Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To establish and optimize an autoverification system for thyroid function test reports of 5 items using historical test data.Methods:Based on the docoment' Autoverification of Clinical Laboratory Quantitative Test Results′, CLSI AUTO 10-A and AUTO 15 guidelines, an autoverification system for thyroid function test reports of 5 items was established combining with manual verification experience. A total of 193 860 thyroid function test reports of 5 items in 2021 were collected for the assessment of the original system. Totally 210 097 thyroid function test reports of 5 items in 2022 and 299 198 reports in 2023 were collected for the optimization of the autoverification system. There were 160 666 thyroid function test reports of 5 items from the first half of 2024 for the manual and autoverification comparison after optimization.Results:The pass rate of the autoverification system based on original thyroid function report in 2021 was 69.56%(134 849/193 860). The optimized system utilizing historical data from 2022 and 2023 covered 21 pattern rules and established verification for different patterns including range rules, delta check rules, and review rules. Taking manual verification as the standard for the data from the first half of 2024, the sensitivity and specificity of the optimized system were 100% (499/499) and 81.57% (130 646/160 167), respectively, with a false-negative rate of 0. The concordance rate between autoverification and manual verification was 81.63% (131 145/160 666), and the pass rate was 81.32% (130 646/160 666).Conclusion:Establishing and optimizing the autoverification system for thyroid function tests of 5 items using historical test data, and formulating verification rules for different patterns can be applied to clinical practise, which not only ensures the accuracy of test reports but also improves work efficiency, allowing continuously optimized and perfected of the system.

Objective To analyze the differences in bone serum protein between patients with osteoporosis accompanied by obstructive sleep apnea syndrome(OSAS)and those with osteoporosis only using proteomics.Methods A total of 80 osteoporosis patients who attended our hospital between June 2022 and June 2024 were enrolled.Based on their polysomnography results,the participants were divided into an OSAS and osteoporosis comorbidity(OSAS-osteoporosis)group(n=42)and an osteoporosis only group(n=38).Propensity score matching was applied to incorporate covariates in logistic regression so that the individual characteristics of the two groups of patients were generally balanced.Following the matching procedure,a final cohort of 20 matched pairs was obtained and subsequently utilized for further analysis.The mass spectrum was obtained using laser desorption ionization mass spectrometry.Principal component analysis(PCA)was performed to assess differences in metabolic patterns between groups.Partial least squares discriminant analysis(PLS-DA)and orthogonal PLS-DA(OPLS-DA)were employed for further data analysis.Variable importance in projection(VIP)scores of each substance were calculated with OPLS-DA to screen the metabolites showing inter-group differences.Heatmaps were generated to visualize metabolic profile differences between the OSAS-osteoporosis group and the osteoporosis group.Enrichment pathway analysis was conducted on the differential identified metabolites.Results After propensity score matching,individual characteristics between the groups were well balanced.Mass spectrometry revealed significant differences between the OSAS-osteoporosis and osteoporosis groups.In the PCA score plot,the separation trend of the two groups was not significant.The PLS-DA score plot showed a discernible separation trend,with R2 and Q2 lower than those of the corresponding results of the real model,confirming the reliability of the model.OPLS-DA showed that the total R2X of the model was 0.635,R2Y was 0.879,and O2Y was 0.728,showing obvious separation trends between the two groups.A total of 16 differential metabolites were identified,including stearyl-oleyl-glycerol phosphate choline,phosphate choline,L-histidine,erucamide,2'-deoxyuridine,1-palmitoyl glycerol,thymine,tyramine,L-pyroglutamic acid,L-glutamic acid,myristate,glycerol-3-phosphate,caprylic acid,pregnenolone,L-arginine,D-4-hydroxyphenylglycine,and isobutyric acid.Heatmaps showed significant differences in metabolic profiles between the OSAS-osteoporosis group and the osteoporosis group.Pathway enrichment analysis showed that 27 metabolic pathways were involved.27 metabolic pathways.Under the conditions of P<0.05 and pathway impact>0.2,the three most significant metabolic pathways identified included mainly alanine,aspartate,and glutamate metabolism,arginine biosynthesis,and histidine metabolism.Conclusion Significant differences were observed in the metabolic profiles between patients with both OSAS and osteoporosis and those with osteoporosis alone.

