[Objective] To study on the genotyping of a sample with inconsistent forward and reverse serological tests, and to conduct a pedigree investigation and molecular biological mechanism study. [Methods] The ABO blood group of the proband and his family members were identified using blood group serological method. The ABO gene exon 1-7 of samples of the proband and his family were sequenced by Sanger and single molecule real-time sequencing (SMRT). DeepTMHMM was used to predict and analyze the transmembrane region of proteins before and after mutation. [Results] The proband and his mother have the Bw phenotype, while his maternal grandfather has ABw phenotype. The blood group results of forward and reverse typing of other family members were consistent. ABO gene sequencing results showed that there was B new mutation of c.3 G>C in exon 1 of ABO gene in the proband, his mother and grandfather, leading to a shift in translation start site. DeepTMHMM analysis indicated that the shift in the translation start site altered the protein topology. [Conclusion] The c.3G>C mutation in the first exon of the ABO gene leads to a shift in the translation start site, altering the protein topology from an α-transmembrane region to a spherical signaling peptide, reducing enzyme activity and resulting in the Bw serological phenotype.

BACKGROUND:Osteosarcoma has a complex pathogenesis and a poor prognosis.While advancements in medical technology have led to some improvements in the 5-year survival rate,substantial progress in its treatment has not yet been achieved. OBJECTIVE:To screen key molecular markers in osteosarcoma,analyze their relationship with osteosarcoma treatment drugs,and explore the potential disease mechanisms of osteosarcoma at the molecular level. METHODS:GSE99671 and GSE284259(miRNA)datasets were obtained from the Gene Expression Omnibus database.Differential gene expression analysis and Weighted Gene Co-expression Network Analysis(WGCNA)on GSE99671 were performed.Functional enrichment analysis was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes separately for the differentially expressed genes and the module genes with the highest positive correlation to the disease.The intersection of these module genes and differentially expressed genes was taken as key genes.A Protein-Protein Interaction network was constructed,and correlation analysis on the key genes was performed using CytoScape software,and hub genes were identified.Hub genes were externally validated using the GSE28425 dataset and text validation was conducted.The drug sensitivity of hub genes was analyzed using the CellMiner database,with a threshold of absolute value of correlation coefficient|R|>0.3 and P<0.05. RESULTS AND CONCLUSION:(1)Differential gene expression analysis identified 529 differentially expressed genes,comprising 177 upregulated and 352 downregulated genes.WGCNA analysis yielded a total of 592 genes with the highest correlation to osteosarcoma.(2)Gene Ontology enrichment results indicated that the development of osteosarcoma may be associated with extracellular matrix,bone cell differentiation and development,human immune regulation,and collagen synthesis and degradation.Kyoto Encyclopedia of Genes and Genomes enrichment results showed the involvement of pathways such as PI3K-Akt signaling pathway,focal adhesion signaling pathway,and immune response in the onset of osteosarcoma.(3)The intersection analysis revealed a total of 59 key genes.Through Protein-Protein Interaction network analysis,8 hub genes were selected,which were LUM,PLOD1,PLOD2,MMP14,COL11A1,THBS2,LEPRE1,and TGFB1,all of which were upregulated.(4)External validation revealed significantly downregulated miRNAs that regulate the hub genes,with hsa-miR-144-3p and hsa-miR-150-5p showing the most significant downregulation.Text validation results demonstrated that the expression of hub genes was consistent with previous research.(5)Drug sensitivity analysis indicated a negative correlation between the activity of methotrexate,6-mercaptopurine,and pazopanib with the mRNA expression of PLOD1,PLOD2,and MMP14.Moreover,zoledronic acid and lapatinib showed a positive correlation with the mRNA expression of PLOD1,LUM,MMP14,PLOD2,and TGFB1.This suggests that zoledronic acid and lapatinib may be potential therapeutic drugs for osteosarcoma,but further validation is required through additional basic experiments and clinical studies.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

ObjectiveTo analyze the influencing factors of pulmonary dysfunction among community-based population at high-risk for chronic obstructive pulmonary disease (COPD), and to establish a risk assessment model to provide a reference basis for accelerating the beforehand prevention and control of COPD and promoting the respiratory health of community-based residents. MethodsIndividuals aged >35 years old, with at least one risk factor except age illustrated in the Guidelines for Primary Diagnosis and Treatment of Chronic Obstructive Lung Disease (2018), and participated in the early screening for COPD from July 2022 to December 2023 were selected as the research subjects, and their lung function was assessed by the forceful expiratory volume in the first second after inhalation of bronchodilator (FEV₁)/ forced vital capacity (FVC) <70% and/or the ratio of FEV₁ to predicted value (FEV₁%Pred) <80% as the diagnostic criteria. In addition, risk factors related to pulmonary dysfunction were analyzed for the establishment of risk assessment model. ResultsA total of 823 individuals aged between 35‒76 years were included, among which 298 (36.21%) were diagnosed with pulmonary dysfunction, 167 (20.29%) with COPD, and 131 (15.92%) with preserved ratio but impaired spirometry. Logistic regression analysis revealed that male gender, increasing age, more frequent smoking, insufficient physical activity, recurrent wheezing, the presence of post-exercise wheezing or coughing, insensitive to airborne allergens, and history of chronic bronchitis or bronchial asthma were correlated with pulmonary dysfunction. The incidence rate of pulmonary dysfunction was 1.99 times higher in males than that in females, 1.81 times more common in those aged between 70‒76 years than those aged <60 years, 2.42 times more common in those who smoked 50‒200 pack-years than in those who smoked 0‒14 pack-years, 1.78 times higher in those who underwent physical activity <600 MET‑min·week^-1 than in those who underwent physical activity ≥600 MET‑min·week^-1, 2.61 times higher in those suffered recurrent wheezing than in those did not, 1.53 times higher in those with symptoms of post-exercise wheezing or coughing than in those without, 1.61 times higher in those insensitive to airborne allergens than those sensitive, 2.02 times higher in patients with chronic bronchitis than in those without, and 2.41 times higher in patients with bronchial asthma than in those without. The risk assessment model for pulmonary dysfunction constructed on this basis had a total score of 28 points, and the area under the subject operating characteristic (ROC) curve was 0.72, reaching the cut-off point of ROC curve while taking scores ≥10 points as the cut-off value for pulmonary dysfunction. ConclusionIn community-based high-risk COPD population, the incidence rate of pulmonary dysfunction is higher in males than that in females, in addition, which increases with the advancement of age. Smoking,insufficient physical activity,recurrent wheezing,post-exercise wheezing or coughing,insensitive to airborne allergens,and history of chronic bronchitis or bronchial asthma are high risk factors for pulmonary dysfunction. The risk assessment model constructed based on these factors has a good predictive effect in screening high-risk population of COPD, but its effectiveness in screening people at general risk needs to be further validated.

