[Objective] To evaluate the efficacy and safety of sacral neuromodulation (SNM) in the treatment of patients with benign prostatic hyperplasia (BPH) complicated with underactive bladder (UAB) who respond poorly to transurethral resection of the prostate (TURP). [Methods] A retrospective analysis was performed on 10 patients with BPH and UAB treated with TURP by the same surgeon in Zhongda Hospital Southeast University during Jan.2018 and Jan.2023.The residual urine volume was not significantly relieved after operation, and the maximum urine flow rate and urine volume per discharge were not significantly improved.All patients underwent phase I SNM, and urinary diaries were recorded before and after surgery to observe the average daily frequency of urination, volume per urination, maximum urine flow rate, and residual urine volume. [Results] The operation time was (97.6±11.2) min.During the postoperative test of 2-4 weeks, if the residual urine volume reduction by more than 50% was deemed as effective, SNM was effective in 6 patients (60.0%). Compared with preoperative results, the daily frequency of urination [(20.2±3.8) times vs. (13.2±3.2) times], volume per urination [(119.2±56.7) mL vs. (246.5±59.2) mL], maximum urine flow rate [(8.7±1.5) mL/s vs. (16.5±2.6) mL/s], and residual urine volume [(222.5±55.0) mL vs. (80.8±16.0) mL] were significantly improved, with statistical significance (P<0.05). There were no complications such as bleeding, infection, fever or pain.The 6 patients who had effective outcomes successfully completed phase II surgery, and the fistula was removed.During the follow-up of 1 year, the curative effect was stable, and there were no complications such as electrode displacement, incision infection, or pain in the irritation sites.The residual urine volume of the other 4 unsuccessful patients did not improve significantly, and the electrodes were removed and the vesicostomy tube was retained. [Conclusion] SNM is safe and effective in the treatment of BPH with UAB patients with poor curative effects after TURP.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

ObjectiveTo improve the quality and service performance of community visual health services in Shanghai, and to establish a set of reasonable and effective evaluation index system for community visual health services. MethodsCentered on the national and Shanghai-based visual health policies and based on the current status and development trends of community visual health service program in Shanghai, the candidate indicators were formed through literature review and expert interviews, firstly. The framework of an evaluation index system was formulated through qualitative research successively, which was further revised and perfected using the Delphi method. Coefficient weights were calculated using the analytic hierarchy process (AHP), culminating in the establishment of the community visual health evaluation index system, lastly. ResultsA total of 22 visual health experts from district-level center for disease control, hospital ophthalmology and leaders in charging of visual health service in community health centers participated in the Delphi questionnaire survey, with a questionnaire recovery rate of 100% and an expert authority coefficient of 0.86, indicating high credibility. After a round of correspondence to experts’ importance ratings and discussions, a comprehensive evaluation index system comprising 3 primary indicators, 12 secondary indicators, and 47 tertiary indicators, along with 5 additional indicators, was finalized. ConclusionAn index system tailored to effective evaluation for community visual health initiatives was drawn up in this study, which can promote the capacity building in community eye health services, facilitating the high-quality development of visual health courses, and enhancing residents’ eye health.

