Explore the vast range of titles available.

Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Post-vaccination inflammatory myositis is a rare but known entity in the literature. We encountered a 46-year-old female patient, who presented with complains of fever, arthralgia, and weakness 1 week after taking the second dose of COVID-19 (Oxford-AstraZeneca) vaccine. On workup she had raised inflammatory markers, evidence of myositis on magnetic resonance imaging of thighs, and evidence of interstitial lung disease on high-resolution computed tomography of the chest. The patient was further found to be positive for anti-Jo-1 antibody. She was managed with steroids and methotrexate initially. She briefly developed pneumocystis pneumonia and recovered. She was later switched to mycophenolate mofetil and responded well. We report the first case of anti-Jo-1 syndrome reported following COVID-19 vaccination in the literature. Our aim is to sensitise the clinicians to such rare but occasionally life-threatening complications that may arise in the post-vaccination period.

Recently an enormous expansion of knowledge is seen in various disciplines of science. This surge of information has given rise to concept of interdisciplinary fields, which has resulted in emergence of newer research domains, one of them is ‘Synthetic Biology’ (SynBio). It captures basics from core biology and integrates it with concepts from the other areas of study such as chemical, electrical, and computational sciences. The essence of synthetic biology is to rewire, re-program, and re-create natural biological pathways, which are carried through genetic circuits. A genetic circuit is a functional assembly of basic biological entities (DNA, RNA, proteins), created using typical design, built, and test cycles. These circuits allow scientists to engineer nearly all biological systems for various useful purposes. The development of sophisticated molecular tools, techniques, genomic programs, and ease of nucleic acid synthesis have further fueled several innovative application of synthetic biology in areas like molecular medicines, pharmaceuticals, biofuels, drug discovery, metabolomics, developing plant biosensors, utilization of prokaryotic systems for metabolite production, and CRISPR/Cas9 in the crop improvement. These applications have largely been dominated by utilization of prokaryotic systems. However, newer researches have indicated positive growth of SynBio for the eukaryotic systems as well. This paper explores advances of synthetic biology in the plant field by elaborating on its core components and potential applications. Here, we have given a comprehensive idea of designing, development, and utilization of synthetic biology in the improvement of the present research state of plant system.

Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to decrease mortality and morbidity. Artificial intelligence's (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents' mental health. This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents' mental health care. We used the Arksey and O'Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.

An endophytic bacterium, AL2-16, was isolated from Achyranthes aspera L. It was characterized and identified as Serratia sp. AL2-16 and was experimented for the presence of plant growth-promoting properties. AL2-16 produced siderophore in iron-deficient conditions. The quantitative estimation of siderophore production unit of AL2-16 was maximum after 48 hours of incubation (83.488%) in the presence of 1 μM of ferric chloride. The fructose followed by glucose and sucrose were proved to be the best carbon sources resulting in appreciable amount of siderophore production, i.e. 77.223%, 73.584%, and 65.363% respectively. AL2-16 also has the ability to produce indole acetic acid in medium supplemented with l-tryptophan. The highest amount of indole acetic acid, in the presence of 1.0% l-tryptophan, was 123.2 μg/mL after 144 hours. This isolate solubilized inorganic phosphate and also gave positive result for ammonia production. Colonization and pot trial experiments were conducted on A. aspera L. plant. The population of AL2-16 increased from 16.2 × 106 to 11.2 × 108 colony forming unit/g between 3rd and 5th days after inoculation. It significantly (p ≤ 0.05) increased shoot length by 95.52%, fresh shoot weight by 602.38%, fresh root weight by 438%, and area of leaves by 127.2% when inoculated with AL2-16, as compared with uninoculated control.

In the realm of machine learning, a discernible surge in research has been observed, focusing on the development of adversarial perturbations with the intent to subvert the capabilities of Deep Neural Networks (DNNs), particularly in the context of object detection and classification. Despite the availability of cutting-edge systems such as the widely acclaimed You Look Only Once (YOLO)v5 model, renowned for its swift image and video classification and detection prowess, our research embarks on a distinctive course exposing the weakness of this detection model and how easily it can be manipulated. This paper seeks to highlight the weaknesses of one of the most advanced neural networks when subjected to carefully crafted adversarial attacks. Our method entails intentionally inserting adversarial perturbations into photos via image-in-image steganography, a technique that is essentially imperceptible to the human eye yet capable of significantly lowering YOLOv5’s confidence levels. This approach was carefully, evaluated on a Magnetic Resonance Imaging (MRI) dataset containing around 1100 brain pictures. A comparison between regular and encoded photos undergoing steganography unveiled a substantial decrease in precision values, plummeting from a noteworthy 0.711 to a mere 0.0346.