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.

Aim To investigate the effect of benzyl iso-thiocyanate (BITC) on the proliferation of mouse U14 cervical cancer cells and to explore the mechanism of cytotoxicity based on transcriptomic data analysis. Methods The effect of BITC on U14 cell activity was detected by MTT, nuclear morphological changes were observed by Hochest 33258 and fluorescent inverted microscope, cell cycle and apoptosis were determined by flow cytometry, and the transcriptome database of U14 cells before and after BITC (20 μmol · L

The cost-effectiveness analysis policy for drugs was institutionalized in Japan since 2019， realizing quantitative adjustment of price across varieties. A hierarchical categorization approach was adopted to select medicines with high expected annual sales. For selected medicines， adjustments were made to the premium and profit components within the existing price structure based on a pre-defined incremental cost-effectiveness ratio （ICER） threshold， effectively resolving the issue of inconsistent criteria and magnitudes caused by subjective judgment. Meanwhile， incentive measures like evaluation exemption or threshold enhancement were granted for specific medicines. Besides， a price adjustment mechanism， which was allowed for upward and downward adjustments， involving tiered ICER threshold and quantified formulas， had been established for the premium and profit components of drug price. In China’s National Reimbursement Drug List （NRDL） access， certain issues remained to be addressed： insufficient clarity in the quantitative mechanism of price formation， incomplete price adjustment measures， and lagging in the communication channels. It is recommended that the following measures could be referred to when further improving the scientificity and fairness of drug pricing during China’s NRDL access， such as enhancing the ICER threshold for medicines catering to special populations， quantifying criteria and extents for price adjustment， granting preferential pricing policies to pharmaceutical companies that present high-quality evidence of effectiveness， preceding communication channels with pharmaceutical companies， as well as exploring a price floor mechanism for the drugs with excessive price reduction.

Objective To investigate the influencing factors of adverse pregnancy outcomes in patients with preeclampsia and the predictive value of serum trace elements in the second trimester.Methods A total of 98 patients with preeclampsia admitted to Qujing First People's Hospital from January 2019 to June 2022 were enrolled in the study.Patients were divided into poor outcome group and good outcome group according to whether they had adverse pregnancy outcomes.The clinical data of all patients enrolled in the study were col-lected and the serum levels of trace elements calcium,copper,zinc and iron were detected in the second trimes-ter.Univariate analysis and multivariate Logistic regression were used to analyze the influencing factors of ad-verse pregnancy outcomes in patients with preeclampsia.The levels of serum trace elements in the second tri-mester of pregnancy were compared between the poor outcome group and the good outcome group.The re-ceiver operating characteristic(ROC)curve was used to evaluate the predictive value of serum trace elements calcium,copper,zinc and iron for adverse pregnancy outcomes in patients with preeclampsia.Results Univari-ate analysis showed that compared with the good outcome group,the poor outcome group had significantly higher systolic blood pressure,24 h urinary protein quantitation,and D-dimer level(P＜0.05)and significantly less gestational age and platelet count at admission(P＜0.05).Multivariate Logistic regression analysis showed that 24 h urinary protein quantification,D-dimer and platelet count were the influencing factors of ad-verse pregnancy outcomes in patients with preeclampsia(P＜0.05).The levels of serum trace elements calci-um,copper,and zinc in the poor outcome group were significantly lower than those in the good outcome group(P＜0.05),and the level of iron was significantly higher than that in the good outcome group(P＜0.05).ROC curve analysis showed that the areas under the curves(AUCs)of serum calcium,copper,zinc,and iron in the second trimester of pregnancy for predicting adverse pregnancy outcomes in preeclampsia patients were 0.830(95％CI:0.780-0.880),0.855(95％CI:0.805-0.905),0.847(0.797-0.897)and 0.861(95％CI:0.811-0.911),respectively.Conclusion Adverse pregnancy outcomes in patients with preeclampsia are re-lated to 24 h urine protein,D-dimer and platelet count.The levels of serum trace elements calcium,copper,zinc and iron in the second trimester of pregnancy change significantly in patients with adverse pregnancy out-comes,which may become predictive markers of adverse pregnancy outcomes.