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Abstract Introduction Continuing Medical Education (CME) is vital for healthcare professionals to remain current with advancements and maintain their competence. In India, CME remains in its early stages, lacking a systematic approach. There is a pressing need for a comprehensive framework that addresses needs assessment, program development, accreditation, and credit allocation. Specifically, there is a gap in delivering targeted CME that meets the needs of different medical professionals at appropriate times. The aim of this study is to employ Kern’s six steps of curriculum design to plan and evaluate a faculty development program focused on Entrustable Professional Activities (EPAs) for MD (Doctor of Medicine) Biochemistry professionals. Methods This study utilized Kern’s six-step approach to design a training program on Entrustable Professional Activities (EPAs) tailored for MD Biochemistry professionals. Approval was obtained from the Institutional Human Ethics Committee. Following problem identification, a targeted needs assessment was conducted through expert consultations. The goal of the program was established, focusing on the development and delivery of an interactive online CME program. The program was developed, implemented and evaluated using surveys and feedback questionnaires. Results A comprehensive literature review revealed a notable gap in EPA-related works for MD Biochemistry program. Expert consultations highlighted significant training needs and barriers, such as limited resources and professional commitments. Consequently, the program’s primary objective was to raise awareness and sensitize participants to EPA development. A 4-h online CME was designed, featuring expert-led sessions and collaborative group activities. The program attracted 152 participants and utilized interactive elements, such as polls and chats, to foster engagement. Group activities allowed participants to apply concepts in EPA validation. Pre- and post-program surveys demonstrated significant improvements in participants’ knowledge and confidence. Feedback highlighted the practical relevance of the content and the interactive, hands-on nature of the sessions. Conclusion Kern’s six-step approach provided a structured and effective framework for developing the CME program, addressing identified needs and barriers, and enhancing faculty development in MD Biochemistry. This study underscores the importance of a systematic approach in CME to improve program effectiveness. Trial registration Not applicable.

Abstract Background Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers. Methods We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). Performance of the input formats was compared using a combined 5 × 2 cross-validation F-test. Results The dataset included 542 pairs of montages: 171 montage pairs from 45 cases at time of cardiomyopathy diagnosis or pre-diagnosis and 371 pairs from 70 at-risk survivors who didn’t develop cardiomyopathy during follow-up (non-case). The DCNN trained to distinguish between non-case and time of cardiomyopathy diagnosis or pre-diagnosis case montages achieved an AUROC of 0.89 ± 0.02, AUPRC 0.83 ± 0.03, and F1-score: 0.76 ± 0.04. When limited to smaller subsets of case data (e.g., ≥ 1 or 2 years pre-diagnosis), performance worsened. Model input format did not impact performance accuracy across models. Conclusions This methodology is a promising first step toward development of a DCNN capable of accurately differentiating pre-diagnosis versus non-case echocardiograms to predict survivors more likely to develop cardiomyopathy. Graphical Abstract

Background: The coronary arteries, being the major vascular channels, are known to affect the complete circulatory system and hemodynamics of the body. However, as a matter of fact, the coronary arteries are known to have many anatomical and developmental variations along all the morphological aspects, which include location of orifices, various routes that the artery and its tributaries follow, and of course, the pattern of branching of the main artery. The developmental anomalies in the arterial structures result in abnormalities of arterial blood flow, hypoxia, and other conditions which may lead to atherosclerosis of the coronary artery. To the best of our knowledge, the current systematic review and meta-analysis aim to assess the association between variations in the branching pattern of the coronary artery. Moreover, we propose future studies to assess the association and preventive strategies for clinicians and various health-care workers. Methodology: We performed an electronic search of PubMed, MEDLINE, Cochrane Central Register, EMBASE, Science Citation Index, Web of Science, and Database of Abstracts of Reviews of Effects, and various clinical tribulation registries till 2022. Two independent reviewers extracted the data, and the present meta-analysis was performed with the help of RevMan version 5.4. For the present meta-analysis, we used 10-year database from 2012 to 2022. Results: Appropriate data were fetched from studies that were included for the meta-analysis, and the data were presented in tables for evaluation and drawing of accurate results. A total of 904 articles were found after searching all of the major medical databases. Thirteen more sources of reference were included in the search. After primary screening, full text articles of around 148 were analyzed, and among them, 114 were excluded because those articles were in the category of #case series, reports, or #letters to the editors, #animal studies or #irrelevant documents, reporting duplicate documents, or broadcasting a certain variant as opposed to occurrence. After the complete screening and analysis of the data were done, the final number of articles that were included in the meta-analysis was 29 (n = 29). Conclusion: The present meta-analysis is an attempt to review and analyze the recent literature published on the variation in anatomy and developmental abnormalities of the coronary arteries in the differing population across the geography of the globe. The structural aspects of the coronary arteries, and not just the lifestyle issues, are the ones which are associated with coronary artery disease (CAD), a major cause of mortality in the population. However, the anatomical factors are not attending limelight as other obesity- or age-related issues for the same. Hence, the present review aimed to deliver a thorough understanding of the most predominant branching pattern of the coronary artery seen among Asian population. This study will be beneficial to cardiologists and radiologists when performing various treatments such as coronary angiograms, coronary angioplasty, bypass grafting, and other medical procedures and can also lead to prevention of the CAD.

Abstract Biofilms in nature often exist as communities. In this study, an experimental mixed-species community consisting of Pseudomonas aeruginosa, Pseudomonas protegens and Klebsiella pneumoniae was used to investigate how AI-2 transporters affect interspecies interactions and composition. The K. pneumoniae lsrB/lsrD deletion mutants had a 10–25-fold higher concentration of extracellular AI-2 compared to the wild-type. Although these deletion mutants produced monospecies biofilms of similar biomass, the substitution of these mutants for the parental strain significantly altered composition. Dual-species biofilm assays demonstrated that the changes in composition were due to the cumulative effect of pairwise interactions. It was further revealed that K. pneumoniae being present physically in the consortium was important in AI-2 mediating composition in the consortium, and that AI-2 transporters were crucial in achieving maximum biomass in the community. In conclusion, these findings demonstrate that AI-2 transporters mediate interspecies interactions and is important in maintaining the compositional equilibrium of the community.

Abstract The concept of a crisp graph is essential in the study of outerplanar graphs because outerplanar graphs are a unique type of planar graphs containing special characteristics. One of the core concepts of crisp graphs, the notion of a subgraph, is utilized primarily to solve difficult data problems. In this paper, researching outerplanar graphs in fuzzy graphs is the main aim of the work. By allowing edges connecting vertices to have varying levels of membership or fuzziness, a fuzzy graph is a mathematical framework that develops on the concept of crisp graphs. Because of their capacity to describe unclear or imprecise relationships between items, fuzzy outerplanar graphs might have potential applications. The subgraphs of fuzzy outerplanar graphs are revealed by eliminating specific vertices or edges from the fuzzy graphs. Furthermore, maximum, and maximal fuzzy outerplanar subgraphs defined for both vertex and edge deletion are examined using examples. Some of the connections between the concepts discussed are represented as theorems and conclusions. An application for the layout of a bypass road is discussed using the proposed concepts.

The initial size distribution and morphological parameters of sodium aerosols are critical in evaluating the accidental suspended aerosol behaviour in Sodium-cooled Fast Reactor (SFR) containment. Mass-based measurements were more familiar in characterizing the sodium aerosols. Real-time number size distribution measurements are carried out in this study. The sensitivity analysis of sodium aerosol effective density (ρe) in deriving the actual number size distributions from the measured Aerodynamic Particle Size Distributions (APSD) and predicting suspended aerosol dynamics is presented. Tests are conducted in a 1 m3 chamber at 47 ± 3% RH for different initial mass concentrations (M0) of 0.1, 1, and 2.9 g/m3. The initial APSDs measured just after the generation completions are observed to be polydisperse with the count median aerodynamic diameter (CMAD)

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

Abstract Background Rare disorders comprise of ~ 7500 different conditions affecting multiple systems. Diagnosis of rare diseases is complex due to dearth of specialized medical professionals, testing labs and limited therapeutic options. There is scarcity of data on the prevalence of rare diseases in different populations. India being home to a large population comprising of 4600 population groups, of which several thousand are endogamous, is likely to have a high burden of rare diseases. The present study provides a retrospective overview of a cohort of patients with rare genetic diseases identified at a tertiary genetic test centre in India. Results Overall, 3294 patients with 305 rare diseases were identified in the present study cohort. These were categorized into 14 disease groups based on the major organ/ organ system affected. Highest number of rare diseases (D = 149/305, 48.9%) were identified in the neuromuscular and neurodevelopmental (NMND) group followed by inborn errors of metabolism (IEM) (D = 47/305; 15.4%). Majority patients in the present cohort (N = 1992, 61%) were diagnosed under IEM group, of which Gaucher disease constituted maximum cases (N = 224, 11.2%). Under the NMND group, Duchenne muscular dystrophy (N = 291/885, 32.9%), trinucleotide repeat expansion disorders (N = 242/885; 27.3%) and spinal muscular atrophy (N = 141/885, 15.9%) were the most common. Majority cases of β-thalassemia (N = 120/149, 80.5%) and cystic fibrosis (N = 74/75, 98.7%) under the haematological and pulmonary groups were observed, respectively. Founder variants were identified for Tay-Sachs disease and mucopolysaccharidosis IVA diseases. Recurrent variants for Gaucher disease (GBA:c.1448T > C), β-thalassemia (HBB:c.92.+5G > C), non-syndromic hearing loss (GJB2:c.71G > A), albinism (TYR:c.832 C > T), congenital adrenal hyperplasia (CYP21A2:c.29–13 C > G) and progressive pseudo rheumatoid dysplasia (CCN6:c.298T > A) were observed in the present study. Conclusion The present retrospective study of rare disease patients diagnosed at a tertiary genetic test centre provides first insight into the distribution of rare genetic diseases across the country. This information will likely aid in drafting future health policies, including newborn screening programs, development of target specific panel for affordable diagnosis of rare diseases and eventually build a platform for devising novel treatment strategies for rare diseases.

Abstract Background Phytochemicals possess a wide range of anti‐tumor properties, including the modulation of autophagy and regulation of programmed cell death. Autophagy is a critical process in cellular homeostasis and its dysregulation is associated with several pathological conditions, such as cancer, neurodegenerative diseases, and diabetes. In cancer, autophagy plays a dual role by either promoting tumor growth or suppressing it, depending on the cellular context. During autophagy, autophagosomes engulf cytoplasmic components such as proteins and organelles. LC3‐II (microtubule‐associated protein 1 light chain 3‐II) is an established marker of autophagosome formation, making it central to autophagy monitoring in mammals. Objective To explore the regulatory role of phytochemicals in LC3‐mediated autophagy and their potential therapeutic impact on cancer. The review emphasizes the involvement of autophagy in tumor promotion and suppression, particularly focusing on autophagy‐related signaling pathways like oxidative stress through the NRF2 pathway, and its implications for genomic stability in cancer development. Methods The review focuses on a comprehensive analysis of bioactive compounds including Curcumin, Celastrol, Resveratrol, Kaempferol, Naringenin, Carvacrol, Farnesol, and Piperine. Literature on these compounds was examined to assess their influence on autophagy, LC3 expression, and tumor‐related signaling pathways. A systematic literature search was conducted across databases including PubMed, Scopus, and Web of Science from inception to 2023. Studies were selected from prominent databases, focusing on their roles in cancer diagnosis and therapeutic interventions, particularly in relation to LC3‐mediated mechanisms. Results Phytochemicals have been shown to modulate autophagy through the regulation of LC3‐II levels and autophagic flux in cancer cells. The interaction between autophagy and other cellular pathways such as oxidative stress, inflammation, and epigenetic modulation highlights the complex role of autophagy in tumor biology. For instance, Curcumin and Resveratrol have been reported to either induce or inhibit autophagy depending on cancer type, influencing tumor progression and therapeutic responses. Conclusion Targeting autophagy through LC3 modulation presents a promising strategy for cancer therapy. The dual role of autophagy in tumor suppression and promotion, however, necessitates careful consideration of the context in which autophagy is induced or inhibited. Future research should aim to delineate these context‐specific roles and explore how phytochemicals can be optimized for therapeutic efficacy. Novel therapeutic strategies should focus on the use of bioactive compounds to fine‐tune autophagy, thereby maximizing tumor suppression and inducing programmed cell death in cancer cells.

