Pallium India

Diabetic Retinopathy caused by Diabetes Mellitus is a major vision threatening condition across the global population. Early detection and grading of diabetic retinopathy are pivotal to avoiding the associated vision impairment, and retinal image analysis serves as an effective method for the screening process. Analysing retinal images manually to detect diabetic retinopathy and grade them is a time-consuming pro cess that necessitates the involvement of experts to perform the classification. Computer-aided diagnosis of diabetic retinopathy through retinal image analysis is an effective tool to reduce the time involved in the screening process. This study proposed a convolutional neural network based Selective Feature Map Fusion architecture that leverages the fused feature maps of ResNet50 and EfficientNetV2L networks for the detection and grading of diabetic retinopathy. Feature set of ResNet50 and EfficientNetV2L captures different aspects of the underlying lesion distribution, resulting in a more comprehensive representation of the features. This approach helps the model generalize better to unseen data by incorporating diverse data aspects, thereby preventing overfitting to a specific feature set and developing a model that is more robust to noise. Selective feature fusion helps to reduce the computational overhead of the the feature fusion computation. Comparative analysis against the ResNet50 and EfficientNetV2L models individually revealed the superior performance of the fused model in both detection and grading of diabetic retinopathy. The proposed model attained an excellent level of accuracy of 95.2% in diabetic retinopathy detection with a sensitivity of 95.2% and a specificity of 95.2%, when tested on the IDRiD dataset. The model achieved a classification accuracy of 92% for diabetic retinopathy grading with a sensitivity of 88%, a specificity of 98% and an F1-score of 0.89. Moreover, the fused framework attained superior performance when compared against existing methodologies on the IDRiD dataset. The developed model exhibited robust performance also in the DeepDRiD dataset, and it can be employed successfully in the screening processes for the diagnosis and grading of diabetic retinopathy.

Ectopic thyroid is a form of thyroid dysgenesis and is defined as the presence of thyroid tissue in an abnormal location. Its prevalence is about 1 per 1,00, 000-3,00,000 people, rising to 1 per 4,000-8,000 patients with thyroid diseases. The most common sites of ectopic thyroid are lingual (90%)

Perinatal palliative care (PPC) addresses the complex needs of families facing life-limiting fetal or neonatal conditions through an interdisciplinary, family-centered approach. Despite growing recognition of its importance, there is a lack of systematically defined research priorities to guide the development of evidence-based PPC practices. To identify and establish research priorities for perinatal palliative care (PPC) using a Delphi method with international experts and parents who experienced perinatal loss. This Delphi study involved three Delphi survey rounds with experts and bereaved parents. Participants provided and refined research priorities across rounds. In the first round, participants provided suggestions for research topics spontaneously. Topics were analyzed using principles of thematic analysis and further rated and ranked based on their importance in two additional rounds of consensual scoring. Consensus was defined as a score above the mean from at least 75% of respondents. A total of 125 experts and 10 parents who experienced a perinatal loss were invited to participate in the study. Response rate in the three rounds was 24, 31, and 34%. A total of 187 research priorities were initially suggested, resulting in 34 topics reaching consensus. Final rankings and priority topics were categorized into seven thematic areas: organization of care, decision-making and communication, staff support and education, ethics, symptom management, bereavement, and family experience. This study identified critical research areas in PPC, providing a roadmap for future research to enhance support for families and healthcare providers in PPC.

IntroductionHome-based palliative care (HBPC) enhances access to palliative care, reduces health-related costs, and improves health outcomes. However, studies centered on the socioeconomic profile of HBPC are limited. This study aimed to describe the socio-economic profile of HBPC recipients in Kerala, India, and to assess the impact of the disease on the financial, educational, and employment status of their family members.MethodsThe socio-economic details of HBPC beneficiaries of a non-profit palliative care organization were collected. Individuals enrolled under HBPC who first received it between March 2020 and April 2024 were included in this study. Data on the socioeconomic details of the beneficiaries were extracted and digitized from registries maintained by Pallium India. Of the 1296 entries received, 964 with complete data were included in the final analysis, which was performed using Jamovi v2.3.28.ResultsThe mean (SD) age of the beneficiaries was 61.0 (16.6) years. Among the total, 52.9% of the beneficiaries were female, and the median (IQR) family size was 9 (6–13). A total of 83.25% lived in their own homes, and 62.97% resided in rural areas. Cancer (21.1%), followed by Cerebrovascular diseases (20.3%), were the two most common conditions requiring HBPC. Debt was reported by 31.3% of families, with a median (IQR) debt of 2389.5 (597.4–4779.0) USD. The primary reason for debt was health-related expenses. 62.5% of families had beneficiaries receiving pensions, and 7.21% received support from another organization. In 4.2% of families, there was no breadwinner, and 9.5% and 66.8% of patients could not afford food and medicines, respectively. A family member lost their job in 8.0% of families, while in 2.8% of families, a child had to discontinue their education due to the patient’s illness.ConclusionHealthcare costs were the major cause of debt, with many HBPC recipients unable to afford medicines. Moreover, the employment and education of other family members were also threatened. Further strengthening of social and economic security measures is essential to safeguard these individuals and their families.

Recently, there has been a growing emphasis on omnidirectional mobile robots. The key focus of this research is to ensure their reliable performance. This strategy integrates both velocity and torque controller originally developed for a mobile manipulator and adapts them to suit the unique dynamics of the omni-drive platform. The controller is robust to system dynamics and uncertainties and thereby provides guaranteed performance in diverse environments. Based on a detailed set of simulations, the study demonstrates that accurate trajectory tracking with minimum position and orientation errors can be achieved using the proposed controller. The results validate the efficacy of the proposed control methodology in optimizing the motion control performance of mobile robots.

Introduction and Objective: Longitudinal analysis: data from Diabetes Tele-Management System (DTMS®) over 25 years (1997 to present) to assess impact of telemedicine (TM)-based follow-up on achieving lipid targets & averting events. Methods: N=7,780 PWD. Baseline and longitudinal lipid parameters analyzed, predictors of CV events identified using Generalized Estimating Equations (GEE) model. Median survival times for follow-up durations calculated for each gender. Results: Follow-up rates showed sharp dropout after 1-2 visits (33%), stabilizing thereafter. Baseline LDL 118 ± 40 mg/dL, dropped significantly (sig) to 56 ± 10.38 mg/dL. 91% achieved ADA lipid targets. Median survival times for follow-up: 3.16 y for females & 2.37 y for males. (Fig 1) 53% on high-intensity statins, 37% on moderate-intensity. Top 3 statins: atorvastatin 10 mg (mean LDL = 78 ± 20.3 mg/dL), atorvastatin 5 mg (mean LDL = 57 ± 9 mg/dL), and rosuvastatin 5 mg (mean LDL = 67 ± 24 mg/dL). Only 3% had CV events. GEE analysis: sig predictors of CV events: age (OR = 1.00093, p < 0.001) and low HDL (OR = 0.99995, p = 0.009). Female sex protective (OR = 0.9958, p = 0.015), while A1C (p = 0.051), SBP (p = 0.095), and high-dose statin (p = 0.093) showed marginal associations. BMI, TG, and diabetes duration were not sig predictors. Conclusion: 25-y analysis highlights critical role of TM follow-up in achieving lipid targets and lower CV events. Disclosure J. Kesavadev: None. A. Shankar: None. G. Krishnan: None. S. Jothydev: None. A. David: None. A. Basanth: None. B.D. Saboo: None. S. Joshi: Advisory Panel; USV Private Limited, Marico, Glenmark Pharmaceuticals, Franco Indian, Twin Health, Biocon, Zydus Lifesciences. Speaker's Bureau; Abbott, Novo Nordisk, MSD.

Abstract Introduction Home-based palliative care (HBPC) enhances access to palliative care, reduces health-related costs, and improves health outcomes. However, studies centered on the socioeconomic profile of HBPC are limited. This study aimed to describe the socio-economic profile of HBPC recipients in Kerala, India and to assess the impact of the disease on the financial, educational, and employment status of their family members. Methods The socio-economic details of HBPC beneficiaries of a non-profit palliative care organization were collected. Individuals enrolled under HBPC who first received it between March 2020 and April 2024 were included in this study. Data on the socioeconomic details of the beneficiaries were extracted and digitized from registries maintained by Pallium India. Of the 1296 entries received, 964 with complete data were included in the final analysis, which was performed using Jamovi v2.3.28. Results The mean (SD) age of the beneficiaries was 61.0 (16.6) years. Among the total, 52.9% of the beneficiaries were female, and the median (IQR) family size was 9 (6–13). A total of 83.25% lived in their own homes, and 62.97% resided in rural areas. Cancer (21.1%), followed by Cerebrovascular diseases (20.3%), were the two most common conditions requiring HBPC. Debt was reported by 31.3% of families, with a median (IQR) debt of 2389.5 (597.4–4779.0) USD. The primary reason for debt was health-related expenses. 62.5% of families had beneficiaries receiving pensions, and 7.21% received support from another organization. In 4.2% of families, there was no breadwinner, and 9.54% and 66.8% of patients could not afford food and medicines, respectively. A family member lost their job in 8.0% of families, while in 2.8% of families, a child had to discontinue their education due to the patient’s illness. Conclusion Healthcare costs were the major cause of debt, with many HBPC recipients unable to afford medicines. Moreover, the employment and education of other family members were also threatened. Further strengthening of social and economic security measures is essential to safeguard these individuals and their families.

An essential challenge in the ever-changing logistics and warehousing sector is the efficient routing of AGVs. This study introduces a new algorithm that enhances AGV routing in smart warehouses by integrating cutting-edge pathfinding techniques with real-time data analytics. The proposed method significantly reduces the amount of time and distance needed to complete tasks for AGVs in comparison to the previous methods. The suggested research employed a simulated smart warehouse environment to develop the algorithm and conduct extensive testing. Our data-driven approach successfully reduces operational time by 20% and average travel distance by 15%, resulting in improvements overall efficiency. Furthermore, the algorithm demonstrated impressive performance when confronted with various operational constraints, such as dynamic obstacle avoidance and real-time re-routing capabilities for adjusting to changing warehouse conditions. Our revised routing algorithm for AGVs allowed smart warehouses to significantly increase their resource efficiency. The algorithm effectively reduced operation time by 20% and travel distance by 15%, demonstrating its efficiency and the potential for substantial cost savings. Furthermore, the system incorporates dynamic obstacle avoidance techniques that have been thoroughly tested and shown to be successful in real-time environments. The algorithm's remarkable flexibility was shown by its ability to successfully re-route AGVs in real-time, ensuring continuous and efficient operation. It handled unforeseen changes to the warehouse's layout and obstacles well. Warehouse operations throughput is increased along with logistics infrastructure robustness and responsiveness via these changes.