Objective To assess the clinical efficacy of sodium hyaluronate （0.3%） eye drops combined with herbal self-heating steam eye mask in the treatment of dry eye disease. Methods A prospective randomized controlled clinical trial was performed on 60 patients diagnosed with dry eye at the ophthalmic clinic of a Grade A, Class Ⅲ hospital in Shanghai from June 2023 to September 2024. Specifically, patients were randomly divided into control group and study group. Patients in the control group were treated with sodium hyaluronate （0.3%） eye drops for six weeks; while in the study group, patients received the eye drops combined with the herbal self-heating steam eye mask mainly containing powders of Flos Buddlejae. Subsequently, comparisons and analysis were performed before and after treatment between the two groups in the clinical symptom questionnaire score traditional Chinese medicine （TCM syndrome score），the Chinese dry eye questionnaire score and determination of tear film fluorscein breakup time （FBUT）, and curative effect. Results The quality control standard of the herbal powder in the self-heating steam eye mask was established through TLC and HPLC, and good heating behavior of the herbal self-heating steam eye mask was ascertained heating temperature （43±5）℃; heating duration （≥20 min）, meeting requirements of the product quality control. After treatment for 6 weeks, FBUT was increased, while TCM syndrome score and the Chinese dry eye questionnaire score were both decreased in the study group （P<0.001）. Besides, compared with the control group, TCM syndrome score and the Chinese dry eye questionnaire score were much lower, while the FBUT were higher in the study group （P<0.001）. Moreover, the overall response rate in the study group （81.7%） was much better than that in the control group （25.9%）. Conclusion The combination of sodium hyaluronate （0.3%） eye drops with herbal self-heating steam eye mask could be applied to the clinical treatment of dry eye disease due to its good clinical effects on relieving dry eye symptoms.

Traditional Chinese medicine （TCM） diagnostics is a discipline that studies the basic theories and fundamental skills of diagnostic methods， disease diagnosis， and differentiation in accordance with the theories of TCM. The artificial intelligence （AI） technology has gained remarkable achievements in the intelligentization of the four diagnostic methods in TCM and the standardization of differentiation and diagnosis. However， it still faces many challenges. The standardization of clinical data collection is difficult， and the data quality is uneven， which affects the usability of the data. The integration of the four diagnostic information is insufficient. Most instruments can only collect data from a single diagnostic method， lacking overall integrity. The scientific nature of the diagnostic model needs to be improved. The existing models lack dynamics and the reasoning logic of TCM differentiation. The accuracy of intelligent methods needs to be improved， and the existing evaluation indicators cannot fully reflect the practical application effect of the model. Furthermore， the relevant laws and regulations are still not perfect， and data security and patient privacy lack guarantees. The cultivation of compound talents is insufficient， and there is a lack of interdisciplinary talents who are proficient in both TCM and AI. On this basis， this paper expounded on the current development status， difficulties， and bottlenecks of AI in TCM diagnosis and then explored the development trend of AI in the field of TCM diagnosis. It proposed solutions such as optimizing the data collection process， constructing multimodal diagnostic models， facilitating multi-disciplinary exchanges and cooperation， improving laws and regulations， and cultivating compound talents. It is hoped that modern， standardized， normalized， and intelligent TCM diagnosis can be further promoted， thereby providing new impetus and methods for the inheritance and innovation of TCM.

OBJECTIVE: To explore the genetic etiology of 46 Chinese pedigrees affected with Hereditary multiple exostoses (HME) and provide genetic counseling and reproductive intervention. METHODS: Whole-exome sequencing and Sanger sequencing were carried out on 87 patients from the 46 pedigrees to analyze the variants of EXT1 and EXT2 genes. Pathogenicity of the variants was assessed based on the guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Prenatal diagnosis and preimplantation genetic testing (PGT) were provided for couples with identified pathogenic mutations. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: LL-SC-SG-2014-010). RESULTS: In total 17 and 22 pathogenic variants were respectively identified in the EXT1 and EXT2 genes, among which 5 EXT1 and 12 EXT2 variants were unreported previously. Three patients with no family history were found to harbor de novo variants of the EXT1 gene. Twenty nine couples had opted for PGT or underwent prenatal diagnosis following natural conception, and 17 healthy babies were born. CONCLUSION This study has clarified the genetic etiology of 45 HME pedigrees and identified 17 novel variants, which has enriched the mutational spectrum of the EXT1 and EXT2 genes. Reproductive intervention through PGT and prenatal diagnosis have prevented the recurrence of HME in these families.
Humans ; Female ; Male ; Pedigree ; Exostoses, Multiple Hereditary/diagnosis* ; N-Acetylglucosaminyltransferases/genetics* ; Adult ; Exostosin 1 ; Asian People/genetics* ; Genetic Testing ; Exostosin 2 ; Mutation ; China ; Prenatal Diagnosis ; Pregnancy ; Genetic Counseling ; Preimplantation Diagnosis ; Exome Sequencing ; East Asian People

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

ObjectiveThis paper aims to systematically reveal the effect difference and mechanisms of Zishenwan against chronic prostatitis （CP） before and after salt-processing of Anemarrhenae rhizoma and Phellodendri chinensis cortex based on an integrated strategy of ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry （UPLC-Q-Orbitrap-MS/MS）， network pharmacology， and serum metabolomics. MethodsZishenwan samples before and after salt-processing of Anemarrhenae rhizoma and Phellodendri chinensis cortex were extracted by alcohol-water dual extraction. The chemical components of each sample were detected by UPLC-Q-Orbitrap-MS/MS， and differential components were screened by multivariate statistical analysis. Network pharmacology analysis was performed based on the identified chemical components of Zishenwan to construct a protein-protein interaction （PPI） network of "component， target， and pathway"， and the core components， targets， and pathways of Zishenwan against CP were screened. Forty-two male Sprague-Dawley （SD） rats were randomly divided into a blank group， a model group， a Qianliekang group （1.54 g·kg^-1）， low- and high-dose raw Zishenwan groups （1.8， 5.4 g·kg^-1）， and low- and high-dose salt-processed Zishenwan groups （1.8， 5.4 g·kg^-1）. The CP rat model was established by intraprostatic injection of carrageenan. After one week of recovery， the rats were administered the corresponding drugs for 21 days， while those in the blank group and model group received the same volume of normal saline. After the experiment， serum and tissue samples were collected to evaluate pharmacodynamic indicators including organ indices， histopathology， and inflammatory factors in serum. Subsequently， untargeted serum metabolomics technology was used to analyze metabolite changes and perform pathway enrichment analysis. The network pharmacology was used to construct a network of "differential metabolite， reaction， enzyme， and gene". ResultsA total of 76 components were identified in raw and salt-processed Zishenwan， and 34 differential components were screened by multivariate statistical analysis. Among them， the contents of 14 components， including berberine， berberrubine， and phellodendrine， increased after salt-processing， while the contents of 20 components， such as neomangiferin， decreased. The 28 active components and 185 potential targets were screened out by network pharmacology. The core components included berberine， phellodendrine， magnoflorine， and jatrorrhizine， and the core targets included signal transducer and activator of transcription 3 （STAT3）， protein kinase B1 （Akt1）， and transcription factor AP-1 （JUN）. These targets were significantly enriched in pro-inflammatory signaling pathways such as phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） and mitogen-activated protein kinase （MAPK）. Compared with the model group， all Zishenwan administration groups showed decreased prostate index， reduced levels of interleukin （IL）-1β， IL-18， and B-cell lymphoma-2 （Bcl-2） in serum （P<0.05， P<0.01）， as well as varying degrees of alleviation in histopathological damage. At the same dose， compared with the raw Zishenwan groups， the salt-processed Zishenwan groups showed lower prostate index， pathological scores， and IL-1β， IL-18， and Bcl-2 levels in serum， but the differences were not statistically significant. Metabolomics reveals that 38 differential metabolites were reversed after salt-processed Zishenwan administration. Both raw and salt-processed Zishenwan regulated pathways such as β-alanine metabolism and tryptophan metabolism. In addition to the common regulated pathways， the salt-processed group specifically regulated pantothenate and coenzyme A biosynthesis， pyrimidine metabolism， and arginine and proline metabolism. The intersecting pathways between network pharmacology and metabolomics were tryptophan metabolism and arginine and proline metabolism， with overlapping targets including monoamine oxidase A （MAOA） and arginase 1 （ARG1）. ConclusionThe increased contents of components such as berberine and phellodendrine in salt-processed Zishenwan may enhance its therapeutic effect on CP by inhibiting the PI3K/Akt and MAPK signaling pathways， along with multi-target regulation of tryptophan， arginine， and pantothenate metabolism pathways to comprehensively regulate inflammatory and immune responses.

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.

