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Interleukin-2-inducible T-cell kinase (ITK) is an essential enzyme that plays a key role in both the activation and differentiation of T-cells. As a member of the Tec family of non-receptor tyrosine kinases, ITK is predominantly expressed in T cells, exerting a critical influence on T-cell receptor signaling and downstream pathways. Moreover, ITK regulates cytokine production, notably interleukin-2 (IL-2), and the differentiation of Th2 cells. In the context of immunology, ITK has garnered significant attention, particularly for its potential to address immune-related conditions such as cancer and autoimmune diseases, including lymphoproliferative diseases. In this study, we performed a structure-based virtual screening utilizing a library of plant-based small molecules to identify inhibitors of ITK. The initial selection of phytochemicals was guided by adherence to the Lipinski rule of five. After molecular docking, top-ranked hits in terms of binding affinity underwent screening for physicochemical and pharmacokinetic properties and PASS analyses. The three selected phytochemicals, Withanolide A, Amorphispironon E, and 27-Deoxy-14-hydroxywithaferin A (27-DHA) demonstrated remarkable binding affinity to ITK with a docking score of − 9.2, − 9.1, and − 9.1 kcal/mol, respectively. All the phytochemicals showed specific binding to the ATP-binding site of ITK as revealed by protein structure network analysis. These selected phytoconstituents underwent all-atom molecular dynamics (MD) simulations, spanning 100 ns each. The simulation results showed that ITK with elucidated compounds exhibited stability with minimal dynamics. In addition, we performed an MM-PBSA analysis, which indicated a strong binding affinity. This study highlights the potential of Withanolide A, Amorphispironon E, and 27-DHA as preliminary leads for further experimental validation and preclinical investigation toward therapeutic development.

Microbial electrosynthesis is a new approach to converting C1 carbon (CO2) to more complex carbon-based products. In the present study, CO2, a potential greenhouse gas, was used as a sole carbon source and reduced to value-added chemicals (acetate, ethanol) with the help of bioelectrochemical reduction in microbial electrosynthesis systems (MES). The performance of MES was studied with varying electrode materials (carbon felt, stainless steel, and cobalt electrodeposited carbon felt). The MES performance was assessed in terms of acetic acid and ethanol production with the help of gas chromatography (GC). The electrochemical characterization of the system was analyzed with chronoamperometry and cyclic voltammetry. The study revealed that the MES operated with hybrid cobalt electrodeposited carbon felt electrode yielded the highest acetic acid (4.4 g/L) concentration followed by carbon felt/stainless steel (3.7 g/L), plain carbon felt (2.2 g/L), and stainless steel (1.87 g/L). The alcohol concentration was also observed to be highest for the hybrid electrode (carbon felt/stainless steel/cobalt oxide is 0.352 g/L) as compared to the bare electrodes (carbon felt is 0.22 g/L) tested, which was found to be in correspondence with the pH changes in the system. Electrochemical analysis revealed improved electrotrophy in the hybrid electrode, as confirmed by the increased redox current for the hybrid electrode as compared to plain electrodes. Cyclic voltammetry analysis also confirmed the role of the biocatalyst developed on the electrode in CO2 sequestration.

Skin cancer is one of the major public health concerns among the white population with more than a hundred thousand cases every year. Melanoma is one of the deadliest forms of skin cancer which is responsible for thousands of deaths in US alone in recent years and, therefore, early diagnosis is very important to increase the survival rate of melanoma patients. In last few years’ Deep neural networks have been utilized by researchers to build best models for classifying or diagnosing skin cancer. In this paper Deep neural network-based CNN architectures to classify Melanoma is proposed. The CNN architecture proposed in this work is implemented on CPU, GPU and TPU and the performance of the model is shown on all these platforms. The proposed model is compared to other works done so far for melanoma diagnosis in terms of various performance metrics like prediction accuracy, specifity, sensitivity and it is observed that the proposed models outperformed them. The dataset utilized in training and testing the proposed models is ISIC archive dataset which contains 4750 skin images for two classes i.e. melanoma and benign. The results of our study have proved that utilizing GPU and TPU speeds up the training 38 times faster than CPU and can accelerate the performance of CNN for features extraction, optimization and classification of skin cancer images and the proposed model has outperformed the other models compared with it.

