Concurrent ruptured endometrioma with appendiceal endometriosis: a case report

Endometriosis is an oestrogen dependent inflammatory disease characterised by presence of endometrial tissue outside the uterine cavity. It affects 15% of female patients in reproductive age. Endometriosis is a very common cause of chronic pelvic pain and subfertility in females. We present a case of a 26-year-old woman with chronic lower abdominal pain on medical management of endometriosis. She presented to us with acute abdominal pain and underwent diagnostic laparoscopy. During surgery, we observed minimal haemoperitoneum with frozen pelvis. The appendix appeared slightly inflamed and an appendicectomy with adhesiolysis was done. The histopathological examination showed endometriosis of appendix. Her postoperative period was uneventful. The patient has been followed up postoperatively and is currently doing well

The Impact of New Media on Tourism: A Study on West Bengal

This research report consists of informative data on tourism and the approach of new media in the tourism industry. The adaptation to new changes towards technology is a dynamic change. In this research proposal, there is a brief discussion on West Bengal tourism and some of the offbeat places in North Bengal. In the tourism industry, social media plays a significant role in communicating with tourists and guides. This research paper talks about tourism in West Bengal. This research paper initiated to discuss all the repercussions and growths of social media referred to tourism. Therefore, the role of social media platforms has a great impact on the tourism industry

VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds

Taste is one of the crucial organoleptic properties involved in the perception of food by humans. Taste of a chemical compound present in food stimulates us to take in food and avoid poisons. Bitter taste of drugs presents compliance problems and early flagging of potential bitterness of a drug candidate may help with its further development. Similarly, the taste of chemicals present in food is important for evaluation of food quality in the industry. In this work, we have implemented machine learning models to predict three different taste endpoints-sweet, bitter and sour. The VirtualTaste models achieved an overall accuracy of 90% and an AUC of 0.98 in 10-fold cross-validation and in an independent test set. The web server takes a two-dimensional chemical structure as input and reports the chemical's taste profile for three tastes-using molecular fingerprints along with confidence scores, including information on similar compounds with known activity from the training set and an overall radar chart. Additionally, insights into 25 bitter receptors are also provided via target prediction for the predicted bitter compounds. VirtualTaste, to the best of our knowledge, is the first freely available web-based platform for the prediction of three different tastes of compounds. It is accessible via http://virtualtaste.charite.de/VirtualTaste/without any login requirements and is free to use

Assessment of variation in cesarean delivery rates between public and private health facilities in India from 2005 to 2016

Importance: The rates of cesarean deliveries have more than doubled in India, from 8% of deliveries in 2005 to 17% of deliveries in 2016. The World Health Organization recommends that cesarean deliveries should not exceed 10% to 15% of all deliveries in any country. An understanding of the association of private and public facilities with the increase in cesarean delivery rates in India is needed. Objective: To assess the association of public vs private sector health care facilities with cesarean delivery rates in India and to estimate the potential cost savings if private sector facilities followed World Health Organization recommendation for cesarean deliveries. Design, Setting, and Participants: This cross-sectional study used institutional delivery data from the representative National Family Health Survey (NFHS) in India, including data from the NFHS-1 (1992-1993), the NFHS-3 (2005-2006), and the NFHS-4 (2015-2016). The NFHS-3 and NFHS-4 provided data on 22 647 deliveries and 195 366 deliveries, respectively. The NHFS-4 was the first survey to provide data on out-of-pocket expenditures for delivery by facility type, allowing for a comparison of cesarean deliveries and costs between public and private facilities. The primary sample comprised all pregnant women who delivered infants in public and private institutional facilities in India and who were included the NFHS-3 and the NFHS-4; data on pregnant women who were included in the NFHS-1 were used for comparison. The study's findings were analyzed through geographic mapping, data tabulation, funnel plots, multivariate logistic regression analyses, and potential cost-savings scenario analyses. Data were analyzed from June to December 2019. Main Outcomes and Measures: The main outcome was the rate of cesarean deliveries by facility type (public vs private) and by participant socioeconomic, demographic, and health characteristics. Secondary outcomes were the potential number of avoidable cesarean deliveries and the potential cost savings if private sector facilities followed the World Health Organization recommendations for cesarean deliveries. Results: In the NFHS-3, 22 610 total births occurred at institutional facilities. Of those, 2178 births (15.2%) were cesarean deliveries in public facilities, and 3200 births (27.9%) were cesarean deliveries in private facilities. Of 195 366 total institutional births in the NFHS-4, 15 165 births (11.9%) were cesarean deliveries in public facilities, and 20 506 births (40.9%) were cesarean deliveries in private facilities. The cesarean delivery rate in public health facilities increased from 7.2% in the NFHS-1 to 11.9% in the NFHS-4, whereas in private health facilities, the rate increased from 12.3% to 40.9% during the same period. A substantial increase was found in cesarean delivery rates between the NFHS-3 (2005-2006) and the NFHS-4 (2015-2016), with 22 states exceeding the World Health Organization's upper threshold of 15% in the NFHS-4. The odds ratio for cesarean deliveries in private facilities compared with public facilities increased from 1.62 (95% CI, 1.49-1.76) in the NFHS-3 to 4.17 (95% CI, 4.04-4.30) in the NFHS-4. The number of avoidable cesarean deliveries would have been 1.83 million, with a potential cost savings of $320.60 million, if private sector facilities in India had followed the 15% threshold for cesarean delivery rates recommended by the World Health Organization. Conclusions and Relevance: In this study, private sector health facilities were associated with a substantial increase in cesarean deliveries in India. Further research is needed to assess the factors underlying the increase in cesarean deliveries in private sector facilities

