Enhancing wheat production and quality in alkaline soil: a study on the effectiveness of foliar and soil applied zinc.

Cultivation of high-yield varieties and unbalanced fertilization have induced micronutrient deficiency in soils worldwide. Zinc (Zn) is an essential nutrient for plant growth and its deficiency is most common in alkaline and calcareous soils. Therefore, this study aimed to evaluate the effect of Zn applied either alone or in combination with foliar application on the quality and production of wheat grown in alkaline soils. Zn was applied in the form of zinc sulfate (ZnSo4) to the soil and as a foliar spray during the sowing and tillering stages, respectively. Results showed that Zn fertilization of wheat, irrespective of modes of application, significantly increased grain and biological yield, grain per spike, and 1,000 grains weight over control; however, its effect was more noticeable when applied as 7.5 kg ha-1 of soil Zn combined with foliar Zn at 2.5 kg ha-1. Zn application significantly increased the grain protein content from 9.40% in the control to a maximum of 11.83% at soil Zn of 10 kg ha-1. Similarly, Zn application improved Zn, phosphorus (P), and potassium (K) concentrations in wheat grains. Moreover, correlation analysis showed that the grain Zn concentration was positively correlated with the grain P concentration. The correlation between P concentration in wheat grains and 1,000 grain weight was not significant. A total of 1,000 grains weight was positively correlated with tillers per plant, grain yield, and biological yield. There were positive correlations between protein content, biological yield, grain yield, and tillers per plant. Therefore, soil-applied Zn + foliar application in alkaline soils with limited Zn availability is crucial for improving wheat yield and grain quality.

Dengue beyond fever-fatal dengue myocarditis and complete heart block: A case report and brief overview of cardiac manifestations of dengue fever.

Dengue is an endemic viral fever transmitted by mosquitoes that may be asymptomatic or cause a nonspecific flu-like illness. The disease's most severe manifestations are dengue hemorrhagic fever and dengue shock syndrome. Various atypical manifestations have been observed that constitute the expanded dengue syndrome. Although uncommon, it is now known to cause cardiac complications that can be life-threatening and difficult to diagnose. We illustrate a case of a 16-year-old boy infected with dengue who experienced syncope, dizziness, and lethargy. His electrocardiogram showed third degree atrioventricular block which did not resolve with atropine and fluid resuscitation. After excluding all possible causes of complete heart block, transvenous pacing was done. A detailed workup was carried out that favored a diagnosis of subclinical myocarditis leading to complete heart block. The patient did not regain a normal rhythm and was considered for permanent pacemaker implantation. Myocarditis, pericarditis, rhythm disturbances, first- and second-degree atrioventricular blocks, and rarely third-degree heart blocks have been seen in dengue patients. However, a case of dengue illness associated complete heart blocks that is irreversible and necessitates a permanent pacemaker has never been described in the literature, and this is the first such case being reported. This article intends to increase clinicians' awareness, particularly those in dengue-endemic regions, about better recognition and comprehension of cardiac problems associated with dengue fever.

Diagnosis and treatment of flurbiprofen-induced Stevens-Johnson syndrome: A rare case report.

Our case highlights the occurrence of severe cutaneous adverse reactions with flurbiprofen use and alerts physicians to its odds with safer drugs.

Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases.

There has been substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remote health monitoring using sensors and smartphones. A variety of AI-based prediction models are available for gastrointestinal, inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, hepatitis-associated fibrosis using electronic medical records, and pancreatic carcinoma utilizing endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patients' treatment employing multiple factors. Enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI-based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitations of AI techniques in such diseases' prognosis, risk assessment, and decision support are discussed.

A deep neural network-based approach for prediction of mutagenicity of compounds.

We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.