In this work, nine nanocrystalline binary Mg alloys were synthesized by high-energy ball milling. The compositions, Mg-5 wt% M (M-Cr, Ge, Mn, Mo, Ta, Ti, V, Y, and Zn), were milled with the objective of achieving non-equilibrium alloying. The milled alloys were consolidated via cold compaction (CC) at 25°C and spark plasma sintering (SPS) at 300°C. X-ray diffraction (XRD) analysis indicated grain refinement below 100 nm, and the scanning electron microscopy revealed homogeneous microstructures for all compositions. XRD analysis revealed that most of the alloys showed a change in the lattice parameter, which indicates the formation of a solid solution. A significant increase in the hardness compared to unmilled Mg was observed for all of the alloys. The corrosion behavior was improved in all of the binary alloys compared to milled Mg. A significant decrease in the cathodic kinetics was evident due to Ge and Zn additions. The influence of the alloying elements on corrosion behavior has been categorized and discussed based on the electrochemical response of their respective binary Mg alloys.

In the present work, a novel biosensor (GCE/RGO/PPy/NR) based on the nanocomposite of reduced graphene oxide (RGO), polypyrrole (PPy) immobilized by nitrate reductase (NR) was developed on a glassy carbon electrode (GCE). The conductive nanocomposite (RGO/PPy) was synthesized by in situ oxidative polymerization of pyrrole in the presence of RGO in acidic medium. A facile and green path was employed to synthesize RGO from graphene oxide (GO). This was performed by a novel route using Abelmoschus esculentus vegetable extract as a stabilizing and reducing agent for GO. The composite of reduced graphene oxide and polypyrrole (RGO/PPy) was deposited onto GCE with subsequent deposition of NR enzyme on the GCE/RGO/PPy to develop GCE/RGO/PPy/NR biosensor. The surface morphology and structural features of the composites were studied by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The electrochemical behavior and electrocatalytic activity of the biosensor were examined by cyclic voltammetry at different scan rates (20–100 mV s−1) in the synthetic nitrate solution. The developed bio-anode achieved a maximum current density of 4.24 mA cm−2 at a scan rate of 100 mV s−1 for 10 mM sodium nitrate solution.

This study illustrates a proof-of-concept model to improve solid waste management (SWM) services by analyzing people’s behavior towards waste. A deep neural network model is implemented to detect and identify the specific types of events/activities in the proximity of the waste bin. This model consists of a three-dimensional convolutional neural network (3D CNN) and a long short-term memory (LSTM)-based recurrent neural network. The model was trained and tested over a handcrafted data set and achieved an average precision of 0.944–0.986. This precision is promising to support the implementation of the model on a large scale in the actual environment. The performance measures of all individual events indicate that the model successfully detected the individual events and has high precision for classifying them. The study also designed and built an experimental setup to record the data set, which comprises 3200 video files duration between 150–1200 s. Methodologically, the research is supported through a case study based on the recorded data set. In this case study, the frequencies of identified events/activities at a bin are plotted and thoroughly analyzed to determine people’s behavior toward waste. This frequency analysis is used to determine the locations where one of the following actions is required to improve the SWM service: (i) people need to be educated about the consequences of waste scattering; (ii) bin capacity or waste collection schedules are required to change; (iii) both actions are required simultaneously; (iv) none of the actions are needed.

Based on traditional therapeutic claims, NEERI KFT (a traditional Ayurvedic polyherbal preparation) has been innovatively developed in recent time on the decades of experience for treating kidney dysfunction. Due to the lack of scientific evidence, the present investigations are needed to support the rationale use of NEERI KFT. Considering the facts, the study investigated the nephroprotective effect of NEERI KFT against kidney dysfunction using in silico, in vitro and in vivo approaches. In this study, phytochemical and network pharmacology studies were performed for the developed formulation to evaluate the molecular mechanism of NEERI KFT in the amelioration of kidney disease. In vitro nephroprotective and antioxidant effect of NEERI KFT was determined on HEK 293 cells against cisplatin-induced cytotoxicity and oxidative stress. In vivo nephroprotective effect of NEERI KFT was determined against cisplatin-induced nephrotoxicity in Wistar rats, via assessing biochemical markers, antioxidant enzymes and inflammatory cytokines such as TNF-α, IL-1β, CASP-3, etc. The results showed that the compounds such as gallic acid, caffeic acid and ferulic acid are the major constituents of NEERI KFT, while network pharmacology analysis indicated a strong interaction between polyphenols and several genes (CASPs, ILs, AGTR1, AKT, ACE2, SOD1, etc.) involved in the pathophysiology of kidney disease. In vivo studies showed a significant (p < 0.05) ameliorative effect on biochemical markers and antioxidant enzymes (SOD, CAT, GSH, etc.), and regulates inflammatory cytokine (TNF-α, IL-1β, CASP-3) expression in kidney tissue. Hence, it can be concluded that NEERI KFT subsequently alleviates renal dysfunction mediated by cisplatin via attenuating oxidative and inflammatory stress, thus preserving the normalcy of kidney function.

The Bacille Calmette-Guérin or BCG vaccine, the only vaccine available against Mycobacterium tuberculosis can induce a marked Th1 polarization of T-cells, characterized by the antigen-specific secretion of IFN-γ and enhanced antiviral response. A number of studies have supported the concept of protection by non-specific boosting of immunity by BCG and other microbes. BCG is a well-known example of a trained immunity inducer since it imparts ‘non-specific heterologous’ immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the recent pandemic. SARS-CoV-2 continues to inflict an unabated surge in morbidity and mortality around the world. There is an urgent need to devise and develop alternate strategies to bolster host immunity against the coronavirus disease of 2019 (COVID-19) and its continuously emerging variants. Several vaccines have been developed recently against COVID-19, but the data on their protective efficacy remains doubtful. Therefore, urgent strategies are required to enhance system immunity to adequately defend against newly emerging infections. The concept of trained immunity may play a cardinal role in protection against COVID-19. The ability of trained immunity-based vaccines is to promote heterologous immune responses beyond their specific antigens, which may notably help in defending against an emergency situation such as COVID-19 when the protective ability of vaccines is suspicious. A growing body of evidence points towards the beneficial non-specific boosting of immune responses by BCG or other microbes, which may protect against COVID-19. Clinical trials are underway to consider the efficacy of BCG vaccination against SARS-CoV-2 on healthcare workers and the elderly population. In this review, we will discuss the role of BCG in eliciting trained immunity and the possible limitations and challenges in controlling COVID-19 and future pandemics.

Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per the problem domain. This paper proposes a new Convolution neural network architecture to classify skin lesions into two classes: benign and malignant. The Google Xception model is used as a base model on top of which new layers are added and then fine-tuned. The model is optimized using various optimizers to achieve the maximum possible performance gain for the classifier output. The results on ISIC archive data for the model achieved the highest training accuracy of 99.78% using Adam and LazyAdam optimizers, validation and test accuracy of 97.94% and 96.8% using RMSProp, and on the HAM10000 dataset utilizing the RMSProp optimizer, the model achieved the highest training and prediction accuracy of 98.81% and 91.54% respectively, when compared to other models.

In this study, a compact cold sprayed (CS) Ti coating was deposited on Mg alloy using a high pressure cold spray (HPCS) system. The wear and corrosion behavior of the CS Ti coating was compared with that of CS Al coating and bare Mg alloy. The Ti coating yielded lower wear rate compared to Al coating and Mg alloy. Electrochemical impedance spectroscopy (EIS) and cyclic potentiodynamic polarization (CPP) tests revealed that CS Ti coating can substantially reduce corrosion rate of AZ31B in chloride containing solutions compared to CS Al coating. Interestingly, Ti-coated Mg alloy demonstrated negative hysteresis loop, depicting repassivation of pits, in contrast to AZ31B and Al-coated AZ31B with positive hysteresis loops where corrosion potential (Ecorr) > repassivation potential (Erp); indicating irreversible growth of pits. AZ31B and Al-coated AZ31B were most susceptible to pitting corrosion, while Ti-coated Mg alloy indicated noticeable resistance to pitting in 3.5 wt % NaCl solution. In comparison to Al coating, Ti coating considerably separated the AZ31BMg alloy surface from the corrosive electrolyte during long term immersion test for 11 days.