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.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Objective To evaluate the bioequivalence and safety of two pyrazinamide tablets in healthy Chinese subjects.Methods An open,randomized,single-dose,two-sequence,two-cycle,double-cross trial design was used.All 48 healthy subjects(24 in fasting and 24 in fed trial)were randomized to receive a single oral dose of a 0.5 g pyrazinamide tablet(test or reference)per cycle.The plasma concentration of the drug was determined by liquid chromatography coupled to tandem mass spectrometry method.The pharmacokinetic parameters were calculated by WinNonlin v8.2,and the bioequivalence was evaluated by SAS 9.4.Results In the fasting group,the Cmax of the test and reference preparation of pyrazinamide tablets were(13.28±2.82)and(12.88±4.49)μg·mL-1,the AUC0-t were(139.17±26.58)and(138.63±28.92)h·μg·mL-1,the AUC0-∞ were(148.96±33.65)and(148.71±36.97)h·μg·mL-1 respectively.In the fed group,the Cmax of the test and reference preparation of pyrazinamide tablets were(11.89±1.96)and(11.99±1.92)μg·mL-1,the AUC0-t were(138.22±37.21)and(141.68±25.80)h·μg·mL-1,the AUC0-∞ were(152.20±32.41)and(151.04±28.05)h·μg·mL-,respectively.The 90％confidence intervals of Cmax,AUC0-t and AUC0-∞ geometric mean ratios of the test and reference preparation were all within 80.00％to 125.00％.The incidence of adverse events was 16.70％for both the test and reference preparation in the fasting group and 8.30％for both the test and reference preparation in the fed group,all of which were mild in severity.Conclusion The test and reference preparation of pyrazinamide tablets were bioequivalent,safe and well tolerated in healthy Chinese subjects under fasting and fed conditions.

Epigenetic pathways play a critical role in the initiation, progression, and metastasis of cancer. Over the past few decades, significant progress has been made in the development of targeted epigenetic modulators (e.g., inhibitors). However, epigenetic inhibitors have faced multiple challenges, including limited clinical efficacy, toxicities, lack of subtype selectivity, and drug resistance. As a result, the design of new epigenetic modulators (e.g., degraders) such as PROTACs, molecular glue, and hydrophobic tagging (HyT) degraders has garnered significant attention from both academia and pharmaceutical industry, and numerous epigenetic degraders have been discovered in the past decade. In this review, we aim to provide an in-depth illustration of new degrading strategies (2017-2023) targeting epigenetic proteins for cancer therapy, focusing on the rational design, pharmacodynamics, pharmacokinetics, clinical status, and crystal structure information of these degraders. Importantly, we also provide deep insights into the potential challenges and corresponding remedies of this approach to drug design and development. Overall, we hope this review will offer a better mechanistic understanding and serve as a useful guide for the development of emerging epigenetic-targeting degraders.

Objective: To study the relationship between sugar sweetened beverages consumption characteristics and insomnia of college students in Yunnan Province, so as to provide evidence for sleep quality improvement of college students. Methods: A cluster random sampling method was used to select 2 515 college students from two universities (Kunming University and Dali Nursing Vocational College) in Kunming and Dali in Yunnan Province for a longitudinal study, including baseline survey (T1, November 2021) and three follow up surveys (T2: June 2022, T3: November 2022, T4: June 2023). Sugar sweetened beverages consumption of college students was collected by Semi quantitative Food Frequency Questionnaire and insomnia was assessed by Insomnia Severity Index Scale. Sugarsweetened beverages consumption was analyzed by Kruskal-Wallis test. The Mann-Whiter U test and Kruskal-Wallis test were used to compare the detection rate of insomnia in college students with different population characteristics, and the generalized estimating equations model was established to analyze the association between sugar sweetened beverages consumption and insomnia. Results: The reported rate of insomnia among college students from T1 to T4 was 21.2%, 23.6%, 30.5 % and 36.0%, respectively. The median of sugar sweetened beverages consumption per week was 5 (1,9) bottles per person, and there were significant differences in sugar sweetened beverages (carbonated beverages, fruit beverages, tea beverages, milk beverages, energy beverages) consumption among college students with different insomnia status ( χ 2=42.91, 23.67, 29.98, 61.70, 30.82, P <0.01). The analysis of the generalized estimating equation model revealed that the consumption of carbonated beverages ( β= 0.04, 95%CI =0.00-0.08) and the consumption of milk beverages among college students ( β=0.04, 95%CI =0.00-0.09) were correlated with insomnia ( P <0.05). The stratified analysis indicated that consumption of carbonated beverages by female college students was associated with insomnia [ β(95%CI )=0.06(0.01-0.11)]; consumption of milk beverages among college students from middle income family was associated with insomnia [ β (95% CI )=0.05(0.00-0.10)], and consumption of carbonated beverages and fruit beverages from college students with high household economic status were both associated with insomnia [ β (95% CI )=0.35(0.23-0.46), 0.12(0.00-0.24)] ( P <0.05). Conclusion Sugar sweetened beverages, especially carbonated beverages, are associated with insomnia among college students in Yunnan Province.

OBJECTIVE To study the effects of methimazole on the urinary metabolomics of hyperthyroidism rats， and to preliminarily investigate its possible mechanism. METHODS Thirty SD rats were randomly divided into control group， model group and methimazole group， with 10 rats in each group. Except for the control group， the rats in the other two groups were given Levothyroxine sodium tablets 160 mg/kg by intragastric administration for 15 days； at the same time， methimazole group was additionally given methimazole 3.6 mg/kg daily by intragastric administration every day. The basic condition of the rats was observed， and the body weight and anal temperature were measured. After the last medication， the serum levels of triiodothyronine （T3）， tetraiodothyronine （T4）， free triiodothyronine （FT3）， free tetraiodothyronine （FT4）， and thyroid stimulating hormone （TSH） were determined； 24-hour urine was collected on the 15th day after administration. UPLC-TOF-MS was used to analyze the urine metabolomics of rats. Principal component analysis and orthogonal partial least squares-discriminant analysis were used to screen out related differential metabolites， and potential metabolic pathways were analyzed by using HMDB and KEGG. RESULTS Compared with the control group， the rectal temperature， serum levels of T3， T4， FT3 and FT4， the expressions of differential metabolites sebacic acid， cholic acid 3-O-glucuronic acid and N6， N6， N6-trimethyl-L-lysine in urine were significantly up-regulated， while body weight， serum level of TSH， the expressions of deoxycytidine and 2-oxo-4-methylthiobutanoic acid in urine were significantly down-regulated （P＜0.01）. Compared with model group， above indexes of rats were reversed significantly in methimazole group （P＜0.01 or P＜0.05）. Above five differential metabolites were mainly involved in four signaling pathways: pentose and glucuronate interaction， lysine degradation， cysteine and methionine metabolism， and pyrimidine metabolism. CONCLUSIONS Methimazole might improve hyperthyroidism by modulating the four pathways of pentose and glucuronate interaction， lysine degradation， cysteine and methionine metabolism， and pyrimidine metabolism.

Objective To investigate the value of qualitative and quantitative characteristics of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI in preoperative prediction of vessels encapsulating tumor clusters(VETC)pattern in hepatocellular carcinoma(HCC).Methods A total of 234 patients diagnosed with HCC by pathology were analyzed retrospectively.A total of 101 VETC-positive HCC patients and 133 VETC-negative HCC patients were included.All patients were divided into training group and validation group according to 7︰3.The training group data were used to construct a prediction model for VETC-positive HCC.Receiver operating characteristic(ROC)curve was drawn and the area under the curve(AUC)was calculated to verify the diagnostic efficiency of the model.Calibration curve was drawn to verify the calibration of the model.Results Multivariate logistic regression analysis predicted the independent risk factors for VETC-positive HCC:portal phase peripheral washout[odds ratio(OR)6.493],necrosis or severe ischemia(OR 4.756),targetoid transitional phase or hepatobiliary phase(OR 0.307),and lesion to liver signal intensity ratio(LLR)on arterial phase(OR 0.074).The AUC of the training group in predicting VETC-positive HCC was 0.790[95%confidence interval(CI)0.720-0.859].The AUC of the validation group in predicting VETC-positive HCC was 0.779(95%CI 0.668-0.889).The calibration curve diagram showed that the calibration curve(the slope was 0.91)almost coincides with the ideal curve,indicating that the prediction model had better calibration.Conclusion The qualitative and quantitative characteristics of Gd-EOB-DTPA enhanced MRI can be used to predict VETC-positive HCC preoperatively,the independent risk factors of VETC include portal phase peripheral washout,necrosis or severe ischemia,targetoid transitional phase or hepatobiliary phase,and LLR on arterial phase.