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 investigate the longitudinal stability of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Activity of Daily Living Scale (ADL).Methods:The longitudinal cognitive assessment results of 68 dementia patients admitted to the Dementia and Leukoencephalopathy Outpatient Clinic, Department of Neurology, Peking Union Medical College Hospital, from January 2021 to January 2024, were retrospectively analyzed, including the total and sub-items scores of the MMSE, MoCA, and ADL. Two different rules were applied to analyze the abnormality rates: rule 1, where the current test result being better than the previous one was considered an abnormality; rule 2, where the current test result being better than the previous average score was considered an abnormality (If a patient had only 2 cognitive assessments, rule 2 was considered the same as rule 1). Two rules were used to analyze the abnormality rates of the scales. The statistical analyses were repeated after excluding patients with possible anxiety and depression status.Results:In assessing the total score stability, MMSE showed the lowest abnormality rates [27.2% (31/114) under rule 1 and 29.8% (34/114) under rule 2], while MoCA had the highest abnormality rates [41.3% (26/63) and 46.0% (29/63), respectively]. The ADL abnormality rates were 27.7% (23/83) and 33.7% (28/83), respectively. Among MoCA sub-items, category cue, multiple choice cue, second memory trial, orientation, and clock showed higher abnormality rates [31.7%(20/63), 30.2%(19/63), 23.8%(15/63), 22.2%(14/63), 22.2%(14/63), respectively]. After excluding population with possible anxiety and depression status, the relative abnormality rates of MMSE and ADL sub-items did not significantly change, while the abnormality rate of orientation in MoCA sub-items decreased relatively.Conclusion:The MMSE and ADL exhibit good stability in long-term monitoring of dementia patients, serving as essential tools for assessing and following up cognitive changes.

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 investigate the longitudinal stability of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Activity of Daily Living Scale (ADL).Methods:The longitudinal cognitive assessment results of 68 dementia patients admitted to the Dementia and Leukoencephalopathy Outpatient Clinic, Department of Neurology, Peking Union Medical College Hospital, from January 2021 to January 2024, were retrospectively analyzed, including the total and sub-items scores of the MMSE, MoCA, and ADL. Two different rules were applied to analyze the abnormality rates: rule 1, where the current test result being better than the previous one was considered an abnormality; rule 2, where the current test result being better than the previous average score was considered an abnormality (If a patient had only 2 cognitive assessments, rule 2 was considered the same as rule 1). Two rules were used to analyze the abnormality rates of the scales. The statistical analyses were repeated after excluding patients with possible anxiety and depression status.Results:In assessing the total score stability, MMSE showed the lowest abnormality rates [27.2% (31/114) under rule 1 and 29.8% (34/114) under rule 2], while MoCA had the highest abnormality rates [41.3% (26/63) and 46.0% (29/63), respectively]. The ADL abnormality rates were 27.7% (23/83) and 33.7% (28/83), respectively. Among MoCA sub-items, category cue, multiple choice cue, second memory trial, orientation, and clock showed higher abnormality rates [31.7%(20/63), 30.2%(19/63), 23.8%(15/63), 22.2%(14/63), 22.2%(14/63), respectively]. After excluding population with possible anxiety and depression status, the relative abnormality rates of MMSE and ADL sub-items did not significantly change, while the abnormality rate of orientation in MoCA sub-items decreased relatively.Conclusion:The MMSE and ADL exhibit good stability in long-term monitoring of dementia patients, serving as essential tools for assessing and following up cognitive changes.

Pan-immune inflammation value (PIV) is a comprehensive immune inflammatory biomarker based on complete blood cell counts, which has been proven to predict treatment response and survival outcomes for different types of tumors. However, the predictive value of the PIV varies in different strategies for tumor treatment. This paper aims to systematically review the latest progress of PIV in predicting survival outcomes and tumor prognosis for immunotherapy, radiotherapy, targeted therapy, endocrine therapy, surgical treatment and neoadjuvant therapy, and analyze its existing challenges and issues, as well as look forward to its future development direction and application prospects.

