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The NLRP3 inflammasome plays a pivotal role in the innate immune system, orchestrating the activation of caspase-1 and the release of proinflammatory cytokines IL-1β and IL-18 in reaction to microbial infections and cellular damage. Despite its crucial function in defending against pathogens, the dysregulated activation of the NLRP3 inflammasome has been associated with various inflammatory disorders. In the current investigation, promising plant-derived alkaloids compounds have been discovered as targeted inhibitors against multiprotein NLRP3 using an in-silico drug development approach. The repurposing of natural compounds as anti-inflammatory agents remains a relevant approach for identifying promising early interventions to prevent and manage inflammatory diseases. In this molecular docking study targeting Chain A of the NLRP3 inflammasome protein, eight plant-derived alkaloids renowned for their anti-inflammatory properties were chosen. Docking analysis of the selected alkaloids showed the lowest/best binding energies of less than − 10 Kcal/mol against NLRP3 Chain A, based on this docking result, which is regarded as an exceptional binding score. Notably, Oxyacanthine, Magnoflorine, Corynoline, and Berbamine demonstrated the most favourable binding energies, displaying unique interactions within the binding pocket of the NACHT/PYD domain of NLRP3 Chain A among all compounds investigated. These findings highlight the potential of these alkaloids as promising therapeutic candidates specifically targeting this trans-activating NACHT/PYD domain of NLRP3 Chain A in the context of anti-inflammatory interventions. Protein-protein interactions (PPIs) play an important role in elucidating protein function and drug interactions. To identify bioactive compounds with anti-inflammatory potential, a functional protein network was constructed from publicly available PPI data. As a result, the findings of this in-silico study may cause researchers to emphasize more on alkaloids when considering natural plant products for the treatment of various illnesses that target the inflammatory intermediates. This computational approach predicted ligands that may modulate inflammatory proteins and support host immunity. However, further in vitro and in vivo studies are still needed to validate these in-silico findings before clinical use. In summary, analysing PPI networks can aid discovery of therapeutic candidates, but experimental validation remains essential.

Background The proliferation of electronic cigarettes (e-cigarettes) has presented new challenges in public health, particularly among adolescents and young adults. While marketed as safer than tobacco and as cessation aids, e-cigarettes have raised concerns about their long-term health and psychosocial impacts, including potential links to increased suicidal behaviors. This study aims to evaluate the relationship between e-cigarette use and suicidal behaviors by conducting a systematic review of the current literature. Methods We searched PubMed, Web of Science, and EMBASE for studies up to March 10, 2024, examining the relationship between e-cigarette use and suicidal behaviors. Eligible studies included cross-sectional, longitudinal, retrospective, prospective, and case–control designs. Meta-analysis was performed to calculate pooled odds ratios (ORs). Newcastle Ottawa scale was used to assess the quality of studies. R software (V 4.3) was used to perform the meta-analysis. Results Our analysis included fourteen studies, predominantly from the US and Korea, with participants ranging from 1,151 to 255,887. The meta-analysis identified a significant association between e-cigarette use and an increased risk of suicidal ideation (OR = 1.489, 95% CI: 1.357 to 1.621), suicide attempts (OR = 2.497, 95% CI: 1.999 to 3.996), and suicidal planning (OR = 2.310, 95% CI: 1.810 to 2.810). Heterogeneity was noted among the studies. Conclusion E-cigarette use is significantly associated with the risk of suicidal behaviors, particularly among adolescents. The findings underscore the necessity for caution in endorsing e-cigarettes as a safer smoking alternative and call for more extensive research to understand the underlying mechanisms. Public health strategies should be developed to address and mitigate the risks of suicidal behaviors among e-cigarette users.

The production and disposal of plastics have become significant concerns for the sustainability of the planet. During the past 75 years, around 80% of plastic waste has either ended up in landfills or been released into the environment. Plastic debris released into the environment breaks down into smaller particles through fragmentation, weathering, and other disintegration processes, generating microplastics (plastic particles ≤ 5 mm in size). Although marine and aquatic ecosystems have been the primary focus of microplastic pollution research, a growing body of evidence suggests that terrestrial ecosystems are equally at risk. Microplastic contamination has been reported in various terrestrial environments from several sources such as plastics mulch, pharmaceuticals and cosmetics, tire abrasions (tire wear particles), textiles industries (microfibers), sewage sludge, and plastic dumping. Recent studies suggest that the soil has become a significant sink for pollutants released into terrestrial ecosystems and is often contaminated with a mixture of organic and inorganic pollutants. This has gradually caused adverse impacts on soil health and fertility by affecting soil pH, porosity, water-holding capacity, and soil microbial enzymatic activities. Microplastics can interact with the co-existing pollutants of the environments by adsorbing the contaminants onto their surfaces through various intermolecular forces, including electrostatic, hydrophobic, non-covalent, partition effects, van der Waals forces, and microporous filling mechanisms. This subsequently delays the degradation process of existing contaminants, thereby affecting the soil and various ecological activities of the ecosystem. Thus, the present article aims to elucidate the deleterious impact of microplastics and their interactions with other pollutants in the terrestrial ecosystem. This review also addresses the impact of microplastics in disrupting the soil sustainability of the planet.

