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6. Assessment of Quality of Life and Determinants Among the Elderly Population in Rural Areas of Puducherry: A Mixed Method Study

Author

Premnath Dhasaram, Lalithambigai Chellamuthu, Karthika Ganesh, Amarnath Santhaseelan, and Srimadhi Muthaiyan

Subjects
quality of life, elderly, who qol-bref, Medicine
Abstract

Background: Elderly people living in rural areas often faces unique challenges that affect their quality of life (QOL), including limited access to healthcare and social support. Understanding the factors influencing their well-being is essential to improve the quality of life. The study aims to assess the QOL and its determinants among the elderly in rural areas of Puducherry. Methods: A community based sequential explanatory mixed method study was conducted among the elderly residing in the rural field practice area of a medical college in Puducherry district. 200 participants were recruited by simple random sampling from the family health records in the Rural Health Training Centre of a medical college. WHO QOL-BREF questionnaire was used to assess the Quality of Life quantitatively and an interview guide to explore its determinants. Data were entered in MS excel and analyzed using SPSS v16.0. Results: The mean age of the participants was 68.8± 2.5 years with majority being females. The environmental domain scored the highest mean QOL and psychological domain the lowest mean QOL. The overall mean QOL was 234.8 ± 65.4. The main determinants of poor QOL are age ≥ 75 years, lower socio-economic class, those who are widow/separated and presence of comorbid conditions. The binary logistic regression predicts the factor for poor QOL was age ≥ 75 years with OR (95% CI) as 6.23 (2.44-15.91). Conclusion: The overall mean QOL was moderate. The factors identified for poor QOL need to be addressed with key intervention strategies. Quality affordable medical services at door step to improve physical domain and targeted health education for family members and the community, who form the immediate environment around the elderly, can play a crucial role in enhancing the social domain.

Published
2024
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7. A Community-Based Assessment of Knowledge, Attitude and Practice on Hepatitis B among Residents in a Coastal Village of Southern India

Author

Lalithambigai Chellamuthu, Senkadhirdasan Dhakshnamurthy, Vinodhini Balamurugan, and Sindu Kanagalingam

Subjects
epidemiology, hepatitis b, knowledge, practice, Medicine, Public aspects of medicine, RA1-1270
Abstract

Introduction: The National Viral Hepatitis Control Program launched by the Government of India aims to end viral hepatitis by the year 2030. The main key objective of the programme is to enhance community awareness about the virus, the disease and the preventive measures for tackling the burden of hepatitis. Lack of knowledge and awareness regarding the disease, its modes of spread and the available preventive strategies would seriously limit in achieving the goal of reduction in burden and elimination of HBV. Objective: To assess the knowledge, attitude and practice on epidemiology of Hepatitis B among residents in a coastal village of Puducherry, Southern India. Method: A community-based, cross-sectional survey was conducted for three months among 796 adults aged ≥18 years residing in a coastal village which was one of the rural field practice areas of a private medical college in Puducherry. Multi-stage sampling technique was followed. A pre-validated, semi-structured questionnaire incorporated in Epicollect 5 software was utilized to capture the data through face-to-face interviews. The data analysis was performed using SPSS, v24.0. Results: About 66.3% of participants had heard of Hepatitis with 55.9% specifically aware of Hepatitis B as a viral disease. More than half (59.3%) believed they could contract Hepatitis B. About three-fourth (73.1%) had not undergone screening for Hepatitis B, and 67.1% had not received the Hepatitis B vaccination. Age, gender and socio-economic status of the respondents were associated with knowledge attitude and practice on Hepatitis B among participants which was found to be statistically significant (p-value < 0.0001). Conclusion: The findings from the study showed that more than two third of participants had heard of Hepatitis. More than half of the respondents believed they could contract Hepatitis B. Nearly two-third respondents had not received Hepatitis B vaccination.

Published
2024
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8. Ocular biometry and anthropometric measurements in young myopes – A case–control study

Author

Venipriya Sigamani, Viswesh Kathavarayan, Lalithambigai Chellamuthu, Ravichandran Kandasamy, and Hannah Ranjee Prasanth

Subjects
myopia in young, height, corneal thickness, axial length, Medicine
Abstract

Background: The prevalence of myopia in the 5–15-year age group in India has been 7.5% over the past four decades. While ocular growth and physical growth occur simultaneously during early life, the existence of a common regulatory system for both is still debated. Aims and Objectives: This study aims to analyze the ocular biometry and anthropometric values of young myopes and emmetropes aged 18–25 years. Materials and Methods: This case–control study involved a sample size of 86 participants. Corneal curvature was measured using keratometry, while A-scan ultrasonography was utilized to measure axial length, lens thickness, anterior chamber depth, and vitreous chamber depth. Results: The results indicated that there were no significant differences in ocular biometry and anthropometric values between the case and control groups, except for corneal thickness and axial length. The mean corneal thickness was found to be 549.64 μm in the case group and 566.05 μm in the control group, while the mean axial length was 24.70 mm in the case group and 23.41 mm in the control group. An increase in height was correlated with longer axial length in myopes. Conclusion: There was no difference in anthropometry and ocular biometry in emmetropes and myopes in the age group of 18–25 years of age. Myopes tend to have thinner corneas, which should be taken into consideration before performing refractive surgeries. The presence of thin corneas in individuals with myopia can result in inaccurately low measurements of intraocular pressure, potentially hampering the early detection of glaucoma within this high-risk demographic.

Published
2024
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9. The Effect of the Psycho-Oncology Program on Depression, Anxiety, and Stress among Breast Cancer Survivors: A Quasi-experimental Study in a Tertiary Care Hospital, South India

Author

T. Bharathi, Srinivasan Chelladurai, and Vasanth Chellamuthu

Subjects
breast cancer survivors, depression, anxiety, and stress, the psycho-oncology program, Psychiatry, RC435-571
Abstract

Background: Breast cancer treatment can affect women both physically and psychologically. Women with breast cancer undergo various painful and debilitating therapies as well as emotional trauma. Health-care providers are facing the challenge of helping breast cancer survivors cope with their physical and psychological problems. In addition, treatment modalities can bring multiple changes. Materials and Methods: Purposive sampling was used to choose 60 breast cancer survivors, with 30 members of the intervention group and 30 members of the control group. While participants in the intervention group received routine therapy along with additional psycho-oncology programs, individuals in the control group received standard care according to hospital guidelines. All participants’ baseline and postintervention levels of stress, anxiety, and depression were assessed using the Depression, Anxiety, and Stress Scale (DASS)-21. For every DASS-21 indicator, the differences between the intervention and control groups were examined using two-sided t-tests. Results: Significant decreases in means were found for DASS-21 indicators from baseline mean scores for depression (15.9 ° 1.7), anxiety (13.3 ° 1.2), Stress (16.4 ° 1.4) to mean scores after the Psycho-oncology program, for depression (10.7 ° 2.0), anxiety (7.4 ° 1.1), stress (10.1 ° 1.1) with p < .001. After the intervention, participants from the intervention group were found to heal from a severely depressed/anxious/stressed state to moderately depressed/anxious and stressed than in the control group. Conclusion: The findings of this study show that depression, anxiety, and stress, which are quite treatable, are common psychiatric morbidities faced by breast cancer patients, which can be treated through psychological intervention (the Psycho-Oncology Program) along with physical measures.

Published
2024
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10. Assessment of nomophobia and its determinants among adults and adolescents in Semi-urban Chennai

Author

Vinodhini Balamurugan, Abinaya Ravi, Hetal Tejas Mer, Lalithambigai Chellamuthu, Usha Devarasu, and Karthik Balamurugan

Subjects
addictive behaviours, mental health, mobile phone, nomophobia, smartphone addiction, Medicine
Abstract

Introduction: The mobile phone has evolved into an indispensable accessory carried by everyone. With its increasing usage, there is a parallel rise in mobile phone addictions. Nomophobia, short for no mobile phone phobia, is characterized as a fear specific to the absence of mobile phones. Objectives: (1) To assess the prevalence of nomophobia among adolescents and adults residing in semi-urban Chennai. (2) To understand the patterns of mobile phone usage and explore the health-related consequences of nomophobia. Methods: This cross-sectional study was conducted among adolescents and adults (15–50 years) in semi-urban Chennai from July to September 2022. The sample size was 220, and the study utilized the Test of Mobile Phone Dependence Brief as an assessment tool. Participants scoring >30 were identified as nomophobic, indicating mobile phone dependence. Results: Among the 220 study participants, there was an almost equal distribution between males and females, with a majority falling within the 21–25 age group. Approximately 26.1% belonged to the upper middle class and 89.1% were married. The prevalence of nomophobia was notably high at 68.6% (151 out of 220). A significant association was found between social class and mobile phone addiction, with the middle class exhibiting higher levels of addiction. Conclusion: The study highlights that a substantial majority of adults exhibit mobile phone addiction, almost two-thirds of the participants. There is a pressing need for health awareness programmes targeting adults to educate them about the adverse effects of mobile phone addiction. Additionally, reinforcing strategies for effective and time-restricted mobile phone usage is essential.

Published
2024
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13. Quantifying the impact of changing rainfall patterns on landslide frequency and intensity in the Nilgiris District of Western Ghats, India

Author

Sabari Nathan Chellamuthu and Ganapathy Pattukandan Ganapathy

Subjects
Landslides, Landslide susceptibility map, Analytical hierarchy process, Precipitation patterns, Microclimate, Environmental sciences, GE1-350, Social sciences (General), H1-99
Abstract

Changing rainfall patterns on vulnerable hill slopes are a significant factor in increasing landslide frequency and resulting damages. In the Nilgiris district of the Western Ghats, India, recent shifts in rainfall patterns, including increased overall precipitation and more erratic downpours, have raised concerns about landslide occurrences. This study examines the correlation between the altering rainfall patterns and the occurrence of landslides in a quantitative manner. A thorough analysis of rainfall data from 1992 to 2022 using R (hydroTSM) is conducted to evaluate its impact on landslides. Using the Analytical Hierarchy Process (AHP), a comprehensive Landslide Susceptibility Map (LSM) is generated by incorporating twelve significant landslide causative factors. The results indicate that 1% of the study area is in the very high susceptibility zone and 18% in the high susceptibility zone. These findings are crucial for developing targeted mitigation strategies, effective land use planning, and ensuring the safety of the region's inhabitants and infrastructure.

Published
2024
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15. Israel−Palestine Conflict: Risk of Sleep Disorders and Post-Traumatic Stress Disorders

Author

Pandi‑Perumal, Seithikurippu R., Gulia, Kamalesh K., Mallick, Hruda Nanda, Shrivastava, Deepak, Mahalaksmi, Arehally Marappa, Chidambaram, Saravana Babu, Kumar, Ramasamy Rajesh, Saravanan, Konda Mani, Ramasubramanian, Chellamuthu, Sivasubramaniam, Sudhakar, Madoro, Derebe, Narasimhan, Meera, Agudelo, Hernán Andrés Marín, Corlateanu, Alexandru, Meira e Cruz, Miguel, Grønli, Janne, van de Put, Willem A. C. M., Hobfoll, Stevan E., van der Velden, Koos, Bjorvatn, Bjørn, Braakman, Mario H., Partinen, Markku, Maercker, Andreas, de Jong, Joop T. V. M., and Berk, Michael

Published
2023
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16. A novel approach for protein secondary structure prediction using encoder–decoder with attention mechanism model

Author

Sonsare Pravinkumar M. and Gunavathi Chellamuthu

Subjects
bioinformatics, machine learning, deep learning, protein secondary structure prediction, Biology (General), QH301-705.5
Abstract

Computational biology faces many challenges like protein secondary structure prediction (PSS), prediction of solvent accessibility, etc. In this work, we addressed PSS prediction. PSS is based on sequence-structure mapping and interaction among amino acid residues. We proposed an encoder–decoder with an attention mechanism model, which considers the mapping of sequence structure and interaction among residues. The attention mechanism is used to select prominent features from amino acid residues. The proposed model is trained on CB513 and CullPDB open datasets using the Nvidia DGX system. We have tested our proposed method for Q 3 and Q 8 accuracy, segment of overlap, and Mathew correlation coefficient. We achieved 70.63 and 78.93% Q 3 and Q 8 accuracy, respectively, on the CullPDB dataset whereas 79.8 and 77.13% Q 3 and Q 8 accuracy on the CB513 dataset. We observed improvement in SOV up to 80.29 and 91.3% on CullPDB and CB513 datasets. We achieved the results using our proposed model in very few epochs, which is better than the state-of-the-art methods.

