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Abstract Gallstone disease is a prevalent biliary disease worldwide, and bacteria play vital roles in the disease development and progression, as well as the prognosis after endoscopic surgery. However, there have been limited studies to explore the key taxa involved. In this study, bile samples from healthy controls (HCs, liver donors without hepatobiliary disease) and three diseased groups, namely patients with gallbladder stones (GBS), patients with common bile duct stones (CBDS), and patients with stricture in the common bile duct (SCBD), were collected and analyzed. Bacterial community characterization based on 16S rRNA amplicon sequencing showed that bacterial diversities did not change significantly alongside gallstone disease development and progression. The predominant phyla in each group were Proteobacteria, Firmicutes, Bacteroidota, and Fusobacteriota, representing over 80% in abundance of the biliary bacteria community. Specifically, the abundance of Proteobacteria decreased greatly while that of Firmicutes and Bacteroidota increased greatly in the diseased groups when compared to that in HCs. Moreover, linear discriminant analysis identified several genera highly represented in the diseased groups. Among them, Klebsiella, Prevotella, Pseudomonas and Veillonella are persistent in both the HCs group and the diseased groups, indicating an enrichment of local bile bacteria in the diseased bile; while Lachnoanerobaculum, Atopobium, Oribacterium, and Stomatobaculum, those aligned to oral cavity taxa, are persistent in the diseased groups but are transient in the HCs group, and their abundances sequentially increased with the disease development and progression (HCs→GBS→CBDS→SCBD), implying a translocation and colonization of the oral cavity bacteria in the diseased bile. Moreover, co-occurrence network analysis revealed that bacterial infection (e.g., Photobacterium and Plesiomonas) from the intestine was developed during endoscopic surgery with reduced bile bacteria diversity. The results of this study revealed that the bile bacterial community is relatively stable and dominated by a few persistent taxa. Moreover, we hypothesized that translocation and colonization of specific bacteria from the oral cavity happens alongside gallstone disease development and progression, and bacterial infection from the intestinal tract results in poor outcomes after endoscopic surgery.

Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image.Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem.Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy.Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors’ experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.

Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem. Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy. Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors' experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures. [ABSTRACT FROM AUTHOR]

Summary: Background: We aimed to develop and validate a prognostic model for predicting malignant brain oedema in patients with acute ischaemic stroke in a real-world setting of practice. Methods: A prospective multicentre study enrolled adult patients with acute ischaemic stroke with brain CT

BackgroundMajor depressive disorder (MDD) is a highly prevalent mental disease. Using magnetic resonance imaging (MRI), although numerous studies have revealed the alterations in structure and function of grey matter (GM), few studies focused on the synchronization of white matter (WM) structure and function in MDD. The aim of this study was to investigate whether functional and structural abnormalities of WM play an essential role in the neurobiological mechanisms of MDD.MethodsGradient-echo imaging sequences at 3.0T were used to gather resting state functional MRI (rsfMRI) data, which were performed on 33 drug-naive first-episode MDD patients and 34 healthy controls (HCs). After data preprocessed, amplitude of low frequency fluctuation (ALFF) of WM was calculated. ALFF values in different frequency bands were analyzed, including typical (0.01–0.15 Hz) band, slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz) bands. In addition, the fractional anisotropy (FA) values in WM in 23 patients and 26 HCs were examined using tract-based spatial statistics (TBSS) and tractography based on diffusion tensor imaging (DTI). Pearson correlation analysis was applied to analyze the relationships between ALFF values and Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA).ResultsCompared with the HCs, MDD patients showed decreased ALFF values in posterior thalamic radiation (PTR) and superior longitudinal fasciculus (SLF) in slow-5 frequency band, no significant differences of ALFF values were found in typical and slow-4 frequency bands. In addition, there were no significant differences in FA values with TBSS analysis as well as the number of fibers in PTR and SLF with tractography analysis between two groups. Further correlation analysis showed that the ALFF value in SLF was negatively correlated with HAMA-2 score (r = −0.548, pFDR = 0.037) in patients.ConclusionOur results indicated that WM dysfunction may be associated with the pathophysiological mechanism of depression. Our study also suggested that the functional damage of the WM may precedes the structural damage in first-episode MDD patients. Furthermore, for mental disorders, slow-5 frequency band may be a more sensitive functional indicator for early detection of abnormal spontaneous brain activity in WM.

