PROPOSE: To investigate the molecular mechanisms underlying the protective effects of ischemic preconditioning (IPC) in patients undergoing total knee arthroplasty. METHODS: GSE21164 was extracted from an online database, followed by an investigation of differentially expressed genes (DEGs) between IPC treatment samples at 2 time points (T0T and T1T). Function and pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed to identify hub genes according to 5 different algorithms, followed by enrichment analysis. In addition, long noncoding RNAs (lncRNAs) were identified between the T0T and T1T samples. Furthermore, a competing endogenous RNA network was predicted based on the identified lncRNA-messenger RNA (mRNA), lncRNA-microRNA (miRNA), and mRNA-miRNA relationships revealed in this study. Finally, a drug-gene network was investigated. Statistical analyses were performed using GraphPad Prism 8.0. Differences between groups were determined using an unpaired t-test. p < 0.05 was considered significant. RESULTS: A total of 343 DEGs at T0 and 10 DEGs at T1 were identified and compared with their respective control groups, followed by 100 DEGs between T0T and T1T. Based on these 100 DEGs, protein-protein interaction network analysis revealed 9 hub genes, mainly with mitochondria-related functions and the carbon metabolism pathway. Six differentially expressed lncRNAs were investigated between T0T and T1T. A competing endogenous RNA network was constructed using 259 lncRNA-miRNA-mRNA interactions, including alpha-2-macroglobulin antisense RNA 1-miR-7161-5p-iron-sulfur cluster scaffold. Finally, 13 chemical drugs associated with the hub genes were explored. CONCLUSION Iron-sulfur cluster scaffold may promote IPC-induced ischemic tolerance mediated by alpha-2-macroglobulin antisense RNA 1-miR-7161-5p axis. Moreover, IPC may induce a protective response after total knee arthroplasty via mitochondria-related functions and the carbon metabolism pathway, which should be further validated in the near future.
Humans ; Arthroplasty, Replacement, Knee ; Ischemic Preconditioning ; RNA, Long Noncoding/genetics* ; Protein Interaction Maps ; MicroRNAs/genetics* ; RNA, Messenger/genetics* ; Gene Regulatory Networks

Objective To investigate the value of hs-cTnT in predicting all-cause death in the elderly with SCAD.Methods A prospective cohort observation study was conducted on 274 old adults with SCAD hospitalized in our department from January 2016 to January 2019.Their hs-cTnT level was measured,and according to the results,they were divided into lower(≤13.0 ng/L,94 cases),middle(14.0-22.0 ng/L,94 cases)and upper(≥23.0 ng/L,86 cases)tertile groups.The general clinical data were compared among the three groups.Kaplan-Meier survival curve was drawn to analyze the survival differences among groups.Cox proportional hazards regression analysis was applied to identify risk factors for mortality.ROC curve analysis was applied to evaluate the predictive value of hs-cTnT for all-cause mortality.Results During a me-dian follow-up period of 32 months,62(22.63％)patients died among the 274 patients,account-ing for 75.8％dying of non-cardiovascular diseases.There were statistically differences in the three tertile groups in terms of age,male ratio,proportions of hypertension,chronic obstructive pulmonary disease and chronic kidney disease,number of comorbidities,estimated glomerular fil-tration rate,albumin and hemoglobin levels,left ventricular ejection fraction,left ventricular mass index,and mortality rate(P＜0.05,P＜0.01).COX proportional hazards regression model showed the upper tertile group had significantly lower cumulative survival rate than the middle and lower tertile groups(Plog rank＜0.01).Multivariate Cox proportional hazards regression analysis indicated that hs-cTnT≥23.0 ng/L level was still a risk factor for death in both model 2(HR=3.749,95％CI:1.703-8.256,P=0.001)and model 3(HR=2.990,95％CI:1.358-6.581,P=0.007).ROC curve analysis revealed that the AUC value of hs-cTnT level in predicting death was 0.736,with a cut-off value of 25 ng/L.Conclusion For elderly SCAD patients,despite the existence of multiple comorbidities and the priority of non-cardiovascular death,hs-cTnT,a marker reflecting myocar-dial injury,is still a predictor for risk of death in the population.