The co-occurrence of diabetes mellitus complicated with depression （DD） is a clinically prevalent comorbidity characterized by complex pathogenesis and challenging therapeutic management， severely impairing patients' quality of life. Recent research indicates that dysregulation in microglia-neuron interaction plays a pivotal role in the pathological progression of DD. Sustained hyperglycemia can activate central microglia， promoting the activation of NOD-like receptor protein 3（NLRP3） inflammasome and the release of abundant pro-inflammatory cytokines， such as interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， which trigger neuroinflammation. This cascade leads to hippocampal neuronal damage， impaired synaptic plasticity， and neurotransmitter imbalances， ultimately exacerbating depressive-like behaviors. Traditional Chinese medicine （TCM） offers advantages in preventing and treating DD through its multi-component， multi-target， and holistic regulatory approach. TCM can intervene in excessive microglial activation and their aberrant interactions with neurons by modulating neurotrophic signaling pathways such as brain-derived neurotrophic factor （BDNF）/tyrosine receptor kinase B （TrkB）， suppressing neuroinflammation， preserving neuronal structure and function， regulating synaptic plasticity， and improving glucose and lipid metabolism， thereby exerting synergistic therapeutic effects. Focusing on the role of microglia-neuron interaction in the pathological progression of DD， this review systematically elaborates on the research progress of TCM in regulating this interactive process， providing new perspectives and potential targets for the clinical prevention and treatment of DD.

The co-occurrence of diabetes mellitus complicated with depression （DD） is a clinically prevalent comorbidity characterized by complex pathogenesis and challenging therapeutic management， severely impairing patients' quality of life. Recent research indicates that dysregulation in microglia-neuron interaction plays a pivotal role in the pathological progression of DD. Sustained hyperglycemia can activate central microglia， promoting the activation of NOD-like receptor protein 3（NLRP3） inflammasome and the release of abundant pro-inflammatory cytokines， such as interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， which trigger neuroinflammation. This cascade leads to hippocampal neuronal damage， impaired synaptic plasticity， and neurotransmitter imbalances， ultimately exacerbating depressive-like behaviors. Traditional Chinese medicine （TCM） offers advantages in preventing and treating DD through its multi-component， multi-target， and holistic regulatory approach. TCM can intervene in excessive microglial activation and their aberrant interactions with neurons by modulating neurotrophic signaling pathways such as brain-derived neurotrophic factor （BDNF）/tyrosine receptor kinase B （TrkB）， suppressing neuroinflammation， preserving neuronal structure and function， regulating synaptic plasticity， and improving glucose and lipid metabolism， thereby exerting synergistic therapeutic effects. Focusing on the role of microglia-neuron interaction in the pathological progression of DD， this review systematically elaborates on the research progress of TCM in regulating this interactive process， providing new perspectives and potential targets for the clinical prevention and treatment of DD.

ObjectiveTo analyze the characteristics of chromosomal variations and disease spectrum in patients with lymphohematopoietic system diseases in Hunan region. MethodsA cross-sectional study was conducted， collecting clinical data of 548 patients with lymphohematopoietic system diseases who underwent bone marrow chromosome karyotype analysis at Hunan Provincial People's Hospital from January 2021 to July 2025. G-banding technique was used to detect bone marrow chromosome karyotypes， and chromosome number and structural abnormalities were determined in accordance with the International System for Human Cytogenomic Nomenclature （ISCN） 2024. Retrospective statistical analysis was performed to correlate karyotype abnormality data with disease phenotypes such as myelodysplastic syndrome and acute/chronic leukemia. Classification of the types， frequencies， and reproducibility of variations was calculated to clarify the correlation between karyotypes and disease phenotypes. ResultsAmong the 548 patients who completed bone marrow chromosome karyotype analysis， 127 had chromosomal abnormalities （84 males and 43 females， mean age 58.26 years）. Among patients with autosomal abnormalities， 64 cases were detected with translocations （a total of 67 mutations）， with t（9；22）（q34；q11.2） （22 cases） and t（15；17）（q24；q21） （10 cases） as high-frequency translocations. Chromosomes 9， 15， etc. were high-frequency variant chromosomes， and 29 cases had complex mutations such as deletions/duplications， with +8 being the common type. There were 27 cases of sex chromosome abnormalities： 7 cases of X chromosome abnormalities （2 cases of chimerism） and 20 cases of Y chromosome deletions （7 cases of 45，X，-Y/46，XY chimerism）. Karyotype-disease correlation showed that t（9；22） was detected 9 times in acute leukemia and 8 times in chronic myeloid leukemia； +8， -Y， etc. were associated with multiple types of hematological diseases， and the same karyotype could correspond to multiple disease phenotypes. ConclusionThe high-frequency chromosomal abnormalities， karyotype-disease correlations， and high-frequency NGS genes in patients with lymphohematopoietic system diseases， providing a basis for their precise diagnosis and treatment.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.