Objective To investigate the correlation between pre-treatment pan-immune inflammation value (PIV) and clinicopathological features in esophageal squamous cell carcinoma (ESCC) patients with postoperative adjuvant radiotherapy and evaluate its value in prognosis assessment combined with T stage. Methods A retrospective analysis was conducted on data of 85 ESCC patients with postoperative adjuvant radiotherapy in the Department of Radiation Oncology of the Affiliated Hospital of Yangzhou University from January 2019 to January 2023. The receiver operating characteristic (ROC) curve was drew to obtain the optimal cut-off value of PIV and other immune-inflammatory biomarkers. The area under the curve (AUC) and clinical applicability of PIV and other immune-inflammatory biomarkers were compared based on the ROC curve and decision curve analysis (DCA). According to the optimal cut-off value, patients were divided into high PIV group and low PIV group, and the correlation between PIV level and clinicopathological features of ESCC was evaluated. Kaplan-Meier method was used for survival analysis, the Cox proportional hazards model was used for multivariate analysis, and a risk stratification model combining PIV and T stage was established by recursive partitioning analysis (RPA). Results The optimal cut-off value of pre-treatment PIV was determined as 187.22 based on the ROC curve. The AUC of PIV was 0.679, which was greater than 0.640, 0.583, 0.656 and 0.644 of the other four immune-inflammatory biomarkers such as the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR). The 85 patients were divided into low PIV group (< 187.22, n=48) and high PIV group (≥187.22, n=37). The level of PIV was significantly correlated with tumor diameter (P < 0.05). The 3-year overall survival (OS) (75.6% versus 30.6%, P < 0.001) and 3-year disease-free survival (DFS) (56.1% versus 21.0%, P < 0.001) were significantly higher in the low PIV group than the high PIV group. Tumor diameter, T stage and PIV were independent factors affecting OS in ESCC patients (P < 0.05), and T stage and PIV were independent factors affecting DFS in ESCC patients (P < 0.05). A new staging system with three risk groups was established by the RPA stratification model based on T stage and PIV, which further improved the predictive value of prognosis compared with T stage or PIV alone. Conclusion Pre-treatment PIV is helpful in predicting the prognosis of ESCC patients with postoperative adjuvant radiotherapy, and the combination of PIV and T stage can improve the predictive value.

Objective:To analyze the clinical and imaging features of patients with COVID-19-related osmotic demyelination syndrome (ODS).Methods:COVID-19-related ODS cases diagnosed in the Department of Neurology, Peking Union Medical College Hospital from January 2020 to September 2023 were retrospectively reviewed. And their past medical history, possible triggers, clinical manifestations, imaging manifestations, treatment and prognosis were summarized.Results:A total of 5 patients with COVID-19-related ODS were included. Electrolyte disturbances acted as an inducement of ODS in all patients (5/5),4 of whom with hyponatremia. Four of 5 patients first presented with disturbance of consciousness, followed by predominant dystonia. Imaging of all patients (5/5) showed isolated extrapontine myelinolysis (EPM). With the prolongation of the course of disease, such signal intensity could return to normal, and lesions showed atrophic changes in some patients. The patients′ clinical symptoms were partly relieved within a few days to a few months after treatment.Conclusions:COVID-19-related ODS is mostly associated with hyponatremia, and EPM is more common. COVID-19 should be considered as a risk factor for ODS.

