ObjectiveTo explore the efficacy and mechanisms of Sini San in the treatment of depression and liver injury based on gut microbiota. MethodsThirty-two male Sprague-Dawley （SD） rats were randomly divided into a normal group， model group （M）， Sini San group （MS， 2.5 g·kg^-1）， and fluoxetine group （MF， 2 mg·kg^-1）. Except for the normal group， rats in the other three groups were subjected to chronic unpredictable mild stress （CUMS）. After 8 weeks， the open-field test and sucrose preference test were conducted. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum corticosterone （CORT）， adrenocorticotropic hormone （ACTH）， corticotropin-releasing factor （CRF）， lipopolysaccharide （LPS）， Zonulin， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， γ-aminobutyric acid （GABA） levels in the hippocampus and prefrontal cortex， and brain-derived neurotrophic factor （BDNF） levels in the hippocampus. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect hippocampal BDNF mRNA expression. Serum alanine aminotransferase （ALT） and aspartate aminotransferase （AST） levels were measured using the ultraviolet lactate dehydrogenase method. The ultrastructure of the intestinal epithelium was observed by electron microscopy， and gut microbiota in rat feces were analyzed using 16S rDNA high-throughput sequencing. ResultsCompared with the normal group， the sucrose preference of rats in the model group was significantly reduced （P0.01）， whereas it was significantly increased in the Sini San group compared with the model group （P0.05）. Compared with the normal group， hippocampal GABA protein levels and BDNF mRNA expression in the model group were significantly decreased （P0.05）， and compared with the model group， both were significantly increased in the Sini San group （P0.05， P0.01）. Compared with the normal group， serum LPS and Zonulin levels in the model group were significantly increased （P0.05， P0.01）， and compared with the model group， Zonulin levels in the Sini San group were significantly decreased （P0.05）. No obvious changes were observed in the ultrastructure of the jejunal mucosa among groups. Compared with the normal group， widened and blurred tight junctions， sparse and shortened microvilli， and mitochondrial swelling with cristae disruption in epithelial cells were observed in the ileal and colonic mucosa of the model group， which were markedly improved in the Sini San and fluoxetine groups. The results of 16S rDNA high-throughput sequencing showed that Sini San improved CUMS-induced dysbiosis of Bacteroidetes and Proteobacteria. Correlation analysis indicated that Bacteroidetes and Proteobacteria were significantly correlated with depression-related indicators， liver function， and intestinal mucosal permeability. ConclusionSini San exerts antidepressant and hepatoprotective effects by improving Bacteroidetes and Proteobacteria and inhibiting the increase in intestinal mucosal permeability in CUMS rats.

ObjectiveTo explore the efficacy and mechanisms of Sini San in the treatment of depression and liver injury based on gut microbiota. MethodsThirty-two male Sprague-Dawley （SD） rats were randomly divided into a normal group， model group （M）， Sini San group （MS， 2.5 g·kg^-1）， and fluoxetine group （MF， 2 mg·kg^-1）. Except for the normal group， rats in the other three groups were subjected to chronic unpredictable mild stress （CUMS）. After 8 weeks， the open-field test and sucrose preference test were conducted. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum corticosterone （CORT）， adrenocorticotropic hormone （ACTH）， corticotropin-releasing factor （CRF）， lipopolysaccharide （LPS）， Zonulin， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， γ-aminobutyric acid （GABA） levels in the hippocampus and prefrontal cortex， and brain-derived neurotrophic factor （BDNF） levels in the hippocampus. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect hippocampal BDNF mRNA expression. Serum alanine aminotransferase （ALT） and aspartate aminotransferase （AST） levels were measured using the ultraviolet lactate dehydrogenase method. The ultrastructure of the intestinal epithelium was observed by electron microscopy， and gut microbiota in rat feces were analyzed using 16S rDNA high-throughput sequencing. ResultsCompared with the normal group， the sucrose preference of rats in the model group was significantly reduced （P<0.01）， whereas it was significantly increased in the Sini San group compared with the model group （P<0.05）. Compared with the normal group， hippocampal GABA protein levels and BDNF mRNA expression in the model group were significantly decreased （P<0.05）， and compared with the model group， both were significantly increased in the Sini San group （P<0.05， P<0.01）. Compared with the normal group， serum LPS and Zonulin levels in the model group were significantly increased （P<0.05， P<0.01）， and compared with the model group， Zonulin levels in the Sini San group were significantly decreased （P<0.05）. No obvious changes were observed in the ultrastructure of the jejunal mucosa among groups. Compared with the normal group， widened and blurred tight junctions， sparse and shortened microvilli， and mitochondrial swelling with cristae disruption in epithelial cells were observed in the ileal and colonic mucosa of the model group， which were markedly improved in the Sini San and fluoxetine groups. The results of 16S rDNA high-throughput sequencing showed that Sini San improved CUMS-induced dysbiosis of Bacteroidetes and Proteobacteria. Correlation analysis indicated that Bacteroidetes and Proteobacteria were significantly correlated with depression-related indicators， liver function， and intestinal mucosal permeability. ConclusionSini San exerts antidepressant and hepatoprotective effects by improving Bacteroidetes and Proteobacteria and inhibiting the increase in intestinal mucosal permeability in CUMS rats.

ObjectiveThe development trend and knowledge structure of the research on human use experience （HUE） of traditional Chinese medicine （TCM） were systematically reviewed， and the core challenges and future directions were identified. This study aims to provide reference for the construction of a scientific and feasible research and development framework and evidence transformation system. MethodsLiterature related to "human use experience" published from January 1， 2019 to July 31， 2025 was retrieved from the China National Knowledge Infrastructure （CNKI）， Wanfang， China Science and Technology Journal Database （VIP）， and PubMed databases. Bibliometric visualization was conducted using Excel， VOSviewer， and CiteSpace， followed by in-depth reading and thematic summarization of core literature. ResultsA total of 181 papers were included for bibliometric analysis， with 45 articles used for in-depth thematic mining. The analysis showed that the number of publications on HUE research has increased in a stepwise manner over the past five years. Yang Zhongqi （24 times） was the core of the author network， the journal with the highest number of publications was China Journal of Chinese Materia Medica， the institutions publishing the most articles were mainly research institutions， regulatory agencies， hospitals， and universities， high-frequency keywords included "new TCM drugs"， "real-world studies"， and "clinical comprehensive evaluation"， keyword clustering analysis formed three major clusters： Policy orientation， application fields， and methodological approaches. Thematic analysis reveals that HUE-based evaluation should be integrated throughout the research and development process， encompassing three dimensions： TCM theory， clinical value， and pharmaceutical fundamentals， with toxic herbs and compatibility contraindications being key foci. Data collection primarily relies on empirical data， while real-world data constitute the primary source for clinical research， with efficacy and safety as the shared core. Data management emphasizes quality control and statistical analysis； however， the management of bias and confounding remains a critical bottleneck in evidence transformation. In practice， HUE-based approaches have successfully supported the registration and evaluation of multiple categories of new TCM drugs. ConclusionThe research on HUE of TCM has formed a policy-driven pattern characterized by， rapid development and close link with regulatory practice. A technical framework covering the whole chain of research and development has been constructed with clinical value as the core， which provides methodological basis and strategy reference for the scientific transformation of HUE of TCM from "experience" to "evidence".