# Background The objective of residency recruitment is to select the most appropriate candidate. While cognitive skills are identified before an interview and can be measured objectively, non-cognitive skills can be harder to discern. These non-cognitive skills though are a good predictors of future residency performance. A structured behavioral interview is better at identifying noncognitive skills compared to a traditional interview. # Objective Compare the noncognitive traits identified in the interviews with those identified in resident evaluations # Methods Using the semi-annual evaluations 6 residents were split between satisfactory group and excellent group. Behavioral-based interviews and traditional unstructured interviews conducted on the same individual were compared and the results of the interview were compared to the semi-annual and annual evaluations submitted by the teaching faculty and the program directors. The interviews were analyzed for non-cognitive skills. # Results Qualitative analysis of behavioral interview narratives and the narrative part of the semiannual evaluations independently identified the non-cognitive characteristics of adaptability, decisiveness, time management, judgment, and the ability to work in a team. It also identified other essential non-cognitive skills necessary for an anesthesiologist, such as ability to prioritize, study techniques, ability to destress. # Conclusion The noncognitive traits found in the excellent group response to the structured interview match those found in the semi-annual evaluations. In this case individuals who are suited to the field of anesthesiology are adaptable, decisive, team-players with excellent judgment.

Background: Avocado intake has been associated with improvements in diet quality. Whether this response is because of avocado intake, per se, or combined with a food and/or nutrient displacement (D) has yet to be determined. Objectives: This secondary analysis, conducted using dietary data from the Habitual Diet and Avocado Trial, sought to assess the effect of consuming a large avocado (168 g, 281 kcal) daily in the avocado-supplemented diet (AD) group compared with the habitual diet (HD) group on food and nutrient D. Methods: Using data from 3 unannounced telephone 24-h dietary recalls conducted during the 6-mo intervention period, food intake data were evaluated for 898 participants, aged 25–87 y, and a BMI range of 20.5–60.3 kg/m2. The food group distribution, energy, and nutrient intake of the AD group (n = 436) were calculated and compared with those of the HD group (n = 462). Results: The AD group had a higher daily intake of energy (159 ± 575 kcal) (P < 0.001), potassium (3193 ± 817 mg compared with 2419 ± 843 mg) (P < 0.005), fiber (30 ± 8 g compared with 19 ± 9 g) (P < 0.05), and a lower daily intake of animal protein (49 ± 33 g compared with 55 ± 24 g) (P = 0.02) compared with the HD group. Partial D with an avocado was observed for energy (43%), total fat (23%), and carbohydrate (87%), indicating a lower intake of these nutrients from non-avocado sources in the AD group. Food group analysis revealed a lower consumption of animal-derived protein from red meat, processed meats, poultry, and fish in the AD group, with no significant differences observed in dairy and egg intake between groups. Conclusions: Incorporating 1 avocado daily led to favorable modifications in the dietary composition of participants, including an increase in potassium and fiber intake, which can improve diet quality.This trial was registered at https://clinicaltrials.gov as NCT03528031.

Postsecondary students with disabilities (SWDs) face significant challenges to their academic and personal well-being. There is a demand for more supportive strategies to facilitate fulfilling and equitable academic experiences for SWDs. This review aimed to (1) determine the effectiveness of various interventions, programs, and accommodations for enhancing the overall well-being of postsecondary SWDs and (2) evaluate the implications of these support strategies for disability service providers at postsecondary institutions. A narrative review was conducted, examining 13 studies aimed at enhancing the well-being of postsecondary SWDs. Studies were classified using the International Classification of Functioning, Disability and Health (ICF) Model. Ergonomics, assistive technology, and counselling programs had positive improvements on the academic success of SWDs. In SWDs, health status was associated with higher community participation, and physical interventions were beneficial to reducing pain over time. Coaching and mentoring opportunities were beneficial for successful goal fulfilment. However, there remains a risk of digital eye strain when using technology to support postsecondary SWDs. The findings indicate that a range of support strategies can benefit several different components of SWDs experiences. This review identifies research gaps and opportunities to examine support strategies to improve the physical and mental well-being of SWDs.

Summary: Background: We aimed to study the clinical characteristics, myocardial injury, and longitudinal outcomes of COVID-19 vaccine-associated myocarditis (C-VAM). Methods: In this longitudinal retrospective observational cohort multicenter study across 38 hospitals in the United States, 333 patients with C-VAM were compared with 100 patients with multisystem inflammatory syndrome in children (MIS-C). We included patients ≤30 years of age with a clinical diagnosis of acute myocarditis after COVID-19 vaccination based on clinical presentation, abnormal biomarkers and/or cardiovascular imaging findings. Demographics, past medical history, hospital course, biochemistry results, cardiovascular imaging, and follow-up information from April 2021 to November 2022 were collected. The primary outcome was presence of myocardial injury as evidenced by late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging. Findings: Patients with C-VAM were predominantly white (67%) adolescent males (91%, 15.7 ± 2.8 years). Their initial clinical course was more likely to be mild (80% vs. 23%, p 15 years, OR 2.74 [95% CI: 1.28, 5.83, p = 0.009]) and when C-VAM occurred after the first or second dose as compared to the third dose of mRNA vaccine. Mid-term clinical outcomes of C-VAM at a median follow-up of 178 days (IQR 114–285 days) were reassuring. No cardiac deaths or heart transplantations were reported until the time of submission of this report. LGE persisted in 60% of the patients at follow up. Interpretation: Myocardial injury at initial presentation and its persistence at follow up, despite a mild initial course and favorable mid-term clinical outcome, warrants continued clinical surveillance and long-term studies in affected patients with C-VAM. Funding: The U.S. Food and Drug Administration.

