This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack ...This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo(MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.展开更多
In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train ...In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.展开更多
Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study comp...Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.展开更多
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while...Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.展开更多
1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, a...1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, and reducing the management costs.1Here, the RUL is usually defined as the length from the current time to the end of the useful life.展开更多
Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS d...Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.展开更多
Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring ...Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,v...The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.展开更多
BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the para...BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.展开更多
The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficultie...The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.展开更多
BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic...BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.展开更多
BACKGROUND Non-ST segment elevation myocardial infarction(NSTEMI)poses significant challenges in clinical management due to its diverse outcomes.Understanding the prognostic role of hematological parameters and derive...BACKGROUND Non-ST segment elevation myocardial infarction(NSTEMI)poses significant challenges in clinical management due to its diverse outcomes.Understanding the prognostic role of hematological parameters and derived ratios in NSTEMI patients could aid in risk stratification and improve patient care.AIM To evaluate the predictive value of hemogram-derived ratios for major adverse cardiovascular events(MACE)in NSTEMI patients,potentially improving clinical outcomes.METHODS A prospective,observational cohort study was conducted in 2021 at the Internal Medicine Clinic of the University Hospital in Tuzla,Bosnia and Herzegovina.The study included 170 patients with NSTEMI,who were divided into a group with MACE and a control group without MACE.Furthermore,the MACE group was subdivided into lethal and non-lethal groups for prognostic analysis.Alongside hematological parameters,an additional 13 hematological-derived ratios(HDRs)were monitored,and their prognostic role was investigated.RESULTS Hematological parameters did not significantly differ between non-ST segment elevation myocardial infarction(NSTEMI)patients with MACE and a control group at T1 and T2.However,significant disparities emerged in HDRs among NSTEMI patients with lethal and non-lethal outcomes post-MACE.Notably,neutrophil-to-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR)were elevated in lethal outcomes.Furthermore,C-reactive protein-to-lymphocyte ratio(CRP/Ly)at T1(>4.737)demonstrated predictive value[odds ratio(OR):3.690,P=0.024].Both NLR at T1(>4.076)and T2(>4.667)emerged as significant predictors,with NLR at T2 exhibiting the highest diagnostic performance,as indicated by an area under the curve of 0.811(95%CI:0.727-0.859)and OR of 4.915(95%CI:1.917-12.602,P=0.001),emphasizing its important role as a prognostic marker.CONCLUSION This study highlights the significant prognostic value of hemogram-derived indexes in predicting MACE among NSTEMI patients.During follow-up,NLR,PLR,and CRP/Ly offer important insights into the inflammatory processes underlying cardiovascular events.展开更多
Precise risk stratification is crucial for selecting the optimal risk-adapted treatment for newly diagnosed multiple myeloma (NDMM) patients. Various prognostic factors and staging systems have been developed to predi...Precise risk stratification is crucial for selecting the optimal risk-adapted treatment for newly diagnosed multiple myeloma (NDMM) patients. Various prognostic factors and staging systems have been developed to predict NDMM patient outcomes. The Durie-Salmon (D-S) staging system reflects tumor burden and clinical progression staging with prognostic value.展开更多
Objective Hepatocellular carcinoma(HCC)is sensitive to ferroptosis,a new form of programmed cell death that occurs in most tumor types.However,the mechanism through which ferroptosis modulates HCC remains unclear.This...Objective Hepatocellular carcinoma(HCC)is sensitive to ferroptosis,a new form of programmed cell death that occurs in most tumor types.However,the mechanism through which ferroptosis modulates HCC remains unclear.This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy.Methods Using clinicopathological parameters and bioinformatic techniques,we comprehensively examined the expression of FANCD2 macroscopically and microcosmically.We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death.Results FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration.As an independent risk factor for HCC,a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade.Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism.Conclusion To the best of our knowledge,this is the first study to comprehensively elucidate the oncogenic role of FANCD2.FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC;hence,it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.展开更多
Background:Acute cholangitis is an infection due to the bile duct obstruction.Despite progress in treat-ment,acute cholangitis remains potentially fatal.Early diagnosis and treatment improve the patient out-comes.The ...Background:Acute cholangitis is an infection due to the bile duct obstruction.Despite progress in treat-ment,acute cholangitis remains potentially fatal.Early diagnosis and treatment improve the patient out-comes.The present study aimed to identify clinical and biological factors at admission associated with 30-day mortality in acute cholangitis,to build an efficient prognostic score based on these parameters and to study the performances of this new score.Methods:We enrolled all adult patients consecutively hospitalized for acute cholangitis between January 2017 and December 2021.We developed a score system named ProChol using variables significantly asso-ciated with 30-day mortality in multivariate logistic analysis and simplified this system(named sProChol)based on a simple points-based approach.Results:In total,528 patients were included,with an average age of 77±13 years,a male predominance(54.2%)and a majority of lithiasis etiology(66.5%).Mortality in 30 days was 11.9%.In multivariate logis-tic analysis,tumor etiology[adjusted odds ratio(aOR)=15.43,95%confidence interval(CI):5.90-40.40],stent obstruction(aOR=5.12,95%CI:2.02-12.99),hypoalbuminemia(aOR=3.50,95%CI:1.25-9.81),renal failure(aOR=6.51,95%CI:2.62-16.18),oxygen therapy(aOR=4.63,95%CI:1.02-20.92)and cu-rative anticoagulation(aOR=2.60,95%CI:1.23-5.52)were independently associated with the 30-day mortality while fever was a protective factor(aOR=0.37,95%CI:0.16-0.84).ProChol score using these 7 parameters and sProChol using the 3 robust factors(etiology,renal failure and anticoagulation)presented respectively an area under receiver operating characteristic(ROC)curves(AUC)of 0.81 and 0.77,higher than Tokyo(AUC=0.72)and Gravito-Soares et al.score(AUC=0.71).Patients with sProChol≥4 had a significantly higher risk of transfer to intensive care unit(13.3%vs.5.1%;P<0.001)and longer length of stay(P=0.0006).Conclusions:ProChol and sProChol constructed from simple clinico-biological parameters at admission,present interesting performances in predicting the 30-day mortality in acute cholangitis.展开更多
Background:Although the prognostic nutritional index(PNI)may predict surgical outcomes in certain cancers,the impact of PNI on surgical prognosis in patients undergoing pylorus-preserving pancreati-coduodenectomy(PPPD...Background:Although the prognostic nutritional index(PNI)may predict surgical outcomes in certain cancers,the impact of PNI on surgical prognosis in patients undergoing pylorus-preserving pancreati-coduodenectomy(PPPD)is unclear.This study aimed to investigate the impact of preoperative PNI on mortality rate and cancer recurrence rate in patients who underwent PPPD.Methods:A total of 718 patients who were diagnosed with periampullary or pancreatic cancer and un-derwent PPPD between January 2012 and December 2016 were analyzed.Patients were categorized into two groups using the optimal cut-offvalue for PNI,determined by calculating the receiver operating characteristic(ROC)curve and the Youden index.We performed propensity score matching(PSM)anal-ysis to compare the mortality rate and cancer recurrence rate between the two groups.In addition,Cox regression analyses were performed to examine the association of PNI with mortality rate and cancer recurrence rate.Results:Using the 1-year mortality as an endpoint,the area under the ROC curve for PNI was 0.620(opti-mal cut-offvalue:41.7).We observed significant differences in 1-year(P=0.001),5-year(P=0.002),and overall(P=0.001)mortality;1-year(P=0.013),5-year(P=0.032),and overall(P=0.017)cancer re-currence between groups after PSM.High PNI was significantly associated with reduced 1-year[adjusted hazard ratio(HR)=0.44,95%confidence interval(CI):0.26-0.74,P=0.020],5-year(HR=0.66,95%CI:0.52-0.84,P<0.001),and overall(HR=0.71,95%CI:0.57-0.88,P=0.002)mortality;1-year(HR=0.70,95%CI:0.52-0.93,P=0.016),5-year(HR=0.78,95%CI:0.62-0.97,P=0.027)and overall(HR=0.78,95%CI:0.63-0.97,P=0.024)cancer recurrence.Conclusions:Preoperative PNI may serve as an independent factor for short-and long-term surgical prog-nosis in cancer patients undergoing PPPD.展开更多
BACKGROUND Older patients are more likely to have a poor performance status and comor-bidities.There is a reluctance to extensively investigate and treat older patients.As elderly individuals and patients with neoplas...BACKGROUND Older patients are more likely to have a poor performance status and comor-bidities.There is a reluctance to extensively investigate and treat older patients.As elderly individuals and patients with neoplasms each increase in number,palliative treatment of older patients is expected to grow as an issue.AIM To investigated the role of palliative radiotherapy in older patients and patients who were expected to demonstrate a therapeutic effect.METHODS From February 2019 to February 2022,33 patients aged≥80 years underwent palliative radiotherapy.The prognosis in palliative care study predictor(PiPS),palliative prognostic index(PPI),and delirium-palliative prognostic score(D-PaP)models were used for prognosis prediction.D-PaP scores calculated according to the doctor's prediction of clinical prediction of survival(CPS)were excluded and then analyzed for comparison.Radiation was prescribed at a dose of 2.5-7 Gy per fraction,up to a median of 39 Gy10(range,28-75 Gy10).RESULTS The median follow-up was 2.4 months(range,0.2-27.5 months),and 28 patients(84.8%)showed subjective symptom improvements following treatment.The 2-and 6-month survival rates of all patients were 91.5%and 91.5%,respectively.According to regression analysis,the performance status index,symptom type,and