Insight into epidemiology of male infertility in central India

Background: Approximately 10% to 15% of couples in developing countries are infertile. Male infertility is responsible for 20-43% of infertility cases and contributes to another 12-20% of cases. Azoospermia, oligozoospermia, asthenozoospermia, teratozoospermia, and oligoasthenoteratozoospermia are abnormal sperm parameters causing male infertility. Male infertility is often poorly responsive to primary treatment and often requires supportive secondary measures. The understanding of causes and modifiable risk factors for male infertility would enable their prevention and primary treatment. Aims and objectives of current study was to analyze the epidemiology and clinical factors of male infertility in Central India and identify its risk factors.Methods: 100 male patients attending outpatient for treatment of infertility were evaluated using a questionnaire. Semen samples were collected and spermatozoa were assessed according to WHO 2021 data for semen analysis. The results were tabulated and analyzed.Results: Amongst patients were semen abnormalities, the majority (34%) of patients had oligoasthenoteratozoospermia. All semen abnormalities were most common in the age group 35-45 years and in patients with 5-10 years duration of infertility. All semen abnormalities except azoospermia were most common in people with a monthly income of >2,000-5,000. The majority of the patients had a past history of urogenital tract infection, except oligoasthenospermic males in whom the majority had varicocele. All semen abnormalities were more common among businessmen and also more prevalent among smokers.Conclusions: Couples should be educated about infertility causes and the contribution of male infertility to it. Multifactorial analysis along with clinicopathological analysis should contribute to accurate diagnosis of the cause of male infertility and proposal of adequate measures

Ultrasonographic placental localisation and extent of invasion in scarred versus non-scarred uterus and its correlation with obstetrical outcomes: a prospective study

Background: The site of implantation and resultant location of the placenta within the uterus are likely important determinants of placental blood flow and therefore pregnancy success. Abnormal placental implantation or ‘placental invasion’ is a rare, but potentially life‐threatening, complication in the third stage of labour. Currently massive obstetric haemorrhage remains one of the leading causes of maternal mortality. Methods: 140 antenatal women at ≥34 weeks of gestation were selected, out of which 70 had the history of previous caesarean section and 70 had the history of previous vaginal delivery. Detailed USG was done with special emphasis on edge to os distance (EOD), extent and depth of invasion of placenta. All cases were followed till delivery and their intraoperative assessment done and correlated with sonographic findings and obstetrical outcomes. Results: With increasing number of previous caesarean section, depth and extent of invasion of placenta increases and edge to os distance (EOD) decreases. 8.5% cases with previous 1 caesarean section, 22.22% cases with previous 2 caesarean section and 50% cases with previous >2 caesarean section had some adherence of placenta. Conclusions: Uterine scar increases chances of low implantation of placenta as well as its adherence as compared with unscarred uterus. This risk increases with number of caesarean sections. These high-risk cases of scarred uterus especially those with multiple scars should be subjected to detailed sonographic scan by expert radiologist

Modern Technologies for Pest Control: A Review

The major concern for farmers is important loss due to pests and diseases, which is regardless of any production system adopted. Plant pathogens, insects, and weed pests devastate over 40% of all possible sustenance creation every year. This loss happens despite utilizing approximately 3 million tons of pesticide per year in addition to the use of a variety of nonchemical controls such as biological controls and crop rotations. If some of this food could be saved from pest attack, it could be utilized to bolster an excess of 3 billion people who are malnourished in the world today. Expansive range of conventional insecticides such as carbamates, organophosphates, pyrethroids, and organochlorines were developed. They have been used to control insect pests in the course of recent decades, resulting in the reduction of the loss of agricultural yield. However, problems of resistance reaching crisis proportions, the extreme unfavorable impacts of pesticides on the environment, and public complaints led to stricter protocols and regulations directed to reduce their utilization. The pest control industry is continuously examining novel technologies and products that will improve the way to manage and prevent pests. The general objective is to likewise diminish the effects of various available pesticides on the environment and on nontarget creatures, besides the economic influence on bottom lines

In Vitro Study on total Phenols, Flavonoids Content and DPPH Activity of Withania Species

The escalating interest in appraisal of antioxidant power of herbal plant as medicine, the current study was carried out to explore the antioxidant potential of aqueous extracts of Withania somnifera root and Withania coagulan fruit in-vitro. Antioxidant activity; total phenol,total flavonoids and DPPH free radical scavenging assay of Withania somnifera root and Withania coagulans fruit aqueous extracts were determined by using reference standards gallic acid, quercetin and ascorbic acid, respectively. The highest total phenols content (mgGAE/g) and total flavonoids content (mgQE/g) was found to be 33.1±0.82 and 1.86±0.01 respectively in aqueous somnifera root extracts as compared to coagulans fruit extract . The DPPH radical scavenging activity of the both extracts was increased with the increasing concentration and was observed high in aqueous extract insomniferaroot (IC50= 54) than coagulans fruit (69μg/ml) aqueous extract.Thus,Withania somnifera root has potent antioxidant activity and may serve as a good pharmacotherapeutic agent which could be explored to provide affordable medicines to masses

