Initial experience of visualized biliary cannulation during ERCP.

BACKGROUND : Selective biliary cannulation is the most challenging step in endoscopic retrograde cholangiopancreatography (ERCP) because only indirect radiographic images can be obtained. Therefore, we developed a novel endoscopic retrograde direct cholangioscopy (ERDC) technology to facilitate visible biliary cannulation. METHODS : In this case series, we used ERDC to treat 21 patients with common bile duct stones who were enrolled consecutively between July 2022 and December 2022. The procedure details and complications were recorded, and all patients were followed up for 3 months after the procedure. The learning curve effect was analyzed by comparing the early and later cases. RESULTS : Biliary cannulation was successful in all patients, and the stones were removed completely. The median (interquartile range [IQR]) time for cholangioscopy-guided biliary cannulation was 240.0 (10.0-430.0) seconds, and the median (IQR) number of cannulation procedures was 2 (1-5). Despite there being one episode of post-ERCP pancreatitis, one of cholangitis, and three patients developing asymptomatic hyperamylasemia, all of the patients recovered after symptomatic treatment, being discharged and with no serious adverse events occurring during the 3-month follow-up period. Compared with the early cases, the number of intubations and the use of guidewire guidance decreased in later cases. CONCLUSION : Our research confirms that ERDC is a feasible technology for biliary cannulation under direct vision.

Novel endoscopic tangential sawing technique in treatment of giant gastric bezoars: a retrospective single-center study (with video).

BACKGROUND AND AIMS: The current methods for treatment of giant gastric bezoars mainly include chemical dissolution, endoscopic fragmentation, and surgical removal, which often have limited curative effects or generate multiple adverse events. Therefore, there is an urgent need to find new methods to overcome such a dilemma. The aim of this study was to evaluate the safety, efficacy, and feasibility of a novel guidewire-based tangential sawing fragmentation (GTSF) technique to treat giant gastric bezoars. METHODS: This retrospective single-center study was performed in the Department of Gastroenterology and Hepatology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital. Ten patients with giant bezoars were consecutively enrolled from December 8, 2019 to April 8, 2021. We treated the 10 patients with the GTSF technique, recorded the GTSF procedure, and followed the patients with gastroscopy 2 weeks after the procedure. RESULTS: All patients were successfully treated by the GTSF technique, and the giant bezoar was broken into small pieces (<2 cm in diameter). The average operation time was 21.73 minutes, and the average fragmentation time was 8.06 minutes. Ten patients treated with the GTSF technique attained satisfactory treatment results, with no acute adverse events or alimentary canal injury during the procedure, and no bezoar residue remained as shown by gastroscopy 2 weeks after the procedure. CONCLUSIONS: The GTSF technique is a safe, effective, and feasible method for removing giant bezoars and can be considered as an alternative treatment of this disease.

COVID-19 vaccination may prevent postoperative delirium in elderly patients undergoing elective non-cardiac surgery: The PNDRFAP and PNDABLE studies.

Background: Postoperative delirium (POD) often occurs in elderly patients after surgery. We conducted two clinical studies to determine whether COVID-19 vaccination has a protective effect on POD and to explore the role of CSF biomarkers in this process. Methods: We conducted two clinical studies, Perioperative Neurocognitive Disorder Risk Factor and Prognosis (PNDRFAP) and Perioperative Neurocognitive Disorder and Biomarker Lifestyle (PNDABLE), in which patients more than or equal to 65 years old who have had elective non-cardiac surgery were enrolled. The preoperative cognitive status of patients were evaluated by Mini-Mental State Examination (MMSE) one day preoperatively. Confusion Assessment Method (CAM) was used to diagnose POD. We used the mediation model to analyze the relationship between CSF biomarkers, COVID-19 vaccination and POD, as well as Dynamic Nomogram to calculate the incidence of Non-Postoperative Delirium (NPOD). The main outcome of these studies was the incidence of POD during seven days postoperatively or before discharge, which was assessed by CAM. Results: In the final, 705 participants were enrolled in the PNDRFAP study, and 638 patients in the PNDABLE. In both studies, we found that the occurrence of POD was lower in patients who had injected COVID-19 vaccination before surgery compared with those without vaccination (PNDRFAP: 10.20 % [21/205] vs 25.80 % [129/500], P < 0.001; PNDABLE: 2.40 % [4/164] vs 34.60 % [164/474], P < 0.001). Mediation analysis showed that the protective effect of preoperative COVID-19 vaccine on POD was significantly mediated by CSF Aß42 (proportion = 17.56 %), T-tau (proportion = 19.64 %), Aß42/T-tau (proportion = 29.67 %), and Aß42/P-tau (proportion = 12.26 %). Conclusions: COVID-19 vaccine is a protective factor for POD in old patients, which is associated with CSF biomarkers.

