Browse Articles
Discover research articles across all indexed journals
Unsung climate guardians: The overlooked role of remnant and spontaneous trees in carbon stocks and gains from tree growth in West African cocoa fields
Cocoa cultivation in West Africa has been a major driver of deforestation, leading to increased greenhouse gas emissions and threatening cocoa yields. Agroforestry, which integrates trees from various origins—remnant, spontaneous, and planted—presents a sustainable solution to enhance carbon sequestration and improve farm resilience. However, the specific contributions of these tree origins and the socio-environmental factors shaping their effectiveness remain poorly understood. This study examines carbon dynamics across 150 cocoa fields in Côte d’Ivoire, analyzing a total of 11,568 trees across 15 sites. Using Bayesian modeling, we assess carbon stocks and gains from tree growth to explore how socio-environmental factors influence carbon balance in cocoa fields. Carbon stocks varied widely with remnant having the highest median carbon stocks (6.33 Mg/ha), followed by spontaneous (2.06 Mg/ha) and planted trees (1.53 Mg/ha). Carbon gains are similar for planted and spontaneous trees up to 7 years, but afterward, spontaneous trees grow faster (11.20 ± 0.87 kg/year) than planted ones (3.96 ± 0.5 kg/year). Carbon stocks rose with informed farmers and former forest use, but declined with higher cocoa density. Carbon gains at the tree level is primarily influenced by ownership and previous forest land use with positive effects, while cocoa density and annual temperature have negative effects. To maximize carbon sequestration and ensure the sustainable management of agroforestry systems, interventions should prioritize securing land tenure, enhancing farmer training in tree botany, and promoting the conservation of remnant and spontaneous trees.
Next generation antimicrobial peptide disinfectant targeting biocide and vancomycin resistant staphylococcus aureus through integrated in Silico and in vitro validation
Evaluation of large language models as a diagnostic tool for medical learners and clinicians using advanced prompting techniques
Background Large language models (LLMs) have demonstrated capabilities in natural language processing and critical reasoning. Studies investigating their potential use as healthcare diagnostic tools have largely relied on proprietary models like ChatGPT and have not explored the application of advanced prompt engineering techniques. This study aims to evaluate the diagnostic accuracy of three open-source LLMs and the role of prompt engineering using clinical scenarios. Methods We analyzed the performance of three open-source LLMs—llama-3.1-70b-versatile, llama-3.1-8b-instant, and mixtral-8x7b-32768—using advanced prompt engineering when answering Medscape Clinical Challenge questions. Responses were recorded and evaluated for correctness, accuracy, precision, specificity, and sensitivity. A sensitivity analysis was conducted presenting the three LLMs with basic prompting challenge questions and excluding cases with visual assets. Results were compared with previously published performance data on GPT-3.5. Results Llama-3.1-70b-versatile, llama-3.1-8b-instant, and mixtral-8x7b-32768 achieved correct responses in 79%, 65%, and 62% of cases, respectively, outperforming GPT-3.5 (74%). Diagnostic accuracy, precision, sensitivity, and specificity responses all outperformed those previously reported for GPT-3.5. Results generated using advanced prompting strategies were superior to those based on basic prompting. Sensitivity analysis revealed similar trends when cases with visual assets were excluded. Discussion Using advanced prompting techniques, LLMs can generate clinically accurate responses. The study highlights the limitations of proprietary models like ChatGPT, particularly in terms of accessibility and reproducibility due to version deprecation. Future research should employ prompt engineering techniques and prioritize the use of open-source models to ensure research replicability.
