Search Results (56)

Background/Objectives: The factors contributing to residual atherosclerotic cardiovascular disease (ASCVD) risk in individuals are not fully understood, but knowledge of the specific type of dyslipoproteinemia may help further refine risk assessment. We developed a novel phenotyping and risk assessment system that may be applied automatically using standard lipid panel parameters. Methods: NHANES data collected from 37,056 individuals during 1999–2018 were used to develop a three-dimensional dyslipidemia phenotype classification system. ARIC data from 14,632 individuals were used to train and validate the risk model. Three-dimensional Cartesian coordinates were converted to spherical coordinates, which were used as features in a logistic regression model that provides a probability of ASCVD. UK Biobank data from 354,344 individuals were used to further validate and test the model. Results: Nine lipidemia phenotypes were defined based on the concentrations of HDLC, non-HDLC and TG. These phenotypes were related to the prevalence of metabolic syndrome, pooled cohort equation (PCE) score and ASCVD-free survival. A logistic regression model including age, sex and the spherical coordinates of the phenotype provided a composite risk score with predictive accuracy comparable to that of the PCEs. Conclusions: We provided an example of how a multidimensional coordinate system may be used to define a novel lipoprotein phenotyping system to examine disease associations. When applied to an ASCVD risk model, the composite spherical coordinate risk marker, which can be fully automated, provided an F1 performance score almost as good as the PCEs, which requires other risk factors besides lipids. Full article

Background/Objectives: Pancreatic cancer (PC) is an aggressive malignancy with a poor prognosis, frequently diagnosed at a metastatic stage. The identification of accessible, cost-effective prognostic biomarkers is critical for optimizing treatment strategies. The Endothelial Activation and Stress Index (EASIX), calculated using lactate dehydrogenase (LDH), creatinine, and platelet count, reflects endothelial dysfunction and has shown prognostic value in hematological cancers. However, its utility in metastatic PC remains unexplored. This study is the first to evaluate the prognostic significance of the EASIX in patients with metastatic PC receiving first-line FOLFIRINOX chemotherapy. Methods: This retrospective cohort study analyzed 204 patients diagnosed with metastatic pancreatic adenocarcinoma at Istanbul Training and Research Hospital between 2020 and 2025. All patients received FOLFIRINOX as first-line therapy. EASIX was calculated as LDH (U/L) × creatinine (mg/dL)/platelet count (10⁹/L). A cut-off value of 1.33 was used to stratify patients into low and high EASIX groups. Overall survival (OS) was assessed using Kaplan–Meier analysis and compared with the log-rank test. Results: The mean patient age was 63.0 ± 9.4 years; 61.8% were male. There were no significant differences in baseline characteristics between groups. Patients with EASIX ≥ 1.33 had significantly lower platelet counts and higher LDH and creatinine levels. Median OS was 14 months for EASIX < 1.33 and 8 months for EASIX ≥ 1.33 (p < 0.001). Conclusions: EASIX is a simple, inexpensive prognostic marker associated with overall survival in metastatic PC. Its integration into clinical practice may facilitate early risk stratification. Further prospective studies are needed to confirm its prognostic utility. Full article

Background: Large language models (LLMs) have emerged as powerful tools in healthcare. In diagnostic radiology, LLMs can assist in the computation of the Spine Instability Neoplastic Score (SINS), which is a critical tool for assessing spinal metastases. However, the accuracy of LLMs in calculating the SINS based on radiological reports remains underexplored. Objective: This study evaluates the accuracy of two institutional privacy-preserving LLMs—Claude 3.5 and Llama 3.1—in computing the SINS from radiology reports and electronic medical records, comparing their performance against clinician readers. Methods: A retrospective analysis was conducted on 124 radiology reports from patients with spinal metastases. Three expert readers established a reference standard for the SINS calculation. Two orthopaedic surgery residents and two LLMs (Claude 3.5 and Llama 3.1) independently calculated the SINS. The intraclass correlation coefficient (ICC) was used to measure the inter-rater agreement for the total SINS, while Gwet’s Kappa was used to measure the inter-rater agreement for the individual SINS components. Results: Both LLMs and clinicians demonstrated almost perfect agreement with the reference standard for the total SINS. Between the two LLMs, Claude 3.5 (ICC = 0.984) outperformed Llama 3.1 (ICC = 0.829). Claude 3.5 was also comparable to the clinician readers with ICCs of 0.926 and 0.986, exhibiting a near-perfect agreement across all individual SINS components [0.919–0.990]. Conclusions: Claude 3.5 demonstrated high accuracy in calculating the SINS and may serve as a valuable adjunct in clinical workflows, potentially reducing clinician workload while maintaining diagnostic reliability. However, variations in LLM performance highlight the need for further validation and optimisation before clinical integration. Full article

Artificial intelligence (AI) shows promise in streamlining MRI workflows by reducing radiologists’ workload and improving diagnostic accuracy. Despite MRI’s extensive clinical use, systematic evaluation of AI-driven productivity gains in MRI remains limited. This review addresses that gap by synthesizing evidence on how AI can shorten scanning and reading times, optimize worklist triage, and automate segmentation. On 15 November 2024, we searched PubMed, EMBASE, MEDLINE, Web of Science, Google Scholar, and Cochrane Library for English-language studies published between 2000 and 15 November 2024, focusing on AI applications in MRI. Additional searches of grey literature were conducted. After screening for relevance and full-text review, 66 studies met inclusion criteria. Extracted data included study design, AI techniques, and productivity-related outcomes such as time savings and diagnostic accuracy. The included studies were categorized into five themes: reducing scan times, automating segmentation, optimizing workflow, decreasing reading times, and general time-saving or workload reduction. Convolutional neural networks (CNNs), especially architectures like ResNet and U-Net, were commonly used for tasks ranging from segmentation to automated reporting. A few studies also explored machine learning-based automation software and, more recently, large language models. Although most demonstrated gains in efficiency and accuracy, limited external validation and dataset heterogeneity could reduce broader adoption. AI applications in MRI offer potential to enhance radiologist productivity, mainly through accelerated scans, automated segmentation, and streamlined workflows. Further research, including prospective validation and standardized metrics, is needed to enable safe, efficient, and equitable deployment of AI tools in clinical MRI practice. Full article

Comprehensive studies are needed to investigate the diversity, abundance, and seed bank of weeds in winter wheat, spring barley, and spring oilseed rape crops due to a lack of experimental studies. Tillage has a long-term impact on agroecosystems. Since 1999, a long-term field experiment has been conducted at the Experimental Station of Vytautas Magnus University. The soil of the experimental site is classified as Epieutric Endocalcaric Planosol (Endoclayic, Episiltic, Aric, Drainic, Endoraptic, Uterquic), according to the World Reference Base. Treatments were arranged using a split-plot design. According to the factorial field experiment, the straw was removed from one part of the experimental field, and on the other part of the field, the straw was chopped and spread at harvesting (factor A). Six tillage systems, conventional (deep) and shallow plowing, shallow loosening, shallow rotovation, catch cropping and rotovation, and no tillage, were used as a subplot (factor B). The current study results show that the number of annual, perennial, and total weeds and the dry matter biomass decreased in shallow-plowed plots compared to deep-plowed plots. Different applied tillage treatments had different effects on perennial weeds. In the upper (0–10 cm) soil layer studied, the number of annual, perennial, and total weed seeds decreased in the fields where the straw was chopped and spread compared to the fields where the straw was removed. In the deeper soil layer (10–25 cm), no tillage with cover crops and direct seeding without cover crops reduced the number of annual and perennial weed seeds compared to deep tillage. The aim of this experiment was to investigate the effects of long-term tillage of different intensities and straw retention systems on weeds in crop fields. The results were obtained in 2019 and 2021 (winter wheat, spring barley, spring oilseed rape). Full article

Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address this issue, we propose DUIncoder, which is an unsupervised framework designed to learn exclusively from normal driving data across diverse scenarios to detect DUI behaviors and provide explanatory insights. DUIncoder aims to address the challenge of collecting DUI data by leveraging diverse normal driving data, which can be readily and continuously obtained from daily driving. Experiments on simulator data show that DUIncoder achieves detection performance superior to that of supervised learning methods which require additional DUI data. Moreover, its generalization capabilities and adaptability to incremental data demonstrate its potential for enhanced real-world applicability. Full article

Background: Childhood vitamin A deficiency leads to increased morbidity and mortality. Human milk is the only source of vitamin A for exclusively breastfed infants. Dried Moringa oleifera leaf powder (moringa) is a good food source of provitamin A and other carotenoids. Its effect during lactation on human milk vitamin A and carotenoid content is unclear. Objectives: Our objective was to investigate the effect of maternal moringa consumption on human milk retinol and carotenoid concentrations and maternal and infant vitamin A status. Methods: We conducted a 3-month pilot single-blinded cluster-randomized controlled trial in breastfeeding mother–infant pairs (n = 50) in Kenya. Mothers received corn porridge with (20 g/d) or without moringa with complete breast expressions and maternal and infant serum collected at enrollment (infant <30 days old) and 3 months. Milk was analyzed for retinol and selected carotenoids; maternal/infant serum was analyzed for retinol binding protein (RBP). Results: 88% (n = 44) pairs completed milk and serum samples. Four mothers (9%) had vitamin A deficiency (RBP <0.07 µmol/L); 11 (25%) were vitamin A insufficient (VAI; RBP <1.05 µmol/L). Alpha-carotene concentration in milk was higher in the moringa than the control group at baseline (p = 0.024) and at exit (least squares means, LSM, 95%CI µg/mL 0.003, 0.003–0.004 moringa vs. 0.002, 0.001–0.003 control, n = 22/cluster; p = 0.014). In mothers with VAI, alpha-carotene was higher in the moringa group than controls at exit (LSM, 95%CI µg/mL 0.005, 0.003–0.009 moringa, n = 3, vs. 0.002, 0.000–0.004 control, n = 8, p = 0.027) with no difference at baseline. Milk carotenoids did not correlate with vitamin A status (serum RBP) in infants or mothers. Conclusions: Maternal moringa consumption did not impact concentration of milk vitamin A and resulted in limited increase in milk carotenoids in this cohort. Full article

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care. Full article

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI’s potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice. Full article

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. Full article

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio^® XT Patch [aged 72–94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (β = 0.30, 95% CI: 0.16–0.44) and executive function scores (β = 0.38, 95% CI: 0.22–0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22–0.67) or dementia (OR = 0.25, 95% CI: 0.08–0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia. Full article

The evolutionarily conserved nonsense-mediated mRNA decay (NMD) pathway is a quality control mechanism that degrades aberrant mRNA containing one or more premature termination codons (PTCs). Recent discoveries indicate that NMD also differentially regulates mRNA from wild-type protein-coding genes despite lacking PTCs. Together with studies showing that NMD is involved in development and adaptive responses that influence health and longevity, these findings point to an expanded role of NMD that adds a new layer of complexity in the post-transcriptional regulation of gene expression. However, the extent of its control, whether different types of NMD play different roles, and the resulting physiological outcomes remain unclear and need further elucidation. Here, we review different branches of NMD and what is known of the physiological outcomes associated with this type of regulation. We identify significant gaps in the understanding of this process and the utility of genetic tools in accelerating progress in this area. Full article

The scientific aim of this article is to elucidate the effects of various tillage practices on soil properties and crop yields; additionally, it seeks to highlight the significant potential of specific farming systems in enhancing soil organic carbon, thereby positively influencing CO₂ emissions from soil. In the experimental station of Vytautas Magnus University, Kaunas District, Lithuania (54°52′50″ N and 23°49′41″ E), a long-term field experiment has been established since 1999, and studies have been conducted since 2003. The soil of the experimental site is classified as Epieutric Endocalcaric Planosol (Endoclayic, Episiltic, Aric, Drainic, Endoraptic, Uterquic), according to the World Reference Base (WRB, 2022). Two primary factors were assessed. Factor A incorporated practices of straw removal versus straw chopping and spreading, while Factor B evaluated a spectrum of tillage techniques: conventional deep plowing and two no-tillage practices, one of which involved cover crops. The findings from this long-term study highlight a significant increase in SOC stocks across all treatments over the 20-year period. Notably, the no-tillage practices, coupled with the spreading of chopped straw, demonstrated the most substantial growth in SOC levels, particularly in the top 0–10 cm soil layer. This trend underscores the effectiveness of minimizing soil disturbance and incorporating organic matter in boosting SOC stocks. The different tillage systems influence CO₂ emissions from soil. Initially, direct sowing into uncultivated land, both with and without cover crops, led to a notable reduction in CO₂ emissions compared to conventional plowing. However, this effect was found to vary over the growth cycle of the plant, highlighting the dynamic interaction between tillage practices, soil properties, and environmental conditions. Collaborative research efforts that involve farmers, scientists, policymakers, and other stakeholders are crucial for the development of holistic, practical, scalable solutions that enhance the sustainability and productivity of agricultural systems. This study contributes to the growing body of knowledge on sustainable agriculture, providing insights for farmers, agronomists, and policymakers in their quest to promote environmentally sound and productive agricultural systems. Full article

Background: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. Objective: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. Methods: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016–2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m–11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). Results: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 10⁶: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). Conclusions: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h. Full article

Emerging evidence indicates a previously unrecognized, clinically relevant spectrum of abnormal aldosterone secretion associated with hypertension severity. It is not known whether excess aldosterone secretion contributes to hypertension during pregnancy. We quantified aldosterone concentrations and angiotensin peptides in serum (using liquid chromatography with tandem mass spectrometry) in a cohort of 128 pregnant women recruited from a high-risk obstetrics clinic and followed prospectively for the development of gestational hypertension, pre-eclampsia, superimposed pre-eclampsia, chronic hypertension, or remaining normotensive. The cohort was grouped by quartile of aldosterone concentration in serum measured in the first trimester, and blood pressure, angiotensin peptides, and hypertension outcomes compared across the four quartiles. Blood pressures and body mass index were greatest in the top and bottom quartiles, with the top quartile having the highest blood pressure throughout pregnancy. Further stratification of the top quartile based on increasing (13 patients) or decreasing (19 patients) renin activity over gestation revealed that the latter group was characterized by the highest prevalence of chronic hypertension, use of anti-hypertensive agents, pre-term birth, and intrauterine growth restriction. Serum aldosterone concentrations greater than 704 pmol/L, the 75th percentile defined within the cohort, were evident across all categories of hypertension in pregnancy, including normotensive. These findings suggest that aldosterone excess may underlie the development of hypertension in pregnancy in a significant subpopulation of individuals. Full article