Effect of α-tocopherol on antitubercular drugs induced hepatotoxicity

Background: Mycobacterium, the causative organism of tuberculosis, is notorious for its ability to develop resistance with monotherapy. To prevent emergence of resistance, combination of antitubercular drugs is given for months to years that can lead to side effects. Hepatotoxicity is one of the commonest side-effect with antitubercular drugs. This study was aimed to explore the hepatoprotective potential of α-tocopherol against experimentally induced hepatotoxicity in albino rabbits.Methods: This experimental study was carried out on 30 rabbits of either sex.  They were divided into three groups comprising 10 animals each. Hepatotoxicity is induced experimentally in rabbits following a standard protocol. Group I received normal saline (10 ml/kg bw). Rabbits in group II were treated with first line antitubercular drugs isoniazid (5 mg/kg bw), rifampicin (20 mg/kg bw) and pyrazinamide (25 mg/kg bw) concurrently. Group III received α-tocopherol 200 mg/kg bw along with group II drugs. All drugs were administered by oral route for 90 days. On last day of experiment blood samples were taken to investigate the plasma levels of alanine aminotransferase (ALT), alkaline phosphatase (ALP) and serum total bilirubin.Results: Serum levels of ALT were found to be markedly elevated upon oral administration of antitubercular drugs for 90 days. A statistically significant reduction in ALT levels was noticed when α-Tocopherol was given in doses of 200mg/kg bw along with antitubercular drugs for same duration. Similar results were obtained with serum ALP & serum total bilirubin.Conclusions: α-tocopherol (200 mg/kg bw, oral) was found to have hepatoprotective effect against antitubercular drugs induced hepatotoxicity in albino rabbits.

Weed dynamics and productivity of wheat (Triticum aestivum) under various tillage and weed management practices

The reduced yield under conventional tillage is due to more crop-weed competition and more dry matter accumulation by the weeds (Kumar et al. 2018). Due to zero weed competition, weed-free treatments yielded the highest grain yield of all weed management practices. In contrast to this, the lowest grain yield was obtained in weedy treatment due to season-long weed competition. Maximum yield under W8 is due to broadspectrum activity of these herbicides (Sharma et al. 2014, Sunil et al. 2021). The use of zero tillage reduced weed incidence and suppression, leading to higher grain yields. Therefore, zero tillage and metsulfuron 20% wp 4 g a.i./ha + clodinafop propargyl 15% wp 60 g a.i./ha should be practiced for minimizing weed growth and maximizing the yield

Pathology of atherosclerotic coronary artery disease in the young Indian population

Atherosclerotic coronary artery disease (CAD) is of great concern in young adults because of its potential to cause great incapacitation. This arena of cardiology has gained importance in South Asian countries, particularly India due to increased prevalence that is related to traditional risk factors, altered life styles and inherent risk factors. In this study, we sought to evaluate, at autopsy, the pathology of atherosclerotic CAD in young patients with ischemic heart disease (IHD). A 10-year retrospective autopsy-based study was carried out in a large tertiary-care centre and patients aged ≤45 years with IHD were selected. Out of 545 autopsied cases of IHD, 95 patients (17.4%) were young. Among these 95 patients, 84 (88.4%) had IHD related to atherosclerotic CAD; the youngest patient was 18 years old. Predictably there was sole involvement of left anterior descending artery and the presence of fibrous plaques. Irrespective of the plaque morphology, the commonest complication was thrombosis produced by plaque erosion seen in 36.9% of patients. Acute coronary insufficiency was noted in 52 patients (61.9%), while healed infarctions were surprisingly noted in 28 patients (33.3%). Screening for IHD in the young population may help to improve prognosis by detecting subclinical disease, although more studies are necessary to establish reference limits for this young population. Additional research must also focus on treatment concerns that are specific to young patients

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization

OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features.METHODS: After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation.RESULTS: Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%.CONCLUSION: Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.</p

A local variance approach to time frequency localization

A new approach towards time frequency localization has been proposed in this paper. This scheme is based on a local variance factor. The framework of the approach has been demonstrated mathematically. The consistency of approach and the resulting methodology have been empirically verified.by Shashank Tyagi, Vibhav Katre and Nithin V. Georg

Quantifying and Mitigating Privacy Risks of Contrastive Learning

Data is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive to collect. To leverage large-scale unlabeled data, self-supervised learning, represented by contrastive learning, is introduced. The objective of contrastive learning is to map different views derived from a training sample (e.g., through data augmentation) closer in their representation space, while different views derived from different samples more distant. In this way, a contrastive model learns to generate informative representations for data samples, which are then used to perform downstream ML tasks. Recent research has shown that machine learning models are vulnerable to various privacy attacks. However, most of the current efforts concentrate on models trained with supervised learning. Meanwhile, data samples' informative representations learned with contrastive learning may cause severe privacy risks as well. In this paper, we perform the first privacy analysis of contrastive learning through the lens of membership inference and attribute inference. Our experimental results show that contrastive models trained on image datasets are less vulnerable to membership inference attacks but more vulnerable to attribute inference attacks compared to supervised models. The former is due to the fact that contrastive models are less prone to overfitting, while the latter is caused by contrastive models' capability of representing data samples expressively. To remedy this situation, we propose the first privacy-preserving contrastive learning mechanism, Talos, relying on adversarial training. Empirical results show that Talos can successfully mitigate attribute inference risks for contrastive models while maintaining their membership privacy and model utility

Observational study of victims of alleged sexual assault in central Mumbai region

According to recent, Criminal Law (Amendment) Act 2013, age of consent in India has been increased from 16 to 18 years. Implications of the amendments made, are vastly reflected in data collected in our study. Aim of the study was to evaluate data in relation to incidences of alleged sexually abused victims and to contemplate their socio demographic profile with history and examination and also to assess the effect of increase in age of consent for sexual intercourse. This study was conducted on 58 cases of alleged sexual assault brought to Seth G.S. Medical College, K.E.M. Hospital, Mumbai, from September 2013 to February 2015. Out of 58 victims of alleged sexual assault, the most affected age group was 10-19 years i.e. 34 cases (58.62 %), while 18 cases (31.03%) were in the age group of 16-18 years. The highest number of victims 18 (31.03 %) were brought for examination more than three weeks after incident resulting in loss of vital trace evidences. 58.62% of victims knew the assailant. In consensual relationships, breach of trust resulted in complaint of sexual assault, thus crime was registered under Section 375 IPC. Our study critically analyzes entailments of Criminal law amendment act, 2013; in relation to the data collected. Reductions in age of consent, in order to protect rights of young persons aged 16-18 years to engage in consensual sexual activity should be deliberated.</p

Effect of 6 months of meditation on blood sugar, glycosylated hemoglobin, and insulin levels in patients of coronary artery disease

Background and Objectives: Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide. It has been recognized that stress, diabetes, and hypertension are important in etiology and progression of CAD. This study is to evaluate the role of meditation in improving biochemical parameters such as blood glucose, glycosylated hemoglobin, and serum insulin levels in known CAD patients. Material and Methods: Sixty CAD patients are divided into two groups of which one group did meditation and other did not. Blood glucose, glycosylated hemoglobin, and fasting serum insulin levels were measured before and at the end of 6 months of study in both the groups. Results: At the end of the study, significant decrease was seen in patients who practiced meditation as compared to other group. Conclusion: Meditation may modulate the physiological response to stress through neurohumoral activation, which may be a novel therapeutic target for the treatment of CAD