Published
2024
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17. Internet of Things enabled open source assisted real-time blood glucose monitoring framework

Author

Abubeker K. M, Ramani. R, Raja Krishnamoorthy, Sreenivasulu Gogula, Baskar. S, Sathish Muthu, Girinivasan Chellamuthu, and Kamalraj Subramaniam

Subjects
Medicine, Science
Abstract

Abstract Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

Published
2024
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19. Design of Robust Evolving Cloud-Based Controller for Type 1 Diabetic Patients Using n-Beats Algorithm

Author

Subasri Chellamuthu Kalaimani and Vijay Jeyakumar

Subjects
Adaptive Model Predictive Control (AMPC), Glucose-Insulin (GI), Lehman Based Diabetic Patient Model (LBDPM), Neural Network (NN), Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), Biotechnology, TP248.13-248.65
Abstract

Abstract Designing and analyzing adaptive controllers to control blood glucose levels by giving insulin in the Lehman-Based Diabetic Patient Model (LBDPM) while considering diverse stochastic environments in gaining popularity is challenging task. RECCo, a notable recent innovation that implements the concept of the ANYA fuzzy rule-based system, is an online adaptive type controller that is used in this study for the application of diabetes. The simulation results show that the suggested controller is used in the model to track standard blood glucose values even in the presence of some unexpected external disturbances. The primary concern in the field of type 1 diabetes is achieving higher accuracy using a deep learning algorithm with data obtained from simulated patient models. To achieve better accuracy, validation of the model is performed using the N-BEATS algorithm. By utilizing an online parameter estimation technique, the RPME is integrated to improve the performance of the adaptive model predictive controller. The system identification technique is used to attain a transfer function that is designed further for implementation of the controller. The experimental validation of the proposed N-BEATS algorithm method is compared with other conventional machine learning algorithms. The proposed controller method attains excellent blood glucose set point tracking and the proposed algorithms give accuracy rates of 97.4% and 96% for the data obtained. It outperforms other state-of-the-art methods with an increase in the accuracy percentage compared with other Benchmark Pima Indian Diabetes Datasets (PIDD).

Published
2024
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20. Clinical effectiveness of various treatments for cartilage defects compared with microfracture: a network meta-analysis of randomized controlled trials

Author

Sathish Muthu, Vibhu Krishnan Viswanathan, Girinivasan Chellamuthu, and Mohammad Thabrez

Subjects
Autologous chondrocyte implantation, Cartilage injury, Microfracture, Mosaicplasty, Osteochondral autograft transfer, Outcome, Diseases of the musculoskeletal system, RC925-935, Other systems of medicine, RZ201-999, Sports medicine, RC1200-1245
Abstract

Background: Advancements have been made in the realm of cartilage-regenerative techniques in the past decades. However, their comparative advantage has not yet been fully studied. Objectives: To comparatively analyze the functional, radiological and histological outcomes, and complications of various procedures available for the treatment of cartilage defects. Data sources: PubMed, Embase, Web of Science, Cochrane, and Scopus. Study eligibility criteria, participants, and interventions: Randomized controlled trials reporting functional, radiological, histological outcomes, or complications of various methods were utilized in the management of cartilage defects. Patients with cartilage defects. Treatment methods include microfracture (MFX), autologous chondrocyte implantation (ACI), osteochondral allograft/autograft transplantation (OAT), mosaicplasty, or acellular implants. Study appraisal and synthesis methods: Cochrane’s Confidence in Network meta-analysis approach. Network meta-analysis was conducted in Stata. Random effects model was used for forest plots. Results: Three thousand one hundred ninety-three patients from 54 randomized controlled trials were included in the analysis. The mean age of included patients was 37.9 (±9.46) years. MFX-I was used as a constant comparator. Among the restorative methods, OAT-II offered significantly better functional outcome at 5 years (weighted mean difference [WMD] = 16.00, 95% confidence interval [CI] [11.66, 20.34], P

Published
2024
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29. Effectiveness of a multimodal intervention in promoting physical activity among sedentary elderly population in socially and economically constrained settings - A quasi-experimental study

Author

Jyothi Vasudevan, Lalithambigai Chellamuthu, RS Swarnalatha, and Meena Ramanathan

Subjects
elderly population, multimodal intervention, physical activity, quasi-experimental study, sedentary, Medicine
Abstract

Introduction: Aging is becoming a major challenge for policymakers. Regular exercise helps keep elderly people mobile, enhances physical and mental abilities, and to some extent delays the effects of chronic illnesses. Objectives: To evaluate the effectiveness of a multimodal intervention to increase physical activity levels among sedentary elderly living in socially and economically constrained settings. Materials and Methods: A quasi-experimental study was conducted in selected old age homes in Puducherry, South India in 2022 for 3 months. Individuals aged ≥60 years, both genders residing in selected old-age homes were included through convenience sampling. The sample size was 36 subjects per arm [three arms namely E1, E2 (intervention arms), and C (control arm)]. Baseline data collection on physical activity was collected using a semi-structured questionnaire in all three arms. The intervention arms (E1 and E2) received a multimodal intervention to promote physical activity. In addition, E1 arms were instructed to perform exercises with an “exercise partner” and to maintain a daily log. At the end of 8 weeks, follow-up data collection was done using the same questionnaire in all three arms. Data entry was done by MS Excel 2010 and analysis using SPSS version 21. Results: The mean (SD) of the days of physical activity per week and time of physical activity per day before and after the intervention among E1 and E2 were compared using paired t-tests. The difference between pre- and post-intervention was found to be statistically significant, that is, P value

Published
2023
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30. A Community-based Epidemiological Study on non-fatal Road Traffic Accidents in Puducherry, South India

Author

Lalithambigai Chellamuthu, Devi Kittu, Yogesh A Bahurupi, and Kavita Vasudevan

Subjects
epidemiology, injury, road traffic accident, Medicine, Public aspects of medicine, RA1-1270
Abstract

Introduction: Road traffic accidents are the sixth leading cause of death in India with a greater share of hospitalization, disabilities, deaths and socio-economic losses. Objective: To identify the pattern of non-fatal road traffic accidents, socio-demographic profile of accident victims and antecedent factors influencing these road traffic injuries. Method: A cross-sectional study was conducted for six months in Puducherry. From existing 27 wards of Lawspet, six wards were selected by simple random sampling technique and all the households in selected wards were included. The minimum required sample size was estimated to be 165 by considering prevalence of non-fatal road traffic accidents in Puducherry as 5.6%. Face-to-face interview with a semi-structured questionnaire was used for data collection. Data entry and analysis were performed using Epi-data manager 4.2.0. Results: Total 169 accident victims were included in the study from the households of selected wards. Mean age of the accident victims was found to be 36.2 (11.4) years. Two‑wheeler accidents accounted for 144 (85.2%) and 123 (72.7%) accident victims were drivers at the time of accident. Majority (95.1 %) of the victims did not wear helmet while driving two-wheelers and none of the four-wheel drivers/pillions wore seat belts. Majority of the accidents occurred on usual tar roads 116 (68.6%) and 42 (24.9%) on highways. 102 (60.4%) accidents occurred in bi-directional roads.Conclusion: Simple or minor injuries were high compared to serious injuries requiring hospitalization. Majority of the accidents occurred during Fridays, Saturdays and Sundays. The accidents exhibited a bimodal distribution with day and night time.

Published
2023
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31. Unobtrusive Skin Temperature Estimation on a Smart Bed

Author

Gary Garcia-Molina, Trevor Winger, Nikhil Makaram, Megha Rajam Rao, Pavlo Chernega, Yehor Shcherbakov, Leah McGhee, Vidhya Chellamuthu, Erwin Veneros, Raj Mills, Mark Aloia, and Kathryn J. Reid

Subjects
unobtrusive, sleep, skin temperature, regression model, temperature sensor strip, smart bed, Chemical technology, TP1-1185
Abstract

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper’s body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed’s mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [−0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.

Published
2024
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33. Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Author

Judge, PK, Staplin, N, Mayne, KJ, Wanner, C, Green, JB, Hauske, SJ, Emberson, JR, Preiss, D, Ng, SYA, Roddick, AJ, Sammons, E, Zhu, D, Hill, M, Stevens, W, Wallendszus, K, Brenner, S, Cheung, AK, Liu, ZH, Li, J, Hooi, LS, Liu, WJ, Kadowaki, T, Nangaku, M, Levin, A, Cherney, D, Maggioni, AP, Pontremoli, R, Deo, R, Goto, S, Rossello, X, Tuttle, KR, Steubl, D, Massey, D, Landray, MJ, Baigent, C, Haynes, R, Herrington, WG, Abat, S, Abd Rahman, R, Abdul Cader, R, Abdul Hafidz, MI, Abdul Wahab, MZ, Abdullah, NK, Abdul-Samad, T, Abe, M, Abraham, N, Acheampong, S, Achiri, P, Acosta, JA, Adeleke, A, Adell, V, Adewuyi-Dalton, R, Adnan, N, Africano, A, Agharazii, M, Aguilar, F, Aguilera, A, Ahmad, M, Ahmad, MK, Ahmad, NA, Ahmad, NH, Ahmad, NI, Ahmad Miswan, N, Ahmad Rosdi, H, Ahmed, I, Ahmed, S, Aiello, J, Aitken, A, AitSadi, R, Aker, S, Akimoto, S, Akinfolarin, A, Akram, S, Alberici, F, Albert, C, Aldrich, L, Alegata, M, Alexander, L, Alfaress, S, Alhadj Ali, M, Ali, A, Alicic, R, Aliu, A, Almaraz, R, Almasarwah, R, Almeida, J, Aloisi, A, Al-Rabadi, L, Alscher, D, Alvarez, P, Al-Zeer, B, Amat, M, Ambrose, C, Ammar, H, An, Y, Andriaccio, L, Ansu, K, Apostolidi, A, Arai, N, Araki, H, Araki, S, Arbi, A, Arechiga, O, Armstrong, S, Arnold, T, Aronoff, S, Arriaga, W, Arroyo, J, Arteaga, D, Asahara, S, Asai, A, Asai, N, Asano, S, Asawa, M, Asmee, MF, Aucella, F, Augustin, M, Avery, A, Awad, A, Awang, IY, Awazawa, M, Axler, A, Ayub, W, Azhari, Z, Baccaro, R, Badin, C, Bagwell, B, Bahlmann-Kroll, E, Bahtar, AZ, Bains, D, Bajaj, H, Baker, R, Baldini, E, Banas, B, Banerjee, D, Banno, S, Bansal, S, Barberi, S, Barnes, S, Barnini, C, Barot, C, Barrett, K, Barrios, R, Bartolomei Mecatti, B, Barton, I, Barton, J, Basily, W, Bavanandan, S, Baxter, A, Becker, L, Beddhu, S, Beige, J, Beigh, S, Bell, S, Benck, U, Beneat, A, Bennett, A, Bennett, D, Benyon, S, Berdeprado, J, Bergler, T, Bergner, A, Berry, M, Bevilacqua, M, Bhairoo, J, Bhandari, S, Bhandary, N, Bhatt, A, Bhattarai, M, Bhavsar, M, Bian, W, Bianchini, F, Bianco, S, Bilous, R, Bilton, J, Bilucaglia, D, Bird, C, Birudaraju, D, Biscoveanu, M, Blake, C, Bleakley, N, Bocchicchia, K, Bodine, S, Bodington, R, Boedecker, S, Bolduc, M, Bolton, S, Bond, C, Boreky, F, Boren, K, Bouchi, R, Bough, L, Bovan, D, Bowler, C, Bowman, L, Brar, N, Braun, C, Breach, A, Breitenfeldt, M, Brettschneider, B, Brewer, A, Brewer, G, Brindle, V, Brioni, E, Brown, C, Brown, H, Brown, L, Brown, R, Brown, S, Browne, D, Bruce, K, Brueckmann, M, Brunskill, N, Bryant, M, Brzoska, M, Bu, Y, Buckman, C, Budoff, M, Bullen, M, Burke, A, Burnette, S, Burston, C, Busch, M, Bushnell, J, Butler, S, Büttner, C, Byrne, C, Caamano, A, Cadorna, J, Cafiero, C, Cagle, M, Cai, J, Calabrese, K, Calvi, C, Camilleri, B, Camp, S, Campbell, D, Campbell, R, Cao, H, Capelli, I, Caple, M, Caplin, B, Cardone, A, Carle, J, Carnall, V, Caroppo, M, Carr, S, Carraro, G, Carson, M, Casares, P, Castillo, C, Castro, C, Caudill, B, Cejka, V, Ceseri, M, Cham, L, Chamberlain, A, Chambers, J, Chan, CBT, Chan, JYM, Chan, YC, Chang, E, Chant, T, Chavagnon, T, Chellamuthu, P, Chen, F, Chen, J, Chen, P, Chen, TM, Chen, Y, Cheng, C, Cheng, H, Cheng, MC, Ching, CH, Chitalia, N, Choksi, R, Chukwu, C, Chung, K, Cianciolo, G, Cipressa, L, Clark, S, Clarke, H, Clarke, R, Clarke, S, Cleveland, B, Cole, E, Coles, H, Condurache, L, Connor, A, Convery, K, Cooper, A, Cooper, N, Cooper, Z, Cooperman, L, Cosgrove, L, Coutts, P, Cowley, A, Craik, R, Cui, G, Cummins, T, Dahl, N, Dai, H, Dajani, L, D'Amelio, A, Damian, E, Damianik, K, Danel, L, Daniels, C, Daniels, T, Darbeau, S, Darius, H, Dasgupta, T, Davies, J, Davies, L, Davis, A, Davis, J, Davis, L, Dayanandan, R, Dayi, S, Dayrell, R, De Nicola, L, Debnath, S, Deeb, W, Degenhardt, S, DeGoursey, K, Delaney, M, DeRaad, R, Derebail, V, Dev, D, Devaux, M, Dhall, P, Dhillon, G, Dienes, J, Dobre, M, Doctolero, E, Dodds, V, Domingo, D, Donaldson, D, Donaldson, P, Donhauser, C, Donley, V, Dorestin, S, Dorey, S, Doulton, T, Draganova, D, Draxlbauer, K, Driver, F, Du, H, Dube, F, Duck, T, Dugal, T, Dugas, J, Dukka, H, Dumann, H, Durham, W, Dursch, M, Dykas, R, Easow, R, Eckrich, E, Eden, G, Edmerson, E, Edwards, H, Ee, LW, Eguchi, J, Ehrl, Y, Eichstadt, K, Eid, W, Eilerman, B, Ejima, Y, Eldon, H, Ellam, T, Elliott, L, Ellison, R, Emberson, J, Epp, R, Er, A, Espino-Obrero, M, Estcourt, S, Estienne, L, Evans, G, Evans, J, Evans, S, Fabbri, G, Fajardo-Moser, M, Falcone, C, Fani, F, Faria-Shayler, P, Farnia, F, Farrugia, D, Fechter, M, Fellowes, D, Feng, F, Fernandez, J, Ferraro, P, Field, A, Fikry, S, Finch, J, Finn, H, Fioretto, P, Fish, R, Fleischer, A, Fleming-Brown, D, Fletcher, L, Flora, R, Foellinger, C, Foligno, N, Forest, S, Forghani, Z, Forsyth, K, Fottrell-Gould, D, Fox, P, Frankel, A, Fraser, D, Frazier, R, Frederick, K, Freking, N, French, H, Froment, A, Fuchs, B, Fuessl, L, Fujii, H, Fujimoto, A, Fujita, A, Fujita, K, Fujita, Y, Fukagawa, M, Fukao, Y, Fukasawa, A, Fuller, T, Funayama, T, Fung, E, Furukawa, M, Furukawa, Y, Furusho, M, Gabel, S, Gaidu, J, Gaiser, S, Gallo, K, Galloway, C, Gambaro, G, Gan, CC, Gangemi, C, Gao, M, Garcia, K, Garcia, M, Garofalo, C, Garrity, M, Garza, A, Gasko, S, Gavrila, M, Gebeyehu, B, Geddes, A, Gentile, G, George, A, George, J, Gesualdo, L, Ghalli, F, Ghanem, A, Ghate, T, Ghavampour, S, Ghazi, A, Gherman, A, Giebeln-Hudnell, U, Gill, B, Gillham, S, Girakossyan, I, Girndt, M, Giuffrida, A, Glenwright, M, Glider, T, Gloria, R, Glowski, D, Goh, BL, Goh, CB, Gohda, T, Goldenberg, R, Goldfaden, R, Goldsmith, C, Golson, B, Gonce, V, Gong, Q, Goodenough, B, Goodwin, N, Goonasekera, M, Gordon, A, Gordon, J, Gore, A, Goto, H, Gowen, D, Grace, A, Graham, J, Grandaliano, G, Gray, M, Greene, T, Greenwood, G, Grewal, B, Grifa, R, Griffin, D, Griffin, S, Grimmer, P, Grobovaite, E, Grotjahn, S, Guerini, A, Guest, C, Gunda, S, Guo, B, Guo, Q, Haack, S, Haase, M, Haaser, K, Habuki, K, Hadley, A, Hagan, S, Hagge, S, Haller, H, Ham, S, Hamal, S, Hamamoto, Y, Hamano, N, Hamm, M, Hanburry, A, Haneda, M, Hanf, C, Hanif, W, Hansen, J, Hanson, L, Hantel, S, Haraguchi, T, Harding, E, Harding, T, Hardy, C, Hartner, C, Harun, Z, Harvill, L, Hasan, A, Hase, H, Hasegawa, F, Hasegawa, T, Hashimoto, A, Hashimoto, C, Hashimoto, M, Hashimoto, S, Haskett, S, Hawfield, A, Hayami, T, Hayashi, M, Hayashi, S, Hazara, A, Healy, C, Hecktman, J, Heine, G, Henderson, H, Henschel, R, Hepditch, A, Herfurth, K, Hernandez, G, Hernandez Pena, A, Hernandez-Cassis, C, Herzog, C, Hewins, S, Hewitt, D, Hichkad, L, Higashi, S, Higuchi, C, Hill, C, Hill, L, Himeno, T, Hing, A, Hirakawa, Y, Hirata, K, Hirota, Y, Hisatake, T, Hitchcock, S, Hodakowski, A, Hodge, W, Hogan, R, Hohenstatt, U, Hohenstein, B, Hooi, L, Hope, S, Hopley, M, Horikawa, S, Hosein, D, Hosooka, T, Hou, L, Hou, W, Howie, L, Howson, A, Hozak, M, Htet, Z, Hu, X, Hu, Y, Huang, J, Huda, N, Hudig, L, Hudson, A, Hugo, C, Hull, R, Hume, L, Hundei, W, Hunt, N, Hunter, A, Hurley, S, Hurst, A, Hutchinson, C, Hyo, T, Ibrahim, FH, Ibrahim, S, Ihana, N, Ikeda, T, Imai, A, Imamine, R, Inamori, A, Inazawa, H, Ingell, J, Inomata, K, Inukai, Y, Ioka, M, Irtiza-Ali, A, Isakova, T, Isari, W, Iselt, M, Ishiguro, A, Ishihara, K, Ishikawa, T, Ishimoto, T, Ishizuka, K, Ismail, R, Itano, S, Ito, H, Ito, K, Ito, M, Ito, Y, Iwagaitsu, S, Iwaita, Y, Iwakura, T, Iwamoto, M, Iwasa, M, Iwasaki, H, Iwasaki, S, Izumi, K, Izumi, T, Jaafar, SM, Jackson, C, Jackson, Y, Jafari, G, Jahangiriesmaili, M, Jain, N, Jansson, K, Jasim, H, Jeffers, L, Jenkins, A, Jesky, M, Jesus-Silva, J, Jeyarajah, D, Jiang, Y, Jiao, X, Jimenez, G, Jin, B, Jin, Q, Jochims, J, Johns, B, Johnson, C, Johnson, T, Jolly, S, Jones, L, Jones, S, Jones, T, Jones, V, Joseph, M, Joshi, S, Judge, P, Junejo, N, Junus, S, Kachele, M, Kadoya, H, Kaga, H, Kai, H, Kajio, H, Kaluza-Schilling, W, Kamaruzaman, L, Kamarzarian, A, Kamimura, Y, Kamiya, H, Kamundi, C, Kan, T, Kanaguchi, Y, Kanazawa, A, Kanda, E, Kanegae, S, Kaneko, K, Kang, HY, Kano, T, Karim, M, Karounos, D, Karsan, W, Kasagi, R, Kashihara, N, Katagiri, H, Katanosaka, A, Katayama, A, Katayama, M, Katiman, E, Kato, K, Kato, M, Kato, N, Kato, S, Kato, T, Kato, Y, Katsuda, Y, Katsuno, T, Kaufeld, J, Kavak, Y, Kawai, I, Kawai, M, Kawase, A, Kawashima, S, Kazory, A, Kearney, J, Keith, B, Kellett, J, Kelley, S, Kershaw, M, Ketteler, M, Khai, Q, Khairullah, Q, Khandwala, H, Khoo, KKL, Khwaja, A, Kidokoro, K, Kielstein, J, Kihara, M, Kimber, C, Kimura, S, Kinashi, H, Kingston, H, Kinomura, M, Kinsella-Perks, E, Kitagawa, M, Kitajima, M, Kitamura, S, Kiyosue, A, Kiyota, M, Klauser, F, Klausmann, G, Kmietschak, W, Knapp, K, Knight, C, Knoppe, A, Knott, C, Kobayashi, M, Kobayashi, R, Kobayashi, T, Koch, M, Kodama, S, Kodani, N, Kogure, E, Koizumi, M, Kojima, H, Kojo, T, Kolhe, N, Komaba, H, Komiya, T, Komori, H, Kon, SP, Kondo, M, Kong, W, Konishi, M, Kono, K, Koshino, M, Kosugi, T, Kothapalli, B, Kozlowski, T, Kraemer, B, Kraemer-Guth, A, Krappe, J, Kraus, D, Kriatselis, C, Krieger, C, Krish, P, Kruger, B, Ku Md Razi, KR, Kuan, Y, Kubota, S, Kuhn, S, Kumar, P, Kume, S, Kummer, I, Kumuji, R, Küpper, A, Kuramae, T, Kurian, L, Kuribayashi, C, Kurien, R, Kuroda, E, Kurose, T, Kutschat, A, Kuwabara, N, Kuwata, H, La Manna, G, Lacey, M, Lafferty, K, LaFleur, P, Lai, V, Laity, E, Lambert, A, Langlois, M, Latif, F, Latore, E, Laundy, E, Laurienti, D, Lawson, A, Lay, M, Leal, I, Lee, AK, Lee, J, Lee, KQ, Lee, R, Lee, SA, Lee, YY, Lee-Barkey, Y, Leonard, N, Leoncini, G, Leong, CM, Lerario, S, Leslie, A, Lewington, A, Li, N, Li, X, Li, Y, Liberti, L, Liberti, ME, Liew, A, Liew, YF, Lilavivat, U, Lim, SK, Lim, YS, Limon, E, Lin, H, Lioudaki, E, Liu, H, Liu, J, Liu, L, Liu, Q, Liu, X, Liu, Z, Loader, D, Lochhead, H, Loh, CL, Lorimer, A, Loudermilk, L, Loutan, J, Low, CK, Low, CL, Low, YM, Lozon, Z, Lu, Y, Lucci, D, Ludwig, U, Luker, N, Lund, D, Lustig, R, Lyle, S, Macdonald, C, MacDougall, I, Machicado, R, MacLean, D, Macleod, P, Madera, A, Madore, F, Maeda, K, Maegawa, H, Maeno, S, Mafham, M, Magee, J, Mah, DY, Mahabadi, V, Maiguma, M, Makita, Y, Makos, G, Manco, L, Mangiacapra, R, Manley, J, Mann, P, Mano, S, Marcotte, G, Maris, J, Mark, P, Markau, S, Markovic, M, Marshall, C, Martin, M, Martinez, C, Martinez, S, Martins, G, Maruyama, K, Maruyama, S, Marx, K, Maselli, A, Masengu, A, Maskill, A, Masumoto, S, Masutani, K, Matsumoto, M, Matsunaga, T, Matsuoka, N, Matsushita, M, Matthews, M, Matthias, S, Matvienko, E, Maurer, M, Maxwell, P, Mazlan, N, Mazlan, SA, Mbuyisa, A, McCafferty, K, McCarroll, F, McCarthy, T, McClary-Wright, C, McCray, K, McDermott, P, McDonald, C, McDougall, R, McHaffie, E, McIntosh, K, McKinley, T, McLaughlin, S, McLean, N, McNeil, L, Measor, A, Meek, J, Mehta, A, Mehta, R, Melandri, M, Mené, P, Meng, T, Menne, J, Merritt, K, Merscher, S, Meshykhi, C, Messa, P, Messinger, L, Miftari, N, Miller, R, Miller, Y, Miller-Hodges, E, Minatoguchi, M, Miners, M, Minutolo, R, Mita, T, Miura, Y, Miyaji, M, Miyamoto, S, Miyatsuka, T, Miyazaki, M, Miyazawa, I, Mizumachi, R, Mizuno, M, Moffat, S, Mohamad Nor, FS, Mohamad Zaini, SN, Mohamed Affandi, FA, Mohandas, C, Mohd, R, Mohd Fauzi, NA, Mohd Sharif, NH, Mohd Yusoff, Y, Moist, L, Moncada, A, Montasser, M, Moon, A, Moran, C, Morgan, N, Moriarty, J, Morig, G, Morinaga, H, Morino, K, Morisaki, T, Morishita, Y, Morlok, S, Morris, A, Morris, F, Mostafa, S, Mostefai, Y, Motegi, M, Motherwell, N, Motta, D, Mottl, A, Moys, R, Mozaffari, S, Muir, J, Mulhern, J, Mulligan, S, Munakata, Y, Murakami, C, Murakoshi, M, Murawska, A, Murphy, K, Murphy, L, Murray, S, Murtagh, H, Musa, MA, Mushahar, L, Mustafa, R, Mustafar, R, Muto, M, Nadar, E, Nagano, R, Nagasawa, T, Nagashima, E, Nagasu, H, Nagelberg, S, Nair, H, Nakagawa, Y, Nakahara, M, Nakamura, J, Nakamura, R, Nakamura, T, Nakaoka, M, Nakashima, E, Nakata, J, Nakata, M, Nakatani, S, Nakatsuka, A, Nakayama, Y, Nakhoul, G, Naverrete, G, Navivala, A, Nazeer, I, Negrea, L, Nethaji, C, Newman, E, Ng, TJ, Ngu, LLS, Nimbkar, T, Nishi, H, Nishi, M, Nishi, S, Nishida, Y, Nishiyama, A, Niu, J, Niu, P, Nobili, G, Nohara, N, Nojima, I, Nolan, J, Nosseir, H, Nozawa, M, Nunn, M, Nunokawa, S, Oda, M, Oe, M, Oe, Y, Ogane, K, Ogawa, W, Ogihara, T, Oguchi, G, Ohsugi, M, Oishi, K, Okada, Y, Okajyo, J, Okamoto, S, Okamura, K, Olufuwa, O, Oluyombo, R, Omata, A, Omori, Y, Ong, LM, Ong, YC, Onyema, J, Oomatia, A, Oommen, A, Oremus, R, Orimo, Y, Ortalda, V, Osaki, Y, Osawa, Y, Osmond Foster, J, O'Sullivan, A, Otani, T, Othman, N, Otomo, S, O'Toole, J, Owen, L, Ozawa, T, Padiyar, A, Page, N, Pajak, S, Paliege, A, Pandey, A, Pandey, R, Pariani, H, Park, J, Parrigon, M, Passauer, J, Patecki, M, Patel, M, Patel, R, Patel, T, Patel, Z, Paul, R, Paulsen, L, Pavone, L, Peixoto, A, Peji, J, Peng, BC, Peng, K, Pennino, L, Pereira, E, Perez, E, Pergola, P, Pesce, F, Pessolano, G, Petchey, W, Petr, EJ, Pfab, T, Phelan, P, Phillips, R, Phillips, T, Phipps, M, Piccinni, G, Pickett, T, Pickworth, S, Piemontese, M, Pinto, D, Piper, J, Plummer-Morgan, J, Poehler, D, Polese, L, Poma, V, Postal, A, Pötz, C, Power, A, Pradhan, N, Pradhan, R, Preiss, E, Preston, K, Prib, N, Price, L, Provenzano, C, Pugay, C, Pulido, R, Putz, F, Qiao, Y, Quartagno, R, Quashie-Akponeware, M, Rabara, R, Rabasa-Lhoret, R, Radhakrishnan, D, Radley, M, Raff, R, Raguwaran, S, Rahbari-Oskoui, F, Rahman, M, Rahmat, K, Ramadoss, S, Ramanaidu, S, Ramasamy, S, Ramli, R, Ramli, S, Ramsey, T, Rankin, A, Rashidi, A, Raymond, L, Razali, WAFA, Read, K, Reiner, H, Reisler, A, Reith, C, Renner, J, Rettenmaier, B, Richmond, L, Rijos, D, Rivera, R, Rivers, V, Robinson, H, Rocco, M, Rodriguez-Bachiller, I, Rodriquez, R, Roesch, C, Roesch, J, Rogers, J, Rohnstock, M, Rolfsmeier, S, Roman, M, Romo, A, Rosati, A, Rosenberg, S, Ross, T, Roura, M, Roussel, M, Rovner, S, Roy, S, Rucker, S, Rump, L, Ruocco, M, Ruse, S, Russo, F, Russo, M, Ryder, M, Sabarai, A, Saccà, C, Sachson, R, Sadler, E, Safiee, NS, Sahani, M, Saillant, A, Saini, J, Saito, C, Saito, S, Sakaguchi, K, Sakai, M, Salim, H, Salviani, C, Sampson, A, Samson, F, Sandercock, P, Sanguila, S, Santorelli, G, Santoro, D, Sarabu, N, Saram, T, Sardell, R, Sasajima, H, Sasaki, T, Satko, S, Sato, A, Sato, D, Sato, H, Sato, J, Sato, T, Sato, Y, Satoh, M, Sawada, K, Schanz, M, Scheidemantel, F, Schemmelmann, M, Schettler, E, Schettler, V, Schlieper, GR, Schmidt, C, Schmidt, G, Schmidt, U, Schmidt-Gurtler, H, Schmude, M, Schneider, A, Schneider, I, Schneider-Danwitz, C, Schomig, M, Schramm, T, Schreiber, A, Schricker, S, Schroppel, B, Schulte-Kemna, L, Schulz, E, Schumacher, B, Schuster, A, Schwab, A, Scolari, F, Scott, A, Seeger, W, Segal, M, Seifert, L, Seifert, M, Sekiya, M, Sellars, R, Seman, MR, Shah, S, Shainberg, L, Shanmuganathan, M, Shao, F, Sharma, K, Sharpe, C, Sheikh-Ali, M, Sheldon, J, Shenton, C, Shepherd, A, Shepperd, M, Sheridan, R, Sheriff, Z, Shibata, Y, Shigehara, T, Shikata, K, Shimamura, K, Shimano, H, Shimizu, Y, Shimoda, H, Shin, K, Shivashankar, G, Shojima, N, Silva, R, Sim, CSB, Simmons, K, Sinha, S, Sitter, T, Sivanandam, S, Skipper, M, Sloan, K, Sloan, L, Smith, R, Smyth, J, Sobande, T, Sobata, M, Somalanka, S, Song, X, Sonntag, F, Sood, B, Sor, SY, Soufer, J, Sparks, H, Spatoliatore, G, Spinola, T, Squyres, S, Srivastava, A, Stanfield, J, Staylor, K, Steele, A, Steen, O, Steffl, D, Stegbauer, J, Stellbrink, C, Stellbrink, E, Stevenson, A, Stewart-Ray, V, Stickley, J, Stoffler, D, Stratmann, B, Streitenberger, S, Strutz, F, Stubbs, J, Stumpf, J, Suazo, N, Suchinda, P, Suckling, R, Sudin, A, Sugamori, K, Sugawara, H, Sugawara, K, Sugimoto, D, Sugiyama, H, Sugiyama, T, Sullivan, M, Sumi, M, Suresh, N, Sutton, D, Suzuki, H, Suzuki, R, Suzuki, Y, Swanson, E, Swift, P, Syed, S, Szerlip, H, Taal, M, Taddeo, M, Tailor, C, Tajima, K, Takagi, M, Takahashi, K, Takahashi, M, Takahashi, T, Takahira, E, Takai, T, Takaoka, M, Takeoka, J, Takesada, A, Takezawa, M, Talbot, M, Taliercio, J, Talsania, T, Tamori, Y, Tamura, R, Tamura, Y, Tan, CHH, Tan, EZZ, Tanabe, A, Tanabe, K, Tanaka, A, Tanaka, N, Tang, S, Tang, Z, Tanigaki, K, Tarlac, M, Tatsuzawa, A, Tay, JF, Tay, LL, Taylor, J, Taylor, K, Te, A, Tenbusch, L, Teng, KS, Terakawa, A, Terry, J, Tham, ZD, Tholl, S, Thomas, G, Thong, KM, Tietjen, D, Timadjer, A, Tindall, H, Tipper, S, Tobin, K, Toda, N, Tokuyama, A, Tolibas, M, Tomita, A, Tomita, T, Tomlinson, J, Tonks, L, Topf, J, Topping, S, Torp, A, Torres, A, Totaro, F, Toth, P, Toyonaga, Y, Tripodi, F, Trivedi, K, Tropman, E, Tschope, D, Tse, J, Tsuji, K, Tsunekawa, S, Tsunoda, R, Tucky, B, Tufail, S, Tuffaha, A, Turan, E, Turner, H, Turner, J, Turner, M, Tye, YL, Tyler, A, Tyler, J, Uchi, H, Uchida, H, Uchida, T, Udagawa, T, Ueda, S, Ueda, Y, Ueki, K, Ugni, S, Ugwu, E, Umeno, R, Unekawa, C, Uozumi, K, Urquia, K, Valleteau, A, Valletta, C, van Erp, R, Vanhoy, C, Varad, V, Varma, R, Varughese, A, Vasquez, P, Vasseur, A, Veelken, R, Velagapudi, C, Verdel, K, Vettoretti, S, Vezzoli, G, Vielhauer, V, Viera, R, Vilar, E, Villaruel, S, Vinall, L, Vinathan, J, Visnjic, M, Voigt, E, von-Eynatten, M, Vourvou, M, Wada, J, Wada, T, Wada, Y, Wakayama, K, Wakita, Y, Walters, T, Wan Mohamad, WH, Wang, L, Wang, W, Wang, X, Wang, Y, Wanninayake, S, Watada, H, Watanabe, K, Watanabe, M, Waterfall, H, Watkins, D, Watson, S, Weaving, L, Weber, B, Webley, Y, Webster, A, Webster, M, Weetman, M, Wei, W, Weihprecht, H, Weiland, L, Weinmann-Menke, J, Weinreich, T, Wendt, R, Weng, Y, Whalen, M, Whalley, G, Wheatley, R, Wheeler, A, Wheeler, J, Whelton, P, White, K, Whitmore, B, Whittaker, S, Wiebel, J, Wiley, J, Wilkinson, L, Willett, M, Williams, A, Williams, E, Williams, K, Williams, T, Wilson, A, Wilson, P, Wincott, L, Wines, E, Winkelmann, B, Winkler, M, Winter-Goodwin, B, Witczak, J, Wittes, J, Wittmann, M, Wolf, G, Wolf, L, Wolfling, R, Wong, C, Wong, E, Wong, HS, Wong, LW, Wong, YH, Wonnacott, A, Wood, A, Wood, L, Woodhouse, H, Wooding, N, Woodman, A, Wren, K, Wu, J, Wu, P, Xia, S, Xiao, H, Xiao, X, Xie, Y, Xu, C, Xu, Y, Xue, H, Yahaya, H, Yalamanchili, H, Yamada, A, Yamada, N, Yamagata, K, Yamaguchi, M, Yamaji, Y, Yamamoto, A, Yamamoto, S, Yamamoto, T, Yamanaka, A, Yamano, T, Yamanouchi, Y, Yamasaki, N, Yamasaki, Y, Yamashita, C, Yamauchi, T, Yan, Q, Yanagisawa, E, Yang, F, Yang, L, Yano, S, Yao, S, Yao, Y, Yarlagadda, S, Yasuda, Y, Yiu, V, Yokoyama, T, Yoshida, S, Yoshidome, E, Yoshikawa, H, Young, A, Young, T, Yousif, V, Yu, H, Yu, Y, Yuasa, K, Yusof, N, Zalunardo, N, Zander, B, Zani, R, Zappulo, F, Zayed, M, Zemann, B, Zettergren, P, Zhang, H, Zhang, L, Zhang, N, Zhang, X, Zhao, J, Zhao, L, Zhao, S, Zhao, Z, Zhong, H, Zhou, N, Zhou, S, Zhu, L, Zhu, S, Zietz, M, Zippo, M, Zirino, F, and Zulkipli, FH

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34. Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

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Staplin, N, Haynes, R, Judge, PK, Wanner, C, Green, JB, Emberson, J, Preiss, D, Mayne, KJ, Ng, SYA, Sammons, E, Zhu, D, Hill, M, Stevens, W, Wallendszus, K, Brenner, S, Cheung, AK, Liu, ZH, Li, J, Hooi, LS, Liu, WJ, Kadowaki, T, Nangaku, M, Levin, A, Cherney, D, Maggioni, AP, Pontremoli, R, Deo, R, Goto, S, Rossello, X, Tuttle, KR, Steubl, D, Petrini, M, Seidi, S, Landray, MJ, Baigent, C, Herrington, WG, Abat, S, Abd Rahman, R, Abdul Cader, R, Abdul Hafidz, MI, Abdul Wahab, MZ, Abdullah, NK, Abdul-Samad, T, Abe, M, Abraham, N, Acheampong, S, Achiri, P, Acosta, JA, Adeleke, A, Adell, V, Adewuyi-Dalton, R, Adnan, N, Africano, A, Agharazii, M, Aguilar, F, Aguilera, A, Ahmad, M, Ahmad, MK, Ahmad, NA, Ahmad, NH, Ahmad, NI, Ahmad Miswan, N, Ahmad Rosdi, H, Ahmed, I, Ahmed, S, Aiello, J, Aitken, A, AitSadi, R, Aker, S, Akimoto, S, Akinfolarin, A, Akram, S, Alberici, F, Albert, C, Aldrich, L, Alegata, M, Alexander, L, Alfaress, S, Alhadj Ali, M, Ali, A, Alicic, R, Aliu, A, Almaraz, R, Almasarwah, R, Almeida, J, Aloisi, A, Al-Rabadi, L, Alscher, D, Alvarez, P, Al-Zeer, B, Amat, M, Ambrose, C, Ammar, H, An, Y, Andriaccio, L, Ansu, K, Apostolidi, A, Arai, N, Araki, H, Araki, S, Arbi, A, Arechiga, O, Armstrong, S, Arnold, T, Aronoff, S, Arriaga, W, Arroyo, J, Arteaga, D, Asahara, S, Asai, A, Asai, N, Asano, S, Asawa, M, Asmee, MF, Aucella, F, Augustin, M, Avery, A, Awad, A, Awang, IY, Awazawa, M, Axler, A, Ayub, W, Azhari, Z, Baccaro, R, Badin, C, Bagwell, B, Bahlmann-Kroll, E, Bahtar, AZ, Bains, D, Bajaj, H, Baker, R, Baldini, E, Banas, B, Banerjee, D, Banno, S, Bansal, S, Barberi, S, Barnes, S, Barnini, C, Barot, C, Barrett, K, Barrios, R, Bartolomei Mecatti, B, Barton, I, Barton, J, Basily, W, Bavanandan, S, Baxter, A, Becker, L, Beddhu, S, Beige, J, Beigh, S, Bell, S, Benck, U, Beneat, A, Bennett, A, Bennett, D, Benyon, S, Berdeprado, J, Bergler, T, Bergner, A, Berry, M, Bevilacqua, M, Bhairoo, J, Bhandari, S, Bhandary, N, Bhatt, A, Bhattarai, M, Bhavsar, M, Bian, W, Bianchini, F, Bianco, S, Bilous, R, Bilton, J, Bilucaglia, D, Bird, C, Birudaraju, D, Biscoveanu, M, Blake, C, Bleakley, N, Bocchicchia, K, Bodine, S, Bodington, R, Boedecker, S, Bolduc, M, Bolton, S, Bond, C, Boreky, F, Boren, K, Bouchi, R, Bough, L, Bovan, D, Bowler, C, Bowman, L, Brar, N, Braun, C, Breach, A, Breitenfeldt, M, Brettschneider, B, Brewer, A, Brewer, G, Brindle, V, Brioni, E, Brown, C, Brown, H, Brown, L, Brown, R, Brown, S, Browne, D, Bruce, K, Brueckmann, M, Brunskill, N, Bryant, M, Brzoska, M, Bu, Y, Buckman, C, Budoff, M, Bullen, M, Burke, A, Burnette, S, Burston, C, Busch, M, Bushnell, J, Butler, S, Büttner, C, Byrne, C, Caamano, A, Cadorna, J, Cafiero, C, Cagle, M, Cai, J, Calabrese, K, Calvi, C, Camilleri, B, Camp, S, Campbell, D, Campbell, R, Cao, H, Capelli, I, Caple, M, Caplin, B, Cardone, A, Carle, J, Carnall, V, Caroppo, M, Carr, S, Carraro, G, Carson, M, Casares, P, Castillo, C, Castro, C, Caudill, B, Cejka, V, Ceseri, M, Cham, L, Chamberlain, A, Chambers, J, Chan, CBT, Chan, JYM, Chan, YC, Chang, E, Chant, T, Chavagnon, T, Chellamuthu, P, Chen, F, Chen, J, Chen, P, Chen, TM, Chen, Y, Cheng, C, Cheng, H, Cheng, MC, Ching, CH, Chitalia, N, Choksi, R, Chukwu, C, Chung, K, Cianciolo, G, Cipressa, L, Clark, S, Clarke, H, Clarke, R, Clarke, S, Cleveland, B, Cole, E, Coles, H, Condurache, L, Connor, A, Convery, K, Cooper, A, Cooper, N, Cooper, Z, Cooperman, L, Cosgrove, L, Coutts, P, Cowley, A, Craik, R, Cui, G, Cummins, T, Dahl, N, Dai, H, Dajani, L, D'Amelio, A, Damian, E, Damianik, K, Danel, L, Daniels, C, Daniels, T, Darbeau, S, Darius, H, Dasgupta, T, Davies, J, Davies, L, Davis, A, Davis, J, Davis, L, Dayanandan, R, Dayi, S, Dayrell, R, De Nicola, L, Debnath, S, Deeb, W, Degenhardt, S, DeGoursey, K, Delaney, M, DeRaad, R, Derebail, V, Dev, D, Devaux, M, Dhall, P, Dhillon, G, Dienes, J, Dobre, M, Doctolero, E, Dodds, V, Domingo, D, Donaldson, D, Donaldson, P, Donhauser, C, Donley, V, Dorestin, S, Dorey, S, Doulton, T, Draganova, D, Draxlbauer, K, Driver, F, Du, H, Dube, F, Duck, T, Dugal, T, Dugas, J, Dukka, H, Dumann, H, Durham, W, Dursch, M, Dykas, R, Easow, R, Eckrich, E, Eden, G, Edmerson, E, Edwards, H, Ee, LW, Eguchi, J, Ehrl, Y, Eichstadt, K, Eid, W, Eilerman, B, Ejima, Y, Eldon, H, Ellam, T, Elliott, L, Ellison, R, Epp, R, Er, A, Espino-Obrero, M, Estcourt, S, Estienne, L, Evans, G, Evans, J, Evans, S, Fabbri, G, Fajardo-Moser, M, Falcone, C, Fani, F, Faria-Shayler, P, Farnia, F, Farrugia, D, Fechter, M, Fellowes, D, Feng, F, Fernandez, J, Ferraro, P, Field, A, Fikry, S, Finch, J, Finn, H, Fioretto, P, Fish, R, Fleischer, A, Fleming-Brown, D, Fletcher, L, Flora, R, Foellinger, C, Foligno, N, Forest, S, Forghani, Z, Forsyth, K, Fottrell-Gould, D, Fox, P, Frankel, A, Fraser, D, Frazier, R, Frederick, K, Freking, N, French, H, Froment, A, Fuchs, B, Fuessl, L, Fujii, H, Fujimoto, A, Fujita, A, Fujita, K, Fujita, Y, Fukagawa, M, Fukao, Y, Fukasawa, A, Fuller, T, Funayama, T, Fung, E, Furukawa, M, Furukawa, Y, Furusho, M, Gabel, S, Gaidu, J, Gaiser, S, Gallo, K, Galloway, C, Gambaro, G, Gan, CC, Gangemi, C, Gao, M, Garcia, K, Garcia, M, Garofalo, C, Garrity, M, Garza, A, Gasko, S, Gavrila, M, Gebeyehu, B, Geddes, A, Gentile, G, George, A, George, J, Gesualdo, L, Ghalli, F, Ghanem, A, Ghate, T, Ghavampour, S, Ghazi, A, Gherman, A, Giebeln-Hudnell, U, Gill, B, Gillham, S, Girakossyan, I, Girndt, M, Giuffrida, A, Glenwright, M, Glider, T, Gloria, R, Glowski, D, Goh, BL, Goh, CB, Gohda, T, Goldenberg, R, Goldfaden, R, Goldsmith, C, Golson, B, Gonce, V, Gong, Q, Goodenough, B, Goodwin, N, Goonasekera, M, Gordon, A, Gordon, J, Gore, A, Goto, H, Gowen, D, Grace, A, Graham, J, Grandaliano, G, Gray, M, Greene, T, Greenwood, G, Grewal, B, Grifa, R, Griffin, D, Griffin, S, Grimmer, P, Grobovaite, E, Grotjahn, S, Guerini, A, Guest, C, Gunda, S, Guo, B, Guo, Q, Haack, S, Haase, M, Haaser, K, Habuki, K, Hadley, A, Hagan, S, Hagge, S, Haller, H, Ham, S, Hamal, S, Hamamoto, Y, Hamano, N, Hamm, M, Hanburry, A, Haneda, M, Hanf, C, Hanif, W, Hansen, J, Hanson, L, Hantel, S, Haraguchi, T, Harding, E, Harding, T, Hardy, C, Hartner, C, Harun, Z, Harvill, L, Hasan, A, Hase, H, Hasegawa, F, Hasegawa, T, Hashimoto, A, Hashimoto, C, Hashimoto, M, Hashimoto, S, Haskett, S, Hauske, SJ, Hawfield, A, Hayami, T, Hayashi, M, Hayashi, S, Hazara, A, Healy, C, Hecktman, J, Heine, G, Henderson, H, Henschel, R, Hepditch, A, Herfurth, K, Hernandez, G, Hernandez Pena, A, Hernandez-Cassis, C, Herzog, C, Hewins, S, Hewitt, D, Hichkad, L, Higashi, S, Higuchi, C, Hill, C, Hill, L, Himeno, T, Hing, A, Hirakawa, Y, Hirata, K, Hirota, Y, Hisatake, T, Hitchcock, S, Hodakowski, A, Hodge, W, Hogan, R, Hohenstatt, U, Hohenstein, B, Hooi, L, Hope, S, Hopley, M, Horikawa, S, Hosein, D, Hosooka, T, Hou, L, Hou, W, Howie, L, Howson, A, Hozak, M, Htet, Z, Hu, X, Hu, Y, Huang, J, Huda, N, Hudig, L, Hudson, A, Hugo, C, Hull, R, Hume, L, Hundei, W, Hunt, N, Hunter, A, Hurley, S, Hurst, A, Hutchinson, C, Hyo, T, Ibrahim, FH, Ibrahim, S, Ihana, N, Ikeda, T, Imai, A, Imamine, R, Inamori, A, Inazawa, H, Ingell, J, Inomata, K, Inukai, Y, Ioka, M, Irtiza-Ali, A, Isakova, T, Isari, W, Iselt, M, Ishiguro, A, Ishihara, K, Ishikawa, T, Ishimoto, T, Ishizuka, K, Ismail, R, Itano, S, Ito, H, Ito, K, Ito, M, Ito, Y, Iwagaitsu, S, Iwaita, Y, Iwakura, T, Iwamoto, M, Iwasa, M, Iwasaki, H, Iwasaki, S, Izumi, K, Izumi, T, Jaafar, SM, Jackson, C, Jackson, Y, Jafari, G, Jahangiriesmaili, M, Jain, N, Jansson, K, Jasim, H, Jeffers, L, Jenkins, A, Jesky, M, Jesus-Silva, J, Jeyarajah, D, Jiang, Y, Jiao, X, Jimenez, G, Jin, B, Jin, Q, Jochims, J, Johns, B, Johnson, C, Johnson, T, Jolly, S, Jones, L, Jones, S, Jones, T, Jones, V, Joseph, M, Joshi, S, Judge, P, Junejo, N, Junus, S, Kachele, M, Kadoya, H, Kaga, H, Kai, H, Kajio, H, Kaluza-Schilling, W, Kamaruzaman, L, Kamarzarian, A, Kamimura, Y, Kamiya, H, Kamundi, C, Kan, T, Kanaguchi, Y, Kanazawa, A, Kanda, E, Kanegae, S, Kaneko, K, Kang, HY, Kano, T, Karim, M, Karounos, D, Karsan, W, Kasagi, R, Kashihara, N, Katagiri, H, Katanosaka, A, Katayama, A, Katayama, M, Katiman, E, Kato, K, Kato, M, Kato, N, Kato, S, Kato, T, Kato, Y, Katsuda, Y, Katsuno, T, Kaufeld, J, Kavak, Y, Kawai, I, Kawai, M, Kawase, A, Kawashima, S, Kazory, A, Kearney, J, Keith, B, Kellett, J, Kelley, S, Kershaw, M, Ketteler, M, Khai, Q, Khairullah, Q, Khandwala, H, Khoo, KKL, Khwaja, A, Kidokoro, K, Kielstein, J, Kihara, M, Kimber, C, Kimura, S, Kinashi, H, Kingston, H, Kinomura, M, Kinsella-Perks, E, Kitagawa, M, Kitajima, M, Kitamura, S, Kiyosue, A, Kiyota, M, Klauser, F, Klausmann, G, Kmietschak, W, Knapp, K, Knight, C, Knoppe, A, Knott, C, Kobayashi, M, Kobayashi, R, Kobayashi, T, Koch, M, Kodama, S, Kodani, N, Kogure, E, Koizumi, M, Kojima, H, Kojo, T, Kolhe, N, Komaba, H, Komiya, T, Komori, H, Kon, SP, Kondo, M, Kong, W, Konishi, M, Kono, K, Koshino, M, Kosugi, T, Kothapalli, B, Kozlowski, T, Kraemer, B, Kraemer-Guth, A, Krappe, J, Kraus, D, Kriatselis, C, Krieger, C, Krish, P, Kruger, B, Ku Md Razi, KR, Kuan, Y, Kubota, S, Kuhn, S, Kumar, P, Kume, S, Kummer, I, Kumuji, R, Küpper, A, Kuramae, T, Kurian, L, Kuribayashi, C, Kurien, R, Kuroda, E, Kurose, T, Kutschat, A, Kuwabara, N, Kuwata, H, La Manna, G, Lacey, M, Lafferty, K, LaFleur, P, Lai, V, Laity, E, Lambert, A, Langlois, M, Latif, F, Latore, E, Laundy, E, Laurienti, D, Lawson, A, Lay, M, Leal, I, Lee, AK, Lee, J, Lee, KQ, Lee, R, Lee, SA, Lee, YY, Lee-Barkey, Y, Leonard, N, Leoncini, G, Leong, CM, Lerario, S, Leslie, A, Lewington, A, Li, N, Li, X, Li, Y, Liberti, L, Liberti, ME, Liew, A, Liew, YF, Lilavivat, U, Lim, SK, Lim, YS, Limon, E, Lin, H, Lioudaki, E, Liu, H, Liu, J, Liu, L, Liu, Q, Liu, X, Liu, Z, Loader, D, Lochhead, H, Loh, CL, Lorimer, A, Loudermilk, L, Loutan, J, Low, CK, Low, CL, Low, YM, Lozon, Z, Lu, Y, Lucci, D, Ludwig, U, Luker, N, Lund, D, Lustig, R, Lyle, S, Macdonald, C, MacDougall, I, Machicado, R, MacLean, D, Macleod, P, Madera, A, Madore, F, Maeda, K, Maegawa, H, Maeno, S, Mafham, M, Magee, J, Mah, DY, Mahabadi, V, Maiguma, M, Makita, Y, Makos, G, Manco, L, Mangiacapra, R, Manley, J, Mann, P, Mano, S, Marcotte, G, Maris, J, Mark, P, Markau, S, Markovic, M, Marshall, C, Martin, M, Martinez, C, Martinez, S, Martins, G, Maruyama, K, Maruyama, S, Marx, K, Maselli, A, Masengu, A, Maskill, A, Masumoto, S, Masutani, K, Matsumoto, M, Matsunaga, T, Matsuoka, N, Matsushita, M, Matthews, M, Matthias, S, Matvienko, E, Maurer, M, Maxwell, P, Mazlan, N, Mazlan, SA, Mbuyisa, A, McCafferty, K, McCarroll, F, McCarthy, T, McClary-Wright, C, McCray, K, McDermott, P, McDonald, C, McDougall, R, McHaffie, E, McIntosh, K, McKinley, T, McLaughlin, S, McLean, N, McNeil, L, Measor, A, Meek, J, Mehta, A, Mehta, R, Melandri, M, Mené, P, Meng, T, Menne, J, Merritt, K, Merscher, S, Meshykhi, C, Messa, P, Messinger, L, Miftari, N, Miller, R, Miller, Y, Miller-Hodges, E, Minatoguchi, M, Miners, M, Minutolo, R, Mita, T, Miura, Y, Miyaji, M, Miyamoto, S, Miyatsuka, T, Miyazaki, M, Miyazawa, I, Mizumachi, R, Mizuno, M, Moffat, S, Mohamad Nor, FS, Mohamad Zaini, SN, Mohamed Affandi, FA, Mohandas, C, Mohd, R, Mohd Fauzi, NA, Mohd Sharif, NH, Mohd Yusoff, Y, Moist, L, Moncada, A, Montasser, M, Moon, A, Moran, C, Morgan, N, Moriarty, J, Morig, G, Morinaga, H, Morino, K, Morisaki, T, Morishita, Y, Morlok, S, Morris, A, Morris, F, Mostafa, S, Mostefai, Y, Motegi, M, Motherwell, N, Motta, D, Mottl, A, Moys, R, Mozaffari, S, Muir, J, Mulhern, J, Mulligan, S, Munakata, Y, Murakami, C, Murakoshi, M, Murawska, A, Murphy, K, Murphy, L, Murray, S, Murtagh, H, Musa, MA, Mushahar, L, Mustafa, R, Mustafar, R, Muto, M, Nadar, E, Nagano, R, Nagasawa, T, Nagashima, E, Nagasu, H, Nagelberg, S, Nair, H, Nakagawa, Y, Nakahara, M, Nakamura, J, Nakamura, R, Nakamura, T, Nakaoka, M, Nakashima, E, Nakata, J, Nakata, M, Nakatani, S, Nakatsuka, A, Nakayama, Y, Nakhoul, G, Naverrete, G, Navivala, A, Nazeer, I, Negrea, L, Nethaji, C, Newman, E, Ng, TJ, Ngu, LLS, Nimbkar, T, Nishi, H, Nishi, M, Nishi, S, Nishida, Y, Nishiyama, A, Niu, J, Niu, P, Nobili, G, Nohara, N, Nojima, I, Nolan, J, Nosseir, H, Nozawa, M, Nunn, M, Nunokawa, S, Oda, M, Oe, M, Oe, Y, Ogane, K, Ogawa, W, Ogihara, T, Oguchi, G, Ohsugi, M, Oishi, K, Okada, Y, Okajyo, J, Okamoto, S, Okamura, K, Olufuwa, O, Oluyombo, R, Omata, A, Omori, Y, Ong, LM, Ong, YC, Onyema, J, Oomatia, A, Oommen, A, Oremus, R, Orimo, Y, Ortalda, V, Osaki, Y, Osawa, Y, Osmond Foster, J, O'Sullivan, A, Otani, T, Othman, N, Otomo, S, O'Toole, J, Owen, L, Ozawa, T, Padiyar, A, Page, N, Pajak, S, Paliege, A, Pandey, A, Pandey, R, Pariani, H, Park, J, Parrigon, M, Passauer, J, Patecki, M, Patel, M, Patel, R, Patel, T, Patel, Z, Paul, R, Paulsen, L, Pavone, L, Peixoto, A, Peji, J, Peng, BC, Peng, K, Pennino, L, Pereira, E, Perez, E, Pergola, P, Pesce, F, Pessolano, G, Petchey, W, Petr, EJ, Pfab, T, Phelan, P, Phillips, R, Phillips, T, Phipps, M, Piccinni, G, Pickett, T, Pickworth, S, Piemontese, M, Pinto, D, Piper, J, Plummer-Morgan, J, Poehler, D, Polese, L, Poma, V, Postal, A, Pötz, C, Power, A, Pradhan, N, Pradhan, R, Preiss, E, Preston, K, Prib, N, Price, L, Provenzano, C, Pugay, C, Pulido, R, Putz, F, Qiao, Y, Quartagno, R, Quashie-Akponeware, M, Rabara, R, Rabasa-Lhoret, R, Radhakrishnan, D, Radley, M, Raff, R, Raguwaran, S, Rahbari-Oskoui, F, Rahman, M, Rahmat, K, Ramadoss, S, Ramanaidu, S, Ramasamy, S, Ramli, R, Ramli, S, Ramsey, T, Rankin, A, Rashidi, A, Raymond, L, Razali, WAFA, Read, K, Reiner, H, Reisler, A, Reith, C, Renner, J, Rettenmaier, B, Richmond, L, Rijos, D, Rivera, R, Rivers, V, Robinson, H, Rocco, M, Rodriguez-Bachiller, I, Rodriquez, R, Roesch, C, Roesch, J, Rogers, J, Rohnstock, M, Rolfsmeier, S, Roman, M, Romo, A, Rosati, A, Rosenberg, S, Ross, T, Roura, M, Roussel, M, Rovner, S, Roy, S, Rucker, S, Rump, L, Ruocco, M, Ruse, S, Russo, F, Russo, M, Ryder, M, Sabarai, A, Saccà, C, Sachson, R, Sadler, E, Safiee, NS, Sahani, M, Saillant, A, Saini, J, Saito, C, Saito, S, Sakaguchi, K, Sakai, M, Salim, H, Salviani, C, Sampson, A, Samson, F, Sandercock, P, Sanguila, S, Santorelli, G, Santoro, D, Sarabu, N, Saram, T, Sardell, R, Sasajima, H, Sasaki, T, Satko, S, Sato, A, Sato, D, Sato, H, Sato, J, Sato, T, Sato, Y, Satoh, M, Sawada, K, Schanz, M, Scheidemantel, F, Schemmelmann, M, Schettler, E, Schettler, V, Schlieper, GR, Schmidt, C, Schmidt, G, Schmidt, U, Schmidt-Gurtler, H, Schmude, M, Schneider, A, Schneider, I, Schneider-Danwitz, C, Schomig, M, Schramm, T, Schreiber, A, Schricker, S, Schroppel, B, Schulte-Kemna, L, Schulz, E, Schumacher, B, Schuster, A, Schwab, A, Scolari, F, Scott, A, Seeger, W, Segal, M, Seifert, L, Seifert, M, Sekiya, M, Sellars, R, Seman, MR, Shah, S, Shainberg, L, Shanmuganathan, M, Shao, F, Sharma, K, Sharpe, C, Sheikh-Ali, M, Sheldon, J, Shenton, C, Shepherd, A, Shepperd, M, Sheridan, R, Sheriff, Z, Shibata, Y, Shigehara, T, Shikata, K, Shimamura, K, Shimano, H, Shimizu, Y, Shimoda, H, Shin, K, Shivashankar, G, Shojima, N, Silva, R, Sim, CSB, Simmons, K, Sinha, S, Sitter, T, Sivanandam, S, Skipper, M, Sloan, K, Sloan, L, Smith, R, Smyth, J, Sobande, T, Sobata, M, Somalanka, S, Song, X, Sonntag, F, Sood, B, Sor, SY, Soufer, J, Sparks, H, Spatoliatore, G, Spinola, T, Squyres, S, Srivastava, A, Stanfield, J, Staylor, K, Steele, A, Steen, O, Steffl, D, Stegbauer, J, Stellbrink, C, Stellbrink, E, Stevenson, A, Stewart-Ray, V, Stickley, J, Stoffler, D, Stratmann, B, Streitenberger, S, Strutz, F, Stubbs, J, Stumpf, J, Suazo, N, Suchinda, P, Suckling, R, Sudin, A, Sugamori, K, Sugawara, H, Sugawara, K, Sugimoto, D, Sugiyama, H, Sugiyama, T, Sullivan, M, Sumi, M, Suresh, N, Sutton, D, Suzuki, H, Suzuki, R, Suzuki, Y, Swanson, E, Swift, P, Syed, S, Szerlip, H, Taal, M, Taddeo, M, Tailor, C, Tajima, K, Takagi, M, Takahashi, K, Takahashi, M, Takahashi, T, Takahira, E, Takai, T, Takaoka, M, Takeoka, J, Takesada, A, Takezawa, M, Talbot, M, Taliercio, J, Talsania, T, Tamori, Y, Tamura, R, Tamura, Y, Tan, CHH, Tan, EZZ, Tanabe, A, Tanabe, K, Tanaka, A, Tanaka, N, Tang, S, Tang, Z, Tanigaki, K, Tarlac, M, Tatsuzawa, A, Tay, JF, Tay, LL, Taylor, J, Taylor, K, Te, A, Tenbusch, L, Teng, KS, Terakawa, A, Terry, J, Tham, ZD, Tholl, S, Thomas, G, Thong, KM, Tietjen, D, Timadjer, A, Tindall, H, Tipper, S, Tobin, K, Toda, N, Tokuyama, A, Tolibas, M, Tomita, A, Tomita, T, Tomlinson, J, Tonks, L, Topf, J, Topping, S, Torp, A, Torres, A, Totaro, F, Toth, P, Toyonaga, Y, Tripodi, F, Trivedi, K, Tropman, E, Tschope, D, Tse, J, Tsuji, K, Tsunekawa, S, Tsunoda, R, Tucky, B, Tufail, S, Tuffaha, A, Turan, E, Turner, H, Turner, J, Turner, M, Tye, YL, Tyler, A, Tyler, J, Uchi, H, Uchida, H, Uchida, T, Udagawa, T, Ueda, S, Ueda, Y, Ueki, K, Ugni, S, Ugwu, E, Umeno, R, Unekawa, C, Uozumi, K, Urquia, K, Valleteau, A, Valletta, C, van Erp, R, Vanhoy, C, Varad, V, Varma, R, Varughese, A, Vasquez, P, Vasseur, A, Veelken, R, Velagapudi, C, Verdel, K, Vettoretti, S, Vezzoli, G, Vielhauer, V, Viera, R, Vilar, E, Villaruel, S, Vinall, L, Vinathan, J, Visnjic, M, Voigt, E, von-Eynatten, M, Vourvou, M, Wada, J, Wada, T, Wada, Y, Wakayama, K, Wakita, Y, Walters, T, Wan Mohamad, WH, Wang, L, Wang, W, Wang, X, Wang, Y, Wanninayake, S, Watada, H, Watanabe, K, Watanabe, M, Waterfall, H, Watkins, D, Watson, S, Weaving, L, Weber, B, Webley, Y, Webster, A, Webster, M, Weetman, M, Wei, W, Weihprecht, H, Weiland, L, Weinmann-Menke, J, Weinreich, T, Wendt, R, Weng, Y, Whalen, M, Whalley, G, Wheatley, R, Wheeler, A, Wheeler, J, Whelton, P, White, K, Whitmore, B, Whittaker, S, Wiebel, J, Wiley, J, Wilkinson, L, Willett, M, Williams, A, Williams, E, Williams, K, Williams, T, Wilson, A, Wilson, P, Wincott, L, Wines, E, Winkelmann, B, Winkler, M, Winter-Goodwin, B, Witczak, J, Wittes, J, Wittmann, M, Wolf, G, Wolf, L, Wolfling, R, Wong, C, Wong, E, Wong, HS, Wong, LW, Wong, YH, Wonnacott, A, Wood, A, Wood, L, Woodhouse, H, Wooding, N, Woodman, A, Wren, K, Wu, J, Wu, P, Xia, S, Xiao, H, Xiao, X, Xie, Y, Xu, C, Xu, Y, Xue, H, Yahaya, H, Yalamanchili, H, Yamada, A, Yamada, N, Yamagata, K, Yamaguchi, M, Yamaji, Y, Yamamoto, A, Yamamoto, S, Yamamoto, T, Yamanaka, A, Yamano, T, Yamanouchi, Y, Yamasaki, N, Yamasaki, Y, Yamashita, C, Yamauchi, T, Yan, Q, Yanagisawa, E, Yang, F, Yang, L, Yano, S, Yao, S, Yao, Y, Yarlagadda, S, Yasuda, Y, Yiu, V, Yokoyama, T, Yoshida, S, Yoshidome, E, Yoshikawa, H, Young, A, Young, T, Yousif, V, Yu, H, Yu, Y, Yuasa, K, Yusof, N, Zalunardo, N, Zander, B, Zani, R, Zappulo, F, Zayed, M, Zemann, B, Zettergren, P, Zhang, H, Zhang, L, Zhang, N, Zhang, X, Zhao, J, Zhao, L, Zhao, S, Zhao, Z, Zhong, H, Zhou, N, Zhou, S, Zhu, L, Zhu, S, Zietz, M, Zippo, M, Zirino, F, and Zulkipli, FH

Published
2024
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36. Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform

Author

Trevor Winger, Vidhya Chellamuthu, Dmytro Guzenko, Mark Aloia, Shawn Barr, Susan DeFranco, Brandon Gorski, Faisal Mushtaq, and Gary Garcia-Molina

Subjects
insomnia risk, personalized machine learning, incremental learning, fine tuning, passive-aggressive learning, Neurology. Diseases of the nervous system, RC346-429
Abstract

IntroductionInsomnia causes serious adverse health effects and is estimated to affect 10–30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform.MethodsUsers of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model.ResultsThe generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions.DiscussionThese are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.

Published
2024
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37. Harbingers of Hope: Scientists and the Pursuit of World Peace

Author

Seithikurippu R. Pandi-Perumal, Willem A. C. M. van de Put, Andreas Maercker, Stevan E. Hobfoll, Velayudhan Mohan Kumar, Corrado Barbui, Arehally Marappa Mahalaksmi, Saravana Babu Chidambaram, Per Olof Lundmark, Tual Sawn Khai, Lukoye Atwoli, Vitalii Poberezhets, Ramasamy Rajesh Kumar, Derebe Madoro, Hernán Andrés Marín Agudelo, Samuel Ratnajeevan Herbert Hoole, Luísa Teixeira-Santos, Paulo Pereira, Konda Mani Saravanan, Anton Vrdoljak, Miguel Meira e Cruz, Chellamuthu Ramasubramanian, Alvin Kuowei Tay, Janne Grønli, Marit Sijbrandij, Sudhakar Sivasubramaniam, Meera Narasimhan, Eta Ngole Mbong, Markus Jansson-Fröjmark, Bjørn Bjorvatn, Joop T. V. M. de Jong, Mario H. Braakman, Maurice Eisenbruch, Darío Acuña-Castroviejo, Koos van der Velden, Gregory M. Brown, Markku Partinen, Alexander C. McFarlane, and Michael Berk

Subjects
sdgs, un, unhcr, covid-19, global health diplomacy, hamas, israel, mental health, psychiatry, middle east, military invasion, palestine, peace, scientist, sustainable development goals, war, Psychology, BF1-990
Abstract

The ongoing wars in many regions—such as the conflict between Israel and Hamas—as well as the effects of war on communities, social services, and mental health are covered in this special editorial. This article emphasizes the need for international efforts to promote peace, offer humanitarian aid, and address the mental health challenges faced by individuals and communities affected by war and violence.

Published
2023
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38. Genome-scale copy number variant analysis in schizophrenia patients and controls from South India

Author

Minali Singh, Dibyabhabha Pradhan, Poornima Kkani, Gundugurti Prasad Rao, Naveen Kumar Dhagudu, Lov Kumar, Chellamuthu Ramasubramanian, Srinivasan Ganesh Kumar, Surekha Sonttineni, and Kommu Naga Mohan

Subjects
schizophrenia, India, CNVs, case–control studies, deletions, duplications, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Copy number variants (CNVs) are among the main genetic factors identified in schizophrenia (SZ) through genome-scale studies conducted mostly in Caucasian populations. However, to date, there have been no genome-scale CNV reports on patients from India. To address this shortcoming, we generated, for the first time, genome-scale CNV data for 168 SZ patients and 168 controls from South India. In total, 63 different CNVs were identified in 56 patients and 46 controls with a significantly higher proportion of medium-sized deletions (100 kb–1 Mb) after multiple testing (FDR = 2.7E-4) in patients. Of these, 13 CNVs were previously reported; however, when searched against GWAS, transcriptome, exome, and DNA methylation studies, another 17 CNVs with candidate genes were identified. Of the total 30 CNVs, 28 were present in 38 patients and 12 in 27 controls, indicating a significantly higher representation in the former (p = 1.87E-5). Only 4q35.1-q35.2 duplications were significant (p = 0.020) and observed in 11 controls and 2 patients. Among the others that are not significant, a few examples of patient-specific and previously reported CNVs include deletions of 11q14.1 (DLG2), 22q11.21, and 14q21.1 (LRFN5). 16p13.3 deletion (RBFOX1), 3p14.2 duplication (CADPS), and 7p11.2 duplication (CCT6A) were some of the novel CNVs containing candidate genes. However, these observations need to be replicated in a larger sample size. In conclusion, this report constitutes an important foundation for future CNV studies in a relatively unexplored population. In addition, the data indicate that there are advantages in using an integrated approach for better identification of candidate CNVs for SZ and other mental health disorders.

Published
2023
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40. A Community-Based Observational Study on Knowledge, Attitude, and Practice of Single-Use Plastics Ban in Rural Puducherry, South India

Author

Devi Kittu, Sivapushani Aruljothi, and Lalithambigai Chellamuthu

Subjects
plastic ban, single-use plastic, observational study, community residents, rural area, Public aspects of medicine, RA1-1270
Abstract

Background: India's industries produce nearly 9,000,000 metric tons of disposable plastic annually. Government of Puducherry implemented a ban on single-use plastics from 1st August 2019. This study aimed to evaluate the knowledge, attitude, and practice (KAP) levels concerning the use and legislation of single-use plastics among rural Puducherry's community residents. Methods: A 6-month community-based observational study was conducted in rural Puducherry using multistage random sampling among 450 households. A semi-structured questionnaire was administered to an adult member (aged > 18 years) in each household before and after the plastic ban. Data collection utilized the Epi-collect 5 application, and SPSS v16 was used for statistical analysis, employing paired t-test and chi-square test (p-value < 0.05) Results: Mean age of study participants was 39.64 (13.23) years, nearly 57% of them were female. Before ban, 80.4% of the subjects were carrying their shopping contents using plastic bags provided by the seller in the rural area, whereas after ban implementation, it has reduced to 16.4%. Mean KAP score before ban was 8 +2.8 (95% CI: 7.7-8.2) and after ban, it increased to 15.2 +1.8 (95% CI: 15-15.4). The pre- and post-ban KAP scores differences were found to be statistically significant (p-value < 0.05). The perception of the law banning the use of plastic bags was found to be significantly higher in younger age group, female gender, and groups with higher educational and occupational status (p = 0.01) Conclusion: The study results will be useful for planning future needs and Information, Education Communication strategies for effective implementation and plastic use reduction in future.

