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Global climate change has drastically affected natural ecosystems and crop productivity. Among several factors of global climate change, CO2 is considered to be the dynamic parameter that will regulate the responses of all biological system on earth in the coming decade. A number of experimental studies in the past have demonstrated the positive effects of elevated CO2 on photosynthesis, growth and biomass, biochemical and physiological processes such as increased C:N ratio, secondary metabolite production, as well as phytohormone concentrations. On the other hand, elevated CO2 imparts an adverse effect on the nutritional quality of crop plants and seed quality. Investigations have also revealed effects of elevated CO2 both at cellular and molecular level altering expression of various genes involved in various metabolic processes and stress signaling pathways. Elevated CO2 is known to have mitigating effect on plants in presence of abiotic stresses such as drought, salinity, temperature etc., while contrasting effects in the presence of different biotic agents i.e. phytopathogens, insects and herbivores. However, a well-defined crosstalk is incited by elevated CO2 both under abiotic and biotic stresses in terms of phytohormones concentration and secondary metabolites production. With this background, the present review attempts to shed light on the major effects of elevated CO2 on plant growth, physiological and molecular responses and will highlight the interactive effects of elevated CO2 with other abiotic and biotic factors. The article will also provide deep insights into the phytohormones modulation under elevated CO2.

Endophytes are microorganisms that inhabit various plant parts and cause no damage to the host plants. During the last few years, a number of novel endophytic fungi have been isolated and identified from medicinal plants and were found to be utilized as bio-stimulants and bio fertilizers. In lieu of this , the present study aims to isolate and identify endophytic fungi associated with the leaves of Anisomeles indica L. an important medicinal plant of the Terai-Duars region of West Bengal. A total of ten endophytic fungi were isolated from the leaves of A. indica and five were identified using ITS1/ITS4 sequencing based on their ability for plant growth promotion, secondary metabolite production, and extracellular enzyme production. Endophytic fungal isolates were identified as Colletotrichum yulongense Ai1, Colletotrichum cobbittiense Ai2, Colletotrichum alienum Ai2.1, Colletotrichum cobbittiense Ai3, and Fusarium equiseti . Five isolates tested positive for their plant growth promotion potential, while isolates Ai4. Ai1, Ai2, and Ai2.1 showed significant production of secondary metabolites viz. alkaloids, phenolics, flavonoids, saponins, etc. Isolate Ai2 showed maximum total phenolic concentration (25.98 mg g −1 ), while isolate Ai4 showed maximum total flavonoid concentration (20.10 mg g −1 ). Significant results were observed for the production of extracellular enzymes such as cellulases, amylases, laccases, lipases, etc. The isolates significantly influenced the seed germination percentage of tomato seedlings and augmented their growth and development under in vitro assay. The present work comprehensively tested these isolates and ascertained their huge application for the commercial utilization of these isolates both in the agricultural and industrial sectors.

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we propose QUEST, a comprehensive and practical framework for human evaluation of LLMs covering three phases of workflow: Planning, Implementation and Adjudication, and Scoring and Review. QUEST is designed with five proposed evaluation principles: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence.

Naphthalene is a widely available moth repellant in the Asian subcontinent. Toxicity can occur either accidentally or intentionally as a suicide attempt. An overdose can lead to a variety of clinical symptoms, including intravascular hemolysis, and can sometimes lead to life-threatening clinical situations. A young male was admitted to our center with an alleged history of ingesting an unknown quantity of naphthalene balls (mothballs). He developed methemoglobinemia, intra-vascular hemolysis, anuria, and acute kidney injury (AKI), followed by cardiorespiratory arrest. He was treated successfully with intravenous methylene blue and dialysis. Naphthalene toxicity can lead to methemoglobinemia and intravascular hemolysis. This can result in AKI caused by pigment nephropathy.

Biofungicides use living organisms and their by-products for the management of severe fungal pathogens in crop plants. They have emerged as a key player in sustainable agriculture as they are eco-friendly and cost-effective in comparison to corresponding synthetic chemical fungicides. Depending on the source of origin, biofungicides have been categorised into microbial biofungicides, including bacterial and fungal, while plant based biofungicides comprises of algal, lichen, and angiosperm based biofungicides. A number of strains of Bacillus , Streptomyces and PGPRs presents positive effects and substantially modulates plant-induced and systemic defense responses thereby strengthening plant immunity towards fungal pathogens. Among the fungal based biofungicides, Trichoderma is a well-known biocontrol agent that is utilized from a long time in disease management. Simultaneously, a number of endophytic fungi also affect plant physiological and biochemical defense by increasing the activity of various defense enzymes, gene transcripts as well as antimicrobial proteins that protects the plant from the harmful effects of pathogens. Plant based biofungicides have gained wide applicability in disease management strategies due to their ease in application. Plants contain a variety of essential oils and volatile chemicals causing deleterious effects on the growth of fungal pathogens. With advancements in separation techniques like GC–MS and LC–MS, novel bioactive secondary metabolites have been studied and isolated from different plant extracts and evaluated for biocontrol activity. The present review aims to highlight the different categories of biofungicides, their synthesis, and mode of application as well as delineate their mechanism for biocontrol of fungal pathogens in various crop plants.

ObjectivesTo date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.MethodsTo address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.ResultsTEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.DiscussionOne major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.ConclusionThe translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.

•Automated perioperative safety event reporting captures safety events more accurately than manual reporting.•Automated perioperative safety event reporting allows for large data sets to be analyzed in near real time.•Automated perioperative safety event reporting models are the foundation for predictive artificial intelligence.

Large language models (LLMs) have generated excitement and interest in their capability to impact various facets of healthcare delivery. However, the rapidly expanding literature on LLMs presents challenges in understanding recent work, associated terminology, and potential applications for healthcare professionals. In this review, we discuss the development and evolution of LLMs, especially in healthcare. We provide a description of the key terminologies associated with LLMs to improve the understanding of these terms and their application context in healthcare. Evaluation of the experiments and research related to LLMs is fundamentally important for clinicians. Thus, we provide a description of evaluation methodologies used in LLM research. Lastly, through illustrative examples of research in application of LLMs in healthcare, we showcase the opportunities to leverage this state-of-the-art artificial intelligence (AI) technique with considerations for clinical and administrative adoption both by the patients and healthcare professionals. Through this review, we hope to equip the healthcare professionals with the knowledge they need to understand LLM in healthcare research.