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Background and Objectives: The study aimed to assess the control of asthma and the severity of chronic obstructive pulmonary disease (COPD) and evaluate the adequacy of treatment in patients presenting to the outpatient department (OPD) across India. The secondary aim was to assess the risk factors associated with poorly controlled asthma and severe COPD. Materials and Methods: This is the analysis of Phase IV of the multicenter questionnaire-based point prevalence SWORD survey, conducted in May 2018, and designed to capture details on disease control and treatment as per the global initiative for asthma and the global initiative for chronic obstructive lung disease guidelines. Results: Of the 5,311 respiratory disease patients presenting to the OPD, there were 1,419 and 412 patients with asthma and COPD, respectively, across 290 sites in India. There were 1,022 (72%) patients having well-controlled asthma, 293 (20.6%) patients with partly controlled asthma, and 104 (7.4%) patients with poorly controlled asthma. Of the 412 patients with COPD, there were 307 (74.5%) in A, 54 (13.1%) in B, and 51 (12.4%) in the E category. In spite of poor control or severe disease, 34.8% of asthmatic and 25.7% of patients in the B and E categories of COPD were not using any medicine. Risk factors for partly and poorly controlled asthma included rain wetting (adjusted odds ratio [AOR]: 1.59, 95% confidence interval [CI]: 1.02--2.47) and gastroesophageal reflux disease (AOR: 1.50, 95%CI: 1.08--2.10). Conclusion: This study identifies a gap in the treatment of both poorly controlled asthma and severe COPD. A substantial number of patients had poorly controlled asthma and severe COPD, and many were either not taking treatment or taking it inappropriately. [ABSTRACT FROM AUTHOR]

In this paper, an analytical approach has been developed for electromagnetic field enhancement in fractal nanostructure under the quasi-static limit. A metallic fractal embedded in an unbounded, isotropic, and lossless chiral medium has been treated under this approach. In addition, the field enhancement is also noted by imposing surface plasmon resonance condition. To achieve the desired objective, Maxwell divergence equations are considered and written in an appropriate form by mean of fractional calculus. [ABSTRACT FROM AUTHOR]

Breast cancer is a major health threat, with early detection crucial for improving cure and survival rates. Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, and improve medical care for breast cancer patients. The study explores the challenges in identifying benign and malignant breast cancer lesions using microscopic image datasets. It introduces a low‐dimensional multiple‐channel feature‐based method for breast cancer microscopic image recognition, overcoming limitations in feature utilization and computational complexity. The method uses RGB channels for image processing and extracts features using level co‐occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. This approach aims to improve diagnostic efficiency and accuracy in breast cancer treatment. The core of our method is the SqE‐DDConvNet algorithm, which utilizes a 3 × 1 convolution kernel, SqE‐DenseNet module, bilinear interpolation, and global average pooling to enhance recognition accuracy and training efficiency. Additionally, we incorporate transfer learning with pre‐trained models, including mVVGNet16, EfficientNetV2B3, ResNet101V2, and CN2XNet, preserving spatial information and achieving higher accuracy under varying magnification conditions. The method achieves higher accuracy compared to baseline models, including texture and deep semantic features. This deep learning‐based methodology contributes to more accurate image classification and unique image recognition in breast cancer microscopic images. Research Highlights: Introduces a low‐dimensional multiple‐channel feature‐based method for breast cancer microscopic image recognition.Uses RGB channels for image processing and extracts features using level co‐occurrence matrix, wavelet, Gabor, and histogram of oriented gradient.Employs the SqE‐DDConvNet algorithm for enhanced recognition accuracy and training efficiency.Transfer learning with pre‐trained models preserves spatial information and achieves higher accuracy under varying magnification conditions.Evaluates predictive efficacy of transfer learning paradigms within microscopic analysis.Utilizes CNN‐based pre‐trained algorithms to enhance network performance. [ABSTRACT FROM AUTHOR]