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed across several assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

Lacticaseibacillus casei KACC92338 was originally isolated from Korean raw milk. The antioxidant activities and protective effect in vitro of this strain were evaluated extensively. The results showed that KACC92338 can tolerate hydrogen peroxide up to 2 mM and cell-free supernatant (CFS) had higher scavenging rates for DPPH, hydroxyl radical, reducing power, and iron chelating activities with 95.61 ± 1.59%, 34.10 ± 1.93%, 2.220 ± 0.82 and 81.06 ± 1.06%, respectively. Meanwhile, the CFS showed a protective effect on yeast cells against 10 mM hydrogen peroxide with a survival rate of 76.05 ± 5.65%. To explore the probiotic potential of KACC92338, whole genome assembly and gene clusters with probiotic properties were further analyzed. The genome size was 3,050,901 bp with a 47.96% GC ratio, and 63 contigs. The genome contains 3,048 genes composed of 2,981 coding sequences and 67 RNAs (including 57 tRNAs +9 rRNAs +1 tmRNA). Average Nucleotide Identity and genome-based taxonomy showed that the KACC92338 genome had close similarity with L. casei strains with 96% ANI. Functional annotation by EggNOG and KEGG revealed the presence of numerous genes putatively involved in carbohydrate- and amino acid-transport and metabolism, genetic information processing, and signaling and cellular processes. Additionally, several genes conferring probiotic characteristics such as tolerance to stress, heat, cold, acid, bile salts, oxidative stress, immunomodulation, and adhesion to intestinal epithelium were identified. Notably absent were acquired antibiotic resistance genes, virulence, and pathogenic factors, that prove KACC92338 is a safe strain. Besides, the defense mechanisms of KACC92338 include six prophage regions and three clustered regularly interspaced short palindromic repeat (CRISPR) arrays as acquired immune systems against mobile elements. Further, the BAGEL4 database determined antimicrobial bacteriocin clusters of class IIb: sakacin-P, Enterolysin_A, sactipeptides, and Enterocin X, which suggests the strain could exhibit a wide range of antimicrobial functions. Together, these findings show that the L. casei KACC92338 strain can be a potential probiotic candidate in producing functional fermented foods-, health care- and skin care products- with antioxidant properties. However, a few more mechanistic studies are necessary on the safety assurance and potential application of the strain as a probiotic agent.

The SARS-CoV-2 main protease (Mpro) is essential for viral replication because it is responsible for the processing of most of the non-structural proteins encoded by the virus. Inhibition of Mpro prevents viral replication and therefore constitutes an attractive antiviral strategy. We set out to develop a high-throughput Mpro enzymatic activity assay using fluorescently labeled peptide substrates. A library of fluorogenic substrates of various lengths, sequences and dye/quencher positions was prepared and tested against full length SARS-CoV-2 Mpro enzyme for optimal activity. The addition of buffers containing strongly hydrated kosmotropic anion salts, such as citrate, from the Hofmeister series significantly boosted the enzyme activity and enhanced the assay detection limit, enabling the ranking of sub-nanomolar inhibitors without relying on the low-throughput Morrison equation method. By comparing cooperativity in citrate or non-citrate buffer while titrating the Mpro enzyme concentration, we found full positive cooperativity of Mpro with citrate buffer at less than one nanomolar (nM), but at a much higher enzyme concentration (∼320 nM) with non-citrate buffer. In addition, using a tight binding Mpro inhibitor, we confirmed there was only one active catalytical site in each Mpro monomer. Since cooperativity requires at least two binding sites, we hypothesized that citrate facilitates dimerization of Mpro at sub-nanomolar concentration as one of the mechanisms enhances Mpro catalytic efficiency. This assay has been used in high-throughput screening and structure activity relationship (SAR) studies to support medicinal chemistry efforts. IC50 values determined in this assay correlates well with EC50 values generated by a SARS-CoV-2 antiviral assay after adjusted for cell penetration.

Background: Weight and waist gain are significant concerns in adulthood. Both weight and waist gain are particularly important among South Asians, known to have an increased risk of developing chronic cardiometabolic complications at any body mass index compared to other racial and ethnic groups. The aim of this study was to investigate factors predicting weight and waist gain in a longitudinal cohort of South Asians living in the US (United States). Methods: This was a prospective analysis using data from exam 1 (2010–2013) and exam 2 (2015–2018) of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study, a prospective cohort study of South Asians (recruited from San Francisco and Chicago), with a mean 4.8 years of follow-up. Results: Of 634 participants studied (42.7 % women, mean age 55 years, BMI 25.7 kg/m2, weight 70.4 kg at exam 1), 34.7 % had gained ≥5 % weight and 32.3 % gained ≥5 % waist at exam 2. In the adjusted models, older age, higher number of years of US residence, and having diabetes were associated with lower odds of weight gain; being female and having higher adiponectin were associated with higher odds of weight gain. Being female and being employed full/part time or being retired predicted lower likelihood of waist gain. Being single, separated/divorced, having a higher leptin and a higher C-reactive protein level predicted higher likelihood of waist gain. Conclusions: The current study identified several social, demographic, and clinical factors that can serve as targets for obesity interventions among US South Asians. In addition, this study also raises hypotheses about associations of adipokine levels with weight and waist gain.

From the inception of artificial intelligence (AI) in optical coherence tomography (OCT) for glaucoma diagnosis, the emphasis has always been on models focusing on visual data. The visual signals are extrapolated from the optic disc and retinal nerve fibre layer (RNFL) region and are processed visually. This manuscript introduces a novel bimodal AI model that combines visual data from OCT with numerical metrics such as RNFL thickness, vCDR, rim area, and cup volume. Furthermore, our unique model utilises an algorithm that goes one step further than existing models in the literature by providing multi-classification (normal vs mild/moderate vs severe) as opposed to binary classification.

Abstract Dynamic regulation of gene expression is fundamental for cellular adaptation to exogenous stressors. P-TEFb-mediated pause-release of RNA polymerase II (Pol II) is a conserved regulatory mechanism for synchronous transcriptional induction in response to heat shock, but this pro-survival role has not been examined in the applied context of cancer therapy. Using model systems of pediatric high-grade glioma, we show that rapid genome-wide reorganization of active chromatin facilitates P-TEFb-mediated nascent transcriptional induction within hours of exposure to therapeutic ionizing radiation. Concurrent inhibition of P-TEFb disrupts this chromatin reorganization and blunts transcriptional induction, abrogating key adaptive programs such as DNA damage repair and cell cycle regulation. This combination demonstrates a potent, synergistic therapeutic potential agnostic of glioma subtype, leading to a marked induction of tumor cell apoptosis and prolongation of xenograft survival. These studies reveal a central role for P-TEFb underpinning the early adaptive response to radiotherapy, opening avenues for combinatorial treatment in these lethal malignancies.

Abstract The genome of Pseudomonas aeruginosa encodes three type VI secretion systems, each comprising a dozen distinct proteins, which deliver toxins upon T6SS sheath contraction. The least conserved T6SS component, TssA, has variations in size which influence domain organisation and structure. Here we show that the TssA Nt1 domain interacts directly with the sheath in a specific manner, while the C-terminus is essential for oligomerisation. We built chimeric TssA proteins by swapping C-termini and showed that these can be functional even when made of domains from different TssA sub-groups. Functional specificity requires the Nt1 domain, while the origin of the C-terminal domain is more permissive for T6SS function. We identify two regions in short TssA proteins, loop and hairpin, that contribute to sheath binding. We propose a docking mechanism of TssA proteins with the sheath, and a model for how sheath assembly is coordinated by TssA proteins from this position.

Abstract Background Current clinical diagnosis pathway for lysosomal storage disorders (LSDs) involves sequential biochemical enzymatic tests followed by DNA sequencing, which is iterative, has low diagnostic yield and is costly due to overlapping clinical presentations. Here, we describe a novel low-cost and high-throughput sequencing assay using single-molecule molecular inversion probes (smMIPs) to screen for causative single nucleotide variants (SNVs) and copy number variants (CNVs) in genes associated with 29 common LSDs in India. Results 903 smMIPs were designed to target exon and exon–intron boundaries of targeted genes (n = 23; 53.7 kb of the human genome) and were equimolarly pooled to create a sequencing library. After extensive validation in a cohort of 50 patients, we screened 300 patients with either biochemical diagnosis (n = 187) or clinical suspicion (n = 113) of LSDs. A diagnostic yield of 83.4% was observed in patients with prior biochemical diagnosis of LSD. Furthermore, diagnostic yield of 73.9% (n = 54/73) was observed in patients with high clinical suspicion of LSD in contrast with 2.4% (n = 1/40) in patients with low clinical suspicion of LSD. In addition to detecting SNVs, the assay could detect single and multi-exon copy number variants with high confidence. Critically, Niemann-Pick disease type C and neuronal ceroid lipofuscinosis-6 diseases for which biochemical testing is unavailable, could be diagnosed using our assay. Lastly, we observed a non-inferior performance of the assay in DNA extracted from dried blood spots in comparison with whole blood. Conclusion We developed a flexible and scalable assay to reliably detect genetic causes of 29 common LSDs in India. The assay consolidates the detection of multiple variant types in multiple sample types while having improved diagnostic yield at same or lower cost compared to current clinical paradigm.

Introduction: Tuberous sclerosis complex (TSC) is a rare autosomal dominant disorder. Facial angiofibromas are the most common cutaneous findings of TSC. Treatment modalities such as laser, surgery, and/or cryotherapy are employed. Topical therapy with Sirolimus, an mTOR inhibitor, showed beneficial effects. Objective: To study the effects of topical sirolimus (0.1%) on Facial Angiofibromas in patients of TSC. Methodology: Four patients with facial angiofibromas were included. They applied Sirolimus preparation twice daily, for 3 months. The Facial Angiofibroma Severity Index (FASI) was recorded pre-intervention, at 3 months and after 6 months. Results: All the patients showed a reduction in the FASI score at the end of three months of therapy. In three patients, on discontinuing therapy, there was no change in the FASI score at the end of six months, i.e., FASI 3 and FASI 6 were the same. Conclusion: Topical sirolimus is an effective treatment for facial angiofibroma in patients with TSC.

Abstract Titanium dioxide nanoparticles (TiO2-NPs) are a promising anode material for Lithium-ion batteries (LIBs) due to their good rate capability, low cost, non-toxicity, excellent structural stability, extended cycle life, and low volumetric change (∼4%) during the Li+ insertion/de-insertion process. In the present paper, anatase TiO2-NPs with an average particle size of ~ 12 nm were synthesized via a green synthesis route using Beta vulgaris (Beetroot) extract, and the synthesized TiO2-NPs were evaluated as anode material in LIBs. Furthermore, we employed an aqueous binder (1:1 mixture of carboxy methyl cellulose and styrene butadiene) for electrode processing, making the process cost-effective and environmentally friendly. The results revealed that the Li/TiO2 half-cells delivered an initial discharge capacity of 209.7 mAh g−1 and exhibited superior rate capability (149 mAh g−1 at 20 C) and cycling performances. Even at the 5C rate, the material retained a capacity of 82.2% at the end of 100 cycles. The synthesis route of TiO2-NPs and the aqueous binder-based electrode processing described in the present work are facile, green, and low-cost and are thus practically beneficial for producing low-cost and high-performance anodes for advanced LIBs.

