Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Renal fibrosis is the common pathological endpoint of many forms of chronic kidney disease (CKD), yet the mechanisms determining effective repair versus persistent fibrotic remodeling remain incompletely understood. Peritubular capillaries (PTCs) are not merely passive targets of fibrotic injury, but form a dynamic vascular microenvironment linking regional perfusion, oxygen delivery, tubular metabolism, endothelial barrier integrity, pericyte support, immune-cell trafficking, and extracellular matrix (ECM) remodeling. We propose that PTC abnormalities should not be interpreted simply as reduced capillary density, but as a multidimensional disturbance of microvascular structure and function. Structural vascular preservation does not necessarily indicate effective microcirculation: anatomically detectable capillaries may still exhibit impaired perfusion, barrier dysfunction, abnormal endothelial-pericyte interactions, disturbed oxygen metabolism, or reduced network connectivity. Conversely, increased angiogenesis does not necessarily signify successful vascular repair if vessels remain immature, poorly perfused, or inadequately integrated into the existing network. To organize these abnormalities, we conceptualize PTC remodeling into four predominant but nonexclusive states: endothelial barrier disruption with inflammatory adhesion; endothelial-pericyte uncoupling with no-reflow; dysregulated angiogenesis with network simplification; and capillary rarefaction with fibrotic matrix locking. These states are not sequential stages of CKD. They may coexist within the same kidney, overlap within the same anatomical region, and vary with disease etiology, injury phase, vascular compartment, and treatment exposure. We further propose six dimensions for evaluating PTC status—structural presence, functional perfusion, barrier integrity, pericyte coupling, oxygen metabolism, and network connectivity—so that vascular quantity, function, and reparative capacity are assessed separately rather than inferred from a single endothelial marker or capillary-density measurement. Mechanistically, these states converge on a bidirectional microvascular-fibrotic feedback loop. Barrier disruption facilitates leukocyte recruitment and alters vascular paracrine signaling. Endothelial-pericyte uncoupling compromises capillary stability, whereas abnormal pericyte contraction may increase local vascular resistance and contribute to no-reflow before overt capillary loss. Dysregulated angiogenesis may generate vessels that fail to mature or connect to an effective perfusion network. Progressive capillary rarefaction compromises oxygen delivery, while sustained tubular transport demand and mitochondrial dysfunction may aggravate regional oxygen supply-demand mismatch. Injured tubules, immune cells, endothelial cells, pericytes, and fibroblasts communicate through soluble mediators, extracellular vesicles, direct cell contact, and cell-matrix signaling. Importantly, fibrosis is not merely a downstream consequence of microvascular injury. Altered ECM may increase diffusion distance, modify mechanotransduction, impair endothelial survival and migration, and restrict capillary reopening and regeneration. We use the term matrix locking to describe this sustained constraint on vascular repair, while emphasizing that current evidence does not establish irreversible disease in humans. This framework also has therapeutic implications. PTC-directed treatment should not be reduced to increasing vessel number or uniformly promoting angiogenesis. Instead, intervention should be matched to the dominant and potentially reversible abnormality: preserving endothelial barrier function; restoring endothelial-pericyte stability and reducing pathological microvascular resistance; normalizing angiogenesis so that vascular growth is accompanied by maturation, barrier competence, network integration, and effective perfusion; and, in advanced rarefaction, relieving matrix-related constraints while supporting vascular regeneration. The context- and time-dependent effects of signaling through VEGF, Tie2, PDGF-B/PDGFRβ, eNOS/NO, and related pathways indicate that the same molecular signal may exert different effects depending on disease context and timing. Therapeutic efficacy should therefore be evaluated by restoration of effective microcirculation and tissue function rather than by isolated molecular changes or capillary counts alone. Despite increasing mechanistic evidence, the translational basis for this framework remains limited. Most causal and interventional data derive from animal or cell models, whereas human evidence is largely limited to pathological observations, patient-sample associations, and prognostic studies. Currently available circulating and urinary candidate biomarkers generally lack sufficient specificity for PTC injury or state assignment, and no standardized quantitative thresholds have been externally validated. The four-state model should therefore be regarded as a hypothesis-generating analytical framework rather than a clinicopathological classification system. Future studies should integrate spatially resolved tissue phenotyping, blood and urine biomarkers, and dynamic microcirculatory assessment in longitudinal cohorts. They should test reproducibility, assess prognostic value beyond conventional pathology, estimated glomerular filtration rate, and proteinuria, and determine whether changes in PTC status precede or mediate therapeutic benefit. Linking reproducible microvascular phenotypes to defined mechanisms, treatment response, and long-term renal outcomes will be essential to determine whether state-directed assessment and restoration of the PTC microenvironment can ultimately inform patient stratification and therapeutic decision-making.

