OBJECTIVE To standardize the main processes and related technical links of the clinical comprehensive evaluation of drugs， and provide guidance and reference for improving the quality of comprehensive evaluation evidence and its transformation and application value. METHODS The construction of Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs was based on the standard guideline formulation method of the World Health Organization （WHO）， strictly followed the latest definition of guidelines by the Institute of Medicine of the National Academy of Sciences of the United States， and conformed to the six major areas of the Guideline Research and Evaluation Tool Ⅱ. Delphi method was adopted to construct the research questions； research evidence was established by applying the research methods of evidence-based medicine. The evidence quality classification system of the Chinese Evidence-Based Medicine Center was adopted for evidence classification and evaluation. The recommendation strength was determined by the recommendation strength classification standard formulated by the Oxford University Evidence-Based Medicine Center， and the recommendation opinions were formed through the expert consensus method. RESULTS & CONCLUSIONS The Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs covers 4 major categories of research questions， including topic selection， evaluation implementation， evidence evaluation， and application and transformation of results. The formulation of this guideline has standardized the technical links of the entire process of clinical comprehensive evaluation of drugs， which can effectively guide the high-quality and high-efficient development of this work， enhance the standardized output and transformation application value of evaluation evidence， and provide high-quality evidence support for the scientific decision-making of health and the rationalization of clinical medication.

Objective:To explore the predictive value of hepatitis B surface antibody (HBsAb) quantitative level for achieving hepatitis B surface antigen (HBsAg) seroclearance and serological conversion in patients with chronic hepatitis B (CHB) treated with nucleos(t)ide analogs (NAs) or interferon (IFN).Methods:A two-center prospective cohort study was conducted, including CHB patients from Nanfang Hospital Southern Medical University and Eastern Theater General Hospital treated with NAs and IFN. All patients were followed up once every three to six months. Basic clinical information and test results were collected at each follow-up. The presence or absence of HBsAg seroclearance and serological conversion rate was evaluated. HBsAg serological conversion was defined as HBsAg quantification continuously below the detection limit (<0.05 IU/mL) at two detection time points at least six months apart. HBsAg serological conversion was defined as HBsAb positivity (≥10 IU/L) at the same time as the first HBsAg seroclearance. The Kruskal-Wallis test was used to compare the quantitative data of multiple groups, and the Wilcoxon rank-sum test was used to compare the data between groups. The chi-square test was used for the count data, and the Fisher exact test was used when the chi-square test was not met. Univariate and multivariate Cox analysis was used to determine the predictors of the study endpoints, and stepwise regression was used for variable screening.Results:A total of 2 266 CHB cases were included, of which 86.5% (1 959/2 266) were NA antiviral-received population. The median treatment duration before baseline was 10.5 (2.5, 37.6) months, and the baseline HBsAg quantification was 3.1 (2.6, 3.5) log 10 IU/mL. A total of 68 cases (3.0%) had HBsAg seroclearance, and 44 cases (1.9%) achieved serological conversion after 85.0 (62.7, 97.3) months of prospective follow-up. The level and positivity rate of HBsAb showed a progressive increase 36 months before and significantly after HBsAg seroclearance. Cox regression analysis results showed that baseline HBsAb level was an independent predictor of HBsAg serological conversion ( HR=2.26, P=0.002) in the overall population, especially in the subgroup with HBsAg between 100 and 1 000 IU/mL, suggesting HBsAb level had important predictive value. In addition, the serological conversion development rate was significantly higher in the GOLDEN model favourable patients than in the unfavourable patients (11.5% vs. 0, P<0.001). Conclusion:The baseline HBsAb quantitative level can predict HBsAg seroclearance and serological conversion for patients with CHB receiving antiviral treatment, which is of significant value in long-term treatment monitoring.

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.

Respiratory viral infections such as influenza and respiratory syncytial virus infections continue to rapidly increase in patients worldwide.Host immune responses to respiratory viruses play a key role in the pathogenesis and clinical manifestations of the disease.Respiratory viruses not only activate antiviral immune responses,but also may lead to uncontrolled inflammatory re-sponses,characterized by significant release of pro-inflammatory cytokines in severely infected patients,resulting in lymphopenia,lymphocyte dysfunction,and abnormalities in immune cells such as neutrophils and macrophages.These respiratory virus-induced im-mune abnormalities may lead to microbial infection,septic shock,and severe multiorgan dysfunction.Therefore,clarifying the immu-nopathogenic mechanisms of patients with respiratory viral infections can guide clinical treatment and patient prognosis;in addition,rational regulation of the immune response of respiratory viruses in the host,including enhancing antiviral immunity while suppressing systemic inflammation,may be the key to successful treatment.This review mainly discusses the immunomodulation and related clini-cal treatment strategies for respiratory viral infections to help develop new therapeutic strategies for respiratory viral infections and pa-tient prognosis.

Objective:To identify the risk factors influencing the prognosis of patients with advanced breast cancer through LASSO-Logistic regression analysis and construct a nomogram model to evaluate their prognostic risk.Methods:A total of 178 patients with advanced breast cancer who visited the Department of Thyroid and Breast Surgery of Chengyang District People’s Hospital of Qingdao City from Jan. 2015 to Jan. 2023 were selected as the research subjects. According to the follow-up results, the patients were divided into a good-prognosis group and a poor-prognosis group. Clinical data of the patients were collected. LASSO-Logistic regression analysis was used to identify the risk factors affecting the prognosis of patients with advanced breast cancer. A nomogram model was constructed based on the analysis results. The predictive efficacy of the model for the prognostic risk of patients with advanced breast cancer was evaluated using the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (H-L) test.Results:During the follow-up, 5 patients were lost to follow-up. Among the final 173 patients included, 60 had poor prognoses (accounting for 34.68%), and 113 had good prognoses (accounting for 65.32%). There were significant differences between the poor prognosis group and the good prognosis group in terms of the number of lymph node metastases ( χ 2=18.12), the number of organ metastases ( χ 2=14.28), the difference in ADC before and after treatment ( t=17.35), the difference in SER before and after treatment ( t=9.57), the enhancement of the echo behind the breast after treatment ( χ 2=13.00), and the proportion of increased calcification ( χ 2=8.06) (both P < 0.05). The clinical data with significant differences in the univariate analysis were included in the LASSO regression analysis. Six factors were finally selected: number of lymph node metastases > 5, number of organ metastases > 1, difference in ADC values before and after treatment, difference in SER values before and after treatment, enhanced echo behind the breast, and increased calcification. These six factors selected by LASSO regression were included in the Logistic regression analysis. The results showed that number of organ metastases > 1 ( OR=2.208, 95% CI: 1.153-3.263), small difference in ADC values before and after treatment ( OR=0.448, 95% CI: 0.287-0.608), enhanced echo behind the breast ( OR=2.474, 95% CI: 1.063-3.886), and increased calcification ( OR=3.762, 95% CI: 1.831-5.693) were independent risk factors for poor prognosis in patients with advanced breast cancer (both P<0.05). A nomogram model was constructed based on the analysis results. The ROC curve showed that the area under the curve (AUC) of the model was 0.778. The H-L test results showed that the calibration curve fit well with the ideal curve, with χ 2 = 0.69 and P = 0.273. Conclusion:The nomogram model constructed based on LASSO-Logistic regression analysis has good predictive efficacy for the prognosis of patients with advanced breast cancer.

Objective:To identify the risk factors influencing the prognosis of patients with advanced breast cancer through LASSO-Logistic regression analysis and construct a nomogram model to evaluate their prognostic risk.Methods:A total of 178 patients with advanced breast cancer who visited the Department of Thyroid and Breast Surgery of Chengyang District People’s Hospital of Qingdao City from Jan. 2015 to Jan. 2023 were selected as the research subjects. According to the follow-up results, the patients were divided into a good-prognosis group and a poor-prognosis group. Clinical data of the patients were collected. LASSO-Logistic regression analysis was used to identify the risk factors affecting the prognosis of patients with advanced breast cancer. A nomogram model was constructed based on the analysis results. The predictive efficacy of the model for the prognostic risk of patients with advanced breast cancer was evaluated using the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (H-L) test.Results:During the follow-up, 5 patients were lost to follow-up. Among the final 173 patients included, 60 had poor prognoses (accounting for 34.68%), and 113 had good prognoses (accounting for 65.32%). There were significant differences between the poor prognosis group and the good prognosis group in terms of the number of lymph node metastases ( χ 2=18.12), the number of organ metastases ( χ 2=14.28), the difference in ADC before and after treatment ( t=17.35), the difference in SER before and after treatment ( t=9.57), the enhancement of the echo behind the breast after treatment ( χ 2=13.00), and the proportion of increased calcification ( χ 2=8.06) (both P < 0.05). The clinical data with significant differences in the univariate analysis were included in the LASSO regression analysis. Six factors were finally selected: number of lymph node metastases > 5, number of organ metastases > 1, difference in ADC values before and after treatment, difference in SER values before and after treatment, enhanced echo behind the breast, and increased calcification. These six factors selected by LASSO regression were included in the Logistic regression analysis. The results showed that number of organ metastases > 1 ( OR=2.208, 95% CI: 1.153-3.263), small difference in ADC values before and after treatment ( OR=0.448, 95% CI: 0.287-0.608), enhanced echo behind the breast ( OR=2.474, 95% CI: 1.063-3.886), and increased calcification ( OR=3.762, 95% CI: 1.831-5.693) were independent risk factors for poor prognosis in patients with advanced breast cancer (both P<0.05). A nomogram model was constructed based on the analysis results. The ROC curve showed that the area under the curve (AUC) of the model was 0.778. The H-L test results showed that the calibration curve fit well with the ideal curve, with χ 2 = 0.69 and P = 0.273. Conclusion:The nomogram model constructed based on LASSO-Logistic regression analysis has good predictive efficacy for the prognosis of patients with advanced breast cancer.

