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.

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.

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.

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Poor water solubility is the primary obstacle preventing the development of many pharmacologically active compounds into oral preparations. Self-nanoemulsifying drug delivery systems(SNEDDS) have become a widely used strategy to enhance the oral bioavailability of poorly soluble drugs by inducing a supersaturated state, thereby improving their apparent solubility and dissolution rate. However, the supersaturated solutions formed in SNEDDS are thermodynamically unstable systems with solubility levels exceeding the crystalline equilibrium solubility, making them prone to drug precipitation in the gastrointestinal tract and ultimately hindering drug absorption. Therefore, maintaining a stable supersaturated state is crucial for the effective delivery of poorly soluble drugs. Incorporating polymers as precipitation inhibitors(PPIs) into the formulation of supersaturated self-nanoemulsifying drug delivery systems(S-SNEDDS) can inhibit drug aggregation and crystallization, thus maintaining a stable supersaturated state. This has emerged as a novel preparation strategy and a key focus in SNEDDS research. This review explores the preparation design of SNEDDS and the technical challenges involved, with a particular focus on polymer-based S-SNEDDS for enhancing the solubility and oral bioavailability of poorly soluble drugs. It further elucidates the mechanisms by which polymers participate in transmembrane transport, summarizes the principles by which polymers sustain a supersaturated state, and discusses strategies for enhancing drug absorption. Altogether, this review provides a structured framework for the development of S-SNEDDS preparations with stable quality and reduced development risk, and offers a theoretical reference for the application of S-SNEDDS technology in improving the oral bioavailability of poorly soluble drugs.
Solubility ; Administration, Oral ; Polymers/chemistry* ; Drug Delivery Systems/methods* ; Humans ; Emulsions/chemistry* ; Biological Availability ; Animals ; Pharmaceutical Preparations/administration & dosage*

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*

L-citrulline is a nonprotein amino acid that plays an important role in human health and has great market demand. Although microbial cell factories have been widely used for biosynthesis, there are still challenges such as genetic instability and low efficiency in the biosynthesis of L-citrulline. In this study, an efficient, plasmid-free, non-inducible L-citrulline-producing strain of Escherichia coli BL21(DE3) was engineered by combined strategies. Firstly, a chassis strain capable of synthesizing L-citrulline was constructed by block of L-citrulline degradation and removal of feedback inhibition, with the L-citrulline titer of 0.43 g/L. Secondly, a push-pull-restrain strategy was employed to enhance the L-citrulline biosynthesis, which realized the L-citrulline titer of 6.0 g/L. Thirdly, the NADPH synthesis and L-citrulline transport were strengthened to promote the synthesis efficiency, which achieved the L-citrulline titer of 11.6 g/L. Finally, fed-batch fermentation was performed with the engineered strain in a 3 L fermenter, in which the L-citrulline titer reached 44.9 g/L. This study lays the foundation for the industrial production of L-citrulline and provides insights for the modification of other amino acid metabolic networks.
Citrulline/biosynthesis* ; Escherichia coli/genetics* ; Metabolic Engineering/methods* ; Fermentation ; NADP/biosynthesis*

