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"Murtaza, Ghulam"
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Upregulation of interleukin-1β and interleukin-18 in traumatic brain injury patients and their potential as biomarkers
Traumatic brain injury (TBI) is diagnosed using radiological imaging and biomarker analysis, each with certain limitations. Accurate and accessible detection of TBI is essential for its treatment. Since interleukin (IL)-1β and IL-18 are key neuroinflammatory cytokines, we aim to investigate the regulation of IL-1β and IL-18 in TBI patients and assess their potential as biomarkers of TBI.
Forty TBI and 40 healthy subjects were recruited for this study. The Glasgow coma scale (GCS) was used to assess the clinical severity of TBI. Spearman's rank-order correlation was employed to find the association between GCS scores and IL levels of TBI patients. An enzyme-linked immunosorbent assay (ELISA) with human IL-1β and IL-18 ELISA kits was conducted to measure the serum IL-1β and IL-18 levels. The area under the curve (AUC), sensitivity, and specificity were measured to assess the potential of IL-1β and IL-18 as biomarkers.
The patients had mild-to-moderate TBI (mTBI) with a mean GCS score of 11.05 ± 1.67. A negative correlation was found between GCS scores and IL levels, while IL-1 β and IL-18 were positively correlated with each other. IL-1β levels in the TBI and healthy subjects were 1882.19 ± 796.72 pg/mL and 1473.50 ± 333.045 pg/mL, respectively, with a significant difference (p = 0.001). Moreover, the IL-18 levels in TBI subjects (25.92 ± 14.81 ng/L) were also significantly higher (p < 0.001) compared to those of the control group subjects (19.14 ± 2.99 ng/L). The IL-1β exhibited an AUC of 0.72 with 72.5% sensitivity and 70% specificity, at a cut-off point of 1527.25 (pg/mL), while IL-18 had an AUC of 0.78 with 77.5% sensitivity and 72.7% specificity, at a cut-off point of 19.645 (ng/L).
IL-1β and IL-18 levels were increased remarkably in mTBI patients and exhibited fair potential as biomarkers for the diagnosis and prognosis of TBI.
Journal Article
Electronic and optical properties of vacancy ordered double perovskites A2BX6 (A = Rb, Cs; B = Sn, Pd, Pt; and X = Cl, Br, I): a first principles study
The highly successful PBE functional and the modified Becke–Johnson exchange potential were used to calculate the structural, electronic, and optical properties of the vacancy-ordered double perovskites A
2
BX
6
(A = Rb, Cs; B = Sn, Pd, Pt; X = Cl, Br, and I) using the density functional theory, a first principles approach. The convex hull approach was used to check the thermodynamic stability of the compounds. The calculated parameters (lattice constants, band gap, and bond lengths) are in tune with the available experimental and theoretical results. The compounds, Rb
2
PdBr
6
and Cs
2
PtI
6
, exhibit band gaps within the optimal range of 0.9–1.6 eV, required for the single-junction photovoltaic applications. The photovoltaic efficiency of the studied materials was assessed using the spectroscopic-limited-maximum-efficiency (SLME) metric as well as the optical properties. The ideal band gap, high dielectric constants, and optimum light absorption of these perovskites make them suitable for high performance single and multi-junction perovskite solar cells.
Journal Article
Metabolic Screening in Psychiatric Patients: Impact of a Quality Improvement Initiative
Aims: Metabolic syndrome is highly prevalent among psychiatric rehabilitation patients, with rates ranging from 40–60% globally. The condition significantly increases the risk of cardiovascular disease and type 2 diabetes, compounded by psychotropic medications, sedentary lifestyles, and poor dietary habits. Despite established guidelines from the National Institute for Health and Care Excellence (NICE), the International Diabetes Federation (IDF), and the World Health Organization (WHO), which recommend regular metabolic screening every 6 months for patients on long-term psychotropic medications, compliance with metabolic screening in psychiatric settings remains inconsistent. This audit aimed to evaluate and improve compliance with these metabolic screening practices in psychiatric rehabilitation units through targeted interventions. Methods: A two-cycle audit was conducted involving 33 patients (26 males, 7 females) across five residential psychiatric units. The first cycle assessed baseline compliance with the 6-month metabolic screening guidelines, revealing significant gaps. Interventions included the implementation of a structured metabolic screening tool, GP coordination, and staff education. Screening was based on the guidelines for waist circumference, fasting glucose or HbA1c, blood pressure, triglycerides, and HDL cholesterol. The second cycle evaluated compliance with the 6-month screening interval. Results: In the first cycle, only 15.15% of patients had complete metabolic screening conducted within the recommended six-month period, while 30.30% had incomplete screenings and 54.55% had missing data. Following the interventions, the second cycle showed improvements in screening compliance. In the second cycle, 66.67% of patients were screened within the recommended six-month period, while the remaining 33.33% were not screened within the recommended period of six months. Conclusion: The structured metabolic screening tool and targeted interventions significantly improved compliance with metabolic screening guidelines as recommended by NICE, IDF, and the WHO. These findings emphasize the importance of regular metabolic screening and the need for continued efforts to improve adherence to established guidelines in psychiatric rehabilitation units.
Journal Article
Nutritional, medicinal and cosmetic value of bioactive compounds in button mushroom (Agaricus bisporus): a review
Fungi are vital to numerous industrial and household processes, especially producing cheeses, beer, wine, and bread, and they are accountable for breaking down organic matter. The remarkable medicinal and nutritional values of the mushrooms have increased their consumption. Agaricus bisporus belongs to the Agaricaceae family, and it is a top-ranked cultivated mushroom that is well known for its edibility. A. bisporus is rich in nutrients such as carbohydrates, amino acids, fats, and minerals and has potential anticancer, antioxidant, anti-obesity, and anti-inflammation properties. The bioactive compounds extracted from this mushroom can be used for the treatment of several common human diseases including cancer, bacterial and fungal infections, diabetes, heart disorder, and skin problems. A. bisporus has opened new horizons for the world to explore mushrooms as far as their culinary and medicinal values are concerned. In recent years, tyrosinase and ergothioneine have been extracted from this mushroom, which has made this mushroom worth considering more for nutritional and medicinal purposes. To emphasize various aspects of A. bisporus, a comprehensive review highlighting the nutritional, medicinal, and cosmetic values and finding out the research gaps is presented. In this way, it would be possible to improve the quality and quantity of bioactive compounds in A. bisporus, ultimately contributing to the discovery of new drugs and the responsible mechanisms. In the present review, we summarize the latest advancements regarding the nutritional, pharmaceutical, and cosmetic properties of A. bisporus. Moreover, research gaps with future research directions are also discussed.
Journal Article
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.
Journal Article
Recent trends and economic significance of modified/functionalized biochars for remediation of environmental pollutants
The pollution of soil and aquatic systems by inorganic and organic chemicals has become a global concern. Economical, eco-friendly, and sustainable solutions are direly required to alleviate the deleterious effects of these chemicals to ensure human well-being and environmental sustainability. In recent decades, biochar has emerged as an efficient material encompassing huge potential to decontaminate a wide range of pollutants from soil and aquatic systems. However, the application of raw biochars for pollutant remediation is confronting a major challenge of not getting the desired decontamination results due to its specific properties. Thus, multiple functionalizing/modification techniques have been introduced to alter the physicochemical and molecular attributes of biochars to increase their efficacy in environmental remediation. This review provides a comprehensive overview of the latest advancements in developing multiple functionalized/modified biochars via biological and other physiochemical techniques. Related mechanisms and further applications of multiple modified biochar in soil and water systems remediation have been discussed and summarized. Furthermore, existing research gaps and challenges are discussed, as well as further study needs are suggested. This work epitomizes the scientific prospects for a complete understanding of employing modified biochar as an efficient candidate for the decontamination of polluted soil and water systems for regenerative development.
Journal Article
Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach
Breast cancer (BC) is the third leading cause of deaths in women globally. In general, histopathology images are recommended for early diagnosis and detailed analysis for BC. Thus, state-of-the-art classification models are required for the early prediction of BC using histopathology images. This study aims to develop an accurate and computationally feasible classification model named Biopsy Microscopic Image Cancer Network (BMIC_Net) to classify BC into eight distinct subtypes through deep learning (DL) and hierarchical classification approach. For experiments, the publicly available dataset BreakHis is used and splitted into training and testing set. Furthermore, data augmentation was performed on training set only and 4096 result-oriented features were extracted through DL. In order to improve the classification performance, feature reduction schemes were experimented to elicit the most discriminative feature subset. Finally, six machine-learning algorithms were analyzed to acquire the best results. The experimental results revealed that BMIC_Net outperformed existing baseline models by obtaining the highest accuracy of 95.48% for first-level classifier and 94.62% and 92.45% for second-level classifiers. Thus, this model can be deployed on a normal desktop machine in any healthcare center of less privileged areas in under-developing countries to serve as second opinion for breast cancer classification.
Journal Article
An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.
Journal Article