Diabetic retinopathy(DR)is one of the most common and serious microvascular complications of diabetes, posing a significant threat to patients' visual health. In recent years, epigenetic mechanisms have garnered increasing attention in the scientific community for their pivotal role in the onset and progression of DR. This paper systematically examines the regulatory roles of epigenetic mechanisms in DR, covering key pathways such as DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs. Under hyperglycemic conditions in diabetes, these epigenetic mechanisms modulate gene expression, thereby influencing critical pathological processes such as oxidative stress, inflammatory responses, mitochondrial dysfunction, and metabolic memory. This article reviews recent advances in epigenetic regulation in DR, providing an in-depth analysis of its underlying molecular mechanisms and complex regulatory networks, and explores the potential of epigenetic markers as diagnostic biomarkers and therapeutic targets. Additionally, this article highlights emerging therapeutic strategies targeting epigenetic modifications, aiming to provide a theoretical foundation and research direction for the early diagnosis and precision treatment of this disease.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Objective To evaluate the short-term effects of comprehensive health management interventions for stroke high-risk population screening on the prevention and treatment of ischemic stroke, and to provide reference and basis for improving and exploring health management and prevention strategies for stroke high-risk population. Methods From 2018 to 2022, 13 community health service centers in Jiading District, Shanghai were selected in the present study. Based on information push platform, stroke risk assessment and health intervention follow-up were conducted for community residents through convenience sampling. The residents were divided into a full course intervention group (intervention group) and a routine intervention group (control group) according to different health intervention measures and forms. The incidence of ischemic stroke in the two groups of survey subjects was tracked within 36 months. Results A total of 52144 subjects were included in the study. The total number of patients in the full course intervention group was 14227, with an incidence density of 577.32/100 000 (556.49/100 000-598.12/100 000), which was lower than that of the conventional intervention group (37 917), with an incidence density of 1 485.47/100 000 (1 464.99/100 000-1 505.94/100 000) (χ²=2490.212, P<0.001). The relative risk of the full course intervention group was 0.39, and the relative risk of stroke risk factors in the full course intervention group from low to high was 0.33, 0.43, 0.45, and 0.49, respectively. The incidence density of males in the full course intervention group was 660.76 (627.46/100 000 - 694.05/100 000), with a relative risk of 0.43, and the incidence density of female patients was 509.71/100 000 (483.37/100 000 - 536.05/100 000), with a relative risk of 0.35. The overall incidence density of the population under 62 years old gourp, 62-75 years old group and over 75 years old group was 197.45/100 000 (173.09/100 000 -221.80/100 000), 608.36/100 000 (580.19/100 000-636.54/100 000), and 1 025.06/100 000 (958.51/100 000-1 091.61/100 000), with relative risks of 0.51, 0.44, and 0.38, respectively. Conclusion Comprehensive health management measures can effectively reduce the short-term risk of ischemic stroke, and should be further promoted and improved to enhance the effectiveness of stroke prevention and control.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

ObjectiveTo compare colonoscopy compliance rates under different screening strategies, to explore ways to enhance colonoscopy compliance among residents with colorectal carcinoma. MethodsResidents aged between 50‒80 years were recruited through extensive community outreach and voluntary participation. A total of 210 630 residents who participated in the colorectal carcinoma screening program in Jiading District, Shanghai, between 2013 and 2019 were selected as the research subjects. All subjects underwent a colorectal carcinoma risk assessment questionnaire survey and two fecal occult blood tests (FOBT). Positive results in the initial screening were defined as a positive questionnaire survey or a positive result in at least one FOBT. Participants with positive initial screening results were advised to undergo colonoscopy screening in a hospital. Colonoscopy results were collected from hospital reports and physician follow-ups. Compliance with colonoscopy was analyzed under different screening strategies to identify possible factors influencing residents’ willingness to undergo the procedure. ResultsA total of 21 403 individuals (10.16%) were identified as positive with the questionnaire survey, 31 595 individuals (15.00%) tested positive with at least one FOBT. Combined questionnaire and FOBT positivity was observed in 3 501 individuals (1.66%). Among the 48 453 individuals with positive initial screening results, 17 230 (35.56%) underwent colonoscopy, and a total of 315 cases of colorectal cancer were detected. The sensitivity, specificity value of FOBT initial screening were 83.81% and 84.66%, respectively. According to the combined risk assessment and FOBT initial screening preliminary screening, the lowest colonoscopy compliance rate (25.63%) was observed among individuals with only a positive questionnaire, and the highest compliance rate (52.55%) was among those with both positive questionnaire survey and two positive FOBT results. Multivariate analysis revealed that FOBT positivity had the greatest impact on colonoscopy compliance. Those with one positive FOBT test result were 2.64 times more likely to undergo colonoscopy screening than those with negative FOBT results, while individuals with two positive FOBT results were 3.18 times more likely to do so. After adjusting for FOBT results, individuals with positive questionnaire survey results were 1.43 times more likely to undergo colonoscopy screening than those with negative results (95%CI: 1.34‒1.52). Compared to questionnaire-based risk assessment, FOBT results were more influential in determining compliance with colonoscopy. ConclusionThe choice of initial screening method significantly impacts residents’ compliance with colonoscopy. While implementing colorectal carcinoma screening programs, it is necessary to strictly adhere to screening protocols, including risk assessment and FOBT. Additionally, efforts should be made to raise public awareness, encouraging residents to actively participate in risk assessments and FOBT, thereby improving their compliance with colonoscopy.

