Objective:To investigate the technical application pathways of artificial intelligence(AI)in laboratory information management systems(LIMS)and its role in promoting laboratory management efficiency and intelli-gence.Methods:Through the integration of traditional AI technologies(e.g.,machine learning,computer vision)with large language models,this study demonstrated the application of various AI technologies in scenarios such as intelligent Q&A for local knowledge bases,comprehensive review of inspection processes,intelligent data visualization,and image recognition.Results:Through the implementation of AI applications in laboratory settings,AI significantly enhanced management efficiency:the intelligent Q&A system achieved over 90％accuracy,auto-mated inspection processes reduced manual workload by 40％,and image recognition precision reached 89％-100％.Conclusion:AI provides efficient and precise solutions for laboratory management via multimodal integration and process optimization.Future efforts should focus on strengthening data security and model interpret-ability to promote comprehensive intelligent development.

Objective:To investigate the technical application pathways of artificial intelligence(AI)in laboratory information management systems(LIMS)and its role in promoting laboratory management efficiency and intelli-gence.Methods:Through the integration of traditional AI technologies(e.g.,machine learning,computer vision)with large language models,this study demonstrated the application of various AI technologies in scenarios such as intelligent Q&A for local knowledge bases,comprehensive review of inspection processes,intelligent data visualization,and image recognition.Results:Through the implementation of AI applications in laboratory settings,AI significantly enhanced management efficiency:the intelligent Q&A system achieved over 90％accuracy,auto-mated inspection processes reduced manual workload by 40％,and image recognition precision reached 89％-100％.Conclusion:AI provides efficient and precise solutions for laboratory management via multimodal integration and process optimization.Future efforts should focus on strengthening data security and model interpret-ability to promote comprehensive intelligent development.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.

Objective To analyze the spatiotemporal distribution characteristics of the syphilis epidemic in Gansu Province from 2005 to 2021, and to provide a reference for the prevention and control of the syphilis epidemic in Gansu Province. Methods ArcGIS 10.7 was used to map the annual incidence of syphilis in Gansu Province from 2005 to 2021, spatial autocorrelation analysis and local autocorrelation analysis were performed, and SaTScan 10.0.2 software was used for spatiotemporal scanning analysis. Results The global autocorrelation results showed that the annual incidence of syphilis in 2005-2021 was >0, Z＞1.96, and the P< 0.0001, showing a spatial clustering distribution, and the local autocorrelation results showed that there was one spatially similar high-high aggregation area and two spatially similar low-low aggregation areas in Gansu Province, and the hot spot analysis showed that there were 9 negative hotspot areas and 2 positive hotspot areas in the syphilis epidemic in Gansu Province. Spatiotemporal scanning analysis detected two high concentration areas, mainly concentrated in Gannan Tibetan Autonomous Prefecture. Conclusion Syphilis in Gansu Province has regional differences in space, and high-high accumulation areas in Gannan Tibetan Autonomous Prefecture persist, and targeted prevention and control strategies should be specified according to temporal and spatial characteristics.

Chloroquine (CQ) phosphate has been suggested to be clinically effective in the treatment of coronavirus disease 2019 (COVID-19). To develop a physiologically-based pharmacokinetic (PBPK) model for predicting tissue distribution of CQ and apply it to optimize dosage regimens, a PBPK model, with parameterization of drug distribution extrapolated from animal data, was developed to predict human tissue distribution of CQ. The physiological characteristics of time-dependent accumulation was mimicked through an active transport mechanism. Several dosing regimens were proposed based on PBPK simulation combined with known clinical exposure-response relationships. The model was also validated by clinical data from Chinese patients with COVID-19. The novel PBPK model allows in-depth description of the pharmacokinetics of CQ in several key organs (lung, heart, liver, and kidney), and was applied to design dosing strategies in patients with acute COVID-19 (Day 1: 750 mg BID, Days 2-5: 500 mg BID, CQ phosphate), patients with moderate COVID-19 (Day 1: 750 mg and 500 mg, Days 2-3: 500 mg BID, Days 4-5: 250 mg BID, CQ phosphate), and other vulnerable populations (.., renal and hepatic impairment and elderly patients, Days 1-5: 250 mg BID, CQ phosphate). A PBPK model of CQ was successfully developed to optimize dosage regimens for patients with COVID-19.

Objective To determine the effect of ostecytic TGF-β/Smad4 signaling on osteoblastic and osteoclastic differentiation in bone marrow stromal cells (BMSCs).Methods Mice with osteocytic TGF-β/Smad4 conditional knock down (Smad4ot CKD) were generated as previously by crossing DMP1-8kb-Cre mice with Smad4lox(ex8)/lox(ex8) mice.The osteocytes were isolated from tibial and femoral diaphysis and co-cultured with wild-type BMSCs.ALP staining, Alizarin red staining and TRAP staining were performed to show osteoblastic and osteoclastic differentiation.Then, their marker genes were detected by qPCR and proteins measured by Western blot.ResultsThe expression of Runx2 and Osterix were reduced in smad4 CKDot co-cultured with BMSCs compared with controls(P<0.01).Similarly, the specific markers of osteoblastic differentiation were decreased (P<0.01).Additionally, the expression of RANKL was not significantly changed in with BMSCs.However, OPG was highly expressed incontrol group compared with smad4 CKD in co-cultured group (P<0.05).Thus, the radio of RANKL/OPG was significantly reduced (P<0.05).Furthermore, the expression of RANK was inhibited.Conclusions The terminally-differentiated osteocytes are the cells regulating bone metabolism, while down-regulation of osteocytic-TGF-β/Smad4 inhibits BMSC osteoblastic and osteoclastic differentiation.