Understanding the impact of diverse land-use systems (LUS) on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different LUSs. Twenty-four sampling sites were identified in four different LUSs across the Bangaluru, and soil samples were collected monthly over five months during the Rabi cropping season of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included soil organic carbon (SOC), pH, dehydrogenase, nitrogen (N), and urease, for different LUSs derived from the MDS. The SQI was highest in the horticulture cropping system (0.58), followed by the agro + horticulture cropping system (0.53) and the vegetable cropping system (0.49), and lowest in the pulse cropping system (0.44). These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across cropping systems.

AIM:Peri-implant mucositis, a dysbiosis-driven inflammatory disease, is a precursor to peri-implantitis, underscoring the need for early disease management. Therefore, we investigated the efficacy of glycine powder in resolving clinical inflammation and restoring host-microbial homeostasis. METHODS:Thirty subjects were randomized to receive either glycine powder air-abrasive debridement or ultrasonic instrumentation. Clinical parameters (probe depth [PD], modified Sulcular Bleeding Index [mSBI], modified Plaque Index [mPlI]), biofilm and peri-implant crevicular fluid were collected at baseline and at 1-day, 1-, 3-, 6-weeks and 3- and 6-months post-therapy. Microbial recolonization was examined using 16S rDNA sequencing and immune response was semi-quantified using a bead-based 17-plex microarray. RESULTS:At 6-months, both groups demonstrated non-significant reductions in mSBI when compared to baseline (p > 0.05, Wald test, mixed model for repeated measures). However, mSBI and PD decreased in the test group from week-1 to 3-months, while control group decreased at 1- and 3-weeks only. mSBI was lower in the test group when compared to controls from Week-1 to 3-months, while PD differed between groups at 6 weeks and 3-months. Glycine group demonstrated significant microbial shifts after 24-h, increases in species richness and health-compatible species, and loss of pathobionts (p < 0.001, Dunn test). Pro-inflammatory cytokines decreased from 1- to 6-weeks or 3-months (p < 0.05, Wald test). Comparable results were obtained in the ultrasonic group at 3-weeks and sustained over 6-weeks post-therapy. CONCLUSIONS:Glycine therapy leads to early and sustained change in host-microbial interactions when compared to ultrasonics, however, the changes wrought by both therapies were sustained for a maximum of 3 months. TRIAL REGISTRATION:ClinicalTrials.gov identifier: NCT05810558.

Vegetables and fruits waste extracts are becoming increasingly popular due to their accessibility, affordability and high concentration of bioactive compounds in environmentally friendly nanoparticle biosynthesis. This is mainly because it utilizes natural sources and helps to reduce bio-waste. The current review is an effort to investigate the use of these extracts as reducing, capping and stabilizing agents in the biosynthesis of nanoparticles. The presented work critically reviews different phytochemical compositions present in fruits and vegetables waste extract and their functioning in the synthesis of nanoparticles. These nanoparticles synthesized by the environmentally friendly method have shown potential applications in a variety of fields. Further, their applications are also reviewed in this study. It is found that the extract made up of Ailanthus altissima fruit as well as the peels waste of lemons and mandarins yields a significant amount of zinc oxide and silver nanoparticles. These nanoparticles effectively interact with viruses, cancer cells, and environmental pollutants. Therefore, these nanoparticles might be a promising candidate for environmental and medical applications. Furthermore, the nanoparticles synthesized through Sterculia acuminata, tangerine peels, and cauliflower waste are useful for eliminating heavy metals and degrading organic dyes. Therefore, it can be concluded that the potential of synthesized nanoparticles derived from fruit and vegetable waste addresses the environmental issues and also propels improvements in environmental remediation and healthcare.

Understanding the impact of urbanization on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different rural‒urban gradient (RUG) zones. Twenty-four sampling sites were identified along the RUG, and soil samples were collected monthly over five months during the October to February of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included microbial biomass carbon (MBC), SOC, N, manganese (Mn), and urease for different RUG zones derived from the MDS. The rural zones had the highest SQI (0.57), followed by the peri-urban (0.47 and 0.48) and urban (0.45 and 0.47) zones. These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across diverse regions, particularly in the face of rapid urbanization and industrialization.

The inaugural microplastic study on Indian lakes, conducted in 2017, marked a significant milestone in addressing the emerging issue of microplastic pollution. Despite the early recognition of microplastics in oceans since the 1970s, our knowledge cutoff reveals that only 12 Indian lakes have been investigated for microplastic contamination. Given the vulnerability of lacustrine ecosystems to pollution, they may face challenges akin to those observed in marine environments. While the contamination of oceans has received considerable attention, the impact on freshwater ecosystems and lakes is increasingly recognized. However, the relatively nascent nature of this field has led to methodological diversity, posing challenges for standardization. This review focuses on three key aspects: (1) assessing microplastic contamination in 12 Indian lakes; (2) exploring methodologies across various compartments, including surface water, sediments, and biota; and (3) examining microplastic characteristics and sources. The analysis concludes with recommendations to guide decision-making by public authorities and foster consensus among research teams in this critical field.