The major arboviruses mainly belong to the Bunyaviridae, Togaviridae, and Flaviviridae families, among which the chikungunya virus and dengue virus have emerged as global public health problems. The main objective of this study was to develop specific, sensitive, and cost-effective molecular multiplex RT-PCR and RT-qPCR assays for the rapid and simultaneous detection of CHIKV and the four serotypes of DENV for arbovirus surveillance. Specific primers for all viruses were designed, and one-step multiplex RT-PCR (mRT-PCR) and RT-qPCR (mRT-qPCR) were developed using reference strains of the CHIKV and DENV serotypes. The specificity of the test for all the viruses was confirmed through sequencing. The standard curves showed a high correlation coefficient, R2 = 0.99, for DENV-2 and DENV-3; R2 = 0.98, for DENV-4; and CHIKV; R2 = 0.93, for DENV-1. The limits of detection were calculated to be 4.1 × 10−1 copies/reaction for DENV-1, DENV-3, and CHIKV and 4.1 × 101 for DENV-2 and DENV-4. The specificity and sensitivity of the newly developed mRT-PCR and mRT-qPCR were validated using positive serum samples collected from India and Burkina Faso. The sensitivity of mRT-PCR and mRT-qPCR are 91%, and 100%, respectively. The specificity of both assays was 100%. mRT-PCR and mRT-qPCR assays are low-cost, and a combination of both will be a useful tool for arbovirus surveillance.

Abstract Background and objectives Hypertriglyceridemia (HT) may increase the risk of stroke. Limited studies have shown that stroke severity and infarction size are smaller in patients with HT. We explored the relationship between triglyceride levels and stroke risk factors, severity and outcome in a large prospective database. Design Prospective Cross-sectional study. Setting We retrospectively interrogated the Qatar Stroke Database in all patients admitted between 2014-2022 with acute ischemic stroke and evaluated the relationship between triglyceride, diabetes, stroke severity (measured on NIHSS), stroke type (TOAST classification) and the short- (mRS at 90 days) and long-term outcomes (MACE at 1 year) in patients with HT. Participants Six thousand five hundred fifty-eight patients ≥20 years were included in this study Results Six thousand five hundred fifty-eight patients with ischemic stroke [mean age 54.6 ± 12. 9; male 82.1%) were included. Triglyceride levels upon admission were low-normal (≤1.1 mmol/L) in 2019 patients, high-normal (1.2-1.7 mmol/L) in 2142 patients, borderline-high (1.8-2.2 mmol/L) in 1072 patients and high (≥2.3 mmol/L) in 1325 patients. Higher triglyceride levels were associated with stroke and increased likelihood of having diabetes, obesity, active smoking, and small vessel/lacunar stroke type. An inverse relationship was noted whereby higher triglyceride levels were associated with lower stroke severity and reduced likelihood of poorer outcome (mRS 3-6) at discharge and 90 days. Long-term MACE events were less frequent in patients with higher triglyceride levels. After adjusting age, gender, diabetes, prior stroke, CAD, and obesity, multivariate analysis showed that hypertension and triglyceride levels were higher in mild ischemic strokes patients. Conclusions Increasing triglycerides are associated with higher risk of small vessel disease and requires further prospective cohort studies for confirmation.

Acute COVID-19 infection typically results in mild disease in children, but can lead to several immune manifestations. One such severe manifestation is immune-mediated thrombotic thrombocytopenia (iTTP). It is exceedingly rare in children, and only a few cases have been reported worldwide. We report a case of severe iTTP in a 17-year-old girl. She was positive for COVID-19 infection by reverse-transcription polymerase chain reaction at presentation and had received a Covaxin injection two weeks earlier. She responded to therapeutic plasma exchange, corticosteroids, and rituximab. Early recognition of this complication and prompt initiation of therapeutic plasma exchange and corticosteroids is paramount to achieving a good outcome in this rare, life-threatening complication.

The experiment consisted of 12 treatments (weed interference until 15, 30, 45, 60, and 75 days after emergence (DAE), weedy until crop maturity, and weed-free until 15, 30, 45, 60, 75, and a weed-free until crop maturity) and the treatments were arranged in a randomized complete block design (RCBD) with three replications. This study indicated increased weed interference period reduced the pod yield by 6.5–36 and 0.7–32%, and haulm yield by 6.4 – 34 and 6.5 – 34% during rainy and winter season respectively. Further, reduction in total biomass production by crop due to weed interference declined the total nutrient uptake by crop, i.e. nitrogen by 7.8–52.1 and 9.6–54.5%, phosphorus by 4.8–45.4 and 6–47.5%, potassium by 6.9–43.7 and 6.3–44.4%, sulphur by 18.4–53.3 and 18.8–53.9%, during rainy and winter season respectively. However, weed-free until crop harvest increased RUE by 293% and 314%, chlorophyll content by 8.7 and 10.7%, respectively, during rainy and winter seasons. This study indicated critical duration for crop-weed interference under 5% relative yield loss (RYL) was 16 to 66 DAE in rainy and 15 to 63 DAE in winter. Similarly, at 10%, RYL were 23 to 62 and 21 to 61 DAE, respectively, during the rainy and winter seasons. As a result, farmers should keep their groundnut crop weed-free between 15 and 66 DAE during the rainy and winter seasons.

