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Mitochondria form a complex, interconnected reticulum that is maintained through coordination among biogenesis, dynamic fission, and fusion and mitophagy, which are initiated in response to various cues to maintain energetic homeostasis. These cellular events, which make up mitochondrial quality control, act with remarkable spatial precision, but what governs such spatial specificity is poorly understood. Herein, we demonstrate that specific isoforms of the cellular bioenergetic sensor, 5′ AMP-activated protein kinase (AMPKα1/α2/β2/γ1), are localized on the outer mitochondrial membrane, referred to as mitoAMPK, in various tissues in mice and humans. Activation of mitoAMPK varies across the reticulum in response to energetic stress, and inhibition of mitoAMPK activity attenuates exercise-induced mitophagy in skeletal muscle in vivo. Discovery of a mitochondrial pool of AMPK and its local importance for mitochondrial quality control underscores the complexity of sensing cellular energetics in vivo that has implications for targeting mitochondrial energetics for disease treatment.

Colorectal cancer (CRC) is the leading cause of cancer disease and poses a significant threat to global health. Although deep learning models have been utilized to accurately diagnose CRC, they still face challenges in capturing the global correlations of spatial features, especially in complex textures and morphologically similar features. To overcome these challenges, we propose a hybrid model using a residual network and transformer encoder with mixed attention. The Residual Next Transformer Network (RNTNet) extracts spatial features from CRC images using ResNeXt. ResNeXt utilizes group convolution and skip connections to capture fine-grained features. Furthermore, a vision transformer (ViT) encoder containing a mixed attention block is designed using multiscale feature aggregation to provide global attention to the spatial features. In addition, a Grad-CAM module is added to visualize the model’s decision process to support oncologists with a second opinion. Two publicly available datasets, Kather and KvasirV1, were utilized for model training and testing. The model achieved classification accuracies of 97.96% and 98.20% on the KvasirV1 and Kather datasets, respectively. Model efficacy is also further confirmed by ROC curve analysis, where AUC values of 0.9895 and 0.9937 on the KvasirV1 and Kather datasets are obtained, respectively. Comparative study findings support that RNTNet delivers improvements in accuracy and efficiency compared to state-of-the-art methods.

Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the cancerous cells make manual diagnosis time-consuming. Recently, machine learning and deep learning methods have been utilized to diagnose bladder cancer. However, manual feature requirements for machine learning and the high volume of data for deep learning make them less reliable for real-time application. This study developed a hybrid model using CNN (Convolutional Neural Network) and less attention-based ViT (Vision Transformer) for bladder lesion diagnosis. Our hybrid model contains two blocks of the inceptionV3 to extract spatial features. Furthermore, the global co-relation of the features is achieved using hybrid attention modules incorporated in the ViT encoder. The experimental evaluation of the model on a dataset consisting of 17,540 endoscopic images achieved an average accuracy, precision and F1-score of 97.73%, 97.21% and 96.86%, respectively, using a 5-fold cross-validation strategy. We compared the results of the proposed method with CNN and ViT-based methods under the same experimental condition, and we achieved much better performance than our counterparts.

Heart disease causing cardiac cell death due to ischemia–reperfusion injury is a major cause of morbidity and mortality in the United States. Coronary heart disease and cardiomyopathies are the major cause for congestive heart failure, and thrombosis of the coronary arteries is the most common cause of myocardial infarction. Cardiac injury is followed by post-injury cardiac remodeling or fibrosis. Cardiac fibrosis is characterized by net accumulation of extracellular matrix proteins in the cardiac interstitium and results in both systolic and diastolic dysfunctions. It has been suggested by both experimental and clinical evidence that fibrotic changes in the heart are reversible. Hence, it is vital to understand the mechanism involved in the initiation, progression, and resolution of cardiac fibrosis to design anti-fibrotic treatment modalities. Animal models are of great importance for cardiovascular research studies. With the developing research field, the choice of selecting an animal model for the proposed research study is crucial for its outcome and translational purpose. Compared to large animal models for cardiac research, the mouse model is preferred by many investigators because of genetic manipulations and easier handling. This critical review is focused to provide insight to young researchers about the various mouse models, advantages and disadvantages, and their use in research pertaining to cardiac fibrosis and hypertrophy.