Prognostic value of myocardial bridging versus non-obstructive CAD: a long-term follow-up study
Increased expression of chaperone proteins in response to DENV 2 infection of Huh-7 liver cells
The mosquito transmitted dengue virus (DENV; family Flaviviridae, genus Orthoflavivirus, species Orthoflavivirus denguei) is a significant public health problem in many tropical and subtropical countries around the world. Human infection by DENV is predominantly asymptomatic in 80% of cases, but the remaining 20% of infections can result in symptoms ranging from a mild undifferentiated fever to life threatening dengue hemorrhagic and dengue shock syndrome. During infection DENV induces changes in the host cell, including changing protein expression, altering the cellular lipids and inducing changes in membrane architecture. A number of cell types have been shown to be permissive for DENV replication, including hepatocytes. This study sought to investigate the protein expression changes induced by DENV infection of a liver cell line, Huh-7, using 2-dimensional (2D) electrophoresis. At 48 hours post infection 14 protein spots were found to have altered expression as compared to mock infected cells at the same time point. In particular four of the proteins showing alterations of expression were chaperone proteins (Stress-70 protein, Endoplasmic reticulum chaperone BiP (GRP78), Heat shock 70 kDa protein 4 and Heat shock protein HSP 90-beta), of which three were upregulated (Stress-70 protein, Endoplasmic reticulum chaperone BiP (GRP78), Heat shock 70 kDa protein 4) and one was downregulated (Heat shock protein HSP 90-beta). GRP78 showed the largest change in expression amongst these four proteins, and so its expression was confirmed by western blot analysis. GRP78 has been shown by many studies to be critically involved in the replication of orthoflaviviruses, and this study further underlines the importance of this protein.
Effective thermal conductivity model of particle-filled composite materials with distributed inner heat sources
Research on the speed fluctuation control of diesel engine under load changes via an improved sparrow algorithm
Given the nonlinear and time-varying characteristics of diesel engine speed control, a conventional proportional integral derivative (PID) controller is inadequate for addressing the lag or overshoot in the system response, and it struggles to adapt to complex dynamic changes under load. This study proposes a fuzzy proportional integral derivative (FPID) control,which is based on an improved sparrow search algorithm(ISSA) with the aim of enhancing the system’s adaptability. By refining the algorithm to augment its parameter control capabilities and employing test functions for experimental comparisons, the improved algorithm exhibited accelerated convergence and increased accuracy. The improved sparrow search algorithm is applied to two controllers for experimental comparison, and the results indicate that, in contrast to the traditional PID control algorithm, the FPID control algorithm reduces the adjustment time by 1.4 s and decreases the overshoot by 6.8% when the speed is adjusted to 2000 revolutions per minute (RPM). The duration for speed fluctuation stabilization under load changes of 8 and 10 is decreased by 18% and 30%, respectively, and the fluctuation deviation of the speed is reduced by 7% and 12%, respectively. Consequently, the implementation of FPID parameters tuned by the improved sparrow algorithm provides robust support for the stable operation of a diesel engine during speed fluctuations.
Cancer treatment and survivorship statistics, 2025
Abstract The number of people living with a history of cancer in the United States continues to rise because of the growth and aging of the population as well as improved survival through advances in early detection and treatment. To assist the public health community serve the needs of these survivors, the American Cancer Society and the National Cancer Institute collaborate triennially to estimate cancer prevalence in the United States using data from the Surveillance, Epidemiology, and End Results cancer registries, the Centers for Disease Control and Prevention's National Center for Health Statistics, and the United States Census Bureau. In addition, cancer treatment patterns are presented from the National Cancer Database along with a brief overview of treatment‐related side effects. As of January 1, 2025, about 18.6 million people were living in the United States with a history of cancer, and this number is projected to exceed 22 million by 2035. The three most prevalent cancers are prostate (3,552,460), melanoma of the skin (816,580), and colorectum (729,550) among males and breast (4,305,570), uterine corpus (945,540), and thyroid (859,890) among females. About one half (51%) of survivors were diagnosed within the past 10 years, and nearly four fifths (79%) were aged 60 years and older. Racial differences in treatment in 2021 were common across disease stage; for example, Black people with stage I‐II lung cancer were less likely to undergo surgery than their White counterparts (47% vs. 52%). Larger disparities exist for rectal cancer, for which 39% of Black people with stage I disease undergo proctectomy or proctocolectomy compared to 64% of their White counterparts. Targeted, multi‐level efforts to expand access to high‐quality care and survivorship resources are vital to reducing disparities and advancing support for all survivors of cancer.
Modelling the melting of „shaft sludge” from shaft furnace dust removal in a short rotary kiln
Exploring the perceptions of living donation among potential Moroccan donors in the Netherlands
Introduction The Moroccan-Dutch population is underrepresented in Dutch donor pools, threatening access to matched blood products and transplants. This study explored Moroccan-Dutch individuals willingness to donate blood, stem cell, and live organ donation, and preferred information dissemination methods. Methods A qualitative and exploratory ethnographic study was employed using a survey followed by a focus group to examine willingness and information needs regarding living donation (blood, stem cells, kidney, liver) among Moroccan-Dutch individuals aged 18–55. Survey results were thematically analysed to inform an interview guide for in-depth focus group. Eight participants (four male, four female) were purposefully invited. The session was recorded, transcribed, and analysed thematically. Results Surveys from 119 persons and one focus group were analysed. Ten themes emerged across tissue types: 1) awareness of need; 2) information and knowledge; 3) donation process; 4) cues to action; 5) attitude; 6) religious legitimacy; 7) health challenges; 8) fear; 9) social cohesion and solidarity; 10) relationship with recipient. Participants were willing to donate blood, but needed reminders to prompt registration. Hesitation around stem cell donation stemmed from unfamiliarity and Islamic uncertainty. For living organ donation, emotional ties to recipients influenced willingness. Most participants were unaware of ethnic matching needs and shortages, and preferred campaigns highlighting these aspects. A multi-component communication strategy was preferred, combining medical, technical, religious, and emotional information, including patient stories, to address gaps and insecurities. Conclusion Participants were unfamiliar with stem cell donation, living organ donation, and ethnic matching. They were willing to donate tissue if aware of the shortage, the need for ethnic matching, and religious legitimacy They sought medical-technical information from donor organisations but preferred religious legitimacy communicated by an Islamic scholar. Participants stressed the need for a tailored campaign addressing barriers, ethnic matching, and reliance on the same community for successful living donation.
Cancer treatment and survivorship statistics, 2025: An urgent call to optimize health after cancer
Neurofeedback training for executive function in ADHD children: a systematic review and meta-analysis
An open-source interactive AI framework for assisting automatic literature review in forensic medicine: Focus on brain injury mechanisms
Background and Objective Systematic reviews and meta-analyses are critical in forensic medicine; however, these processes are labor-intensive and time-consuming. ASReview, an open-source machine learning framework, has demonstrated potential to improve the efficiency and transparency of systematic reviews in other disciplines. Nevertheless, its applicability to forensic medicine remains unexplored. This study evaluates the utility of ASReview for forensic medical literature review. Methods A three-stage experimental design was implemented. First, stratified five-fold cross-validation was conducted to assess ASReview’s compatibility with forensic medical literature. Second, incremental learning and sampling methods were employed to analyze the model’s performance on imbalanced datasets and the effect of training set size on predictive accuracy. Third, gold standard were translated into computational languages to evaluate ASReview’s capacity to address real-world systematic review objectives. Results ASReview exhibited robust viability for screening forensic medical literature. The tool efficiently prioritized relevant studies while excluding irrelevant records, thereby improving review productivity. Model performance remained stable when labeled training data constituted less than 80% of the total sample size. Notably, when the training set proportion ranged from 10% to 55%, ASReview’s predictions aligned closely with human reviewer decisions. Conclusion ASReview represents a promising tool for forensic medical literature review. Its ability to handle imbalanced datasets and gather goal-oriented information enhances the efficiency and transparency of systematic reviews and meta-analyses in forensic medicine. Further research is required to optimize implementation strategies and validate its utility across diverse forensic medical contexts.
Acute lymphoblastic leukemia diagnosis using machine learning techniques based on selected features
Abstract Cancer is considered one of the deadliest diseases worldwide. Early detection of cancer can significantly improve patient survival rates. In recent years, computer-aided diagnosis (CAD) systems have been increasingly employed in cancer diagnosis through various medical image modalities. These systems play a critical role in enhancing diagnostic accuracy, reducing physician workload, providing consistent second opinions, and contributing to the efficiency of the medical industry. Acute lymphoblastic leukemia (ALL) is a fast-progressing blood cancer that primarily affects children but can also occur in adults. Early and accurate diagnosis of ALL is crucial for effective treatment and improved outcomes, making it a vital area for CAD system development. In this research, a CAD system for ALL diagnosis has been developed. It contains four phases which are preprocessing, segmentation, feature extraction and selection phase, and classification of suspicious regions as normal or abnormal. The proposed system was applied to microscopic blood images to classify each case as ALL or normal. Three classifiers which are Naïve Bayes (NB), Support Vector Machine (SVM) and K-nearest Neighbor (K-NN) were utilized to classify the images based on selected features. Ant Colony Optimization (ACO) was combined with the classifiers as a feature selection method to identify the optimal subset of features among the extracted features from segmented cell parts that yield the highest classification accuracy. The NB classifier achieved the best performance, with accuracy, sensitivity, and specificity of 96.15%, 97.56, and 94.59%, respectively.
