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Rampengan, Derren DCH.
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Biochemistry, Genetics & Molecular Biology Chemistry Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health

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Journal : Narra X

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Ecological study on child nutrition in Indonesia: National urban–rural patterns and local-level variation Qanita, Intan; Abshori, Nuril F.; Rampengan, Derren DCH.; Ramadhan, Roy; Adji, Arga S.; Nurkolis, Fahrul; Al-Abdullah, Hatem B.; Al-Dubai, Sami A.
Narra X Vol. 3 No. 3 (2025): December 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v3i3.235

Abstract

Indonesia continues to face a double burden of malnutrition, characterized by persistent undernutrition and a growing prevalence of overweight among children. Although urban children typically show lower rates of stunting and underweight, emerging evidence indicates rising obesity due to unhealthy behavior. At the same time, national indicators may mask substantial heterogeneity at the provincial and district levels. This study aimed to compare urban–rural disparities in child nutrition and maternal care at the national level and examines intra-provincial variation that may be obscured by aggregated statistics. An ecological analysis was conducted using data from the 2024 Indonesian Nutritional Status Survey. Urban–rural differences were evaluated using odds ratios (OR) and Chi-square tests. Sub-provincial analyses were undertaken in selected districts of Central Java and South Sulawesi to assess patterns of variation across smaller administrative units. At the national level, urban children exhibited lower odds of severe underweight (OR: 0.78; 95%CI: 0.75–0.81), underweight (OR: 0.82; 95%CI: 0.80–0.84), and stunting (OR: 0.77; 95%CI: 0.75–0.78). In contrast, they had higher odds of being at risk of overweight (OR: 1.35; 95%CI: 1.31–1.40) and of consuming unhealthy foods (OR: 1.22; 95%CI: 1.19–1.25). Rural areas showed poorer dietary diversity and lower coverage of antenatal care. District-level analyses revealed marked contrasts, where in Central Java, Magelang Municipality had lower odds of severe underweight than Surakarta and Tegal Municipalities. Meanwhile, in South Sulawesi, Makassar Municipality performed better than Pare-pare Municipality but still lagged behind Tana Toraja. These intra-provincial patterns suggest that urban residence does not uniformly confer nutritional advantage. Significant inequities persist not only between urban and rural populations but also across districts within the same province. Smaller cities with stronger health service access, such as Magelang Municipality, tend to show better child nutrition outcomes.
Prevalence of surgical site infections in Gulf Cooperation Council countries: A systematic review and meta-analysis Al-Gunaid, Seba T.; Rampengan, Derren DCH.; Khadra , Jomana B.; Elgohari, Aya T.; Mouzhir, Rim M.; Alzahrani, Abdulrahman A.; Osman, Mousab MAH.; Alabbad, Zahra A.; Adista, Muhammad A.; Al-Dubai, Sami A.; Aleid , Layan K.
Narra X Vol. 4 No. 1 (2026): April 2026 (In Press)
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v4i1.243

Abstract

Surgical site infections (SSIs) rank among the most prevalent healthcare-associated infections, leading to higher patient morbidity, extended hospitalizations, and increased healthcare expenses. Despite advancements in surgical practices within the Gulf Cooperation Council (GCC) nations, information on SSI prevalence remains fragmented and inconsistent. The aim of this study was to determine the overall prevalence of SSIs in GCC countries and to assess variations according to surgical procedure type. A systematic search of PubMed, PubMed Central (PMC), ScienceDirect, and Google Scholar was conducted for studies reporting SSI prevalence in the six GCC countries up to May 2025. The quality of the study was evaluated using the Newcastle-Ottawa Scale. Pooled prevalence estimates were calculated using a random effects model, with subgroup analyses performed based on surgical procedure type. A total of 23 studies involving 32,366 patients were included in the analysis. The overall pooled prevalence of SSIs was 7% (95%CI: 4–10%; I²=92.9%), which suggests a significant level of variability. The highest SSI prevalence was observed in coronary artery bypass graft (CABG) procedures (42%), followed by colorectal surgeries (28%) and coronary artery surgeries (18%). Lower prevalence rates were reported for laparotomies (2%) and cholecystectomies (1%). Caesarean section, the most frequently reported procedure (n=12,419), had an SSI prevalence of 3% (95%CI: 2–4%; I²=84.5%). Smaller studies tended to report higher SSI prevalence estimates. In conclusion, the elevated incidence of SSIs in high-risk procedures, particularly CABG and colorectal surgeries, highlights the necessity for enhanced regional surveillance systems and targeted preventive measures across GCC healthcare settings.
Random forest-based QSAR modeling for predicting the potency of neprilysin inhibitors using Mordred molecular descriptors Albar, Nizam; Rampengan, Derren DCH.; Azhari, Saiful; Mahmudi, Mahmudi; Fahdhienie, Farrah; Susilawati, Anggi; Habiburrahman, Muhammad
Narra X Vol. 4 No. 1 (2026): April 2026 (In Press)
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v4i1.242

