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.

Malignant tumors are one of the major diseases that endanger human life and health. Chinese medicine has unique advantages in clinical anti-tumor treatment. However， how to translate the anti-tumor effects of Chinese medicine into clinical practice is the core issue that must be addressed in the process of treating malignant tumors with traditional Chinese medicine （TCM）. Unlike modern chemical drugs， the compatibility application of Chinese medicine is the key factor that determines whether Chinese medicine can achieve optimal anti-tumor efficacy and realize the goal of "enhancing efficacy and reducing toxicity". The formulation structure based on this compatibility is the basic form for the safe， efficient， and rational clinical use of anti-tumor Chinese medicine， and it mainly includes three categories： herb pairs， tri-herbal combinations， and compound compatibility. Although herb pairs have the characteristics of a simple structure and strong targeting （enhancing efficacy and reducing toxicity）， they often have a single effect and cannot fully address the complex pathogenesis of tumors. As a result， herb pairs are rarely used alone in practice. Compared to herb pairs， tri-herbal combinations broaden the application scope of herbs in clinical treatment， but their therapeutic range remains limited. The traditional "sovereign， minister， assistant， and guide" compound prescription， which includes herb pairs and tri-herbal combinations， improves the efficacy of herbs in treating serious diseases， hypochondriasis， chronic diseases， and miscellaneous disorders. However， due to the limitations of its historical background， it has not been integrated with modern clinical practice and modern pharmacological research， which restricts the development of compound compatibility theory. With the emergence of modern medical technology， it has been combined with traditional compatibility theory of Chinese medicine to create an innovative modern compatibility theory. This includes the "aid medicine" theory derived from modern Chinese medicine pharmacology， which compensates for the inability of the "sovereign， minister， assistant， and guide" theory to accurately apply medicine. Additionally， the "state-targeted treatment based on syndrome differentiation" theory， developed from pharmacology and modern medicine， addresses the deficiency in disease cognition in the "sovereign， minister， assistant， and guide" theory. Under the guidance of these compatibility forms and theories， clinical anti-tumor Chinese medicine can exert its maximum anti-tumor efficacy， which is of great significance for the application of Chinese medicine in clinical tumor treatment.

OBJECTIVE: To precise classify sacral meningeal cysts, effective guide minimally invasive neurosurgery and postoperative personalized rehabilitation by multiple dimensions radiographic reconstruction MRI. METHODS: From March to December 2021, based on the original 3D-fast imaging employing steadystate acquisition (FIESTA) scanning sequence, 92 patients with sacral meningeal cysts were pre-operatively evaluated by multiple dimensional reconstruction MRI. The shape of nerve root and the leakage of cyst were reconstructed according to the direction of nerve root or leakage track showed on original MRI scans. Sacral canal cysts were accurately classified as including nerve root and without nerve root, so as to accurately design the incision of skin and formulate corresponding open range of the posterior wall of the sacral canal. Under the microscope intraoperation, the shape of the nerve roots inside cysts or leakage track of the cysts without nerve roots were verified and explored. After the reinforcement and shaping operation, several reexaminations of multiple dimensional reconstruction MRI were performed to understand the deformation of the nerve root and hydrops in the operation cavity, so as to formulate a persona-lized rehabilitation plan for the patients. RESULTS: Among the 92 patients with sacral mengingeal cyst, 58 (63.0%) cysts with nerve root cyst, 29 (31.5%) cysts without nerve root cyst, and 5 (5.4%) cysts with mixed sacral canal cyst. In 58 patients with nerve root cysts, the accuracy of preoperative clinical classification on MRI image reached 96.6% (56/58) through confirmation by operating microscope. Only 2 cases of large single cyst with nerve root on the head of cyst were mistaken for without nerve root type. In 29 patients with sacral cyst without nerve root, the accuracy of preoperative image reached 100% through confirmation by operating microscope. The accuracy of judging the internal nerve root and leakage of 12 cases with recurrent sacral cyst was also 100%. Two cases of delayed postoperative hydrops were found one month after operation. After rehabilitation treatment by moxibustion and bathing, the hydrops disappeared 4-6 months after operation. CONCLUSION Multiple dimensional reconstruction MRI can precisely make clinical classification of sacral meningeal cysts before operation, guide minimally invasive neurosurgery effectively, and improve the rehabilitation effect.
Humans ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Sacrum/surgery* ; Adult ; Middle Aged ; Imaging, Three-Dimensional/methods* ; Cysts/rehabilitation* ; Aged ; Adolescent ; Young Adult ; Spinal Nerve Roots/diagnostic imaging* ; Minimally Invasive Surgical Procedures ; Neurosurgical Procedures/methods*

