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 molecular mechanism of verbascoside(OC1) in promoting acetylcholine(ACh) release in the pathogenesis of Alzheimer's disease(AD) was studied. Adrenal pheochromocytoma cells(PC12) of rats induced by β-amyloid protein(1-42)(Aβ_(1-42)) were used as AD models in vitro and were divided into control group, model group(Aβ_(1-42) 10 μmol·L~(-1)), OC1 treatment group(2 and 10 μg·mL~(-1)). The effect of OC1 on phosphorylated proteins in AD models was analyzed by whole protein phosphorylation quantitative omics, and the selectivity of OC1 for calcium channel subtypes was virtually screened in combination with computer-aided drug design. The fluorescence probe Fluo-3/AM was used to detect Ca~(2+) concentration in cells. Western blot analysis was performed to detect the effects of OC1 on the expression of phosphorylated calmodulin-dependent protein kinase Ⅱ(p-CaMKⅡ, Thr286) and synaptic vesicle-related proteins, and UPLC/Q Exactive MS was used to detect the effects of OC1 on ACh release in AD models. The effects of OC1 on acetylcholine esterase(AChE) activity in AD models were detected. The results showed that the differentially modified proteins in the model group and the OC1 treatment group were related to calcium channel activation at three levels: GO classification, KEGG pathway, and protein domain. The results of molecular docking revealed the dominant role of L-type calcium channels. Fluo-3/AM fluorescence intensity decreased under the presence of Ca~(2+) chelating agent ethylene glycol tetraacetic acid(EGTA), L-type calcium channel blocker verapamil, and N-type calcium channel blocker conotoxin, and the effect of verapamil was stronger than that of conotoxin. This confirmed that OC1 promoted extracellular Ca~(2+) influx mainly through its interaction with L-type calcium channel protein. In addition, proteomic analysis and Western blot results showed that the expression of p-CaMKⅡ and downstream vesicle-related proteins was up-regulated after OC1 treatment, indicating that OC1 acted on vesicle-related proteins by activating CaMKⅡ and participated in synaptic remodeling and transmitter release, thus affecting learning and memory. OC1 also decreased the activity of AChE and prolonged the action time of ACh in synaptic gaps.
Animals ; Rats ; Glucosides/administration & dosage* ; Acetylcholine/metabolism* ; Alzheimer Disease/genetics* ; PC12 Cells ; Phenols/chemistry* ; Neurotransmitter Agents/metabolism* ; Drugs, Chinese Herbal ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/genetics* ; Humans ; Phosphorylation/drug effects* ; Calcium/metabolism* ; Polyphenols

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*

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

Objective:To investigate the differences in dosimetric parameters for target volumes and organs at risk (OARs), radiation doses to reconstructed tissues, and beam-on time in radiotherapy among helical tomotherapy (HT), volumetric modulated arc therapy (VMAT), and fixed-field intensity-modulated radiotherapy (F_IMRT) following immediate breast reconstruction for breast cancer, thereby providing a reference for the selection of clinical radiotherapy techniques.Methods:This study retrospectively investigated 15 breast cancer patients who underwent radiotherapy following modified radical mastectomy and immediate breast reconstruction at the Liuzhou Worker′s Hospital from August 2018 to July 2023. During target volume delineation, precautions were taken to avoid the reconstructed tissues, which were delineated separately. Customized HT, VMAT, and F_IMRT treatment plans were designed for each patient. The plans were categorized into the HT, VMAT, and F_IMRT groups based on different radiotherapy techniques employed. They were comparatively analyzed through one-way analysis of variance (ANOVA), with multiple comparisons further conducted in the case of significant differences.Results:Statistical analyses reveal significant differences in various parameters of target volumes among the three groups of plans ( F = 38.73, 14.95, 37.01, 48.05, 35.55, 22.56, 34.30, P < 0.05). Pairwise comparisons indicate that the maximum dose ( D2%), minimum dose ( D98%), mean dose ( Dmean), and the proportion of high-dose volumes within the target volume ( V107%and V110%) in both the HT and VMAT groups were significantly better than those in the F_IMRT group. The HT group demonstrated the optimal conformity index (CI), while the VMAT group displayed the superior homogeneity index (HI) compared to the other two groups. In terms of OAR, the V20 of the ipsilateral lung was the lowest in the HT group ( F = 14.31, P < 0.05) and the highest in the F_IMRT group ( F = 14.31, P < 0.05). However, the V5 and Dmean for both the ipsilateral and contralateral lungs in the HT group significantly surpassed those of the other groups ( F = 39.16, 31.91, P < 0.05). The mean dose Dmean ( F = 5.57, P < 0.05) of the contralateral breast was significantly reduced in the VMAT group compared to the other two groups. No statistically significant differences were observed for other OARs, including the heart, spinal cord PRV, thyroid, and humeral head ( P > 0.05). The radiation doses to reconstructed tissues ( Dmax, V53.5, Dmean) ascended in the order of HT, VMAT, and F_IMRT groups ( F = 17.69, 17.53, 15.11, P < 0.05). The HT and F_IMRT groups showed similar beam-on times ( P > 0.05), both exceeding that of the VMAT group by several folds ( F = 28.72, P < 0.05). Conclusions:The comparative analysis indicates that the three radiotherapy techniques exhibit distinct advantages and limitations, with F_IMRT demonstrating the least comprehensive advantage. HT can enhance the conformity of target volumes while reducing the overall radiation doses to reconstructed tissues and the crucial indicator V20 in the ipsilateral lung. VMAT demonstrates the highest treatment efficiency, yielding improved dose uniformity in the target volume and reduced radiation doses to the contralateral breast. It is advisable to prioritize HT or VMAT based on actual clinical conditions.

