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Wounds are structural and functional disruptions of skin that occur because of trauma, surgery, acute illness, or chronic disease conditions. Chronic wounds are caused by a breakdown in the finely coordinated cascade of events that occurs during healing. Wound healing is a long process that split into at least three continuous and overlapping processes: an inflammatory response, a proliferative phase, and finally the tissue remodeling. Therefore, these processes are extensively studied to develop novel therapeutics in order to achieve maximum recovery with minimum scarring. Several growth hormones and cytokines secreted at the site of lesions tightly regulates the healing processes. The traditional approach for wound management has been represented by topical treatments. Metal nanoparticles (e.g., silver, gold and zinc) are increasingly being employed in dermatology due to their favorable effects on healing, as well as in treating and preventing secondary bacterial infections. In the current review, a brief introduction on traditional would healing approach is provided, followed by focus on the potential of wound dressing therapeutic techniques functionalized with Ag-NPs.

Mining of minerals exerts adverse pressure on different compartments of environment directly or indirectly. Air is the worst affected environmental matrix, and it can carry the harmful effect of pollutants generated from mining activity even to distant places. The present study was undertaken to estimate the emission of particulate matter (PM 2.5 and PM 10 ) from different activities undertaken in stone quarrying in Mahendragarh, Haryana. The results obtained from the present study indicated that drilling, blasting, crushing, and transport of mined material are chiefly responsible for the generation of dust. Whereas drilling, blasting, and loading were responsible for emission of higher fraction of PM 10 , crushing and re-suspension of roadside dust from movement of vehicles resulted in generation of relatively higher fraction of finer dust (PM 2.5 ). Modelling the transport of dust over the Hybrid Single-Particle Lagrangian Integrated Trajectory model revealed that the emitted particle may move up to the distance of about 40 km within 4 h of emission under average meteorological conditions. Fourier transform infrared (FTIR) spectroscopy analysis of dust confirmed the presence of calcite and gypsum, thus confirming the source as mining. The study concluded that generation of PM 2.5 -sized particles may impose serious respiratory health effects over the workers engaged in mining, crushing, and transportation of sandstone. Apart from it, population residing downwind of the mining area is particularly vulnerable to the pulmonary effects due to inhalation of dust.

Marine natural products with antibiotic activity have been a rich source of drug discovery; however, the emergence of antibiotic-resistant bacterial strains has turned attention towards the discovery of alternative innovative strategies to combat pathogens. In many pathogenic bacteria, the expression of virulence factors is under the regulation of quorum sensing (QS). QS inhibitors (QSIs) present a promising alternative or potential synergistic treatment since they disrupt the signaling pathway used for intra- and interspecies coordination of expression of virulence factors. This review covers the set of molecules showing QSI activity that were isolated from marine organisms, including plants (algae), animals (sponges, cnidarians, and bryozoans), and microorganisms (bacteria, fungi, and cyanobacteria). The compounds found and the methods used for their isolation are the emphasis of this review.

Arterial pulse wave measurement is beneficial in clinical health assessment and is important for effectively diagnosing different types of cardiovascular disease. Computational pulse signal analysis utilizes sensors and signal processing techniques to understand, classify, and predict disease pulse patterns. However, the choice of sensor types impacts the measurement results. This study presents the first comprehensive quantitative comparison of three sensor modalities (acoustic, optical, and pressure) for radial pulse measurement, employing a novel multi-parameter analysis framework that combines time-domain, frequency-domain, and PRV measures. Among various available types, three types of sensors are compared: an acoustic sensor, an optical sensor, and a pressure sensor. Pulse wave signals were recorded from the radial artery of 30 participants using these three sensors, and the performance was evaluated using various feature extraction methods like time domain, frequency domain and pulse rate variability (PRV) measures. Further, statistical analysis (ANOVA) of the PRV measures was carried out to compare the differences in the means of the various PRV measures. Time and frequency domain features varied across sensor types, but no statistical differences were found in PRV measures across sensors. Based on the experimental results, the pressure sensor was found to perform better in capturing comprehensive wrist pulse information. The research provides evidence-based guidelines for sensor selection in pulse wave analysis applications. The findings have direct applications in developing wearable cardiovascular monitoring devices, where sensor choice critically impacts device accuracy and reliability. and clinical settings requiring pulse wave analysis for cardiovascular disease diagnosis.

Morphological adaptation is vital for biological organisms navigating changing environments. While robots have sought to emulate this adaptability with adjustable body structures, practical robotic applications remain constrained by the complexity of integrating advanced materials, sophisticated control systems, and novel design approaches. This paper introduces a bioinspired quadruped robot featuring both a laterally undulating spine and posture-changing mechanism, specifically designed for adaptation in complex terradynamic environments. The robot utilizes a symmetrical parallelogram mechanism to precisely control its height and width, enabling it to navigate diverse terrains adeptly, avoid collisions, pass through narrow channels, and negotiate obstacles. Furthermore, the robot achieves stability through lateral undulation, which actively counteracts instability arising from posture changes. This ensures the center of gravity remains within its support triangle for the majority of the locomotion cycle, thereby obviating the reliance on intricate posture-stabilizing sensors or learning algorithms. The experimental results demonstrate the robot’s capability to traverse both flat and significantly inclined surfaces (10° uphill and downhill), as well as successfully navigate confined tunnels, down to a narrow width. We observed notable variations in locomotion speed based on posture: certain configurations exhibited speeds that were up to 30% faster than others on surfaces with the least roughness, with similar trends holding for intermediate and maximum roughness. Furthermore, the robot demonstrates energy efficiency; while zero-degree posture showed a modest increase in average power consumption (around 18%) compared to others, the overall energy expenditure across various gaits remained consistently low. This work contributes to the development of versatile and autonomous robotic systems capable of operating in unstructured and unpredictable real-world scenarios, bridging the gap between theoretical adaptability and practical deployment in fields ranging from exploration to disaster response.