Global spread of KPC poses to be a serious threat complicating treatment options in hospital settings. The present study investigates the genetic environment of blaKPC-2 among clinical isolates of Pseudomonas aeruginosa from a tertiary referral hospital of India. The study isolates were collected from different wards and clinics of Silchar Medical College and Hospital, India, from 2012-2013. The presence of blaKPC was confirmed by genotypic characterization followed by sequencing. Cloning of the blaKPC-2 gene was performed and the genetic environment of this gene was characterized as well. Transferability of the resistance gene was determined by transformation assay and Southern hybridization. Additionally, restriction mapping was also carried out. Two isolates of P. aeruginosa were found to harbor blaKPC-2, were resistant towards aminoglycosides, quinolone and β-lactam-β-lactamase inhibitor combination. In both the isolates, the resistance determinant was associated with class 1 integron and horizontally transferable. Both the isolates were co-harboring blaNDM-1. The first detection of this integron mediated blaKPC-2 coexisting with blaNDM-1 in P. aeruginosa from India is worrisome, and further investigation is required to track the gene cassette mediated blaKPC-2 in terms of infection control and to prevent the spread of this gene in hospitals as well as in the community.

Background Lactobacilli are gatekeepers of vaginal ecosystem impeding growth of pathogenic microbes and their diversity varies across populations worldwide. The present study investigated diversity of human vaginal microbiota among females of Northeast India, who are distinct in dietary habits, lifestyle, and genomic composition from rest of India. Results Altogether, 154 bacterial isolates were obtained from vaginal swab samples of 40 pregnant and 29 non-pregnant females. The samples were sequenced for 16 s rRNA gene and analysed for identification using a dual approach of homology search and maximum likelihood based clustering. Molecular identification based on 16S rRNA gene sequence confirmed the isolates belonging to 31 species. Lactobacilli constituted 37.7% of the bacterial isolates with 10 species and other Lactic Acid Bacteria (39.61%) represented another 10 species, some of which are opportunistic pathogens. The remaining of the communities are mostly dominated by species of Staphylococcus (14.28%) and rarely by Propionibacterium avidum (3.90%) , Bacillus subtilis, Escherchia coli, Janthinobacterium lividum, and Kocuria kristinae (each 0.64%). Interestingly Lactobacillus mucosae and Enterococcus faecalis, which are globally uncommon vaginal microbes is found dominant among women of Northeast India. This tentatively reflects adaptability of particular Lactobacillus species, in distinct population, to better compete for receptors and nutrients in vaginal epithelium than other species. Further, intrageneric 16S rRNA gene exchange was observed among Enterococcus, Staphylococcus , and two species of Lactobacillus , and deep intraspecies divergence among L. mucosae, which pinpointed possibility of emergence of new strains with evolved functionality. Lactobacilli percentage decreased from young pregnant to aged non-pregnant women with maximum colonization in trimester II. Conclusion The study highlighted importance of assessment of vaginal microbiota, Lactobacillus in particular, across different population to gain more insight on female health.

The class of integron carried out by blaNDM-1 was determined by integrase gene PCR [9] . blaNDM-1 positive bacterial isolates were cultured in Luria-Bertani (LB) broth (Hi-Media, Mumbai, India) containing 0.25 μg/ml of imipenem. In our study also, similar kind of association of ISAba125 with blaNDM-1 in the upstream region was observed, which may be due to the horizontal transmission of this gene along with ISAba125 at interspecies level. [...]this mobile genetic element may act as a unit of interspecies transmission in our setting. [...]carriage of blaNDM-1 in different Inc type plasmids within a single hospital setting and their expansion may be a serious matter of concern in combating the carbapenem resistance.

There are many environmental problems around us that can harm our health. Among them, environmental pollution, food contamination and adulteration, and antinutrients of foods are listed at the top. Persistent organic pollutants (POPs) are one of them that focus the attention of every researcher, industrialist, and policymaker due to their ill effects on animal and human populations nowadays. Industrialization is the main causative agent that has even put them as part of our food web. The severity of their impact can rely on their transportation across international boundaries. To date, about 26 pesticides have been identified under the POP category. These are toxic, persistent, and bioaccumulated among living beings, whereas polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) are as toxic as industrial effluents and also travel too long by way of air. Sometimes, they may be generated by unintentional means during various agricultural practices, too. Besides all these, food by way of processing, packaging, transportation, and storage could be aligned as potential sources for POP invasion in the food supply chain. There may be chances that POPs contaminate food through raw materials. The International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) has identified some of them as a part of the \"dirty dozen\" due to their role in cancer development. The traditional methods to manage the POPs are neither efficient nor eco-friendly. The only sustainable way to break down POPs is the application of microorganisms. Keeping in mind the severity aspect, resistant to degradation and dangerous to living creatures of POPs. This review provides the insights and details of various extraction, detection and degradation techniques of POPs.