Respiratory viral infections such as influenza and respiratory syncytial virus infections continue to rapidly increase in patients worldwide.Host immune responses to respiratory viruses play a key role in the pathogenesis and clinical manifestations of the disease.Respiratory viruses not only activate antiviral immune responses,but also may lead to uncontrolled inflammatory re-sponses,characterized by significant release of pro-inflammatory cytokines in severely infected patients,resulting in lymphopenia,lymphocyte dysfunction,and abnormalities in immune cells such as neutrophils and macrophages.These respiratory virus-induced im-mune abnormalities may lead to microbial infection,septic shock,and severe multiorgan dysfunction.Therefore,clarifying the immu-nopathogenic mechanisms of patients with respiratory viral infections can guide clinical treatment and patient prognosis;in addition,rational regulation of the immune response of respiratory viruses in the host,including enhancing antiviral immunity while suppressing systemic inflammation,may be the key to successful treatment.This review mainly discusses the immunomodulation and related clini-cal treatment strategies for respiratory viral infections to help develop new therapeutic strategies for respiratory viral infections and pa-tient prognosis.

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 explore the predictive value of hepatitis B surface antibody (HBsAb) quantitative level for achieving hepatitis B surface antigen (HBsAg) seroclearance and serological conversion in patients with chronic hepatitis B (CHB) treated with nucleos(t)ide analogs (NAs) or interferon (IFN).Methods:A two-center prospective cohort study was conducted, including CHB patients from Nanfang Hospital Southern Medical University and Eastern Theater General Hospital treated with NAs and IFN. All patients were followed up once every three to six months. Basic clinical information and test results were collected at each follow-up. The presence or absence of HBsAg seroclearance and serological conversion rate was evaluated. HBsAg serological conversion was defined as HBsAg quantification continuously below the detection limit (<0.05 IU/mL) at two detection time points at least six months apart. HBsAg serological conversion was defined as HBsAb positivity (≥10 IU/L) at the same time as the first HBsAg seroclearance. The Kruskal-Wallis test was used to compare the quantitative data of multiple groups, and the Wilcoxon rank-sum test was used to compare the data between groups. The chi-square test was used for the count data, and the Fisher exact test was used when the chi-square test was not met. Univariate and multivariate Cox analysis was used to determine the predictors of the study endpoints, and stepwise regression was used for variable screening.Results:A total of 2 266 CHB cases were included, of which 86.5% (1 959/2 266) were NA antiviral-received population. The median treatment duration before baseline was 10.5 (2.5, 37.6) months, and the baseline HBsAg quantification was 3.1 (2.6, 3.5) log 10 IU/mL. A total of 68 cases (3.0%) had HBsAg seroclearance, and 44 cases (1.9%) achieved serological conversion after 85.0 (62.7, 97.3) months of prospective follow-up. The level and positivity rate of HBsAb showed a progressive increase 36 months before and significantly after HBsAg seroclearance. Cox regression analysis results showed that baseline HBsAb level was an independent predictor of HBsAg serological conversion ( HR=2.26, P=0.002) in the overall population, especially in the subgroup with HBsAg between 100 and 1 000 IU/mL, suggesting HBsAb level had important predictive value. In addition, the serological conversion development rate was significantly higher in the GOLDEN model favourable patients than in the unfavourable patients (11.5% vs. 0, P<0.001). Conclusion:The baseline HBsAb quantitative level can predict HBsAg seroclearance and serological conversion for patients with CHB receiving antiviral treatment, which is of significant value in long-term treatment monitoring.

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.