High temperature stress affects tomato production both in tropical and sub-tropical environments worldwide. To explore genetic variation for heat tolerance in tomato, 329 transcontinental tomato genotypes were evaluated at the Ministry of Municipality and Environment (MME) greenhouses near Doha, Qatar, where the average daytime temperature was 38 °C with a big fluctuation during the tomato growth season. A preliminary phenotypic analysis identified a panel of 71 hybrid and pure-line tomato genotypes for more detailed studies. The selected subset was examined in the greenhouse using a randomized complete block design under heat stress. The materials were phenotyped for fruit size, fruit weight, fruit hardness, fruit locules, fruit set, total soluble solids (TSS), and fruit yield. Significant phenotypic differences among genotypes were observed for all the traits assessed. To explore the genetic basis of the variation among the examined genotypes, the subset was genotyped using 104 SNP markers identified in previous heat-tolerance genome-wide association studies (GWAS). Nineteen QTL-associated SNP markers could reliably select heat-tolerant genotypes in terms of better fruit yield, fruit set, and TSS. These markers are located on chromosome 1, 5, 6, 8, 9, and 12. Interestingly, two clusters of markers on chromosome 6 were linked to significant effects on yield, fruit set, and TSS under high temperature. Eighteen out of nineteen SNP markers were mapped within a gene body. Based on the phenotypic and the genotypic analysis, an elite set of five genotypes was selected for approval for heat stress environments in Qatar. The aim of the present work is to provide significant results that are exploitable not only in the Qatar region but also worldwide. Specifically, the 19 molecular markers identified in this study can serve as useful tools for breeders in selecting heat-tolerant material. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large‐scale drivers in the agro‐ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation‐dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade−1) and cold spells (−1.28 days·decade−1). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation‐dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper‐level jet by a high‐pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea‐surface temperature, low sea‐level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate‐induced disasters occurring in various AEZs of Pakistan. [ABSTRACT FROM AUTHOR]

Dr. Tariq Mahmood, a Professor Emeritus of Radiation Oncology at Jinnah Postgraduate Medical Centre in Karachi, has been awarded a Fellowship by the Royal College of Radiologists in the UK. The presentation ceremony took place in London and recognized Dr. Mahmood's work in the field of radiology and radiation oncology in Pakistan. The Royal College of Radiologists is the largest organization of its kind and awards the Honorary Fellowship to a select number of individuals worldwide. Dr. Mahmood has also received other prestigious honors for his contributions to medicine, including the Tamgha-e-Imtiaz for public service and an honorary Fellowship from the Royal College of Edinburgh. The Cyber knife- Radiation Oncology services at JPMC Karachi, where Dr. Mahmood works, have also been recognized by the Royal College of Radiologists. This recognition is significant for the province of Sindh and Pakistan as it acknowledges the free cancer treatment facility provided through a public-private partnership. The JPMC is the only center in the world to offer free treatment of cancer with Cyber knife and Tom therapy, benefiting patients from various countries and cities. The Radiation Oncology department at JPMC is equipped with advanced technology and offers highly precise treatments for various types of cancer. Additionally, the department provides free PET-CT scans and plans to conduct over 4000 scans in the next fiscal year. Prof. Yan Chen, a Professor of Digital Screening at the University of Nottingham, was also awarded the Fellowship at the [Extracted from the article]

Summary: This research aimed to explore the influence of nutritional adjustments and storage stability on a functional, reduced‐calorie guava jam incorporating Aloe vera. Over two months, comprehensive analysis assessed physicochemical properties, sensory traits, microbial stability, and shelf‐life. The addition of Aloe vera gel resulted in significant improvements in pH (3.00 to 3.65), total soluble solids (40.10 to 42.20°Brix), antioxidant activity (36.85% to 81.09%), moisture content (29.48% to 38.82%), water activity (0.78 to 0.84), ash content (0.29% to 0.45%), fat content (0.14% to 0.19%), fibre content (1.05% to 1.86%), and the colour values. Moreover, b* scores for colour indication improved from 15.09 to 18.86. Texture attributes of cohesiveness and firmness improved significantly. Sensory evaluation favoured the T2 variant (20% Aloe vera gel), suggesting it as the optimal formulation. Furthermore, artificial neural networks (ANNs), a technique of machine learning, were utilised to predict guava jam behaviour, with 99% accuracy. The study discovered substantial changes in pH, total soluble solids, antioxidant activity, moisture content, and textural qualities, indicating that Aloe vera supplementation could improve guava jam quality and shelf‐life. The results of ANN predictions about antioxidants and cohesiveness provide information about the product's performance during storage. [ABSTRACT FROM AUTHOR]

