Browse Articles

A novel global health index framework for asset prognostics and health management in the oil and gas industry

Spatial interaction between retinal and choroidal circulation in retinal vein occlusion

Greenhouse gas balance of solar parks built on peatlands in Germany

Co-staining microplastics with Nile Red and Rose Bengal for improved optical quantification

Abstract Accurate assessment of microplastic (MP) contamination in environmental samples is crucial not only for understanding the scope of this growing environmental threat but also for quantifying its magnitude and enabling proper risk assessment. However, current methodologies for MP quantification often suffer from inaccuracies due to the difficulty in distinguishing plastic particles from natural organic matter, also due to incomplete digestion of natural polymers during sample treatment. Moreover, the techniques commonly employed are highly time-consuming, further limiting their routine application. This research presents an innovative solution for optical microscopy evaluation: a sequential co-staining technique employing Nile Red (NR) and Rose Bengal (RB) to identify natural vs. synthetic polymer fragments as well as false positives. Two experiments were implemented staining natural polymers (cellulose, protein, lignin, and chitin) and synthetic polymers (Polyvinyl Chloride (PVC), Polystyrene (PS), Polyethylene Terephthalate (PET), Polypropylene (PP), Nylon (NY), High-Density Polyethylene (HDPE) and Low-Density Polyethylene (LDPE)) with the two dyes. The results showed that co-staining is an effective way of separating natural and synthetic fragments and a significant improvement in the accuracy of visual MP identification. Additionally, co-staining the same filter allows to obtain relevant time saving as well as reducing counting and identification errors, since no sample exchange is needed. Application of this novel technique will allow for more reliable monitoring of MP concentrations in various environmental matrices, leading to better-informed risk assessments and mitigation strategies.

RU 486 blocks inhibitory effect of neonatal corticosterone administration on sertoli cell proliferation in mice

FreFilterTST: a dynamic channel graph sparsification approach to multivariate time series anomaly detection with frequency-domain restoration

Consistency evaluation and performance optimization of deep learning-based auto-contouring for nasopharyngeal carcinoma

Abstract Contour delineation is crucial for ensuring the efficacy and side effects of radiotherapy (RT), but it inevitably involves inter-observer variability (IOV). Deep learning (DL) models have been used to assist in contour delineation, but further evaluation is needed to guide healthcare professionals in the judicious application of DL models. The contours of 22 anatomical structures and the gross tumor volume (GTV) for 30 patients with nasopharyngeal carcinoma were delineated using four DL models: AccuContour, RT-Viewer-contour, RT-Mind, and PVmed Contouring. The overall kappa values and generalized conformity indices of these contours were calculated to assess consistency. The Dice similarity coefficient (DSC), Relative Volume Difference (RVD), 95th percentile Hausdorff Distance (HD95), and Average Symmetric Surface Distance (ASSD) were calculated to evaluate the accuracy of the contours. Additionally, two innovative model frameworks were introduced to improve the fidelity and reliability of patient contour delineation. The consistency of the contours generated by the four DL models was poor for GTV, pituitary gland, temporal lobes, and temporomandibular joints. Marked differences were still observed between the contours generated by the models and the manual delineations by oncologists for the GTV, lens, optic nerves, pituitary glands temporomandibular joints, temporal lobes, and trachea. The model frameworks we proposed can effectively optimize the contours of GTV, brainstem, eyes, lens, and temporomandibular joints. The contours generated by DL models still have deficiencies in the application of nasopharyngeal carcinoma radiotherapy. To address this, two model frameworks were proposed to increase the robustness of automatic contouring.

