Drug screening using shape-based virtual screening and in vitro experimental models of cutaneous Leishmaniasis

Cutaneous leishmaniasis (CL) is one of the most disregarded tropical neglected disease with the occurrence of self-limiting ulcers and triggering mucosal damage and stigmatizing scars, leading to huge public health problems and social negative impacts. Pentavalent antimonials are the first-line drug for CL treatment for over 70 years and present several drawbacks in terms of safety and efficacy. Thus, there is an urgent need to search for non-invasive, non-toxic and potent drug candidates for CL. In this sense, we have implemented a shape-based virtual screening approach and identified a set of 32 hit compounds. In vitro phenotypic screenings were conducted using these hit compounds to check their potential leishmanicidal effect towards Leishmania amazonensis (L. amazonensis). Two (Cp1 and Cp2) out of the 32 compounds revealed promising antiparasitic activities, exhibiting considerable potency against intracellular amastigotes present in peritoneal macrophages (IC₅₀ values of 9.35 and 7.25 μm, respectively). Also, a sterile cidality profile was reached at 20 μm after 48 h of incubation, besides a reasonable selectivity (≈8), quite similarly to pentamidine, a diamidine still in use clinically for leishmaniasis. Cp1 with an oxazolo[4,5-b]pyridine scaffold and Cp2 with benzimidazole scaffold could be developed by lead optimization studies to enhance their leishmanicidal potency

Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m(3)/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation

Towards smart healthcare: UAV-based optimized path planning for delivering COVID-19 self-testing kits using cutting edge technologies

Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ sample, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based sample collection procedure through self-collection kits delivery to the potential patients and bringing the samples back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare

Reduced-Complexity LDPC Decoding for Next-Generation IoT Networks

Low-density parity-check (LDPC) codes have become the focal choice for next-generation Internet of things (IoT) networks. This correspondence proposes an efficient decoding algorithm, dual min-sum (DMS), to estimate the first two minima from a set of variable nodes for check-node update (CNU) operation of min-sum (MS) LDPC decoder. The proposed architecture entirely eliminates the large-sized multiplexing system of sorting-based architecture which results in a prominent decrement in hardware complexity and critical delay. Specifically, the DMS architecture eliminates a large number of comparators and multiplexors while keeping the critical delay equal to the most delay-efficient tree-based architecture. Based on experimental results, if the number of inputs is equal to 64, the proposed architecture saves 69%, 68%, and 52% area over the sorting-based, the tree-based, and the low-complexity tree-based architectures, respectively. Furthermore, the simulation results show that the proposed approach provides an excellent error-correction performance in terms of bit error rate (BER) and block error rate (BLER) over an additive white Gaussian noise (AWGN) channel

Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)

With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace

Methotrexate-Loaded Gelatin and Polyvinyl Alcohol (Gel/PVA) Hydrogel as a pH-Sensitive Matrix

The aim was to formulate and evaluate Gel/PVA hydrogels as a pH-sensitive matrix to deliver methotrexate (MTX) to colon. The primed Gel/PVA hydrogels were subjected to evaluation for swelling behavior, diffusion coefficient, sol-gel characteristic and porosity using an acidic (pH 1.2) and phosphate buffer (PBS) (pH 6.8 & pH 7.4) media. Fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analysis (TGA) were performed to evaluate the chemical compatibility of the Gel/PVA hydrogel. The shape alteration and release of Gel/PVA hydrogel was conducted at pH 1.2, pH 6.8 and pH 7.4. The drug release kinetic mechanism was determined using various kinetic equations. The physicochemical evaluation tests and drug release profile results were found to be significant (p < 0.01). However, it was dependent on the polymers' concentration, the pH of the release media and the amount of the cross-linking agent. Hydrogels containing the maximum amount of gel showed a dynamic equilibrium of 10.09 ± 0.18 and drug release of 93.75 ± 0.13% at pH 1.2. The kinetic models showed the release of MTX from the Gel/PVA hydrogel was non-Fickian. The results confirmed that the newly formed Gel/PVA hydrogels are potential drug delivery systems for a controlled delivery of MTX to the colo

Production of Organic Fertilizers from Rocket Seed (Eruca Sativa L.), Chicken Peat and Moringa Oleifera Leaves for Growing Linseed under Water Deficit Stress

Linseed is an important industrial crop cultivated for its edible seeds and fiber linen. Organic fertilizers have beneficial effects on soil properties and quality of crops. Therefore, we conducted two field experiments during 2018&ndash;2019 and 2019&ndash;2020 to determine the effect of organic fertilizers on soil fertility, yield and fiber quality of linseed varieties Roshni, BL1 and Chandni under low soil moisture conditions. We prepared organic fertilizers from seed cake of Eruca sativa, leaves of Moringa oleifera and chicken peat in various combinations by composting method. The various formulations of organic fertilizers included OF1(1 kg seed cake of Eruca sativa), OF2 (1 kg seed cake of Eruca sativa + 1 kg chicken peat), OF3 (1 kg seed cake of Eruca sativa + 0.5 kg chicken peat + 0.25 kg Moringa oliefera leaves) and OF4 (1 kg seed cake of Eruca sativa + 0.250 kg chicken peat + 0.5 kg Moringa oliefera leaves). Compositional analysis of organic fertilizers indicated that OF3 and OF4 had higher and may potentially sufficient quantities of NPK and organic matter. Both of these fertilizers significantly improved soil total N, available P, K, Zn and Fe contents. Growth response of linseed varieties to organic fertilizers was evaluated under water deficit stress (40% field capacity of soil) at tillering stage for one month. Water stress had significantly adverse effects on plant height, production of tillers per plant, leaf relative water content (LRWC), number of capsules per plant, thousand seed weight, total seed yield, straw yield, fiber length and fiber weight of linseed varieties. However, the application of OF3 and OF4 significantly enhanced plant height, tillers production, LRWC, seed yield, straw yield, fiber length and fiber weight under water deficit stress. Water deficit stress also resulted in a significant increase in the content of phenolics of both the leaves and roots. For each measured quality parameter of linseed varieties, organic fertilizer treatments resulted in higher values than untreated and irrigated control. We concluded that organic fertilizers particularly OF3 and OF4 significantly improved soil fertility and minimized negative effect of water deficit stress on plant height, tillers production, LRWC, seed yield, straw yield, fiber length and fiber weight of linseed varieties

Lycopene: A Natural Arsenal in the War against Oxidative Stress and Cardiovascular Diseases

Lycopene is a bioactive red pigment found in plants, especially in red fruits and vegetables, including tomato, pink guava, papaya, pink grapefruit, and watermelon. Several research reports have advocated its positive impact on human health and physiology. For humans, lycopene is an essential substance obtained from dietary sources to fulfil the body requirements. The production of reactive oxygen species (ROS) causing oxidative stress and downstream complications include one of the major health concerns worldwide. In recent years, oxidative stress and its counter strategies have attracted biomedical research in order to manage the emerging health issues. Lycopene has been reported to directly interact with ROS, which can help to prevent chronic diseases, including diabetes and neurodegenerative and cardiovascular diseases. In this context, the present review article was written to provide an accumulative account of protective and ameliorative effects of lycopene on coronary artery disease (CAD) and hypertension, which are the leading causes of death worldwide. Lycopene is a potent antioxidant that fights ROS and, subsequently, complications. It reduces blood pressure via inhibiting the angiotensin-converting enzyme and regulating nitrous oxide bioavailability. It plays an important role in lowering of LDL (low-density lipoproteins) and improving HDL (high-density lipoproteins) levels to minimize atherosclerosis, which protects the onset of coronary artery disease and hypertension. Various studies have advocated that lycopene exhibited a combating competence in the treatment of these diseases. Owing to all the antioxidant, anti-diabetic, and anti-hypertensive properties, lycopene provides a potential nutraceutical with a protective and curing ability against coronary artery disease and hypertension

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. &ldquo;Omics&rdquo; technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer