Direct compression is an ideal method for tablet preparation, but it requires the powder's high functional properties. The functional properties of the powder during compression directly affect the quality of the tablet. 15 parameters such as Py, FES-8KN, FES-12KN, FES-16KN, CR-8KN, CR-12KN, and CR-16KN were used as the characteristic variables in this paper. Unsupervised learning methods like principal component analysis, cluster analysis, and factor analysis were applied to analyze and classify the compression behavior data of 36 traditional Chinese medicine powders. The results showed that both different dimensionality reduction classification methods could effectively differentiate the compression behavior characteristics of 36 traditional Chinese medicine compound powders. The hierarchical cluster analysis results showed a better agreement with the actual compression phenomena of the powders, where group 1 was high elasticity and low compressibility, group 2 was easily compressed and hard to break, group 3 was excellent compressibility and compactibility. This study is expected to provide references and ideas for predicting the behavior of traditional Chinese medicine powders and the screening of tablet formulations.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

OBJECTIVE To explore the construction of selection system for drug directory of the county-level medical community based on multi-criteria decision analysis， and provide decision-making basis for the selection of drug directory of medical community. METHODS Taking county-level medical community in Chongqing as an example，Delphi method and analytic hierarchy process were employed to construct the selection system for drug directory of the county-level medical community. Selected drugs were quantitatively scored based on the constructed index system， and the drug directory was selected according to the drug’s comprehensive score. The implementation effect of the directory was then evaluated through questionnaire surveys one year after the implementation of the directory. RESULTS The expert authority coefficients of the two rounds of consultation were＞ 0.8， with Kendall’s W values of 0.213 and 0.196， respectively （P＜0.001）. Finally， the selection system for drug directory of the medical community was determined to include five evaluation dimensions： safety， effectiveness， economy， accessibility， and innovation， along with eight evaluation indicators. In the drug directory selected according to the above method， the proportions of centrally procured drugs， medical insurance drugs， and essential drugs had all increased compared to before the selection； the comprehensive scores of chemical drugs ranged from 50.25 to 96.31 scores， and the proportion of drugs scoring between 70 and 100 scores had increased from 78.06% before selection to 85.82%. Among them， antiparasitic drugs had the highest comprehensive scores， while drugs for the digestive tract and metabolism were the most numerous. The evaluation scores of each indicator and the comprehensive scores of drugs in the drug directory after the selection process increased significantly than before selection （P＜ 0.05）. CONCLUSIONS The selection system for drug directory of the county-level medical community constructed in this study is scientific， objective and operable. This process facilitates the promotion of standardized and unified management of drugs in the medical community.

Objective To understand the situation of dental cone beam computed tomography (CBCT) quality control testing phantoms in radiation health technical service institutions in Guangdong province, analyze the differences among different phantoms, and provide a reference for dental CBCT quality control testing. Methods The testing phantoms of 49 radiation health technical service institutions were used as the research objects. The designated CBCT equipment was used for scanning and imaging. The Z-score method was used to evaluate the high-contrast resolution, low-contrast resolution, and distance measurement deviation of each phantom. Results The satisfaction rates of various items for the phantoms in 49 institutions ranged from 85.7% to 100%. The distance measurement deviations of four institutions were “suspicious”, and the high-contrast resolution of four institutions and the distance measurement deviation of one institution were “unsatisfactory”. Conclusion The overall performance of dental CBCT quality control testing phantoms in radiological health technical service institutions in Guangdong province is satisfactory. However, there are still some phantoms with poor results in items such as distance measurement deviation and high-contrast resolution. The structural design, material selection, and manufacturing process of the phantom may all affect the results of quality control testing. Therefore, appropriate phantoms, optimized exposure conditions, and suitable reconstruction algorithms should be used in CBCT quality control testing to ensure accurate and reliable measurements.

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.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Pyroptosis is the programmed death of cells accompanied by an inflammatory response and is widely involved in the development of a variety of diseases, such as infectious diseases, cardiovascular diseases, and neurodegeneration. It has been shown that cellular scorching is involved in the pathogenesis of pulmonary arterial hypertension ( PAH) in cardiovascular diseases. Patients with PAH have perivascular inflammatory infiltrates in lungs, pulmonary vasculopathy exists in an extremely inflam-matory microenvironment, and pro-inflammatory factors in cellular scorching drive pulmonary vascular remodelling in PAH patients. This article reviews the role of cellular scorch in the pathogenesis of PAH and the related research on drugs for the treatment of PAH, with the aim of providing new ideas for clinical treatment of PAH.

Depression is a kind of complex mental illness， which is mainly treated by western medicine at present， but the effect of western antidepressant drugs is not good due to the combined influence of side effects and individual differences of patients. Depression is a "stagnation syndrome" in traditional Chinese medicine， and its treatment principle is to disperse stagnated liver Qi for relieving Qi stagnation. The classic traditional Chinese medicine formula Chaihu Shugansan （CHSGS） has a long history of treating depression and demonstrates significant therapeutic efficacy. Clinically， the addition and subtraction of CHSGS is flexible， but the properties of the active ingredients are vague， and the mechanism and function are unclear. In order to elucidate the pharmacodynamic basis and antidepressant mechanism of CHSGS， this article reviews the pharmacodynamic material basis of CHSGS， clinical research and antidepressant mechanism research progress. Clinically， CHSGS can treat various types of depression such as primary depression， post-stroke depression， and postpartum depression. This article summarizes 32 main ingredients of CHSGS， among which albiflorin， ferulic acid， naringin， hesperidin， saikosaponin a， glycyrrhetinic acid， tangeretin， meranzin hydrate， nobiletin and glycyrrhizic acid are the quality markers （Q-markers） for the antidepressant effect of CHSGS. The antidepressant mechanism of CHSGS is complex， including regulating monoamine neurotransmitters， hypothalamic-pituitary-adrenal （HPA） axis， neurotrophic factors， inflammatory response， cell damage-related pathways， oxidative stress， etc. This article helps to deeply understand the pharmacodynamic basis and mechanism of CHSGS in treating depression， and provides a theoretical basis for the clinical application of CHSGS in treating depression and the development of antidepressant drugs.

Based on the interaction between supramolecule of traditional Chinese medicine and enterobacteria, the material basis of Rhei Radix et Rhizoma and Coptidis Rhizoma was explored. Scanning electron microscopy (SEM) and dynamic light scattering (DLS) were used to characterize the morphological differences of Rhubarb single decoction, Coptis single decoction and Rhubarb and Coptis co-decoction. An in vitro antibacterial model (E. coli, E. faecium and B. subtilis) was established to evaluate the damage effect of the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma on enterobacteria. Ultra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the changes of chemical components of single decoctions and co-decoctions. The co-decoction of Rhei Radix et Rhizoma and Coptidis Rhizoma was turbid after decocting. The spherical particles of 300-400 nm were observed under SEM, and the co-decoction was more uniform and stable than that of single decoction. The interaction between supramolecules formed after the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma and enterobacteria was significantly different from that of single decoction. In the process of interaction between supramolecules and enterobacteria, the spherical state was maintained, and the medicinal ingredients in Coptidis Rhizoma or Rhei Radix et Rhizoma were blocked, which could effectively alleviate the damage to enterobacteria. This study provided a reference for subsequent studies on the regulation of intestinal flora homeostasis by the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma.