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All Journal Medicinus : Jurnal Kedokteran ACTIVE: Journal of Physical Education, Sport, Health and Recreation Proceedings Book of International Conference and Exhibition on The Indonesian Medical Education and Research Institute
Amanah, Salma Rizqi
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Education Health Professions Medicine & Pharmacology Neuroscience Public Health

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Association Between Physical Activity, Sleep Quality and Handgrip Strength in Medical Student Amanah, Salma Rizqi; Citrawati, Mila
ACTIVE: Journal of Physical Education, Sport, Health and Recreation Vol 9 No 2 (2020)
Publisher : Department of Physical Education, Sport, Health and Recreation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (284.708 KB) | DOI: 10.15294/active.v9i1.37172

Abstract

This study was conducted to determine the association between physical activity and sleep quality with handgrip muscles strength of medical students at Universitas Pembangunan Nasional “Veteran” Jakarta. This study was an analytical observational with cross sectional design. The participants were 75 male students aged 18-22 years old. Random sampling was used for this study. Measurement of physical activity was carried out using the Global Physical Activity Questionnaire (GPAQ) and sleep quality using the Pitsburgh Sleep Quality Index (PSQI) questionnaire. The strength of handgrip muscle was measured using Camry Handgrip Dynamometer. Result showed there was a significant association between physical activity with handgrip strength with significance value of p =0,000 (p <0,05) and sleep quality with p = 0,003 (p <0,05) using 95% CI. Multivariate test showed physical activity had more dominant association with handgrip strength with an OR score of 4,608 . Based on the result, it can be concluded student with good sleep quality and higher level of physical activity tend to have stronger handgrip muscle with physical activity as dominant factor.
Efficacy and Safety of Stem Cell Therapy for Spinal Cord Injury in Adults: A Systematic Review and Meta-Analysis Rhadika, Anadya; Romano, Sultan Adhitya; Widyatmiko, Himawan; Tanuwijaya, Andrew Wilbert; Putra, Putu Surya Pradipta Hariantha; Amanah, Salma Rizqi; Elashry, Abdelrahman Ramadan; Javaid, Sarmad; Inggas, Made Agus Mahendra; Wijaya, Jeremiah Hilkiah
Medicinus Vol. 15 No. 1 (2025): October
Publisher : Fakultas Kedokteran Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/med.v15i1.10762

Abstract

Background: Despite encouraging early results, clinical outcomes remain inconsistent across trials. This study aimed to systematically evaluate the efficacy and safety of stem cell therapy in adults with spinal cord injury (SCI). Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. PubMed, EMBASE, and Scopus were searched until 18 October 2025. Eligible studies included adult SCI patients receiving stem cell therapy with measurable neurological outcomes. Data synthesis was performed using Review Manager 5.4 under a random-effects model, reporting pooled risk ratios (RR) with 95% confidence intervals (CIs). Risk of bias was assessed using ROBINS-I, and evidence certainty was graded via GRADE. Result: Thirteen studies involving 470 participants (286 intervention, 184 control) were included. Stem cell therapy significantly improved neurological recovery compared with controls (RR = 2.64; 95% CI 1.70–4.10; p < 0.0001; I² = 0%). Subgroup analyses showed consistent benefits across baseline AIS classifications (RR = 2.61; 95% CI 1.71–3.98) and cell doses (RR = 2.75; 95% CI 1.63–4.64). No major safety signals were identified. GRADE assessment rated the certainty of efficacy evidence as moderate. Conclusions: Stem cell therapy yields significant neurological improvement in adult SCI with a favorable safety profile. The findings support its regenerative potential through neuroprotective and remyelinating mechanisms. However, larger randomized controlled trials are required to validate efficacy, optimize protocols, and assess long-term safety.
Applications of Artificial Intelligence in Peripheral Neuropathy: A Systematic Review Sutha, Anak Agung Ngurah Agung Bayu; Sharon; Rahman, Dea Nabila; Amanah, Salma Rizqi; Wicaksono, Teguh Budi; Sofiana, Dina; Hermawan, Galih Muchlis
Medicinus Vol. 13 No. 1 (2023): October
Publisher : Fakultas Kedokteran Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/med.v13i1.10778

Abstract

Background: Peripheral neuropathy (PN) is a common complication of metabolic and systemic diseases, particularly diabetes mellitus, resulting in sensory loss, pain, and motor impairment. Conventional diagnostic tools often detect PN only after irreversible nerve injury. Artificial intelligence (AI), especially machine learning (ML), has emerged as a promising tool for early diagnosis and risk prediction by integrating clinical, imaging, and genetic data. Methods: Following PRISMA 2020 guidelines, PubMed, EMBASE, IEEE Xplore, and Scopus were systematically searched up to September 2025. Studies applying ML or deep learning algorithms to PN were included, while reviews, grey literature, and studies lacking methodological details or performance metrics were excluded. Result: Our study included participants with diabetic, chemotherapy-induced, or pain-related neuropathies. Deep learning models, such as multilayer perceptrons and neural networks, achieved diagnostic accuracies of 87–93%, while classical algorithms including random forest, XGBoost, and SVM reported AUCs of 0.80–0.93. Radiomics-based SVMs using ultrasound showed external validation AUCs of 0.70–0.90. Key predictors included HbA1c, diabetes duration, lipid profile, and BMI. Conclusions: Machine learning demonstrates strong potential for improving the prediction, diagnosis, and phenotypic classification of PN. However, heterogeneity in datasets and limited external validation restrict clinical translation. Future work should focus on standardized data, multicenter validation, and interpretable AI models to facilitate integration into clinical practice.
Relationship between Emergency Department Triage Data and 24- and 48- Hour Mortality in an Academic Teaching Hospital Aviesena Zairinal, Ramdinal; Amanah, Salma Rizqi
Proceedings Book of International Conference and Exhibition on The Indonesian Medical Education Research Institute Vol. 9 No. - (2025): Proceedings Book of International Conference and Exhibition on The Indonesian M
Publisher : Writing Center IMERI FMUI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69951/proceedingsbookoficeonimeri.v9i-.319

Abstract

Introduction: Triage data plays an essential role in the initial assessment and prioritization of emergency patients. However, the association between triage parameters and short- term mortality remains underexplored. Evaluating 24-hour and 48-hour mortality may serve as an indicator of the effectiveness of triage and early resuscitation efforts. Objectives: To determine the relationship between emergency department triage parameters and short-term mortality (24-hour and 48-hour) among patients treated at an academic teaching hospital. Methods: A retrospective cohort study was conducted on all patients admitted to the emergency department from January to February 2024. Demographic and clinical data obtained during triage were analyzed. Bivariate and multivariate binary logistic regression analyses were performed to identify factors associated with 24-hour and 48-hour mortality. Results: A total of 1,976 patients were included. The 24-hour and 48-hour mortality rates were 1% and 5%, respectively. Significant predictors of 24-hour mortality were triage category (OR = 4.42; 95% CI 1.93–10.09), respiratory rate (OR = 1.09; 95% CI 1.02–1.16). Predictors of 48-hour mortality included age (OR = 1.02; 95% CI 1.008–1.036), triage category (OR = 3.23; 95% CI 2.23–4.67), respiratory rate (OR = 1.08; 95% CI 1.03–1.13), systolic blood pressure (OR = 0.98; 95% CI 0.97–0.99), and mental status (OR = 3.58; 95% CI 2.11–6.07). Conclusion: Several routinely collected data during initial admission to the emergency unit are independently associated with both 24-hour & 48-hour mortality. These results highlight that triage data can serve as meaningful predictors of early mortality and may support rapid risk stratification, resource allocation, and operational decision-making in the Emergency Department.
Co-Authors Aviesena Zairinal, Ramdinal Elashry, Abdelrahman Ramadan Hermawan, Galih Muchlis Javaid, Sarmad Made Agus Mahendra Inggas, Made Agus Mahendra Mila Citrawati Putra, Putu Surya Pradipta Hariantha Rahman, Dea Nabila Rhadika, Anadya Romano, Sultan Adhitya Sharon Sofiana, Dina Sutha, Anak Agung Ngurah Agung Bayu Tanuwijaya, Andrew Wilbert Teguh Budi Wicaksono Widyatmiko, Himawan Wijaya, Jeremiah Hilkiah