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Objective The high post-surgery recurrence rate of endometriosis(EMs)has emerged as a challenge in the long-term manaagement of the condition.This study is aimed at investigating the mechanisms of Neiyiting(NYT)decoction in preventing postoperative recurrence of EMs.Methods An animal model of EMs postoperative recurrence and a model of endometrial stromal cells(hEM15A)cocultured with macrophages(RAW 264.7 cell line)were established for both in vivo and in vitro experiments.An autotransplantation method was used to establish a rat model of EMs.The rats were divided into 4 groups(6 rats per group)and received the corresponding treatments:a Model group receiving distilled water,a Gestrinone group receiving gestrinone at 0.325 mg/kg,a low-dose NYT(NYT-L)group receiving NYT decoction at 5.04 g/(kg-d),and a high-dose NYT(NYT-H)group receiving NYT decoction at 10.08 g/(kg-d).The treatment was administered for 3 weeks via intragastric gavage.In addition,6 SD rats were randomly selected for the control group(Control group),and were given distilled water for 3 weeks via intragastric gavage.The sizes and pathological changes of recurrent lesions in EMs rats were observed.Immunohistochemistry and qRT-PCR were performed to assess the expression of M1 macrophage marker CD86 protein and mRNA in vivo.Additionally,immunohistochemistry and qRT-PCR were used to assess the expression of indicator proteins related to the triggering receptor expressed on myeloid cells 1(TREM1)/Toll-like receptor 4(TLR4)/nuclear factor kappa B(NF-κB)signaling pathway and mRNA.The proliferation of hEM15A cells in the coculture experiment was observed.Flow cytometry was performed to determine the polarization of RAW264.7 macrophages,and qRT-PCR was used to determine the expression levels of inducible nitric oxide synthase(iNOS)and interleukin 1β(IL-1β)mRNA.Western blot was performed to determine the expression of signaling pathway-related indicator proteins in vitro.ELISA was performed to determine the levels of inflammatory factors in vitro.Results Compared with the Model group,the volume of recurrent lesions in the NYT-H group was reduced(P＜0.01).Findings from the macrophage M1 polarization assessment showed that the expression levels of CD86 protein and mRNA in the recurrent lesions of the Model group were higher than those in the control group(P＜0.01).The expression levels of CD86 protein and mRNA in the recurrent lesions of the NYT-H group were lower than those of the Model group(P＜0.01).In addition,the RAW 264.7 cell experiment further verified that NYT decoction could reduce the number of CD86-positive macrophages induced by plasmids overexpressing TREM1 and reduce the expression of IL-1β and iNOS mRNA(P＜0.01).The results of the hEM15A cell proliferation assay showed that NYT decoction down-regulated KI-67 protein expression in hEM15A cells induced by macrophage M1 polarization(P＜0.01).The results of TREM1/TLR4/NF-κB signaling pathway showed that the protein and mRNA expression levels of TREM1,TLR4,and NF-κB in the recurrent lesions of the Model group were higher than those of the control group(P＜0.01).Compared with those in the Model group,the protein and mRNA expression levels of TREM1,TLR4,and NF-κB in the recurrent lesions of the NYT-H group were lower(P＜0.01).In addition,the coculture experiment of RAW264.7 and hEM15A cells further confirmed that NYT decoction reduced the expression of TREM1,TLR4,and P-P65 proteins(P＜0.01).Conclusion NYT decoction can inhibit macrophage M1 polarization through the TREM1/TLR4/NF-κB signaling pathway,improve the inflammation level,and inhibit the formation of ectopic endometrial lesions,thereby preventing postoperative recurrence of EMs.

Objective:To assess the accuracy of preoperative enhanced CT in evaluating the degree of lymph node metastasis in gallbladder cancer.Methods:A retrospective analysis was performed on the enhanced CT imaging data of 124 gallbladder cancer patients who underwent surgical treatment at Henan Provincial People's Hospital from Jan 2017 to Dec 2018. Imaging staging was used to classify lymph node metastasis. Pathological and imaging data of 70 patients with confirmed postoperative lymph node pathology were compared to evaluate the accuracy of imaging methods in detecting lymph node involvement.Results:Lymph node metastasis in the 124 surgical patients was categorized into three groups using imaging evaluation methods. The overall accuracy of determining lymph node positivity and negativity was 63%, with a sensitivity of 64% and specificity of 62%. The accuracy of detecting lymph node metastasis in gallbladder cancer was higher when lymph node fusion and internal necrosis were observed. The overall survival rate differed significantly among gallbladder cancer patients at different lymph node imaging stages ( P<0.05). Conclusion:CT imaging evaluation has diagnostic value for lymph node metastasis in gallbladder cancer and has a certain predictive effect on the prognosis of patients.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective To evaluate the recognition capability of AI-enabled Cellsee CS-BM1 automatic cell morphology analyzer for pe-ripheral blood smears and its roles in assisting manual classification,and explore the application value of AI system in the diagnosis network of tiered primary medical units.Methods The blood samples which triggered the re-examination rules were collected from six primary medical units,including the Laboratory Department of Shanghai Jiahui International Hospital,and so on,from March to No-vember 2023.The smears of peripheral blood were prepared and AI analyzer was used for pre-classification to evaluate its recognition performance in identifying the samples with abnormal WBC and RBC.The sensitivity,specificity,and accuracy of WBC classification by six junior and intermediate technicians,both with and without AI assistance,were analyzed.Additionally,the roles of the AI system in tiered diagnosis of primary medical units were also evaluated.Results The sensitivity,specificity,and accuracy of AI system in recognizing malignant primitive cells were 92.86％,95.16％,and 95.10％,respectively.The sensitivities of AI system in recognizing immature granulocytes,reactive lymphocytes,and nucleated RBCs were all greater than 90％.The sensitivity of AI system in identif-ying abnormal morphology of RBCs reached 99.59％,along with rapid quantitative analysis for various anomalous types of RBCs.In AI-assisted mode,the sensitivity of recognition for all cell types was improved to varying degrees by junior and intermediate technicians,and the sensitivity for recognizing malignant primitive cells,reactive lymphocytes,and immature granulocytes increased to 58.24％,53.39％,and 62.37％for junior technicians,and to 92.06％,83.24％,and 83.12％for intermediate technicians,respectively.The improvements for junior technicians were particularly significant,with increases of 12.46％,10.61％,and 3.71％for each cell type,respectively.Both groups achieved higher specificity and accuracy.Through AI pre-classification and manual review,a variety of pe-ripheral blood cell-related diseases were accurately diagnosed in the tiered healthcare practice of primary medical units,including 339 cases(11.13％)of red blood cell diseases,5 cases(0.16％)of platelet diseases,2 343 cases(76.90％)of infection-related disea-ses,and 28 cases(0.92％)of malignant hematological diseases.In addition,332 cases(10.90％)which lacked an obvious related cause or required further examinations were identified as well.Conclusion AI pre-classification has demonstrated strong cell recogni-tion capabilities and may assist technicians in improving the sensitivity,specificity,and accuracy of blood cell classification.AI could en-hance the disease-screening capabilities in the tiered diagnosis network of primary medical units,presenting a broad application prospect.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective To evaluate the recognition capability of AI-enabled Cellsee CS-BM1 automatic cell morphology analyzer for pe-ripheral blood smears and its roles in assisting manual classification,and explore the application value of AI system in the diagnosis network of tiered primary medical units.Methods The blood samples which triggered the re-examination rules were collected from six primary medical units,including the Laboratory Department of Shanghai Jiahui International Hospital,and so on,from March to No-vember 2023.The smears of peripheral blood were prepared and AI analyzer was used for pre-classification to evaluate its recognition performance in identifying the samples with abnormal WBC and RBC.The sensitivity,specificity,and accuracy of WBC classification by six junior and intermediate technicians,both with and without AI assistance,were analyzed.Additionally,the roles of the AI system in tiered diagnosis of primary medical units were also evaluated.Results The sensitivity,specificity,and accuracy of AI system in recognizing malignant primitive cells were 92.86％,95.16％,and 95.10％,respectively.The sensitivities of AI system in recognizing immature granulocytes,reactive lymphocytes,and nucleated RBCs were all greater than 90％.The sensitivity of AI system in identif-ying abnormal morphology of RBCs reached 99.59％,along with rapid quantitative analysis for various anomalous types of RBCs.In AI-assisted mode,the sensitivity of recognition for all cell types was improved to varying degrees by junior and intermediate technicians,and the sensitivity for recognizing malignant primitive cells,reactive lymphocytes,and immature granulocytes increased to 58.24％,53.39％,and 62.37％for junior technicians,and to 92.06％,83.24％,and 83.12％for intermediate technicians,respectively.The improvements for junior technicians were particularly significant,with increases of 12.46％,10.61％,and 3.71％for each cell type,respectively.Both groups achieved higher specificity and accuracy.Through AI pre-classification and manual review,a variety of pe-ripheral blood cell-related diseases were accurately diagnosed in the tiered healthcare practice of primary medical units,including 339 cases(11.13％)of red blood cell diseases,5 cases(0.16％)of platelet diseases,2 343 cases(76.90％)of infection-related disea-ses,and 28 cases(0.92％)of malignant hematological diseases.In addition,332 cases(10.90％)which lacked an obvious related cause or required further examinations were identified as well.Conclusion AI pre-classification has demonstrated strong cell recogni-tion capabilities and may assist technicians in improving the sensitivity,specificity,and accuracy of blood cell classification.AI could en-hance the disease-screening capabilities in the tiered diagnosis network of primary medical units,presenting a broad application prospect.