Diabetic kidney disease(DKD)is one of the common microvascular complications of diabetes mellitus,and it has become the main cause of chronic kidney disease and end stage renal disease.Traditional Chinese medicine can delay the progress of DKD by inhibiting oxidative stress,improving renal tissue damage,restoring renal function.This paper will summarize the relationship between oxidative stress and DKD and the prevention and treatment of DKD by traditional Chinese medicine,so as to provide reference for clinical drug application,basic research and new drug research and development of DKD.

Objective To explore the pharmacokinetic(PK)characteristics of desloratadine tablets and reference drugs in healthy subjects,and evaluate their bioequivalence and safety.Methods The random,open,two-period,cross-over pharmacokinetic study method was adopted,each subject received a single oral dose of desloratadine tablets test drug(T)or reference drug(R)for 5 mg.The concentrations of desloratadine and 3-hydroxy desloratadine in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS);and the PK parameters were calculated by WinNonlin 8.1 software to evaluate the bioequivalence.Results The main PK parameters of T and R of desloratadine were as follows:the fasting condition Cmax were respectively(3 809.82±1 016.54)and(3 642.36±777.07)pg·mL-1;AUC0-120h were respectively(5.75 ×104±5.03 ×104)and(5.51 × 104±4.00 × 104)pg·h·mL-1;AUC0-∞ were respectively(6.85× 104±1.03× 104)and(6.37 × 104±7.92 × 104)pg·h·mL-1.The fed condition Cmax were respectively(4 398.98±1 191.22)and(4 744.4±1 511.97)pg·mL-1;AUC0-120h were respectively(5.25 × 104±1.82 × 104)and(5.55 × 104±1.98 × 104)pg·h·mL-1;AUC0-∞ were respectively(5.37 × 104±1.86 × 104)and(5.68 × 104±2.04 × 104)pg·h·mL-1.The 90％confidence interval of Cmax,AUC0-t and AUC0-∞ of desloratadine were all within 80.00％～125.00％.Conclusion There was no significant difference in the main PK parameters between T tablets and R under fasting or high-fat postprandial conditions,and desloratadine tablets were bioequivalent,safe and well tolerated.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Diabetic kidney disease(DKD)is one of the common microvascular complications of diabetes mellitus,and it has become the main cause of chronic kidney disease and end stage renal disease.Traditional Chinese medicine can delay the progress of DKD by inhibiting oxidative stress,improving renal tissue damage,restoring renal function.This paper will summarize the relationship between oxidative stress and DKD and the prevention and treatment of DKD by traditional Chinese medicine,so as to provide reference for clinical drug application,basic research and new drug research and development of DKD.