OBJECTIVE To analyze the chemical constituents of Heihuang Chizhu Granules and the blood composition of rats af-ter administration by UPLC-Q-TOF-MS/MS.METHODS A Waters ACQUITY UPLC HSS T3 column(3 mm×100 mm,1.8 μm)was eluted with acetonitrile-0.1%formic acid as mobile phase,and the data were collected in electrospray ion source positive and neg-ative ion mode and then identified with the reference retention time,precise molecular weight,secondary fragment ions,and references to relevant literature.RESULTS A total of 104 chemical components were identified from Heihuang Chizhu Granules,including 26 flavonoids,24 organic acids,14 triterpenoids,8 terpenoids,7 phenylpropanoids,11 monoglycosides,and 14 other components(phe-nols,alkaloids,etc.).On this basis,39 blood-entering components were identified in the plasma of rats administered via gavage,in-cluding 28 prototypes and 11 metabolites.CONCLUSION The chemical constituents of Heihuang Chizhu Granules and the compo-nents entering the blood of rats are analyzed and identified for the first time in this study,and the results provide a scientific basis for the basic research of Heihuang Chizhu Granules and the establishment of process quality control standards.

OBJECTIVE To analyze the chemical constituents of Heihuang Chizhu Granules and the blood composition of rats af-ter administration by UPLC-Q-TOF-MS/MS.METHODS A Waters ACQUITY UPLC HSS T3 column(3 mm×100 mm,1.8 μm)was eluted with acetonitrile-0.1%formic acid as mobile phase,and the data were collected in electrospray ion source positive and neg-ative ion mode and then identified with the reference retention time,precise molecular weight,secondary fragment ions,and references to relevant literature.RESULTS A total of 104 chemical components were identified from Heihuang Chizhu Granules,including 26 flavonoids,24 organic acids,14 triterpenoids,8 terpenoids,7 phenylpropanoids,11 monoglycosides,and 14 other components(phe-nols,alkaloids,etc.).On this basis,39 blood-entering components were identified in the plasma of rats administered via gavage,in-cluding 28 prototypes and 11 metabolites.CONCLUSION The chemical constituents of Heihuang Chizhu Granules and the compo-nents entering the blood of rats are analyzed and identified for the first time in this study,and the results provide a scientific basis for the basic research of Heihuang Chizhu Granules and the establishment of process quality control standards.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To establish and optimize an autoverification system for thyroid function test reports of 5 items using historical test data.Methods:Based on the docoment' Autoverification of Clinical Laboratory Quantitative Test Results′, CLSI AUTO 10-A and AUTO 15 guidelines, an autoverification system for thyroid function test reports of 5 items was established combining with manual verification experience. A total of 193 860 thyroid function test reports of 5 items in 2021 were collected for the assessment of the original system. Totally 210 097 thyroid function test reports of 5 items in 2022 and 299 198 reports in 2023 were collected for the optimization of the autoverification system. There were 160 666 thyroid function test reports of 5 items from the first half of 2024 for the manual and autoverification comparison after optimization.Results:The pass rate of the autoverification system based on original thyroid function report in 2021 was 69.56%(134 849/193 860). The optimized system utilizing historical data from 2022 and 2023 covered 21 pattern rules and established verification for different patterns including range rules, delta check rules, and review rules. Taking manual verification as the standard for the data from the first half of 2024, the sensitivity and specificity of the optimized system were 100% (499/499) and 81.57% (130 646/160 167), respectively, with a false-negative rate of 0. The concordance rate between autoverification and manual verification was 81.63% (131 145/160 666), and the pass rate was 81.32% (130 646/160 666).Conclusion:Establishing and optimizing the autoverification system for thyroid function tests of 5 items using historical test data, and formulating verification rules for different patterns can be applied to clinical practise, which not only ensures the accuracy of test reports but also improves work efficiency, allowing continuously optimized and perfected of the system.

Currently,human health due to corona virus disease 2019(COVID-19)pandemic has been seriously threatened.The coronavirus severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)spike(S)protein plays a crucial role in virus transmission and several S-based therapeutic approaches have been approved for the treatment of COVID-19.However,the efficacy is compromised by the SARS-CoV-2 evolvement and mutation.Here we report the SARS-CoV-2 S protein receptor-binding domain(RBD)inhibitor licorice-saponin A3(A3)could widely inhibit RBD of SARS-CoV-2 variants,including Beta,Delta,and Omicron BA.1,XBB and BQ1.1.Furthermore,A3 could potently inhibit SARS-CoV-2 Omicron virus in Vero E6 cells,with EC50 of 1.016 pM.The mechanism was related to binding with Y453 of RBD deter-mined by hydrogen-deuterium exchange mass spectrometry(HDX-MS)analysis combined with quan-tum mechanics/molecular mechanics(QM/MM)simulations.Interestingly,phosphoproteomics analysis and multi fluorescent immunohistochemistry(mIHC)respectively indicated that A3 also inhibits host inflammation by directly modulating the JNK and p38 mitogen-activated protein kinase(MAPK)path-ways and rebalancing the corresponding immune dysregulation.This work supports A3 as a promising broad-spectrum small molecule drug candidate for COVID-19.

ObjectiveTo understand the prevalence of hookworm infection and its relevant behavioral factors in rural areas of Tiantai County， Zhejiang Province， and to provide scientific evidence for prevention and control of hookworm disease. MethodsBy using a stratified cluster random sampling strategy， local residents aged ≥3 years was divided into 5 districts according to geographical location； furthermore， those in one administrative village （surveillance site） were investigated in each district. Species of hookworm were identified by filter paper culture in vitro， and enterobius vermicularis eggs were detected by cellophane anal swab in children aged 3‒9 year. Risk factors were determined by questionnaire. ResultsA total of 1 013 residents were investigated in 5 surveillance sites. Thirty nine cases with hookworm infection were detected， with the total infection rate of 3.85% . All species detected were determined to be Necator americanus. The infection rate significantly differed across the towns （χ²=48.32， P<0.05）， with the highest rate in Nanping Town （10.95%） . It significantly differed by age groups （χ²=65.65， P<0.05）， with the highest rate in those aged >70 years （9.75%）. Furthermore， it decreased with educational background. It was significantly associated with fertilize with fresh manure （χ²=6.87， P<0.05） and barefoot labor （χ²=157.69， P<0.05）. ConclusionThe overall infection rate of hookworm in Tiantai County remains low. Dominant species of hookworm is hookworm Necator americanus. It is necessary to strengthen the advocacy of hookworm prevention and control knowledge， improve hygiene in work and life style， and increase self-protection awareness.

Objective:To analyze the correlation between elastic modulus of shear wave elastography of periurethral prostatic tissue and serum prostate-specific antigen(PSA)in patients with benign prostatic hyperplasia(BPH).Methods:A total of 200 BPH patients who received treatment in the Second People's Hospital of Hefei from October 2019 to October 2022 were collected by convenient sampling method,and all patients were subjected to ultrasound shear wave elastography and serum PSA examination,and the elastic modulus of periurethral prostate tissue was measured by shear wave elastography,and the mean of them was obtained.They were divided into mild group(96 cases),moderate group(59 cases)and severe group(45 cases)according to the results of the International Prostate Symptom Score(IPSS).At the same time,the related information of 30 cases without prostatic lesions who were selected from the prostate examination population were included into healthy control groups.The correlation between the elastic modulus of periurethral prostatic tissue and serum PSA was analyzed and compared.Results:Statistical analysis showed that there was significant difference in the elastic modulus value of prostate among various groups(F=190.914,P<0.05).Compared with the healthy control group,the elastic modulus values of prostate of the mild,moderate and severe groups were significantly increased,and the differences were significant(t=6.572,14.172,18.441,P<0.05),respectively.Compared with the mild group,the elastic modulus values of prostate of the moderate and severe groups were significantly increased(t=7.853,18.274,P<0.05),respectively.Compared with the moderate group,the elasticity modulus value of prostate of the severe group significantly increased(t=11.371,P<0.05).There was significant difference in serum PSA among BPH patients with different degrees(F=126.143,P<0.05).Compared with the healthy control group,the serum PSA values of the mild,moderate and severe groups significantly increased(t=10.694,14.368,13.804,P<0.001),respectively.Compared with the mild group,the serum PSA values of the moderate and severe group significantly increased(t=6.401,13.047,P<0.05),respectively.Compared with the moderate group,the serum PSA value of the patients in the severe group significantly increased(t=7.293,P<0.001).There was no significant correlation between serum PSA and the elastic modulus of periurethral tissues in the healthy control group(P>0.05).There were significant positive correlation between serum PSA and the elastic modulus of periurethral tissues in the mild,moderate and severe groups(r=0.314,0.296,0.354,P<0.05),respectively.Conclusion:Both serum PSA level and elastic modulus value of periurethral prostate tissue of BPH patients significantly increase,and there is a positive correlation between them.