OBJECTIVE To introduce the clinical value assessment model for innovative drugs in Canada's health insurance access,providing a reference for improving the clinical value assessment system for innovative drugs in China.METHODS The clinical value assessment system for innovative drugs in Canada's health insurance access was organized from four aspects:the assessment body,the assessment process,the assessment dimensions,and the application of assessment results.A deep analysis was also conducted with the clinical value assessment of health insurance access for blinatumomab as an example.Then the suggestions were proposed for the improvement of relevant work in China.RESULTS & CONCLUSIONS Canada has established an independent clinical value assessment agency,the Canadian Agency for Drug and Technologies in Health(CADTH),which is responsible for the health technology assessment of innovative drugs.The health insurance access to clinical value assessment system for innovative drugs is built with patient needs as the guide,and the review process includes stages such as opinion review and expert assessment.Different evaluation dimensions are set for oncology and non-oncology drugs,and the assessment is based on sufficient evidence and a transparent process.The assessment results include four types:reimbursement,conditional reimbursement,time-limited reimbursement,and non-reimbursement,balancing efficacy and accessibility.It is suggested that China should strengthen the clinical value assessment system for innovative drugs in health insurance access from four aspects:establishing a specialized institution for the clinical value assessment of innovative drugs,increasing the clarity of policy expectations,including patient benefit assessment indicators,and adding special reimbursement pathways for drugs.

Objective:To investigate the expression and clinical significance of decoy receptor 3 (DcR3) and its signal pathway-related molecules in PBMCs of patients with ankylosing spondylitis (AS).Methods:Peripheral blood samples, clinical data and laboratory test results were collected from 100 patients with ankylosing spondylitis [50 patients with AS activity (ASA), 50 patients with AS stability (ASS)], 30 patients with osteoarthritis and 30 patients with gouty arthritis (as disease control group), and 60 healthy controls (HC). The mRNA expression levels of DcR3 and its signal pathway related genes (DR3, TL1A, Fas, FasL, LIGHT, LIGHTR, LTβR) were measured by real-time fluorescence quantitative polymerase chain reaction. Measurement data among the three groups in normal distribution were analyzed by t test or one-way analysis of variance, pairwise comparisons using LSD- t test, non-normal distribution data were analyzed by Mann-Whitney test or Kruskal-Wallis H test, χ2 test was used for correlation analysis of categorical variables. Correlation analysis between variables were analyzed using Spearman correlation analysis. Results:① By comparing the AS group, disease control group and HC group, the expression levels of DcR3 mRNA and DR3 mRNA in the AS group were lower than those in disease control group and HC group, and DcR3 mRNA and DR3 mRNA in disease control group were lower than those in the HC group {DcR3mRNA: [6.21 (3.89, 10.70)]×10 -4vs [9.51 (5.89, 16.65)]×10 -4vs [17.81 (11.27, 24.20)]×10 -4, H=55.28, P<0.001; DR3 mRNA: [41.05 (24.09, 66.95)]×10 -4vs [58.28 (28.41, 94.38)]×10 -4vs [94.79 (54.07, 144.51)]×10 -4, H=37.10, P<0.001}. The expression level of TL1A mRNA in the AS group was higher than that in disease control group {[14.71(4.91, 42.22)]×10 -4vs [4.00(1.07, 16.60)]×10 -4vs [7.70 (3.52, 27.83)]×10 -4, H=17.71, P<0.001}; The expression level of Fas mRNA in AS group and disease control group was lower than that in HC group {[20.99(4.63, 62.89)]×10 -4vs [23.97(15.82, 38.99)]×10 -4vs [78.45 (27.32, 146.46)]×10 -4, H=31.17, P<0.001}. The expression level of FasL mRNA in AS group was higher than that in disease control group and HC group {[42.87(6.57, 91.21)]×10 -4vs [5.45(2.83, 10.32)]×10 -4vs [6.88 (4.57, 23.79)]×10 -4, H=46.42, P<0.001}. The expression level of LIGHTR mRNA in AS group was lower than that in disease control group {[52.66 (7.20, 143.21)]×10 -4vs [98.80 (53.11, 166.24)]×10 -4vs [63.47(40.85, 138.07)]×10 -4, H=11.96, P<0.001}. There were no significant differences in LIGHT mRNA and LTβR mRNA among all groups ( H=0.86, P>0.05; H=3.18, P>0.05). ②The expression levels of DcR3 mRNA, DR3 mRNA and Fas mRNA in ASA group and ASS group were lower than those in HC group. DcR3 mRNA in ASA group was higher than that in ASS group, and DR3 mRNA in ASA group was lower than that in ASS group {DcR3 mRNA: [7.28 (4.92, 16.56)]×10 -4vs [4.59 (2.49, 7.03)]×10 -4vs [17.81 (11.27, 24.20)]×10 -4, H=62.63, P<0.001; DR3 mRNA: [30.93(16.18, 66.66)]×10 -4vs [47.17(29.91, 67.40)]×10 -4vs [94.79(54.07, 144.51)]×10 -4, H=41.48, P<0.001; Fas mRNA: [20.04(3.29, 62.30)]×10 -4vs [22.49(5.63, 64.79)]×10 -4vs [78.45(27.32, 146.46)]×10 -4, H=23.54, P<0.001}. The expression levels of TL1A mRNA and LTβR mRNA in the ASA group were higher than those in the ASS group and the HC group {TL1A mRNA: [32.36(10.09, 97.84)]×10 -4vs [9.98(1.29, 21.63)]×10 -4vs [7.70(3.52,27.83)]×10 -4, H=21.14, P<0.001; LTβR mRNA: [6.13(2.16,20.06)×10 -4vs [2.13(0.53,8.04)]×10 -4vs [2.72 (1.24,5.73)]×10 -4, H=12.86, P<0.001}. The expression level of FasL mRNA in the ASA group and the ASS group was higher than that in the HC group {[60.70 (8.16, 106.16)]×10 -4vs [30.14 (5.37, 78.40)]×10 -4vs [6.88 (4.57, 23.79)]×10 -4, H=18.99, P<0.001}. The expression level of LIGHTR mRNA in ASS group was lower than that in HC group {[49.79(10.75, 168.48)]×10 -4vs [15.92(3.27, 105.91)]×10 -4vs [63.47(40.85, 138.07)]×10 -4, H=11.80, P<0.001]. There was no significant difference in LIGHT mRNA among all groups ( H=4.15, P>0.05). ③Spearman correlation analysis showed that DcR3 level was positively correlated with BASDAI score and hsCRP in AS patients ( r=0.52, P<0.001; r=0.35, P<0.01), and DR3 level was negatively correlated with BASDAI score, ESR and hsCRP level ( r=-0.28, P<0.001; r=-0.25, P<0.001; r=-0.31, P<0.001). TL1A was positively correlated with BASDAI score, ESR and hsCRP level ( r=0.23, P=0.046; r=0.26, P=0.015; r=0.25, P=0.017). Conclusion:DcR3 and its signal pathway-related molecules are differentially expressed in PBMCs of patients with AS, suggesting that they may participate in the occurrence and development of AS.