Published
2023

42. Effectiveness of selected small group teaching methods for undergraduate medical students on basic concepts of epidemiology: A quasi-experimental study

Author

Jyothi Vasudevan, Lalithambigai Chellamuthu, Lokeshmaran Anandaraj, and Ajith Kumar Chalil

Subjects
fishbowl technique, medical education, problem-based learning, quasi-experimental study, tutorials, Special aspects of education, LC8-6691, Public aspects of medicine, RA1-1270
Abstract

BACKGROUND: Teaching epidemiology to young medical students using traditional teaching techniques is fraught with myriad challenges. Incorporating innovative small group teaching (SGT) approaches that promote active learning, practical application, and critical thinking can help in overcoming these challenges. AIM/OBJECTIVE: To identify the most effective SGT method from selected three approaches [tutorial technique (TT), problem-based learning (PBL), and fishbowl technique (FBT)] to teach the basic concepts of epidemiology to the third-year undergraduate medical students of a private medical college in Puducherry, Southern India. MATERIALS AND METHODS: A quasi-experimental study was conducted among third-year undergraduate medical students for 6 months. The sample size was calculated to be 60 using the nMaster 2.0 sample size software. Three groups were formed with 20 students each. A pre-test, which included fifty multiple-choice questions covering topic one, was conducted for students in all three groups. An SGT session on topic one (dynamics of disease transmission) was held on the same day by different facilitators for three groups A, B, and C using the TT, PBL, and FBT, respectively. After 6 weeks of the SGT session for topic one, a post-test using the same questions was organized for all three groups to identify the effectiveness of each SGT method. The above sequence of events was followed for topic two (study designs) and topic three (investigation of disease outbreak) among all groups in the subsequent months. A written informed consent was sought from all students. The collected data was entered in MS Excel 2010 and analyzed using SPSS 21. The pre- and post-tests for all topics in all three groups were compared using a paired t-test, and an ANOVA test was used to find any difference between the groups. RESULTS: The mean post-test score in each of the three groups for all topics had improved when compared with the mean pre-test score, which was significantly different between the three groups. Further, the mean score of group B (PBL group) was found to be higher than group C (FBT) but not significantly higher compared to group A (TT). The mean score of the feedback where the participants were asked to rate the overall session was found to be high in group B (PBL) followed by group A (TT). CONCLUSION: PBL and TT were found to be an equally effective way of small group methods for teaching–learning epidemiology in medical school.

Published
2024
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43. U-WIN to Win at Immunisation

Author

Neeharika Boreddy, Jyothi Vasudevan, and Lalithambigai Chellamuthu

Subjects
U-WIN, Immunization, Universal Immunization Programme, Mission Indradhanush, Digital India, Public aspects of medicine, RA1-1270
Abstract

India launched the Co-WIN app in 2021 which gained great success in vaccinating the huge population against COVID-19. Motivated by its success, the Government of India has launched the U-WIN app to reach its goals in the immunisation of the country in regards to the UIP (Universal Immunisation Programme). This article will mainly be shedding light on the rationale behind its development, the functionality of the app, training appropriate personnel for its usage, integration with other health platforms, challenges faced previously by the Co-WIN app which could also be faced by the U-WIN app and finally the way forwards regarding the digitalisation of health care in India.

Published
2023
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44. Outcome Analysis Comparing Muscle and Fasciocutaneous Free Flaps for Heel Reconstruction: Meta-Analysis and Case Series

Author

Abiramie Chellamuthu, Sathish Kumar Jayaraman, and Ramesh B. A.

Subjects
weight-bearing heel, reconstruction, fasciocutaneous, musculocutaneous, Surgery, RD1-811
Abstract

Background Choosing the components of free flap (fasciocutaneous or muscle) is one of the crucial but controversial decisions in heel reconstruction. This meta-analysis aims to provide an up-to-date comparison of fasciocutaneous flaps (FCFs) and muscle flaps (MFs) for heel reconstruction and to ascertain if one flap has an advantage over the other. Methods Following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses guidelines, a systematic literature review was performed identifying studies on heel reconstruction with FCF and MF. Primary outcomes were survival, time of ambulation, sensation, ulceration, gait, need for specialized footwear, revision procedures, and shear. Meta-analyses and Trial Sequential Analysis (TSA) were performed to estimate the pooled risk ratios (RRs) and standardized mean difference (SMD) with fixed effects and random effects models, respectively. Results Of 757 publications identified, 20 were reviewed including 255 patients with 263 free flaps. The meta-analysis showed no statistically significant difference between MF and FCF in terms of survival (RR, 1; 95% confidence interval [CI], 0.83, 1.21), gait abnormality (RR, 0.55; 95% CI, 0.19, 1.59), ulcerations (RR, 0.65; 95% CI, 0.27, 1.54), footwear modification (RR, 0.52; 95% CI, 0.26, 1.09), and revision procedures (RR, 1.67; 95% CI, 0.84, 3.32). FCF had superior perception of deep pressure (RR, 1.99; 95% CI, 1.32, 3.00), light touch, and pain (RR, 5.17; 95% CI, 2.02, 13.22) compared with MF. Time to full weight-bearing (SMD, –3.03; 95% CI, –4.25, –1.80) was longer for MF compared with FCF. TSA showed inconclusive results for comparison of the survival of flaps, gait assessment, and rates of ulceration. Conclusion Patients reconstructed with FCF had superior sensory recovery and early weight bearing on their reconstructed heels, hence faster return to daily activities compared with MFs. In terms of other outcomes such as footwear modification and revision procedure, both flaps had no statistically significant difference. The results were inconclusive regarding the survival of flaps, gait assessment, and rates of ulceration. Future studies are required to investigate the role of shear on the stability of the reconstructed heels.

Published
2023
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45. Association of socio-demographic determinants with quality of life among road traffic accident victims

Author

Lalithambigai Chellamuthu, Devi Kittu, Yogesh A Bahurupi, Kavita Vasudevan, and Sadhana Subramanian

Subjects
community-based study, longitudinal study, quality-of-life, road traffic accidents, socio-demographic determinants, Medicine, Nursing, RT1-120
Abstract

Background: Road traffic accidents are the sixth leading cause of death in India with a greater share of hospitalization, disabilities, deaths, and socio-economic losses. Objectives: To assess the socio-demographic determinants associated with quality-of-life among road traffic accident victims. Methodology: A community-based, longitudinal study was conducted for 2 years in South India. Simple random sampling was employed to include 169 accident victims. Baseline data were collected with a semi-structured questionnaire on socio-demographic details and using SF-32. Follow-up was at the 6th and 12th month from the day of accident. Data entry and analysis were performed using Epi-data manager 4.2.0. Written informed consent from each participant was sought. Ethical clearance was obtained. Results: The mean ± standard deviation age of the subjects was found to be 36.2 ± 11.4 years. The results of the study showed that there was a statistically significant incremental change in quality of life among the accident survivors at baseline, 6 and 12 months (P < 0.05). Socio-economic determinants such as education, occupation, marital status, and religion were found to be associated with different domains of quality of life. (P < 0.05). Conclusion: The improvement in the quality of life of the victims over 1-year period was found to be significant.

Published
2023
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46. Assessment of knowledge gap on cigarettes and other tobacco products act (COTPA) among tobacco vendors in Puducherry: A mixed-method study

Author

Lalithambigai Chellamuthu, J Jenifer Florence Mary, and T D Subhaashini

Subjects
cigarettes and other tobacco products act, educational institutions, knowledge gap, mixed-method study, points of sale, tobacco vendors, Public aspects of medicine, RA1-1270
Abstract

Background: Tobacco usage has been identified as a leading preventable cause of mortality and is responsible for six million fatalities per year globally. India had enacted COTPA in 2003. Tobacco vendors are one of the key stakeholders for Cigarettes and Other Tobacco Products Act (COTPA) implementation. Objective: To assess the knowledge gap on COTPA and to explore the perceived factors influencing implementation of COTPA among tobacco vendors in Puducherry. Materials and Methods: A mixed-method study was conducted among tobacco vendors from Point of Sale (PoS) around 230 educational institutions (schools and degree colleges) in Puducherry for three months. Simple random sampling was applied to select educational institutions and the tobacco vendors from PoS located around these institutions were included. Data capture was done using a pretested, face-validated questionnaire incorporated in Epicollect software 5 and data analysis by SPSSv24. Purposive sampling was employed to conduct in-depth interviews among tobacco vendors till the point of saturation and manual content analysis performed. Written informed consents were sought. Institutional Ethical Committee approval was obtained. Results: Majority (95.7%) reported that they were aware of tobacco control legislation, but only one person had heard of COTPA. Awareness about the display of signage boards at PoS was observed in 75.7% vendors. Around 41.7% reported that they should not advertise any kind of tobacco products. Three major themes were identified: facilitators, barriers, and suggested measures for COTPA implementation. Conclusion: Tobacco vendors in Puducherry were aware of tobacco control legislations but not familiar with the COTPA and its provisions. It is necessary to put forth efforts to educate these important stakeholders to be more cognizant of COTPA and to effectively include them in anti-tobacco programs.

Published
2023
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47. Assessment on quality of healthcare services during childbirth: A community-based mixed-method study in the women of Puducherry

Author

Lalithambigai Chellamuthu, Sadhvika Kanagat, Senkadhirdasan Dakshinamurthy, and Abhijit Vinodrao Boratne

Subjects
mistreatment during childbirth, physical abuse, quality of healthcare, verbal abuse, Public aspects of medicine, RA1-1270
Abstract

Background: A women's right to a positive childbirth experience should be the heart of any care provided. To assess the quality of childbirth services and mistreatment by healthcare providers among reproductive age group women and to explore factors influencing the same between women and stakeholders. Material and Methods: A community-based, mixed-method study was conducted from April to September 2021 in field practice areas of a medical college in Puducherry district. The sample size for the quantitative study was 348 and the women were chosen using a multi-stage sampling technique. Women were interviewed with a semi-structured questionnaire. In-depth and key informant interviews between women and stakeholders were done for the qualitative data collection. Results: Three-fourths (77.0%) of women preferred government tertiary healthcare facilities for obstetric care. Although 69.0% and 75.6% of the participants did not experience any verbal and physical abuse, respectively, the qualitative study results were quite the opposite. While 92.8% of the women complained that no birth companion was allowed during their delivery. Moreover, 79.9% of the women did not have the freedom to choose their comfortable birthing position. The levels of mistreatment in the rural areas were slightly higher than that of the urban areas. Conclusion: Quality care is a fundamental approach to maternity care. A fair bit of women experiences mistreatment during childbirth in healthcare setups. However, the chief concern here is the perception of such abuse by the mothers as normal due to their lack of knowledge regarding women's rights.

Published
2023
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49. Physico Chemical and Mechanical Properties of Natural Cellulosic Water Hyacinth Fiber and Its Composites

Author

Amalraju Dass and Sabarinathan Chellamuthu

Subjects
wh fiber, epoxy composites, mechanical properties, structure of wh fiber, chemical properties, sem, Science, Textile bleaching, dyeing, printing, etc., TP890-933
Abstract

Physicochemical and mechanical characterization of Water Hyacinth (WH) fiber was reported in this paper. Compression molding technique is used to fabricate WH fiber-reinforced epoxy composites. The chemical analysis indicates that the WH fiber contains higher percentage of cellulose and lignin content and lesser amount of ash and wax content. Structure of WH fiber shown from Scanning Electron Microscopy (SEM) analysis. Four different ratio’s of fiber and epoxy resin were used to fabricate the composites (25:75, 30:70, 35:65, 40:60), first two digit mention the fiber content and last two digit are mention resin. The result shows that at 35 wt.% of the fiber content in the composite provides a better improvement in properties. Results shown from the SEM depicts that the reason for fiber breakage, matrix fracture and pull-out of fiber is mechanical failure which stands as main mechanism. It stands as victim that WH fiber-reinforced composites can be used in various industrial applications.

Published
2022
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50. Revolutionizing Biomedicine: A Comprehensive Review of Polymer Composite Materials

Author

Manickam Ramesh, Tamil Selvan Manickam, Felix Sahayaraj Arockiasamy, Balakrishnan Ponnusamy, Shruthi Senthilraj, Dharshini Chellamuthu, and Ponraj Palanisamy

Subjects
polymer composites, biomedical applications, biocompatible materials, biomaterials, nanocomposites, tissue engineering, Engineering machinery, tools, and implements, TA213-215
Abstract

Polymer composites have been increasingly used in biomedical applications because of their unique combination of mechanical, chemical, and biological properties. These materials have shown promising results in various fields such as tissue engineering, drug delivery, and implant design. In this review, we examine the current state of polymer composites for biomedical applications, including the materials used, their properties, and processing methods. The advantages and limitations of these materials are also discussed, along with future perspectives and challenges that need to be addressed to fully realize their potential. This review aims to provide a comprehensive overview of this field, highlight recent advances, and encourage further research on the development of polymer composites for biomedical applications.

Published
2024
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