Sodium glucose cotransporter(SGLT)2 inhibitors,initially developed as antihyperglycemic agents,have demonstrated remarkable cardioprotective effects in numerous clinical trials and are now utilized in the treat-ment of heart failure across a range of ejection fractions.SGLT2 is predominantly expressed in the renal proximal tubules,brain,and epicardial adipose tissue within the human body.Research indicates that the protective effects of SGLT2 inhibi-tors in heart failure are linked to their direct modulation of SGLT2 receptors in both the kidneys and epicardial adipose tis-sue.Furthermore,SGLT is also present in the brain,and recent studies have revealed that SGLT2 inhibitors can enhance sympathetic activity in patients with metabolic syndrome,suggesting a potential neuroregulatory role.Given that excessive activation of the sympathetic nervous system can contribute to the onset and progression of heart failure,this paper reviews the neural regulatory mechanisms of SGLT2 inhibitors to provide insights for a deeper understanding of heart failure and to optimize their clinical application.

Objective To survey the free influenza vaccination status and willingness among the elderly in Deqing County,Zhejiang Province,and analyze the influencing factors.Methods Based on population data and the electronic medical system of Deqing County,the influenza vaccine coverage from 2018 to 2022 after the implementation of the free influenza vaccination policy was calculated.A proportional stratified random sampling method was used to select elderly individuals aged 60 and above from six streets/towns in Deqing County.A questionnaire survey was conducted to assess influenza vaccination behaviors from 2020 to 2022 and vaccination willingness for the 2023 influenza season.The chi-square test and logistic regression were used to analyze potential factors associated with vaccination behavior and subsequent willingness among the elderly.Results An average of influenza vaccines for the elderly per year were 56.2 thousand,with influenza vaccination coverage rate of 46.16%in average over the five years.Among 729 subjects,the vaccination rate increased from 69.3%in 2020 to 75.3%in 2022,and 81.9%of them were willing to receive influenza vaccines in the next flu season.Logistic regression analysis revealed that individuals with agricultural household registration(OR=3.05,95%CI:1.82-8.99),lower education levels(OR=2.82,95%CI:1.15-6.93),awareness of the free vaccine policy(OR=4.65,95%CI:1.82-11.91),and belief in vaccine efficacy(OR=6.61,95%CI:3.20-13.61)had higher vaccination rates.Factors associated with a more positive vaccination willingness included an annual household income of<150 thousand CNY(OR=1.65,95%CI:1.04-2.60),awareness of the free vaccine policy(OR=3.07,95%CI:1.84-5.12),perceived vaccine efficacy(OR=4.39,95%CI:2.77-6.96),and perceived vaccine safety(OR=1.57,95%CI:1.01-2.43).The main reasons for non-vaccination were concerned about the side effects of the vaccine(37.9%),thoughts that they were in good health and did not need to be vaccinated(30.3%),and influenza vaccines were ineffective(21.2%).The main reasons for vaccination were to protect themselves(89.1%),vaccination was free(65.5%)and to protect their families(60.6%).Conclusion The elderly in Deqing County had a relatively high influenza vaccination rate and follow-up vaccination willingness rate,but there still existed a gap to reach at target rate recommended by WHO.The local government should strengthen health education related to influenza vaccine knowledge,and publicize free vaccination policy to improve the vaccination rate and willingness in next season.

Aim To elucidate the underlying mecha-nism by which total triterpenoids extracted from Hove-nia dulcis(H-TP)enhance the sensitivity of A549/DDP cells to cisplatin.Methods The ARE-Nrf2 lu-ciferase reporter assay was applied to investigate the impact of H-TP on Nrf2 expression.Western blot was used to detect the protein levels of Keap-1/Nrf2/HO-1,Nrf2-GPX4 signaling pathway,apoptosis-related proteins of Bcl-2 and Bax.Further validation of its effects on Nrf2 was conducted by using Nrf2 activator/inhibitor.Results H-TP could enhance the sensitivi-ty of A549/DDP cells to cisplatin by modulating the expression of apoptosis-related proteins Bax and Bcl-2,inhibiting the Keap-1/Nrf2/HO-1/GPX4 signating pathway in A549/DDP cells,and inducing ferroptosis.Conclusion H-TP enhances the sensitivity of A549/DDP cells to cisplatin by inducing the Nrf2-mediated ferroptosis pathway.

Aim To elucidate the underlying mecha-nism by which total triterpenoids extracted from Hove-nia dulcis(H-TP)enhance the sensitivity of A549/DDP cells to cisplatin.Methods The ARE-Nrf2 lu-ciferase reporter assay was applied to investigate the impact of H-TP on Nrf2 expression.Western blot was used to detect the protein levels of Keap-1/Nrf2/HO-1,Nrf2-GPX4 signaling pathway,apoptosis-related proteins of Bcl-2 and Bax.Further validation of its effects on Nrf2 was conducted by using Nrf2 activator/inhibitor.Results H-TP could enhance the sensitivi-ty of A549/DDP cells to cisplatin by modulating the expression of apoptosis-related proteins Bax and Bcl-2,inhibiting the Keap-1/Nrf2/HO-1/GPX4 signating pathway in A549/DDP cells,and inducing ferroptosis.Conclusion H-TP enhances the sensitivity of A549/DDP cells to cisplatin by inducing the Nrf2-mediated ferroptosis pathway.

