Objective To investigate the effect of sodium-glucose cotransporter 2 inhibitor (empagliflozin, EMPA) on myocardial remodeling in a mouse uremic cardiomyopathy (UCM) model induced by 5/6 nephrectomy, through the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (PKB/AKT)/p65 signaling pathway. Methods The animals were divided into three groups: Sham group (n=6), UCM group (n=8), and UCM＋EMPA group (n=8). A UCM model was established in C57BL/6N mice using the 5/6 nephrectomy. Starting from 5 weeks post-surgery, EMPA or a placebo was administered. After 16 weeks, blood pressure, serum creatinine, blood urea nitrogen, 24-hour urine glucose and urine sodium were measured. Cardiac structure and function were assessed by echocardiography. Hematoxylin-eosin (HE) staining and Masson trichrome staining were used to observe pathological changes in the heart and kidneys. Wheat germ agglutinin (WGA) staining was used to evaluate myocardial hypertrophy. The real-time quantitative PCR (RT-qPCR) was used to detect the expression levels of myocardial hypertrophy- and fibrosis-related mRNAs. Western blotting was used to detect the expression levels of PI3K, AKT and p65 in myocardial tissues. Results After 16 weeks, UCM group exhibited significantly higher blood pressure, serum creatinine, blood urea nitrogen than sham group (P＜0.01); UCM＋EMPA group exhibited lower blood pressure, serum creatinine, blood urea nitrogen, and higher 24 h urine sodium and glucose than UCM group (P＜0.05). Echocardiographic results showed ventricular remodeling in the UCM group, evidenced by left ventricular wall thickening, left ventricular enlargement, increased left ventricular mass, and decreased systolic function (P＜0.05); ventricular remodeling was alleviated (P＜0.05), though there was no significant improvement in systolic function in UCM＋EMPA group. HE and Masson stainings revealed myocardial degeneration, necrosis, and interstitial fibrosis in UCM group (P＜0.01); the myocardial pathology improved with reduced collagen deposition in UCM＋EMPA group (P＜0.01). WGA staining confirmed myocardial hypertrophy in UCM group (P＜0.01), while myocardial hypertrophy was alleviated in UCM＋EMPA group (P＜0.01). RT-qPCR results showed myocardial hypertrophy- and fibrosis-related genes (NPPA, NPPB, MYH7, COL1A1, COL3A1, TGF-β1) were upregulated in UCM group (P＜0.05), but downregulated in UCM＋EMPA group. Western blotting showed PI3K, p-AKT/AKT ratio, and p-p65/p65 ratio were increased in UCM group, but decreased in UCM＋EMPA group (P＜0.05). Conclusion EMPA can improve myocardial hypertrophy and fibrosis in the UCM mouse model, and it may play the role through inhibiting the PI3K/AKT/p65 signaling pathway.

OBJECTIVES: To develop an E-selectin-targeting nanomedicine delivery system that competitively inhibits E-selectin-neutrophil ligand binding to block neutrophil adhesion to vessels and suppress their recruitment to the lesion sites. METHODS: Doxorubicin hydrochloride (DOX)-loaded liposomes (IEL-Lip/DOX) conjugated with E-selectin-affinity peptide IELLQARC were developed using a post-insertion method. Two formulations [2-1P: Mol(PC): Mol(DPI)=100:1; 2-3P: 100:3] were prepared and their modification density and in vitro release characteristics were determined. Their targeting efficacy was assessed in a cell model of LPS-induced inflammation, a mouse model of acute lung injury (ALI), a rat femoral artery model of physical injury-induced inflammation, and a zebrafish model of local inflammation. RESULTS: The prepared IEL-Lip/DOX 2-1P and 2-3P had peptide modification densities of 4.76 and 7.57 pmoL/cm², respectively. Compared with unmodified liposomes, IEL-Lip/DOX exhibited significantly reduced 48-h cumulative release rates at pH 5.5. In the inflammation cell model, IEL-Lip/DOX showed increased uptake by activated inflammatory endothelial cells, and 2-1P exhibited a higher trans-endothelial ability. In ALI mice, the fluorescence intensity of IEL-Lip/Cy5.5 increased significantly in lung tissues by 53.71% [Z-(2-1P)] and 93.41% [Z-(2-3P)], and 2-1P had an increased distribution by 24.19% in the inflammatory lung tissue compared to normal mouse lung tissue. In rat femoral artery models, 2-1P had greater injured/normal vessel fluorescence intensity contrast. In the zebrafish models, both 2-1P and 2-3P showed increased aggregation at the site of inflammation. CONCLUSIONS This E-selectin-targeting nanomedicine delivery system efficiently targets activated inflammatory endothelial cells to increase drug concentration at the inflammatory site, which sheds light on new strategies for treating neutrophil-mediated inflammatory diseases and practicing the concept of "one drug for multiple diseases".
Animals ; Liposomes ; Rats ; Nanomedicine ; E-Selectin ; Drug Delivery Systems ; Inflammation/drug therapy* ; Mice ; Doxorubicin/analogs & derivatives* ; Zebrafish ; Acute Lung Injury/drug therapy*

Noise-induced hearing loss is a worldwide public health issue that is characterized by temporary or permanent changes in hearing sensitivity. This condition is closely linked to inflammatory responses, and interventions targeting the inflammatory gene tumor necrosis factor-alpha (TNFα) are known to mitigate cochlear noise damage. TNFα-induced proteins (TNFAIPs) are a family of translucent acidic proteins, and TNFAIP6 has a notable association with inflammatory responses. To date, there have been few reports on TNFAIP6 levels in the inner ear. To elucidate the precise mechanism, we generated transgenic mouse models with conditional knockout of Tnfaip6 (Tnfaip6 cKO). Evaluation of hair cell morphology and function revealed no significant differences in hair cell numbers or ribbon synapses between Tnfaip6 cKO and wild-type mice. Moreover, there were no notable variations in hair cell numbers or hearing function in noisy environments. Our results indicate that Tnfaip6 does not have a substantial impact on the auditory system.
Animals ; Mice, Knockout ; Hair Cells, Auditory, Inner/pathology* ; Mice ; Mice, Transgenic ; Hearing Loss, Noise-Induced ; Evoked Potentials, Auditory, Brain Stem/physiology*

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Background Staff in biosafety laboratories (BSL) are more likely to experience occupational burnout and other psychological issues due to their unique working environment and high job demands. However, current research in this field tends to focus on overall analyses, overlooking the internal differences within this group. Objective To explore latent profiles of occupational burnout among BSL workers and their influencing factors, providing a reference for targeted burnout interventions. Methods In 2022, cluster random sampling was used to select 8023 BSL workers in Xinjiang. Data were collected via a questionnaire survey, including a self-designed general information questionnaire, the Maslach Burnout Inventory-General Survey (MBI-GS), and the Effort-Reward Imbalance (ERI) scale, to analyze demographic characteristics, occupational burnout, and job stress status. After data cleaning, latent profile analysis (LPA) was applied to identify burnout profiles, followed by K-means clustering for verification. Logistic regression was used to analyze influencing factors with burnout profiles as the dependent variable. Results After excluding invalid questionnaires, 7924 valid questionnaires were retained, with an recovery rate of 98.77%. The LPA results indicated that occupational burnout among BSL staff was categorized into three latent profiles: a low burnout profile (2208 individuals, 27.86%), a moderate burnout profile (4636 individuals, 58.51%), and a high burnout profile (1080 individuals, 13.63%). A similar grouping was derived from K-means clustering analysis (28.71%, 53.37%, and 17.92%), and a Sankey diagram demonstrated significant overlap between the two classification results. Statistically significant differences were observed across the three latent profiles in all three dimensions of burnout: emotional exhaustion, cynicism, and reduced personal accomplishment. The final logistic regression model revealed that the influencing factors exhibited characteristics of partial overlap and partial differentiation. Among the common influencing factors, the shared protective factors included: age ≥50 years (OR_{moderate burnout}=0.42, OR_{high burnout}=0.45), monthly income of 4000-6000 RMB (OR_{moderate burnout}=0.83, OR_{high burnout}=0.71), good sleep quality (OR_{moderate burnout}=0.70, OR_{high burnout}=0.13) and very good sleep quality (OR_{moderate burnout}=0.51, OR_{high burnout}=0.12), and engaging in physical exercise ≥5 times per week (OR_{moderate burnout}=0.74, OR_{high burnout}=0.48). The protective effects were more pronounced for the high burnout profile. The common risk factors included: 21–30 years of service (OR_{moderate burnout}=1.38, OR_{high burnout}=3.00), ≥31 years of service (OR_{moderate burnout}=1.47, OR_{high burnout}=3.39), working in "other positions" (OR_{moderate burnout}=1.46, OR_{high burnout}=1.56), and occupational stress (ERI>1) (OR_{moderate burnout}=2.62, OR_{high burnout}=3.25), with the risk effects being more significant for the high burnout profile. Regarding differentiated influencing factors, protective factors exclusively associated with the moderate burnout profile included having an associate degree (OR=0.70) and engaging in physical exercise 3-4 times per week (OR=0.74). For the high burnout profile, exclusive protective factors included age 40-50 years (OR=0.56), while exclusive risk factors included having a senior professional title (OR=1.85), holding a managerial role (OR=1.78), a high number of night shifts per month (OR=2.66), and a very high number of night shifts per month (OR=3.93). Conclusion BSL workers exhibit three distinct latent burnout profiles. Occupational burnout requires greater attention, and targeted prevention and intervention strategies should be developed based on worker subtypes.

Objective:To develop a 5-year mortality risk prediction model for patients with small cell neuroendocrine carcinoma of the cervix(SCNEC).Methods:Based on the Surveillance,Epidemiology,and End Results(SEER)database and R software version 4.3.3,variables were screened via Lasso regression,followed by multivariable logistic regression and stepwise regression to develop a 5-year mortality risk prediction model for SCNEC patients.The Akaike Information Criterion(AIC),C-index,receiver operating characteristic(ROC)curve,Hosmer-Lemeshow test,and calibration curve were employed to evaluate the model.Results:Age,M stage,surgical status,and lymph node metastasis were ultimately selected as variables to construct the 5-year mortality risk prediction model for SCNEC patients.The model demonstrated superior predictive performance compared to FIGO staging(P＜0.01).The Hosmer-Lemeshow test yielded a P-value＞0.05.The C-index values for the training and validation sets were 0.808 and 0.755,respectively,with the areas under the ROC curves of 0.826 and 0.744.The calibration curves of the model fluctuated near the diagonal line,indicating good agreement between predicted and observed outcomes.The decision curve analysis demonstrated significant clinical net benefit.Results showed that higher mortality risk was associated with advanced age,M1 status,lymph node metastasis,and lack of surgical opportunity.Conclusions:The model exhibits good discriminatory power and accuracy,providing significant benefits to patients.Enhanced management should be implemented for patients with advanced age,distant metastasis,lymph node metastasis,or ineligibility for surgery.Lymph node metastasis is an independent risk factor for 5-year mortality in patients with SCNEC.

Adrenocortical carcinoma（ACC）is a rare and highly aggressive malignant tumor. Currently，mitotane is the first-line treatment. However，reports on neoadjuvant therapy for ACC using mitotane combined with immune checkpoint inhibitors remain scarce. This article reports a case of ACC. The patient was asymptomatic，and a right adrenal mass was detected during examination. Diagnostic imaging and endocrine evaluation confirmed the diagnosis of ACC. Due to the large tumor size，radical resection was initially considered unfeasible. After 7 months of mitotane therapy and two courses of tislelizumab，significant tumor shrinkage was achieved，allowing for successful open resection of the large right adrenal tumor combined with right nephrectomy. Postoperative histopathological examination confirmed the diagnosis of ACC. During the 3-month postoperative follow-up，no evidence of recurrence or metastasis was observed.

BACKGROUND: Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated. METHODS: Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups. RESULTS: A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes. CONCLUSIONS The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.