[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 investigate the application of the novel inflammatory factor adsorption column CA280 combined with low-dose plasma exchange （LPE） in patients with acute-on-chronic liver failure （ACLF）. MethodsA prospective cohort study was designed， and a total of 93 ACLF patients who were admitted to The Ninth Hospital of Nanchang from June 2023 to January 2025 were enrolled and randomly divided into DPMAS+LPE group with 50 patients and CA280+LPE group with 43 patients. In addition to comprehensive medical treatment， the patients in the DPMAS+LPE group received DPMAS and LPE treatment， and those in the CA280+LPE group received CA280 and LPE treatment. The two groups were observed in terms of routine blood test results， liver function parameters， renal function markers， electrolytes， coagulation function parameters， cytokines， adverse events， and 28-day prognosis before surgery （baseline）， during surgery （DPMAS or CA280）， and after surgery （after sequential LPE treatment）. The paired t-test was used for comparison of normally distributed continuous data before and after treatment within each group， and the independent-samples t test was used for comparison between groups； the Wilcoxon signed-rank test was used for comparison of non-normally distributed continuous data before and after treatment within each group， and the Mann-Whitney U test was used for comparison between groups. The chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups， and the Spearman test was used for correlation analysis. ResultsAfter CA280 treatment， the ACLF patients had significant reductions in the levels of cytokines （IL-6， IL-8， IL-10， TNF-α， and IFN-γ）， liver function parameters （ALT， AST， ALP， TBil， DBil， Alb， and glutathione reductase）， and the renal function marker urea nitrogen （all P<0.05）， and in terms of coagulation function parameters， there were significant increases in prothrombin time， activated partial thromboplastin time （APTT）， thrombin time， and international normalized ratio （INR） and significant reductions in prothrombin activity （PTA） and fibrinogen （FIB） （all P<0.05）. Compared with the DPMAS+LPE group， the CA280+LPE group showed better improvements in the serum cytokines IL-8 （Z=-2.63， P=0.009）， IL-10 （Z=-3.94， P<0.001）， and TNF-α （Z=-1.53， P=0.023）， and the two artificial liver support systems had a similar effect in improving liver function （ALT， AST， GGT， GR， TBil， and DBil） （all P >0.05）， but the CA280+LPE group showed a significantly greater reduction in Alb （Z=-2.08， P=0.037）. CA280+LPE was more effective in reducing uric acid （Z=-2.97， P=0.003）. Compared with DPMAS+LPE， CA280+LPE treatment resulted in a significant reduction in INR （Z=-4.01， P<0.001）， a significant increase in APTT （Z=-2.53， P=0.011）， and significant greater increases in PTA （Z=-6.28， P<0.001） and FIB （Z=-3.93， P<0.001）. There were no significant differences in the incidence rates of adverse reactions and the rate of improvement at discharge between the two groups （all P>0.05）. The Spearman correlation analysis showed that IL-6 was significantly correlated with WBC （r=0.22， P=0.042）， TBil （r=0.29， P=0.005）， and FIB （r=-0.33， P=0.003）； IL-8 was positively correlated with APTT （r=0.37， P<0.001） and INR （r=0.25， P=0.013）； TNF-α was significantly correlated with WBC （r=0.40， P<0.001） and TBil （r=0.34， P<0.001）. ConclusionCompared with DPMAS， CA280 combined with LPE can effectively clear proinflammatory cytokines and improve liver function in ACLF patients， but it has a certain impact on Alb and coagulation function. This regimen provides a new option for the individualized treatment of ACLF and can improve the short-term prognosis of patients， but further studies are needed to verify its long-term efficacy.

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

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Objective:To investigate the relationship between the Palatopharyngeal Arch Staging System（PASS） and the severity of Obstructive Sleep Apnea（OSA）, as well as the patterns of airway collapse, while further assessing its clinical applicability. Methods:A total of 98 patients diagnosed with OSA at the Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University Affiliated Shenzhen Hospital, were recruited for this study. Data collected included basic demographic information, oropharyngeal laryngoscopy videos, results from awake laryngoscopy Muller tests, and indicators from sleep respiratory monitoring. The distribution of each PASS stage among patients with varying severities of OSA was compared. Additionally, both objective and subjective sleep indicators along with occurrences of airway collapse in OSA patients across different PASS stages were analyzed. Results:In total, 98 patients participated in this study. Statistically significant differences were observed in neck circumference, weight, Body Mass Index（BMI）, tongue position, and PASS stage when comparing mild-to-moderate OSA patients to those with severe OSA（P<0.05）. Furthermore, there were statistically significant variations in Apnea-Hypopnea Index（AHI）, minimum blood oxygen saturation levels, average blood oxygen saturation levels, oxygen desaturation index values, and total oxygen desaturation indices among OSA patients categorized by different PASS stages. Multiple comparisons revealed statistically significant differences in AHI as well as minimum and average blood oxygen saturation levels between patients at PASS 1 versus those at PASS 3（P<0.05）. Additionally, notable differences regarding oropharyngeal collapse rates among OSA patients across various PASS stages were identified; specifically between those at PASS stage 1 and those at PASS stage 3. Conclusion:The proportion of PASS stages for OSA varies across different severity levels. The severity of OSA and the degree of airway collapse in patients with varying PASS stages also exhibit significant differences. Patients classified as PASS 3 demonstrate a more severe form of OSA compared to those at PASS 1, with stage 3 being more susceptible to oropharyngeal collapse than its stage 1 counterpart. This assessment system is anticipated to address the current limitations in evaluating the lateral pharyngeal wall within the oropharynx.
Humans ; Sleep Apnea, Obstructive/pathology* ; Male ; Severity of Illness Index ; Female ; Middle Aged ; Polysomnography ; Adult ; Pharynx/physiopathology* ; Aged

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

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.