Seroprevalence and risk factor analysis of brucellosis among dairy farmers in Aligarh region, North India: creating awareness of a neglected disease.

Introduction. Brucellosis, a globally distributed zoonotic disease, is caused by the Gram-negative bacteria known as Brucella. Humans acquire infection through direct contact with the blood, urine and placenta of animals, inhalation of dust or aerosols at infected animal farms, and raw milk and meat intake. This study aimed to assess the prevalence of brucellosis in dairy farmers in and around the Aligarh region of North India, to document various clinical signs and symptoms in Brucella-positive individuals, and to create awareness in dairy farmers concerning brucellosis and ways to prevent it. Methods. This was an observational study that included 125 dairy farmers in and around the Aligarh region. Serum samples were taken from this high-risk group after obtaining informed consent. Further, a pre-designed proforma was used to collect information about their knowledge, attitude and practices (KAP) concerning brucellosis and assess the risk factors for the disease. The Rose Bengal test (RBT), serum agglutination test (SAT) and enzyme-linked immunosorbent assay (ELISA) were performed to detect the seroprevalence of brucellosis. Result.Brucella infection was diagnosed in 64 (51.20 %) cases by indirect ELISA (IgM+IgG), 41 (32.8 %) by RBT and 4 (3.2 %) by SAT. Significant clustering of patients was seen in the 20-55 years age group. The most common symptoms in ELISA IgM-positive patients were joint pain (16.07 %), fatigue (14.28 %), anorexia (12.50 %), weight loss (8.92 %), malaise (5.35 %), undulant fever (3.57 %), night sweats (3.57 %) and headache (1.78 %). The findings of this study indicate that ELISA (IgM+IgG) exhibits great sensitivity as compared to SAT and RBT. KAP was very poor among dairy farmers. Conclusion. In India, Brucella is a frequent but severely underreported illness. ELISA is the most sensitive serological test for diagnosing brucellosis. No potential vaccine has yet been introduced for humans against brucellosis. Thus, it is necessary to impart awareness and sensitize high-risk groups concerning brucellosis.

Insight into the seroepidemiology and dynamics of circulating serotypes of dengue virus over a 4 year period in western Uttar Pradesh, India.

An important public health problem in India is dengue infection, with every year seeing an increase in cases of dengue fever. Dengue affects all individuals irrespective of their gender and age, although the infection rate is higher among males and younger people. Despite low severity in general, dengue virus can cause severe health conditions in some individuals. Genetic characterization of circulating endemic dengue virus (DENV) serotypes plays a significant role in providing epidemiological knowledge and subsequent vaccine development. In the present study, over a 4 year period, we assessed DENV transmission dynamics in major regions of western Uttar Pradesh in North India. ELISA tests were used to diagnose dengue, and PCRs were used to determine the circulating serotype. We found that dengue infection peaks after the rainy season and affects all sexes and ages. A total of 1277 individuals were found positive for dengue; among them, 61.7 % were male and 38.3 % were female. DEN-1 was found in 23.12 %, DEN-2 in 45 %, DEN-3 in 29.06 % and DEN-4 in 1.5 % of the dengue-infected individuals. All four DENV serotypes were circulating in the study area, and DENV serotype-2 (DEN-2) was the most prevalent serotype.

Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy.

