Objective: This prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO). Materials and Methods: A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived magnetization transfer ratio (MTR) and MTR asymmetry (MTRasym) at 1 ppm, 2 ppm, and 3.5 ppm were calculated. Clinical data, MTR, and MTRasym values for the extraocular muscles (one representative muscle per eye, yielding two measurements per participant) were compared among the groups. Spearman’s correlation was used to examine associations between imaging parameters and the clinical activity score (CAS) in patients with TAO. Logistic regression analysis was used to identify independent associations between imaging parameters and disease activity in patients with TAO (active vs. inactive). Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance for discriminating active from inactive TAO. Results: Patients with active TAO showed lower MTR values (P < 0.001) and higher MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) (all P < 0.001) compared with those with inactive TAO. MTR was negatively correlated with CAS (r = -0.402; P < 0.001), while MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) were positively correlated with CAS (r = 0.369, 0.350, and 0.349, respectively;all P < 0.001). MTR and MTRasym (1 ppm) were independently associated with TAO activity. The areas under the ROC curve (AUCs) for MTR and MTRasym (1 ppm) in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTRasym (1 ppm) significantly improved diagnostic performance compared with either parameter alone, achieving an AUC of 0.805 (P = 0.029 and 0.001). Conclusion MTR and MTRasym (1 ppm) were independently associated with TAO activity. Their combination further enhanced diagnostic performance in distinguishing active from inactive TAO, suggesting their potential as quantitative imaging biomarkers to guide treatment in patients with TAO.

ObjectiveTo construct and evaluate a mouse model that simulates the pathological progression of immune checkpoint inhibitor-associated pneumonitis (CIP), in order to address the critical issue of the lack of standardized animal models in this research field. MethodsThirty-five SPF-grade 6-week-old male C57BL/6J mice were randomly assigned to a control group (n=5), a bleomycin (BLM) group (n=15), and a BLM+programmed death-1 (PD-1) monoclonal antibody (BLM+PD-1) group (n=15). Corresponding drugs were administered via intraperitoneal injection for intervention. The general condition, body weight changes, survival rate, and pulmonary histopathological alterations were systematically monitored. ResultsCompared with the BLM group, the BLM+PD-1 group exhibited significantly more severe pulmonary fibrosis, characterized by progressive body weight loss and more severe deterioration of general conditions. Histopathological examination showed that pulmonary inflammation was alleviated 4 weeks post-modeling in the BLM group. In contrast, pulmonary lesions continued to worsen in the BLM+PD-1 group, with no evidence of resolution of inflammation 28 days post-modeling. Quantitative immunohistochemical analysis revealed that the positive expression rate of interleukin-17A (IL-17A) in lung tissues reached 34.28% in the BLM+PD-1 group 14 days post-modeling, which was markedly higher than that in the BLM group (24.02%, P < 0.000 1). The positive expression rate of α-smooth muscle actin (α-SMA) was 12.40% in the BLM+PD-1 group 14 days post-modeling, nearly twice the level in the BLM group (5.87%). 28 days post-modeling, the positive expression rate of α-SMA remained at 7.72% in the BLM+PD-1 group, while it declined to 3.38% in the BLM group. ConclusionThe addition of a PD-1 inhibitor to bleomycin treatment accelerates the progression of pulmonary interstitial fibrosis and effectively recapitulates the pathological process of lung injury and fibrosis driven by aberrant immune activation during clinical immunotherapy. Accordingly, this study successfully establishes a dynamic CIP animal model recapitulating the progression from early inflammatory response to pulmonary fibrosis. This model addresses the critical shortage of animal models for this condition and offers an experimental platform for further mechanistic studies and the development of therapeutic strategies.

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.

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.

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 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.