Reassessment of pioglitazone and bladder cancer based on FAERS database.

BACKGROUND: The association between pioglitazone (PLZ) and bladder cancer (BC) remains controversial in several randomized control trials, meta-analyses of multiple prospective studies, and large-scale observational studies. RESEARCH DESIGN AND METHODS: Adverse event (AE) data from 1 January 2004 to 31 March 2024 were extracted from the Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionality analysis were applied to quantify the signals of PLZ related BC. RESULTS: In total, 17,627,524 AE reports were recorded in the FAERS database, of which 1366 were PLZ-related BCs. More male than female patients were reported. The median age of patients was 70 years old. The peak in the annual report occurred in 2011. A total of 602 AEs reported time to onset (TTO) and the median TTO was 1023 days. In this study, BC and BC recurrence were strong signal, whereas BC stage 0 (with cancer in situ), stage ii and iii were weak signals. CONCLUSIONS: This study comprehensively demostrated the PLZ-induced risk of BC in patients with diabetes mellitus using the FAERS database. The results demonstrated that the patients treated with PLZ were more likely to develop BC. The male and aging attributed more cases to BC-related reports of PLZ treated patients.

Effects of tanshinone IIA on endothelial cell dysfunction in uremic condition.

An arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis in uremic patients, yet its dysfunction poses a significant clinical challenge. Venous stenosis, primarily caused by venous neointimal hyperplasia, is a key factor in the failure of vascular access. During vascular access dysfunction, endothelial cells (ECs) transform mechanical stimuli into intracellular signals and interact with vascular smooth muscle cells. Tanshinone IIA, an important compound derived from Salvia miltiorrhiza, has been widely used to treat cardiovascular diseases. However, its role in modulating ECs under uremic conditions remains incompletely understood. In this research, ECs were exposed to sodium tanshinone IIA sulfonate (STS) and subjected to shear stress and uremic conditions. The results indicate that STS can reduce the suppressive effects on the expression of NF-κB p65, JNK and Collagen I in uremia-induced ECs. Moreover, the downregulation of NF-κB p65, JNK and Collagen I can be enhanced through the inhibition of ERK1/2 and the upregulation of Caveolin-1. These findings suggest that tanshinone IIA may improve EC function under uremic conditions by targeting the Caveolin-1/ERK1/2 pathway, presenting tanshinone IIA as a potential therapeutic agent against AVF immaturity caused by EC dysfunction.

Comparison of the recovery time of remimazolam besylate and propofol for gastrointestinal endoscopy sedation in elderly patients.

Background: Recovery time is a crucial factor in ensuring the safety and effectiveness of both patients and endoscopy centers. Propofol is often preferred due to its fast onset and minimal side effects. Remimazolam is a new intravenous sedative agent, characterized by its rapid onset of action, quick recovery and organ-independent metabolism. Importantly, its effect can be specifically antagonized by flumazenil. The primary goal of this study is to compare the recovery time of remimazolam besylate and propofol anesthesia during endoscopic procedures in elderly patients. Methods: 60 patients aged 65-95 years who underwent gastrointestinal endoscopy were randomly and equally assigned to two groups: the remimazolam group (Group R) and the propofol group (Group P). The primary measure was the recovery time, defined as the time from discontinuing remimazolam or propofol until reaching an Observer's Assessment of Alertness and Sedation scale (OAA/S) score of 5 (responds readily to name spoken in normal tone). The time required to achieve an OAA/S score of 3 (responds after name spoken loudly or repeatedly along with glazed marked ptosis) was also recorded and compared. Results: The recovery time for Group R (2.6 ± 1.6 min) was significantly shorter than that for Group P (10.8 ± 3.0 min), with a 95% confidence interval (CI): 6.949-9.431 min, p <0.001. Similarly, the time to attain an OAA/S score of 3 was significantly less in Group R (1.6 ± 0.9 min) compared to Group P (9.6 ± 2.6 min), with a 95% CI: 6.930-8.957 min, p <0.001. Conclusion: Our study demonstrated that remimazolam anesthesia combined with flumazenil antagonism causes a shorter recovery time for elderly patients undergoing gastrointestinal endoscopy compared to propofol. Remimazolam followed by flumazenil antagonism provides a promising alternative to propofol for geriatric patients, particularly during gastrointestinal endoscopy.

Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation.

RATIONALE AND OBJECTIVES: We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs). METHODS: Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups. RESULTS: PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively. CONCLUSION: Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.

Virtual reality-based surgical planning simulator for tumorous resection in FreeForm Modeling: an illustrative case of clinical teaching.

The importance of virtual reality (VR) has been emphasized by many medical studies, yet it has been relatively under-applied to surgical operation. This study characterized how VR has been applied in clinical education and evaluated its tutorial utility by designing a surgical model of tumorous resection as a simulator for preoperative planning and medical tutorial. A 36-year-old male patient with a femoral tumor who was admitted to the Affiliated Jiangmen Traditional Chinese Medicine Hospital was randomly selected and scanned by computed tomography (CT). The data in digital imaging and communications in medicine (*.DICOM) format were imported into Mimics to reconstruct a femoral model, and were generated to the format of *.stl executing in the computer-aided design (CAD) software SenSable FreeForm Modeling (SFM). A bony tumor was simulated by adding clay to the femur, the procedure of tumorous resection was virtually performed with a toolkit called Phantom, and its bony defect was filled with virtual cement. A 3D workspace was created to enable the individual multimodality manipulation, and a virtual operation of tumorous excision was successfully carried out with indefinitely repeated running. The precise delineation of surgical margins was shown to be achieved with expert proficiency and inexperienced hands among 43 of 50 participants. This simulative educator presented an imitation of high definition, those trained by VR models achieved a higher success rate of 86% than the rate of 74% achieved by those trained by conventional methods. This tumorous resection was repeatably handled by SFM, including the establishment of surgical strategy, whereby participants felt that respondent force feedback was beneficial to surgical teaching programs, enabling engagement of learning experiences by immersive events which mimic real-world circumstances to reinforce didactic and clinical concepts.

Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors.

RATIONALE AND OBJECTIVES: To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods. MATERIALS AND METHODS: A total of 147 patients with 155 lesions confirmed by pathology were retrospectively included. Clinical-demographic and radiological CT features was collected. The entire cohort was separated into training and validation groups at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were used to select features. Three ML methods, namely logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) were used to build a differential model. Receiver operating characteristic (ROC) curves and precision-recall curves for each ML method were generated. The area under the curve (AUC), accuracy rate, sensitivity, and specificity were calculated. RESULTS: G3 PNETs were more likely to present with invasive behaviors and lower enhancement than G1/G2 PNETs. The LR classifier yielded the highest AUC of 0.964 (95% confidence interval [CI]: 0.930, 0.972), with 95.4% accuracy rate, 95.7% sensitivity, and 92.9% specificity, followed by SVM (AUC: 0.957) and KNN (AUC: 0.893) in the training group. In the validation group, the SVM classier reached the highest AUC of 0.952 (95% CI: 0.860, 0.981), with 91.5% accuracy rate, 97.3% sensitivity, and 70% specificity, followed by LR (AUC: 0.949) and KNN (AUC: 0.923). CONCLUSIONS: The LR and SVM classifiers had the best performance in the training group and validation group, respectively. ML method could be helpful in differentiating between G1/G2 and G3 PNETs.

A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.

OBJECTIVE: To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). MATERIALS AND METHODS: This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. RESULTS: The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842-0.993) and 0.894 (95% CI: 0.766-0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. CONCLUSION: Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis.

OBJECTIVE: To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients. MATERIALS AND METHODS: This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44). RESULTS: Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes. CONCLUSION: We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Electroacupuncture Attenuates Neuropathic Pain in a Rat Model of Cervical Spondylotic Radiculopathy: Involvement of Spinal Cord Synaptic Plasticity.

Purpose: Cervical spondylotic radiculopathy (CSR) is a common neurologic condition that causes chronic neck pain and motor functions, with neuropathic pain (NP) being the primary symptom. Although it has been established that electroacupuncture (EA) can yield an analgesic effect in clinics and synaptic plasticity plays a critical role in the development and maintenance of NP, the underlying mechanisms have not been fully elucidated. In this study, we explored the potential mechanisms underlying EA's effect on synaptic plasticity in CSR rat models. Materials and Methods: The CSR rat model was established by spinal cord compression (SCC). Electroacupuncture stimulation was applied to LI4 (Hegu) and LR3 (Taichong) acupoints for 20 min once a day for 7 days. Pressure pain threshold (PPT) and mechanical pain threshold (MPT) were utilized to detect the pain response of rats. A gait score was used to evaluate the motor function of rats. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), immunohistochemistry (IHC), immunofluorescence (IF), and transmission electron microscopy (TEM) were performed to investigate the effects of EA. Results: Our results showed that EA alleviated SCC-induced spontaneous pain and gait disturbance. ELISA showed that EA could decrease the concentration of pain mediators in the cervical nerve root. WB, IHC, and IF results showed that EA could downregulate the expression of synaptic proteins in spinal cord tissues and promote synaptic plasticity. TEM revealed that the EA could reverse the synaptic ultrastructural changes induced by CSR. Conclusion: Our findings reveal that EA can inhibit SCC-induced NP by modulating the synaptic plasticity in the spinal cord and provide the foothold for the clinical treatment of CSR with EA.

A noninvasive nomogram model based on CT features to predict DNA mismatch repair deficiency in gastric cancer.

Objectives: DNA mismatch repair deficiency (dMMR) status has served as a positive predictive biomarker for immunotherapy and long-term prognosis in gastric cancer (GC). The aim of the present study was to develop a computed tomography (CT)-based nomogram for preoperatively predicting mismatch repair (MMR) status in GC. Methods: Data from a total of 159 GC patients between January 2020 and July 2021 with dMMR GC (n=53) and MMR-proficient (pMMR) GC (n=106) confirmed by postoperative immunohistochemistry (IHC) staining were retrospectively analyzed. All patients underwent abdominal contrast-enhanced CT. Significant clinical and CT imaging features associated with dMMR GC were extracted through univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and internal validation of the cohort data were performed. Results: The nomogram contained four potential predictors of dMMR GC, including gender (odds ratio [OR] 9.83, 95% confidence interval [CI] 3.78-28.20, P < 0.001), age (OR 3.32, 95% CI 1.36-8.50, P = 0.010), tumor size (OR 5.66, 95% CI 2.12-16.27, P < 0.001) and normalized tumor enhancement ratio (NTER) (OR 0.15, 95% CI 0.06-0.38, P < 0.001). Using an optimal cutoff value of 6.6 points, the nomogram provided an area under the curve (AUC) of 0.895 and an accuracy of 82.39% in predicting dMMR GC. The calibration curve demonstrated a strong consistency between the predicted risk and observed dMMR GC. The DCA justified the relatively good performance of the nomogram model. Conclusion: The CT-based nomogram holds promise as a noninvasive, concise and accurate tool to predict MMR status in GC patients, which can assist in clinical decision-making.