Single-cell metabolite analysis at the small molecule level reveals intercellular heterogeneity and molecular diversity,especially living cell metabolite analysis which can provide more accurate biochemical information.In this study,a comprehensive single-cell metabolomics mass spectrometry analysis platform was constructed based on continuous ultrasonic sample introduction,aiming to improve the utilization rate of single cells and the efficiency of mass spectrometry detection.This platform utilized mechanical motion generated by a miniaturized ultrasound module,which minimally affected cell integrity and viability,enabling cell suspension and dispersion for up to 60 min,with cell viability exceeding 70%.By comparing cell suspension densities and the cell number of mass spectrometry detections between static and ultrasound groups,the results showed that the ultrasound treatment significantly reduced cell sedimentation rate and increased single-cell mass spectrometry detection efficiency.Applying this platform to single-cell analysis of cell line of mouse cerebellar astrocytes(C8D1A)and mouse glioma(GL261)cells achieved clustering and differential analysis of different cell types,demonstrating the method's potential in analyzing cellular heterogeneity and identifying cells.This approach promised to provide new insights and solutions for single-cell analysis.

Objective:This study aims to investigate the predictive performance of serum levels of white blood cell count(WBC),D-dimer(D-D)and N-terminal pro-brain natriuretic peptide(NT-proBNP)for major adverse cardiovascular events(MACE)in elderly patients with acute ST-segment elevation myocardial infarction(ASTE-MI).Methods:A total of 70 elderly patients with ASTEMI treated in Bozhou People's Hospital between April 2020 and May 2023 were prospectively selected as observation group.Incidence of MACE during 1-year follow-up were recorded,another 50 patients with unstable angina pectoris treated in our hospital simultaneously were selected as control group.Serum levels of WBC,D-D and NT-proBNP were compared among above groups.The receiver operating characteristic(ROC)curve was used to evaluate the predictive value of serum WBC,D-D and NT-proBNP for MACE in elderly patients with ASTEMI.A nomogram was established,and calibration curve and deci-sion curve analysis(DCA)were used to evaluate the performance of model.Results:A total of 40 cases(59.70％)experienced MACE during one-year follow-up.Compared to those in control group,patients in observation group had significant higher serum WBC[(11.43±1.98)×109/Lvs.(6.30±1.99)× 109/L],D-D[(0.91±0.20)mg/L vs.(0.47±0.18)mg/L]andNT-proBNP[(192.31±63.19)pg/ml vs.(114.05±22.79)pg/ml](P＜0.001 all).Compared to participants without MACE,those with MACE had significantly higher serum WBC[(13.33±1.90)× 109/L vs.(10.27±0.98)× 109/L],D-D[(1.11±0.25)mg/L vs.(0.87±0.21)mg/L]and NT-proBNP[(238.73±50.22)pg/ml vs.(150.70±39.16)pg/ml](P＜0.001 all).ROC analysis showed that the ar-ea under the curve(AUC)of the combined detection of serum WBC(AUC=0.791,95％CI 0.677～0.879),D-D(AUC=0.767,95％CI 0.650～0.859)and NT-proBNP(AUC=0.733,95％CI 0.614～0.832)was 0.916(95％CI 0.825～0.969),which was significantly higher than those of single detections(Z=2.386,4.953,3.190,P=0.017,0.004,＜0.001).The total score of the nomogram model constructed based on the levels of WBC,D-D and NT-proBNP ranged from 70 to 126 points.The predicted incidence was basically consistent with the actual in-cidence.For the internal verification of the model,the AUC of ROC curve of the training set and the validation set was 0.863 and 0.926 respectively.The DCA curve was located above the critical curve,indicating that the model had a net benefit and good clinical effectiveness.Conclusion:Serum WBC,D-D and NT-proBNP significantly el-evated in elderly patients with ASTEMI.The combined detection of serum WBC,D-D and NT-proBNP levels has good predictive value for MACE in these patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

This study aims to explore the effects and mechanisms of 4'-O-methylbavachalcone(MeBavaC), an active compound from Psoraleae Fructus, in regulating white matter neuroinflammation to improve vascular cognitive impairment. Male Sprague-Dawley(SD) rats were randomly divided into four groups: sham group, model group, high-dose MeBavaC group(14 mg·kg~(-1)), and low-dose MeBavaC group(7 mg·kg~(-1)). The rat model of chronic cerebral hypoperfusion(CCH) was established using bilateral common carotid artery occlusion. The Morris water maze test was performed to evaluate the learning and memory abilities of the rats. Luxol fast blue staining, Nissl staining, immunofluorescence, immunohistochemistry, and transmission electron microscopy were utilized to observe the morphology and ultrastructure of the white matter myelin sheaths, axon integrity, the morphology and number of hippocampal neurons, and the loss and activation of glial cells in the white matter. Transcriptome analysis was performed to explore the potential mechanisms of white matter injury induced by CCH. Western blot and quantitative real-time polymerase chain reaction(qRT-PCR) assays were conducted to measure the expression levels of NOD-like receptor protein 3(NLRP3), absent in melanoma 2(AIM2), gasdermin D(GSDMD), cysteinyl aspartate-specific proteinase-1(caspase-1), interleukin-18(IL-18), interleukin-1β(IL-1β), erythropoietin(EPO), nuclear factor erythroid 2-related factor 2(Nrf2), and heme oxygenase-1(HO-1) in the white matter of rats. The results showed that compared with the model group, MeBavaC significantly improved the learning and memory abilities of rats with CCH, improved the damage of white matter myelin sheath, maintained axonal integrity, reduced the loss of hippocampal neurons and oligodendrocytes in the white matter, inhibited the activation of microglia and the proliferation of astrocytes in the white matter, and suppressed the NLRP3/AIM2/caspase-1/GSDMD pathway. The expression levels of inflammatory cytokines IL-1β and IL-18 were significantly reduced, while EPO expression and the expression of Nrf2/HO-1 antioxidant pathway were notably elevated. In conclusion, MeBavaC can alleviate cognitive impairment in rats with CCH and suppress neuroinflammation in cerebral white matter. The mechanism of action may involve activation of EPO activity, promotion of endogenous antioxidant pathways, and inhibition of neuroinflammation in the white matter. This study suggests that MeBavaC exhibits antioxidant and anti-neuroinflammatory effects, showing potential application in improving cognitive dysfunction.