ObjectiveTo determine the different clinical features of patients with vertigo attacks alone and of those with weakness accompanying vertigo attacks before the vertebrobasilar ischemic stroke.MethodsIn this 4-year retrospective study, we manually screened the medical records of 209 patients, hospitalized with vertigo attack as the main complaint who were finally diagnosed with acute vertebrobasilar ischemic stroke. Patients were divided into two groups according to their symptoms: patients who only experienced vertigo attacks prior to the vertebrobasilar stroke (VO group) and patients who had both vertigo and weakness attacks (VW group) prior to the stroke. Clinical parameters, such as infarction site and volume, relative risk factors, ABCD2 score, and medical intervention, were compared between the two groups.ResultsThe prevalence of hypertension was higher in the Vertigo attacks only (VO) group (42.2 vs. 29.0%, p < 0.05). The total cerebral infarction volume in the VO group was larger than the Vertigo and weakness attacks (VW) group (4.44 vs. 2.12 cm3, p < 0.05). Additionally, the cerebellum was more likely to be affected in the VO group. In contrast, patients in the VW group had higher carotid stenosis (14.2 vs. 27.2%, p < 0.05) and ABCD2 score (2.1 ± 1.2 vs. 3.6 ± 1.5, p = 0.02). The percentage of patients with medullary infarctions also increased in the VW group. Vertigo attack events occurred more frequently in the VW group (median 2.4 vs. 4.3, p < 0.04). We also found that the patients in the VW group were more likely to seek medical intervention after vertigo.ConclusionsClinical parameters, such as infarction location, relative risk factors, and ABCD2 score, differed between patients with vertigo symptoms with or without weakness attacks. These findings highlight the different clinical features of patients with vertigo attack only and those with weakness attacks accompanying vertigo prior to vertebrobasilar ischemic stroke.

Abstract Background To analyze the clinical features of brain tumor-related epilepsy (BTRE) and explore the factors influencing the identification of epilepsy-associated tumor (EAT), in order to advance the clinical understanding of BTRE and EAT. Methods Intracranial tumor origin and location as well as the type of epilepsy were retrospectively reviewed in 153 BTRE patients. The patients were further divided into the EAT and non-EAT groups, and comparisons were made for age, sex, tumor origin and location, and epilepsy type between the two groups. Results The 153 BTRE patients were divided into 78 cases with primary intracranial tumor and 75 cases with tumor originating from extracranial metastasis, according to the origin of tumor. According to the location of tumor, 116 cases had tumor lesions located in the brain parenchyma, and 37 cases had tumor lesions located in the meninges. Further, in the group with a brain parenchyma location, 77 cases had single lobular involvement, and 39 cases had multiple-lobular involvement; 84 cases had tumor lesions located in one hemisphere and 32 cases in both hemispheres. According to the type of epilepsy, 92 cases had generalized seizures, and 61 cases had focal seizures. The type of epilepsy did not significantly correlate with the origin of intracranial tumor, the location of tumor lesions (in brain parenchyma or meninges) (P > 0.05), or the hemispherical location (in one or two hemispheres) of lesions (P > 0.05), but was significantly related with the lobular localization of lesions (P

Background: Recurrent attacks of vertigo account for 2.6 million emergency department visits per year in the USA, of which more than 4% are attributable to ischemic infarction. However, few studies have investigated the frequency of attacks of vertigo before an ischemic stroke.Methods: We conducted this retrospective analysis and manually screened the medical records of 231 patients who experienced recurrent attacks of vertigo prior to an ischemic stroke. Patients were divided into four different groups based on the frequency of vertigo attacks as well as the region of ischemic infarction. Those with ≤2 attacks of vertigo preceding the ischemic stroke were defined as the low-frequency group. Those with ≥3 attacks were defined as the high-frequency group. Clinical parameters, including vascular risk factors, average National Institutes of Health Stroke Scale (NIHSS) score, and infarction volume, were compared between the groups.Results: On analysis, we found that patients with posterior infarction in the high-frequency group exhibited a higher prevalence of vertebral artery stenosis. However, the incidence of diabetes mellitus (DM) was higher in the low-frequency group. In addition, patients with posterior infarction in the low-frequency group were more active in seeking medical intervention after an attack of vertigo. Notably, the brain stem, especially the lateral medullary region, had a higher probability of being involved in posterior infarction in the high-frequency group. However, the cerebellum was more commonly involved in posterior infarction in the low-frequency group.Conclusions: Our findings indicate that the clinical parameters, including arterial stenosis, DM, and magnetic resonance imaging (MRI) findings, differed between the low- and high-frequency groups. We also found that patients in the low-frequency group were more willing to seek medical intervention after the attacks of vertigo. These findings could be valuable for clinicians to focus on specific examination of the patients according to the frequency of vertigo attacks.