Spike substitutions E484D, P812R and Q954H mediate ACE2-independent entry of SARS-CoV-2 across different cell lines
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has evolved into variants with multiple spike protein coding mutations that affect its transmissibility, infectivity, and immune evasion, in particular from neutralizing antibodies. Several of these amino acid changes have been associated with reduced dependency on the principal angiotensin converting enzyme-2 (ACE2) receptor for cell entry. The present study investigates the role of spike protein changes observed in a cell-culture adapted SARS-CoV-2 isolate (DK-AHH1) in modulating entry, ACE2 dependency, and neutralization across different cells, including human liver and lung cell lines. Using a pseudoparticle system, spike proteins with substitutions E484D, P812R, Q954H, and deletion Δ68−76 were evaluated in Vero E6 and Huh7.5, as well as in A549 cells with and without ACE2 overexpression. Pseudoparticles carrying E484D or P812R individually permitted entry in Huh7.5 cells, and their combination further enhanced this capacity. ACE2 blocking experiments revealed the differential roles of these mutations in mediating entry across cell lines. In Vero E6 cells, P812R was the primary driver for ACE2-independent entry, while E484D facilitated ACE2-independent entry in Huh7.5 cells. In A549 cells, all three substitutions (E484D+P812R + Q954H) were required for ACE2-independent entry. Addition of the Δ68−76 deletion did not increase infectivity in any cell line. Notably, pseudoparticles carrying these mutations, maintained susceptibility to neutralization by convalescent plasma from subjects with COVID-19, regardless of the cell line used. These findings highlight the adaptability of SARS-CoV-2 in utilizing alternative entry mechanisms across various cell types, with E484D and P812R playing critical roles in ACE2-independent entry in cell culture. Overall, this study provides valuable insights into how SARS-CoV-2 can alter its receptor usage to ensure robust infectivity of human cell lines while preserving neutralization sensitivity, contributing to our understanding of viral evolution, and informing potential therapeutic strategies targeting viral entry.
Hepatotoxicity evaluation of cannabidiol, cannabinol, cannabichromene and cannabigerol using a human quad culture liver chip
Correction: Comprehensive analysis of bioinformatics and system biology reveals the association between Girdin and hepatocellular carcinoma
Strengthening strategies for unreinforced stone masonry walls using FRP and CFM composites
Thermal processing and geographical origin as drivers of terpenoids variation in Anethum graveolens L. essential oils: A biplot analysis
Dill (Anethum graveolens L.), a medicinal-vegetable plant renowned for its aromatic and functional properties, exhibits significant variation in essential oil composition due to geographical origin (genotypic diversity) and post-harvest drying temperatures (DTs). This study aimed to (1) quantify the effects of geographical origin (as a proxy for genotype) and DTs on essential oil yield and composition, and (2) evaluate how specific genotypes respond to thermal processing. Six A. graveolens genotypes from distinct Iranian regions (Mashhad, Ardabil, Parsabad, Bushehr, Esfahan, and Kerman) were cultivated under uniform field conditions in Ardabil, Iran, to isolate genotypic variation. Post-harvest treatments included environmental shade drying and oven drying at 40°C and 60°C, creating unique combinations of genotype-treatment (CGT). Using CGT × character biplot analysis, we assessed interactions between genotype, DT, and essential oil compositions. The results revealed significant CGT-driven variation: shade drying enhanced α-Phellandrene levels in Kerman and Esfahan genotypes (57.49% and 55.51%), while oven drying at 40°C maximized Myristicin content (1.72%) in the Ardabil genotype and essential oil yield in Parsabad (1.86% w/v). High-temperature drying (60°C) reduced essential oil content in sensitive genotypes. β-Pinene and γ-Terpinene emerged as discriminative markers for genotype performance. Critically, the Parsabad genotype at 40°C and the Ardabil genotype demonstrated superior essential oil yields, whereas genotype-specific responses to DT highlighted the need for tailored post-harvest protocols. This study establishes CGT interactions as pivotal drivers of A. graveolens essential oil chemotypes, offering actionable strategies for genotype-specific drying protocols to optimize industrial production and breeding programs.