Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

Objective To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.

Objective The study aimed to elucidate the population composition and seasonal distribution characteristics of mosquitoes in Laoshan District, Qingdao City, providing a scientific basis for the prevention and control of mosquito-borne infectious diseases. Methods Mosquito surveillance was conducted in Laoshan District in Qingdao City using light traps from April to November 2020-2022. Dominant mosquito species were identified using the Berger-Parker dominance index(I)and mosquito density was compared using the Kruskal-Wallis H test. The seasonal distribution characteristics of adult mosquitoes were analyzed using concentration and circular distribution method. Spearman rank correlation analysis was used to study the relationship between mosquito populations and meteorological factors with mean monthly temperature, mean monthly relative humidity, and mean monthly rainfall. Results The mean mosquito density for the trapping period was 9.771 females/(trap·night)in 2020,9.771 in 2021, and 9.427 in 2022. There was no statistically significant difference in mosquito density between different years(H = 0.095, P = 0.954). In total, 2 781 adult female mosquitoes comprising five species from four genera were captured over three years. Culex pipiens(I = 0.654), Aedes albopictus(I = 0.202), and Aedes aegypti(I = 0.118)were identified as dominant species. Seasonal fluctuations in the adult mosquitoes were unimodal, and primarily concentrated from June to September. The peak period of adult mosquito varied between years with statistical significance. (F = 3.838, P ＜ 0.05). Mosquito density was highly correlated with mean monthly temperature, mean monthly relative humidity and mean monthly precipitation(P＜ 0.05). Conclusions Cx. pipiens pallens, Ae. albopictus, and Ar. inamoratus are the dominant mosquito species in the Laoshan District of Qingdao City. The activity of adult mosquitoes is seasonal, and primarily concentrated in summer and autumn, during which the risk of mosquito-borne diseases such as dengue fever is increased.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

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.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Objective:To compare the differences in repair of rabbit bone defects between allogeneic platelet rich plasma (PRP) gel and autologous PRP gel.Methods:Thirty-six healthy New Zealand white rabbits were selected and randomly divided into an autologous group, an allogeneic group, and a control group ( n=12). A model of bilateral forelimb bone defects was established in each group. The autologous group was repaired with self-made deproteinized bone scaffold materials + autologous bone marrow mesenchymal stem cells (BMSCs) + autologous PRP gel, the allogeneic group with self-made deproteinized bone scaffold materials + autologous BMSCs + allogeneic PRP gel, and the control group with only self-made deproteinized bone scaffold materials + autologous BMSCs. At postoperative 1, 2, and 3 months, 4 animals were euthanized in each group, respectively, for gross observation, X-ray examination, Micro-CT examination, biomechanical testing and histological analysis (HE staining for tissue morphology) to compare the differences in repair of bone defects. Results:The formation of trabecular bone, cortical reconstruction, and medullary recanalization occurred earlier in the autologous and allogeneic groups than in the control group. Micro-CT analysis at postoperative 2 months showed that bone mineral density [(281.51±33.69) mg/mL and (266.13±37.13) mg/mL], bone volume fraction (23.52%±2.81% and 21.91%±1.94%), and trabecular number [(1.68±0.29) mm -1 and (1.63±0.22) mm -1] in the autologous and allogeneic groups were significantly higher than those in the control group [(197.47±18.61) mg/mL, 16.54%±3.06%, and (1.06±0.11) mm -1] ( P<0.05). No significant differences were found among the 3 groups in trabecular thickness [(0.33±0.09) mm, (0.42±0.16) mm, and (0.28±0.13) mm] or in the maximum compressive load ( P>0.05). HE staining revealed a significantly greater number and earlier formation of chondrocytes and osteoblasts in the autologous and allogeneic groups than in the control group. Conclusion:Since allogeneic PRP exhibits similar efficacy in promoting new bone formation compared with autologous PRP in a rabbit bone defect model, it may serve as a viable substitute for autologous PRP.