Jurnal Health Sains

STAPHYLOCOCCAL SCALDED SKIN SYNDROME WITH ATYPICAL PRESENTATION IN AN INFANT: A CASE REPORT

Laila Kurnia Fitri — 2025-04-30

Staphylococcal scalded skin syndrome (SSSS) is a rare, potentially fatal illness caused by Staphylococcus aureus toxins, mainly affecting children under six due to immature immunity and renal function. It presents with fever, rash, blistering, and skin peeling. This report highlights an atypical chronic case with unusual skin presentation. A 7-month-old infant presented with a 3-month history of widespread skin peeling, starting from the face and spreading to the body. No fever or prior drug use was reported. Physical exam showed generalized erythema, desquamation (rough scales), positive Nikolsky sign, and signs of dehydration because of acute diarrhea. Histopathology confirmed Staphylococcal Scalded Skin Syndrome (SSSS). The patient improved after 8 days of hospitalization with supportive and antibiotic therapy. Staphylococcal Scalded Skin Syndrome (SSSS) is caused by epidermolytic exotoxins from certain Staphylococcus aureus strains, leading to skin peeling and large superficial blisters. While often diagnosed clinically, atypical cases may mimic other conditions, making skin biopsy or culture necessary for confirmation.

Community Perception and Spatial Diaper Waste in Liliba Village, Kupang City

Lidia Br Tarigan — 2025-05-13

The improper management of diaper waste in Liliba Village, Indonesia, poses a serious environmental and public health concern. Currently, diaper waste is often discarded indiscriminately without prior processing, resulting in unpleasant odors and public complaints. This study aims to analyze community perceptions regarding diaper waste and to map its spatial distribution within Liliba Village. Employing an observational research design, the study investigated two main variables: public perception of diaper waste and the spatial distribution of diaper waste. A total of 111 respondents, all of whom were parents of toddlers, were selected as the study sample. Data were collected using structured questionnaires and Geographic Information System (GIS) applications. Analysis was conducted using univariate and bivariate statistical techniques. The results indicate that both gender and occupation significantly influence community perceptions of diaper waste and their views on proper waste management. Spatial analysis revealed that diaper waste is primarily found in temporary disposal sites (TPS) and in various unregulated locations, especially within the buffer zones surrounding residential areas of the respondents. These findings highlight the urgent need for targeted waste management education and infrastructure development to improve sanitation practices in Liliba Village. The integration of spatial data through GIS provides valuable insights for local authorities to implement geographically focused waste intervention strategies.

Comparison of Machine Learning Performance with TIMI and GRACE Score for Cardiovascular Risk Prediction in Acute Coronary Syndrome: Meta-Analysis

Indah Pertiwi — 2025-05-22

Acute Coronary Syndrome (ACS) risk stratification relies on TIMI and GRACE scores, which lack accuracy for individual-level predictions. Machine Learning (ML) offers promising alternatives but faces challenges in interpretability and clinical adoption. This meta-analysis compares ML models (DNN, XGBoost, Random Forest, GBDT, SVM) with TIMI/GRACE scores in predicting cardiovascular events, while addressing implementation barriers. Following PRISMA guidelines, we analyzed 50 studies (1,592,034 patients) from PubMed, Scopus, and Web of Science (2015–2025). Performance metrics (AUC, sensitivity, specificity) were pooled using random-effects models, and publication bias was assessed via funnel plots. ML models significantly outperformed conventional scores, with Random Forest (AUC=0.99), XGBoost (AUC=0.98), and DNN (sensitivity=99%) demonstrating superior discrimination. However, heterogeneity in validation (e.g., Asian vs. European cohorts) and "black-box" limitations were identified. The study advocates for explainable AI, multi-center validation, and clinician training to facilitate ML integration into Electronic Health Records (EHRs). These steps could establish ML as the new standard in ACS care, improving outcomes while reducing healthcare costs.