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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.
Deformation mechanism of surrounding rock in weakly cemented thick coal seam roadways and research on graded anchor composite support technology
Assessment of retinal and choroidal structural and microvascular changes in early diabetic retinopathy using swept-source optical coherence tomography angiography
Objective It was to assess changes in structural parameters in early diabetic retinopathy (DR). Materials and methodologies This study is a retrospective analysis that included patients with early DR admitted to the Affiliated Third Hospital of Nantong University from January 2024 to December 2024. The participants were divided into the non-DR group (NDR group) and the non-proliferative DR group (NPDR group, which included mild, moderate, and severe subgroups) using swept-source optical coherence tomography angiography (SS-OCTA) technology. One-way analysis of variance (ANOVA) and the Kruskal-Wallis test were used to compare parameter differences among the groups. Results A total of 208 diabetic patients were included (55 in the NDR group, 153 in the NPDR group) and 51 healthy controls. The results showed that the FAZ area in the NPDR group was significantly larger than that in the control group (CG) (mean difference: +0.38 ± 0.10 mm2, 95% CI [0.25-0.51], P < 0.001), and it was positively correlated with disease severity (trend test P < 0.001). Relative to the CG, NDR group and various stages of NPDR group exhibited greatly lower values in choroidal vascular index (CVI), peripapillary vascular density (ppVD), peripapillary retinal nerve fiber layer thickness (pRNFL), vascular density (VD) in both the superficial and deep retinal vascular complexes, total perfusion area (PA), small vessel density (SVD), disc area, vascular density (FD300) within a 300 µm radius of the foveal center, and capillary plexus blood flow density (P<0.05). NPDR group showed progressively lower values than NDR group, with severity increasing as the condition worsened (P<0.05). Conclusion SS-OCTA can effectively monitor changes in structural parameters and serves as a valuable tool for evaluating the progression of early DR.
Changes in prefrontal hemodynamics and mood states during screen use: a functional near-infrared spectroscopy study
Abstract Screen use has been associated with poor cognitive and mental health, yet few studies have examined its effects on brain activity. Our aims were to describe changes in brain activity and mood states following brief exposure to screen-based content; assess the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure these effects; and gather preliminary data to inform future investigations. Twenty-seven young people (age = 21.5 ± 2.8 years; range = 18–25) completed six consecutive 3-min screen conditions in a psuedorandomized cross over design. All screen exposures were presented on an iPhone 12-Max while sitting. Hemodynamic changes in the dorsolateral prefrontal cortex (dlPFC) were measured continuously using fNIRS (Portalite Mk II). Changes in mood states (energy, tension, focus, happiness) were assessed before and after each condition. Condition exposure altered the hemodynamic response in the dlPFC, where oxygenated hemoglobin (HbO) increased more compared to baseline after exposure to social media (largest increase), gaming, and TV-viewing (smallest increase), respectively. Deoxygenated hemoglobin (HbR) and total hemoglobin (HbT) increased more following exposure to gaming (largest increase), social media, and TV-viewing (smallest increase), respectively. Both TV-viewing and gaming were associated with increased focus relative to baseline, whereas social media use was associated with decreased focus. Findings indicate that even short durations of screen use have measurable effects on brain regions involved in cognitive control, emotion, and social decision making. These effects are nuanced and context dependent, rather than universally beneficial or detrimental. fNIRS is a feasible method for measuring these effects.