radiation dose all showed no significant correlation with the treatment re-sponse.When survival was expected for>55 days in the PiPS model,the 2-month survival rate was 94.4%.For patients with PPI and D-PaP-CPS values of 0-3.9 points,the 2-month survival rates were 90.0%and 100%,respectively.For patients with a score of≥4 points,the 2-month survival rates were 37.5%and 0%,res-pectively.Core Tip:This is a retrospective study to investigate the role of palliative radiotherapy in older patients and patients who were expected to demonstrate a great therapeutic effect.The prognosis in palliative care study predictor,palliative prognostic index,and delirium-palliative prognostic score models were used for prognosis prediction.Most of patients showed subjective symptom improvements following treatment.The prognosis prediction model showed good correlation with survival.In order to increase the therapeutic effectiveness in palliative radiotherapy,it is necessary to assess a patient's exact prognosis and select appropriate patients accordingly.INTRODUCTION The incidence of cancer is high among individuals 60-69 years old and is 11 times greater among those≥65-years-old compared to those<65-years-old.For this reason,about half of all cancer cases are diagnosed in individuals aged≥70 years,and older patients account for a large portion of the total population regarding the prevalence of cancer[1].Cancer is one of the most significant diseases in older patients.About 60%of all cancer-related deaths occur in older patients aged 70 years[1,2].Moreover,cancer accounts for about one-third of the causes of death in the elderly population[1,2].When choosing a cancer treatment,both the characteristics of the cancer and the overall health status of the patient,such as their general condition and any underlying diseases,should be considered[2].Older patients have a shorter life expectancy than younger patients;moreover,they typically have many accompanying underlying diseases and have a poorer general condition.For this reason,older patients are often rejected from receiving active testing and treatment services.Therefore,even if other factors,such as the underlying disease,are the same in young and old patients,older patients typically receive less treatment due to the simple fact that they are older[3].Palliative treatment is a treatment approach that improves the pain and symptoms of a patient and their quality of life.Although palliative treatment is applicable regardless of patient age and the type and severity of their disease,most patients requiring palliative treatment are cancer patients.Palliative radiotherapy is relatively effective for cancer patients and tends to be a well-tolerated treatment.Although some studies have reported the usefulness of palliative radiotherapy in elderly patients,a large number of patients and caregivers are not receiving treatment due to fears of treatment,the risks of side effects,and doubts about treatment effectiveness[1].Since actual age is not always associated with physical ability,the determination of treatment based solely on age can be an obstacle preventing appropriate treatment opportunities.The importance of palliative care is increasing due to the recent growth of the elderly population,as well as,the increase in cancer incidence,and the changes in traditional views or perceptions,such as a growing acceptance of the pursuit of a dignified death[4].Therefore,in this study,we investigated the role of palliative radiotherapy in older patients and in patients who are expected to show a great therapeutic effect.展开更多
Objective:To explore the application value of a machine learning-based prediction model in assessing the prognosis of septic children in the pediatric intensive care unit(PICU)and provide data support for clinical dec...Objective:To explore the application value of a machine learning-based prediction model in assessing the prognosis of septic children in the pediatric intensive care unit(PICU)and provide data support for clinical decision-making.Methods:A total of 180 septic children admitted to the PICU of a tertiary hospital from January 2020 to December 2024 were selected.They were divided into a control group(90 cases,using traditional scoring methods to predict prognosis)and an observation group(90 cases,using a multivariable model based on machine learning algorithms to predict prognosis)according to the random number table method.General information,laboratory indicators,and clinical interventions were collected.Various models such as Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR)were established.The model performance was evaluated using ROC curve,AUC value,accuracy,sensitivity,and specificity.Results:The machine learning models performed better than traditional scoring methods in predicting the 28-day mortality rate of septic children.Among them,the RF model achieved an AUC value of 0.921,a sensitivity of 85.6%,and a specificity of 88.1%,which were significantly higher than the PIM3 score(AUC 0.762).The prediction accuracy and timeliness of clinical intervention in the observation group were significantly improved,leading to a shortened hospital stay and reduced mortality rate(p<0.05).Conclusion:The prediction model based on machine learning can more accurately assess the prognostic risk of septic children in PICU,showing good clinical application prospects and providing references for individualized treatment and optimal resource allocation.展开更多
基金partially funded by the National Natural Science Foundation of China (No.51805262)
文摘This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo(MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.
基金supported by National Natural Science Foundation of China(U2268206,T2222015)Beijing Natural Science Foundation(4232031)+1 种基金Key Fields Project of DEGP(2021ZDZX1110)Shenzhen Science and Technology Program(CJGJZD20220517141801004).
文摘In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.
基金financially supported by the vice chancellor for research and technology of Urmia University
文摘Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20242194)the National Natural Science Foundation of China(Grant Nos.52175251 and 52205268)+1 种基金the Industry Key Technology Research Fund Project of Northwestern Polytechnical University(Grant No.HYGJXM202318)the National Basic Scientific Research Program(Grant No.JCKY2021206B005).
文摘Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.
基金supported by the National Natural Science Foundation of China (Nos. 62450056 and 62233017).
文摘1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, and reducing the management costs.1Here, the RUL is usually defined as the length from the current time to the end of the useful life.
基金supported by the National Social Science Fund of China(No.22BTJ005)the Key Project of National Key Research and Development Plan(No.2023YFF1304002-05)+1 种基金supported by the National Natural Science Foundation of China(No.32001252)the International Center for Bamboo and Rattan(Nos.1632022024,1632020029,1632021024).
文摘Remote sensing(RS)facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes.The Conventional model-based(CMB)estimator supports wall-to-wall RS data,while Hybrid estimators support surveys where RS data are available as a sample.However,the connection between these two types of monitoring procedures has been unclear,hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring.Consequently,our objectives are to(1)shed further light on the connections between different types of Hybrid estimators,and between CMB and Hybrid estimators,through mathematical analyses and Monte Carlo simulations;and(2)compare the effects and explore the tradeoffs related to the RS sampling design,coverage rate,and cluster size on estimation precision.Primary findings are threefold:(1)the CMB estimator represents a special case of Hybrid estimators,signifying that wallto-wall RS data is a particular instance of sample-based RS data;(2)the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data;(3)otherwise costprohibitive sensing,such as LiDAR and UAV,can support large scale monitoring through collecting RS data as a sample.These conclusions may reconcile different perspectives regarding choice of RS instruments,data acquisition,and cost for continuous observations,particularly in the context of surveys aiming at providing data for mitigating climate change.
基金co-supported by the National Natural Science Foundation of China(Nos.U223321251875014)+1 种基金the Beijing Natural Science Foundation,China(No.L221008)the China Scholarship Council(No.202106020001).
文摘Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
文摘The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.
基金Supported by Gansu Province Joint Fund General Program,No.24JRRA878Gansu Provincial Science and Technology Program Project,No.24JRRA1020+2 种基金Gansu Province Key Talent Program,No.2025RCXM006Teaching Research and Reform Program for Postgraduate Education at Gansu University of Traditional Chinese Medicine(GUSTCM),No.YBXM-202406Special Fund for Mentors of“Qihuang Talents”in the First-Level Discipline of Chinese Medicine,No.ZYXKBD-202415。
文摘BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.
文摘The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.
基金Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region,No.2022D01C297.
文摘BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.
文摘BACKGROUND Non-ST segment elevation myocardial infarction(NSTEMI)poses significant challenges in clinical management due to its diverse outcomes.Understanding the prognostic role of hematological parameters and derived ratios in NSTEMI patients could aid in risk stratification and improve patient care.AIM To evaluate the predictive value of hemogram-derived ratios for major adverse cardiovascular events(MACE)in NSTEMI patients,potentially improving clinical outcomes.METHODS A prospective,observational cohort study was conducted in 2021 at the Internal Medicine Clinic of the University Hospital in Tuzla,Bosnia and Herzegovina.The study included 170 patients with NSTEMI,who were divided into a group with MACE and a control group without MACE.Furthermore,the MACE group was subdivided into lethal and non-lethal groups for prognostic analysis.Alongside hematological parameters,an additional 13 hematological-derived ratios(HDRs)were monitored,and their prognostic role was investigated.RESULTS Hematological parameters did not significantly differ between non-ST segment elevation myocardial infarction(NSTEMI)patients with MACE and a control group at T1 and T2.However,significant disparities emerged in HDRs among NSTEMI patients with lethal and non-lethal outcomes post-MACE.Notably,neutrophil-to-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR)were elevated in lethal outcomes.Furthermore,C-reactive protein-to-lymphocyte ratio(CRP/Ly)at T1(>4.737)demonstrated predictive value[odds ratio(OR):3.690,P=0.024].Both NLR at T1(>4.076)and T2(>4.667)emerged as significant predictors,with NLR at T2 exhibiting the highest diagnostic performance,as indicated by an area under the curve of 0.811(95%CI:0.727-0.859)and OR of 4.915(95%CI:1.917-12.602,P=0.001),emphasizing its important role as a prognostic marker.CONCLUSION This study highlights the significant prognostic value of hemogram-derived indexes in predicting MACE among NSTEMI patients.During follow-up,NLR,PLR,and CRP/Ly offer important insights into the inflammatory processes underlying cardiovascular events.
基金supported by the National Natural Science Foundation of China (Grant nos. 82470209 and 82170141)the Jiaxing Key Discipiline of Medcine-Nephrology (Grant no. 2023-ZC-011)。
文摘Precise risk stratification is crucial for selecting the optimal risk-adapted treatment for newly diagnosed multiple myeloma (NDMM) patients. Various prognostic factors and staging systems have been developed to predict NDMM patient outcomes. The Durie-Salmon (D-S) staging system reflects tumor burden and clinical progression staging with prognostic value.
基金supported by Beijing Science and Technology Commission(grant number Z211100002921059)National Science and Technology Major Projects of China(2017ZX10201201-001-006,2017ZX10201201-002-006,and 2018ZX10715-005-003-005)+5 种基金Digestive Medical Coordinated Development Center of Beijing Hospital Authority(XXZ0302 and XXT28)National Key R&D Program China(2022YFC2603505)Beijing Hospital Authority Clinical Medicine Development with Special Funding Support(XMLX 202127)High-Level Public Health Technical Personnel Training Program of Beijing the Municipal Health Commission(2022-3-050)Capital Health Research and Development of Special(2022-1-2172)HBV infection,Clinical Cure and Immunology Joint Laboratory for Clinical Medicine Capital Medical University.
文摘Objective Hepatocellular carcinoma(HCC)is sensitive to ferroptosis,a new form of programmed cell death that occurs in most tumor types.However,the mechanism through which ferroptosis modulates HCC remains unclear.This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy.Methods Using clinicopathological parameters and bioinformatic techniques,we comprehensively examined the expression of FANCD2 macroscopically and microcosmically.We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death.Results FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration.As an independent risk factor for HCC,a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade.Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism.Conclusion To the best of our knowledge,this is the first study to comprehensively elucidate the oncogenic role of FANCD2.FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC;hence,it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
文摘Background:Acute cholangitis is an infection due to the bile duct obstruction.Despite progress in treat-ment,acute cholangitis remains potentially fatal.Early diagnosis and treatment improve the patient out-comes.The present study aimed to identify clinical and biological factors at admission associated with 30-day mortality in acute cholangitis,to build an efficient prognostic score based on these parameters and to study the performances of this new score.Methods:We enrolled all adult patients consecutively hospitalized for acute cholangitis between January 2017 and December 2021.We developed a score system named ProChol using variables significantly asso-ciated with 30-day mortality in multivariate logistic analysis and simplified this system(named sProChol)based on a simple points-based approach.Results:In total,528 patients were included,with an average age of 77±13 years,a male predominance(54.2%)and a majority of lithiasis etiology(66.5%).Mortality in 30 days was 11.9%.In multivariate logis-tic analysis,tumor etiology[adjusted odds ratio(aOR)=15.43,95%confidence interval(CI):5.90-40.40],stent obstruction(aOR=5.12,95%CI:2.02-12.99),hypoalbuminemia(aOR=3.50,95%CI:1.25-9.81),renal failure(aOR=6.51,95%CI:2.62-16.18),oxygen therapy(aOR=4.63,95%CI:1.02-20.92)and cu-rative anticoagulation(aOR=2.60,95%CI:1.23-5.52)were independently associated with the 30-day mortality while fever was a protective factor(aOR=0.37,95%CI:0.16-0.84).ProChol score using these 7 parameters and sProChol using the 3 robust factors(etiology,renal failure and anticoagulation)presented respectively an area under receiver operating characteristic(ROC)curves(AUC)of 0.81 and 0.77,higher than Tokyo(AUC=0.72)and Gravito-Soares et al.score(AUC=0.71).Patients with sProChol≥4 had a significantly higher risk of transfer to intensive care unit(13.3%vs.5.1%;P<0.001)and longer length of stay(P=0.0006).Conclusions:ProChol and sProChol constructed from simple clinico-biological parameters at admission,present interesting performances in predicting the 30-day mortality in acute cholangitis.
基金supported by a grant from the National Re-search Foundation of Korea(grant number:RS-2022-00165755).
文摘Background:Although the prognostic nutritional index(PNI)may predict surgical outcomes in certain cancers,the impact of PNI on surgical prognosis in patients undergoing pylorus-preserving pancreati-coduodenectomy(PPPD)is unclear.This study aimed to investigate the impact of preoperative PNI on mortality rate and cancer recurrence rate in patients who underwent PPPD.Methods:A total of 718 patients who were diagnosed with periampullary or pancreatic cancer and un-derwent PPPD between January 2012 and December 2016 were analyzed.Patients were categorized into two groups using the optimal cut-offvalue for PNI,determined by calculating the receiver operating characteristic(ROC)curve and the Youden index.We performed propensity score matching(PSM)anal-ysis to compare the mortality rate and cancer recurrence rate between the two groups.In addition,Cox regression analyses were performed to examine the association of PNI with mortality rate and cancer recurrence rate.Results:Using the 1-year mortality as an endpoint,the area under the ROC curve for PNI was 0.620(opti-mal cut-offvalue:41.7).We observed significant differences in 1-year(P=0.001),5-year(P=0.002),and overall(P=0.001)mortality;1-year(P=0.013),5-year(P=0.032),and overall(P=0.017)cancer re-currence between groups after PSM.High PNI was significantly associated with reduced 1-year[adjusted hazard ratio(HR)=0.44,95%confidence interval(CI):0.26-0.74,P=0.020],5-year(HR=0.66,95%CI:0.52-0.84,P<0.001),and overall(HR=0.71,95%CI:0.57-0.88,P=0.002)mortality;1-year(HR=0.70,95%CI:0.52-0.93,P=0.016),5-year(HR=0.78,95%CI:0.62-0.97,P=0.027)and overall(HR=0.78,95%CI:0.63-0.97,P=0.024)cancer recurrence.Conclusions:Preoperative PNI may serve as an independent factor for short-and long-term surgical prog-nosis in cancer patients undergoing PPPD.
文摘BACKGROUND Older patients are more likely to have a poor performance status and comor-bidities.There is a reluctance to extensively investigate and treat older patients.As elderly individuals and patients with neoplasms each increase in number,palliative treatment of older patients is expected to grow as an issue.AIM To investigated the role of palliative radiotherapy in older patients and patients who were expected to demonstrate a therapeutic effect.METHODS From February 2019 to February 2022,33 patients aged≥80 years underwent palliative radiotherapy.The prognosis in palliative care study predictor(PiPS),palliative prognostic index(PPI),and delirium-palliative prognostic score(D-PaP)models were used for prognosis prediction.D-PaP scores calculated according to the doctor's prediction of clinical prediction of survival(CPS)were excluded and then analyzed for comparison.Radiation was prescribed at a dose of 2.5-7 Gy per fraction,up to a median of 39 Gy10(range,28-75 Gy10).RESULTS The median follow-up was 2.4 months(range,0.2-27.5 months),and 28 patients(84.8%)showed subjective symptom improvements following treatment.The 2-and 6-month survival rates of all patients were 91.5%and 91.5%,respectively.According to regression analysis,the performance status index,symptom type,and radiation dose all showed no significant correlation with the treatment re-sponse.When survival was expected for>55 days in the PiPS model,the 2-month survival rate was 94.4%.For patients with PPI and D-PaP-CPS values of 0-3.9 points,the 2-month survival rates were 90.0%and 100%,respectively.For patients with a score of≥4 points,the 2-month survival rates were 37.5%and 0%,res-pectively.Core Tip:This is a retrospective study to investigate the role of palliative radiotherapy in older patients and patients who were expected to demonstrate a great therapeutic effect.The prognosis in palliative care study predictor,palliative prognostic index,and delirium-palliative prognostic score models were used for prognosis prediction.Most of patients showed subjective symptom improvements following treatment.The prognosis prediction model showed good correlation with survival.In order to increase the therapeutic effectiveness in palliative radiotherapy,it is necessary to assess a patient's exact prognosis and select appropriate patients accordingly.INTRODUCTION The incidence of cancer is high among individuals 60-69 years old and is 11 times greater among those≥65-years-old compared to those<65-years-old.For this reason,about half of all cancer cases are diagnosed in individuals aged≥70 years,and older patients account for a large portion of the total population regarding the prevalence of cancer[1].Cancer is one of the most significant diseases in older patients.About 60%of all cancer-related deaths occur in older patients aged 70 years[1,2].Moreover,cancer accounts for about one-third of the causes of death in the elderly population[1,2].When choosing a cancer treatment,both the characteristics of the cancer and the overall health status of the patient,such as their general condition and any underlying diseases,should be considered[2].Older patients have a shorter life expectancy than younger patients;moreover,they typically have many accompanying underlying diseases and have a poorer general condition.For this reason,older patients are often rejected from receiving active testing and treatment services.Therefore,even if other factors,such as the underlying disease,are the same in young and old patients,older patients typically receive less treatment due to the simple fact that they are older[3].Palliative treatment is a treatment approach that improves the pain and symptoms of a patient and their quality of life.Although palliative treatment is applicable regardless of patient age and the type and severity of their disease,most patients requiring palliative treatment are cancer patients.Palliative radiotherapy is relatively effective for cancer patients and tends to be a well-tolerated treatment.Although some studies have reported the usefulness of palliative radiotherapy in elderly patients,a large number of patients and caregivers are not receiving treatment due to fears of treatment,the risks of side effects,and doubts about treatment effectiveness[1].Since actual age is not always associated with physical ability,the determination of treatment based solely on age can be an obstacle preventing appropriate treatment opportunities.The importance of palliative care is increasing due to the recent growth of the elderly population,as well as,the increase in cancer incidence,and the changes in traditional views or perceptions,such as a growing acceptance of the pursuit of a dignified death[4].Therefore,in this study,we investigated the role of palliative radiotherapy in older patients and in patients who are expected to show a great therapeutic effect.
文摘Objective:To explore the application value of a machine learning-based prediction model in assessing the prognosis of septic children in the pediatric intensive care unit(PICU)and provide data support for clinical decision-making.Methods:A total of 180 septic children admitted to the PICU of a tertiary hospital from January 2020 to December 2024 were selected.They were divided into a control group(90 cases,using traditional scoring methods to predict prognosis)and an observation group(90 cases,using a multivariable model based on machine learning algorithms to predict prognosis)according to the random number table method.General information,laboratory indicators,and clinical interventions were collected.Various models such as Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR)were established.The model performance was evaluated using ROC curve,AUC value,accuracy,sensitivity,and specificity.Results:The machine learning models performed better than traditional scoring methods in predicting the 28-day mortality rate of septic children.Among them,the RF model achieved an AUC value of 0.921,a sensitivity of 85.6%,and a specificity of 88.1%,which were significantly higher than the PIM3 score(AUC 0.762).The prediction accuracy and timeliness of clinical intervention in the observation group were significantly improved,leading to a shortened hospital stay and reduced mortality rate(p<0.05).Conclusion:The prediction model based on machine learning can more accurately assess the prognostic risk of septic children in PICU,showing good clinical application prospects and providing references for individualized treatment and optimal resource allocation.