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate the optimal termination time for acupuncture in treating patients with acute peripheral facial paralysis and its cost-effectiveness. MethodsA total of 120 eligible patients with acute-stage peri-pheral facial paralysis were randomly assigned to either the mild dysfunction termination group and the complete recovery termination group， with 60 patients in each group. Both groups received the standard acupuncture treatment protocol. Treatment in the mild dysfunction termination group was terminated when the Sunnybrook facial grade scale （SFGS） score first reached or exceeded 83 points， while that in the complete recovery termination group was terminated when the SFGS score first reached or exceeded 95 points. Assessments were conducted before treatment， 6 and 12 months after onset. SFGS， facial disability index （FDI） including physical function （FDIp） and social function （FDIs）， self-rating anxiety scale （SAS）， and self-rating depression scale （SDS） scores were assessed before treatment， and 6 and 12 months after onset. Any acupuncture-related adverse events during treatment were recorded for safety evaluation. Treatment sessions and medical costs including direct costs， indirect costs， insurance coverage， total societal costs， and patient out-of-pocket expenses were also recorded， and an economic evaluation was conducted including cost-effectiveness ratio （CER） and incremental cost-effectiveness ratio （ICER）. ResultsUltimately， 56 patients in the mild dysfunction termination group and 55 in the complete recovery termination group completed the follow-up. At 6 and 12 months after onset， SFGS and FDIp scores in both groups improved significantly while FDIs， SAS and SDS scores decreased （P＜0.05）. Comparison of scores between groups 6 months and 12 months after onset showed no statistically significant differences （P＞0.05）. During the trial， the incidence of adverse events was 13.3% （8/60） in the mild dysfunction termination group and 18.3% （11/60） in the complete recovery termination group， with no statistically significant difference （P＞0.05）. The number of treatment sessions， total social costs， and out-of-pocket expenses in the mild dysfunction termination group were significantly lower than those in the complete recovery termination group （P＜0.05）. The CER of the mild dysfunction termination group in SFGS， FDIp， FDIs， SAS， and SDS scores was lower than that of the complete recovery termination group. The ICER analysis showed that continuing treatment until full recovery incurred an additional cost of 573.30 CNY/point in SFGS improvement， whereas 1-point improvement in FDIp， FDIs， SAS， and SDS required 21,355.25 CNY， 1779.60 CNY， 3713.96 CNY， and 2755.52 CNY， respectively. ConclusionFor acupuncture in treating acute peripheral facial palsy， terminating treatment when mild dysfunction is achieved yields long-term efficacy comparable to that of continuing treatment until complete recovery， while significantly reducing medical costs and socioeconomic burden.

To analyze global trends and research hotspots in exercise intervention for overweight and obesity from 2010 to 2024, to provide novel perspectives for comprehensive management research on overweight and obesity.

Objective To explore the teaching model of the "LungSmart" smart healthcare system in clinical pulmonology teaching and its effectiveness in enhancing the clinical reasoning skills of postgraduate students. Methods A single-center, single-group pretest-posttest educational intervention study was conducted among 30 postgraduate students who participated in respiratory medicine-related teaching activities and enrolled in the "LungSmart" smart healthcare course at Shanghai Pulmonary Hospital from 2024 to 2025 academic year. The course was structured around three core components, namely an AI case repository, dynamic simulation, and immediate feedback, and was delivered over 16 weeks with a total of 64 class hours. Teaching effectiveness was assessed using pretest and posttest clinical reasoning ability scores, while students’ acceptance of the course was evaluated using a 5-point Likert questionnaire. Results All 30 students completed the teaching activities and were included in the final analysis. The pretest score was (78.83±6.25) points, and the posttest score increased to (93.50±4.18) points, with a mean improvement of (14.67±7.06) points (95%CI, 12.03 to 17.30), indicating a statistically significant improvement after the intervention (t=11.37, P<0.001). A total of 30 valid questionnaires were collected at the end of the course, with a response rate of 100.0%. The overall satisfaction score was (4.67±0.15) points, and the mean scores for content satisfaction, practical value, interest stimulation, and professional competence were (4.72±0.23) points, (4.53±0.35) points, (4.72±0.39) points, and (4.75±0.43) points, respectively. Conclusion These findings suggest that the clinical teaching model based on the "LungSmart" smart healthcare system is feasible and well accepted, and may help improve postgraduate students’ clinical reasoning ability.