Over the last 5 years, IR-MALDESI-MS (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry) has been demonstrated for use in a range of high-throughput biochemical and cellular assays with remarkable sample acquisition rates up to 22 Hz for a single 384-well assay plate. With such high single plate acquisition rates, the rate limiting step becomes how fast subsequent plates can be presented to the MS for analysis. To make this transfer as fast as possible while maintaining safe operation in a laboratory environment, we developed a collaborative robotic plate transfer system (CRPTS) that combines a 6-axis robot with dual plate grippers, a 7th axis conveyor stage, and a 420-plate capacity sample loading window. As a demonstration of the throughput and flexibility of CRPTS, we performed a biochemical assay that monitored the oxidation of tris(2-carboxyethyl)phosphine (TCEP) to screen for nuisance compounds. Using continuous and step motion scan profiles, we analyzed 158,799 compounds contained in 448 assay plates over the course of 12.5 h (Z-Factor=0.87) and 17.5 h (Z-factor=0.99), respectively. Extrapolating these results enables the screening of a million compounds within 6–7 working days.

IntroductionTuberculosis (TB) remains a significant health concern in India, and its complexity is exacerbated by the rising occurrence of non-communicable diseases such as diabetes mellitus (DM). Recognizing that DM is a risk factor for active TB, the emerging comorbidity of TB and PDM (TB-PDM) presents a particular challenge. Our study focused on the impact of PDM on cytokine and chemokine profiles in patients with pulmonary tuberculosis TB) who also have PDM.Materials and methodsWe measured and compared the cytokine (GM-CSF, IFN-γ, IL-1α/IL-1F1, IL-1β/IL-1F2, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, IL-13, IL-17/IL-17A, IL-18/IL-1F4, TNF-α) and chemokine (CCL1, CCL2, CCL3, CCL4, CCL11, CXCL1, CXCL2, CXCL9, CXCL10, and CXCL11) levels in plasma samples of TB-PDM, only TB or only PDM using multiplex assay.ResultsWe observed that PDM was linked to higher mycobacterial loads in TB. Patients with coexisting TB and PDM showed elevated levels of various cytokines (including IFNγ, TNFα, IL-2, IL-17, IL-1α, IL-1β, IL-6, IL-12, IL-18, and GM-CSF) and chemokines (such as CCL1, CCL2, CCL3, CCL4, CCL11, CXCL1, CXCL9, CXCL10, and CXCL11). Additionally, cytokines such as IL-18 and GM-CSF, along with the chemokine CCL11, were closely linked to levels of glycated hemoglobin (HbA1c), hinting at an interaction between glycemic control and immune response in TB patients with PDM.ConclusionOur results highlight the complex interplay between metabolic disturbances, immune responses, and TB pathology in the context of PDM, particularly highlighting the impact of changes in HbA1c levels. This emphasizes the need for specialized approaches to manage and treat TB-PDM comorbidity.

Objective: This study investigated the association between Coronavirus Disease 2019 mRNA vaccination and stroke in Qatar. Methods: Between December 1, 2020, and April 11, 2023, a matched case-control study was conducted to investigate the association between 3036 acute stroke cases and 3036 controls drawn from the entire population of Qatar. Results: The adjusted odds ratio (aOR) for vaccination among cases compared to controls was 0.87 (95% CI: 0.75-1.00). The aOR was 0.74 (95% CI: 0.45-1.23) for a single vaccine dose, 0.87 (95% CI: 0.73-1.04) for primary-series vaccination (two doses), and 0.91 (95% CI: 0.66-1.25) for booster vaccination (three or more doses). The aOR was 0.87 (95% CI: 0.72-1.04) for BNT162b2 and 0.86 (95% CI: 0.67-1.11) for mRNA-1273. Subgroup analyses, considering different durations since vaccination, also demonstrated no association. Subgroup analyses based on nationality, age, number of coexisting conditions, or prior infection status yielded similar results. Subgroup analysis, stratified by stroke type, suggested an association between vaccination and cerebral venous sinus thrombosis (aOR of 2.50 [95% CI: 0.97-6.44]), but it did not reach statistical significance. Conclusion: There was no evidence of an increased risk of stroke following vaccination, both in the short term and in the long term, extending beyond a year after receiving the vaccine.

Aggregate wick system (AWS) or capillary action technique is a kind of sub-surface irrigation system gaining popularity owing to its profound advantages of improved water and nutrient use efficiency resulting in superior plant growth and development. However, its effect on growth and physiology of gerbera crop has not been studied yet. Therefore, an experiment was conducted during 2021–23 at ICAR-Indian Institute of Horticultural Research, Bengaluru, Karnataka to assess the effect of aggregate wick system on growth, flowering, vase life, yield and physiological aspects of gerbera var. Arka Nesara as compared to the conventional drip systems. It was apparent from the results that higher overall factor productivity in terms of number of leaves/plant (12.92), earlier first flower harvest (149.80 days) and copious flower production (38.58/plant/year) with enhanced chlorophyll content and optimal starch content were observed with AWS on the soil bed. However, AWS with pots on the ground resulted in extended vase life (9.20 days) with enhanced leaf area (115.31 cm2), chlorophyll-b, reducing sugars and decreased total and non-reducing sugars in leaves. Therefore, it can be concluded that the improved flower yield and vase life of gerbera in aggregate wick systems might be attributed to the greater chlorophyll production and reducing sugars in the leaves, respectively.

Body fluids carry several biomarkers and genetic material that enable the detection of carcinogenesis and tumor metastasis. Owing to this, liquid biopsy is gaining importance and entering clinical practice as an adjunct modality to the current standard of practice in cancer detection. Liquid biopsy involving specimens such as urine is non-invasive and has biomarkers reflecting the tumor microenvironment. We investigate the normal and altered levels of cellular metabolic by-products in liquid phantoms and demonstrate their photon-induced excited-state interaction behaviour for potential diagnostic applications. Short-span temporal analysis of metabolic by-products FAD and NADH in liquid phantoms with fiber optic fluorescence spectroscopy is carried out. The mixed liquid phantom of FAD and NADH, showed excited state interactions that manifested as simultaneous inverse variation in the fluorescence emission from FAD and NADH in varying pH environments. Linear discriminant analysis of the time-varying integrated fluorescence intensity resulted in a 100% accuracy in classifying liquid samples between high and low metabolite concentrations. An extension of application of the experiment model for analysis of body fluids such as urine is also discussed.

