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Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates., Objective: To develop a lightweight, explainable, self-supervised approach with personalized alert thresholds to detect adverse health events in PLwD, using changes in home activity., Methods: We hypothesized that health downturns manifest as detectable shifts in household movement patterns. Our approach leverages a Graph Barlow Twins contrastive model, which uses granular activity data and a macroscopic view to extract noise-robust, high-level and low-level discriminative features that represent daily activity patterns. Household-personalized alert thresholds are calculated based on clinician-set target alert rates, and daily anomaly scores are compared against these thresholds, triggering alerts for the clinical monitoring team. Model attention weights support explainability. Data were collected from a real-world dataset by the UK Dementia Research Institute (August 2019-April 2022)., Results: Our model outperformed state-of-the-art temporal graph algorithms in detecting agitation and fall events across three patient cohorts, achieving 81% average recall and 88% generalizability at a target alert rate of 7%., Conclusion: We developed a novel, lightweight, explainable, and personalized Graph Barlow Twins model for real-world remote health monitoring in dementia care, with potential for broader applications in healthcare and sensor-based environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

OrthoTV has emerged as a pioneering platform in the field of orthopedic education, leveraging technology to create a comprehensive and accessible knowledge repository. Originating from the vision of the Indian Orthopedic Research Group in 2013, OrthoTV has evolved into a global educational hub, streaming thousands of hours of surgical videos, webinars, and podcasts. It provides a dynamic learning environment through live interactive sessions, fostering real-time engagement with experts and facilitating a global exchange of knowledge. OrthoTV's expansion during the COVID-19 pandemic underscored its vital role in continuing medical education amidst travel restrictions. The platform extends beyond educational resources to include career support, collaborative research initiatives, and even virtual fellowships, embodying a multifaceted approach to professional development. With the introduction of OrthoAI, OrthoTV integrates advanced artificial intelligence to offer personalized, evidence-based responses to queries, setting a new standard in digital medical education. This narrative review highlights OrthoTV's foundational principles, its significant impact on orthopedic education and practice, and its commitment to democratizing knowledge across the globe. OrthoTV's innovative use of technology bridges geographical barriers enriches learning experiences, and fosters a vibrant community of orthopedic professionals, marking a significant advance in the field of medical education., Competing Interests: Conflict of Interest: Nil, (Copyright: © Indian Orthopaedic Research Group.)

Background: Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability., Objective: This study aims to develop an online, lightweight unsupervised learning-based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations., Methods: We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia., Results: The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns., Conclusions: To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios., (©Nivedita Bijlani, Ramin Nilforooshan, Samaneh Kouchaki. Originally published in JMIR Aging (https://aging.jmir.org), 19.09.2022.)

Purpose: To present clinical profile and risk factors of sight-threatening diabetic retinopathy (STDR) among people with age of onset of diabetes (AOD) <25 versus ≥25 years., Methods: A retrospective chart analysis of consecutive patients with diabetic retinopathy (DR) n = 654) treated at 14 eye care centers across India between 2018 and 2019 was performed. Patients were divided into two groups, Group 1: AOD <25 years and Group 2: AOD ≥25 years. DR and diabetic macular edema (DME) were classified using the International Clinical Classification of DR severity scale. STDR included severe nonproliferative DR (NPDR), proliferative DR (PDR), and moderate to severe DME. A multilevel mixed-effects model was used for comparison between two groups: 1) Patients with DR and AOD <25 years and 2) Patients with DR and AOD ≥25 years. Bivariate and multivariate regression analyses were used to evaluate risk factors between the two groups., Results: A total of 654 patients were included, 161 (307 eyes) in AOD <25 and 493 (927 eyes) in AOD >25 group. There was a higher prevalence of PDR with high-risk characteristics in AOD <25 group (24% vs. 12%) at baseline and 12-month follow-up (25% vs. 6%); P < 0.001. Systolic hypertension and poor glycemic control were risk factors in both groups, with no difference in these modifiable risk factors between groups., Conclusion: People with youth-onset DM are likely to present with severer form of STDR despite similar modifiable risk factors. Therefore, strict control of systolic blood pressure, glycemic status, and regular screening for DR are recommended to reduce the risk of STDR irrespective of the age of onset of diabetes., Competing Interests: None

Purpose: COVID-19-associated rhino-orbital-cerebral mucormycosis (ROCM) has reached epidemic proportion during India's second wave of COVID-19 pandemic, with several risk factors being implicated in its pathogenesis. This study aimed to determine the patient demographics, risk factors including comorbidities, and medications used to treat COVID-19, presenting symptoms and signs, and the outcome of management., Methods: This was a retrospective, observational study of patients with COVID-19-associated ROCM managed or co-managed by ophthalmologists in India from January 1, 2020 to May 26, 2021., Results: Of the 2826 patients, the states of Gujarat (22%) and Maharashtra (21%) reported the highest number of ROCM. The mean age of patients was 51.9 years with a male preponderance (71%). While 57% of the patients needed oxygen support for COVID-19 infection, 87% of the patients were treated with corticosteroids, (21% for > 10 days). Diabetes mellitus (DM) was present in 78% of all patients. Most of the cases showed onset of symptoms of ROCM between day 10 and day 15 from the diagnosis of COVID-19, 56% developed within 14 days after COVID-19 diagnosis, while 44% had delayed onset beyond 14 days. Orbit was involved in 72% of patients, with stage 3c forming the bulk (27%). Overall treatment included intravenous amphotericin B in 73%, functional endoscopic sinus surgery (FESS)/paranasal sinus (PNS) debridement in 56%, orbital exenteration in 15%, and both FESS/PNS debridement and orbital exenteration in 17%. Intraorbital injection of amphotericin B was administered in 22%. At final follow-up, mortality was 14%. Disease stage >3b had poorer prognosis. Paranasal sinus debridement and orbital exenteration reduced the mortality rate from 52% to 39% in patients with stage 4 disease with intracranial extension (p < 0.05)., Conclusion: : Corticosteroids and DM are the most important predisposing factors in the development of COVID-19-associated ROCM. COVID-19 patients must be followed up beyond recovery. Awareness of red flag symptoms and signs, high index of clinical suspicion, prompt diagnosis, and early initiation of treatment with amphotericin B, aggressive surgical debridement of the PNS, and orbital exenteration, where indicated, are essential for successful outcome.

Objective: The current study was designed to determine the effect of natural calcium carbonate toothpaste containing Perlite and microgranules (Whitening toothpaste) on extrinsic tooth stain compared to a standard commercial toothpaste formulation with precipitated calcium carbonate (PCC) as abrasive and a commercial toothpaste with dicalcium phosphate dihydrate (DCPD) as abrasive., Methods: The toothpastes were evaluated in a double blind, three-cell, stratified (tobacco use; baseline tooth stain level), parallel group design study involving 600 subjects with extrinsic tooth stain. Subjects brushed twice daily with their allocated toothpaste for four weeks. Extrinsic tooth stain was measured using the Macpherson modification of the Lobene stain index., Results: ANCOVA showed significant differences between toothpastes (p=0.037). Subsequent multiple comparisons using pairwise t-tests, showed the Whitening toothpaste to be superior to the DCPD toothpaste (p=0.014) and the PCC toothpaste (p=0.067). When a Box-Cox transformation was made to the data (y0.6) to improve normality, these two differences were more accurately estimated at p=0.004 and p=0.03 respectively., Conclusion: The Whitening toothpaste has been shown to be significantly more effective in tooth stain removal than the two standard commercial toothpaste formulations.

Objective: To compare the reduction of plaque and gingivitis from the following three toothpaste formulations containing: 0.3% Triclosan, 1,000ppm fluoride and 0.13% calcium glycerophosphate in a natural calcium carbonate base (a new paste); 0.2% Triclosan and 1,000ppm fluoride in a precipitated calcium carbonate base; 0.3% Triclosan, 1,000ppm fluoride and Gantrez in a silica base. A control group was maintained, which used commercially available toothpastes without the above actives., Methods: A double-blind, four-cell, stratified, parallel group design study. Subjects brushed twice daily with their allocated toothpaste for 12 weeks. Plaque, MGI and gingival bleeding assessments were carried out at baseline, after 6 and 12 weeks of product use., Results and Conclusion: An analysis of covariance of the 12-week results showed significant differences between products [p<0.0001] for plaque, MGI and gingival bleeding. The new toothpaste formulation containing 0.3% Triclosan, 1,000ppm fluoride and 0.13% calcium glycerophosphate in a natural calcium carbonate base was shown to be significantly more effective in reducing plaque and gingivitis than the other toothpaste formulations in the study.