This study aims to develop a fully automated method for tooth segmentation and root canal measurement based on cone beam computed tomography (CBCT) images, providing objective, efficient, and accurate measurement results to guide and assist clinicians in root canal diagnosis grading, instrument selection, and preoperative planning. The method utilized Attention U-Net to recognize tooth descriptors, cropped regions of interest (ROIs) based on the center of mass of these descriptors, and applied an integrated deep learning method for segmentation. The segmentation results were mapped back to the original coordinates and position-corrected, followed by automatic measurement and visualization of root canal lengths and angles. The results indicated that the Dice coefficient for segmentation was 96.42%, the Jaccard coefficient was 93.11%, the Hausdorff Distance was 2.07 mm, and the average surface distance was 0.23 mm, all of which surpassed existing methods. The relative error of the root canal working length measurement was 3.15% (< 5%), the curvature angle error was 2.85 °, and the correct classification rate of the treatment difficulty coefficient was 90.48%. The proposed methods all achieved favorable results, which can provide an important reference for clinical application.
Cone-Beam Computed Tomography/methods* ; Deep Learning ; Humans ; Dental Pulp Cavity/diagnostic imaging* ; Image Processing, Computer-Assisted/methods*

Peptide receptor radionuclide therapy (PRRT) with radiolabeled SSTR2 agonists is a treatment option that is highly effective in controlling metastatic and progressive neuroendocrine tumors (NETs). Previous studies have shown that an SSTR2 agonist combined with albumin binding moiety Evans blue (denoted as ¹⁷⁷Lu-EB-TATE) is characterized by a higher tumor uptake and residence time in preclinical models and in patients with metastatic NETs. This study aimed to enhance the in vivo stability, pharmacokinetics, and pharmacodynamics of ¹⁷⁷Lu-EB-TATE by replacing the maleimide-thiol group with a polyethylene glycol chain, resulting in a novel EB conjugated SSTR2-targeting radiopharmaceutical, ¹⁷⁷Lu-LNC1010, for PRRT. In preclinical studies, ¹⁷⁷Lu-LNC1010 exhibited good stability and SSTR2-binding affinity in AR42J tumor cells and enhanced uptake and prolonged retention in AR42J tumor xenografts. Thereafter, we presented the first-in-human dose escalation study of ¹⁷⁷Lu-LNC1010 in patients with advanced/metastatic NETs. ¹⁷⁷Lu-LNC1010 was well-tolerated by all patients, with minor adverse effects, and exhibited significant uptake and prolonged retention in tumor lesions, with higher tumor radiation doses than those of ¹⁷⁷Lu-EB-TATE. Preliminary PRRT efficacy results showed an 83% disease control rate and a 42% overall response rate after two ¹⁷⁷Lu-LNC1010 treatment cycles. These encouraging findings warrant further investigations through multicenter, prospective, and randomized controlled trials.

Objective:To explore the characteristics of the brain functional networks in children with spastic cerebral palsy (SCP) while at rest and to correlate them with motor functioning.Methods:Thirty-six children with SCP were enrolled as the SCP group, while thirty-four age-matched healthy children were recruited as the control group (the HC group). Functional near-infrared spectroscopy was used to detect changes in the concentration of oxygenated hemoglobin in the children′s cerebral cortex while at rest. The left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left motor cortex (LMC), and right motor cortex (RMC) were selected as regions of interest. Phase locking values (PLVs) were used to evaluate the strength of functional connectivity (FC) among these brain regions, and graph theory methods were applied to analyze the topological properties of the brain networks. Motor functioning was assessed using the gross motor function measure (GMFM).Results:The analyses of FC strength revealed that the SCP group had significantly weaker FC among all of the regions of interest while at rest compared to the HC group. Their PLVs for LPFC-RPFC, LPFC-RMC, RPFC-RMC and LMC-RMC connectivity were all significantly smaller. Graph theory analysis showed that the SCP group had significantly lower global efficiency (GE) and smaller clustering coefficients (CCs) and network density (D), while their characteristic path lengths were significantly longer. According to the correlation analysis, the PLVs for LMC-RMC connections in the SCP group were positively correlated with their scores on dimensions D and E of the GMFM ( r=0.496 and r=0.579 respectively). GE ( r=0.587 and r=0.642) and CC ( r=0.318 and r=0.759) showed similar significant positive correlations with GMFM dimensions D and E. Conclusions:At rest, the functional networks in the brains of children with SCP exhibit abnormalities closely associated with their motor dysfunction.