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 increasing prevalence of aging has led to a rising incidence of comorbidity of sarcopenia and obesity, posing significant burdens on socioeconomic and public health. Current research has systematically explored the pathogenesis of each condition; however, the mechanisms underlying their comorbidity remain unclear. This study reviews the current literature on sarcopenia and obesity in the aging population, focusing on their shared biological mechanisms, which include loss of autophagy, abnormal macrophage function, mitochondrial dysfunction, and reduced sex hormone secretion. It also identifies metabolic mechanisms such as insulin resistance, vitamin D metabolism abnormalities, dysregulation of iron metabolism, decreased levels of nicotinamide adenine dinucleotide, and gut microbiota imbalances. Additionally, this study also explores the important role of genetic factors, such as alleles and microRNAs, in the co-occurrence of sarcopenia and obesity. A better understanding of these mechanisms is vital for developing clinical interventions and preventive strategies.
Humans ; Sarcopenia/physiopathology* ; Obesity/physiopathology* ; Aging/physiology* ; Autophagy/physiology* ; Insulin Resistance ; Comorbidity ; Vitamin D/metabolism* ; Gonadal Steroid Hormones/metabolism* ; Gastrointestinal Microbiome ; Mitochondria ; MicroRNAs

Prostate cancer(PCa) is a common malignant tumor among elderly men, with high incidence and mortality rates worldwide, posing a serious threat to human health. Traditional treatments face limitations, highlighting the urgent need for novel therapeutic strategies. Recent studies on the regulatory mechanisms of micro ribonucleic acid(microRNA, miRNA) in tumor development has identified miRNA as new targets for PCa diagnosis and treatment. Traditional Chinese medicine(TCM), with its multi-mechanism, multi-target, and multi-pathway regulatory properties, shows promising potential in miRNA-based PCa therapy. This review summarized recent findings on miRNA' roles in PCa and research progress of TCM intervention and found that a variety of miRNA played important regulatory roles in cell differentiation, proliferation, apoptosis, invasion, metastasis, immune microenvironment, and drug resistance, and their potential as biomarkers for PCa diagnosis, prognosis, and therapy, indicating the potential to be a biomarker for the diagnosis, prognosis evaluation, and treatment of PCa. The review concluded that the active components of TCM(terpenoids, flavonoids, alkaloids, and others) and compounds(Yishen Tonglong Decoction, Shenhu Decoction, Zhoushi Qiling Decoction, Fuzheng Yiliu Decoction, and Qilan Formula) could regulate the expression of their downstream target genes by acting on specific miRNA and affect the above biological behaviors of PCa cells, thus playing a role in the treatment of PCa. This review aims to provide a theoretical basis for miRNA as potential biomarkers and therapeutic targets for PCa and suggest new avenues for further development of targeted therapy strategies against miRNA.
Humans ; MicroRNAs/metabolism* ; Prostatic Neoplasms/metabolism* ; Male ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Animals ; Gene Expression Regulation, Neoplastic/drug effects*

OBJECTIVE: To investigate the impact of bone mass and volume of low-density zones beneath the tibial plateau on the maximum von Mises stresses experienced by the cartilage and meniscus in the knee joint. METHODS: The study included one healthy adult volunteer, from whom CT scans were obtained, and one patient diagnosed with knee osteoarthrisis (KOA), for whom X-ray films were acquired. A static model of the knee joint featuring a low-density zone was established based on a normal knee model. In the finite element analysis, axial loads of 1 000 N and 1 800 N were applied to the weight-bearing region of the upper surface of the femoral head for model validation and subsequent finite element studies, respectively. The maximum von Mises stresses in the femoral cartilage, as well as the medial and lateral tibial cartilage and menisci, were observed, and the stress percentage of the medial and lateral components were concurrently analyzed. Additionally, HE staining, as well as alkaline magenta staining, were performed on the pathological specimens of patients with KOA in various low-density regions. RESULTS: The results of model validation indicated that the model was consistent with normal anatomical structures and correlated with previous calculations documented in the literature. Static analysis revealed that the maximum von Mises stress in the medial component of the normal knee was the lowest and increased with the advancement of the hypointensity zone. In contrast, the lateral component exhibited an opposing trend, with the maximum von Mises stress in the lateral component being the highest and decreasing as the hypointensity zone progressed. Additionally, the medial component experienced an increasing proportion of stress within the overall knee joint. HE staining demonstrated that the chondrocyte layer progressively deteriorated and may even disappear as the hypointensity zone expanded. Furthermore, alkaline magenta staining indicated that the severity of microfractures in the trabecular bone increased concurrently with the expansion of the hypointensity zone. CONCLUSION The presence of subtalar plateau low-density zone may aggravate joint degeneration. In clinical practice, it is necessary to pay attention to the changes in the subtalar plateau low-density zone and actively take effective measures to strengthen the bone status of the subtalar plateau low-density zone and restore the complete biomechanical function of the knee joint, in order to slow down or reverse the progression of osteoarthritis.
Humans ; Finite Element Analysis ; Knee Joint/physiology* ; Tibia/anatomy & histology* ; Cartilage, Articular/physiology* ; Menisci, Tibial/physiopathology* ; Tomography, X-Ray Computed ; Osteoarthritis, Knee/diagnostic imaging* ; Weight-Bearing ; Bone Density ; Adult ; Stress, Mechanical ; Male ; Middle Aged ; Biomechanical Phenomena ; Female

OBJECTIVE: To analyze the molecular genetic spectrum of children with acute myeloid leukemia (AML), and explore its correlation with clinical characteristics and prognosis. METHODS: The clinical and molecular genetic data of 116 children with newly diagnosed AML in Wuhan Children's Hospital from September 2015 to August 2022 were retrospectively analyzed. The Fisher's exact test was used to analyze the correlation of gene mutations with clinical features, and Kaplan-Meier curve was used to analyze the influences of gene mutations on the prognosis. RESULTS: NRAS (22%), KRAS (14.9%), and KIT (14.7%) mutations were the most common genetic abnormalities in 116 children with AML. Children with KIT, CEBPA and GATA2 mutations showed a higher median onset-age than those without mutations (all P < 0.05). Children with FLT3-ITD mutation exhibited a higher white blood cell count at initial diagnosis compared to those without mutations (P < 0.05). Children with ASXL2 mutation had lower platelet count and hemoglobin at initial diagnosis than those without mutations (both P < 0.05). KIT mutations were often co-occurred with t(8;21)(q22;q22). There was no significant relationship between gene mutation and minimal residual disease (MRD) remission rate after the first and second induction therapy (P >0.05). KIT and NRAS mutations were not associated with prognosis significantly (P >0.05). The overall survival (OS) rates of children with CEBPA and FLT3-ITD mutations were superior to those without mutations, but the differences were not statistically significant (P >0.05). The 3-year OS rate of 61 children treated by allogeneic hematopoietic stem cell transplantation was 89.8%, which was significantly higher than 55.2% of those only treated by chemotherapy (P < 0.001). CONCLUSIONS Gene mutations are common in children with AML, and next-generation sequencing can significantly improve the detection rate of gene mutations, which can guide the risk stratification therapy. In addition, FLT3-ITD and KIT mutations may no longer be poor prognostic factors.
Humans ; Leukemia, Myeloid, Acute/genetics* ; Mutation ; Prognosis ; Retrospective Studies ; fms-Like Tyrosine Kinase 3/genetics* ; Child ; Proto-Oncogene Proteins c-kit/genetics* ; Male ; Female ; CCAAT-Enhancer-Binding Proteins/genetics* ; Membrane Proteins/genetics* ; Child, Preschool ; Adolescent ; GATA2 Transcription Factor/genetics* ; GTP Phosphohydrolases/genetics* ; Proto-Oncogene Proteins p21(ras)/genetics*

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.

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

Objective To investigate the relationship between single nucleotide polymorphisms(SNPs)in the eNOS gene and genetic susceptibility to systemic lupus erythematosus(SLE)in Hainan.Methods Blood samples were collected from SLE patients(SLE group,n=214)and healthy controls(control group,n=214)from January 2020 to December 2022 at the First Affiliated Hospital of Hainan Medical College and Hainan Provincial People's Hospital.The bases of eNOS gene rs3918188,rs1799983 and rs1007311 loci in each group were detected by SNaPshot sequencing technology.Logistic regression was used to analyze the correlation between genotypes,alleles and gene models(dominant model,recessive model,and overdominant model)of the above 3 target loci of the eNOS gene and genetic susceptibility to SLE.Haplotype analysis was conducted using HaploView 4.2 software to investigate the relationship between haploid and genetic susceptibility to SLE at each site.Results The results of logistic regression analysis revealed that the CC genotype and the C allele at rs3918188 locus were risk factors for genetic susceptibility to SLE(CC vs.AA:OR=2.449,P<0.05;C vs.A:OR=2.133,P<0.001).In recessive model at rs3918188 locus,CC genotype carriers had an increased risk of SLE development compared with AA+AC genotype carriers(OR=2.774,P<0.001).In contrast,in overdominant model at this locus,AC genotype carriers had a decreased risk of SLE occurrence compared with AA+CC genotype carriers(OR=0.385,P<0.001).In addition,polymorphisms of rs1799983 and rs1007311 were not associated with susceptibility to SLE in genotype,allele type and the 3 genetic models(P>0.05).Haplotype analysis revealed a strong linkage disequilibrium between the rs1007311 and rs1799983 loci of the eNOS gene,but no significant correlation was found between haplotype and genetic susceptibility to SLE(P>0.05).Conclusion The CC genotype and C allele at rs3918188 locus of eNOS gene may be risk factors for SLE in Hainan,while the risk of SLE occurrence is reduced in carriers of AC genotype under the overdominant model.

Objective:To compare the therapeutic effects of 0.05% cyclosporine and 0.1% fluorometholone eye drops in patients with moderate and severe dry eye.Methods:A randomized controlled study was conducted.Fifty-two patients (52 eyes) with moderate to severe dry eye in Tianjin Medical University Eye Hospital from August 2021 to December 2022 were enrolled and randomly divided into 0.05% cyclosporine group and 0.1% fluorometholone group by random number table method, with 26 cases (26 eyes) in each group.Patients received 0.05% cyclosporine eye drops (2 times/day) and 0.1% fluorometholone eye drops (2 times/day) combined with calf blood deproteinized extract eye drops (4 times/day) according to the grouping.Before and 1, 3 and 6 months after treatment, clinical symptoms and signs were observed and Ocular Surface Disease Index (OSDI) score, corneal fluorescein staining (CFS) score, Schirmer Ⅰ test (SⅠT), non-invasive first tear film break-up time (NIBUTf), and conjunctival goblet cell (CGC) density were recorded.Before treatment and after 6 months of treatment, changes in corneal nerves and dendritic cells (DC) were observed by in vivo confocal microscopy (IVCM).This study adhered to the Declaration of Helsinki and was approved by the Medical Ethics Committee of Eye Hospital of Tianjin Medical University (No.2021KY-17).Written informed consent was obtained from each subject. Results:Compared with the 0.1% fluorometholone group, CFS score decreased after 1 month of treatment, but SⅠT, NIBUTf and CFS score increased after 3 months of treatment, and OSDI score, SⅠT and CFS score decreased after 6 months of treatment in the 0.05% cyclosporine group, showing statistically significant differences (all at P<0.05).Compared with baseline, in the 0.05% cyclosporine group, NIBUTf increased and CFS score decreased after 1 month of treatment, OSDI score and CFS score decreased, SⅠT and NIBUTf increased after 3 and 6 months of treatment, showing statistically significant differences (all at P<0.05).In the 0.1% fluorometholone group, CFS score decreased after 3 months of treatment, OSDI score and CFS score decreased, SⅠT increased after 6 months of treatment compared to baseline, showing statistically significant differences (all at P<0.05).OSDI score and CFS score decreased, SⅠT increased after 6 months of treatment compared to 3 months of treatment in the 0.05% cyclosporine group, and the differences were statistically significant (all at P<0.05).Baseline and CGC densities after 1, 3 and 6 months of treatment were (147.66±17.29), (195.44±15.46), (210.36±19.15) and (282.09±22.63)cells/mm 2 in the 0.05% cyclosporine group and (138.09±17.29), (95.67±15.46), (117.77±19.15) and (109.13±22.63)cells/mm 2 in the 0.1% fluorometholone group, respectively, with a statistically significant overall difference ( Fgroup=11.724, P<0.001; Ftime=4.837, P=0.005).Compared with the 0.1% fluorometholone group, CGC density in the 0.05% cyclosporine group increased after 1, 3 and 6 months of treatment, with statistically significant differences (all at P<0.05).Compared with baseline, the CGC density increased in the 0.05% cyclosporine group after 1, 3 and 6 months of treatment, and the differences were statistically significant (all at P<0.05).Compared with the 0.1% fluorometholone group, the corneal nerve fiber density in the 0.05% cyclosporine group increased after 6 months of treatment, and corneal DC density, area and dendrite number decreased, showing statistically significant differences (all at P<0.05). Conclusions:Cyclosporine 0.05% eye drops combined with calf blood deproteinized extract eye drops can improve symptoms and signs in patients with moderate to severe dry eye, and the long-term effect is better than that of 0.1% fluorometholone plus calf blood deproteinized extract eye drops.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.