Background: The world was struggling in lockdown for months since December of 2019 due to novel coronavirus disease (COVID-19) outbreak, a pandemic declared by the World Health Organization. Research evidence is growing on the role of several symptoms, comorbidities, inflammation and hypercoagulability markers in relation to disease progression and deaths in COVID-19 patients. The incidence of diabetes, one of the leading causes of morbidity has been shown to be high and is associated with disease progression in COVID-19. More than 425 million individuals have diabetes worldwide and projections show this number rising to 629 million by 2045. This Study was done with fallowing objectives-To compare markers of coagulation dysfunction and markers of inflammation in Diabetic and Non-Diabetic Post COVID-19 patients. Material and method: This is an observational cross -sectional study assessing Post COVID-19 . In this study among 214 patients participated, in which 129 patients were diabetic and 85 patients were non- diabetic. Result: The following variables were significantly associated (p<0.05) with the diabetic Post COVID-19 patients: Age (Years), TLC (/cu.mm), S.Ferritin (ng/mL), S.Procalcitonin (ng/mL), D-Dimer (µg/mL), S.LDH (U/L), SpO2 (%), FBS (mg/dL), PPBS (mg/dL), A1c (%), PT (Sec), aPTT (Sec), INR Conclusion: The presence of diabetes could further influence the magnitude of inflammatory and coagulation dysfunction in Post COVID-19. Strikingly, a recent study showed a significant increase in these markers in diabetic group as compared to non-diabetic group of Post-COVID-19 patients without other comorbidities, indicating the independent impact of diabetes. [ABSTRACT FROM AUTHOR]

The study of length-weight relationship and condition factor was carried out on of freshwater catfish, Clupisoma garua. Altogether 280 samples were collected from three different rivers of North India (Gomti, Ganga and Rapti). The value of b ranges from 2.571-2.720 for the length-weight relationship in the current study. The growth pattern showed significant deviation from isometric value (b=3), therefore all the three populations are supposed to follow negative allometric growth pattern. The coefficient of determination (r2) was found to be in the range of 0.8059-0.8587. The value of condition factor in the present study was found to be more than '1' in all the three populations of the fish which indicated the good health conditions. The present study is considered to be important for assessment of the biology of the fish that could help in conservation and management of C. garua in the North Indian Rivers. [ABSTRACT FROM AUTHOR]

A potamodromous species found in both fresh and brackish water, Clupisoma garua (Hamilton, 1822) is also known as "garuabachcha". It belongs to the order Siluriformes and the family Schilbeidae. The species mostly inhabits Asian countries such as Bangladesh, India, Pakistan and Nepal and is predominantly found in expansive freshwater reservoirs. The species are classified as Least Concern (LC) by the IUCN, although they are classified as Vulnerable (Vu) according to the CAMP and CAFF studies. The species is an omnivorous carnivore that consumes insects, molluscs, crustaceans and small fish in its natural habitat. This species possesses both utilitarian and aesthetic qualities and individuals of moderate size are also regarded as sport fish in India. The species is reported to have a broad distribution; however, it is at risk in certain areas, such as southern West Bengal, due to excessive fishing and human activity. This review aims to provide comprehensive information on the species 'garuabachcha', including its characteristics, significance and recommended conservation strategies. [ABSTRACT FROM AUTHOR]