Cell models can simulate a variety of life states and disease developments, including single cells, two-dimensional (2D) cell cultures, three-dimensional (3D) multicellular spheroids, and organoids. They are essential tools for addressing complex biochemical questions. With continuous advancements in biological and cellular analysis technologies, in vitro cellular models designed to answer scientific questions have evolved rapidly. Early in vitro models primarily relied on 2D systems, which failed to accurately replicate the complex cellular compositions and microenvironmental interactions observed in vivo, let alone support sophisticated investigations into cellular biological functions. Subsequent improvements in cell culture techniques led to the development of 3D culture-based models, such as cellular spheroids. The advent of pluripotent stem cell technology further advanced the development of organoid systems, which closely mimic human organ development. Compared to traditional 2D models, both 3D cellular models and organoids offer significant advantages, including personalization and enhanced physiological relevance, making them particularly suitable for exploring molecular mechanisms of disease progression, discovering novel cellular and biomolecular functions, and conducting related studies. The imaging analysis of common cellular models primarily employs labeling-based methods for in situ imaging of targeted genes, proteins, and small-molecule metabolites, enabling further research on cell types, states, metabolism, and drug efficacy. However, these approaches have drawbacks such as poor labeling specificity and complex experimental procedures. By using cells as experimental models, mass spectrometry technology combined with morphological analysis can reveal quantitative changes and spatial distributions of various biological substances at the spatiotemporal level, including metabolites, proteins, lipids, peptides, drugs, environmental pollutants, and metals. This allows for the investigation of cell-cell interactions, tumor microenvironments, and cellular bioinformational heterogeneity. The application of these cutting-edge imaging technologies generates vast amounts of cellular data, necessitating the development of rapid, efficient, and highly accurate image data algorithms for precise segmentation and identification of single cells, multi-organelle structures, rare cell subpopulations, and complex cellular morphologies. A critical focus lies in creating deep learning models and algorithms that enhance the accuracy of cellular visualization. At the same time, establishing more robust data integration tools is essential not only for analyzing and interpreting outputs but also for effectively uncovering the biological significance of spatially resolved mass spectrometry data. Developing a cell imaging platform with high versatility, operational stability, and specificity to enable data interoperability will significantly enhance its utility in clinical research, thereby advancing investigations into disease molecular mechanisms and supporting precision diagnostics and therapeutics. In contrast to genomic, transcriptomic, and proteomic information, the metabolome can rapidly respond to external stimuli and cellular physiological changes within a short timeframe. This rapid and precise reflection of ongoing cellular state alterations has positioned spatial metabolomics as a pivotal approach for exploring the molecular mechanisms underlying physiological and pathological processes in cells, tissues, and organisms. In this review, we summarize research on cell imaging based on mass spectrometry technologies, including the selection and preparation of cell models, morphological analysis of cell models, spatial omics techniques based on mass spectrometry, mass cytometry, and their applications. We also discuss the current challenges and propose future directions for development in this field.

Objective: To investigate the distribution patterns and risk factors for multidrug-resistant bacterial pulmonary infections in patients with oral squamous cell carcinoma (OSCC) undergoing flap reconstruction surgery, and to provide evidence for infection prevention and treatment in this population. Methods: This study was approved by the institutional medical ethics committee. We retrospectively analyzed sputum culture results, antimicrobial susceptibility testing data, and clinical records of 109 OSCC patients undergoing flap reconstruction. Chi-square tests were employed to identify pathogens and risk factors for multidrug-resistant bacteria (MDR) in postoperative pulmonary infections. Multivariate logistic regression analysis was conducted to determine MDR risk factors and establish a nomogram prediction model. The model’s discriminatory power, accuracy, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Among the 109 patients, 52 had negative sputum cultures and 57 tested positive, of whom 14 developed multidrug-resistant (MDR) pulmonary infections. Chi-square analysis revealed that blood transfusion, pre-existing pulmonary diseases, operation time ≥ 490 min, intraoperative blood loss ≥ 400 mL, and abnormal BMI were significant risk factors for postoperative MDR infections (P < 0.05). Multivariate logistic regression identified pre-existing pulmonary diseases, intraoperative blood loss ≥ 400 mL, abnormal BMI, and operative duration ≥ 490 min as independent risk factors for MDR infections (P < 0.05). The nomogram prediction model for MDR infections demonstrated an area under the ROC curve (AUC) of 0.874 (95% CI: 0.775-0.973). The calibration plot showed good agreement between predicted and observed outcomes. DCA indicated a net clinical benefit when the threshold probability for high-risk MDR infections ranged from 0.000 to 0.810. Common MDR pathogens included MDR Pseudomonas aeruginosa, MDR Klebsiella pneumoniae, carbapenem-resistant Acinetobacter baumannii (CRAB), and methicillin-resistant Staphylococcus aureus (MRSA). Conclusion Among OSCC patients undergoing flap reconstruction, MDR pulmonary infections were predominantly caused by gram-negative bacteria (including CRAB, MDR Pseudomonas aeruginosa, and MDR Klebsiella pneumoniae along with the gram-positive pathogen MRSA. Pre-existing pulmonary comorbidities, prolonged surgery duration (≥ 490 min), significant intraoperative blood loss (≥ 400 mL), and abnormal BMI were confirmed as independent risk factors for these MDR infections. The nomogram predictive model incorporating these four variables demonstrated clinically reliable accuracy in risk stratification for postoperative MDR pulmonary infections in this patient population.

Objective To explore the efficacy and safety of dapagliflozin in patients with paroxysmal atrial fibrillation (PAF) combined with heart failure with preserved ejection fraction (HFpEF). Methods A total of 120 patients with PAF combined with HFpEF treated at Jinshan Hospital of Fudan University from July 2022 to July 2023 were selected and randomly divided into the dapagliflozin group (n＝60, standard treatment combined with dapagliflozin) and the control group (n＝60, standard treatment combined with placebo). After 12 months of follow-up, the Kansas City Cardiomyopathy Questionnaire-Total Symptom Score (KCCQ-TSS), PAF duration, recurrence rate and frequency of PAF, left atrial diameter, left ventricular end-systolic diameter, left ventricular end-diastolic diameter, left ventricular ejection fraction, P-wave dispersion, blood pressure, plasma N-terminal pro-brain natriuretic peptide (NT-proBNP), estimated glomerular filtration rate (eGFR), and glycated hemoglobin A_1C (HbA_1C) were compared between the two groups. Cardiovascular outcomes and adverse events were observed. Results A total of 10 patients lost to follow-up, and 110 patients were included in the analysis (55 in each group). After 12 months of treatment, the KCCQ-TSS in the dapagliflozin group was significantly higher than that in the control group ([61.68±2.65] points vs [44.98±4.76] points, P＜0.001). The PAF duration in the dapagliflozin group was significantly shorter than that in the control group ([144±18] min vs [270±24] min, P＝0.045). After treatment, frequency of PAF, NT-proBNP levels, left ventricular end-systolic diameter, left ventricular end-diastolic diameter, left atrial diameter, P-wave dispersion, and HbA_1C levels showed statistical differences between the two groups (P＜0.05). The heart failure readmission rate and PAF recurrence rate in the dapagliflozin group were significantly lower than those in the control group (P＜0.05). There was no significant difference in the incidence of adverse events between the two groups during treatment. Conclusions Dapagliflozin improves patients’ quality of life, reduces PAF duration and recurrence rate, decreases heart failure readmission rate, lowers NT-proBNP levels, reverses cardiac remodeling, and demonstrates favorable safety in patients with PAF combined with HFpEF.