Objective:To perform RHD gene detection on a blood sample with serological weak D phenotype.Methods:A specimen received by the People's Hospital of Zhijin County was serologically identified by the microcolumn gel method and saline method.RHD gene detection was conducted by the PCR-SSP method,and the full sequence determination of the 10 exons amplified was performed.The sequencing results were compared with the ISBT database to determine the genotype.Bioinformatics tool was used to predict the functional damage of mutant proteins,and Alphafold-3 was used for tertiary structural modeling of wild-type and mutant RhD proteins,and the structures of the two proteins were compared and analyzed to explore the reasons why mutations lead to weak serological manifestations.Results:The patient's genotype was identified as RHD*DV.5/RHD*01N.01 heterozygote,with the complete deletion of RHD genes on one chromosome,unable to express the D antigen.On the other chromosome,a G＞A mutation occurred at the 697th base of the 5th exon,resulting in a partial D phenotype.This mutation causes internal hydrogen bond changes at the 233 position of RhD protein,resulting in a change in the conformation of the protein,affecting binding to the corresponding antibody.Conclusion:The patient is a heterozygous mutant individual with RHD*DV.5/RHD*01N.01,exhibiting a partial D phenotype serologically.This variation is extremely rare and has been scarcely reported globally.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Infectious keratitis (IK) is a leading cause of blindness worldwide, primarily resulting from improper contact lens use, trauma, and a compromised immune response. The pathogenic microorganisms responsible for IK include bacteria, fungi, viruses, and Acanthamoeba. This review examines standard therapeutic agents for treating IK, including broad-spectrum empiric antibiotics for bacterial keratitis (BK), antifungals such as voriconazole and natamycin for fungal infections, and antiviral nucleoside analogues for viral keratitis (VK). Additionally, this review discusses therapeutic agents, such as polyhexamethylene biguanide (PHMB), for the treatment of Acanthamoeba keratitis (AK). The review also addresses emerging drugs and the challenges associated with their clinical application, including anti-biofilm agents that combat drug resistance and nuclear factor kappa-B (NF-κB) pathway-targeted therapies to mitigate inflammation. Furthermore, methods of Photodynamic Antimicrobial Therapy (PDAT) are explored. This review underscores the importance of integrating novel and traditional therapies to tackle drug resistance and enhance drug delivery, with the goal of advancing treatment strategies for IK.

This study aims to reveal the effect and mechanism of Gentianella turkestanorum total extract(GTI) in ameliorating non-alcoholic steatohepatitis(NASH). UPLC-Q-TOF-MS was employed to identify the chemical components in GTI. SwissTarget-Prediction, GeneCards, OMIM, and TTD were utilized to screen the targets of GTI components and NASH. The common targets shared by GTI components and NASH were filtered through the STRING database and Cytoscape 3.9.0 to identify core targets, followed by GO and KEGG enrichment analysis. AutoDock was used for molecular docking of key components with core targets. A mouse model of NASH was established with a methionine-choline-deficient high-fat diet. A 4-week drug intervention was conducted, during which mouse weight was monitored, and the liver-to-brain ratio was measured at the end. Hematoxylin-eosin staining, Sirius red staining, and oil red O staining were employed to observe the pathological changes in the liver tissue. The levels of various biomarkers, including aspartate aminotransferase(AST), alanine aminotransferase(ALT), hydroxyproline(HYP), total cholesterol(TC), triglycerides(TG), low-density lipoprotein cholesterol(LDL-C), high-density lipoprotein cholesterol(HDL-C), malondialdehyde(MDA), superoxide dismutase(SOD), and glutathione(GSH), in the serum and liver tissue were determined. RT-qPCR was conducted to measure the mRNA levels of interleukin 1β(IL-1β), interleukin 6(IL-6), tumor necrosis factor α(TNF-α), collagen type I α1 chain(COL1A1), and α-smooth muscle actin(α-SMA). Western blotting was conducted to determine the protein levels of IL-1β, IL-6, TNF-α, and potential drug targets identified through network pharmacology. UPLC-Q-TOF/MS identified 581 chemical components of GTI, and 534 targets of GTI and 1 157 targets of NASH were screened out. The topological analysis of the common targets shared by GTI and NASH identified core targets such as IL-1β, IL-6, protein kinase B(AKT), TNF, and peroxisome proliferator activated receptor gamma(PPARG). GO and KEGG analyses indicated that the ameliorating effect of GTI on NASH was related to inflammatory responses and the phosphoinositide 3-kinase(PI3K)/AKT pathway. The staining results demonstrated that GTI ameliorated hepatocyte vacuolation, swelling, ballooning, and lipid accumulation in NASH mice. Compared with the model group, high doses of GTI reduced the AST, ALT, HYP, TC, and TG levels(P<0.01) while increasing the HDL-C, SOD, and GSH levels(P<0.01). RT-qPCR results showed that GTI down-regulated the mRNA levels of IL-1β, IL-6, TNF-α, COL1A1, and α-SMA(P<0.01). Western blot results indicated that GTI down-regulated the protein levels of IL-1β, IL-6, TNF-α, phosphorylated PI3K(p-PI3K), phosphorylated AKT(p-AKT), phosphorylated inhibitor of nuclear factor kappa B alpha(p-IκBα), and nuclear factor kappa B(NF-κB)(P<0.01). In summary, GTI ameliorates inflammation, dyslipidemia, and oxidative stress associated with NASH by regulating the PI3K/AKT/NF-κB signaling pathway.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Mice ; Network Pharmacology ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Chromatography, High Pressure Liquid ; Liver/metabolism* ; Mice, Inbred C57BL ; Humans ; Mass Spectrometry ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.