ObjectiveTo explore and analyze the influencing factors of myopia among the primary and middle school students, and to provide an evidence for the prevention and control of myopia in students through the combination of scientific use in electronic products and adherence improvement to outdoor activities. MethodsStratified cluster sampling method was used to select 4 schools in Jiading District, in which all the students in grades 3‒5 of primary school and grades 7‒9 of middle school were enrolled into the study for a questionnaire survey and refraction examination. The χ² test was used to compare the data between the myopic and non-myopic group. Logistic regression analysis was used to analyze the influencing factors of myopia. ResultsThe total myopia detection rate among the primary and middle school students in the industrial zone of Jiading District was 62.8%, with a detection rate of 46.9% for primary school students and 75.6% for middle school students, respectively. Logistic regression analysis showed that middle school (OR=3.639, 95%CI=3.045‒4.349, P<0.001), female students (OR=1.278, 95%CI=1.081‒1.510, P=0.004), the frequency of school desks and chairs was adjusted>1 semester (OR=1.227, 95%CI=1.031‒1.461, P=0.021), the length of time spent on extracurricular tutorial classes for main courses ≥1 hour in a week (OR=1.205,95%CI=1.009‒1.440, P=0.040), parents reduced the length of time that their children spent on exercise (OR=1.205, 95%CI=1.009‒1.440, P=0.040), and parental myopia (OR=2.611, 95%CI=2.157‒3.160, P<0.001) were associated with myopia among the primary and middle school students in the industrial zone of Jiading District. ConclusionThe detection rate of myopia among students in Jiading District was relatively high. More attention should be paid to the effect of school desks and chairs’ adjustment frequency, the length of time spent on extracurricular tutorial classes for main courses, electronic screen exposure time on students’ vision, so as to prevent and slow down the occurrence and development of myopia.

Objective To investigate the population characteristics,distribution of traditional Chinese medicine(TCM)syndromes and influencing factors of long-term prognosis of diarrhea-predominant irritable bowel syndrome(IBS-D),and to provide evidence for the formulation of intervention program for IBS-D patients.Methods A total of 124 patients with IBS-D admitted to the medical institutions of the project team members from July 2020 to August 2022 were selected.According to the scoring results of IBS Quality of Life Measure(IBS-QOL),the patients were divided into the good prognosis group(81 cases)and the poor prognosis group(43 cases).The distribution of TCM syndromes in patients with IBS-D was explored,and the difference of IBS-QOL scores of the patients between good prognosis group and poor prognosis group was compared.Univariate logistic regression analysis and multivariate logistic regression analysis were used to determine the main risk factors for poor prognosis in patients with IBS-D.Results(1)The analysis of population characteristics showed that there was no significant difference in the proportion of male and female patients with IBS-D.The patients with IBS-D were usually middle-aged,and had a large interval span of the course of disease.The severity of their symptoms was mostly moderate.All of the patients with IBS-D had various degrees of anxiety and depression,and had nutritional imbalance.(2)The distribution of TCM syndromes in the patients with IBS-D were shown as the following:78 cases were identified as liver depression and spleen deficiency type,accounting for 62.90％;26 cases were identified as spleen-qi deficiency type,accounting for 20.97％;20 cases were identified as spleen and kidney yang deficiency type,accounting for 16.13％.(3)Analysis of IBS-QOL score showed that compared with the good prognosis group,the items scores of negative emotion,physical function,behavioral disorder,health status,being fastidious about food,social function,sexual behavior and interpersonal relationship of IBS-QOL in the poor prognosis group were significantly lowered(P＜0.01).(4)The univariate analysis showed that the risk of poor prognosis in patients with IBS-D would be increased by the factors of age,education level,course of disease,severity of symptoms,anxiety state,depression state,TCM syndrome types,Acute Physiology and Chronic Health Evaluation scoring system Ⅱ(APACHE 11)score,complication of neurological diseases,hemoglobin level,albumin level and total protein level(P＜0.01).(5)The multivariate Logistic regression analysis showed that the risk factors for poor prognosis of IBS-D patients involved age,education level below junior high school,the severity of symptoms being severe,Self-Rating Anxiety Scale(SAS)score,Self-Rating Depression Scale(SDS)score,TCM syndrome being liver depression and spleen deficiency type,hemoglobin level,albumin level and total protein level(P＜0.01).Conclusion Most of IBS-D patients exert long-term poor prognosis,and their long-term prognosis is affected by the factors of age,education level,severity of symptoms,anxiety and depression state,nutritional imbalance and TCM syndrome being liver depression and spleen deficiency type.The identification of the risk factors of poor prognosis will provide evidence for the formulation and adjustment of clinical intervention programs.