Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) block downstream signaling pathways by inhibiting receptor tyrosine kinase activity, consequently suppressing proliferation, invasion and metastasis of tumor cells. EGFR-TKIs have been proven to be highly effective in patients with late non-small cell lung cancer (NSCLC) harboring EGFR sensitive mutations, significantly better than chemotherapy. Third-generation EGFR-TKIs, such as osimertinib, have emerged as the first-line treatment for advanced NSCLC patients with sensitive EGFR mutations. However, there are still some patients who exhibit primary resistance upon initial treatment with EGFR-TKIs. The exact mechanism of primary resistance remains unknown, and may be related to factors such as the structure of EGFR mutation subtypes, concurrent mutations, BIM deletion polymorphism, and high expression of programmed cell death-ligand 1. This review summarizes the molecular mechanisms of primary resistance to EGFR-TKIs and discusses potential therapeutic strategies, with the goal of optimizing precision targeted therapy for NSCLC patients.

Diabetic cataract is a prevalent and severe complication of diabetes mellitus. Its pathogenesis is complex and driven by multiple metabolic disorders. Conventional research methods are insufficient to fully clarify its complex pathophysiological processes. As a high-throughput and unbiased systems biology approach, metabolomics allows comprehensive profiling of small-molecule metabolites in biological systems, serving as a robust tool for exploring the metabolic signatures underlying this disease. This review systematically summarizes recent progress in the application of metabolomics to diabetic cataract research, with a focus on technological innovations in sample pretreatment, data acquisition, and bioinformatics analysis. This article elaborates on how these advances contribute to the identification of disease-specific biomarkers, the clarification of intrinsic molecular mechanisms, and the discovery of potential therapeutic targets, ultimately advancing the translational application of metabolomics in the early clinical diagnosis, prognostic assessment, and development of innovative therapeutic strategies for diabetic cataract.

Objective: To create a prediction model for stroke risk among middle-aged and elderly populations, so as to provide a basis for early identification of high-risk population for stroke. Methods: From October to December 2023, residents aged ≥45 years in Chongchuan District, Nantong City, Jiangsu Province were selected using a multi-stage stratified random sampling method. The demographic information, life behavior, and chronic disease data were collected through a questionnaire survey. The standardized prevalence of stroke was calculated using data from the seventh National Population Census. The subjects were randomly divided into the training set and the internal validation set according to the ratio of 8∶2. The basic demographic information, life behavior, and chronic diseases of residents aged ≥45 years in Rugao City were collected from July to August 2023 as the external validation set. Predictive factors were selected using multivariable logistic regression model, and a nomogram for stroke among residents aged ≥45 years was established. The prediction effect was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curve, and Hosmer-Lemeshow goodness of fit test. Results: A total of 6 290 residents aged ≥45 years were included, including 2 975 males (47.30%) and 3 315 females (52.70%). The average age was (61.90±10.20) years. The prevalence of stroke was 3.80%, and the standardized prevalence was 3.36%. The multivariable logistic regression showed that age, smoking, hypertension, and hyperlipidemia were predictors of stroke risk among residents aged ≥45 years, and the prediction model was ln[p/(1-p)]=-4.619+0.046×age+0.383×smoking+0.887×hypertension+0.678×hyperlipidemia. The AUC values of the training set, internal validation set, and external validation set were 0.748, 0.755, and 0.738, respectively. The consistency indexes were 0.748, 0.755, and 0.738, respectively. The Hosmer-Lemeshow goodness of fit test showed a good fitting effect (P>0.05). Conclusion The prediction model based on age, smoking, hypertension, and hyperlipidemia has good discrimination and calibration, and can be used to predict the risk of stroke among middle-aged and elderly populations aged ≥45 years.

OBJECTIVE To investigate the effects of evolocumab combined with atorvastatin on post-percutaneous coronary intervention （PCI） efficacy and coronary microcirculation in patients with acute ST-segment elevation myocardial infarction （STEMI）. METHODS This retrospective cohort study included 194 hospitalized patients with acute STEMI who underwent PCI in the First Affiliated Hospital of Nanyang Medical College from Jan. 2022 to Dec. 2024. The patients were divided into control group （100 cases） and combination therapy group （94 cases） according to the different therapy plans. The control group was given Atorvastatin calcium tablets orally， at a dose of 20 mg， once a day. On the basis of the control group， the combination therapy group received an initial injection of Evolocumab injection 140 mg within 24 hours after PCI， followed by subsequent injections every 2 weeks. Both groups received continuous treatment for at least 30 days. Blood lipid indicators [total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C） and high-density lipoprotein cholesterol]， inflammation indicators [C-reactive protein （CRP） and interleukin-6 （IL-6）]， vascular Δ 基金项目河南省医学科技攻关计划项目（No.LHGJ20230971） endothelial function and microcirculation indicators [fibrinogen， ankle-brachial index （ABI） and nitric oxide （NO）]， cardiac function indicators [left ventricular ejection （LVEF）， left ventricular end-diastolic diameter and left ventricular end-systolic diameter] before treatment and after 30 days of treatment， as well as the occurrence of adverse reactions during the treatment， were compared between the two groups. RESULTS Before treatment， there were no statistically significant differences in the levels of the indicators of blood lipid， inflammation， vascular endothelial function and microcirculation， and cardiac function between the two groups （P＞0.05）. After 30 days of treatment， the levels of TC， TG， LDL-C， CRP， IL-6 and fibrinogen in both groups were significantly reduced compared to those before treatment within the same group. The levels of NO and LVEF in both groups， as well as ABI in the combination therapy group， were significantly elevated compared to those before treatment within the same group. Moreover， the improvements in the levels of TC， LDL-C， CRP， IL-6， fibrinogen and NO in the combination therapy group were more pronounced than those in the control group during the same period （P＜0.05）. There were no statistically significant differences in the incidence of liver function impairment， gastrointestinal adverse reactions， or the overall incidence of adverse reactions between the two groups （P＞0.05）. CONCLUSIONS Compared with atorvastatin alone， evolocumab combined with atorvastatin more effectively reduces the blood lipid levels， improves the inflammatory status， vascular endothelial function and coronary microcirculation in patients with acute STEMI after PCI， while the safety is comparable to that of atorvastatin alone.

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

OBJECTIVES: To explore the predictive factors for hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants and to construct a nomogram prediction model for hsPDA occurrence in this population. METHODS: A retrospective analysis was conducted on the clinical data of preterm infants with gestational age <32 weeks diagnosed with patent ductus arteriosus (PDA) who were delivered at Nanjing Women and Children's Healthcare Hospital from January 2020 to December 2022. The subjects were divided into an hsPDA group (52 cases) and a non-hsPDA group (176 cases) based on the presence of hsPDA. Univariate analysis and multivariate logistic regression analysis were performed to screen predictive variables regarding the general information of the infants at birth, maternal pregnancy and delivery conditions, and relevant indicators during hospitalization. A nomogram prediction model for hsPDA occurrence was constructed using R software in preterm infants. Internal validation was performed using the Bootstrap method. Finally, the predictive model was evaluated for calibration, discrimination ability, and clinical utility. RESULTS: Multivariate regression analysis showed that the ratio of the left atrium to aorta diameter (LA/AO), mode of delivery (vaginal), and duration of mechanical ventilation were independent predictive factors for hsPDA in preterm infants (P<0.05). Based on the results of univariate analysis and multivariate logistic regression analysis, variables used to construct the nomogram prediction model for hsPDA risk included: LA/AO ratio, mode of delivery (vaginal), duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy. The area under the receiver operating characteristic curve for this model was 0.876 (95%CI: 0.824-0.927), and the calibrated curve was close to the ideal reference line, indicating good calibration. The Hosmer-Lemeshow test demonstrated that the model fit well, and the clinical decision curve was above the extreme curves. CONCLUSIONS The nomogram prediction model, constructed using five variables (LA/AO ratio, vaginal delivery, duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy), has reference significance for predicting the occurrence of hsPDA in preterm infants and provides valuable guidance for the early clinical identification of hsPDA.
Humans ; Ductus Arteriosus, Patent/etiology* ; Nomograms ; Female ; Infant, Newborn ; Infant, Premature ; Retrospective Studies ; Male ; Hemodynamics ; Logistic Models ; Pregnancy

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

Objective:To compare the application of Micronutritional Risk Assessment(MNA),Universal Screening Tool for Malnutrition(MUST)and Nutritional Risk Screening 2002(NRS2002)in nutritional risk assessment among patients with digestive tract tumors during perichemotherapy,based on the Global Leadership Initiative on Malnutrition(GLIM)standard.Methods:A prospective cross-section study was conducted,including 114 patients with digestive tract tumors hospitalized by Department of General Surgery,Huangshan Shoukang Hospital from January 2020 to December 2021.All patients were evaluated by GLIM assessment,the correlation between GLIM and MNA,MUST and NRS 2002 screening results was compared,and the consistency among different methods was compared.Patients were divided into malnourished group(nutritional risk group)or normal nourished group according to the results of the three tools.The differences in single anthropometric or test indicators between the groups were compared.Results:According to GLIM,the proportion of malnutrition was 36.8%.The proportion of malnutrition evaluated by MNA,MUST,and NRS2002 were 63.2%,47.4%,and 32.5%,respectively.The sensitivity and negative predictive value of MNA in assessing nutrition-related risks were the highest,while the specificity,Jorden index,Kappa value and positive predictive value of NRS2002 were the highest.There were statistical differences in levels of body mass index,hemoglobin(Hb),triglyceride,total cholesterol,albumin,prealbumin(P-ALB),blood creatinine,lymphocyte counts,and hospitalization costs between two groups assessed by three different tools(P<0.05).Levels of Hb and P-ALB were statistically different between the two groups of the three screening tools.Conclusion:Based on GLIM evaluation results,MNA and other nutritional screening tools are applicable to the assessment of nutritional risks of patients with gastrointestinal cancer during perichemotherapy due to the joint evaluation of measurement indicators.The MNA is more recommended with the highest detection rate and sensitivity for nutritional risks assessment.