SmartSat Constellation - A Deep Reinforcement Learning Approach for Decentralized Coordination

With rapid advancements in satellite technology, the amount of low earth orbit satellites has grown significantly which are primarily deployed for weather monitoring, earth observation or military purposes. Due to this reason, there has been an increased interest in enhancing the level of autonomy and cognition, onboard satellites to achieve optimal data collection. Optimal data is said to be collected when the satellites in a small sat constellation work together to collect information. This means that even if one of the satellites has missed out on some important information, the others can still collect them. A satellite constellation can be considered as a multi-agent reinforcement learning system. Having these agents coordinate with one another, can reduce the amount of time required to perform a task. The state-of-the-art satellite constellations follow a centralized coordination mechanism in which one primary satellite controls the rest of the satellites. This process is computationally more expensive and requires substantial communication between the satellites.It has a single point of failure and communication might be affected if the primary satellite fails. On the other hand, decentralized coordination allows agents to control their behavior themselves without the command of a supervised master. In this case, there is less inter-satellite communication which reduces the requirement for specialized onboard computational hardware. The proposal constitutes leveraging the Multi-Agent Deep Deterministic Policy Gradient [2] (MADDPG) algorithm to train the agents (satellites) to achieve optimal data collection. There are multiple use cases for the proposed solution such as illegal maritime activity tracking, natural disaster detection and assessing building damage after a natural disaster. The proposed solution focuses on tracking of ships in an extensively simulated environment for which a custom ship environment was created by leveraging OpenAI Gym [12]. By providing on-board autonomy, we aim to reduce frequent Earth Station (ES) communication significantly and enhance data collection capability

Role of Click Chemistry in Organic Synthesis

Click chemistry involves highly efficient organic reactions of two or more highly functionalized chemical entities under eco-benign conditions for the synthesis of different heterocycles. Several organic reactions such as nucleophilic ring-opening reactions, cyclo-additions, nucleophilic addition reactions, thiol-ene reactions, Diels Alder reactions, etc. are included in click reactions. These reactions have very important features i.e. high functional group tolerance, formation of a single product, high atom economy, high yielding, no need for column purification, etc. It also possesses several applications in drug discovery, supramolecular chemistry, material science, nanotechnology, etc. Being highly significant and valuable, we have elaborated on several aspects of click reactions in organic synthesis in this chapter. Recent advancements in the field of organic synthesis using click chemistry approach have been deliberated by citing last five years articles

Metal Catalyzed Oxidation Reactions of Alkenes Using Eco-Friendly Oxidants

Oxidation of alkenes is an important reaction in academia, industry and science as it is used to develop epoxides, carbonyls, allylic compounds, 1,2-diols, etc. Metal catalyzed oxidation of alkenes has aroused as a significant tool in modern organic synthesis. Several techniques are available; however some of them suffer from few shortcomings viz. high cost, toxic nature, harsh reaction condition, solid waste generation, etc. In view of these drawbacks, green oxidants i.e. O2, H2O2, TBHP, etc. have shown noteworthy prospects due to their nature, low cost, high atom economy and high sustainability in metal catalyzed reactions. This chapter highlights the metal catalyzed green oxidation of alkenes and shall provide new strategies for the functionalization and transformation of alkenes

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct and a definitive answer. However, in real-world applications, users often ask questions that don't have a definitive answer. Incorrectly answering such questions certainly hampers a system's reliability and trustworthiness. Can SOTA models accurately identify such questions and provide a reasonable response? To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don't have definitive answers. Furthermore, for each QnotA instance, we also provide a corresponding QA instance i.e. an alternate question that ''can be'' answered. With this data, we formulate three evaluation tasks that test a system's ability to 'identify', 'distinguish', and 'justify' QnotA questions. Through comprehensive experiments, we show that even SOTA models including GPT-3 and Flan T5 do not fare well on these tasks and lack considerably behind the human performance baseline. We conduct a thorough analysis which further leads to several interesting findings. Overall, we believe our work and findings will encourage and facilitate further research in this important area and help develop more robust models.Comment: TrustNLP Workshop at ACL 202

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction

Aim: Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods: This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist’s findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results: Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist’s findings. Substantial agreement observed between the classification by model and the radiologist’s given classification [K-0.695 (K is Cohen’s kappa score for interrater reliability)]. Conclusions: The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology