This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlyin...This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.展开更多
Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geos...Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.展开更多
Triple-negative breast cancer(TNBC)is the most challenging breast cancer subtype.Molecular stratification and target therapy bring clinical benefit for TNBC patients,but it is difficult to implement comprehensive mole...Triple-negative breast cancer(TNBC)is the most challenging breast cancer subtype.Molecular stratification and target therapy bring clinical benefit for TNBC patients,but it is difficult to implement comprehensive molecular testing in clinical practice.Here,using our multi-omics TNBC cohort(N=425),a deep learning-based framework was devised and validated for comprehensive predictions of molecular features,subtypes and prognosis from pathological whole slide images.The framework first incorporated a neural network to decompose the tissue on WSIs,followed by a second one which was trained based on certain tissue types for predicting different targets.Multi-omics molecular features were analyzed including somatic mutations,copy number alterations,germline mutations,biological pathway activities,metabolomics features and immunotherapy biomarkers.It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation,germline BRCA2 mutation and PD-L1 protein expression(area under the curve[AUC]:0.78,0.79 and 0.74 respectively).The molecular subtypes of TNBC can be identified(AUC:0.84,0.85,0.93 and 0.73 for the basal-like immune-suppressed,immunomodulatory,luminal androgen receptor,and mesenchymal-like subtypes respectively)and their distinctive morphological patterns were revealed,which provided novel insights into the heterogeneity of TNBC.A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes(log-rank P<0.001).Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA(N=143)and appeared robust to the changes in patient population.For potential clinical translation,we built a novel online platform,where we modularized and deployed our framework along with the validated models.It can realize real-time one-stop prediction for new cases.In summary,using only pathological WSIs,our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making.It had the potential to be clinically implemented and promote the personalized management of TNBC.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
As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice E...As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.展开更多
Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponi...Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.展开更多
Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with...Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.展开更多
Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al report...Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al reports on red cell distribution width(RDW)/platelet ratio(RPR)as a non-invasive biomarker in predicting decompensation of metabolic dysfunction-associated steatotic liver disease(MASLD)-related cACLD.Differently from other biomarkers and algorithms,RPR is inexpensive and widely available,based on parameters which are included in a complete blood count.RPR is computed on the grounds of two different items,one of which,RDW,mirrors the host’s response to a variety of disease stimuli and is non-specific.The second parameter involved in RPR,platelet count,is more specific and has been used in the hepatological clinic to discriminate cirrhotic from non-cirrhotic chronic liver disease for decades.Cardiovascular disease is the primary cause of mortality among MASLD subjects,followed by extra-hepatic cancers and liver-related mortality.Therefore,MASLD biomarkers should be validated not only in terms of liver-related events but also in the prediction of major adverse cardiovascular events and cardiovascular mortality and extra-hepatic cancers.Adequately sized multi-ethnic confirmatory investigation is required to define the role and significance of RPR in the stratification of MASLD-cACLD.展开更多
BACKGROUND Minimally invasive esophagectomy(MIE)is a widely accepted treatment for esophageal cancer,yet it is associated with a significant risk of surgical adverse events(SAEs),which can compromise patient recovery ...BACKGROUND Minimally invasive esophagectomy(MIE)is a widely accepted treatment for esophageal cancer,yet it is associated with a significant risk of surgical adverse events(SAEs),which can compromise patient recovery and long-term survival.Accurate preoperative identification of high-risk patients is critical for improving outcomes.AIM To establish and validate a risk prediction and stratification model for the risk of SAEs in patients with MIE.METHODS This retrospective study included 747 patients who underwent MIE at two centers from January 2019 to February 2024.Patients were separated into a train set(n=549)and a validation set(n=198).After screening by least absolute shrinkage and selection operator regression,multivariate logistic regression analyzed clinical and intraoperative variables to identify independent risk factors for SAEs.A risk stratification model was constructed and validated to predict the probability of SAEs.RESULTS SAEs occurred in 10.2%of patients in train set and 13.6%in the validation set.Patients with SAE had significantly higher complication rate and a longer hospital stay after surgery.The key independent risk factors identified included chronic obstructive pulmonary disease,a history of alcohol consumption,low forced expiratory volume in the first second,and low albumin levels.The stratification model has excellent prediction accuracy,with an area under the curve of 0.889 for the training set and an area under the curve of 0.793 for the validation set.CONCLUSION The developed risk stratification model effectively predicts the risk of SAEs in patients undergoing MIE,facilitating targeted preoperative interventions and improving perioperative management.展开更多
Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serio...Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.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
A recent single-center retrospective study proposed novel combinations of hematological parameters and scoring systems for predicting severe acute pancre-atitis.While these combinations showed promising predictive per...A recent single-center retrospective study proposed novel combinations of hematological parameters and scoring systems for predicting severe acute pancre-atitis.While these combinations showed promising predictive performance,several limitations warrant consideration,including the lack of calibration,the absence of key inflammatory markers such as procalcitonin,and practical challenges in integrating these models into routine clinical workflows.To improve predictive accuracy and clinical applicability,prospective validation and the inclusion of additional variables are recommended.展开更多
BACKGROUND Acute liver failure(ALF)with sepsis is associated with rapid disease progression and high mortality.Therefore,early detection of high-risk sepsis subgroups in patients with ALF is crucial.AIM To develop and...BACKGROUND Acute liver failure(ALF)with sepsis is associated with rapid disease progression and high mortality.Therefore,early detection of high-risk sepsis subgroups in patients with ALF is crucial.AIM To develop and validate an accurate nomogram model for predicting the risk of sepsis in patients with ALF.METHODS We retrieved data from the Medical Information Mart for Intensive Care(MIMIC)IV database and the Fifth Medical Center of Chinese PLA General Hospital(FMCPH).Univariate and multivariate logistic regression analysis were used to identify risk factors for sepsis in ALF and were subsequently incorporated to construct a nomogram model[sepsis in ALF(SIALF)].The discrimination ability,calibration,and clinical applicability of the SIALF model were evaluated by the area under receiver operating characteristic curve,calibration curves,and decision curve analysis,respectively.The Kaplan-Meier curves were used for robustness check.The SIALF model was internally validated using the bootstrapping method with the MIMIC validation cohort and externally validated by the FMCPH cohort.RESULTS A total of 738 patients with ALF patients were included in this study,with 510 from the MIMIC IV database and 228 from the FMCPH cohort.In the MIMIC IV cohort,387(75.89%)patients developed sepsis.Multivariate logistic regression analysis revealed that age[odds ratio(OR)=1.016,95%confidence interval(CI):1.003-1.028,P=0.017],total bilirubin(OR=1.047,95%CI:1.008-1.088,P=0.017),lactate dehydrogenase(OR=1.001,95%CI:1.000-1.001,P<0.001),albumin(OR=0.436,95%CI:0.274-0.692,P=0.003),and mechanical ventilation(OR=1.985,95%CI:1.269-3.105,P=0.003)were independent risk factors associated with sepsis in patients with ALF.The SIALF model demonstrated satisfactory accuracy and clinical utility with area under receiver operating characteristic curve values of 0.849,0.847,and 0.835 for the internal derivation,internal validation,and external validation cohort,respectively,which outperformed the Sequential Organ Failure Assessment scores of 0.733,0.746,and 0.721 and systemic inflammatory response syndrome scores of 0.578,0.653,and 0.615,respectively.The decision curve analysis and calibration curves indicated superior clinical utility and efficiency than other score systems.Based on the risk stratification score derived from the SIALF model,the Kaplan-Meier curves effectively discriminated the real high-risk subpopulation.To enhance the clinical utility,we constructed an online dynamic version,enabling physicians to evaluate patients’condition and track disease progression in real-time.CONCLUSION Based on easily identifiable clinical data,we developed the SIALF model to predict the risk of sepsis in patients with ALF.The model demonstrated robust predictive efficiency,outperformed Sequential Organ Failure Assessment and systemic inflammatory response syndrome scores,and was validated in an external cohort.The model-based risk stratification and online calculator might further facilitate the early detection and appropriate treatment for this subpopulation.展开更多
