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.

OBJECTIVE: To investigate the characteristics of Vitamin D (VitD) and bone metabolism in patients with knee osteoarthritis (KOA) concurrent with osteoporosis (OP). METHODS: A retrospective analysis was performed on 240 patients who were admitted to the orthopedics department between March 2019 and March 2024. Patients were stratified into four distinct groups according to their respective disease categories.There were 90 patients in the simple KOA group, comprising 13 males and 77 females, age ranged from 50 to 91 years old with an average of (68.48±8.96) years old. There were 90 patients in the simple OP group, comprising 7 males and 83 females, age ranged from 52 to 88 years old with an average of (69.60±8.94 )years old. There were 30 patients in the KOA with OP group, comprising 1 male and 29 females, age ranged from 51 to 91 years old with an average of(69.03±7.93) years old. There were 30 patients in the physical examination group, comprising 5 males and 25 females, age ranged from 53 to 79 years old with an average of(64.93±6.51) years old. The general data and the levels of osteocalcin (OC), β-CrossLaps, parathyroid hormone(PTH) and VitD in each group were observed. RESULTS: The level of VitD in KOA with OP group (19.62±10.38) ng·ml^-1 and OP group (20.65±10.50) ng·ml^-1 was lower than that in physical examination group (27.46±8.00) ng·ml^-1 and KOA group (24.01±9.11) ng·ml^-1 (P<0.05). There were significant differences in β- CrossLaps and PTH levels among the four groups (P<0.001, P=0.019, respectively), while there was no significant difference in OC levels (P=0.763). Compared with the two simple disease groups, the KOA with OP group had higher levels of β - CrossLaps(0.81±0.30) ng·ml^-1 (P<0.001). There were significant differences in β-CrossLaps and PTH between the simple KOA group(0.54±0.22) ng·ml^-1, (46.03±18.08) pg·ml^-1 and the physical examination group (0.44±0.19) ng·ml^-1, (36.65±9.63) pg·mL^-1(P=0.038;P=0.006). There was a significant difference in PTH between the OP group(43.85±14.30) ng·ml-1, and the physical examination group, P=0.004. There was a significant difference in Kallgren-Lawrence grading between KOA with OP group and KOA group (P=0.006). Within KOA with OP group, the differences of β-CrossLaps and VitD levels among different K-L grades were statistically significant (P=0.016). The level of OC, β-CrossLaps and PTH within KOA with OP group was significantly different at different VitD levels (P=0.013, P=0.033, P=0.046). CONCLUSION Patients with KOA complicated by OP exhibit greater disturbances in bone metabolism and reduced VitD levels, particularly reflected by elevated β-CrossLaps. These findings underscore the importance of early monitoring of bone turnover and VitD supplementation in advanced-stage KOA with bone loss.
Humans ; Female ; Male ; Middle Aged ; Aged ; Vitamin D/blood* ; Osteoporosis/complications* ; Aged, 80 and over ; Osteoarthritis, Knee/complications* ; Retrospective Studies ; Bone and Bones/metabolism* ; Parathyroid Hormone/metabolism* ; Osteocalcin/metabolism*

OBJECTIVE: To investigate the role of MK2 inhibitor MMI-0100 on inflammatory response after allogeneic hematopoietic stem cell transplantation (allo-HSCT) and related mechanisms. METHODS: An allo-HSCT mouse model was established. Recipient rats were randomly divided into BMT+NaCl group and BMT+MMI-0100 group, and were injected with NaCl and MMI-0100 every day after transplantation, respectively. Samples of the two groups were collected on d 7 and 14, femur paraffin sections were stained with HE, and pathological changes in the bone marrow cavity were observed under the light microscope. The gene and protein expression levels of pro-inflammatory cytokines IL-1β and IL-18 were detected by qPCR and Western blot. Macrophage typing was detected by flow cytometry. The expression levels of NLRP3 and Caspase-1 were detected by Western blot. RESULTS: Inflammatory cell infiltration in the bone marrow cavity was significantly reduced in the BMT+MMI-0100 group. Western blot results showed that the protein expression levels of IL-1β and IL-18 in the BMT+MMI-0100 group were decreased compared to the BMT+NaCl group on day 7 and day 14 (all P <0.01). The qPCR results showed that compared to the BMT+NaCl group, the IL-18 gene expression levels in the BMT+MMI-0100 group were significantly reduced on day 7 and day 14 (both P <0.01). In the BMT+MMI-0100 group, the expression level of IL-1β gene decreased on day 7 (P <0.05), but increased and was higher than that in the BMT+NaCl group on day 14 (P <0.05). Flow cytometry results showed that the expression of M1 macrophages and M1/M2 ratio decreased in the BMT+MMI-0100 group compared to BMT+NaCl group (all P <0.05). Western blot results showed that the protein expression levels of NLRP3 and Caspase-1 in the BMT+MMI-0100 group were lower than those in the BMT+NaCl group (all P <0.05). CONCLUSION MMI-0100 can ameliorate bone marrow inflammatory injury after allo-HSCT and may act by reducing NLRP3 expression to promote M2 polarization.
Animals ; Interleukin-1beta/metabolism* ; Rats ; Interleukin-18/metabolism* ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammation ; Bone Marrow/pathology* ; Protein Serine-Threonine Kinases/metabolism* ; Intracellular Signaling Peptides and Proteins/antagonists & inhibitors* ; Caspase 1/metabolism* ; Macrophages ; Transplantation, Homologous

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.