Weeds interfere with lima bean production by reducing crop yield, hindering harvest, and contributing contaminants to harvested beans, yet there are very few trials documenting the impact of weeds on lima bean. This research was designed to evaluate weeds on lima bean yield, quality, as well as Rhizoctonia solani and pod-feeding insects in order to assist in implementing a more integrated approach to pest management. Field studies at four sites evaluated the impact of common cocklebur (Xanthium strumarium L.), jimsonweed (Datura stramonium L.), and ivyleaf morningglory [Ipomoea hederacea (L.) Jacq.] at densities of 0, 7, 10, or 20 plants 10 m-1 row in the presence and absence of Rhizoctonia solani on lima bean (Phaseolus lunatus L.). The planting dates of late June to mid-July represented a typical planting period for the mid-Atlantic region of USA, while one site represented an early planting date in this region (28 May). Differences in response to weed competition for total lima bean yield, marketable yield, yield components, and R. solani discoloration on lima bean occurred at one or more sites. Weed competition from 7 plants 10 m-1 of row or higher, reduced number lima bean pods by as much as 40%. However, weed density had little impact on percentage of flat, plump, or dry pods. Marketable yield was reduced at two sites in response to 7 plants 10-1 row (19% yield loss) and higher weed densities resulted in 29 to 33% yield loss. The presence of lima bean resulted in 40 to 60% reduction of common cocklebur and jimsonweed biomass and burs or seeds compared to weeds grown without crop competition. Weed competition in lima bean was influenced by many factors including weed species and planting date. Weed management is important to not only preserve yield but limit weed seed return to the soil seedbank and maintain harvest efficiency.

IntroductionThe assessment of tuberculosis (TB) treatment outcomes predominantly relies on sputum culture conversion status. To enhance treatment management, it is crucial to identify non-sputum-based biomarkers that can predict unfavorable outcomes. Cytokines are widely studied as diagnostic biomarkers for active TB. However, their potential as indicators for unfavorable treatment outcomes remains uncertain.MethodologyThis study was conducted within a well-characterized cohort comprising newly diagnosed patients with drug-sensitive pulmonary TB, confirmed through sputum smear and culture positivity. Our objective was to elucidate the TB antigen-stimulated cytokine profile at pre-treatment and at 2 months into anti-TB treatment (ATT) in patients with unfavorable treatment outcomes (cases, n = 27) in comparison to recurrence-free, microbiologically cured controls (n = 31). Whole blood was stimulated with TB antigens using the QuantiFERON In-tube gold method, and plasma supernatants were subjected to a panel of 14 cytokine measurements.ResultsIn our study, pre-treatment analysis revealed that eight cytokines (IL-2, IFN-γ, TNF-α, IL-6, IL-10, IL-17A, IL-18, and GM-CSF) were significantly elevated at baseline in cases compared to cured controls, both in unstimulated conditions and following TB antigen (CFP10, ESAT6, and TB7.7) stimulation. A similar pattern was observed at the 2-month mark of ATT, with eight cytokines (IL-2, IL-10, IL-13, IFN-γ, IL-6, IL-12p70, IL-17A, and TNF-α) showing significant differences between the groups. Importantly, no variations were detected following mitogen stimulation, underscoring that these distinctive immune responses are primarily driven by TB-specific antigens.ConclusionOur findings indicate that individuals with unfavorable TB treatment outcomes display a characteristic cytokine profile distinct from TB-cured patients, even before commencing ATT. Therefore, the levels of specific cytokine pre-treatment and at the 2-month point in the course of treatment may serve as predictive immune markers for identifying individuals at risk of unfavorable TB treatment outcomes, with these responses being predominantly influenced by TB-specific antigens.

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in an IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16 % for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

Cloud-based deployments face increasing threats from various types of attacks, necessitating robust anomaly detection frameworks to safeguard against potential security breaches. Existing solutions, such as RSSI, GTM, and APG, though effective to a certain extent, exhibit limitations in terms of precision, accuracy, and scalability. To address these shortcomings, this paper proposes a novel anomaly detection framework that integrates multimodal feature analysis, deep learning models, and QoS-aware sidechains to enhance the prediction accuracy of cloud attacks and optimize blockchain-based cloud installations. By maximizing feature variance across different sample types and leveraging advanced deep learning techniques, the proposed approach significantly outperforms conventional methods in terms of precision, accuracy, recall, and AUC performance. Furthermore, the framework demonstrates superior efficiency in block mining delay, energy consumption, and throughput, making it highly suitable for real-time cloud attack prediction scenarios. The proposed methodology represents a significant advancement in anomaly detection and cloud security, offering a comprehensive solution for addressing challenges in blockchain-based cloud deployments. Thus, the proposed anomaly detection framework employs both Deep Learning and Blockchain technologies. Using Recurrent Neural Networks (RNN) with Convolutional Neural Networks (CNN), the system examines system logs and identifies unusual behavior patterns associated with different attacks. Using Blockchain technology, the framework ensures the transparency and integrity of system logs, and Deep Learning models provide precise and timely anomaly detection. The decision to combine Deep Learning and Blockchain technology is justified by the merits of each technique. The distributed, immutable ledger provided by blockchain technology makes it impossible to tamper with system logs and ensures the accuracy of anomaly detection. While, deep learning models, have exceptional pattern recognition abilities and can adapt to changing attack methods, resulting in high precision, accuracy, recall, and AUC metrics. Analyses of experimental data demonstrate that the proposed framework is effective. The framework achieves impressive performance metrics, such as low delays, 98.5% precision, 99.4% accuracy, 98.3% recall, and 99.2% Area Under the Curve (AUC).