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.

[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.

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.

BACKGROUND: Pre-transplant exposure to immune checkpoint inhibitors (ICIs) significantly increases the risk of allograft rejection after liver transplantation (LT); however, whether ICI-related rejection leads to increased graft loss remains controversial. Therefore, this study aimed to investigate the association between ICI-related allograft rejection and perioperative graft loss. METHODS: This was a retrospective analysis of adult liver transplant recipients with early biopsy-proven T-cell-mediated rejection (TCMR) at Liver Transplantation Center of Sun Yat-sen Memorial Hospital from June 2019 to September 2024. The pathological features, clinical characteristics, and perioperative graft survival were analyzed. RESULTS: Twenty-eight patients who underwent early TCMR between June 2019 and September 2024 were included. Based on pre-LT ICI exposure, recipients were categorized into ICI-related TCMR (irTCMR, n = 12) and conventional TCMR (cTCMR, n = 16) groups. Recipients with irTCMR had a higher median Banff rejection activity index (RAI) (6 vs . 5, P = 0.012) and more aggressive tissue damage and inflammation. Recipients with irTCMR showed higher proportion of treatment resistance, achieving a complete resolution rate of only 8/12 compared to 16/16 for cTCMR. Graft loss occurred in 5/12 of irTCMR recipients within 90 days after LT, with no graft loss in cTCMRs recipients. Cox analysis demonstrated that irTCMR with an ICI washout period of <30 days was an independent risk factor for perioperative graft loss (hazard ratio [HR], 6.540; 95% confidence interval [CI], 1.067-40.067, P = 0.042). CONCLUSION IrTCMR is associated with severe pathological features, increased resistance to treatment, and higher graft loss in adult liver transplant recipients.
Humans ; Liver Transplantation/adverse effects* ; Male ; Female ; Middle Aged ; Retrospective Studies ; Graft Rejection/immunology* ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; T-Lymphocytes/drug effects* ; Graft Survival/immunology* ; Aged

OBJECTIVE: To explore clinical efficacy of percutaneous endoscopic discectomy with a lateral approach and dual-channel method in treating highly free lumbar disc herniation(LDH). METHODS: A retrospective analysis was conducted on 54 patients with highly free LDH who were treated with spinal endoscopic techniques from January 2021 to December 2022. Twenty-seven patients were treated with lateral approach dual-channel(lateral approach dual-channel group), including 16 males and 11 females, with an average age of (54.6±10.5) years old. Twenty-seven patients were treated with unilateral biportal endoscopic (UBE group), including 17 males and 10 females, with an average age of (52.9±12.3) years old. The number of intraoperative fluoroscopy, operation time and hospital stay, as well as visual analogue scale (VAS) and Oswestry diability index (ODI) of low back and leg pain between two patients before operation, 1 day, 1, 3, and 12 months after operation, and the efficacy was evaluated by the modified MacNab criteria at 12 mohths after operation. RESULTS: All patients were successfully completed surgical and were followed up, the time raged from 12 to 22 months with an average of (13.57±4.12) months. There was no statistically significant difference in operation time between two groups (P>0.05). The hospital stay of lateral approach dual-channel group was (3.9±1.1) days, which was shorter than that of UBE group (6.5±1.4) days, the number of intraoperative fluoroscopy in lateral approach dual-channel group was (12.7±2.1) times, which was more than that in UBE group (6.6±1.3) times, the differences were statistically significant (t=5.197, -7.532;P<0.05). VAS and ODI for low back pain at 1 day and 1 month after operation, and VAS for leg pain at 1 day after operation of lateral approach dual-channel group were superior to those of UBE group, and the differences were statistically significant (P<0.05). However, there were no statistically significant differences in VAS and ODI for low back and leg pain between two groups before operation and 3 and 12 months after operation (P>0.05). VAS and ODI of low back and leg pain were significantly improved at each time point before and after operation in both groups, and the difference were statistically significant (P<0.05). At 12 months after operation, according to the modified MacNab criteria, the excellent and good rates of therapeutic effects between lateral approach dual-channel group and UBE group were 92.6% (25/27) and 88.9% (24/27), respectively, and the difference was not statistically significant (χ²=0.22, P>0.05). CONCLUSION For patients with highly free lumbar intervertebral disc protrusion, both of lateral approach dual-channel method and UBE endoscopic surgery are safe and effective. Endoscopic surgery with lateral approach and dual-channel method could be performed under local anesthesia, allowing for the removal of the nucleus pulposus under direct vision. It is simpler, more efficient.
Humans ; Male ; Female ; Intervertebral Disc Displacement/surgery* ; Middle Aged ; Diskectomy, Percutaneous/methods* ; Lumbar Vertebrae/surgery* ; Endoscopy/methods* ; Adult ; Retrospective Studies ; Aged

OBJECTIVES: To investigate the prognostic value of molecular minimal residual disease (Mol-MRD) monitored before and after allogeneic hematopoietic stem cell transplantation (HSCT) in pediatric acute myeloid leukemia (AML). METHODS: Clinical data of 71 pediatric AML patients who underwent HSCT between August 2016 and December 2023 were analyzed. Mol-MRD levels were dynamically monitored in MRD-positive patients, and survival outcomes were evaluated. RESULTS: No significant difference in the 3-year overall survival (OS) rate was observed between patients with pre-HSCT Mol-MRD ≥0.01% and <0.01% (77.3% ± 8.9% vs 80.4% ± 7.9%, P=0.705). However, patients with pre-HSCT Mol-MRD <1.75% had a significantly higher 3-year OS rate than those with Mol-MRD ≥1.75% (86.6% ± 5.6% vs 44.4% ± 16.6%, P=0.020). The median Mol-MRD level in long-term survivors was significantly lower than in non-survivors [0.61% (range: 0.04%-51.58%)] vs 10.60% (range: 1.90%-19.75%), P=0.035]. Concurrent flow cytometry-based MRD positivity was significantly higher in non-survivors (80% vs 24%, P=0.039). There was no significant difference in the 3-year overall survival rate between patients with Mol-MRD ≥0.01% and those with <0.01% at 30 days post-HSCT (P=0.527). For children with Mol-MRD <0.22% at 30 days post-HSCT, the 3-year overall survival rate was 80.4% ± 5.9%, showing no significant difference compared to those with molecular negativity (87.0% ± 7.0%) (P=0.523). CONCLUSIONS Patients with pre-HSCT Mol-MRD <1.75% or post-HSCT Mol-MRD <0.22% may achieve long-term survival outcomes comparable to Mol-MRD-negative cases through HSCT and targeted interventions.
Humans ; Hematopoietic Stem Cell Transplantation ; Neoplasm, Residual ; Leukemia, Myeloid, Acute/genetics* ; Child ; Male ; Female ; Child, Preschool ; Prognosis ; Adolescent ; Infant ; Transplantation, Homologous

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers