Category: Nevin Manimala

J Chem Inf Model. 2025 Dec 1. doi: 10.1021/acs.jcim.5c01941. Online ahead of print.

ABSTRACT

Large language models (LLMs) have the potential to serve as collaborative assistants in scientific research. However, adapting them to specialized domains is difficult because it requires the integration of domain-specific knowledge. We propose Materials Dual-Source Knowledge Retrieval-Augmented Generation (MDSK-RAG), a retrieval-augmented generation (RAG) framework that enables domain specialization of LLMs for materials development under fully offline (no-Internet) operation to ensure data confidentiality. The framework unifies two complementary knowledge sources, experimental CSV data (practical knowledge) and scientific PDF literature (theoretical insights), by converting tabular records into template-based text, retrieving relevant passages from each source, summarizing them with a local LLM, and merging the summaries with the user query prior to generation. As a case study, we applied the framework to metal-sulfide photocatalysts using 740 in-house experimental records and 20 scientific PDFs. We evaluated the framework on a benchmark consisting of 14 expert-defined questions and used two-sided Wilcoxon signed-rank tests for paired comparisons. Models with fewer than 10 billion parameters were executed on a laptop, whereas larger models were run on a dedicated local server; the cloud-based LLM (GPT-4o) was evaluated via the cloud service. For practical deployment, gemma-2-9b-it (<10 billion parameters) was chosen as the primary local model; we additionally tested Qwen2.5-7B-Instruct and a larger gemma-2-27b-it to assess model choice and scalability. For gemma-2-9b-it, the framework increased the median cosine similarity to expert reference answers from 0.63 to 0.71, an absolute increase of 0.08 (corresponding to a relative percentage gain of 12.70%; Wilcoxon signed-rank test statistic: W = 14.0, two-sided p-value: p = 1.34 × 10^-2) and improved the median expert 5-point rating from 2 to 3, an absolute increase of 1 point (corresponding to a relative percentage gain of 50.00%; Wilcoxon signed-rank test statistic: W = 3.5, two-sided p-value: p = 7.00 × 10^-3). For reasoning-type questions, incomplete context retrieved by MDSK-RAG sometimes disrupted the model’s reasoning process and led to incorrect conclusions, indicating remaining room for improvement. Comparable, statistically significant improvements were observed for the other local models (Qwen2.5-7B-Instruct and a larger gemma-2-27b-it) between conditions with and without the framework in the evaluation by cosine similarity to expert reference answers. In comparison to a cloud-based LLM, the gemma-2-9b-it with the framework outperformed GPT-4o. In this case study, the framework effectively incorporated practical experimental knowledge and theoretical literature into local LLM responses, improving accuracy for domain-specific queries. The framework presented here offers a practical and extensible adaptation of local LLMs to domain-specific scientific research.

PMID:41325550 | DOI:10.1021/acs.jcim.5c01941

Acta Ophthalmol. 2025 Dec 1. doi: 10.1111/aos.70049. Online ahead of print.

ABSTRACT

Elevated corticosteroid levels are the strongest known risk factor for central serous chorioretinopathy (CSC), and previous studies have explored if alterations in systemic immunity could play a role in CSC. Here, we explored if elevated systemic C-reactive protein (CRP), a marker of systemic low-grade inflammation, is associated with CSC. We systematically searched 12 literature databases on 12 April 2025 for studies in which blood CRP is measured in both patients with CSC and a comparable control group. Studies were reviewed qualitatively. Meta-analysis using the random-effects model was performed on the weighted mean difference in systemic CRP levels between patients with CSC and controls. Six studies comprising 544 patients with CSC and 655 control individuals were eligible for this review. The meta-analysis of the difference in CRP between patients with CSC and controls showed no statistically significant difference at 0.86 mg/L (95% CI: -1.03-2.75 mg/L; p = 0.37). One study reported a very high degree of association between elevated CRP and CSC, which was not reproduced in the other studies. The lack of association remained consistent in the sensitivity analyses. Current evidence does not suggest that elevated systemic CRP levels are associated with CSC. Further studies on CSC pathophysiology are warranted.

PMID:41325540 | DOI:10.1111/aos.70049

Proc Natl Acad Sci U S A. 2025 Dec 9;122(49):e2519112122. doi: 10.1073/pnas.2519112122. Epub 2025 Dec 1.

ABSTRACT

Fisheries are critical for sustaining waterfront communities. However, subsistence fishing is not well understood in the United States, despite its potential contributions to health and culture. We piloted a multivariable construct to classify subsistence vs. nonsubsistence fishers, identified the strongest predictor of participating in this practice, and tested for differences in place-based fishing motivations, behaviors, and community sharing. Among shore-based fishers in coastal Alabama, lower household income was the most powerful predictor of subsistence fishing. Subsistence fishers held more fishing motivations, targeted more specific fish groups, were more efficient in catching and keeping fish, and more frequently shared fish across social groups. Informed by these findings, we discussed management strategies to addressopportunities and barriers for shore-based subsistence fishing in coastal Alabama. More broadly, the framework piloted here offers a pathway to integrate subsistence fisheries into management using place-based evidence.

PMID:41325526 | DOI:10.1073/pnas.2519112122

J Chem Inf Model. 2025 Dec 1. doi: 10.1021/acs.jcim.5c01982. Online ahead of print.

ABSTRACT

Reliable prediction of compound potency and the ADME profile is crucial in drug discovery. With the recent surge of AI and deep learning frameworks, it remains unclear whether these modern techniques offer statistically significant improvement over the well-established classical methods. The 2025 ASAP-Polaris-OpenADMET Antiviral Challenge provided a unique benchmarking opportunity to address this question, with over 65 teams of computational scientists worldwide. Our submissions were among the top performers in terms of Pearson r correlation, ranked first in pIC₅₀ prediction for SARS-CoV-2 Mpro and fourth in aggregated ADME. In this work, we present a retrospective analysis of our modeling strategies and highlight our lessons learned. Through rigorous statistical benchmarking, we demonstrate that while classical methods remain highly competitive for predicting potency, modern deep learning algorithms significantly outperformed traditional machine learning in ADME prediction. We also illustrate the importance of appropriate data curation and the benefits of leveraging public datasets via feature augmentation. Finally, we outline current limitations and identify future opportunities including the integration of structure-guided modeling. Overall, these results not only provide practical guidance for building robust predictive models but also offer valuable insights into the field of computational drug discovery.

PMID:41325513 | DOI:10.1021/acs.jcim.5c01982

PLOS Glob Public Health. 2025 Dec 1;5(12):e0005545. doi: 10.1371/journal.pgph.0005545. eCollection 2025.

ABSTRACT

Improving antiretroviral therapy (ART) adherence among adolescents living with HIV (ALHIV) improves outcomes, but with resource implications. We conducted a cost-effectiveness analysis extrapolating the costs and benefits of a community-based peer-support intervention (Zvandiri) among ALHIV in Zimbabwe. We used a de-novo multistate Markov decision-analytic model that simulated Zvandiri lifetime costs and benefits on viral suppression, death rates, life-years (LY) and quality-adjusted-life-years (QALYs) gained from the healthcare system perspective. We estimate the incremental cost-effectiveness ratio (ICER) per LY and QALY gained and compare the ICER to proposed cost-effectiveness thresholds of $500 and $700 per LY or QALY gained. We explore parameter uncertainty using probabilistic sensitivity analyses. Cohort-microsimulation suggests that after 40 years under SoC, 21% of 280 ALHIV will have undetectable viral-load (VL), 12% will have low VL (<1000 copies/mL), 10% will have high VL (≥1000 copies/mL) and 57% would have died. With Zvandiri, ART adherence improves, decreasing annual probability of virological failure or death. After 40 years, 65% will have undetectable viral load, 23% low VL, 3% high VL and 9% would have died. Zvandiri results in 1,345 LYs gained at incremental cost of $500,587, yielding a discounted ICER of $372 per LY gained. Zvandiri also results in 1,246 QALYs at incremental cost of $123,645, yielding a discounted ICER of $99 per QALY. The ICER is highly sensitive to programme costs, health-related utilities, and the discount rate. Zvandiri is a cost-effective intervention for reducing virological failure and death in ALHIV. Our analysis likely underestimates the full benefits of the intervention by not accounting for reductions in HIV transmissions resulting from higher virological suppression observed in full transmission models.

PMID:41325500 | DOI:10.1371/journal.pgph.0005545

PLoS One. 2025 Dec 1;20(12):e0336022. doi: 10.1371/journal.pone.0336022. eCollection 2025.

ABSTRACT

BACKGROUND: Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium transmitted through the bite of infected female Anopheles mosquitoes, which act as vectors of the disease. It affects approximately 219 million people globally and results in 435,000 deaths each year. Fever, chills, and exhaustion are among of the signs of this illness. If left untreated, these symptoms can develop into serious problems like anemia, respiratory distress, and even organ failure. By identifying determinants related to malaria prevalence, this study supports evidence-based national malaria prevention and control initiatives. The results help improve decision-making for malaria control efforts and guide focused public health initiatives by identifying areas with a high malaria burden.

METHODS: Data from the 2021 Niger Malaria Indicator Survey (NMIS) is used, focusing on RDT-confirmed malaria cases in children aged 6-59 months. The dataset includes individual, household, and community-level variables, such as age, household income, education, healthcare access, and geographic coordinates. Spatial distribution of malaria prevalence is first visualized through maps and hot spot analysis to identify areas with high and low malaria rates. Random effects are incorporated to capture unobserved heterogeneity between regions and communities, allowing for more accurate estimates of malaria prevalence by adjusting for spatial clustering. Multilevel logistic regression models are applied to account for the hierarchical structure of the data. Model fit is evaluated using standard criteria (AIC, BIC and DIC), and diagnostics are performed to ensure reliability.

RESULTS: 1121 (23.7%) of the 4724 children aged 6 to 59 months who were examined had positive RDT results for malaria. Malaria prevalence in Niger among children aged 6-59 months is significantly clustered (Moran’s I = 0.434, p < 0.001), revealing distinct hotspots and cold spots unlikely due to chance. Model III provides a better fit for RDT prevalence among children aged 6-59 months with malaria, as indicated by the smallest AIC, BIC, and deviation statistics compared to other reduced models. Malaria prevalence was associated with factors, including child age, anemia levels, maternal education, the number of children sleeping under bed nets, the use of insecticide-treated nets, the number of children aged 5 and under, as well as residence and region.

CONCLUSION: The findings show that malaria prevalence among children aged 6-59 months in Niger is significantly influenced by factors such as child age, anemia levels, maternal education, and bed net usage, emphasizing the need for improved coverage of insecticide-treated nets and tailored interventions based on local conditions.

PMID:41325497 | DOI:10.1371/journal.pone.0336022

PLoS One. 2025 Dec 1;20(12):e0336495. doi: 10.1371/journal.pone.0336495. eCollection 2025.

ABSTRACT

This paper introduces a novel statistical test, the Policy Effects Lagrange Multiplier (PELM) test, to detect stabilization policy effects in the distribution of forecast errors from dynamic financial models. Traditional analyses of policy impact typically rely on explicit policy information or direct intervention data, which are often unavailable or incomplete. In contrast, the proposed PELM test infers policy footprints from the distribution of forecast errors alone. Empirically applied to sovereign bond yield data from 33 countries before the Russian financial crisis of 2014, the test identifies countries showing stabilization policy footprints. Subsequent analysis shows that significant budgetary improvements were observed for years following the crisis in the group of countries where our test statistically confirmed stabilization policies. This confirms the rationale of test foundations and also indicates its predictive properties. Robustness checks further validate these findings across various model specifications and sensitivity scenarios. The proposed PELM test offers policymakers and researchers a powerful tool for evaluating stabilization policies, facilitating better forecasting and assessing policy efficiency in diverse economic contexts without necessitating detailed policy intervention data.

PMID:41325492 | DOI:10.1371/journal.pone.0336495

PLoS One. 2025 Dec 1;20(12):e0336899. doi: 10.1371/journal.pone.0336899. eCollection 2025.

ABSTRACT

Previous research has shown that the causal structure of events influences how well they are recalled in episodic memory later on. Here, we aimed to investigate whether these effects apply not only to events that are passively observed but also situations directly shaped by an individual’s decisions. We designed a task in which participants had to traverse decision trees of varying causal structure: ‘Coherent’ trees where each decision followed from the consequences of the preceding decision, and ‘fragmented’ trees where each subsequent decision was only statistically (but not causally) contingent on the preceding decision. In a between-subjects experiment, participants first completed an exploration phase in which they had to explore the decision trees without a specific goal; in a subsequent search phase, they had to reach a target outcome in as few attempts as possible. Analyses of participants’ performance showed that those in the coherent group required significantly fewer attempts to reach a correct outcome than those in the fragmented group. A follow-up experiment surprisingly found that the advantage of causal structure does not depend on episodic memory: Removing the exploration phase barely diminished the positive effect causal coherence had on participants’ performance. In further follow-up experiments without an exploration phase, neither the additional removal of ‘process images’ that show how a choice leads to an outcome, nor the removal of text labels describing decisions, was individually sufficient to equalize performances. Only when both were eliminated at once did participants perform equally well on coherent and fragmented trees. This indicates that cues relating to causal mechanisms (images) and predictive cues (text) each facilitate goal-directed decision making without relying on extensive learning, and that only the absence of both is sufficient to suppress the advantage causal structure provides.

PMID:41325484 | DOI:10.1371/journal.pone.0336899

PLoS One. 2025 Dec 1;20(12):e0337076. doi: 10.1371/journal.pone.0337076. eCollection 2025.

ABSTRACT

Shale samples from source rocks of the Upper Triassic Yanchang Formation (Chang 9 member) in the Ansai area, Ordos Basin, North China, were analyzed using gas chromatography – mass spectrometry (GC-MS) to investigate the distribution, abundance, and enrichment mechanisms of rearranged hopanes. Four rearranged hopane series were detected, with all four present simultaneously in individual samples. Analysis of the C₃₀ hopane series (regular C₃₀H, diahopane C₃₀D, and neohopane C₃₀E) using a ternary diagram revealed a distinct linear trend, demonstrating a systematic, inverse relationship between the abundance of regular hopane and the combined abundance of its rearranged counterparts. These results provide strong evidence that C₃₀D and C₃₀E in the Chang 9 shales are diagenetic products derived from C₃₀H, sharing a common biological precursor. Both diasteranes and regular steranes with the ββ configuration were correlated positively in abundance with rearranged hopanes, further supporting a common origin linked to specific organism assemblages rather than widespread organisms. Samples deposited under highly saline, suboxic sedimentary environments displayed relatively high abundances of rearranged hopanes, indicating the critical role of depositional conditions in their enrichment. Multi-proxy analysis revealed a complex, non-linear control of thermal maturity on rearranged hopane abundance. The C₃₀ Rearranged Hopane Index showed statistically significant positive correlations with multiple maturity parameters (including sterane and hopane isomerization ratios), indicating maturity as a primary driver in the early oil window. However, this trend diverged at higher maturity levels, suggesting that other factors, such as the catalytic activity of the mineral matrix, become dominant. Our findings establish a robust biomarker-based framework for interpreting oil-source correlations and informing petroleum exploration in the Ordos Basin, particularly for the Chang 9 member source rocks.

PMID:41325482 | DOI:10.1371/journal.pone.0337076

PLoS Comput Biol. 2025 Dec 1;21(12):e1013758. doi: 10.1371/journal.pcbi.1013758. Online ahead of print.

ABSTRACT

Antibodies play a crucial role in adaptive immunity. They develop as B cell receptors (BCRs): membrane-bound forms of antibodies that are expressed on the surfaces of B cells. BCRs are refined through affinity maturation, a process of somatic hypermutation (SHM) and natural selection, to improve binding to an antigen. Computational models of affinity maturation have developed from two main perspectives: molecular evolution and language modeling. The molecular evolution perspective focuses on nucleotide sequence context to describe mutation and selection; the language modeling perspective involves learning patterns from large data sets of protein sequences. In this paper, we compared models from both perspectives on their ability to predict the course of antibody affinity maturation along phylogenetic trees of BCR sequences. This included models of SHM, models of SHM combined with an estimate of selection, and protein language models. We evaluated these models for large human BCR repertoire data sets, as well as an antigen-specific mouse experiment with a pre-rearranged cognate naive antibody. We demonstrated that precise modeling of SHM, which requires the nucleotide context, provides a substantial amount of predictive power for predicting the course of affinity maturation. Notably, a simple nucleotide-based convolutional neural network modeling SHM outperformed state-of-the-art protein language models, including one trained exclusively on antibody sequences. Furthermore, incorporating estimates of selection based on a custom deep mutational scanning experiment brought only modest improvement in predictive power. To support further research, we introduce EPAM (Evaluating Predictions of Affinity Maturation), a benchmarking framework to integrate evolutionary principles with advances in language modeling, offering a road map for understanding antibody evolution and improving predictive models.

PMID:41325480 | DOI:10.1371/journal.pcbi.1013758