Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in re...Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.展开更多
This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the...This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.展开更多
Efficient drug response prediction is crucial for reducing drug development costs and time,but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and i...Efficient drug response prediction is crucial for reducing drug development costs and time,but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings.To address this,we introduced drug response prediction meta-learner(metaDRP),a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks.metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer(GDSC)drug screening and in vivo datasets,while effectively mitigating out-of-distribution problems,making it reliable for translating findings from controlled environments to clinical applications.Additionally,metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action,such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed.This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction,while promoting the integration of few-shot learning in this field.展开更多
Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunat...Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction(FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation,we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods,the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime(3.514 s).展开更多
High-entropy alloys(HEA)are novel materials obtained by introducing chemical disorder through mixing multiple-principal components,performing rather attractive features together with charming and exceptional propertie...High-entropy alloys(HEA)are novel materials obtained by introducing chemical disorder through mixing multiple-principal components,performing rather attractive features together with charming and exceptional properties in comparison with traditional alloys.However,the trade-off relationship is still present between strength and ductility in HEAs,significantly limiting the practical and wide application of HEAs.Moreover,the preparation of HEAs by trial-and-error method is time-consuming and resource-wasting,hindering the high-speed and high-quality development of HEAs.Herein,the primary objective of this work is to summarize the latest advancements in HEAs,focusing on methods for predicting phase structures and the factors influencing mechanical properties.Additionally,strengthening and toughening strategies for HEAs are highlighted,thus maximizing their application potential.Besides,challenges and future investigation direction of HEAs are also identified and proposed.展开更多
Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT h...Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.展开更多
Objective:To investigate whether the presence or absence of improvement in chronic severe functional constipation(CSFC)at the early stage of treatment with electroacupuncture predicts subsequent response or non-respon...Objective:To investigate whether the presence or absence of improvement in chronic severe functional constipation(CSFC)at the early stage of treatment with electroacupuncture predicts subsequent response or non-response,and to determine the optimal treatment duration for assessing subsequent responses to electroacupuncture.Methods:This is a post hoc analysis using data pooled from two large-scale randomized controlled trials.Patients with CSFC were recruited,and those in the electroacupuncture groups were included in the present study.Early improvement was defined as a weekly increase of≥1 complete spontaneous bowel movement(CSBM)compared to baseline.Three treatment response criteria were evaluated:≥3CSBMs per week,overall CSBM response,and sustained CSBM response.Predictive statistics,including sensitivity,specificity,positive predictive value,and negative predictive value,were calculated at weeks1–4.Receiver operating characteristic curves and accuracy rates were used to determine the optimal timepoint for differentiation between responders and non-responders.Results:Cases from a total of 813 participants who received electroacupuncture were analyzed.The proportion of improvers was 40.34%by week 1,increasing to 52.52%by week 4.After 8 weeks of treatment,the response rates were 30.14%,25.83%and 25.58%according to the three aforementioned criteria,respectively.Early improvement was a strong predictor of treatment response,with week 3 demonstrating the highest predictive accuracy.Conclusion:Early improvement with electroacupuncture,especially at week 3,can predict subsequent outcomes.Our findings suggest that acupuncturists may identify non-responders who might require adjustments to therapeutic strategies early in treatment.展开更多
BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumo...BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumor size and control micrometastases,has emerged as a crucial therapeutic strategy for locally advanced gastric cancer.Pathological complete response(pCR),characterized by the absence of viable tumor cells in the resected specimen after neoadjuvant treatment,is recognized as a strong predictor of favorable prognosis.However,the factors influencing the achievement of pCR remain incompletely understood.AIM To identify and analyze the predictive factors associated with achieving pCR after NAC in gastric cancer patients,thereby providing evidence-based guidance for clinical decision-making.METHODS A retrospective analysis was performed on 215 patients from Shandong Cancer Hospital and Tai’an Central Hospital with locally advanced gastric cancer who underwent NAC followed by radical surgery at our hospital between January 2015 and December 2023.Comprehensive clinical and pathological data were collected,including age,gender,tumor location,Lauren classification,clinical staging,chemotherapy regimens,number of chemotherapy cycles,and baseline hematological indicators.The baseline hematological indicators included neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio,albumin level,carcinoembryonic antigen(CEA),and carbohydrate antigen 19-9.Univariate and multivariate logistic regression analyses were employed to determine the independent predictive factors for pCR.RESULTS Among 215 gastric cancer patients,41(19.1%)achieved pCR after NAC.Multivariate analysis identified five independent predictive factors for pCR:Lauren intestinal type[odds ratio(OR)=3.28],lower clinical T stage(OR=2.75),CEA decrease≥70%after NAC(OR=3.42),pre-treatment NLR<2.5(OR=2.13),and≥4 chemotherapy cycles(OR=2.87).The fluorouracil,leucovorin,oxaliplatin,docetaxel regimen achieved the highest pCR rate(27.5%),and oxaliplatin-containing regimens were superior to cisplatin-containing regimens(22.3%vs 12.7%,P=0.034).Patients with both low NLR and platelet-to-lymphocyte ratio had the highest pCR rate(33.8%),while those with both high inflammatory markers had the lowest rate(10.7%).Earlier clinical stage disease(cT3N+vs cT4N+:28.6%vs 13.0%)and lower lymph node burden were associated with higher pCR rates.CONCLUSION The achievement of pCR after NAC in gastric cancer patients is closely associated with Lauren intestinal type,lower clinical T stage,a significant decrease in CEA after chemotherapy,low pre-treatment NLR,and an adequate number of chemotherapy cycles.展开更多
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw...To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.展开更多
BACKGROUND Colorectal cancer(CRC)remains one of the leading causes of cancer-related morbidity and mortality worldwide.Growing evidence suggests that gut microbial dysbiosis plays a crucial role in tumorigenesis and c...BACKGROUND Colorectal cancer(CRC)remains one of the leading causes of cancer-related morbidity and mortality worldwide.Growing evidence suggests that gut microbial dysbiosis plays a crucial role in tumorigenesis and can influence therapeutic responses.AIM To explore the associations between serum S100A12 and soluble CD14(sCD14)levels and gut microbiota alterations in patients with CRC,and to assess the predictive utility of these biomarkers in forecasting chemotherapy response.METHODS A retrospective analysis was conducted on 104 patients diagnosed with advanced CRC(CRC group)and 104 age-matched and sex-matched healthy controls.Serum concentrations of S100A12 and sCD14 were measured using enzyme-linked immunosorbent assay.Fecal samples collected before chemotherapy were subjected to 16S rRNA sequencing to profile gut microbial composition.Pearson correlation analysis was used to evaluate the relationship between biomarker levels and microbial abundance.Receiver operating characteristic(ROC)curves were used to assess the predictive performance of S100A12 and sCD14 for chemotherapy response.RESULTS CRC patients exhibited significantly higher serum levels of S100A12 and sCD14 compared to healthy individuals(P<0.05).Patients with moderate to severe gut dysbiosis showed the highest elevations of these biomarkers(P<0.05).Elevated levels of S100A12 and sCD14 were positively correlated with Fusobacterium nucleatum and Prevotella abundance,and negatively correlated with Faecalibacterium prausnitzii and Akkermansia muciniphila(P<0.05).Both biomarkers significantly decreased following chemotherapy(P<0.05).Non-responders to chemotherapy had higher pre-treatment levels of S100A12 and sCD14 compared to responders(P<0.05).Combined ROC analysis showed improved diagnostic accuracy compared to either marker alone.CONCLUSION Serum S100A12 and sCD14 levels are closely associated with gut microbiota imbalance and chemotherapy response in CRC patients.These markers may serve as promising predictive indicators for treatment efficacy and offer potential value in individualized treatment strategies.展开更多
BACKGROUND Juvenile arthritis damage index(JADI)is a tool that measures the degree of aggressiveness of the juvenile idiopathic arthritis(JIA)course and assesses articular[JADI-articular damage(JADI-A)]and extraarticu...BACKGROUND Juvenile arthritis damage index(JADI)is a tool that measures the degree of aggressiveness of the juvenile idiopathic arthritis(JIA)course and assesses articular[JADI-articular damage(JADI-A)]and extraarticular[JADI-extraarticular damage(JADI-E)]damage.While aggressive JIA often requires early bio-logic disease-modified antirheumatic drugs(bDMARDs),the utility of JADI as a predictor of treatment response remains underexplored.AIM To evaluate the potential of JADI as a predictor of bDMARD treatment response in JIA patients.METHODS This prospective study included 112 highly active non-systemic JIA biologic-naïve patients with a mean age of 12.2±4.6 years and a median disease duration of 2.5(interquartile range:1-5)years.Their clinical and radiological assessment,juvenile arthritis disease activity score 71,JADI-A,and JADI-E,were evaluated twice:Before the biologic initiation(baseline)and 12 months after(end of study).At baseline,50%had any damage,with 43%with articular damage and 23%with extraarticular damage.RESULTS During the study,JADI-A/JADI-E improved(33.9%/9.8%),worsened(8.9%/5.4%),or remained unchanged(57.1%/84.8%).Patients with baseline damage had higher markers of JIA activity:Polyarticular course,earlier onset age,ANA-positivity,and more active joints.Patients without initial structural damage(JADI“-”)were more likely(odds ratio=3.8,95%confidence interval:1.6-9.0,P<0.004)to achieve a low degree of activity or remission(46.2%),while on biological therapy,their scores were comparable to JADI-positive(18.3%).Pre-biological joint damage according to the JADI-A index(P=0.003),wrist(P=0.035),elbow(P=0.027),cervical spine limitation of motion(P=0.051),and erosions confirmed by magnetic resonance imaging(P=0.002),were associated with poor response to biological treatment and follow-up JIA activity.CONCLUSION Baseline structural damage in JIA is associated with diminished bDMARDs efficacy,increased disability,and shorter remission duration.JADI enhances conventional clinical risk stratification by facilitating timely initiation of bDMARDs,adherence to treat-to-target strategy and tailored patient care.展开更多
The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption an...The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption and total discharge of industrial waste water, gas and residue of China via the energy and environmental quality data from 1978 to 2009 in China by use of GM（1,1） model and gravity center model, based on which the paper also analyzes the dynamic variation in regional difference in energy production, consumption and environmental quality and their relationship. The results are shown as follows. 1） The gravity center of energy production is gradually moving southwestward and the entire movement track approxi-mates to linear variation, indicating that the difference of energy production between the east and west, south and north is narrowing to a certain extent, with the difference between the east and the west narrowing faster than that between the south and the north. 2） The gravity center of energy consumption is moving southwestward with perceptible fluctuation, of which the gravity center position from 2000 to 2005 was relatively stable, with slight annual position variation, indicating that the growth rates of all provinces and autonomous regions are basically the same. 3） The gravity center of the total discharge of industrial waste water, gas and residue is characterized by fluctuation in longitude and latitude to a certain degree. But, it shows a southwestward trend on the whole. 4） There are common ground and discrepancy in the variation track of the gravity center of the energy production consumption of China, and the comparative analysis of the gravity center of them and that of total discharge of industrial waste water, gas and residue shows that the environmental quality level is closely associated with the energy production and consumption （especially the energy consumption）, indicating that the environment cost in economy of energy is higher in China.展开更多
Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0...Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0 hormone receptor （HR）-positive invasive breast cancer were treated with anastrozole for 16 weeks before surgery. New slides of tumor specimens taken before and after treatment were conducted centrally for biomarker analysis and classified using the Applied Imaging Ariol MB-8 system. The pathological response was evaluated using the Miller ＆ Payne classification. The cell cycle response was classified according to the change in the Ki67 index after treatment. Multivariable logistic regression analysis was used to calculate the combined index of the biomarkers. Receiver operating characteristic （ROC） curves were used to determine whether parameters may predict response. Results： The correlation between the pathological and cell cycle responses was low （Spearman correlation coefficient =0.241, P〈0.001; Kappa value =0.119, P=0.032）. The cell cycle response was significantly associated with pre-treatment estrogen receptor （ER） status （P=0.001）, progesterone receptor （PgR） status （P〈0.001）, human epidermal growth factor receptor 2 （Her-2） status （P=0.050） and the Ki67 index （P〈0.001）, but the pathological response was not correlated with these factors. Pre-treatment ER levels [area under the curve （AUC） =0.634, 95% confidence interval （95% CI）, 0.534-0.735, P=0.008] and combined index of pre-treatment ER and PgR levels （AUC =0.684, 95% CI, 0.591-0.776, P〈0.001） could not predict the cell cycle response, but combined index including per-treatment ER/PR/Her-2/Ki67 expression levels could （AUC =0.830, 95% CI, 0.759-0.902, P〈0.001）. Conclusions： The combined use of pre-treatment ER/PgR/Her-2/Ki67 expression levels, instead of HR expression levels, may predict the cell cycle response to NET.展开更多
Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining.To address the comprehensive proble...Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining.To address the comprehensive problem of stability in the double mined-out area of the Tong-Lv-Shan(TLS)mine,which employed the dry stacked gangue technology,this paper applies the function fitting theory and a regression analysis method to screen the sensitive interval of four influencing factors based on single-factor experiments and the numerical simulation software FLAC3D.The influencing factors of the TLS mine consist of the column thickness(d),gob area span(D),boundary pillar thickness(h)and height of tailing gangue(H).The fitting degree between the four factors and the displacement of the gob roof(W)is reasonable because the correlation coefficient(R2)is greater than0.9701.After establishing29groups that satisfy the principles of Box-Behnken design(BBD),the dry gangue tailings process was re-simulated for the selected sensitive interval.Using a combination of an analysis of variance(ANOVA),regression equations and a significance analysis,the prediction results of the response surface methodology(RSM)show that the significant degree for the stability of the mined-out area for the factors satisfies the relationship of h>D>d>H.The importance of the four factors cannot be disregarded in a comparison of the prediction results of the engineering test stope in the TLS mine.By comparing the data of monitoring points and function prediction,the proposed method has shown promising results,and the prediction accuracy of RSM model is acceptable.The relative errors of the two test stopes are1.67%and3.85%,respectively,which yield satisfactory reliability and reference values for the mines.展开更多
Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural netw...Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.展开更多
Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack ...Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).展开更多
Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by impleme...Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer,the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general,but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease.However,these new therapeutic strategies do not uniformly benefit all patients.Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies.Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophagogastric cancer to neoadjuvant therapy.Exploring and validating tissue-based biomarkers are ongoing processes.In this review,we discuss the status of several targeted therapies for gastric cancer,as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.展开更多
Patients with locally advanced esophageal cancer have a dismal prognosis when treated exclu- sively by surgery. This fact prompted many investigators to apply neoadjuvant treatment strategies in an e?ort to improve su...Patients with locally advanced esophageal cancer have a dismal prognosis when treated exclu- sively by surgery. This fact prompted many investigators to apply neoadjuvant treatment strategies in an e?ort to improve survival. Results from phase III randomized trials are encouraging however, they revealed 五笔字型计算机汉字输入技术 that only patients with major histopathological response will bene?t from treatment. Therefore, predic- tive molecular markers indicating response or non-response to neoadjuvant treatment would be extremely helpful in selecting patients for current and future treatment protocols. In this paper we review the role of the molecular markers ERCC1 (excision repair cross-complementing 1 gene) and c-erbB-2 (synonym: HER2/neu) in predicting response to radiochemotherapy and outcome for patients with locally advanced resectable esophageal cancers (cT2-4, Nx, M0). The results are promising and it appears that we might expect to unequivocally identify with ERCC1 and c-erbB-2 respectively, approximately up to one third of patients who ful?l the criteria for neoadjuvant treatment for locally advanced esophageal cancer but will not bene?t from our treatment protocol. Integration of such markers in the clinical setting might prevent a substantial number of patients from expensive, non-e?ective and potentially harmful therapies, and could lead to a more individualized type of combined multimodality treatment in the near future.展开更多
The load/unload experiments on rock failure under pressure have been carried out in Material Test System (MTS) in the Laboratory for Non-linear Mechanics of Continuous Media (LNM), Institute of Mechanics, Chinese Acad...The load/unload experiments on rock failure under pressure have been carried out in Material Test System (MTS) in the Laboratory for Non-linear Mechanics of Continuous Media (LNM), Institute of Mechanics, Chinese Academy of Sciences, and load/unload response ratio (LURR) values with strain as response (i.e. inverse elastic constant as response rate) have been obtained. The experimental results are in accordance with theoretical results and those in real earthquakes: LURR rises just before rock failure. So LURR can be used as the precursor of rock failure and earthquake prediction.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金supported by the National Natural Science Foun-dation of China(Grant Nos.:32170680 and T2122018)the Natural Science Foundation of Shanghai,China(Grant No.:21ZR1476000)the CAS Youth Innovation Promotion Association,China(Grant No.:Y2022076).
文摘Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.
基金Project(52108433)supported by the National Natural Science Foundation of ChinaProject(HSR202004)supported by the Open Foundation of National Engineering Research Center of High-Speed Railway Construction Technology(CSU),China+3 种基金Projects(2024RC3170,2021RC4031)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(2024JJ5018,2024JJ5427)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(KQ2402027)supported by the Changsha City Natural Science Foundation,ChinaProjects(2021-Special-08,2022-Special-09)supported by the Science and Technology Research and Development Program Project of China Railway Group Limited。
文摘This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.
基金the grants from the National Natural Science Foundation of China(Grant No.:22173065)the funding from Science&Technology Department of Sichuan Province(Grant No.:2023NSFSC0633).
文摘Efficient drug response prediction is crucial for reducing drug development costs and time,but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings.To address this,we introduced drug response prediction meta-learner(metaDRP),a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks.metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer(GDSC)drug screening and in vivo datasets,while effectively mitigating out-of-distribution problems,making it reliable for translating findings from controlled environments to clinical applications.Additionally,metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action,such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed.This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction,while promoting the integration of few-shot learning in this field.
基金sponsored by the Innovation Foundation for National Natural Science Foundation of China(No.11872312)。
文摘Response prediction is a fundamental yet challenging task in aeronautical engineering,requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction(FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation,we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods,the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime(3.514 s).
基金supported by the National Natural Science Foundation of China(Nos.52375451,52005396)Shandong Provincial Natural Science Foundation,China(Nos.ZR2023YQ052,ZR2023ME087)+6 种基金Shandong Provincial Technological SME Innovation Capability Promotion Project,China(No.2023TSGC0375)Young Taishan Scholars Program of Shandong Province,China(No.tsqn202306041)Guangdong Basic and Applied Basic Research Foundation,China(No.2023 A1515010044)Shandong Provincial Youth Innovation Team,China(No.2022KJ038)Open Project of State Key Laboratory of Solid Lubrication,China(No.LSL-22-11)Young Talent Fund of University Association for Science and Technology in Shaanxi,China(No.20210414)Qilu Youth Scholar Project Funding of Shandong University,China。
文摘High-entropy alloys(HEA)are novel materials obtained by introducing chemical disorder through mixing multiple-principal components,performing rather attractive features together with charming and exceptional properties in comparison with traditional alloys.However,the trade-off relationship is still present between strength and ductility in HEAs,significantly limiting the practical and wide application of HEAs.Moreover,the preparation of HEAs by trial-and-error method is time-consuming and resource-wasting,hindering the high-speed and high-quality development of HEAs.Herein,the primary objective of this work is to summarize the latest advancements in HEAs,focusing on methods for predicting phase structures and the factors influencing mechanical properties.Additionally,strengthening and toughening strategies for HEAs are highlighted,thus maximizing their application potential.Besides,challenges and future investigation direction of HEAs are also identified and proposed.
基金supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-JKCS-23)the Special Research Fund for Central Universities,Peking Union Medical College[No.2022-I2M-C&T-A-014,CAMS Innovation Fund for Medical Sciences(CIFMS)]。
文摘Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.
基金supported by High-Level Chinese Medical Hospital Promotion ProjectHLCMHPP2023089.
文摘Objective:To investigate whether the presence or absence of improvement in chronic severe functional constipation(CSFC)at the early stage of treatment with electroacupuncture predicts subsequent response or non-response,and to determine the optimal treatment duration for assessing subsequent responses to electroacupuncture.Methods:This is a post hoc analysis using data pooled from two large-scale randomized controlled trials.Patients with CSFC were recruited,and those in the electroacupuncture groups were included in the present study.Early improvement was defined as a weekly increase of≥1 complete spontaneous bowel movement(CSBM)compared to baseline.Three treatment response criteria were evaluated:≥3CSBMs per week,overall CSBM response,and sustained CSBM response.Predictive statistics,including sensitivity,specificity,positive predictive value,and negative predictive value,were calculated at weeks1–4.Receiver operating characteristic curves and accuracy rates were used to determine the optimal timepoint for differentiation between responders and non-responders.Results:Cases from a total of 813 participants who received electroacupuncture were analyzed.The proportion of improvers was 40.34%by week 1,increasing to 52.52%by week 4.After 8 weeks of treatment,the response rates were 30.14%,25.83%and 25.58%according to the three aforementioned criteria,respectively.Early improvement was a strong predictor of treatment response,with week 3 demonstrating the highest predictive accuracy.Conclusion:Early improvement with electroacupuncture,especially at week 3,can predict subsequent outcomes.Our findings suggest that acupuncturists may identify non-responders who might require adjustments to therapeutic strategies early in treatment.
文摘BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumor size and control micrometastases,has emerged as a crucial therapeutic strategy for locally advanced gastric cancer.Pathological complete response(pCR),characterized by the absence of viable tumor cells in the resected specimen after neoadjuvant treatment,is recognized as a strong predictor of favorable prognosis.However,the factors influencing the achievement of pCR remain incompletely understood.AIM To identify and analyze the predictive factors associated with achieving pCR after NAC in gastric cancer patients,thereby providing evidence-based guidance for clinical decision-making.METHODS A retrospective analysis was performed on 215 patients from Shandong Cancer Hospital and Tai’an Central Hospital with locally advanced gastric cancer who underwent NAC followed by radical surgery at our hospital between January 2015 and December 2023.Comprehensive clinical and pathological data were collected,including age,gender,tumor location,Lauren classification,clinical staging,chemotherapy regimens,number of chemotherapy cycles,and baseline hematological indicators.The baseline hematological indicators included neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio,albumin level,carcinoembryonic antigen(CEA),and carbohydrate antigen 19-9.Univariate and multivariate logistic regression analyses were employed to determine the independent predictive factors for pCR.RESULTS Among 215 gastric cancer patients,41(19.1%)achieved pCR after NAC.Multivariate analysis identified five independent predictive factors for pCR:Lauren intestinal type[odds ratio(OR)=3.28],lower clinical T stage(OR=2.75),CEA decrease≥70%after NAC(OR=3.42),pre-treatment NLR<2.5(OR=2.13),and≥4 chemotherapy cycles(OR=2.87).The fluorouracil,leucovorin,oxaliplatin,docetaxel regimen achieved the highest pCR rate(27.5%),and oxaliplatin-containing regimens were superior to cisplatin-containing regimens(22.3%vs 12.7%,P=0.034).Patients with both low NLR and platelet-to-lymphocyte ratio had the highest pCR rate(33.8%),while those with both high inflammatory markers had the lowest rate(10.7%).Earlier clinical stage disease(cT3N+vs cT4N+:28.6%vs 13.0%)and lower lymph node burden were associated with higher pCR rates.CONCLUSION The achievement of pCR after NAC in gastric cancer patients is closely associated with Lauren intestinal type,lower clinical T stage,a significant decrease in CEA after chemotherapy,low pre-treatment NLR,and an adequate number of chemotherapy cycles.
基金supported by the China State Railway Group Co.,Ltd.Science and Technology Research and Development Program Project(Grant No.L2024G007)the Natural Science Foundation of Hunan Province(Grant No.2024JJ5427)+1 种基金the National Natural Science Foundation of China(Grant No.52478321,52078485)the Science and Technology Research and Development Program Project of China Railway Group Limited(Grant No.2021-Special-08,2022-Key-06&2023-Key-22).
文摘To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.
文摘BACKGROUND Colorectal cancer(CRC)remains one of the leading causes of cancer-related morbidity and mortality worldwide.Growing evidence suggests that gut microbial dysbiosis plays a crucial role in tumorigenesis and can influence therapeutic responses.AIM To explore the associations between serum S100A12 and soluble CD14(sCD14)levels and gut microbiota alterations in patients with CRC,and to assess the predictive utility of these biomarkers in forecasting chemotherapy response.METHODS A retrospective analysis was conducted on 104 patients diagnosed with advanced CRC(CRC group)and 104 age-matched and sex-matched healthy controls.Serum concentrations of S100A12 and sCD14 were measured using enzyme-linked immunosorbent assay.Fecal samples collected before chemotherapy were subjected to 16S rRNA sequencing to profile gut microbial composition.Pearson correlation analysis was used to evaluate the relationship between biomarker levels and microbial abundance.Receiver operating characteristic(ROC)curves were used to assess the predictive performance of S100A12 and sCD14 for chemotherapy response.RESULTS CRC patients exhibited significantly higher serum levels of S100A12 and sCD14 compared to healthy individuals(P<0.05).Patients with moderate to severe gut dysbiosis showed the highest elevations of these biomarkers(P<0.05).Elevated levels of S100A12 and sCD14 were positively correlated with Fusobacterium nucleatum and Prevotella abundance,and negatively correlated with Faecalibacterium prausnitzii and Akkermansia muciniphila(P<0.05).Both biomarkers significantly decreased following chemotherapy(P<0.05).Non-responders to chemotherapy had higher pre-treatment levels of S100A12 and sCD14 compared to responders(P<0.05).Combined ROC analysis showed improved diagnostic accuracy compared to either marker alone.CONCLUSION Serum S100A12 and sCD14 levels are closely associated with gut microbiota imbalance and chemotherapy response in CRC patients.These markers may serve as promising predictive indicators for treatment efficacy and offer potential value in individualized treatment strategies.
文摘BACKGROUND Juvenile arthritis damage index(JADI)is a tool that measures the degree of aggressiveness of the juvenile idiopathic arthritis(JIA)course and assesses articular[JADI-articular damage(JADI-A)]and extraarticular[JADI-extraarticular damage(JADI-E)]damage.While aggressive JIA often requires early bio-logic disease-modified antirheumatic drugs(bDMARDs),the utility of JADI as a predictor of treatment response remains underexplored.AIM To evaluate the potential of JADI as a predictor of bDMARD treatment response in JIA patients.METHODS This prospective study included 112 highly active non-systemic JIA biologic-naïve patients with a mean age of 12.2±4.6 years and a median disease duration of 2.5(interquartile range:1-5)years.Their clinical and radiological assessment,juvenile arthritis disease activity score 71,JADI-A,and JADI-E,were evaluated twice:Before the biologic initiation(baseline)and 12 months after(end of study).At baseline,50%had any damage,with 43%with articular damage and 23%with extraarticular damage.RESULTS During the study,JADI-A/JADI-E improved(33.9%/9.8%),worsened(8.9%/5.4%),or remained unchanged(57.1%/84.8%).Patients with baseline damage had higher markers of JIA activity:Polyarticular course,earlier onset age,ANA-positivity,and more active joints.Patients without initial structural damage(JADI“-”)were more likely(odds ratio=3.8,95%confidence interval:1.6-9.0,P<0.004)to achieve a low degree of activity or remission(46.2%),while on biological therapy,their scores were comparable to JADI-positive(18.3%).Pre-biological joint damage according to the JADI-A index(P=0.003),wrist(P=0.035),elbow(P=0.027),cervical spine limitation of motion(P=0.051),and erosions confirmed by magnetic resonance imaging(P=0.002),were associated with poor response to biological treatment and follow-up JIA activity.CONCLUSION Baseline structural damage in JIA is associated with diminished bDMARDs efficacy,increased disability,and shorter remission duration.JADI enhances conventional clinical risk stratification by facilitating timely initiation of bDMARDs,adherence to treat-to-target strategy and tailored patient care.
基金National Natural Science Foundation of China,No.41071057National Natural Science Foundation of China,No.41001388 Key Research Institute of Humanities and Social Sciences under the Ministry of Education,No.2009JJD770025
文摘The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption and total discharge of industrial waste water, gas and residue of China via the energy and environmental quality data from 1978 to 2009 in China by use of GM（1,1） model and gravity center model, based on which the paper also analyzes the dynamic variation in regional difference in energy production, consumption and environmental quality and their relationship. The results are shown as follows. 1） The gravity center of energy production is gradually moving southwestward and the entire movement track approxi-mates to linear variation, indicating that the difference of energy production between the east and west, south and north is narrowing to a certain extent, with the difference between the east and the west narrowing faster than that between the south and the north. 2） The gravity center of energy consumption is moving southwestward with perceptible fluctuation, of which the gravity center position from 2000 to 2005 was relatively stable, with slight annual position variation, indicating that the growth rates of all provinces and autonomous regions are basically the same. 3） The gravity center of the total discharge of industrial waste water, gas and residue is characterized by fluctuation in longitude and latitude to a certain degree. But, it shows a southwestward trend on the whole. 4） There are common ground and discrepancy in the variation track of the gravity center of the energy production consumption of China, and the comparative analysis of the gravity center of them and that of total discharge of industrial waste water, gas and residue shows that the environmental quality level is closely associated with the energy production and consumption （especially the energy consumption）, indicating that the environment cost in economy of energy is higher in China.
文摘Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0 hormone receptor （HR）-positive invasive breast cancer were treated with anastrozole for 16 weeks before surgery. New slides of tumor specimens taken before and after treatment were conducted centrally for biomarker analysis and classified using the Applied Imaging Ariol MB-8 system. The pathological response was evaluated using the Miller ＆ Payne classification. The cell cycle response was classified according to the change in the Ki67 index after treatment. Multivariable logistic regression analysis was used to calculate the combined index of the biomarkers. Receiver operating characteristic （ROC） curves were used to determine whether parameters may predict response. Results： The correlation between the pathological and cell cycle responses was low （Spearman correlation coefficient =0.241, P〈0.001; Kappa value =0.119, P=0.032）. The cell cycle response was significantly associated with pre-treatment estrogen receptor （ER） status （P=0.001）, progesterone receptor （PgR） status （P〈0.001）, human epidermal growth factor receptor 2 （Her-2） status （P=0.050） and the Ki67 index （P〈0.001）, but the pathological response was not correlated with these factors. Pre-treatment ER levels [area under the curve （AUC） =0.634, 95% confidence interval （95% CI）, 0.534-0.735, P=0.008] and combined index of pre-treatment ER and PgR levels （AUC =0.684, 95% CI, 0.591-0.776, P〈0.001） could not predict the cell cycle response, but combined index including per-treatment ER/PR/Her-2/Ki67 expression levels could （AUC =0.830, 95% CI, 0.759-0.902, P〈0.001）. Conclusions： The combined use of pre-treatment ER/PgR/Her-2/Ki67 expression levels, instead of HR expression levels, may predict the cell cycle response to NET.
基金Project(2017YFC0602902) supported by the National Science and Technology Pillar Program during the 13th Five-Year Plan Period,ChinaProject(2015CX005) supported by the Innovation Driven Plan of Central South University,ChinaProject(2016zzts445) supported by the Fundamental Research Funds for the Central Universities,China
文摘Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining.To address the comprehensive problem of stability in the double mined-out area of the Tong-Lv-Shan(TLS)mine,which employed the dry stacked gangue technology,this paper applies the function fitting theory and a regression analysis method to screen the sensitive interval of four influencing factors based on single-factor experiments and the numerical simulation software FLAC3D.The influencing factors of the TLS mine consist of the column thickness(d),gob area span(D),boundary pillar thickness(h)and height of tailing gangue(H).The fitting degree between the four factors and the displacement of the gob roof(W)is reasonable because the correlation coefficient(R2)is greater than0.9701.After establishing29groups that satisfy the principles of Box-Behnken design(BBD),the dry gangue tailings process was re-simulated for the selected sensitive interval.Using a combination of an analysis of variance(ANOVA),regression equations and a significance analysis,the prediction results of the response surface methodology(RSM)show that the significant degree for the stability of the mined-out area for the factors satisfies the relationship of h>D>d>H.The importance of the four factors cannot be disregarded in a comparison of the prediction results of the engineering test stope in the TLS mine.By comparing the data of monitoring points and function prediction,the proposed method has shown promising results,and the prediction accuracy of RSM model is acceptable.The relative errors of the two test stopes are1.67%and3.85%,respectively,which yield satisfactory reliability and reference values for the mines.
文摘Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.
基金supported by the National Natural Science Foundation of China(81825009,82071505,81901358)the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2021-I2MC&T-B-099,2019-I2M-5–006)+2 种基金the Program of Chinese Institute for Brain Research Beijing(2020-NKX-XM-12)the King’s College London-Peking University Health Science Center Joint Institute for Medical Research(BMU2020KCL001,BMU2019LCKXJ012)the National Key R&D Program of China(2021YFF1201103,2016YFC1307000).
文摘Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).
基金Supported by Ministry of Education and Research of the Federal Republic of Germany,Grant No.0315508A and No.01IB10004E(to AW),SYS-Stomach to BL,FL and AW)the Deutsche Forschungsgemeinschaft,Grant No.HO 1258/3-1,No.SFB 824 TP Z02 and No.WA 1656/3-1(to AW)
文摘Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer,the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general,but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease.However,these new therapeutic strategies do not uniformly benefit all patients.Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies.Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophagogastric cancer to neoadjuvant therapy.Exploring and validating tissue-based biomarkers are ongoing processes.In this review,we discuss the status of several targeted therapies for gastric cancer,as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.
文摘Patients with locally advanced esophageal cancer have a dismal prognosis when treated exclu- sively by surgery. This fact prompted many investigators to apply neoadjuvant treatment strategies in an e?ort to improve survival. Results from phase III randomized trials are encouraging however, they revealed 五笔字型计算机汉字输入技术 that only patients with major histopathological response will bene?t from treatment. Therefore, predic- tive molecular markers indicating response or non-response to neoadjuvant treatment would be extremely helpful in selecting patients for current and future treatment protocols. In this paper we review the role of the molecular markers ERCC1 (excision repair cross-complementing 1 gene) and c-erbB-2 (synonym: HER2/neu) in predicting response to radiochemotherapy and outcome for patients with locally advanced resectable esophageal cancers (cT2-4, Nx, M0). The results are promising and it appears that we might expect to unequivocally identify with ERCC1 and c-erbB-2 respectively, approximately up to one third of patients who ful?l the criteria for neoadjuvant treatment for locally advanced esophageal cancer but will not bene?t from our treatment protocol. Integration of such markers in the clinical setting might prevent a substantial number of patients from expensive, non-e?ective and potentially harmful therapies, and could lead to a more individualized type of combined multimodality treatment in the near future.
基金This project was sponsored by the National Natural Science Foundation (No. 19732006), China and Ninth Five-year Plan, China Seismological Bureau.
文摘The load/unload experiments on rock failure under pressure have been carried out in Material Test System (MTS) in the Laboratory for Non-linear Mechanics of Continuous Media (LNM), Institute of Mechanics, Chinese Academy of Sciences, and load/unload response ratio (LURR) values with strain as response (i.e. inverse elastic constant as response rate) have been obtained. The experimental results are in accordance with theoretical results and those in real earthquakes: LURR rises just before rock failure. So LURR can be used as the precursor of rock failure and earthquake prediction.