Subsistence agriculture-the cultivation of subsistence cereals including millets, oilseeds, and pulses is the major occupation and source of livelihood of people of the Garhwal region. Over time, the production and yield of subsistence agriculture have reduced drastically. This study examines the practices of subsistence agriculture, the major drivers of declining agriculture, and its implications on the livelihood of marginal farmers. Data were gathered from eight villages of four districts, using a household-level survey. A total of 376 households inhabit these villages. We have surveyed 207 households (55%), who were fully engaged in practicing subsistence agriculture, purposively. A structured questionnaire was constructed and the questions were asked from the heads of households on the area, production, and productivity of the principal crops during 2000-2020. Data were analyzed using statistical methods. This study depicted that traditional agriculture has been declining, as a 50% decrease in the area, production, and productivity of crops was noticed during the last 20 years. The decrease was noted mainly due to climate change, extreme geo-hydrological disasters, crops damaged by wildlife, increasing population, decreasing landholdings, low soil fertility, low output from arable land, mono-cropping, and a lack of use of modern technology. The exodus out-migration, land abandonment, and livelihood insecurity were noticed as the implications of declining subsistence agriculture.

Many difficulties are encountered during evacuation from construction sites in hazardous situations, which may lead to severe fatalities. These fatalities, especially caused by fire, may be significantly reduced by ensuring personal protective equipment (PPE) compliance of construction site workers and fire detection through proper surveillance. Thus, the detection of PPEs, fire and injured or trapped persons, can greatly assist in the reduction of fatalities and economic loss. This article presents a novel approach towards the detection of fire and PPEs to assist in the monitoring and evacuation tasks. This work utilizes the YOLOv4 and YOLOv4-tiny algorithms based on deep learning for carrying out the detection task. A self-made dataset has been utilized to train the model in the Darknet neural network framework. Moreover, a comparative analysis with previous works has been carried out in order to endorse the real-time efficacy of the proposed work. The results verify the strength of YOLOv4 algorithm in real-time detection and surveillance at construction sites with maximum mean average precision (mAP) of 76.86 %.

The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

Neurotropic mouse hepatitis virus (MHV-A59/RSA59) infection in mice induces acute neuroinflammation due to direct neural cell dystrophy, which proceeds with demyelination with or without axonal loss, the pathological hallmarks of human neurological disease, Multiple sclerosis (MS). Recent studies in the RSA59-induced neuroinflammation model of MS showed a protective role of CNS-infiltrating CD4 + T cells compared to their pathogenic role in the autoimmune model. The current study further investigated the molecular nexus between CD4 + T cell-expressed CD40Ligand and microglia/macrophage-expressed CD40 using CD40L -/- mice. Results demonstrate CD40L expression in the CNS is modulated upon RSA59 infection. We show evidence that CD40L -/- mice are more susceptible to RSA59 induced disease due to reduced microglia/macrophage activation and significantly dampened effector CD4 + T recruitment to the CNS on day 10 p.i. Additionally, CD40L -/- mice exhibited severe demyelination mediated by phagocytic microglia/macrophages, axonal loss, and persistent poliomyelitis during chronic infection, indicating CD40-CD40L as host-protective against RSA59-induced demyelination. This suggests a novel target in designing prophylaxis for virus-induced demyelination and axonal degeneration, in contrast to immunosuppression which holds only for autoimmune mechanisms of inflammatory demyelination.

Chitosan nanoparticles (NPs) are well-recognized as promising vehicles for delivering anticancer drugs due to their distinctive characteristics. They have the potential to enclose hydrophobic anticancer molecules, thereby enhancing their solubilities, permeabilities, and bioavailabilities; without the use of surfactant, i.e ., through surfactant-free solubilization. This allows for higher drug concentrations at the tumor sites, prevents excessive toxicity imparted by surfactants, and could circumvent drug resistance. Moreover, biomedical engineers and formulation scientists can also fabricate chitosan NPs to slowly release anticancer agents. This keeps the drugs at the tumor site longer, makes therapy more effective, and lowers the frequency of dosing. Notably, some types of cancer cells (fallopian tube, epithelial tumors of the ovary, and primary peritoneum; lung, kidney, ependymal brain, uterus, breast, colon, and malignant pleural mesothelioma) have overexpression of folate receptors (FRs) on their outer surface, which lets folate-drug conjugate–incorporated NPs to target and kill them more effectively. Strikingly, there is evidence suggesting that the excessively produced FR&αgr (isoforms of the FR) stays consistent throughout treatment in ovarian and endometrial cancer, indicating resistance to conventional treatment; and in this regard, folate-anchored chitosan NPs can overcome it and improve the therapeutic outcomes. Interestingly, overly expressed FRs are present only in certain tumor types, which makes them a promising biomarker for predicting the effectiveness of FR-targeted therapy. On the other hand, the folate-modified chitosan NPs can also enhance the oral absorption of medicines, especially anticancer drugs, and pave the way for effective and long-term low-dose oral metronomic scheduling of poorly soluble and permeable drugs. In this review, we talked briefly about the techniques used to create, characterize, and tailor chitosan-based NPs; and delved deeper into the potential applications of folate-engineered chitosan NPs in treating various cancer types. Graphical Abstract Schematic illustration of target ligand-drug incorporated chitosan nanoparticles and its advantage in treating cancer. Figure created with BioRender.Com.