Objective To investigate the predictive value of D-dimer for in-hospital mortality and heart failure with reduced ejection fraction (HFrEF) in patients with acute myocardial infarction (AMI). Methods A total of 688 patients with AMI who were hospitalized at Zhongshan Hospital, Fudan University, from January 2020 to December 2023 were retrospectively enrolled. Demographic and clinical characteristics, laboratory findings, echocardiographic parameters, and in-hospital outcomes were collected. Patients were divided into four groups according to D-dimer quartiles, and trend tests were used to compare changes in each indicator with increasing D-dimer levels. The primary endpoints were in-hospital mortality and HFrEF. Multivariable logistic regression analysis was performed to assess the independent associations of D-dimer with in-hospital mortality and HFrEF. Receiver operating characteristic (ROC) curve analysis was used to evaluate its predictive performance. Results Differences and trend tests among four groups for age, creatinine, peak cardiac troponin T, N-terminal pro-brain natriuretic peptide (NT-proBNP), left ventricular ejection fraction, HFrEF incidence, and in-hospital mortality were statistically significant (P＜0.05). Multivariable logistic regression analysis showed that elevated D-dimer was associated with an increased risk of in-hospital mortality in patients with AMI (OR＝1.251, 95%CI 1.093–1.432, P＝0.001), and HFrEF (OR＝1.075, 95%CI 1.028–1.124, P＝0.002). ROC curve analysis showed that the area under the curve (AUC) of D-dimer for predicting in-hospital mortality and HFrEF were 0.871 and 0.716, respectively. At a cutoff value of 0.47 mg/L, the sensitivity and specificity of D-dimer in predicting HFrEF were 81.9% and 52.1%, respectively; at a cutoff value of 2.18 mg/L, the sensitivity and specificity of D-dimer in predicting in-hospital mortality were 63.6% and 93.8%, respectively. Restricted cubic spline analysis demonstrated significant non-linear associations between D-dimer and the risks of in-hospital mortality and HFrEF (P＜0.001). Conclusions Elevated D-dimer level is significantly associated with increased risks of in-hospital mortality and HFrEF in patients with AMI and shows acceptable predictive performance. As a simple and readily available laboratory biomarker, D-dimer may provide useful information for early risk stratification and clinical management in patients with AMI.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.

A 58-year-old female patient with anti-synthetase syndrome received compound sulfamethoxazole [containing trimethoprim (TMP) 80 mg and sulfamethoxazole (SMZ) 0.4 g, SMZ-TMP] 3 tablets thrice daily orally for the treatment of Pneumocystis jirovecii pneumonia. Before medication, the patient′s blood potassium was 3.3 mmol/L and blood chlorine was 116 mmol/L. Three days after SMZ-TMP treatment, the patient′s blood potassium was 5.7 mmol/L, blood chlorine was 114 mmol/L, blood pH was 7.3, urine pH was <5.5, blood chlorine was 114 mmol/L, and bicarbonate was 15 mmol/L. Hyperkalemia type renal tubular acidosis due to SMZ-TMP was considered. The dosage of SMZ-TMP was reduced to 2 tablets once daily orally. After 1 day of diuretic and potassium excretion treatments, the patient′s blood potassium levels returned to normal; after 2 days of the treatments, her blood chlorine was 109 mmol/L and bicarbonate was 17 mmol/L; after 3 days of the treatments, her chest CT showed emphysema in the neck and mediastinum. The dose of SMZ-TMP was changed to 3 tablets thrice daily orally, and at the same time intravenous infusion of ganciclovir 0.3 g twice daily was given. And again, her blood potassium increased and blood pH decreased. Sodium bicarbonate 1 g thrice daily orally was given to correct the acidosis. After adding SMZ-TMP for 2 days, SMZ-TMP dosage was reduced to 2 tablets once daily orally again. Seven days later, the patient′s vital signs were stable, her mediastinal emphysema was significantly improved, her blood potassium was 4.7 mmol/L, and blood pH was 7.4.

A 58-year-old female patient with anti-synthetase syndrome received compound sulfamethoxazole [containing trimethoprim (TMP) 80 mg and sulfamethoxazole (SMZ) 0.4 g, SMZ-TMP] 3 tablets thrice daily orally for the treatment of Pneumocystis jirovecii pneumonia. Before medication, the patient′s blood potassium was 3.3 mmol/L and blood chlorine was 116 mmol/L. Three days after SMZ-TMP treatment, the patient′s blood potassium was 5.7 mmol/L, blood chlorine was 114 mmol/L, blood pH was 7.3, urine pH was <5.5, blood chlorine was 114 mmol/L, and bicarbonate was 15 mmol/L. Hyperkalemia type renal tubular acidosis due to SMZ-TMP was considered. The dosage of SMZ-TMP was reduced to 2 tablets once daily orally. After 1 day of diuretic and potassium excretion treatments, the patient′s blood potassium levels returned to normal; after 2 days of the treatments, her blood chlorine was 109 mmol/L and bicarbonate was 17 mmol/L; after 3 days of the treatments, her chest CT showed emphysema in the neck and mediastinum. The dose of SMZ-TMP was changed to 3 tablets thrice daily orally, and at the same time intravenous infusion of ganciclovir 0.3 g twice daily was given. And again, her blood potassium increased and blood pH decreased. Sodium bicarbonate 1 g thrice daily orally was given to correct the acidosis. After adding SMZ-TMP for 2 days, SMZ-TMP dosage was reduced to 2 tablets once daily orally again. Seven days later, the patient′s vital signs were stable, her mediastinal emphysema was significantly improved, her blood potassium was 4.7 mmol/L, and blood pH was 7.4.