To investigate the clinical application value of indocyanine green(ICG) fluorescence-guided thoracoscopic lung lesion resection under the diagnosis related group(DRG) payment system, and to provide reference for the management of new medical technologies in healthcare institutions.

Objective To construct a differential diagnosis model for systemic lupus erythematosus(SLE)from other autoimmune diseases based on the conventional test indicators in clinical laboratory,so as to improve the diagnostic efficacy of the existing test indicators.Methods The data of 178 SLE patients(SLE group)and 196 patients with other autoimmune diseases(control group)diagnosed in Sichuan Provincial People's Hospital from Apr.2022 to Mar.2023 were retrospectively analyzed.The differences in the levels of 19 clinical routine indicators between the 2 groups were analyzed.The least absolute shrinkage and selection operator(LASSO)regression was used to screen for test indicators with non-zero coefficients.These indicators were then used in logistic regression to construct a Nomogram model for SLE differential diagnosis.Model performance was assessed using receiver operating characteristic(ROC)curves and decision curve analysis.Results The levels of anti-cardiolipin antibody immunoglobulin(Ig)G,anti-cardiolipin antibody IgA,high-sensitivity C reactive protein(hs-CRP),D-dimer,and thrombin time(TT)in the SLE group were significantly higher than those in the control group(all P＜0.05),while the levels of IgM,complement 3(C3),complement 4(C4),prothrombin time(PT),and activated partial thromboplastin time(APTT)in the SLE group were significantly lower than those in the control group(all P＜0.05).Through LASSO regression,IgM,C3 and C4 were selected as the most likely indicators with non-zero coefficients.Multivariate logistic regression analysis showed that the differential diagnosis model was Logit P=4.18-1.34 × IgM-1.70 × C3-6.61 × C4.The area under the curve of this model was 0.80(95％confidence interval 0.76-0.85),with a sensitivity of 0.77 and a specificity of 0.74.Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.2-0.9.Conclusion The present model,constructed using the clinical routine indicators,such as IgM,C3 and C4,is helpful for the differential diagnosis of SLE from other autoimmune diseases and has good clinical application value.

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.

Objective To discuss the correlation between free prostate specific antigen(fPSA),serum ferritin(SF),blood uric acid(SUA)levels and Gleason grading in patients with prostate cancer(PCa).Methods The clinical data of 61 patients with prostate biopsy treated in 411 Hospital of Shanghai University from January to December of 2023 were retrospectively analyzed.According to the results of puncture,the patients were divided into benign prostatic hyperpla-sia(BPH)group(31 cases)and PCa group(30 cases).The levels of fPSA,SF and SUA in patients and Gleason grade in biopsy cases were analyzed.The correlation between fPSA,SF and SUA levels and Gleason grade was analyzed by the method of Spearman.And the diagnostic efficacy of fPSA,SF and SUA levels on PCa was analyzed by receiver operating characteristic curve(ROC).Results The levels of fPSA,SF and SUA in PCa group were significantly higher than those in BPH group(P＜0.05).There were statistically significant differences in levels of fPSA SF and SUA in PCA patients with different Gleason grades(P＜0.05).With the specificity reaching 96.08％and sensitivity reaching 94.35％,the ar-ea under the curve(AUC)of the combined fPSA,SF and SUA levels in the diagnosis of PCa was 0.982,which was higher than that of the single fPSA,SF and SUA levels(P＜0.05).SF and SUA levels in PCa patients were positively correlated with Gleason grade(P＜0.05),while fPSA levels were not correlated with Gleason grade(P＞0.05).Conclusion The levels of SF and SUA in PCa patients are positively correlated with Gleason grade,which can be used as an important index to predict Gleason grade in PCa patients.

Acetaminophen (APAP) is the primary cause of drug-induced acute liver failure. Ovarian tumor deubiquitinase 6A (OTUD6A), a recently discovered deubiquitinase of the OTU family, has been primarily studied in tumor contexts. However, its role in APAP-induced liver injury (AILI) remains unclear. Therefore, this study aimed to investigate the involvement of OTUD6A in the pathogenesis of AILI. Our findings demonstrated a substantial upregulation of OTUD6A in both the liver tissue and isolated hepatocytes of mice following APAP stimulation. OTUD6A knockout exacerbated APAP-induced inflammation, hepatocyte necrosis, and liver injury, whereas OTUD6A overexpression alleviated these pathologies. Mechanistically, OTUD6A directly interacted with the enhancer of zeste homolog 2 (EZH2) and selectively removed K48-linked polyubiquitin chains from EZH2, enhancing its stability. This resulted in increased protein levels of EZH2 and H3K27me3, as well as reduced endoplasmic reticulum (ER) stress and cell death in hepatocytes. Collectively, our research uncovers a novel role for OTUD6A in mitigating APAP-induced liver injury by promoting EZH2 stabilization.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

Purpose To investigate the value of non-invasive preoperative prediction of programmed death ligand 1 expression status in extrahepatic cholangiocarcinoma using MRI radiomics combined with clinical features through nomograms.Materials and Methods A retrospective collection was made of 87 cases of extrahepatic cholangiocarcinoma diagnosed through surgical pathology in the Affiliated Hospital of Southwest Medical University from January 2011 to December 2021.These were randomly divided into training and testing sets at a 7∶3 ratio.Using 3D-Slicer software,regions of interest were manually delineated layer-by-layer on MRI images,and radiomic features were extracted.Data normalization,feature dimensionality reduction and selection were then performed.A Gaussian naive Bayes classifier was used to construct the radiomics model,and radiomics scores were obtained.Multivariate Logistic regression was used to screen clinical features,and individual clinical and combined models were constructed.The predictive performances of the three models were evaluated using the area under the receiver operating characteristic curve(AUC),and the goodness of fit and clinical net benefit of the combined model nomogram were assessed through calibration and decision curves.Results Nine radiomic features and three clinical features were finally selected.The clinical features included alanine transaminase(P=0.020),aspartate transaminase(P=0.025)and total bilirubin(P=0.026).The predictive performance of the combined model(training set AUC 0.813,testing set AUC 0.818)was superior to that of the individual clinical model(training set AUC 0.711,testing set AUC 0.705)and the radiomics model(training set AUC 0.769,testing set AUC 0.767).Calibration and decision curves indicated good fit and better clinical net benefit for the combined model nomogram.Conclusion Based on preoperative multi-sequence MRI images and the radiomics score,along with alanine transaminase,aspartate transaminase and total bilirubin as clinical features,the constructed combined model nomogram effectively predicts the programmed death ligand 1 expression status in extrahepatic cholangiocarcinoma.This provides guidance for the precise and personalized immunotherapy for patients.

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.

Chest CT pulmonary angiography (CTPA) has certain auxiliary diagnostic value for the clinical diagnosis of invasive pulmonary aspergillosis (IPA) . Three patients with hematological malignancies were reported, including 2 ones after allogeneic hematopoietic stem cell transplantation and 1 ones after chemotherapy for refractory recurrent leukemia. Each patient was treated with antibiotics for at least 48 hours after the onset of fever, they all underwent chest high-resolution CT (HRCT) scans without fever resolution. CT revealed at least one dense pulmonary consolidation shadow with a diameter greater than 10 mm, and subsequently a CTPA examination was performed to observe the effect of CTPA imaging signs for the diagnosis of IPA. There were 2 patients with positive vascular occlusion sign (VOS) and 1 patient with negative VOS detected by CTPA. Among them, 2 patients with positive VOS were diagnosed with possible IPA and received with diagnosis-driven antifungal treatment, which improved their conditions. One patient with negative VOS sign was diagnosed with diffuse large B-cell lymphoma involving the lungs. After receiving anti-lymphoma treatment, the lesions significantly reduced in size. The vascular occlusion sign detected by CTPA is relatively characteristic. For high-risk IPA patients, it helps to improve the specificity of imaging diagnosis and guide clinical treatment decisions.

Objective To observe the value of deep learning image reconstruction(DLIR)algorithm in brain CT perfusion(CTP)using a protocol of 70 kVp and 40 ml contrast agent dose.Methods Totally 105 patients with suspected acute ischemic stroke(AIS)were prospectively enrolled and randomly divided into 3 groups,who underwent standard dose CTP scanning with 80 kVp and 150 mA combined with reconstruction as adaptive statistic iterative reconstruction V(ASIR-V)at 50％level(CN group,n=35),low dose(LD)scanning with 70 kVp and 100 mA combined with DLIR reconstruction at the highest level(DLIR-H)(LD group,n=35),or ultra-low dose(ULD)scanning with 70 kVp and 70 mA combined with DLIR-H reconstruction(ULD group,n=35).Radiation doses were compared among 3 groups.CT values and standard deviations(SDCT)of ROI of gray matter and white matter in the frontal,parietal and temporal lobes were measured.Signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)between gray and white matter were calculated and compared among groups.Then pseudo-color images of cerebral blood volume(CBV),cerebral blood flow(CBF),mean transit time(MTT)and time to maximum of the tissue residual function(Tmax)were generated.The imaging quality of CTP pseudo-color images was evaluated,and the compatibility of the subjective scores within every kind of CTP pseudo-color images were assessed using Kappa test.Quantitative perfusion parameters were measured and compared among groups.Results Compared with CN group,both LD and ULD groups demonstrated significantly reduced volume CT dose index(CTDIvol),dose-length product(DLP)and effective dose(ED)(all adjusted P＜0.05).In ULD group,SDCT of white matter in frontal,parietal and temporal lobes were higher than those in CN group,and SDCT of white matter in parietal lobe was also higher than that in LD group(all adjusted P＜0.05).No significant difference of SDCT of gray matter was observed among groups(all P＞0.05).SNR of white matter in parietal and temporal lobes in both LD and ULD groups were lower than those in CN group(all P＜0.05),while no significant difference of SNR of white matter in frontal lobe,nor of gray matter in frontal,parietal and temporal lobes was found among groups(all P＞0.05).CNR of gray and white matter in the frontal,parietal and temporal lobes were not significantly different among groups(all P＞0.05).High consistency of inter-observer subjective scores of CBV maps,CBF maps and Tmax maps(Kappa of 0.623,0.644 and 0.638,respectively)were noticed,which of MTT maps had moderate consistency(Kappa=0.560).No significant difference of intra-obsever subjective scores of CTP pseudo-color images was found among groups(all P＞0.05).CBV,CBF,MTT and Tmax values of gray and white matter in frontal,parietal and temporal lobes were not significantly different among groups(all P＞0.05).Conclusion DLIR algorithm applicated in low radiation dose and reduced contrast agent dosage might ensure imaging quality.