The IoT and AI are transforming social connections and communication. The article investigates how this symbiotic relationship impacts Twitter sentiment. Using the Twitter Sentiment Dataset," this study analyzes how IoT and AI are changing linked experiences. Analyzing Twitter sentiment on this historic merger shows positive and negative sentiments. Social media allows people to express their thoughts and feelings. This evolving digital environment reflects society and reveals technology adoption sentiment. The merging of IoT with AI is a key technological achievement. Tweets provide a real-time snapshot of worldwide dialogues and reactions to understand public opinion on integration. This study examines the complicated sentiment landscape using the "Twitter Sentiment Dataset," which captures IoT and AI sentiments. Social media sentiment analysis has been used to study public reactions to technological advances. Several studies show that sentiment analysis can predict technological adoption, examine attitudes and concerns, uncover issues, influence legislation, and address ethical issues. Research also emphasizes sentiment polarization's enthusiasm-realism balance and how sentiment predicts technical impact. The research used the "Twitter Sentiment Dataset," with over 1.6 million tweets. Lowercase, punctuation, and stopwords were removed from the dataset. After that, NLP algorithms like sentiment analysis examined the text. The training and test sets were split, and TF- IDF vectorization retrieved features. This research tested sentiment analysis machine learning models (Bernoulli Naive Bayes, Linear Support Vector Classification, and Logistic Regression). Later, the best model was stored. The main measure of sentiment analysis model accuracy was accuracy. Logistic regression outperformed with 83% accuracy. The models accurately captured IoT and AI integration tweets, including joy and concerns. Sentiment analysis can disclose society's feelings, making IoT-AI integration promising. This study reveals that algorithms and human sentiment drive technology. Technology- emotion interactions affect public opinion, innovation ethics, and policy. The study shows that societal emotion is essential to technical advancement and lays the framework for human-valued IoT and AI.

The COVID-19 outbreak served as a stark reminder that the global community is not fully prepared for pandemics. In order to effectively deal with potential future health risks, such as diseases that might be more lethal and widespread than COVID-19, strong and adaptable health systems will need to be built. The provision of relief by the government in the form of aid for healthcare and contingency planning becomes a source of delight in light of the insights gained from the present predicament. The advancement of general healthcare as well as the identification and control of epidemics might benefit significantly from the use of AI. The use of artificial intelligence (AI) in healthcare has expanded more rapidly in recent years as a direct result of the pandemic; yet, there are still a great number of challenging issues that need to be handled before the approaches can be used in real-world contexts. It is imperative that the World Health Organization's Department of Health Research and Technology, which is in charge of digitizing COVID-19, be established in order to provide assistance to nations whose levels of digital development differ greatly. The World Health Organization (WHO) is dedicated to assisting nations in the use of these cutting-edge technologies in order to improve the ability of health systems to respond to outbreaks and prevent future ones.

In this paper, we’ll take a look at how AI has been used, and where it is now, in the interpretation of breast-mammography images. Some other prospective applications of AI in diagnostic imaging are also addressed. In women, breast cancer is the most frequent form of cancer. While it may be challenging, finding breast irregularities greatly reduces the risk of getting breast cancer and dying from it. This is true even in cases when anomalies in the breast are obscured by clothing or other factors. Using a combination of imaging methods and the possibilities offered by AI, computers aid radiologists in their quest for an accurate diagnosis of breast diseases. This means that computers are now far more efficient than humans in carrying out imaging tasks. Because of recent improvements in breast cancer detection methods, including mammography, ultrasonography, and magnetic resonance imaging, the number of confirmed cases has grown. The development of AI has the potential to impact almost every facet of contemporary life.

This paper provides a summary of recent investigations on the use of algorithms based on deep learning for detecting and classifying breast cancer, with the goal of improving timely diagnosis and treatment outcomes. Machine learning has an accuracy of 91% in identifying cancer, whereas human specialists have an accuracy of just 79%. In this study, we analyze and contrast the two most current machine learning algorithms for detecting and classifying breast cancer: RetinaNet and YOLO (You Only Look Once). This study adds support to the theory that machine learning-based technologies may be able to make more precise diagnoses of cancer than human doctors. When compared to other breast cancer screening and categorization systems using prominent public datasets, RetinaNet and YOLO fared the best.