Methyl red, a synthetic azo dye, was reported for not only being mutagenic but also its persistence has severe consequences on human health, such as cancer, alongside detrimental environmental effects. In the present study, the Pseudomonas putida OsEnB_HZB_G20 strain was isolated from the soil sample to study the catalytic activity for the degradation of methyl red dye. Another isolated strain, the Pseudomonas aeruginosa PA 1_NCHU strain was used as an electroactive anodophile and mixed with the Pseudomonas putida OsEnB_HZB_G20 strain to see the effect of co-culturing on the power generation in single-chambered microbial fuel cells (MFCs). The Pseudomonas putida OsEnB_HZB_G20 and Pseudomonas aeruginosa PA 1_NCHU strains were used as co-culture inoculum in a 1:1 ratio in MFCs. This work uses isolated bacterial strains in a co-culture to treat wastewater with varying methyl red dye concentrations and anolyte pH to investigate its effect on power output in MFCs. This co-culture produced up to 7.3 W/m3 of power density with a 250 mgL−1 of dye concentration, along with 95% decolorization, indicating that the symbiotic relationship between these bacteria resulted in improved MFC performance simultaneous to dye degradation. Furthermore, the co-culture of Pseudomonas putida and Pseudomonas aeruginosa in a 1:1 ratio demonstrated improved power generation in MFCs at an optimized pH of 7.

In an agriculture sector, the quality of several raw crops depends upon the time factor. After harvesting, it is necessary to bring the crops either to cold storage or directly to customer though wholesale dealers. Keeping crops in cold storage decrease the nutrition value and also increase the overall cost of the crop leading to price hike. So, it will be best if the crops are put in the market as soon as possible. This can only be possible if the logistic system is updated and can handle the real-time requirement of agriculture product transport. This study investigates and highlights the possible IoT-based logistic support using artificial intelligence for the farmers such that a fast corridor is created between farmers’ lands to end-user customers. This will benefit the farmers in two-fold; first, it will increase the revenue of farmers by decreasing the time span avoiding cold storage fees, and second, it will maintain the quality of crops.

Background Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 can lead to severe cardiovascular complications. Anakinra, an interleukin-1 receptor antagonist, is proposed to benefit the hyperinflammatory state of MIS-C, potentially improving cardiac function. This systematic review evaluated the effectiveness of early Anakinra administration on cardiac outcomes in children with MIS-C. Methods A comprehensive search across PubMed, Embase, and Web of Science until March 2024 identified studies using Anakinra to treat MIS-C with reported cardiac outcomes. Observational cohorts and clinical trials were included, with data extraction focusing on cardiac function metrics and inflammatory markers. Study quality was assessed using the Newcastle-Ottawa Scale. Results Six studies met the inclusion criteria, ranging from retrospective cohorts to prospective clinical studies, predominantly from the USA. Anakinra dosages ranged from 2.3 to 10 mg/kg based on disease severity. Several studies showed significant improvements in left ventricular ejection fraction and reductions in inflammatory markers like C-reactive protein, suggesting Anakinra’s role in enhancing cardiac function and mitigating inflammation. However, findings on vasoactive support needs were mixed, and some studies did not report significant changes in acute cardiac support requirements. Conclusion Early Anakinra administration shows potential for improving cardiac function and reducing inflammation in children with MIS-C, particularly those with severe manifestations. However, the existing evidence is limited by the observational nature of most studies and lacks randomized controlled trials (RCTs). Further high-quality RCTs are necessary to conclusively determine Anakinra’s effectiveness and optimize its use in MIS-C management for better long-term cardiac outcomes and standardized treatment protocols.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is a highly contagious respiratory disease with widespread societal impact. The symptoms range from cough, fever, and pneumonia to complications affecting various organs, including the heart, kidneys, and nervous system. Despite various ongoing efforts, no effective drug has been developed to stop the spread of the virus. Although various types of medications used to treat bacterial and viral diseases have previously been employed to treat COVID-19 patients, their side effects have also been observed. The way SARS-CoV-2 infects the human body is very specific, as its spike protein plays an important role. The S subunit of virus spike protein cleaved by human proteases, such as furin protein, is an initial and important step for its internalization into a human host. Keeping this context, we attempted to inhibit the furin using phytochemicals that could produce minimal side effects. For this, we screened 408 natural phytochemicals from various plants having antiviral properties, against furin protein, and molecular docking and dynamics simulations were performed. Based on the binding score, the top three compounds (robustaflavone, withanolide, and amentoflavone) were selected for further validation. MM/GBSA energy calculations revealed that withanolide has the lowest binding energy of −57.2 kcal/mol followed by robustaflavone and amentoflavone with a binding energy of −45.2 kcal/mol and −39.68 kcal/mol, respectively. Additionally, ADME analysis showed drug-like properties for these three lead compounds. Hence, these natural compounds robustaflavone, withanolide, and amentoflavone, may have therapeutic potential for the management of SARS-CoV-2 by targeting furin.

Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and p values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real-time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: the fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset.