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Introduction: The problem of infertility is experienced as a crisis or major life stressor with potentially serious negative effects on the couple's relationship and preparation toward the parenthood. According to Sharlip et al., it has been estimated that about 15% of couple would have trouble in conception, and at present, 48.5 million couples experience infertility globally. To decrease the stress among parents to be and to promote healthy parenting for bringing out healthy parent–child relationships, an intervention of Parent Education Program was planned and executed. A study was conducted with the aim to assess the effectiveness of parental preparedness package (PPP) on physical health among couple conceived with artificial reproductive techniques (ART). Materials and Methods: The study was conducted to evaluate the effectiveness of PPP in fifty couples conceived with ART at Dayanand Medical College and Hospital, Ludhiana. Quantitative approach was used to collect the data of couples by doing pre- and postinterventional assessment of physical health of couple using the Structured Physical Health Assessment Scale. Analysis was done with the help of descriptive and inferential statistics. Results: The study revealed that mean score of physical health of couple (both husband and wife) increased significantly after the intervention which infers the effectiveness of PPP (P = 0.001). Conclusion: Our study concludes that PPP should be incorporated in the antenatal care protocol in all couples who conceive using ART. [ABSTRACT FROM AUTHOR]

Uremic encephalopathy presenting as involuntary movements of the orofacial region is important to recognize because of reversibility seen with resolution of azotaemia though residual neurological dysfunction might persist. Neuroimaging is important part of diagnosis with typical lesions involving basal ganglia seen mostly in diabetic patients. Our case highlights a patient with non-diabetic uremic encephalopathy with facial dyskinesia which is rare with a lesion in unilateral lentiform nucleus and small white matter hyperintensities. Resolution following dialysis pointed to uraemia as aetiology. [ABSTRACT FROM AUTHOR]

Influence maximization is a fundamental problem in the study of complex relationship networks, such as viral marketing in business application areas. It is directed towards extracting a minimal (or k-sized) subset of most influential nodes with largest cascading effect across the network as per seeding budget. The problem is categorized as NP hard and hence greedy/heuristic techniques are extensively studied in the literature for generating reasonably acceptable solutions. This article proposes a novel nature based heuristic optimization algorithm IM-GSO to dynamically evolve near to optimal K-sized influential seed nodes for varied structural real world networks. IM-GSO smartly incorporates hidden structural patterns like communities, node degrees, betweenness and similarities for efficient candidate population generation. This smartly initialized population is then evolved using a discrete adaption of Group Search Optimization (GSO) algorithm. Correctness of IM-GSO is verified by optimizing two prominent spread estimation functions SIMPATH and MAGA, on varied sized (small/medium/large) networks. Detailed experimental evaluations by execution of 10,000 Monte Carlo simulations under Information Cascade (IC) model indicates a significantly high influence spread for IM-GSO seeds in contrast to standard heuristics techniques. [ABSTRACT FROM AUTHOR]

Bimetallic nanoparticles, which are composed of two metal elements in a particle, exhibit much higher catalytic activity than respective monometallic ones due to new bi-functional or synergistic effects, so-called a ligand and/or an ensemble effect. In this work, a novel homogeneous as well as heterogeneous Au Ag bimetallic nanocatalyst have been synthesized and supported on layer double hydroxide (LDH) by a simple wet chemical process. The support of LDH has reduced the size as well as capped the bimetallic nanoparticles and hence prevents the agglomeration. The surface morphology and chemical composition of these bimetallic nanoparticles were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. It was found that alloy (40–60 nm) and core-shell (15–30 nm) Au Ag bimetallic nanoparticles were formed for heterogeneous and homogeneous composites, respectively. Moreover, the different compositions of as-synthesized Au Ag bimetallic nanoparticles were utilized for the catalytic reduction of 4-nitrophenol. The highest activity was observed for the Au Ag nanoparticles prepared with Au Ag ratio at 1:3 and the activity became 10 to 45 times higher than that of original monometallic Au or Ag nanoparticles. The rate kinetics was studied for both homogeneous and heterogeneous system on the reduction of 4-nitrophenol and observed that the rate of reduction was greater in case of homogeneous catalysts as compared to heterogeneous catalysts. Also, the heterogeneous catalysts were effortlessly recovered and reused (up to 5 cycles) after completion of the reduction reaction. [ABSTRACT FROM AUTHOR]