Prediction is a balancing act: importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets

Increase in the number of new chemicals synthesized in past decades has resulted in constant growth in the development and application of computational models for prediction of activity as well as safety profiles of the chemicals. Most of the time, such computational models and its application must deal with imbalanced chemical data. It is indeed a challenge to construct a classifier using imbalanced data set. In this study, we analyzed and validated the importance of different sampling methods over non-sampling method, to achieve a well-balanced sensitivity and specificity of a machine learning model trained on imbalanced chemical data. Additionally, this study has achieved an accuracy of 93.00%, an AUC of 0.94, F1 measure of 0.90, sensitivity of 96.00% and specificity of 91.00% using SMOTE sampling and Random Forest classifier for the prediction of Drug Induced Liver Injury (DILI). Our results suggest that, irrespective of data set used, sampling methods can have major influence on reducing the gap between sensitivity and specificity of a model. This study demonstrates the efficacy of different sampling methods for class imbalanced problem using binary chemical data sets

Computational methods for prediction of in vitro effects of new chemical structures

Background With a constant increase in the number of new chemicals synthesized every year, it becomes important to employ the most reliable and fast in silico screening methods to predict their safety and activity profiles. In recent years, in silico prediction methods received great attention in an attempt to reduce animal experiments for the evaluation of various toxicological endpoints, complementing the theme of replace, reduce and refine. Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning. Within the “Toxicology in the 21st Century” screening initiative, a crowd-sourcing platform was established for the development and validation of computational models to predict the interference of chemical compounds with nuclear receptor and stress response pathways based on a training set containing more than 10,000 compounds tested in high-throughput screening assays. Results Here, we present the results of various molecular similarity-based and machine-learning based methods over an independent evaluation set containing 647 compounds as provided by the Tox21 Data Challenge 2014. It was observed that the Random Forest approach based on MACCS molecular fingerprints and a subset of 13 molecular descriptors selected based on statistical and literature analysis performed best in terms of the area under the receiver operating characteristic curve values. Further, we compared the individual and combined performance of different methods. In retrospect, we also discuss the reasons behind the superior performance of an ensemble approach, combining a similarity search method with the Random Forest algorithm, compared to individual methods while explaining the intrinsic limitations of the latter. Conclusions Our results suggest that, although prediction methods were optimized individually for each modelled target, an ensemble of similarity and machine-learning approaches provides promising performance indicating its broad applicability in toxicity prediction

Pharmacological activities of pyrazolone derivatives

Pyrazoline is a five member heterocyclic ring which is a versatile lead compound for designing potent bioactive agents. The review of the literature shows that the pyrazoline derivatives are quite stable and has inspired the chemists to synthesize the new pyrazoline derivatives. The past studies of pyrazoline derivative revealed that they are useful in pharmaceutical and agrochemical research. Pyrazoline derivatives display various pharmacological activities such as antitumor, antitubercular, antimicrobial, antibacterial, anti-inflammatory and antioxidant etc. and the pharmacological activities of different synthesized compound are reviewed in the present article

A short glimpse on promising pharmacological effects of Begenia ciliata

Bergenia ciliata is a potent indigenous folk medicine that has been proved fruitful in the treatment of various adverse conditions of the body. The major chemical constituents of plant include tannic acid, gallic acid, glucose, metarbin, albumen, bergenin, (+)-catechin, gallicin. Bergenia ciliata was subjected to bioactivity analysis. The plant has antitussive, antiulcer, antioxidant, antibacterial, hypoglycemic, toxicological activity. It was observed that root and leaves extract were promising as antifungal agent. The root and leaves extract were effective against Microsporum canis, Pleuroetus oustreatus and Candida albicans.   All the extracts except chloroform extract of root and leaves of Bergenia ciliata were found to possess hypoglycemic activity in Streptozotocin (STZ) treated rats. The methanolic extract exhibited significant anti-tussive activity in a dose-dependent manner. B. ciliata bear potent anti-neoplastic activities that may have prospective clinical use as precursor for preventive medicine. Methanolic and aqueous B. ciliata rhizome extracts were found to possess antioxidant activity, including reducing power, free radical scavenging activity and lipid peroxidation inhibition potential. Bergenia ciliata extracts exhibit a narrow spectrum antibacterial activity. The results obtained thus suggest that extracts of B. ciliata have promising therapeutic potential and could be considered as potential source for drug development by pharmaceutical industries

Super Natural II - a database of natural products

Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ∼50 000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ∼326 000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ∼170 000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins