Two-dimensional materials have attracted great scientific attention due to their unusual and fascinating properties for use in electronics, spintronics, photovoltaics, medicine, composites, etc. Graphene, transition metal dichalcogenides such as MoS₂, phosphorene, etc., which belong to the family of two-dimensional materials, have shown great promise for gas sensing applications due to their high surface-to-volume ratio, low noise and sensitivity of electronic properties to the changes in the surroundings. Two-dimensional nanostructured semiconducting metal oxide based gas sensors have also been recognized as successful gas detection devices. This review aims to provide the latest advancements in the field of gas sensors based on various two-dimensional materials with the main focus on sensor performance metrics such as sensitivity, specificity, detection limit, response time, and reversibility. Both experimental and theoretical studies on the gas sensing properties of graphene and other two-dimensional materials beyond graphene are also discussed. The article concludes with the current challenges and future prospects for two-dimensional materials in gas sensor applications.

Background: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer’s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the “gold standard” is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. Purpose: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. Methods: The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. Conclusion: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Background: Sleep and immune function are interconnected aspects of health that mutually impact each other in disease development and inflammatory homeostasis. Different aspects of immunology are regulated by different sleep characteristics, impacting on specific aspects of immune function including cytokine production and T-cell activity. Ongoing disruptions of sleep have been linked to heightened inflammation and are suspected in the pathogenesis and disease course of a range of life-style-related illnesses, including diabetes and neurodegenerative diseases. Summary: This review provides a comprehensive overview of knowledge on the interaction of sleep with the immune system, its modulation of inflammatory balance, and the pathogenesis of many diseases. It emphasizes how sleep deficiency compromises immune function by means of a systemic, low-grade inflammatory response, while adequate sleep promotes intense immune responses and thus enables efficient pathogen clearance and the maintenance of immune memory. The mutual influence of sleep on the immune system underlines its critical involvement in health preservation and the course of disease. Key Message: Sleep plays an indispensable role in immune health, mediating the efficiency of immune responses and the course of the regulation of inflammation. Chronic sleep deprivation can result in a low-grade inflammation that substantially contributes to the onset and exacerbation of metabolic and neurodegenerative disorders. The intimate linkage between sleep and immune function can be one strategic approach to therapy, improving health outcomes by leveraging this sleep-immune connection.

Cancer is complex because of the critical imbalance in genetic regulation as characterized by both the overexpression of oncogenes (OGs), mainly through mutations, amplifications, and translocations, and the inactivation of tumor-suppressor genes (TSGs), which entail the preservation of genomic integrity by inducing apoptosis to counter the malignant growth. Reviewing the intricate molecular interplay between OGs and TSGs draws attention to their cell cycle, apoptosis, and cancer metabolism regulation. In the present review, we discuss seminal discoveries, such as Knudson’s two-hit hypothesis, which framed the field’s understanding of cancer genetics, leading to the next breakthroughs with next-generation sequencing and epigenetic profiling, revealing novel insights into OG and TSG dysregulation with opportunities for targeted therapy. The key pathways, such as MAPK/ERK, PI3K/AKT/mTOR, and Wnt/β-catenin, are presented in the context of tumor progression. Importantly, we further highlighted the advances in therapeutic strategies, including inhibitors of KRAS and MYC and restoration of TSG function, despite which mechanisms of resistance and tumor heterogeneity pose daunting challenges. A high-level understanding of interactions between OG-TSGs forms the basis for effective, personalized cancer treatment—something to strive for in better clinical outcomes. This synthesis should integrate foundational biology with translation and, in this case, contribute to the ongoing effort against cancer.

Agriculture is the backbone of Indian economy and farmers play significant role in sustaining the sector. However, farmers face several challenges such as poor market access and limited access to market information which adversely affect their marketing performance. Therefore, this study aimed to examine the marketing performance and factors influencing farmers choice for agricultural output marketing channels in garden pea in the Indian state of Himachal Pradesh. The study utilized primary data collected through structured questionnaires administered via the interview method to a sample size of 400 farmers selected through a multistage stratified random sampling technique. The data collected were analyzed using descriptive statistics and acharya approach to study the marketing performance. Further, a multinomial logistic regression model was used to determine the factors that influence farmers marketing choice of marketing channels. Additionally, the constraints experienced by farmers during marketing were investigated using garret ranking. The study revealed that farmers who sold their pea directly to consumer had higher marketing performance than those who sold through market intermediaries such as local traders, commission agents and wholesalers. Further, the results indicated that farm income, farm experience, distance to the market and market information were significant determinants of farmers choice for marketing channels. Whereas, lack of market consultancy, high commission chargers and poor transportation were the major constraints faced by farmers during marketing of pea. The findings of this study highlight the need for policymakers to develop and implement interventions that enhance farmers marketing skill, facilitate access to markets, improve negotiation power which can lead to higher incomes for farmers, improve their livelihoods, better quality produce for consumers and a more efficient and sustainable food system.