Aim We investigated the relationship between periodontal disease, a clinical manifestation of periodontal infection, and pre-diabetes. Methods The National Health and Nutrition Examination Survey, 2009-2010 enrolled 1165 diabetes-free adults (51% female) aged 30-80 years (mean ± SD=50 ± 14) who received a full-mouth periodontal examination and an oral glucose tolerance test. Participants were classified as having none/mild, moderate or severe periodontitis and also according to mean probing depth ≥2.19 mm or attachment loss ≥1.78 mm, (respective 75th percentiles). Pre-diabetes was defined according to ADA criteria as either: (i) impaired fasting glucose ( IFG) or impaired glucose tolerance ( IGT). In multivariable logistic regression models, the odds of IFG and IGT were regressed on levels of periodontitis category. Results The odds ratios and 95% confidence intervals for having IGT among participants with moderate or severe periodontitis, relative to participants with none/mild periodontitis were 1.07 [0.50, 2.25] and 1.93 [1.18, 3.17], p = 0.02. The ORs for having IFG were 1.14 [0.74, 1.77] and 1.12 [0.58, 2.18], p = 0.84. PD ≥75th percentile was related to a 105% increase in the odds of IGT: OR [95% CI] = 2.05 [1.24, 3.39], p = 0.005. Conclusions Periodontal infection was positively associated with prevalent impaired glucose tolerance in a cross-sectional study among a nationally representative sample. [ABSTRACT FROM AUTHOR]

The paper demonstrates an efficient use of intelligent system for solving the classification problem in the sector of health insurance. A model based on adaptive neuro-fuzzy inference system (ANFIS) is proposed to deal with the fuzziness in the real-life environments. This approach enables the interpretation of majority of health factors of an insurance seeker through a set of fuzzy rules to determine the degree of risk to an individual. A fuzzy neural network has been trained with fuzzy inputs like age, occupation, family size, smoking habits, drinking habits, diabetes history, heart disease and other relevant inputs of individual for risk calculation. The model gets importance in health insurance sector because risk determination is fuzzy in nature, and fuzzy calculations are done more accurately by machines rather than human beings especially for the problems which are repetitive in nature and have large number of vague parameters. The proposed model can help the insurance seeker to identify the degree of risk he is having if he is not taking health insurance. This serves a dual purpose of attracting the insurance seeker to acquire the insurance and facilitate generating business to insurance company. Indicative results are presented and discussed in detail in terms of accuracy and solution interpretability. [ABSTRACT FROM AUTHOR]

A perfect mix of the air and fuel in internal combustion engines is desirable for proper combustion of fuel with air. The vehicles running on road emit harmful gases due to improper combustion. This problem is severe in heavy vehicles like locomotive engines. To overcome this problem, generally an operator opens or closes the valve of fuel injection pump of locomotive engines to control amount of air going inside the combustion chamber, which requires constant monitoring. A model is proposed in this paper to alleviate combustion process. The method involves recording the time-varying flow of fuel components in combustion chamber. A Fuzzy Neural Network is trained for around 40 fuels to ascertain the required amount of air to form a standard mix to produce non-harmful gases and about 12 fuels are used for testing the network's performance. The network then adaptively determines the additional/subtractive amount of air required for proper combustion. Mean square error calculation ensures the effectiveness of the network's performance. [ABSTRACT FROM AUTHOR]

Keyword query processing over graph structured data is beneficial across various real world applications. The basic unit, of search and retrieval, in keyword search over graph, is a structure (interconnection of nodes) that connects all the query keywords. This new answering paradigm, in contrast to single web page results given by search engines, brings forth new challenges for ranking. In this paper, we propose a simple but effective Fuzzy set theory based Ranking measure, called FRank. Fuzzy sets acknowledge the contribution of each individual query keyword, discretely, to enumerate node relevance. A novel aggregation operator is defined, to combine the content relevance based fuzzy sets and, compute query dependent edge weights. The final rank, of an answer, is computed by non-monotonic addition of edge weights, as per their relevance to keyword query. FRank evaluates each answer based on the distribution of query keywords and structural connectivity between those keywords. An extensive empirical analysis shows superior performance by our proposed ranking measure as compared to the ranking measures adopted by current approaches in the literature. [ABSTRACT FROM AUTHOR]

• History of benzodiazepine motivates researchers to explore their new potentials. • Novel and highly efficient methodologies developed for the synthesis of benzodiazepines have been described in the article. • Newer finding related to biological activities of benzodiazepines have been discussed in the manuscript. • SAR analysis, Docking studies with idetified hits and mechanistic studies are also included. • The article provides detailed insight of current and future prospective in the area of benzodiazepine research. Benzodiazepines (BZDs) represent a diverse class of bicyclic heterocyclic molecules. In the last few years, benzodiazepines have emerged as potential therapeutic agents. As a result, several mild, efficient and high yielding protocols have been developed that offer access to various functionalized benzodiazepines (BZDs). They are known to possess a wide array of biological activities such as anxiolytic, anticancer, anticonvulsant, antipsychotics, muscle relaxant, anti-tuberculosis, and antimicrobial activities. The fascinating spectrum of biological activities exhibited by BZDs in various fields has prompted the medicinal chemist to design and discover novel benzodiazepine-based analogs as potential therapeutic candidates with the desired biological profile. In this review, an attempt has been made by to summarize (1) Recent advances in the synthetic chemistry of benzodiazepines which enable their synthesis with desired substitution pattern; (2) Medicinal chemistry of BZDs as therapeutic candidates with promising biological profile including insight of mechanistic studies; (3) The correlation of biological data with the structure i.e. structure-activity relationship studies were also included to provide an insight into the rational design of more active agents. [ABSTRACT FROM AUTHOR]

• A variety of natural and synthetic quinolones exhibit diverse array of pharmacological activities. • This review article focused on recent advances in the synthetic and medicinal prospective of quinolone derivatives. • The pipeline candidates in various stages of clinical development also included. • Structure-activity relationship (SAR) analysis, docking studies with identified hits and mechanistic studies are discussed. In the modern scenario, the quinolone scaffold has emerged as a very potent motif considering its clinical significance. Quinolones possess wide range of pharmacological activities such as anticancer, antibacterial, antifungal, antiprotozoal, antiviral, anti-inflammatory, carbonic anhydrase inhibitory and diuretic activity etc. The versatile synthetic approaches have been successfully applied and several of the resulted synthesized compounds exhibit fascinating biological activities in numerous fields. This has prompted to discover quinolone-based analogues among the researchers due to its great diversity in biological activities. In the past few years, various new, efficient and convenient synthetic approaches (including green chemistry and microwave-assisted synthesis) have been designed and developed to synthesize diverse quinolone-based scaffolds which represent a growing area of interest in academic and industry as well as to explore their biological activities. In this review, an attempt has been made by the authors to summarize (1) One of the most comprehensive listings of quinolone-based drugs or agents in the market or under various stages of clinical development; (2) Recent advances in the synthetic strategies for quinolone derivatives as well as their biological implications including insight of mechanistic studies. (3) Further, the biological data is correlated with structure-activity relationship studies to provide an insight into the rational design of more active agents. [ABSTRACT FROM AUTHOR]

Sickle cell disease (SCD) is a known risk factor for the development of severe dengue, however, literature documenting dengue in SCD is scarce. Dengue fever further triggers the sickling process in a patient with SCD by augmenting endothelial dysfunction, the main identifiable cause behind organ dysfunction. Hepatic involvement in SCD due to enhanced sickling can be in the form of acute viral hepatitis, cholecystitis, acute sickle hepatic crisis, and more severe sickle cell intrahepatic cholestasis (SCIC). Initially starting as an acute sickle hepatic crisis, SCIC progresses to striking jaundice, enhanced bleeding tendency coupled with mostly renal failure. We report a rare case of a female, native of Chhattisgarh with SCD and dengue shock syndrome who had fatal hepatic complications resulting from accelerated severe endothelial dysfunction due to concurrent illnesses. [ABSTRACT FROM AUTHOR]

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control. [ABSTRACT FROM AUTHOR]

Abstract: Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology. [Copyright &y& Elsevier]

Abstract: Conformational modeling has been successfully applied to the design of cyclic bioisosteres used to replace a conformationally rigid amide bond in a series of thiophene carboxylate inhibitors of HCV NS5B polymerase. Select compounds were equipotent with the original amide series. Single-point mutant binding studies, in combination with inhibition structure–activity relationships, suggest this new series interacts at the Thumb-II domain of NS5B. Inhibitor binding at the Thumb-II site was ultimately confirmed by solving a crystal structure of 8b complexed with NS5B. [Copyright &y& Elsevier]

Abstract: Biacore T100 technology was used in conjunction with a van’t Hoff analysis to characterize the thermodynamic binding parameters of 85 small-molecule inhibitors of adenosine triphosphate (ATP) binding to p38 mitogen-activated protein (MAP) kinase. The compounds were selected from a large panel of azaindole and pyrazole derivatives for which IC50 data exist. We showed a strong relationship between the K D and IC50 of a compound, but only a modest relationship between k off and IC50 was detected and an apparent relationship between a compound’s k on and its IC50 could not be discerned. Similarly, a correlation between a compound’s IC50 and its thermodynamic parameters ΔH° and ΔS° could not be established. The lack of a predominant kinetic or thermodynamic signature associated with the inhibitory potential of these compounds demonstrates that there exists, even within a single well-defined system, a library of kinetic routes or, alternatively, a library of initial and final enthalpic and entropic states from which to effect inhibition. As a complement to these studies, selected double mutant thermodynamic cycles were performed to probe the energetic coupling, if any, between common sites of fluorination in both the azaindole and pyrazole classes and two different substituents. Although both cycles indicated negligible coupling free energies, both revealed significant coupling enthalpies, an observation made in other similarly dissected systems. The possible significance and caveats associated with these findings along with the advantages of using Biacore technology to derive thermodynamic parameters in drug discovery efforts are discussed. [Copyright &y& Elsevier]

