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 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.

Objective To analyze the economic cost of self-monitoring of gestational diabetes mellitus, and provide a basis for measuring the economic burden of gestational diabetes mellitus, and to provide a reference for the formulation of intervention development and the adjustment of resource allocation. Methods The individual economic cost of self-monitoring for gestational diabetes mellitus was measured based on a decision tree model, and the total economic cost of self-monitoring for gestational diabetes mellitus in Beijing was estimated. The uncertainty of the model parameters was analyzed using one-way sensitivity analysis. Results The average individual economic cost of gestational diabetes self-monitoring was 1184 RMB, and the individual cost incurred by choosing different types of blood glucose meters ranged from 403 to 18 000 RMB. The average individual economic cost of finger-stick blood glucose monitoring was 606 RMB and the average individual economic cost of continuous glucose monitoring was 2 374 RMB. The total economic cost of gestational diabetes self-monitoring in Beijing was 23.818 0 million RMB, and the total economic cost incurred by choosing different types of blood glucose meters ranged from 0.292 5 to 9.027 9 million RMB. The proportion of the finger-stick blood glucose monitoring had the greatest impact on the robustness of the results. Conclusion Finger-stick blood glucose monitoring is still the dominant self-monitoring method and is less costly than continuous glucose monitoring. Self-monitoring of pregnant women with gestational diabetes mellitus incurs certain economic cost and causes an economic burden on society.

Hypertension, a common risk factor for cardiovascular diseases, has aroused global concern. As breakthroughs have been achieved in the traditional Chinese medicine(TCM) and western medicine theories related to the heart and brain, top international journals such as Science pay increasing attention to the functional interaction between the heart and brain in modern medicine, known as the "heart-brain" axis, also referred to as the "cardiovascular-brain" circuit. The heart and brain interact and influence each other through the "heart-brain" axis. Increasing evidence suggests that the inflammation-regulated "heart-brain" axis plays a crucial role in the occurrence and development of hypertension, offering new insights for the treatment of cardiovascular diseases. In TCM, there is a connection between the heart and brain by the sharing of blood essence, interconnected blood vessels, and shared governance over the mind. Diseases of the heart and brain share common pathological and physiological foundations, similar risk factors, and TCM pathogeneses, which form the basis for simultaneous treatment of heart and brain diseases in TCM. The principle of simultaneous treatment of the heart and brain diseases aligns with the theory of "heart-brain" axis. Modern research has found that the heart and brain are the main target organs of hypertension. Long-term high blood pressure can easily cause structural changes, mainly characterized by left ventricular hypertrophy and dilation, leading to hypertensive heart disease. Hypertension can change the structure, blood supply, and function of the brain, being closely related to cerebral atherosclerosis, cerebral infarction, cerebral hemorrhage, cognitive dysfunction, dementia and other brain diseases. TCM treatment of hypertension has a long history. According to the pathogenesis(Yang hyperactivity and blood stasis) of hypertension, the team has developed the core treatment principle of subduing Yang and activating blood. Through extensive clinical exploration and experimental research, the team has developed an effective prescription called Tianxiong Granules. This prescription has shown definite efficacy in stabilizing blood pressure, ameliorating clinical symptoms, and reducing target organ damage. The protective effects of Tianxiong Granules on the heart and brain are reflected in aspects such as symptoms related to the heart and brain, pharmacological effects on ventricular hypertrophy, and brain protection. The preliminary research by the team found that Tianxiong Granules might treat hypertension by inhibiting sympathetic nerve excitation and renin-angiotensin-aldosterone system(RAAS) and targeting mitochondrial autophagy to regulate the activation of the NOD-like receptor family pyrin domain containing 3(NLRP3) inflammasome. The activation of the NLRP3 inflammasome mediates pyroptosis, which is a key mechanism of hypertension. Next, the team will construct the adeno-associated viruses with downregulated NLRP3 expression via adenoviral vectors and use viral tracing technology, left stellate ganglionectomy, and a cardiac denervation model to reveal the mechanism of Tianxiong Granules in regulating the heart-brain interaction in hypertensive rats, from both in vivo and in vitro perspectives. In summary, exploring clinical treatment strategies for hypertension from both heart and brain based on the "heart-brain" axis is likely to be a new direction for the development of drugs for hypertension and offers a new target and basis for intervention in hypertension.
Humans ; Hypertension/physiopathology* ; Drugs, Chinese Herbal/administration & dosage* ; Brain/physiopathology* ; Animals ; Heart/physiopathology*

The quest to decipher the determinants of human longevity has intensified with the rise in global life expectancy. Long-lived individuals (LLIs), who exceed the average life expectancy while delaying age-related diseases, serve as a unique model for studying human healthy aging and longevity. Longevity is a complex phenotype influenced by both genetic and non-genetic factors. This review paper delves into the genetic, epigenetic, metabolic, immune, and environmental factors underpinning the phenomenon of human longevity, with a particular focus on LLIs, such as centenarians. By integrating findings from human longevity studies, this review highlights a diverse array of factors influencing longevity, ranging from genetic polymorphisms and epigenetic modifications to the impacts of diet and physical activity. As life expectancy grows, understanding these factors is crucial for developing strategies that promote a healthier and longer life.
Humans ; Healthy Aging/physiology* ; Longevity/physiology* ; Epigenesis, Genetic ; Life Expectancy ; Exercise ; Aging/genetics* ; Diet ; Aged, 80 and over

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease (CKD). However, existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes. This study presents a smartphone-integrated handheld automated biochemical analyzer (sHABA) designed for point-of-care testing of urinary albumin. The sHABA features a pre-loaded, disposable reagent cassette with reagents for the albumin assay arranged in the order of their addition within a hose. The smartphone-integrated analyzer can drive the reagents following a preset program, to enable automatic sequential addition. The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L. The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity (95.78%) and specificity (90.16%). This research advances the field by providing an automated detection method for albumin in a portable device, allowing even untrained individuals to monitor CKD in real time at the patient's bedside. In the context of promoting tiered diagnosis and treatment, the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

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.