Circular RNAs (circRNAs), covalently closed continuous RNA loops, are generated from cognate linear RNAs through back splicing events, and alternative splicing events may gener-ate different circRNA isoforms at the same locus. However, the challenges of reconstruction and quantification of alternatively spliced full-length circRNAs remain unresolved. On the basis of the internal structural characteristics of circRNAs, we developed CircAST, a tool to assemble alter-natively spliced circRNA transcripts and estimate their expression by using multiple splice graphs.Simulation studies showed that CircAST correctly assembled the full sequences of circRNAs with a sensitivity of 85.63％-94.32％and a precision of 81.96％-87.55％. By assigning reads to specific iso-forms, CircAST quantified the expression of circRNA isoforms with correlation coefficients of 0.85-0.99 between theoretical and estimated values. We evaluated CircAST on an in-house mouse testis RNA-seq dataset with RNase R treatment for enriching circRNAs and identified 380 cir-cRNAs with full-length sequences different from those of their corresponding cognate linear RNAs. RT-PCR and Sanger sequencing analyses validated 32 out of 37 randomly selected isoforms, thus further indicating the good performance of CircAST, especially for isoforms with low abundance. We also applied CircAST to published experimental data and observed substantial diversity in circular transcripts across samples, thus suggesting that circRNA expression is highly regulated. CircAST can be accessed freely at https://github.com/xiaofengsong/CircAST.