Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

[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.

ObjectiveTo observe the intervention effect of Fufang Kangjiaolv capsules on anxiety-like behaviors in the rat model of chronic restraint stress （CRS） and explore the mechanism underlying the anti-anxiety effect via the microbiota-gut-brain axis. MethodsRats were assigned into blank， model， positive drug （diazepam， 1 mg·kg^-1）， and low-， medium-， and high-dose （0.75， 1.5， 3 g·kg^-1， respectively） Fufang Kangjiaolv capsules groups. After 14 days of administration， the elevated plus maze test， open field test， light and dark box test， and marble burying test were performed. Hematoxylin-eosin staining was employed to observe the pathological changes in the hippocampus and colon of rats， and Nissl staining was conducted to observe the damage of hippocampal neurons. The gut microbiota was analyzed by 16S rRNA gene sequencing. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the mRNA levels of zonula occludens-1 （ZO-1） and occludin in the colon of rats. The levels of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β） in the colon， serum， and hippocampus were determined by enzyme-linked immunosorbent assay. Western blot was employed to determine the protein levels of ZO-1， occludin， nuclear factor-κB p65 （NF-κB p65） in the colon tissue and NF-κB p65 and brain-derived neurotrophic factor （BDNF） in the hippocampal tissue. ResultsCompared with the blank group， the model group showed reductions in the time and frequency ratio of rats entering the elevated plus maze， the time and frequency of rats entering the central area of the open field， the time of entering the open box， the times of passing through the light and dark box， and the number of unburied beads （P<0.05， P<0.01）. Compared with the model group， Fufang Kangjiaolv capsules ameliorated the anxiety of the model rats to varying degrees， and the high-dose group had the best effect， with increases in the proportions of time and frequency of rats entering the open arm in the elevated plus maze （P<0.05）， the number of rats entering the central area in the open field （P<0.05）， the time of entering the open box， the times of passing through the light and dark boxes， and the number of unburied beads （P<0.01）. Moreover， the high-dose group showed alleviated pathological damage of hippocampal neurons and colon. The results of 16S rRNA gene sequencing showed that the model group had increased relative abundance of Firmicutes， Deferribacterota， Romboutsia， and Phascolarctobacterium， while it had decreased relative abundance of Bavcteroidota and Lactobacillus. The drug administration groups showed increased relative abundance of Bavcteroidota， Bacteroides， norank f norank o Clostridia UCG-014， and Blautia and decreased relative abundance of Firmicutes and Deferribacterota. Compared with the blank group， the model group showed down-regulated protein and mRNA levels of ZO-1 and occludin in the colon （P<0.01）， elevated levels of TNF-α， IL-6， and IL-β in the colon， serum， and hippocampus （P<0.01）， up-regulated protein level of NF-κB p65 in the colon and hippocampus （P<0.01）， and down-regulated protein level of BDNF in the hippocampus （P<0.05）. Compared with the model group， high-dose Fufang Kangjiaolv capsules up-regulated the mRNA levels of ZO-1 and occludin in the colon （P<0.01）， lowered the levels of TNF-α， IL-6， and IL-β in the colon， serum， and hippocampus （P<0.01）， up-regulated the protein levels of ZO-1 （P<0.01） and occludin （P<0.05） in the colon， down-regulated the protein level of NF-κB p65 in the colon and hippocampus （P<0.05）， and up-regulated the protein level of BDNF in the hippocampus. ConclusionFufang Kangjiaolv capsules can reduce the anxiety-like behaviors in the rat model of CRS by regulating the gut microbiota disturbance， up-regulating the expression of tight junction proteins in the colon， repairing intestinal mucosal mechanical barrier， and down-regulating NF-κB/BDNF signaling pathway， thereby reducing peripheral and central inflammation. This study proves the hypothesis that Fufang Kangjiaolv capsules play an anti-anxiety role via the microbiota-gut-brain axis， providing a new idea for further research.

ObjectiveTo explore the mechanism of Shouhui Tongbian capsules in treating constipation based on the research foundation of its active components combined with network pharmacology and animal experiments. MethodsThe drug components were imported into SwissTargetPrediction to predict the targets of Shouhui Tongbian capsules， and constipation-related targets were collected from disease databases. A protein-protein interaction （PPI） network was constructed for the common targets shared by Shouhui Tongbian capsules and constipation to screen key targets， which was followed by gene ontology （GO） function and Kyoto encyclopedia of genes and genomes （KEGG） pathway enrichment analyses. A "bioactive component-target-pathway" network was constructed， and the core components of Shouhui Tongbian capsules in treating constipation were screened based on the topological parameters of this network. Molecular docking was employed to predict the binding affinity of core components to key targets. A mouse model of constipation was constructed to screen the key pathways and targets of the drug intervention in constipation. ResultsThe PPI network revealed six key constipation-related targets： protein kinase B （Akt1）， B-cell lymphoma-2 （Bcl-2）， glycogen synthase kinase-3β （GSK-3β）， cyclooxygenase-2 （PTGS2）， estrogen receptor 1 （ESR1）， and epidermal growth factor receptor （EGFR）. The KEGG pathway analysis showed that the phosphatidylinositol 3-kinase （PI3K）/Akt signaling pathway was the most enriched. The topological parameter analysis of the "bioactive component-target-pathway" network screened out the top 10 core components： auranetin， isosinensetin， naringin， diosmetin， quercetin， apigenin， luteolin， hesperidin， isorhapontigenin， and chrysophanol. Molecular docking results showed that the 10 core components had strong binding affinity with the 6 key targets. Animal experiments showed that after intervention with different doses of Shouhui Tongbian capsules， the time to the first black stool excretion was reduced and the fecal water content and small intestine charcoal propulsion rate of mice were improved. After treatment with Shouhui Tongbian capsules， the colonic mucosal injury and glandular arrangement were alleviated， and the muscle layer thickness was increased. Western blot results showed that Shouhui Tongbian capsules recovered the expression of apoptosis-related molecules mediated by the PI3K/Akt pathway in the colonic tissue of constipated mice. Terminal-deoxynucleotidyl transferase-mediated nick end labeling （TUNEL） results showed that the cell apoptosis rate of the colon significantly reduced after intervention with Shouhui Tongbian capsules. ConclusionThe results of network pharmacology and animal experiments confirmed that Shouhui Tongbian capsules can treat constipation through multiple targets and pathways. The capsules can effectively intervene in loperamide-induced constipation in mice by regulating the constipation indicators and reducing cell apoptosis in the colon tissue via activating the PI3K/Akt signaling pathway.

As a chronic and lifelong disease， diabetes mellitus occurs across all age groups and gender groups. Since the disease requires lifelong treatment， it seriously affects the quality of life of patients. With the rising incidence on a global scale， diabetes mellitus has become a global problem that seriously affects public health. Moreover， the complications of this disease have aroused concern from the global medical research community， the World Health Organization， and the public. In the past， Western medicine was used in the clinical treatment of diabetes mellitus， which， however， had drug dependence， unsatisfactory efficacy， and side effects. Long-term oral administration of antidiabetics may cause liver and kidney function damage， hypoglycemia and other adverse symptoms. The treatment of diabetes mellitus has been faced with challenges such as limited efficacy and obvious side effects. Therefore， exploring more effective treatment means， especially tapping the potential of traditional Chinese medicine （TCM） in the treatment of diabetes mellitus， is a major issue to be solved. TCM has shown a great application value and a broad prospect in the treatment of diabetes mellitus because of multi-target regulation， a holistic view， synergistic effects， and high safety. TCM has a history of thousands of years in the prevention and treatment of diabetes mellitus， with rich experience accumulated and remarkable results achieved. Particularly， TCM demonstrates definite therapeutic effects on the complications. The application of TCM in the treatment of complications has been recognized and accepted by patients because of the definite therapeutic effect. In recent years， great progress has been achieved in the treatment of diabetes mellitus by the combination of Chinese and western medicine， which has made important contributions to the control of diabetes mellitus. This paper reviews the articles about the treatment of diabetes mellitus with TCM classic prescriptions， summarizes the treatment of clinical cases regarding the indications of these prescriptions， and provides an overview of the treatment mechanisms， aiming to offer fresh insights and strategies for the clinical diagnosis and treatment of diabetes mellitus.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: Intraoperative hypercapnia reduces the time to emergence from volatile anesthetics, but few clinical studies have explored the effect of hypercapnia on the emergence time from intravenous (IV) anesthesia. We investigated the effect of inducing mild hypercapnia during the recovery period on the emergence time after total IV anesthesia (TIVA). Methods: Adult patients undergoing transurethral lithotripsy under TIVA were randomly allocated to normocapnia group (end-tidal carbon dioxide [ETCO2] 35–40 mmHg) or mild hypercapnia group (ETCO2 50-55 mmHg) during the recovery period. The primary outcome was the extubation time. The spontaneous breathing-onset time, voluntary eye-opening time, and hemodynamic data were collected. Changes in the cerebral blood flow velocity in the middle cerebral artery were assessed using transcranial Doppler ultrasound. Results: In total, 164 patients completed the study. The extubation time was significantly shorter in the mild hypercapnia (13.9 ± 5.9 min, P = 0.024) than in the normocapnia group (16.3 ± 7.6 min). A similar reduction was observed in spontaneous breathing-onset time (P = 0.021) and voluntary eye-opening time (P = 0.008). Multiple linear regression analysis revealed that the adjusted ETCO2 level was a negative predictor of extubation time. Middle cerebral artery blood flow velocity was significantly increased after ETCO2 adjustment for mild hypercapnia, which rapidly returned to baseline, without any adverse reactions, within 20 min after extubation. Conclusions Mild hypercapnia during the recovery period significantly reduces the extubation time after TIVA. Increased ETCO2 levels can potentially enhance rapid recovery from IV anesthesia.

Objective To clarify the current development status and research hotspots in the field of experimental drug management in China through data mining by bibliometric. Methods Key words such as “experiment”, “drug”, and “management” were used to search the Chinese literature in China National Knowledge Infrastructure （CNKI）. The title, author name, author affiliation, Chinese abstract, Chinese keywords, publication period, journal name, and other content of the literature were extracted from the literature. Cluster analysis was performed by CNKI literature visualization analysis system, CiteSpace and other software, and a network knowledge map was drawn. Results The literature in the field of experimental drug management in China was first published in 1994, and a total of 140 articles were published until 2022. Among them, 20 articles were supported by relevant funds, and the keyword co-occurrence frequency was highest among “subjects”. The most frequently published medium was the Chinese Pharmacological Yearbook. Conclusion At present, the quantity and quality of literature in the field of experimental drug management in China were relatively small, and the cooperation and communication among authors were not close. The funding from various fund projects in this field was also lacking. These factors led to a lower overall development level and slower development speed in this field.

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.