The role of gut microbiota in clinical complications, disease severity, and treatment response in severe alcoholic hepatitis.

BACKGROUND: Dysbiotic gut bacteria engage in the development and progression of severe alcoholic hepatitis (SAH). We aimed to characterize bacterial communities associated with clinical events (CE), identify significant bacteria linked to CE, and define bacterial relationships associated with specific CE and outcomes at baseline and after treatment in SAH. METHODS: We performed 16-s rRNA sequencing on stool samples (n=38) collected at admission and the last follow-up within 90 days in SAH patients (n=26; 12 corticosteroids; 14 granulocyte colony-stimulating factor, [G-CSF]). Validated pipelines were used to plot bacterial communities, profile functional metabolism, and identify significant taxa and functional metabolites. Conet/NetworkX® was utilized to identify significant non-random patterns of bacterial co-presence and mutual exclusion for clinical events. RESULTS: All the patients were males with median discriminant function (DF) 64, Child-Turcotte-Pugh (CTP) 12, and model for end-stage liver disease (MELD) score 25.5. At admission, 27%, 42%, and 58% had acute kidney injury (AKI), hepatic encephalopathy (HE), and infections respectively; 38.5% died at end of follow-up. Specific bacterial families were associated with HE, sepsis, disease severity, and death. Lachnobacterium and Catenibacterium were associated with HE, and Pediococcus with death after steroid treatment. Change from Enterococcus (promotes AH) to Barnesiella (inhibits E. faecium) was significant after G-CSF. Phenylpropanoid-biosynthesis (innate-immunity) and glycerophospholipid-metabolism (cellular-integrity) pathways in those without infections and the death, respectively, were upregulated. Mutual interactions between Enterococcus cecorum, Acinetobacter schindleri, and Mitsuokella correlated with admission AKI. CONCLUSIONS: Specific gut microbiota, their interactions, and metabolites are associated with complications of SAH and treatment outcomes. Microbiota-based precision medicine as adjuvant treatment may be a new therapeutic area.

Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN.

Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications.

In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.

Compliance with the published RCOG guidelines in women undergoing hysterectomy for menorrhagia in a district general hospital.

The Royal College of Obstetricians and Gynaecologists (RCOG) published guidelines describing the management of menorrhagia in primary and secondary care. In this study, we reviewed retrospectively, the compliance with these guidelines in women who underwent a hysterectomy for menorrhagia over a 3-year period in a District General Hospital. Case notes were reviewed for 22 women in whom the uterus was reported normal on histology. There was a high level of compliance in clinical assessment before hysterectomy. Nearly 70% of women received some form of medical treatment, however only 50% were offered endometrial ablation. Compliance was high in imparting information about the risk factors of hysterectomy and in administering thromboprophylaxis.