The study evaluates the efficacy of parenteral administration of different antimicrobial agents for treatment of mastitis in buffaloes. A total of 42 buffaloes, diagnosed with mastitis were randomly divided into three groups i.e. group-1, 2 and 3. In group 1 (n=14), Amoxycillin + Cloxacillin, in group 2 (n=14), Cefoperazone + Sulbactam and in group 3 (n=14), Cefquinome was administered for 4-7 days, respectively. Other supportive treatments given to all groups included injection Meloxicam, Chlorpheniramine maleate and Vitamin B1, B6 and B12 for 4-7 days. The clinical efficacy was assessed by gradual clinical improvements in term of swelling, pain, consistency of affected quarter/s of udder and teats, visual observation of milk, its characteristics and pH of milk. The results indicated that recovery rate from mastitis in buffaloes after four and seven days of therapies were 42.85% (6/14), 57.14% (8/14), 71.42% (10/14) and 71.42% (10/14), 85.70% (12/14) and 92.85% (13/ 14), respectively, in Amoxicillin + Cloxacillin, Cefoperazone + Sulbactam and Cefquinome treated groups of animals. [ABSTRACT FROM AUTHOR]

Objective: To evaluate first-trimester screening protocols for detecting trisomy 21 in an Indian population.Methods: The present prospective study collected data from women with singleton pregnancies and a crown-to-rump length of 45-84 mm who presented at the fetal medicine unit of a tertiary care center in North India between June 1, 2006, and December 31, 2015, for combined first-trimester screening. Maternal age, nuchal translucency, nasal bone, and maternal serum levels of free beta human chorionic gonadotropin and pregnancy-associated plasma protein A were assessed for calculating the risk of trisomy 21. Tricuspid regurgitation and qualitative analysis of ductus venosus data were available from June 2010, and were included where available. Trisomy-21 detection rates were calculated for various screening protocols and were compared.Results: There were 4523 women screened and 24 records of trisomy 21. Combined screening with maternal age, nuchal translucency, nasal bone, tricuspid regurgitation, and ductus venosus demonstrated optimal detection and false-positive rates of 93.8% and 1.9%, respectively. Screening using only maternal age yielded a detection rate of 37.5%; using fixed nuchal translucency cut-off values of 2.5 and 3 mm resulted in detection rates of 66.7% and 37.5%, respectively.Conclusion: Combined first-trimester screening performed well in an Indian population; combining maternal age, nuchal translucency, nasal bone, ductus venosus, and tricuspid regurgitation yielded the most accurate screening. [ABSTRACT FROM AUTHOR]

Aim. To find out the changing trends in indications for use of laparoscopy for diagnostic or operative procedures in gynaecology. Methods. This was a clinical audit of 417 women who underwent laparoscopic procedures over a period of 8 years from January 2005 to December 2012 in the Department of Obstetrics and Gynaecology at a tertiary care centre in Delhi. Results. A total of 417 diagnostic and operative laparoscopic procedures were performed during the period from January 2005 to December 2012. Out of 417 women, 13 women were excluded from the study due to inadequate data. 208 (51.4%) women had only diagnostic laparoscopy whereas 196 (48.6%) patients had operative laparoscopy after the initial diagnostic procedure. Change in trend of diagnostic versus operative procedures was observed from 2005 to 2012. There was increase in operative procedures from 10 (37.03%) women in 2005 as compared to 51 (73.91%) in 2012. The main indication for laparoscopy was infertility throughout the study period (61.38%), followed by chronic pelvic pain (CPP) (11.38%) and abnormal uterine bleeding (AUB) (9.4%). Conclusion. Over the years, there has been a rise in the rate of operative laparoscopy. Though the indications for laparoscopy have remained almost similar during the years, laparoscopy for diagnosis and treatment of CPP and AUB has now increased. [ABSTRACT FROM AUTHOR]

Abstract: Learnings from previous Roche p38-selective inhibitors were applied to a new fragment hit, which was optimized to a potent, exquisitely selective preclinical lead with a good pharmacokinetic profile. [Copyright &y& Elsevier]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]