Abstract

Neprilysin (NEP) is a zinc-dependent metallopeptidase, considered a key therapeutic target in heart failure management. Efficient identification of potent NEP inhibitors remains a challenge in drug discovery. The aim of this study was to develop a quantitative structure–activity relationship (QSAR) model using 2D Mordred molecular descriptors and Random Forest algorithms to predict the inhibitory potency (pIC50) of drug candidates. A curated dataset of compounds with experimentally determined IC₅₀ values (in nM) against NEP was preprocessed and converted to pIC50. Mordred was used to calculate 2D molecular descriptors, and descriptors with missing values were excluded. The dataset was split into training, internal validation, and external test sets. A Random Forest regression model was trained using 500 estimators, and model performance was evaluated using R2, root mean square error (RMSE), mean absolute error (MAE), and concordance correlation coefficient (CCC), while a binary classification model was also constructed. Feature importance, residual analysis, and chemical space visualization were conducted to assess model interpretability and reliability. The regression model demonstrated moderate to strong predictive performance, with R2 of 0.286, RMSE of 0.949, MAE of 0.723, and CCC of 0.532 in the internal validation. External validation showed improved generalization, with R2=0.659, RMSE=0.858, MAE=0.630, and CCC=0.763. Binary classification revealed an accuracy of 0.953, precision of 1.000, recall of 0.943, and an F1-score of 0.971, indicating strong discriminative ability in classifying inhibitory versus non-inhibitory compounds. Top contributing descriptors included ATSC2p (feature importance=0.0505), GATS2p (0.0408), and SaasC (0.0317). Principal component analysis (PCA) and Williams plots confirmed that test compounds lie within the model’s applicability domain, with no major outliers in leverage or residual distribution. The developed Random Forest-based QSAR model demonstrates strong predictive power and interpretability for identifying NEP inhibitors. This study provides a valuable tool for virtual screening and highlights the relevance of 2D structural features in governing NEP inhibitory activity. It is the first dedicated QSAR analysis of neprilysin inhibition using Mordred descriptors with rigorous internal and external validation.
Co-Authors Abshori, Nuril F. Adista, Muhammad A. Adji, Arga S. Al-Abdullah, Hatem B. Al-Dubai, Sami A. Al-Gunaid, Seba T. Alabbad, Zahra A. Albar, Nizam Aleid , Layan K. Alzahrani, Abdulrahman A. Anggi Susilawati Ar, Ardiyana Azhari, Saiful Bulandari, Beatrice LA. Catharina Suharti Elgohari, Aya T. Farrah Fahdhienie Fuadi, Ahmad F. Gunawan, Felicia A. Ignatius Riwanto, Ignatius Iqhrammullah, Muhammad Khadra , Jomana B. Mahmudi Mahmudi MOCHAMMAD THAHA Mouzhir, Rim M. Muhammad Habiburrahman, Muhammad Multazam, Chaq ECZ. Neni Susilaningsih Nurkolis, Fahrul Osman, Mousab MAH. Prabowo, Erik Puling, Imke MDR. Purnomo, Hery D. Qanita, Intan Ramadhan, Roy Ramadhan, Roy N. Rampengan, Jade AH. Respati, Danendra RK. Suryantoro, Satriyo Tjandra, Kevin C. Willyanto, Sebastian E. Yufika, Amanda