BACKGROUND: Esophagogastroduodenoscopy (EGD) is generally a safe procedure, but adverse events often occur. This highlights the necessity of the quality control of EGD. Complete visualization and photo documentation of upper gastrointestinal (UGI) tracts are important measures in quality control of EGD. To evaluate these measures in large scale, we developed an AI-driven quality control system for EGD through convolutional neural networks (CNNs) using archived endoscopic images. METHODS: We retrospectively collected and labeled images from 250 EGD procedures, a total of 2599 images from eight locations of the UGI tract, using the European Society of Gastrointestinal Endoscopy (ESGE) photo documentation methods. The label confirmed by five experts was considered the gold standard. We developed a CNN model for multi-class classification of EGD images to one of the eight locations and binary classification of each EGD procedure based on its completeness. RESULTS: Our CNN model successfully classified the EGD images into one of the eight regions of UGI tracts with 97.58% accuracy, 97.42% sensitivity, 99.66% specificity, 97.50% positive predictive value (PPV), and 99.66% negative predictive value (NPV). Our model classified the completeness of EGD with 89.20% accuracy, 89.20% sensitivity, 100.00% specificity, 100.00% PPV, and 64.94% NPV. We analyzed the credibility of our model using a probability heatmap. CONCLUSIONS: We constructed a CNN model that could be used in the quality control of photo documentation in EGD. Our model needs further validation with a large dataset, and we expect our model to help both endoscopists and patients by improving the quality of EGD procedures.

Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object localization, semantic segmentation, and object instance segmentation. Accordingly, the applicability of deep learning to the analysis of medical images has increased. This paper presents a novel application of state-of-the-art CNN models, such as DenseNet, to the automatic detection of the tympanic membrane (TM) and middle ear (ME) infection. We collected 2,484 oto-endoscopic images (OEIs) and classified them into one of three categories: normal, chronic otitis media (COM) with TM perforation, and otitis media with effusion (OME). Our results indicate that CNN models have significant potential for the automatic recognition of TM and ME infections, demonstrating a competitive accuracy of 95% in classifying TM and middle ear effusion (MEE) from OEIs. In addition to accuracy measurement, our approach achieves nearly perfect measures of 0.99 in terms of the average area under the receiver operating characteristics curve (AUROC). All these results indicate robust performance when recognizing TM and ME effusions in OEIs. Visualization through a class activation mapping (CAM) heatmap demonstrates that our proposed model performs prediction based on the correct region of OEIs. All these outcomes ensure the reliability of our method; hence, the study can aid otolaryngologists and primary care physicians in real-world scenarios.

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods.

CARLo-7-A plausible biomarker for bladder cancer.

Cancer is defined as undifferentiated and unchecked growth of cells damaging the surrounding tissue. Cancers manifest altered gene expression. Gene expression is regulated by a diverse array of non-protein-coding RNA. Aberrant expression of long non-coding RNAs (lncRNAs) has been recently found to have functional consequences in cancers. In the current study, we report CARLo-7 as the only bladder cancer-specific lncRNA from the CARLos cluster. The expression of this lncRNA correlates with bladder cancer grade. We propose that CARLo-7 has an oncogenic potential and might be regulator of cell proliferation. Furthermore, by comparison the expression of proto-oncogene MYC, which is the only well-annotated gene close to the cancer - associated linkage disequilibrium blocks of this region, does not show a pronounced change in expression between the low- and high-grade tumours. Our results indicate that CARlo-7 can act as a prognostic marker for bladder cancer.

Revisiting ligand-induced conformational changes in proteins: essence, advancements, implications and future challenges.

Ligand-induced conformational changes are of immense importance for the biological activity of a protein. An in-depth understanding of salutary and deleterious effects of ligand-induced conformational alterations in single- and multi-chain proteins would lend a hand in human welfare. Unlike single-chain proteins, the function of multichain proteins depends upon the inherent properties of the subunit interfaces. The interfaces of temporary oligomeric proteins and the active sites of enzymes are of similar characteristics but the interfaces are more conservative than the active sites. Therefore, these interfaces may possibly be represented as drug targets by inhibition or induction of the oligomerization process. Thus without detailed structural understanding of ligand-induced conformational changes in a protein, structure-based rational drug designing is a great challenging task. So the purpose of this review is to clarify or enlighten the reader at the degree of internal motions related to protein backbone and side-chain flexibility which occur on binding of small molecule to a protein target. This can prove helpful to improve the conformational prediction for a protein-ligand complex. Besides a detailed description of protein-ligand interaction, this review also focuses on structure-activity relationships of protein which will surely help in the rational drug designing.