Objective:To explore the clinical manifestations,diagnosis,pathological features and treatment of small-cell carci-noma of the prostate(SCCP).Methods:We conducted a retrospective analysis of the clinical and pathological data of 2 cases of confirmed SCCP treated from November 2017 to March 2018,and reviewed relevant literature.Results:Both the patients had the symptoms of frequent,urgent and difficult urination,with an elevated level of PSA and grades Ⅱ-Ⅲ enlargement of the prostate at palpation.One underwent prostate puncture biopsy and the other received transurethral 1470 laser vaporization resection of the tumor.Postoperative pathology indicated prostate adenocarcinoma accompanied by SCCP in both of the cases.One of them was treated by eto-poside-platinum(EP)chemotherapy and died of systemic multiple organ failure 20 months after diagnosis,while the other underwent endocrine therapy and has lived with tumor up to the present day.Conclusion:The incidence rate of SCCP is low,its malignancy is high,and its prognosis is poor.The average survival of the patient is about 7 to 10 months after diagnosis.Currently there is no effec-tive management of the dissease,except by relying on the experience of the treatment of small-cell lung cancer,with chemotherapy as the main option.

AIM To establish the quality standard of the zhujieshen Formula Granules based on standard decoction.METHODS The contents and transfer rates of ginsenoside Ro and chikusetsu saponin Ⅳa in standard decoction and formula granules were determined by HPLC,after which the transfer rates were calculated.The HPLC characteristic chromatograms of standard decoctions were established,after which cluster analysis and principal component analysis were adopted.Then the HPLC characteristic chromatograms of formula granules were established.RESULTS Nine common peaks were found in the HPLC characteristic chromatograms of seventeen batches of standard decoctions with the similarities of more than 0.9(except for S6,S12),which were clustered into two categories,and the accumulative variance contribution rate of three principal components reached 86.7％.The contents of ginsenoside Ro in three batches of formula granules were 83.1-88.6 mg/g,and the transfer rates were 53.1％-55.5％.The contents of chikusetsusaponin Ⅳa were 14.8-15.0 mg/g,and the transfer rates were 47.4％-48.1％.Nine common peaks were found in the HPLC characteristic chromatograms of three batches of formula granules with the similarities of 0.998,0.998 and 0.999,respectively.CONCLUSION This reasonable and reliable method can comprehensively evaluate the quality of Zhujieshen Formula Granules,and provide a reference for the quality control.

Objective To noninvasively assess the cerebral ischemic status using the velocity profile of the carotid and vertebral arteries measured by Doppler ultrasound and a neural network model.Methods Imaging data were collected from patients who underwent computed tomography perfusion(CTP)and Doppler ultrasound.Hemodynamic parameters were extracted from the ultrasound images.These parameters were used to train a fully connected neural network model.The model was validated using the CTP results.Results Sixty-two eligible patients were included;44 were randomly selected as the training dataset and 18 were designated for validation.In the training set,the area under the curve(AUC)of the receiver operating characteristic,sensitivity,specificity,and accuracy were 0.95,0.833,0.923,and 0.886,respectively.In the test set,the AUC,sensitivity,specificity,and accuracy were 0.860,0.714,1.000,and 0.889,respectively.Conclusions The model based on Doppler ultrasound and neural network was clinically verified and had good accuracy for assessing cerebral ischemia,showing its clinical potential for the early screening of cerebral ischemia.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Cervical spondylotic radiculopathy(CSR)is a condition caused by the degeneration of cervical intervertebral discs and facet joints,primarily manifesting as the pain,sensory abnormalities,and motor dysfunction in the cervical nerve innervation area of neck,shoulder,and upper limb.For the treatment of CSR,tendon-bone syndrome differentiation in traditional Chinese medicine often faces the issues of conceptual confusion and non-standard syndrome differentiation.Based on the traditional tendon-bone syndrome differentiation and by integrating modern anatomical insights,Professor LIN Ding-Kun,an esteemed scholar of Traditional Chinese Medicine,proposed a classification system for the cervical spine that includes the categories of tendons,joints,bones and marrow.This paper explored the thoughts of Professor LIN for the tendon-bone syndrome differentiation of CSR,summarized the targets of manual therapy,and proposed the four kinds of pathological changes such as tendon overstrain,joint dislocation,bone lesion,and marrow injury,as well as the four techniques of traditional Chinese medicine manipulations,i.e.relaxation of tendons,reduction of joints,protection of marrow,and treatment of bones.The aim is to improve the syndrome-differentiation and treatment for CSR with orthopedic and traumatologic manipulations,and to provide reference for clinical practice.

Objective To explore the method for measurement of tritium in nuclear power plant liquid effluent purified by a mixed ion-exchange resin, to verify the feasibility of the method by experiments, and to provide technical support for the development of standard methods for the measurement of tritium in liquid effluent of nuclear power plants. Methods The purification effect of the mixed ion-exchange resin was determined by measuring the quenching factor, conductivity, and β-nuclide adsorption efficiency of the samples purified using the resin. A comparison was made between the ion-exchange resin method and the atmospheric distillation method for tritium determination. The precision and accuracy of the method were verified by calculating the relative standard deviation and the recovery in repeated measurement of samples with different activity concentrations and spiked samples. Results There were no significant differences in quenching factor, conductivity, and tritium activity concentration between the two methods. The adsorption efficiencies of EC20MB resin for common β-nuclides such as carbon, iron, nickel, strontium, yttrium, and cesium ranged from 99.28% to 99.88%. Repeated measurement of the same sample showed relative standard deviations of 5.2%-9.4% and recoveries of 86.8%-107%. Conclusion There were no significant differences between the results of the ion-exchange resin purification method and the atmospheric distillation method. The precision and accuracy of the method met the requirements of the ecological and environmental management authorities for monitoring tritium in liquid effluent from nuclear power plants. This method can be widely used in daily monitoring work.