Seismic characterization of lava flow facies in the critical zone of the deccan traps using shear wave velocity models
Abstract The critical zone is the uppermost layer of Earth’s crust, where the geosphere, hydrosphere, atmosphere, and biosphere interact to sustain life. In continental flood basalt provinces, its structure and evolution remain poorly understood due to lithological complexities and variable weathering patterns. Geological and geophysical characterization of the subsurface is essential to unravel these factors. Despite advances in understanding basalt lava flow stratigraphy in the Deccan Volcanic Province (DVP), field-scale seismic velocity variations within these flows and their internal structure remain largely unknown. This study integrates seismic data with volcanological information to investigate weathering patterns in the uppermost 50 m of basalt lava flows around Pune city in the western DVP. Using the multi-channel analysis of surface waves technique, we estimate shear wave velocity variations across flow units and dykes. By co-analyzing seismic data with morphological variations across outcrops, we develop a field-scale velocity characterization across basalt lavas and dykes. Critical zone facies, identified and validated through outcrop studies, include soil, weathered bedrock with vesicular basalt, columnar-jointed lava cores, red bole, and intrusive dykes. An analysis of vegetation distribution, landscape morphology, and lithological variability provides insights into key weathering and erosional processes shaping the critical zone in this volcanic terrain.
Brain activation and heart rate variability as markers of autonomic function under stress
Abstract Efficient brain–heart interactions, mediated by the central autonomic network (CAN), are crucial in regulating physiological and psychological stress. The ability of the autonomic nervous system to adapt to stress predicts resilience to cardiovascular, anxiety, and mood disorders. Since the neural dynamics underlying brain–heart interactions remain poorly understood, this study investigated brain activation and heart rate variability (HRV) during stress and relaxation. Functional magnetic resonance imaging (fMRI) and peripheral heart rate assessment were used to assess brain–heart coupling during breathing-induced relaxation, psychosocial stress and stress recovery in 32 healthy participants. We assessed the relation between perceived stress and brain activation, and employed non-linear generalized additive models to forecast changes in HR based on brain activation in the CAN. Both breathing-induced relaxation and stress induction significantly affected HR variation and triggered brain activation in CAN-related regions. HR variation was related to CAN activity during stress induction, and that chronic stress was linked to reduced brain activation during stress recovery. Finally, we demonstrated that brain activation within the CAN predicts changes in HRV. Our results offer novel insights into dynamic brain–heart interactions during stress-related autonomic regulation and emphasize the brain–heart axis’s potential as a target for therapeutic interventions aimed at enhancing stress resilience.
Management of T‐cell malignancies: Bench‐to‐bedside targeting of epigenetic biology
Abstract The peripheral T‐cell lymphomas (PTCL) are the only disease for which four histone deacetylase (HDAC) inhibitors have been approved globally as single agents. Although it is not clear why the PTCL exhibit such a vulnerability to these drugs, understanding the biological basis for this activity is essential. Many lines of data have established that the PTCL exhibit marked sensitivity to other epigenetically targeted drugs, including EZH2 and DNMT3 (DNA‐methyltransferase 3) inhibitors. Even more compelling is the finding that combinations of drugs targeting the epigenetic biology of PTCL are beginning to produce provocative data, leading some to wonder if these agents can replace historical chemotherapy regimens routinely used for patients with the disease. Simultaneously, the field has identified a spectrum of mutations in genes governing epigenetic biology in many subtypes of PTCL, although the T follicular helper lymphomas, including angioimmunoblastic T‐cell lymphoma, appear to be particularly enriched for these genetic features. While the direct relationship between the presence of any one of these mutations and responsiveness to a particular epigenetic drug has yet to be established, it is increasingly accepted that the PTCL may be the prototypical epigenetic disease as no other form of cancer has exhibited such a vulnerability to this diversity of epigenetically targeted agents. Herein, we comprehensively review this esoteric and rapidly evolving field to identify themes and lessons from these experiences that may guide efforts to improve outcomes of patients with T‐cell neoplasms. Furthermore, we will discuss how these concepts might be applied to the broader field of cancer medicine.