BrainFounder: Towards Brain Foundation Models for Neuroimage Analysis.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to interpret and analyze neurological data. This study introduces a novel approach towards the creation of medical foundation models by integrating a large-scale multi-modal magnetic resonance imaging (MRI) dataset derived from 41,400 participants in its own. Our method involves a novel two-stage pretraining approach using vision transformers. The first stage is dedicated to encoding anatomical structures in generally healthy brains, identifying key features such as shapes and sizes of different brain regions. The second stage concentrates on spatial information, encompassing aspects like location and the relative positioning of brain structures. We rigorously evaluate our model, BrainFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the complexity of the model and the volume of unlabeled training data derived from generally healthy brains, which enhances the accuracy and predictive capabilities of the model in complex neuroimaging tasks with MRI. The implications of this research provide transformative insights and practical applications in healthcare and make substantial steps towards the creation of foundation models for Medical AI. Our pretrained models and training code can be found at https://github.com/lab-smile/GatorBrain.

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This work introduces a novel approach towards creating 3-dimensional (3D) medical foundation models for multimodal neuroimage segmentation through self-supervised training. Our approach involves a novel two-stage pretraining approach using vision transformers. The first stage encodes anatomical structures in generally healthy brains from the large-scale unlabeled neuroimage dataset of multimodal brain magnetic resonance imaging (MRI) images from 41,400 participants. This stage of pertaining focuses on identifying key features such as shapes and sizes of different brain structures. The second pretraining stage identifies disease-specific attributes, such as geometric shapes of tumors and lesions and spatial placements within the brain. This dual-phase methodology significantly reduces the extensive data requirements usually necessary for AI model training in neuroimage segmentation with the flexibility to adapt to various imaging modalities. We rigorously evaluate our model, BrainSegFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainSegFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the model complexity and the volume of unlabeled training data derived from generally healthy brains. Both of these factors enhance the accuracy and predictive capabilities of the model in neuroimage segmentation tasks. Our pretrained models and code are at https://github.com/lab-smile/BrainSegFounder.

Potential value of serum prealbumin and serum albumin in the identification of postoperative delirium in patients undergoing knee/hip replacement: an observational study and internal validation study.

Background: Postoperative delirium (POD) is a common postoperative neurological complication that can lead to a variety of postoperative complications. At present, the pathogenesis of POD is unclear. This study aims to explore the relationship between serum prealbumin and serum albumin and POD and whether serum prealbumin and serum albumin influence POD through POD core pathology. Objective: We enrolled 500 Chinese Han patients between September 2020 to January 2023. We analyzed the risk and protective factors of POD using the multivariate logistic regression. We also assessed the predictive power of serum prealbumin, serum albumin, and both in combination with CSF POD biomarkers. We used Stata MP16.0. to examine whether the association between serum prealbumin and serum albumin and POD was mediated by CSF POD biomarkers, and conducted an internal validation study to verify the accuracy of the combination of serum prealbumin + serum albumin + CSF POD biomarkers for predicting POD. The model was visualized using ROC curve and decision curve analysis (DCA). DynNom and Shiny packages were used to create an online calculator. Ten patients who had POD occurring from February 2023 to October 2023 were selected for internal verification. Results: Finally, a total of 364 patients were included in our study. Levels of serum prealbumin, serum albumin in the POD group were lower than those in the NPOD group. The lever of serum prealbumin, serum albumin were protective factors for POD. The relationship between serum prealbumin, serum albumin and POD was partially mediated by T-tau (12.28%) and P-tau (20.61%). The model combining serum prealbumin and serum albumin and POD biomarkers exhibited a relatively better discriminatory ability to predict POD. DCA also showed that the combination of serum prealbumin and serum albumin and POD biomarkers brought high predictive benefits to patients. The dynamic online calculator can accurately predict the occurrence of POD in the internal validation study. Conclusion: Preoperative low serum prealbumin and serum albumin levels were the preoperative risk factors for POD, which is partly mediated by T-tau and P-tau. The model combining serum prealbumin and serum albumin and CSF POD biomarkers can accurately predict the occurrence of POD. Clinical trial registration: http://www.clinicaltrials.gov, identifier ChiCTR2000033439.

Comparison of the clinical value of multi-band mucosectomy versus endoscopic mucosal resection for the treatment of patients with early-stage esophageal cancer.

The present study aimed to compare the clinical value of multi-band mucosectomy (MBM) versus endoscopic mucosal resection (EMR) for the treatment of patients with early-stage esophageal cancer. Between January 2011 and December 2012, 68 patients with early-stage esophageal cancer who underwent MBM and EMR were enrolled into the present study. The curative resection rate, duration of surgery, complications and follow-up records were retrospectively analyzed. Of the 68 patients included, 33 were treated with MBM and 35 with EMR. There was no significant difference in the rate of complete resection between the MBM and EMR groups (P>0.05). The mean duration of surgery in the MBM group was statistically lower than that in the EMR group (P<0.05). There was no statistically significant difference in the intraoperative and post-operative complications between the MBM and EMR groups (P>0.05). Esophageal cancer reoccurred in 2 patients treated with MBM and 1 patient treated with EMR during the follow-up period (range, 3-24 months). Overall, MBM can be considered a better surgical option for the management of patients with early-stage esophageal cancer, as it offers higher histological curative resection rates and improved safety. However, further studies and a larger follow-up period are required to confirm the long-term curative effect.