Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE To explore the effects of prescription pre-review system on rational drug use and off-label drug use management in outpatient and emergency department. METHODS A retrospective analysis was conducted on outpatient and emergency department prescription data from three phases in our hospital： January to May 2023 （silent review phase， control group）， June to October 2023 （systematic automatic review phase， intervention group 1）， and November 2023 to March 2024 （phase combining systematic automatic review with centralized feedback from pharmacists to physicians regarding irrational prescriptions， intervention group 2）. These phases followed the implementation of our hospital’s pre-prescription review software. Statistical analysis of the prompt rate of alert， rate of irrational prescriptions， registered the off-label drug use rate and false positive irrationality prescription rate were conducted. Meanwhile， the composition of irrational prescriptions was analyzed， and evidence- based evaluation of the off-label drug use proposed by clinicians was also conducted. RESULTS Compared with control group， the prompt rate of alert and the rate of irrational prescriptions in intervention group 1 were all decreased significantly after receiving pop-up notification， with statistically significant differences （P＜0.05）. With the help of system warning and the pharmacists feedback， the prompt rate of alert and the rate of irrational prescriptions declined further in the intervention group 2， but there was no statistically significant difference when compared with intervention group 1 （P＞0.05）. The main type of irrational drug use was improper administration routes. When comparing intervention group 1 with the control group， as well as intervention group 2 with intervention group 1， a significant decrease in the rate of improper administration routes was observed， with statistically significant differences （P＜0.05）. Compared with control group， there was no significant difference in the registered off-label drug use rate of intervention group 1 and intervention group 2 （P＞0.05）. The doctor’s awareness of off-label drug use registration increased due to the real-time alerts from the pre-prescription review software， along with the pharmacists’ regular summarization and feedback. Total 13 items registrations of off-label drug use were proposed by clinicians from June 2023 to March 2024， all of which were supported by evidence of varying levels； among them， 3 items received FDA approval， 4 items were included in the Micromedex database， and the remaining 6 items were supported by evidence from system reviews or randomized controlled trials. CONCLUSIONS Prescription pre-review system can improve the level of rational drug use and assist in the standardized management of off-label drug use.

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease (AD), but the neurobiological mechanisms linking synaptic pathology, neural oscillatory dynamics, and brain network reorganization remain unclear. This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments, molecular profiling, and neurophysiological monitoring. MethodsIn this prospective double-blind trial, 12 AD patients underwent a 14-day protocol of 20 Hz rTMS, with comprehensive multimodal assessments performed pre- and post-intervention. Cognitive functioning was quantified using the mini-mental state examination (MMSE) and Montreal cognitive assessment (MOCA), while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living (ADL) scale and combined neuropsychiatric inventory (NPI)-Hamilton depression rating scale (HAMD). Peripheral blood biomarkers, specifically Aβ1-40 and phosphorylated tau (p-tau181), were analyzed to investigate the effects of rTMS on molecular metabolism. Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients, while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization. Furthermore, systematic assessment of correlations between cognitive scale scores, blood biomarkers, and network characteristics was performed to elucidate cross-modal therapeutic associations. ResultsClinically, MMSE and MOCA scores improved significantly (P<0.05). Biomarker showed that Aβ1-40 level increased (P<0.05), contrasting with p-tau181 reduction. Moreover, the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores. Post-intervention analyses revealed significant modulations in oscillatory power, characterized by pronounced reductions in delta (P<0.05) and theta bands (P<0.05), while concurrent enhancements were observed in alpha, beta, and gamma band activities (all P<0.05). Network analysis revealed frequency-specific reorganization: clustering coefficients were significantly decreased in delta, theta, and alpha bands (P<0.05), while global efficiency improvement was exclusively detected in the delta band (P<0.05). The alpha band demonstrated concurrent increases in average nodal degree (P<0.05) and characteristic path length reduction (P<0.05). Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181. Additionally, the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band. However, the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands. Conclusion20 Hz rTMS targeting dorsolateral prefrontal cortex (DLPFC) significantly improves cognitive function and enhances the metabolic clearance of β-amyloid and tau proteins in AD patients. This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation, which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks. These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales, blood biomarkers, and EEG——in understanding and monitoring the progression of AD. This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.