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.

A 12-year-old boy was admitted with symptoms of cough and fever lasting over a month, accompanied by weight loss 2 kg. Prior anti-infective treatments proved ineffective in alleviating the symptoms. Chest imaging revealed diffuse interstitial pulmonary edema in the right lung with obstructed lymphatic drainage. Combined with histopathological examinations, the diagnosis was confirmed as lung cancer with pulmonary lymphangitic carcinomatosis. The patient underwent chemotherapy with docetaxel and carboplatin, yet the disease progressively worsened, resulting in death three months after diagnosis. This case highlights lung cancer should not be overlooked in patients with persistent respiratory symptoms of unknown etiology. Early imaging examinations, along with necessary pathological evaluations, are crucial for timely detection and diagnosis. The presence of pulmonary lymphangitic carcinomatosis often indicates an advanced-stage of cancer, associated with a poor prognosis.
Humans ; Male ; Lung Neoplasms/complications* ; Child ; Carcinoma/drug therapy*

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

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

Myocardial fibrosis is a serious cause of heart failure and even sudden cardiac death. However, the mechanisms underlying myocardial ischemia-induced cardiac fibrosis remain unclear. Here, we identified that the expression of sterile alpha and TIR motif containing 1 (SARM1), was increased significantly in the ischemic cardiomyopathy patients, dilated cardiomyopathy patients (GSE116250) and fibrotic heart tissues of mice. Additionally, inhibition or knockdown of SARM1 can improve myocardial fibrosis and cardiac function of myocardial infarction (MI) mice. Moreover, SARM1 fibroblasts-specific knock-in mice had increased deposition of extracellular matrix and impaired cardiac function. Mechanically, elevated expression of SARM1 promotes the deposition of extracellular matrix by directly modulating P4HA1. Notably, by using the Click-iT reaction, we identified that the increased expression of ZDHHC17 promotes the palmitoylation levels of SARM1, thereby accelerating the fibrosis process. Based on the fibrosis-promoting effect of SARM1, we screened several drugs with anti-myocardial fibrosis activity. In conclusion, we have unveiled that palmitoylated SARM1 targeting P4HA1 promotes collagen deposition and myocardial fibrosis. Inhibition of SARM1 is a potential strategy for the treatment of myocardial fibrosis. The sites where SARM1 interacts with P4HA1 and the palmitoylation modification sites of SARM1 may be the active targets for anti-fibrosis drugs.

Amorphous solid dispersion (ASD) is one of the most effective formulation approaches to enhance the water solubility and oral bioavailability of poorly water-soluble drugs. However, maintenance of physical stability of amorphous drug is one of the main challenges in the development of ASD. Crystallization is a process of nucleation and crystal growth. The nucleation is the key factor that influences the physical stability of the ASD. However, a theoretical framework to describe the way to inhibit the nucleation of amorphous drug is not yet available. We reviewed the methods and theories of nucleation for amorphous drug. Meanwhile, we also summarized the research progress on the mechanism of additives influence on nucleation and environmental factors on nucleation. This review aims to enhance the better understanding mechanism of nucleation of amorphous drug and controlling over the crystal nucleation during the ASD formulation development.

OBJECTIVE To study the effect of different doses of aspirin on clinical efficacy in acute stage of Kawasaki’s disease, and to explore the optimal dose of aspirin. METHODS A total of 150 patients suffered from Kawasaki’s disease were randomly selected by hospital information system from March to May 2022 for retrospective analysis. According to different doses of aspirin, they were divided into three groups: high dose group(>50 mg·kg−1·d−1), medium dose group(30−50 mg·kg−1·d−1) and low dose group(<30 mg·kg−1·d−1). The antipyretic time, the incidence of non-response to intravenous human immunoglobulin, the improvement of laboratory indexes and prevalence of adverse drug reaction were compared among the three groups. RESULTS There was no significant difference in body temperature recovery among the three groups under different doses of aspirin. There was no significant difference in patients with non-response to intravenous human immunoglobulin among the three groups. Before treatment, there were no significant differences in white blood cell(WBC) count, blood platelet(PLT) count and C-reactive protein(CRP) concentration among the three groups. After treatment, the count of WBC, PLT and CRP in the three groups was significantly improved compared with that before treatment, and the difference was statistically significant(P<0.05). However, there was no significant difference in the above indexes among the three groups after treatment. There was a higher incidence of adverse reactions in children treated with medium or high dose aspirin. CONCLUSION Different doses of aspirin combined with intravenous human immunoglobulin have good therapeutic effect on Kawasaki’s disease, but considering the safety and economy of aspirin, low dose administration is recommended.

Bone Transplantation/adverse effects* ; Transplantation, Autologous/adverse effects*

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.

Objective To detect the expression changes of interleukin-10(IL-10)and transforming growth factor-β1(TGF-β1)during the development of deep vein thrombosis in mice,and to explore the application value of them in thrombus age estimation.Methods The mice in the experimental group were subjected to ligation of inferior vena cava.The mice were sacrificed by excessive anesthesia at 1 d,3 d,5 d,7 d,10 d,14 d and 21 d after ligation,respectively.The inferior vena cava segment with thrombosis was extracted below the ligation point.The mice in the control group were not ligated,and the inferior vena cava segment at the same position as the experimental group was extracted.The ex-pression changes of IL-10 and TGF-β1 were detected by immunohistochemistry(IHC),Western blot-ting and real-time qPCR.Results IHC results revealed that IL-10 was mainly expressed in monocytes in thrombosis and TGF-β1 was mainly expressed in monocytes and fibroblast-like cells in thrombosis.Western blotting and real-time qPCR showed that the relative expression levels of IL-10 and TGF-β1 in each experimental group were higher than those in the control group.The mRNA and protein levels of IL-10 reached the peak at 7 d and 10 d after ligation,respectively.The mRNA expression level at 7 d after ligation was 4.72±0.15 times that of the control group,and the protein expression level at 10 d after ligation was 7.15±0.28 times that of the control group.The mRNA and protein levels of TGF-β1 reached the peak at 10 d and 14 d after ligation,respectively.The mRNA expression level at 10 d after ligation was 2.58±0.14 times that of the control group,and the protein expression level at 14 d after ligation was 4.34±0.19 times that of the control group.Conclusion The expressions of IL-10 and TGF-β1 during the evolution of deep vein thrombosis present time-dependent sequential changes,and the expression levels of IL-10 and TGF-β1 can provide a reference basis for thrombus age estimation.