Background & problem statement Human metacognition, defined as the awareness and regulation of one’s cognitive processes, plays a critical role in effective learning and decision-making. With the advancement of Artificial Intelligence (AI), educational technologies now offer personalized support to human being to accomplish certain tasks. It may indirectly, enhance or hinder human metacognitive skills. However, despite growing research interest, a comprehensive understanding of global research trends in understanding how AI intermediates with human metacognition remains lacking. Purpose This study aims to systematically map the scientific landscape of how AI intermediates with human metacognition through a bibliometric analysis, identifying key contributors, influential publications, and emerging research themes. Methodology A total of 144 articles published between 1985 and 2024 were retrieved from the Scopus database. Using bibliometric tools such as VOSviewer and Bibliometrix (R-package), the study employed performance analysis, co-authorship analysis, and co-word analysis to examine publication trends, leading countries, collaboration networks, prolific journals, and thematic clusters within the field. Findings & contributions The analysis reveals a gradual increase in publications with AI and human metacognition as common point of study from 1985, with a sharp rise post-2009 and a peak in 2023. The United States leads in research output and the most cited work is by Graesser (2005), focusing on metacognitive scaffolding through intelligent tutoring systems such as iSTART and AutoTutor. The study also highlighted the limited number of empirical studies discussing the negative effects of AI on human metacognitive abilities although few studies discussed about overreliance on AI, fear of failure and other aspects. Theoretically, this research repositions AI not only as a facilitator of technology but as a cognitive collaborator that directly influences metacognitive activities such as planning, monitoring, and reflective judgment. This theoretical framework helps to develop metacognitive theory in the digital era by situating AI as a collaborator in, not a substitute for, human cognitive control.

The current investigation was carried out to study the effect of different doses of gamma irradiation on in vitro shoot cultures of carnation ( Dianthus caryophyllus L.) and characterization of mutants using ISSR markers. The trial was undertaken at the experimental farm and laboratory of the Department of Floriculture and Landscaping, Dr. YS Parmar University of Horticulture and Forestry, Nauni, Solan (HP). The experiment on ten carnation genotypes i.e. 7 mutants, 3 parents viz.; ‘Dark Rendezvous’, ‘Madras’ and ‘Tempo’ was laid out in Randomized Block Design (RBD) under polyhouse conditions. All the mutants developed after the irradiation with gamma rays showed variation in color. In this study, different morphological parameters of the mutants and their parents were recorded. Among different mutants, maximum plant height was recorded in mutant of ‘Dark Rendezvous’ i.e. ‘Rendez-Vous A 2 ’ (67.70 cm) and maximum flower size in ‘Rendez-Vous C 2 ’ (8.40 cm). Duration of flowering was recorded maximum in ‘Tempo A 1 ’ (67.67 days) and vase life in ‘Rendez-Vous C 1 ’ (14.79 days). For the majority of the characters analyzed, all mutants performed better in comparison to parents. Moreover, for the characterization of mutants at the molecular level, ten ISSR primers were used and DNA polymorphism was observed with four primers. A total of fifteen polymorphic bands were produced by these four primers. No monomorphic band was observed with any primer resulting in 100% polymorphism. Variations in the plant material can be explained through genetic polymorphism. These results directly strengthen the significance of mutation breeding in developing improved carnation cultivars and substantiate the use of molecular characterization for validating genetic variation. Furthermore, they emphasize the scope for the commercial expansion of carnation cultivation through the selection of promising mutants.

Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Public perception plays a crucial role in shaping attitudes that can facilitate the adoption of new technologies. Similarly, the acceptance of AI-driven mental health interventions is crucial in determining their effectiveness and widespread adoption. Therefore, it is essential to study public perceptions and usage of existing AI-driven mental health interventions by exploring user experiences and opinions on their future applicability, particularly in comparison to traditional, human-based interventions. This study aims to explore the use, perception, and acceptance of AI-driven mental health interventions in comparison to traditional, human-based interventions. A total of 466 adult participants from India voluntarily completed a 30-item web-based survey on the use and perception of AI-based mental health interventions between November and December 2023. Of the 466 respondents, only 163 (35%) had ever consulted a mental health professional. Additionally, 305 (65.5%) reported very low knowledge of AI-driven interventions. In terms of trust, 247 (53%) expressed a moderate level of Trust in AI-Driven Mental Health Interventions, while only 24 (5.2%) reported a high level of trust. By contrast, 114 (24.5%) reported high trust and 309 (66.3%) reported moderate Trust in Human-Based Mental Health Interventions; 242 (51.9%) participants reported a high level of stigma associated with using human-based interventions, compared with only 50 (10.7%) who expressed concerns about stigma related to AI-driven interventions. Additionally, 162 (34.8%) expressed a positive outlook toward the future use and social acceptance of AI-based interventions. The majority of respondents indicated that AI could be a useful option for providing general mental health tips and conducting initial assessments. The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma. Major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis. There is a general lack of awareness about AI-driven mental health interventions. However, AI shows potential as a viable option for prevention, primary assessment, and ongoing mental health maintenance. Currently, people tend to trust traditional mental health practices more. Stigma remains a significant barrier to accessing traditional mental health services. Currently, the human touch remains an indispensable aspect of human-based mental health care, one that AI cannot replace. However, integrating AI with human mental health professionals is seen as a compelling model. AI is positively perceived in terms of accessibility, availability, and destigmatization. Knowledge and perceived trustworthiness are key factors influencing the acceptance and effectiveness of AI-driven mental health interventions.

Organic farming is an influential practice for minimizing the environmental and ecological impact of sustainable development. The use of organic materials in agricultural practices can reduce the adverse effects on the environment by preserving its natural cycles during the recovery process. The study evaluated the effects of different organic growing media and liquid organic fertilizer i.e. Jeevamrit on foliage expansion, aesthetic appeal and nutritional properties of ornamental kale genotype ‘Kt OK-2’. The growing media tested included a combination of leaf mould collected from ban oak soil and cocopeat in a (1:1, v/v), cocopeat alone and leaf mould alone. Jeevamrit was soil drenched at weekly, fortnightly and monthly intervals and was compared with a control using the recommended dose of fertilizers. Results indicated that the growing medium of leaf mould and cocopeat (1:1, v/v) demonstrated superior qualitative and quantitative effects on plant growth compared to the other media, owing to its high nutritional content. Jeevamrit @ 20% at weekly intervals further enhanced plant performance. This combination of growing medium and jeevamrit led to the highest measurements of plant height (22.06 cm), spread (35.75 cm), number of leaves (46.43), rosette diameter (23.55 cm), percentage of green and coloured leaves per plant (36.84% and 67.73%) and pot display life (68.55 days). Furthermore, total chlorophyll content (32.88 mg/100 g), anthocyanin content (0.64 mg/100 g) and total carotenoid content (2.13 mg/100 g) of the plant was recorded highest for the same treatment combination. Additionally, the study assessed physico-chemical properties of the soil. The results underscore the effectiveness of using a balanced growing medium with regular organic manure applications to optimize plant health and aesthetic quality in ornamental kale.

Obesity-linked (type 2) diabetic nephropathy (T2DN) has become the largest contributor to morbidity and mortality in the modern world. Recent evidences suggest that inflammation may contribute to the pathogenesis of T2DN and T-regulatory cells (Treg) are protective. We developed a novel cytokine (named IL233) bearing IL-2 and IL-33 activities in a single molecule and demonstrated that IL233 promotes Treg and T-helper (Th) 2 immune responses to protect mice from inflammatory acute kidney injury. Here, we investigated whether through a similar enhancement of Treg and inhibition of inflammation, IL233 protects from T2DN in a genetically obese mouse model, when administered either early or late after the onset of diabetes. In the older mice with obesity and microalbuminuria, IL233 treatment reduced hyperglycemia, plasma glycated proteins, and albuminuria. Interestingly, IL233 administered before the onset of microalbuminuria not only strongly inhibited the progression of T2DN and reversed diabetes as indicated by lowering of blood glucose, normalization of glucose tolerance and insulin levels in islets, but surprisingly, also attenuated weight gain and adipogenicity despite comparable food intake. Histological examination of kidneys showed that saline control mice had severe inflammation, glomerular hypertrophy, and mesangial expansion, which were all attenuated in the IL233 treated mice. The protection correlated with greater accumulation of Tregs, group 2 innate lymphoid cells (ILC2), alternately activated macrophages and eosinophils in the adipose tissue, along with a skewing toward T-helper 2 responses. Thus, the novel IL233 cytokine bears therapeutic potential as it protects genetically obese mice from T2DN by regulating multiple contributors to pathogenesis. A novel bifunctional cytokine IL233, bearing IL-2 and IL-33 activities reverses inflammation and protects from type-2 diabetic nephropathy through promoting T-regulatory cells and type 2 immune response.

Over the last few years, the microbiome has emerged as a high-priority research area to discover missing links between brain health and gut dysbiosis. Emerging evidence suggests that the commensal gut microbiome is an important regulator of the gut–brain axis and plays a critical role in brain physiology. Engaging microbiome-generated metabolites such as short-chain fatty acids, the immune system, the enteric nervous system, the endocrine system (including the HPA axis), tryptophan metabolism or the vagus nerve plays a crucial role in communication between the gut microbes and the brain. Humans are exposed to a wide range of pollutants in everyday life that impact our intestinal microbiota and manipulate the bidirectional communication between the gut and the brain, resulting in predisposition to psychiatric or neurological disorders. However, the interaction between xenobiotics, microbiota and neurotoxicity has yet to be completely investigated. Although research into the precise processes of the microbiota–gut–brain axis is growing rapidly, comprehending the implications of environmental contaminants remains challenging. In these milieus, we herein discuss how various environmental pollutants such as phthalates, heavy metals, Bisphenol A and particulate matter may alter the intricate microbiota–gut–brain axis thereby impacting our neurological and overall mental health.