Animals ; Male ; Rats, Sprague-Dawley ; NF-E2-Related Factor 2/immunology* ; Rats ; Chalcones/administration & dosage* ; Cognitive Dysfunction/metabolism* ; Signal Transduction/drug effects* ; Neuroinflammatory Diseases/drug therapy* ; Heme Oxygenase-1/metabolism* ; Humans ; Heme Oxygenase (Decyclizing)/genetics*

This study applied the grading of recommendations assessment, development and evaluation(GRADE) system and the integrated evidence chain-based effectiveness evaluation of traditional Chinese medicine(Eff-iEC) to evaluate the evidence for 12 commonly used Chinese patent medicines for the treatment of osteoporosis, which are frequently recommended in guidelines or expert consensuses. The results showed that Xianling Gubao Capsules/Tablets were rated as C(low-level evidence) according to the GRADE system, and as BA~+B~+(intermediate evidence) according to the Eff-iEC system. Jintiange Capsules were rated as C(low-level evidence) by the GRADE system, and as AA~+B(high-level evidence) by the Eff-iEC system. Gushukang Granules/Capsules were rated as C(low-level evidence) by GRADE system, and as BA~+B~+(intermediate evidence) by Eff-iEC system. Zuogui Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Qianggu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B~+(high-level evidence) by Eff-iEC system. Zhuanggu Zhitong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BA~+B(intermediate evidence) by Eff-iEC system. Jingui Shenqi Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B(high-level evidence) by Eff-iEC system. Quanduzhong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AD~+B~+(low-level evidence) by Eff-iEC system. Epimedium Total Flavones Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AAB~+(high-level evidence) by Eff-iEC system. Yougui Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~(++)B~(+ )(high-level evidence) by Eff-iEC system. Qigu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BB~+B(intermediate evidence) by Eff-iEC system. Liuwei Dihuang Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Overall, the Eff-iEC system provides a more comprehensive assessment of the effectiveness evidence for traditional Chinese medicine(TCM) than the GRADE system. However, it still has certain limitations that hinder its wider promotion and application. In terms of clinical evidence evaluation, both the Eff-iEC and GRADE systems reflect that the current clinical research quality on Chinese patent medicines for the treatment of osteoporosis is generally low. High-quality clinical trials are still needed in the future to further validate clinical efficacy.
Drugs, Chinese Herbal/therapeutic use* ; Osteoporosis/drug therapy* ; Humans ; Nonprescription Drugs/therapeutic use* ; Evidence-Based Medicine ; Medicine, Chinese Traditional

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

PURPOSE: Early mortality in major trauma has decreased, but MODS remains a leading cause of poor outcomes, driven by trauma-induced cytokine storms that exacerbate injuries and organ damage. METHODS: This prospective cohort study included 79 major trauma patients (ISS >15) treated in the National Center for Trauma Medicine, Peking University People's Hospital, from September 1, 2021, to July 31, 2023. Patients (1) with ISS >15 (according to AIS 2015), (2) aged 15-80 years, (3) admitted within 6 h of injury, (4) having no prior treatment before admission, were included. Exclusion criteria were (1) GCS score <9 or AIS score ≥3 for TBI, (2) confirmed infection, infectious disease, or high infection risk, (3) pregnancy, (4) severe primary diseases affecting survival, (5) recent use of immunosuppressive or cytotoxic drugs within the past 6 months, (6) psychiatric patients, (7) participation in other clinical trials within the past 30 days, (8) patients with incomplete data or missing blood samples. Admission serum inflammatory cytokines and pathophysiological data were analyzed to develop machine learning models predicting MODS within 7 days. LR, DR, RF, SVM, NB, and XGBoost were evaluated based on the area under the AUROC. The SHAP method was used to interpret results. RESULTS: This study enrolled 79 patients with major trauma, and the median (Q₁, Q₃) age was 51 (35, 59) years (52 males, 65.8%). The inflammatory cytokine data were collected for all participants. Among these patients, 35 (44.3%) developed MODS, and 44 (55.7%) did not. Additionally, 2 patients (2.5%) from the MODS group succumbed. The logistic regression model showed strong performance in predicting MODS. Ten key cytokines, IL-18, Eotaxin, MCP-4, IP-10, CXCL12, MIP-3α, MCP-1, IL-1RA, Cystatin C, and MRP8/14 were identified as critical to the trauma-induced cytokine storm and MODS development. Early elevation of these cytokines achieved high predictive accuracy, with an AUROC of 0.887 (95% CI 0.813-0.976). CONCLUSION Trauma-induced cytokine storms are strongly associated with MODS. Early identification of inflammatory cytokine changes enables better prediction and timely interventions to improve outcomes.
Humans ; Prospective Studies ; Middle Aged ; Male ; Female ; Adult ; Aged ; Cytokine Release Syndrome/etiology* ; Adolescent ; Young Adult ; Aged, 80 and over ; Wounds and Injuries/complications* ; Cytokines/blood* ; Multiple Organ Failure/diagnosis* ; Machine Learning