ObjectiveTo investigate the effect and potential mechanism of caffeic acid （CA） on severe acute pancreatitis （SAP） induced by caerulein combined with lipopolysaccharide （LPS）， and to provide a basis for the research on novel drugs for the treatment of SAP. MethodsC57BL/6J mice， aged 6 weeks， were divided into control group， model group， CA group， and octreotide acetate （OA） group， with 6 mice in each group. The mice in the control group were given injection of normal saline， and those in the other groups were given intraperitoneal injection of caerulein combined with LPS to establish a mouse model of SAP. At 1 hour after the first injection of caerulein， the mice in the CA group and the OA group were given intraperitoneal injection of CA or subcutaneous injection of OA at an interval of 8 hours. The general status of the mice was observed after 24 hours of modeling， and serum， pancreas， lung， and colon samples were collected. HE staining was used to observe the histopathological changes of the pancreas and lungs， and the serum levels of α-amylase， lipase， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， alanine aminotransferase， aspartate aminotransferase， and creatinine were measured. RT-PCR was used to measure the expression of proinflammatory factors in the pancreas and lungs； myeloperoxidase （MPO） immunohistochemistry was used to observe the degree of neutrophil infiltration； Western blot was used to measure the activation of nuclear factor-kappa B （NF-κB） and the level of citrullinated histone H3 （CitH3）， a marker for the formation of neutrophil extracellular traps （NETs）， in the pancreas and lungs， as well as the expression level of ZO-1 in colon tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the Dunnett’s t-test was used for further comparison between two groups. ResultsCompared with the control group， the model group had severe injury in the pancreas and lungs and significant increases in the activity of serum α- amylase and lipase and the levels of the proinflammatory cytokines IL-6， interleukin-1β （IL-1β）， and TNF-α in serum and lung tissue （all P<0.05）， as well as significant increases in NF-κB activation， neutrophil infiltration， and the formation of NETs in the pancreas and lungs （all P<0.05）. Compared with the model group， the CA group had alleviated pathological injury of the pancreas and lungs and significant reductions in the activity of serum α-amylase and the levels of the proinflammatory cytokines IL-6， IL-1β， and TNF-α in serum and lung tissue （all P<0.05）， as well as significant reductions in NF-κB activation， neutrophil infiltration， and the formation of NETs in the pancreas and lungs （all P<0.05）. ConclusionCA can alleviate SAP induced by caerulein combined with LPS in mice， possibly by inhibiting neutrophil recruitment and the formation of NETs.

Background: Immunocompromised patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection often have prolonged viral shedding, and some are clinically suspected of reinfection with different SARSCoV-2 variants. However, data on this issue are limited. This study investigated the SARS-CoV-2 variants in serially collected respiratory samples from immunocompromised patients with prolonged viral shedding for over 12 weeks or relapsed viral shedding after at least 2 weeks of viral clearance. Materials and Methods: From February 2022 to September 2023, we prospectively enrolled immunocompromised patients with coronavirus disease 2019 who had hematologic malignancies or had undergone transplantation and were admitted to a tertiary hospital. Weekly saliva or nasopharyngeal swabs were collected from enrolled patients for at least 12 weeks after diagnosis. Genomic RNA polymerase chain reaction (PCR) was performed on samples, and those testing positive underwent viral culture to isolate the live virus. Spike gene full sequencing via Sanger sequencing and real-time reverse transcription-PCR for detecting mutation genes were conducted to identify SARSCoV-2 variants. Results: Among 116 enrolled patients, 20 with prolonged or relapsed viral shedding were screened to identify the variants. Of these 20 patients, 7 (35%) exhibited evidence of re-infection; one of 8 patients with prolonged viral shedding and 6 of 12 with relapsed viral shedding were reinfected with SARS-CoV-2. Conclusion Our data suggest that approximately one-third of immunocompromised patients with persistent or relapsed viral shedding had reinfection with different variants of SARS-CoV-2.