Idiopathic pulmonary fibrosis (IPF) is a progressive disease lacking effective therapy. Metformin, an antidiabetic medication, has shown promising therapeutic properties in preclinical fibrosis models; however, its precise cellular targets and associated mechanisms in fibrosis resolution remain incompletely defined. Most research on metformin's effects has focused on mesenchymal and inflammatory responses with limited attention to epithelial cells. In this study, we utilized Sftpc lineage-traced and Fgfr2b conditional knockout mice, along with BMP2/PPARγ and AMPK inhibitors, to explore metformin's impact on alveolar epithelial cells in a bleomycin-induced pulmonary fibrosis model and cell culture. We found that metformin increased the proliferation and differentiation of alveolar type 2 (AT2) cells, particularly the recently identified injury-activated alveolar progenitors (IAAPs)-a subpopulation characterized by low SFTPC expression but enriched for PD-L1. Single-cell RNA sequencing revealed a reduction in apoptosis among mature AT2 cells. Interestingly, metformin's therapeutic effects were not significantly affected by BMP2 or PPARγ inhibition, which blocked the lipogenic differentiation of myofibroblasts. However, Fgfr2b deletion in Sftpc lineage cells significantly impaired metformin's ability to promote fibrosis resolution, a process linked to AMPK signaling. In conclusion, metformin alleviates fibrosis by directly activating AT2 cells, especially the IAAPs, through a mechanism that involves AMPK and FGFR2b signaling, but is largely independent of BMP2/PPARγ pathways.

Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with in vitro experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for in vitro assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC₅₀) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

OBJECTIVE To explore the implementation and standardized management of infection control core ele-ments in grass-roots medical institutions within county-level medical communities.METHODS From Mar.2024 to Apr.2024,the current status of implementation of infection control core elements in the grass-roots medical institu-tions within county-level medical communities was investigated by means of questionnaire survey and qualita-tive interview,and the implementation strategies were further explored.RESULTS The infection management or-ganizational system and functions of the two county-level medical institutions within the county medical communi-ties were completed,there is no independent hospital infection management department in the primary medical in-stitutions.The infection management personnel in the 16 grass-roots medical institutions were part-time person-nel,the personnel with the educational background below junior college accounted for 84.21％,the personnel with the professional background of nursing accounted for 100.00％,the personnel with less than 5 years of working experience accounted for 78.95％,none of them had an on-the-job training certificate.The monitoring programs of the county-level medical institutions within the county medical communities were completed,there was no infec-tion management monitoring information platform in the grass-roots medical institutions.The infection cases,hand hygiene,environmental health and occupational exposures were monitored by people.The grass-roots medi-cal institutions had the highest requirements for various professional trainings and increase of training contents of prevention and control of public health infectious diseases(100.00％).The county-level medical institutions had inadequate capabilities of professional examination of medical equipment replacement and construction of medi-cal architecture.CONCLUSION It is necessary for the country and local levels of governments to attach great importance to the implementation of the infection control core elements in the grass-roots medical institutions within the county-lev-el medical communities,establish the county-level regional information platform,formulate the corresponding surveil-lance indexes and homogenized management systems,complete the cultivation of talents,and offer financial support.

Objective To develop a method for rapidly identifying Clostridioides difficile(C.difficile)and de-termining high-producing toxin strains,conduct clinical evaluation.Methods The loop-mediated isothermal amplifi-cation(LAMP)method was used to identify C.difficile based on the tcdC,tcdA,and tcdB genes.The sensitivi-ty,specificity,and overall consistency of the detection method were evaluated.Results Feces specimens from 499 hospitalized patients suspected of C.difficile-associated diarrhea were detected,with C.difficile detection rate of 12.8％(64/499),out of which the detection rate of toxin-producing C.difficile was 10.8％(54/499).The sensi-tivity,specificity,positive predictive value,and negative predictive value of the detection method for tcdA were 87.2％,98.9％,89.1％,and 98.6％,respectively,and 88.2％,99.6％,90.0％,and 98.73％for tcdB,respec-tively.The total toxin levels of different strains were different,but the average toxin production level of A+B+strains(1.79 μg/mL)was higher than those of A-B+strains(0.72 μg/mL)and A-B-strains(＜0.10 μg/mL).Conclusion The portable high-throughput LAMP detection method can rapidly and efficiently identify C.difficile and determine high-producing toxin strains.

Objective:To study the application value of analytic hierarchy process(AHP)-risk analysis management strategy in management for medical equipment of hospital,so as to improve utilization efficiency of equipment.Methods:A decision model of managing equipment was established by adopting AHP-risk analysis management strategy,and Entropy method was used to optimize and improve the combined weight of AHP algorithm,so as to obtain the comprehensive weight of classification decision for medical equipment.The equipment was classified into high risk,second highest risk,medium risk,second lowest risk and low risk to receive different management strategy.A total of 103 medical equipment in clinical use at People's Hospital of Xinjiang Uygur Autonomous Region from January 2023 to February 2024 were selected,and the conventional management method was adopted to manage these equipment during January 2023 and July 2024,and AHP-risk analysis management strategy was adopted to manage them during August 2023 and February 2024.The differences of the management effects of medical equipment in clinical use,and technical support for equipment between two management methods were compared.Results:The average use rate,rate of growth amplitude of cost-effectiveness,and rate of growth amplitude of social benefit of equipment of using the AHP risk analysis management method were respectively(95.34±3.75)%,(7.65±2.18)%,and(10.81±2.42)%,all of which were higher than those of the conventional management method.However,the average relative operating rate and relative turnover rate of using the AHP risk analysis management method were respectively(9.26±2.46)%and(10.63±2.07)%,all of which were lower than those of the conventional management method,and the differences were statistically significant(t=9.223,24.681,17.813,15.764,18.873,P<0.05).The average self-repair rate,upgrade efficiency,and coincidence rate of scrapping equipment of using the AHP-risk analysis management method were respectively(91.54±4.25)%,(94.52±3.77)%,and(93.53±4.72)%,all of which were higher than those of the conventional management method.However,the average failure rate of that was(0.74±0.38)%,which was lower than the average failure rate of the conventional management method,and the differences of them were statistically significant(t=8.445,9.624,14.046,22.794,P<0.05).Conclusion:The AHP-risk analysis management strategy can improve the management effect of clinical use of medical equipment,and the technical support for medical equipment,and enhance usage and maintenance efficiency of equipment,and achieve refined management for medical equipment.

Objective:To investigate the clinical phenotypes and genetic variant characteristics in children with epilepsy.Methods:A total of 91 children with epilepsy admitted to the Women′s and Children′s Hospital Affiliated to Ningbo University from July 2021 to October 2022 were selected as the study subjects. Peripheral blood samples were collected from the children for whole exome sequencing. Candidate genetic variants were validated by Sanger sequencing and copy number variation sequencing (CNV-seq). The clinical phenotypes and treatment outcomes of the children with epilepsy were followed up, and an analysis of the relationship between genotype and phenotype was conducted. This study was approved by the Women′s and Children′s Hospital Affiliated to Ningbo University (Ethics No.: EC2020-048).Results:Among the 91 children with epilepsy, 21 cases (23.08%, 21/91) were found to carry pathogenic or likely pathogenic variants. Of these, 18 cases had involved single base variant or insertional deletion, while 3 cases involved copy number variations. The gene with the highest detection rate was PRRT2 (38.10%, 8/21). Among the children with genetic variants, 47.62% (10/21) had onset during infancy, with 8 diagnosed with Benign familial infantile epilepsy (BFIE), 8 with Developmental epileptic encephalopathy (DEE), and 3 with Epileptic encephalopathy (EE). One case of Dravet syndrome (DS) and one case of Infantile spasms (IS) were also noted. The clinical manifestations of children were diverse and primarily included generalized tonic-clonic seizures and focal seizures. Among them, 52.38% (11/21) had exhibited cluster seizures, 23.81% (5/21) showed fever sensitivity, and 14.29% (3/21) experienced status epilepticus. After pharmacological treatment, 42.86% (9/21) of children had achieved complete seizure control, while 61.90% (13/21) had intellectual disability and 19.05% (4/21) had co-morbid autism spectrum disorder. Conclusion:Pathogenic or likely pathogenic variants were identified in 23.08% of the pediatric epilepsy cases, with the PRRT2 gene being the most frequently involved. Among children carrying genetic variants, 47.62% had seizure onset during infancy. Genetic factors are an important cause of epilepsy, and early genetic testing may facilitate precise diagnosis, treatment, and prognostic evaluation.