Amrita Institute of Medical Sciences (AIMS), also referred to as Amrita Hospital, is a super-specialty quaternary care health center and medical school in Kochi, India. It is one of the largest medical facilities in the country with a total built-up area of over 3.33 million sq.ft, spread over 125 acres of land. It is a 1,450-bed hospital which supports a daily patient volume of about 3,000 outpatients with 95 percent inpatient occupancy. It was inspired by Mata Amritanandamayi and inaugurated on 17 May 1998 by the then Prime Minister, Atal Bihari Vajpayee. The Mata Amritanandamayi Math is its parent organisation. Ron Gottsegen is the executive director and Prem Nair is the medical director of AIMS. Dr.(Col). Vishal Marwaha is the Principal/Dean of AIMS.AIMS is part of the Health Sciences campus of Amrita Vishwa Vidyapeetham (Amrita University). The hospital has received the ISO 9001:2008 accreditation and also enjoys accreditation from the National Accreditation Board for Testing and Calibration Laboratories (NABL) for its laboratories and the National Accreditation Board for Hospitals and Healthcare Providers NABH for the hospital overall. In April 2019, Amrita School of Medicine was ranked the fifth best medical college in India by the Ministry of Human Resource Development in their annual NIRF rankings.
To map and characterise major transnational initiatives in education and training in geriatrics, and to explore complementarities to support a more coherent and equitable global framework. Multiple transnational programmes operate across a wide spectrum of structures, educational approaches, and content, reflecting diverse regional priorities and stages of development. Coordinated collaboration amongst initiatives is essential to build global capacity, promote equity, and ensure sustainability in geriatrics education and workforce development. To map and characterise major transnational initiatives in geriatrics education and training, and explore complementarities as a basis for a more integrated and equitable global framework. A mapping exercise and expert consultation were undertaken by the European Geriatric Medicine Society (EuGMS) Special Interest Group on Education and Training between January and October 2025, including a meeting of international experts during the Twenty-First EuGMS Congress in Reykjavík. Eligible initiatives operated across national borders with an explicit mandate in education and training related to geriatrics and were not confined to a specific topic or subspecialty. Each initiative was profiled by scope, target audience, and contributions, and classified within a three-tier framework: (1) foundational capacity-building, (2) professional and interprofessional development, and (3) leadership and specialist advancement. Seventeen initiatives were identified. Tier 1 included the International Federation on Ageing (IFA), International Institute on Ageing, United Nations–Malta (INIA), PAHO’s ACAPEM (Basic), ASEAN’s Centre for Active Ageing and Innovation (ASEAN–ACAI), IAGG’s e-Training in Gerontology and Geriatrics (e-TRIGGER) programmes, WHO’s Integrated Care for Older People (WHO ICOPE approach), and AfriAGE. Tier 2 included the IAGG, EuGMS, EICA, PROGRAMMING CA2112, Victorian Geriatric Medicine Training Programme (VGMTP), and ACAPEM (Intermediate); and Tier 3 was represented by leadership academies (EAMA, ALMA, MEAMA/MENAAA, and AAMA), and UEMS–GMS. Collectively, these programmes form a considerably disjointed but potentially complementary global ecosystem for geriatrics education. Greater mutual awareness and alignment, anchored in equity and interprofessional inclusion, could enhance efficiency and sustainability in developing the global geriatrics workforce.
An inequality by Samorodnitsky states that if f : 𝔽_2^n →ℝ is a nonnegative boolean function, and S ⊆ [n] is chosen by randomly including each coordinate with probability a certain λ= λ(q,ρ) < 1, then logT_ρf_q ≤𝔼_Slog𝔼(f|S)_q . Samorodnitsky's inequality has several applications to the theory of error-correcting codes. Perhaps most notably, it can be used to show that any binary linear code (with minimum distance ω(log n)) that has vanishing decoding error probability on the BEC(λ) (binary erasure channel) also has vanishing decoding error on all memoryless symmetric channels with capacity above some C = C(λ). Samorodnitsky determined the optimal λ= λ(q,ρ) for his inequality in the case that q ≥ 2 is an integer. In this work, we generalize the inequality to f : Ω^n →ℝ under any product probability distribution μ^⊗ n on Ω^n; moreover, we determine the optimal value of λ= λ(q,μ,ρ) for any real q ∈ [2,∞], ρ∈ [0,1], and distribution μ. As one consequence, we obtain the aforementioned coding theory result for linear codes over any finite alphabet.
Background: Cirrhosis represents the final common pathway of chronic liver injury, arising from diverse etiologies such as metabolic, viral, autoimmune, and alcohol-related liver diseases. Despite similar exposures, disease progression varies considerably among individuals, suggesting a genetic contribution to susceptibility and outcome. Objective: This narrative review examines how specific genetic variants influence the risk, progression, and phenotypic expression of cirrhosis. It provides a structured synthesis of established and emerging gene associations, emphasizing their biological mechanisms and potential clinical relevance. Methods: This narrative review synthesizes evidence from all major biomedical and scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar, as well as reference lists of relevant articles, covering literature published between 2005 and 2025 on genetic polymorphisms associated with cirrhosis and its etiological subtypes. Content: Variants are categorized into four mechanistic domains-metabolic regulation, immune modulation, liver enzyme activity, and ancestry-linked expression patterns-representing a novel integrative framework for understanding genetic risk in cirrhosis. Well-characterized variants such as PNPLA3, TM6SF2, HSD17B13, and MBOAT7, along with less commonly studied loci and chromosomal alterations, are discussed in relation to major etiologies, including MASLD/MASH, viral hepatitis, alcohol-related liver disease, and autoimmune conditions. Conclusions: Genetic insights into cirrhosis offer pathways toward early risk stratification and personalized disease management. While polygenic risk scores and multi-omic integration show promise, their clinical translation remains exploratory and requires further validation through large-scale prospective studies.
The Coronal Plane Alignment of the Knee (CPAK) classification describes knee morphology using arithmetic hip-knee-ankle angle (aHKA) and joint line obliquity (JLO). Currently there is uncertainty about the relevance and restoration of coronal alignment following total knee arthroplasty (TKA), particularly among Asian populations. This study examined changes in coronal alignment following robotic TKA using functional alignment, along with concordance between CPAK JLO and knee joint line obliquity (KJLO) – this would help develop strategies to optimize outcomes of TKA among Asian patients. A retrospective analysis was undertaken for 333 knees that received cruciate-retaining robotic TKA during 2019–2023. Pre- and 6-week postoperative radiographs were used to assess aHKA, JLO, CPAK phenotypes, and KJLO. Maintenance of constitutional CPAK alignment and crossover in aHKA and JLO categories were estimated. The concordance between CPAK JLO and KJLO at pre- and post-operative levels were also estimated. Preoperatively, 72
Chronic kidney disease affects 788-844 million adults worldwide and is projected to become the fifth leading cause of death by 2040. Global burden estimates remain limited by ascertainment bias and inadequate access to testing, particularly in low-income and middle-income countries. Advances in detection include improved estimation of glomerular filtration rate (GFR) using cystatin C and the recognition of albuminuria as a key marker for screening and risk stratification. Kidney biopsy is improving diagnostic accuracy and prognostic prediction, and multiomics approaches are advancing our understanding of disease mechanisms and hold promise for precision medicine. Advanced imaging and artificial intelligence are enabling non-invasive diagnostics and case detection. Population screening strategies using estimated GFR and albuminuria are increasingly cost-effective, particularly with the availability of novel therapies. Disease-specific prediction models support individualised risk stratification and clinical decision-making. However, inequitable access, limited validation across diverse populations, gaps in biomarker standardisation, and insufficient health-care system capacity constrain implementation globally. Addressing these challenges requires coordinated investment in diagnostics, workforce training, laboratory infrastructure, and health-care system strengthening alongside continued technological advancement.