The mathematical model for non-Newtonian magnetohydrodynamics flows across a vertically stretched surface with non-linear thermal radiation,mass and heat transfer rates,thermophoretic and Brownian movements,bio-convec...The mathematical model for non-Newtonian magnetohydrodynamics flows across a vertically stretched surface with non-linear thermal radiation,mass and heat transfer rates,thermophoretic and Brownian movements,bio-convection,and motile microbes considered in the present work.It is possible to regulate the nanomaterial suspension in the nanofluid using the growth of microbes.With the use of boundary layer approximation,highly nonlinear partial differential equations were derived for the present flow model.The nonlinear partial differential equations are converted into ordinary differential equations by utilizing similarity transmutations,which simplify them.Numerical elixirs for ordinary differential equations are found through bvp4c.This guarantees accurate results for profiles of temperature,concentration,velocity,and motile density.There is a good match between the numerical values shown graphically and the existing data.As the thermal radiation parameter rises,the flow temperature grows.Increasing Lewis number values is a sharp drop in the nanoparticle volume fraction.Bioconvection Lewis number reduces microorganism profiles.The research work focused on electrical systems,heat transfer,acoustics,chemical processing,rigid body dynamics,fluid mechanics,and solid mechanics,among others.展开更多
Background:Previous researches mainly focused on whether cancer stem cells exist in diffuse large B-cell lym-phoma(DLBCL).However,subgroups with dismal prognosis and stem cell-like characteristics have been over-looke...Background:Previous researches mainly focused on whether cancer stem cells exist in diffuse large B-cell lym-phoma(DLBCL).However,subgroups with dismal prognosis and stem cell-like characteristics have been over-looked.Methods:Using large scale data(n=2133),we conducted machine learning algorithms to identify a high risk DLBCL subgroup with stem cell-like features,and then investigated the potential mechanisms in shaping this subgroup using transcriptome,genome and single-cell RNA-seq data,and in vitro experiments.Results:We identified a high-risk subgroup(25.6%of DLBCL)with stem cell-like characteristics and dismal prog-nosis.This high-risk group(HRG)was featured by upregulation of key enzyme(ODC1)in polyamine metabolism and cold tumor microenvironment(TME),and had a poor prognosis with lower 3-year overall survival(OS)(54.3%vs.83.6%,P<0.0001)and progression-free survival(PFS)(42.8%vs.74.7%,P<0.0001)rates com-pared to the low-risk group.HRG also exhibited malignant proliferative phenotypes similar to Burkitt lymphoma.Patients with MYC rearrangement,double-hit,double-expressors,or complete remission might have either favor-able or poor prognosis,which could be further distinguished by our risk stratification model.Genomic analysis revealed widespread copy number losses in the chemokine and interferon coding regions 8p23.1 and 9p21.3 in HRG.We identified ODC1 as a therapeutic vulnerability for HRG-DLBCL.Single-cell analysis and in vitro ex-periments demonstrated that ODC1 overexpression enhanced DLBCL cell proliferation and drove macrophage polarization towards the M2 phenotype.Conversely,ODC1 inhibition reduced DLBCL cell proliferation,induced cell cycle arrest and apoptosis,and promoted macrophage polarization towards the M1 phenotype.Finally,we developed a comprehensive database of DLBCL for clinical application.Conclusions:Our study effectively advances the precise risk stratification of DLBCL and reveals that ODC1 and immune-deserted microenvironment jointly shape a group of DLBCL patients with stem cell-like features.Target-ing ODC1 regulates immunotherapies in DLBCL,offering new insights for DLBCL treatment.展开更多
Unheralded cardiac arrest among previously healthy young people without antecedent illness,months or years after coronavirus disease 2019(COVID-19)vaccination,highlights the urgent need for risk stratification.The mos...Unheralded cardiac arrest among previously healthy young people without antecedent illness,months or years after coronavirus disease 2019(COVID-19)vaccination,highlights the urgent need for risk stratification.The most likely underlying pathophysiology is subclinical myopericarditis and reentrant ventri-cular tachycardia or spontaneous ventricular fibrillation that is commonly preci-pitated after a surge in catecholamines during exercise or the waking hours of terminal sleep.Small patches of inflammation and/or edema can be missed on cardiac imaging and autopsy,and the heart can appear grossly normal.This paper reviews evidence linking COVID-19 vaccines to cardiac arrest where unfortu-nately the majority of victims have had no antecedent clinical evaluation.We propose a comprehensive strategy for evaluating cardiovascular risk post-vaccination,incorporating detailed patient history,antibody testing,and cardiac diagnostics in the best attempt to detect abnormalities before sudden cardiac death.This approach aims to identify individuals at higher risk of cardiac events after COVID-19 vaccination and guide appropriate clinical management.It is prudent for each primary care physician to have a pre-established plan when addressing this issue in their practice.展开更多
Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heter...Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.展开更多
The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide,having applications in environmental and geophysical engineering.This study explores the impact of ...The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide,having applications in environmental and geophysical engineering.This study explores the impact of Navier slip conditions on exchange flows within a long channel connecting two large reservoirs of differing salinity.These horizontal density gradients drive the flow.We modify the recent one-dimensional theory,developed to avoid runaway stratification,to account for the presence of uniform slip walls.By adjusting the parameters of the horizontal density gradient based on the slip factor,we resolve analytically various flow regimes ranging from high diffusion to transitional high advection.These regimes are governed by physical parameters like channel aspect ratio,slip factor,Schmidt number,and gravitational Reynolds number.Our solutions align perfectly with ones in the no-slip limit.More importantly,under the conditions of no net flow across the channel and high Schmidt number(where stratification is concentrated near the channel’s mid-layer),we derive a closed-form solution for the slip parameter,aspect ratio,and gravitational Reynolds number that describes the interface’s behavior as a sharp interface separating two distinct zones.This interface,arising from hydrostatic wall gradients,ultimately detaches the low-and high-density regimes throughout the channel when the gravitational Reynolds number is inversely proportional to the aspect ratio for a fixed slip parameter.This phenomenon,observed previously in 2D numerical simulations with no-slip walls in the literature,is thus confirmed by our theoretical results.Our findings further demonstrate that wall slip leads to distinct and diverse flow regimes.展开更多
BACKGROUND The categorization and assessment of diabetes-related risks during Ramadan have evolved significantly over three decades.Research interest in the health effects of fasting has grown significantly,with diabe...BACKGROUND The categorization and assessment of diabetes-related risks during Ramadan have evolved significantly over three decades.Research interest in the health effects of fasting has grown significantly,with diabetes emerging as the most extensively studied condition.AIM To explore the historical development of risk stratification approaches for Ramadan fasting in people with diabetes,culminating in the 2021 International Diabetes Federation-Diabetes and Ramadan(IDF-DAR)risk assessment tool.We also evaluated the evidence for its validation and real-world utility.METHODS The PubMed and Google Scholar databases were searched using the term“Diabetes AND Ramadan AND Risk Assessment”.Eligible studies included fulltext articles on risk stratification concept and tools for diabetes during Ramadan.Selected studies were reviewed and synthesized thematically.RESULTS Risk categorization began with a dichotomy and tripartite models and progressed to a four-tier narrative scale.In 2021,the IDF-DAR tool introduced a point-based system with three risk categories.Validation studies across diverse populations demonstrated strong predictive value,though moderate inter-clinician variability and potential overestimation in type 2 diabetes cases were noted.CONCLUSION The IDF-DAR risk stratification tool significantly advances individualized diabetes care during Ramadan.Its conservative bias in some populations and variability in physician scoring suggests the need for standardized training.展开更多
The property of water mass plays an important role in determining the distribution of phytoplankton in the ocean.In the Yellow Sea,summer stratification constrains water exchange and differentiates the properties of t...The property of water mass plays an important role in determining the distribution of phytoplankton in the ocean.In the Yellow Sea,summer stratification constrains water exchange and differentiates the properties of the Yellow Sea Cold Water Mass(YSCWM)and surface water,which in turn affects the spatiotemporal patterns of phytoplankton communities.Here,based on four summer cruises in the Yellow Sea,we examined the response of phytoplankton pigment assemblages to three water masses,including surface water(water massⅠ,WM-Ⅰ),thermocline water(WM-Ⅱ),and the YSCWM(WM-Ⅲ).Based on the opportunities for group dominance across the four cruises,Cyanophyceae,Haptophyceae,Chlorophyceae,and Cryptophyceae preferred living in WM-Ⅰ,characterized by relatively higher temperature and light intensity but lower nutrients;Bacillariophyceae,Chlorophyceae,Cyanophyceae,and Dinophyceae dominated in WM-Ⅲ,with relatively lower temperature and light intensity but higher nutrients.In comparison,the highest diversity of the dominant pigment groups was observed in WM-Ⅱ with intermediate temperature,light,and nutrient levels.The Dirichlet regression model identified the key environmental factors driving changes in phytoplankton assemblages in WM-Ⅰ,Ⅱ,and Ⅲ as dissolved inorganic phosphate(DIP),DIP and light,and temperature and ammonium,respectively.Under the impact of global environmental change,the fluctuations of key driving forces and their potential ecological implications need further investigation.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42261134532,42405059,and U2342212)。
文摘This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.
文摘Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.
基金supported by grants from the National Key Research and Development Program of China(2021YFF1201300 and 2021YFF1201305)the National Natural Science Foundation of China(82103039,81572583,81922048,91959207,U1809205,92159301,61771249 and 62171230)。
文摘Triple-negative breast cancer(TNBC)is the most challenging breast cancer subtype.Molecular stratification and target therapy bring clinical benefit for TNBC patients,but it is difficult to implement comprehensive molecular testing in clinical practice.Here,using our multi-omics TNBC cohort(N=425),a deep learning-based framework was devised and validated for comprehensive predictions of molecular features,subtypes and prognosis from pathological whole slide images.The framework first incorporated a neural network to decompose the tissue on WSIs,followed by a second one which was trained based on certain tissue types for predicting different targets.Multi-omics molecular features were analyzed including somatic mutations,copy number alterations,germline mutations,biological pathway activities,metabolomics features and immunotherapy biomarkers.It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation,germline BRCA2 mutation and PD-L1 protein expression(area under the curve[AUC]:0.78,0.79 and 0.74 respectively).The molecular subtypes of TNBC can be identified(AUC:0.84,0.85,0.93 and 0.73 for the basal-like immune-suppressed,immunomodulatory,luminal androgen receptor,and mesenchymal-like subtypes respectively)and their distinctive morphological patterns were revealed,which provided novel insights into the heterogeneity of TNBC.A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes(log-rank P<0.001).Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA(N=143)and appeared robust to the changes in patient population.For potential clinical translation,we built a novel online platform,where we modularized and deployed our framework along with the validated models.It can realize real-time one-stop prediction for new cases.In summary,using only pathological WSIs,our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making.It had the potential to be clinically implemented and promote the personalized management of TNBC.
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.
基金supported by the National Natural Science Foundation China(Grant No.42176222).
文摘As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.
文摘Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.
文摘Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.
文摘Prognostication of compensated advanced chronic liver disease(cACLD)is of paramount importance for the physician-and-patient communication and for rational clinical decisions.The paper published by Dallio et al reports on red cell distribution width(RDW)/platelet ratio(RPR)as a non-invasive biomarker in predicting decompensation of metabolic dysfunction-associated steatotic liver disease(MASLD)-related cACLD.Differently from other biomarkers and algorithms,RPR is inexpensive and widely available,based on parameters which are included in a complete blood count.RPR is computed on the grounds of two different items,one of which,RDW,mirrors the host’s response to a variety of disease stimuli and is non-specific.The second parameter involved in RPR,platelet count,is more specific and has been used in the hepatological clinic to discriminate cirrhotic from non-cirrhotic chronic liver disease for decades.Cardiovascular disease is the primary cause of mortality among MASLD subjects,followed by extra-hepatic cancers and liver-related mortality.Therefore,MASLD biomarkers should be validated not only in terms of liver-related events but also in the prediction of major adverse cardiovascular events and cardiovascular mortality and extra-hepatic cancers.Adequately sized multi-ethnic confirmatory investigation is required to define the role and significance of RPR in the stratification of MASLD-cACLD.
基金Supported by Joint Funds for the Innovation of Science and Technology,Fujian Province,No.2023Y9187 and No.2021Y9057.
文摘BACKGROUND Minimally invasive esophagectomy(MIE)is a widely accepted treatment for esophageal cancer,yet it is associated with a significant risk of surgical adverse events(SAEs),which can compromise patient recovery and long-term survival.Accurate preoperative identification of high-risk patients is critical for improving outcomes.AIM To establish and validate a risk prediction and stratification model for the risk of SAEs in patients with MIE.METHODS This retrospective study included 747 patients who underwent MIE at two centers from January 2019 to February 2024.Patients were separated into a train set(n=549)and a validation set(n=198).After screening by least absolute shrinkage and selection operator regression,multivariate logistic regression analyzed clinical and intraoperative variables to identify independent risk factors for SAEs.A risk stratification model was constructed and validated to predict the probability of SAEs.RESULTS SAEs occurred in 10.2%of patients in train set and 13.6%in the validation set.Patients with SAE had significantly higher complication rate and a longer hospital stay after surgery.The key independent risk factors identified included chronic obstructive pulmonary disease,a history of alcohol consumption,low forced expiratory volume in the first second,and low albumin levels.The stratification model has excellent prediction accuracy,with an area under the curve of 0.889 for the training set and an area under the curve of 0.793 for the validation set.CONCLUSION The developed risk stratification model effectively predicts the risk of SAEs in patients undergoing MIE,facilitating targeted preoperative interventions and improving perioperative management.
文摘Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.
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.
文摘A recent single-center retrospective study proposed novel combinations of hematological parameters and scoring systems for predicting severe acute pancre-atitis.While these combinations showed promising predictive performance,several limitations warrant consideration,including the lack of calibration,the absence of key inflammatory markers such as procalcitonin,and practical challenges in integrating these models into routine clinical workflows.To improve predictive accuracy and clinical applicability,prospective validation and the inclusion of additional variables are recommended.
基金Supported by National Key Research and Development Program,No.2022YFA1103501。
文摘BACKGROUND Acute liver failure(ALF)with sepsis is associated with rapid disease progression and high mortality.Therefore,early detection of high-risk sepsis subgroups in patients with ALF is crucial.AIM To develop and validate an accurate nomogram model for predicting the risk of sepsis in patients with ALF.METHODS We retrieved data from the Medical Information Mart for Intensive Care(MIMIC)IV database and the Fifth Medical Center of Chinese PLA General Hospital(FMCPH).Univariate and multivariate logistic regression analysis were used to identify risk factors for sepsis in ALF and were subsequently incorporated to construct a nomogram model[sepsis in ALF(SIALF)].The discrimination ability,calibration,and clinical applicability of the SIALF model were evaluated by the area under receiver operating characteristic curve,calibration curves,and decision curve analysis,respectively.The Kaplan-Meier curves were used for robustness check.The SIALF model was internally validated using the bootstrapping method with the MIMIC validation cohort and externally validated by the FMCPH cohort.RESULTS A total of 738 patients with ALF patients were included in this study,with 510 from the MIMIC IV database and 228 from the FMCPH cohort.In the MIMIC IV cohort,387(75.89%)patients developed sepsis.Multivariate logistic regression analysis revealed that age[odds ratio(OR)=1.016,95%confidence interval(CI):1.003-1.028,P=0.017],total bilirubin(OR=1.047,95%CI:1.008-1.088,P=0.017),lactate dehydrogenase(OR=1.001,95%CI:1.000-1.001,P<0.001),albumin(OR=0.436,95%CI:0.274-0.692,P=0.003),and mechanical ventilation(OR=1.985,95%CI:1.269-3.105,P=0.003)were independent risk factors associated with sepsis in patients with ALF.The SIALF model demonstrated satisfactory accuracy and clinical utility with area under receiver operating characteristic curve values of 0.849,0.847,and 0.835 for the internal derivation,internal validation,and external validation cohort,respectively,which outperformed the Sequential Organ Failure Assessment scores of 0.733,0.746,and 0.721 and systemic inflammatory response syndrome scores of 0.578,0.653,and 0.615,respectively.The decision curve analysis and calibration curves indicated superior clinical utility and efficiency than other score systems.Based on the risk stratification score derived from the SIALF model,the Kaplan-Meier curves effectively discriminated the real high-risk subpopulation.To enhance the clinical utility,we constructed an online dynamic version,enabling physicians to evaluate patients’condition and track disease progression in real-time.CONCLUSION Based on easily identifiable clinical data,we developed the SIALF model to predict the risk of sepsis in patients with ALF.The model demonstrated robust predictive efficiency,outperformed Sequential Organ Failure Assessment and systemic inflammatory response syndrome scores,and was validated in an external cohort.The model-based risk stratification and online calculator might further facilitate the early detection and appropriate treatment for this subpopulation.
基金U.F.-G.was supported by the Mobility Lab Foundation,a governmental organization of the Provincial Council of Araba,and the local council of Vitoria-Gasteiz.S.Noeiaghdam was supported by the Henan Academy of Sciences(Project No.241819246).
文摘The mathematical model for non-Newtonian magnetohydrodynamics flows across a vertically stretched surface with non-linear thermal radiation,mass and heat transfer rates,thermophoretic and Brownian movements,bio-convection,and motile microbes considered in the present work.It is possible to regulate the nanomaterial suspension in the nanofluid using the growth of microbes.With the use of boundary layer approximation,highly nonlinear partial differential equations were derived for the present flow model.The nonlinear partial differential equations are converted into ordinary differential equations by utilizing similarity transmutations,which simplify them.Numerical elixirs for ordinary differential equations are found through bvp4c.This guarantees accurate results for profiles of temperature,concentration,velocity,and motile density.There is a good match between the numerical values shown graphically and the existing data.As the thermal radiation parameter rises,the flow temperature grows.Increasing Lewis number values is a sharp drop in the nanoparticle volume fraction.Bioconvection Lewis number reduces microorganism profiles.The research work focused on electrical systems,heat transfer,acoustics,chemical processing,rigid body dynamics,fluid mechanics,and solid mechanics,among others.
基金supported by grants from the National Natural Science Foundation of China(grant number:82170181)Beijing Natural Science Foundation(grant number:7222027)+4 种基金Beijing Physician Scientist Train-ing Project(grant number:BJPSTP-2024-01)the National Key R&D Program of China(grant number:2022YFF1502000)to Liang Wangthe National Key R&D Program of China(grant number:2022YFF1502000)Beijing Municipal Fund for Distinguished Young Scholars(grant number:JQ22022)to Lin FengHebei province government funding for clinical medical talents training and basic research projects(grant number:361007)to Youchao Jia.
文摘Background:Previous researches mainly focused on whether cancer stem cells exist in diffuse large B-cell lym-phoma(DLBCL).However,subgroups with dismal prognosis and stem cell-like characteristics have been over-looked.Methods:Using large scale data(n=2133),we conducted machine learning algorithms to identify a high risk DLBCL subgroup with stem cell-like features,and then investigated the potential mechanisms in shaping this subgroup using transcriptome,genome and single-cell RNA-seq data,and in vitro experiments.Results:We identified a high-risk subgroup(25.6%of DLBCL)with stem cell-like characteristics and dismal prog-nosis.This high-risk group(HRG)was featured by upregulation of key enzyme(ODC1)in polyamine metabolism and cold tumor microenvironment(TME),and had a poor prognosis with lower 3-year overall survival(OS)(54.3%vs.83.6%,P<0.0001)and progression-free survival(PFS)(42.8%vs.74.7%,P<0.0001)rates com-pared to the low-risk group.HRG also exhibited malignant proliferative phenotypes similar to Burkitt lymphoma.Patients with MYC rearrangement,double-hit,double-expressors,or complete remission might have either favor-able or poor prognosis,which could be further distinguished by our risk stratification model.Genomic analysis revealed widespread copy number losses in the chemokine and interferon coding regions 8p23.1 and 9p21.3 in HRG.We identified ODC1 as a therapeutic vulnerability for HRG-DLBCL.Single-cell analysis and in vitro ex-periments demonstrated that ODC1 overexpression enhanced DLBCL cell proliferation and drove macrophage polarization towards the M2 phenotype.Conversely,ODC1 inhibition reduced DLBCL cell proliferation,induced cell cycle arrest and apoptosis,and promoted macrophage polarization towards the M1 phenotype.Finally,we developed a comprehensive database of DLBCL for clinical application.Conclusions:Our study effectively advances the precise risk stratification of DLBCL and reveals that ODC1 and immune-deserted microenvironment jointly shape a group of DLBCL patients with stem cell-like features.Target-ing ODC1 regulates immunotherapies in DLBCL,offering new insights for DLBCL treatment.
文摘Unheralded cardiac arrest among previously healthy young people without antecedent illness,months or years after coronavirus disease 2019(COVID-19)vaccination,highlights the urgent need for risk stratification.The most likely underlying pathophysiology is subclinical myopericarditis and reentrant ventri-cular tachycardia or spontaneous ventricular fibrillation that is commonly preci-pitated after a surge in catecholamines during exercise or the waking hours of terminal sleep.Small patches of inflammation and/or edema can be missed on cardiac imaging and autopsy,and the heart can appear grossly normal.This paper reviews evidence linking COVID-19 vaccines to cardiac arrest where unfortu-nately the majority of victims have had no antecedent clinical evaluation.We propose a comprehensive strategy for evaluating cardiovascular risk post-vaccination,incorporating detailed patient history,antibody testing,and cardiac diagnostics in the best attempt to detect abnormalities before sudden cardiac death.This approach aims to identify individuals at higher risk of cardiac events after COVID-19 vaccination and guide appropriate clinical management.It is prudent for each primary care physician to have a pre-established plan when addressing this issue in their practice.
基金supported by the Shanghai Municipal Health Commission(201840063,201801075)the Science and Technology Commission of Shanghai Municipality(18441903300).
文摘Background:Spontaneous intracerebral hemorrhage(ICH)is a severe neurological emergency with high morbidity and mortality.The effectiveness of surgical intervention remains controversial,partly due to significant heterogeneity among patients.Traditional clinical criteria often fail to identify those most likely to benefit from surgery.Methods:This nationwide retrospective study in China included 2,167 ICH patients from 31 hospitals.Using machine learning techniques,we integrated clinical and radiomic data to perform unsupervised clustering and identify distinct phenogroups.Dimensionality reduction and cross-validation were applied to minimize overfitting.External validation was conducted using data from the INTERACT3 trial,and a prospective cohort was used to assess real-world applicability.Results:Three phenogroups were identified.Among them,only Phenogroup 1-characterized by older age,moderate hematoma volume,and intermediate Glasgow Coma Scale scores-showed significant benefit from early surgical intervention,with a 42%reduction in 3-month mortality and improved functional outcomes.In contrast,surgery did not significantly affect outcomes in Phenogroups 0 and 2.These findings were consistent across multiple machine learning models and validated externally.Conclusion:Machine learning-driven phenotypic stratification can effectively identify ICH patients who are most likely to benefit from surgical treatment.This approach supports personalized treatment strategies and may improve clinical decision-making in ICH management.Further validation in diverse populations is warranted.
文摘The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide,having applications in environmental and geophysical engineering.This study explores the impact of Navier slip conditions on exchange flows within a long channel connecting two large reservoirs of differing salinity.These horizontal density gradients drive the flow.We modify the recent one-dimensional theory,developed to avoid runaway stratification,to account for the presence of uniform slip walls.By adjusting the parameters of the horizontal density gradient based on the slip factor,we resolve analytically various flow regimes ranging from high diffusion to transitional high advection.These regimes are governed by physical parameters like channel aspect ratio,slip factor,Schmidt number,and gravitational Reynolds number.Our solutions align perfectly with ones in the no-slip limit.More importantly,under the conditions of no net flow across the channel and high Schmidt number(where stratification is concentrated near the channel’s mid-layer),we derive a closed-form solution for the slip parameter,aspect ratio,and gravitational Reynolds number that describes the interface’s behavior as a sharp interface separating two distinct zones.This interface,arising from hydrostatic wall gradients,ultimately detaches the low-and high-density regimes throughout the channel when the gravitational Reynolds number is inversely proportional to the aspect ratio for a fixed slip parameter.This phenomenon,observed previously in 2D numerical simulations with no-slip walls in the literature,is thus confirmed by our theoretical results.Our findings further demonstrate that wall slip leads to distinct and diverse flow regimes.
文摘BACKGROUND The categorization and assessment of diabetes-related risks during Ramadan have evolved significantly over three decades.Research interest in the health effects of fasting has grown significantly,with diabetes emerging as the most extensively studied condition.AIM To explore the historical development of risk stratification approaches for Ramadan fasting in people with diabetes,culminating in the 2021 International Diabetes Federation-Diabetes and Ramadan(IDF-DAR)risk assessment tool.We also evaluated the evidence for its validation and real-world utility.METHODS The PubMed and Google Scholar databases were searched using the term“Diabetes AND Ramadan AND Risk Assessment”.Eligible studies included fulltext articles on risk stratification concept and tools for diabetes during Ramadan.Selected studies were reviewed and synthesized thematically.RESULTS Risk categorization began with a dichotomy and tripartite models and progressed to a four-tier narrative scale.In 2021,the IDF-DAR tool introduced a point-based system with three risk categories.Validation studies across diverse populations demonstrated strong predictive value,though moderate inter-clinician variability and potential overestimation in type 2 diabetes cases were noted.CONCLUSION The IDF-DAR risk stratification tool significantly advances individualized diabetes care during Ramadan.Its conservative bias in some populations and variability in physician scoring suggests the need for standardized training.
基金Supported by the National Natural Science Foundation of China(No.42030402)the Program of Shanghai Subject Chief Scientist(No.23XD1401200)collected onboard of R/Vs Dongfanghong 2 and Lanhai 101 implementing the open research cruises(Cruise Nos.NORC 2013-01,NORC 2015-01,NORC 2018-01,NORC 2021-01)supported by NSFC Shiptime Sharing Project(Nos.41249901,41449901,41749901,42049901)。
文摘The property of water mass plays an important role in determining the distribution of phytoplankton in the ocean.In the Yellow Sea,summer stratification constrains water exchange and differentiates the properties of the Yellow Sea Cold Water Mass(YSCWM)and surface water,which in turn affects the spatiotemporal patterns of phytoplankton communities.Here,based on four summer cruises in the Yellow Sea,we examined the response of phytoplankton pigment assemblages to three water masses,including surface water(water massⅠ,WM-Ⅰ),thermocline water(WM-Ⅱ),and the YSCWM(WM-Ⅲ).Based on the opportunities for group dominance across the four cruises,Cyanophyceae,Haptophyceae,Chlorophyceae,and Cryptophyceae preferred living in WM-Ⅰ,characterized by relatively higher temperature and light intensity but lower nutrients;Bacillariophyceae,Chlorophyceae,Cyanophyceae,and Dinophyceae dominated in WM-Ⅲ,with relatively lower temperature and light intensity but higher nutrients.In comparison,the highest diversity of the dominant pigment groups was observed in WM-Ⅱ with intermediate temperature,light,and nutrient levels.The Dirichlet regression model identified the key environmental factors driving changes in phytoplankton assemblages in WM-Ⅰ,Ⅱ,and Ⅲ as dissolved inorganic phosphate(DIP),DIP and light,and temperature and ammonium,respectively.Under the impact of global environmental change,the fluctuations of key driving forces and their potential ecological implications need further investigation.
