Atma Jaya Catholic University of Indonesia

Climate change profoundly affects the phytochemical profiles and therapeutic potentials of medicinal plants through environmental stressors such as rising temperatures, altered precipitation patterns, and increased atmospheric CO2 levels. This review critically examines the mechanisms underlying these impacts, focusing on physiological plant responses, shifts in primary and secondary metabolite biosynthesis, and the consequent effects on medicinal efficacy and toxicity. Our findings indicate that elevated CO2 often enhances biomass production but exerts variable effects on bioactive compound concentrations; temperature fluctuations disrupt phenological phases, thereby altering medicinal quality; and water stress significantly modulates secondary metabolite profiles. While these environmental challenges threaten plant-based healthcare, potential mitigation strategies—including sustainable agricultural practices, genetic engineering, and conservation approaches—are discussed as viable solutions. We recommend future research to emphasize metabolomics, interdisciplinary methodologies, and integration of traditional knowledge to bolster resilience and preserve the therapeutic efficacy of medicinal plants amid ongoing climatic uncertainties.

Maternal nutrition plays a pivotal role in ensuring optimal health outcomes for both mother and fetus. However, natural bioactive compounds such as omega-3 fatty acids, polyphenols, and probiotics face major limitations, including poor stability, low solubility, and limited bioavailability during pregnancy. Food-grade polymers have emerged as promising delivery platforms to overcome these challenges by enhancing stability, protecting against degradation, masking undesirable flavors, and enabling controlled or site-specific release. This review synthesizes recent advances in polymer-based encapsulation strategies for maternal nutrition, focusing on biopolymers such as alginate, pectin, chitosan, gum arabic, and protein–polysaccharide composites. Encapsulation techniques including spray-drying, complex coacervation, hydrogels, and nanoparticles are highlighted for their ability to improve bioactive delivery and efficacy. We further examine preclinical and clinical evidence, safety considerations, and regulatory challenges that must be addressed before translation into maternal health interventions. Overall, food-grade polymers represent a promising tool to transform maternal nutrition into more effective, sustainable, and personalized strategies; however, industrial scalability, long-term safety, and standardized clinical validation remain pressing gaps for future research.

Introduction Insomnia affects up to 48% of older adults globally, often treated with Z-drugs that increase the risk of cognitive impairment and falls.1,4 Dual Orexin Receptor Antagonists (DORAs) offer a targeted alternative by specifically suppressing wakefulness-promoting pathways to minimize these adverse effects.7,8 Method This systematic review of Randomized Controlled Trials was conducted using the PRISMA guideline with references found in multiple online databases including PubMed, ScienceDirect, Proquest and Sage Journals. The search strategy utilized a combination of keywords relevant to this study and the screening process was conducted by implementing exclusion and inclusion criteria developed using the PRISMA guideline.9 The studies recovered were then assessed by the Cochrane RoB 2 risk of bias tool.12 Result Seven RCTs were evaluated. DORAs demonstrated significantly superior efficacy over Z-drugs, with Least Squares Mean differences of -6.7 and -8.0 minutes for 5 mg and 10 mg doses, respectively.14 Furthermore, safety analyses revealed DORAs had a significantly lower relative incidence of residual effects and cognitive impairment (<5%) compared to Z-drugs (>15%).16 Conclusion Although both DORAs and Z-drugs help mitigate insomnia symptoms, DORAs have shown lesser adverse side effects in the treatment regimen, most notably in safety, DORAs have also shown lower levels of falls during treatment. Heterogeneity still is a limitation and restricts data power, standardized studies are needed to show a more comprehensive outcome in studies.

Dengue (DENV), chikungunya (CHIKV), and Zika (ZIKV) viruses are arboviruses with the same main vectors, Aedes aegypti and Ae. albopictus. These three viruses pose significant public health challenges globally, especially with their recent increase in prevalence and reports of co-infection worldwide. This is concerning, as they have similar early symptoms but may need different treatment. However, a comprehensive and up-to-date analysis of the global prevalence of these co-infections is lacking, and many health providers in DENV-endemic countries are unaware of other arbovirus infections. To address this issue, we conducted a systematic review and meta-analysis to determine the prevalence of co-infection among DENV, CHIKV, and ZIKV based on molecular and antigen detections. We searched and screened original studies from 2013 to 2023 from Medline, Proquest, OVID, EBSCO, medRxiv, and bioRxiv. Using a random effects model, 29 studies from 16 countries were analysed. The estimated co-infection rates were 2.10% (95% CI = 0.88%-4.91%; I2 = 92%) for DENV-CHIKV, 1.91% (95% CI = 0.65-5.43; I2 = 80%) for DENV-ZIKV, 1.30% (95% CI = 0.35%-4.68%; I2 = 85%) for CHIKV-ZIKV, and 0.59% (95% CI = 0.20%-1.71%; I2 = 71%) for DENV-CHIKV-ZIKV, with variations (0.30%-9.83%) observed across different regions. These findings will aid in raising awareness of arboviral co-infections and formulating better strategies for diagnosing and managing these infections.

Large Language Models (LLMs) such as ChatGPT are transforming how scientists conduct and validate research, offering promise as tools to improve scientific reproducibility. However, computational reproducibility and error detection remain expensive and labor-intensive. We experimentally test how collaboration between researchers and LLM assistants influences the reproduction of quantitative social science findings across different levels of AI autonomy. We randomly assigned 288 researchers to 103 teams working under three conditions: human-only, AI-assisted (using ChatGPT as a collaborative tool), or AI-led (ChatGPT operating with minimal human oversight). Teams reproduced published results from leading social science journals, detected coding errors, and proposed robustness checks. Human-only and AI-assisted teams achieved comparable reproduction rates (94% vs. 91%) and performed similarly on most outcomes, except human-only teams identified significantly more major coding errors. Both substantially outperformed AI-led teams, which achieved only a 37% reproduction rate, detected fewer errors across all categories, proposed weaker robustness checks, and required more time. This autonomous approach, however, likely represents only a lower bound of AI capabilities. Despite rapid model advances, expert human judgment currently remains indispensable for reliable empirical verification. While AI assistance did not degrade most outcomes, it provided no measurable advantages and was associated with reduced detection of major errors. However, the 37% autonomous reproduction rate indicates that AI could provide value in settings where scale or cost constraints preclude human review of papers, even though general-purpose LLMs offer no immediate advantages for human-supervised verification.