Scientific Nexus

Sanggenon C from Morus alba as a promising Bcl-2 inhibitor: a molecular docking study targeting apoptosis in lung cancer

Adelweis Putrinda — 2026-08-22

Bcl-2 overexpression is a key driver of apoptosis evasion and chemoresistance in lung cancer. Morus alba L. harbors bioactive phytochemicals with reported anticancer properties, yet their binding potential against Bcl-2 remains computationally unevaluated. This study aims to assess the binding affinity and interaction profiles of five M. alba-derived compounds—sanggenon C, morusin, albanol B, rutin, and kuwanon C—against Bcl-2 using molecular docking. Molecular docking was performed using AutoDockTools® (v1.5.7) with the Bcl-2 crystal structure (PDB ID: 6O0K) and venetoclax as the reference ligand. Binding free energy (ΔG), binding site similarity (%BSS), and hydrogen bond interactions were analyzed. Redocking yielded an RMSD of 1.41 Å, confirming protocol validity. Sanggenon C exhibited the most favorable binding profile (ΔG = −8.46 kcal/mol; %BSS = 81.25%), sharing 13 of 16 key residues with venetoclax, including Phe112, Val156, Met115, Phe153, Arg146, and Gly145, with three hydrogen bonds predominantly within the optimal range. These findings establish a computational basis for prioritizing sanggenon C as a Bcl-2 inhibitor lead from M. alba, warranting further validation through ADMET profiling, molecular dynamics simulation, and in vitro cytotoxicity and apoptosis assays in lung cancer cell models.

Trend analysis of extreme climate indices in coastal Semarang using ETCCDI and Mann–Kendall Test (1995–2024)

Ghefira Tsuraya Rafli — 2026-06-29

This study analyzes trends in seven ETCCDI extreme climate indices at the Tanjung Emas Maritime Meteorological Station, coastal Semarang, Indonesia, over 1995–2024. Daily rainfall, minimum temperature, and maximum temperature records underwent quality control (RClimdex) and homogeneity testing (RHtestsV4). The Mann–Kendall test assessed trend significance and Sen's slope quantified trend magnitude, applied to the full 30-year period and two 15-year sub-periods (1995–2009 and 2010–2024). Among precipitation indices, total annual rainfall (PRCPTOT) and maximum 1-day rainfall (RX1day) showed significant increasing trends, with Sen's slopes of +16.53 mm/year and +2.09 mm/year, respectively. Consecutive dry and wet day indices showed no significant trends. Among temperature indices, warm night frequency (TN90p) and warm day frequency (TX90p) increased significantly at +0.50 and +0.54 days/year, respectively, while the diurnal temperature range showed no significant change. Sub-period analysis revealed acceleration of TX90p in 2010–2024 (Sen's slope = +1.19 days/year), indicating intensifying daytime warming in the more recent period. No precipitation index reached significance within either sub-period, suggesting that rainfall trends accumulate over decadal rather than sub-decadal timescales. These results provide quantitative evidence of strengthening warming signals and increasing rainfall intensity in coastal Semarang, with implications for flood risk management and climate adaptation planning.

Comparative analysis of exponential and logistic growth models for predicting motorized vehicle population in Indonesia

Suli Windi Astuti — 2026-06-29

The increasing mobility of the Indonesian population has led to a high demand for motorized vehicles, posing significant challenges for infrastructure management and environmental sustainability. This study aims to compare the accuracy of exponential and logistic growth models and to predict the motorized vehicle population in Indonesia. Motorized vehicle data for the period 2015–2022 from the Central Bureau of Statistics (BPS) were processed using MS Excel to determine the parameters for both models and subsequently simulated using MATLAB. The optimal model was selected based on graphical plot results and the lowest Mean Absolute Percentage Error (MAPE) value. The analysis results indicate that the logistic model provides higher accuracy (MAPE = 0.54%) compared to the exponential model (MAPE = 1.50%). The motorized vehicle population is projected to reach 152,520,752 units in 2023 and is expected to continue increasing to 174,657,192 units by 2030. Furthermore, the logistic model demonstrates that the population growth rate will decelerate as it approaches its carrying capacity (K = 189,659,261 units). These findings serve as critical considerations for the government in formulating infrastructure planning and environmental protection strategies against emissions from operating motorized vehicles.