Effects of different growing environments on strawberry growth and yield
Evaluating the effectiveness of flight simulator training on developing perceptual-motor skills among flight cadets: a pilot study
New approach methodologies: EU regulatory horizons
Synthesis and evaluation of novel thiohydantoin derivatives for antidiabetic activity using in silico in vitro and in vivo methods
Abstract Diabetes mellitus remains a global health challenge, necessitating the development of novel therapeutic agents. In this study, a series of thiohydantoin derivatives (FP1–FP7) were synthesized and evaluated for their in silico, in vitro, and in vivo antidiabetic potential. Molecular docking studies revealed strong binding affinities of derivatives towards α-glucosidase (PDB: 3wy1) and α-amylase (PDB: 3dhp), with FP4 exhibiting the most favorable interactions (− 7.8 kcal/mol with α-amylase and − 7.0 kcal/mol with α-glucosidase), involving hydrogen bonding and π-π stacking. During in vitro enzyme inhibition assay, FP4 demonstrated potent inhibitory activity against α-glucosidase and α-amylase, with IC₅₀ values of 129.40 and 128.90 µg/mL, respectively. The DPPH scavenging assay also indicated that FP4 had relatively strong antioxidant activity, with an IC₅₀ value of 39.7 µg/mL. In vivo antidiabetic efficacy was evaluated in STZ-induced diabetic rats over a period of 6-week. FP4 treated diabetic rats exhibited significantly reduced fasting blood glucose by 28.9% than in diabetic controls. In addition, HbA1C levels and diabetes-associated weight loss were significantly curtailed in these animals than untreated diabetic group. Further FP4 treated animals exhibited significantly decreased LDL and triglyceride levels and elevated HDL levels, suggesting a broader metabolic benefit. Taken together, our results suggest that thiohydantoin derivatives, particularly FP4, exhibited interesting antidiabetic and antihyperlipidemic activities warranting further pharmacokinetic and mechanistic investigations also potential for clinical translation and long term safety assessment.
Cancer in rural America: Improving access to clinical trials and quality of oncologic care
Abstract Individuals from rural areas in the United States suffer higher rates of morbidity and mortality from cancer than their urban counterparts. This review is based on the idea that equity—the elimination of unnecessary and preventable differences between groups of individuals—should underlie access to cancer care resources for patients from rural areas. Access to cancer clinical trials serves as the framework for identifying and understanding barriers in access to quality oncologic care. The authors discuss the interplay between rural living, socioeconomic status, culture, and health; and they highlight how economic considerations in rural areas often limit access to clinical trials and oncologic care because economies of scale do not apply in these regions given the requirement for high‐quality oncology care even with lower patient volumes. The authors propose solutions to enhance access to clinical trials and improve the quality of oncologic care in rural areas, viewing these aims as ethical and moral imperatives.
Engineering nitrogen and oxygen functionalities in naturally sourced activated carbon for multicomponent gas adsorption
Abstract Nitrogen doping is a widely adopted strategy to enhance the gas adsorption performance of activated carbon (AC) adsorbents. However, the simultaneous evolution of oxygen and nitrogen functional groups—especially in carbon precursors with high oxygen content—has received limited attention. In this study, coal-derived ACs with high surface areas (up to 940 m2/g) and micropore volumes (0.36 cm3/g) were synthesized via K2CO3-assisted physical activation, followed by nitrogen doping through co-pyrolysis with melamine. By regulating the doping temperature (600–900 °C), the nitrogen content of the resulting samples ranged from 1.44 to 7.68 at%, while the oxygen content varied from 6.89 to 10.39 at%. After decoupling the influences of porosity, we found that a well-balanced distribution of N and O functionalities, especially pyrrolic nitrogen, ether (C–O–C), and hydroxyl (C–O–H) groups, was critical for enhancing CO2 and H2O adsorption. NAC-600 exhibited the most favorable surface chemistry for the adsorption of CO2 (15 vol%) and H2O (20% RH), achieving capacities of 41 mg/g and 59.9 mg/g, respectively. In contrast, NAC-900, prepared at the highest N-doping temperature, exhibited the best surface chemistry for toluene adsorption (550 mg/cm3), attributed to its higher degree of graphitization and the presence of graphitic N and ether groups. This work offers a rational design strategy for improving the multicomponent gas adsorption performance of activated carbons for flue gas treatment.