An ethnic-sensitive hybrid framework for T2D prediction with explainable AI and weighted ensembles

Using explainable AI to align pre-university profiles with bachelor’s degree success

Comparative analysis of OECD guideline data and Tox21 assays to improve reproductive and developmental toxicity prediction

The low sidelobe level high-gain adaptive beamforming method for uniform subarrays

Rebar free high performance ductile concrete beams powered by CFRP and post-tensioning

Comprehensive analysis of malignant subtypes of lung adenocarcinoma based on multi-omics landscape and functional validation of prognostic biomarker BAIAP2L2

Correction: Similarity and dissimilarity in alterations of the gene expression profile associated with inhalational anesthesia between sevoflurane and desflurane

Developing ammonium persulfate activation by tri-metal oxide-based nanocomposite for upgraded organic pollutants degradation

School-to-work transition: The role of life satisfaction, risk perception, and resilience in youth career decision-making

Young people often face uncertainty during the transition from education to work, along with high unemployment and job dissatisfaction, which is addressed in the EU Youth Strategy, highlighting the need for better career support. This study aimed to identify main factors influencing youth career decisions and to develop a decision-making model. Five core constructs were defined through literature review: Dealing with Uncertainty, Risk Preference, Adaptability and Resilience, Education and Support, and Life Satisfaction. Data were collected from 673 engineering students. Regression analysis was used to test the proposed model and hypotheses, while Mann-Whitney and Kruskal-Wallis tests examined group differences. The developed model accounts for 46.2% (R² = 0.462) of the variability in students’ career choices. Adaptability and resilience emerged as the most influential factor (β = 0.557). Certain differences, for specific constructs, were also observed in relation to different groups of family income, gender and extracurricular activity engagement. The model supports more informed career decisions and provides insights that may help improve career guidance and educational policy. The findings also may contribute to bridging theory and practice in career development research. The study is limited by its sample, which included only engineering students from the Republic of Serbia, potentially restricting the generalizability of the results.

Development and application of SNP markers to discriminate Korean Perilla (Perilla frutescens) varieties using genomic sequence variations

A study protocol for a multi-specialty observational cohort comparing robotic stapler and bedside stapler outcomes in robotic-assisted surgeries

Introduction Surgical staplers are essential tools in minimally invasive surgery (MIS), enabling tissue division, hemostasis, and secure anastomoses. With the growth of robotic-assisted surgery, robotic staplers such as SureForm have recently become available. These staplers offer precise articulation and real-time tissue compression monitoring. However, the clinical advantages of robotic staplers over bedside staplers remain uncertain. Studies show mixed results across specialties, mainly due to small sample sizes, outdated data, and data heterogeneity. This study protocol proposes a series of future analyses that will evaluate the clinical outcomes and resource utilization of robotic versus bedside staplers in robotic-assisted surgeries across multiple specialties using recent real-world data. Methods and analysis This retrospective cohort study will use data from the Premier Healthcare Database (PHD), a large hospital-based database covering patients with varied payers across the United States. Adult patients (≥18 years) who underwent elective, fully robotic-assisted lung, colorectal, gastric, or bariatric surgeries from 2019 to 2023 will be included. Each surgical specialty will be analyzed in a separate paper. Patients will be categorized into two groups based on the type of surgical stapler used: robotic staplers (SureForm) and bedside staplers (manual or powered). The primary outcome will be postoperative leak (air leak for lung resection; anastomotic leak for colorectal, gastrectomy, and bariatric). Key secondary outcomes are other complications, conversion to open surgery, operative time, transfusion requirements, length of stay (LOS), and cost. Overlap weighting will be applied to minimize bias. Dissemination Results will be disseminated through peer-reviewed surgical journals and presentations at relevant surgical meetings.

Knowledge, attitude, and self-medication practices among healthcare students from the metropolitan City of South India

Systematic review of mHealth and digital health interventions to improve childhood vaccination uptake in 19 Sub-Saharan African countries

Mobile health and digital health (mHealth/DH) interventions have been shown to support immunisation programmes in Sub-Saharan Africa (SSA) and improve uptake of life-saving vaccines. As 19 SSA countries were targeted to begin rolling out the two new malaria vaccines (RTS,S/AS01 and R21/Matrix-M) in 2024, this systematic review aims to investigate which mHealth/DH interventions are most effective at increasing vaccination uptake (by assessing vaccination coverage and timeliness outcomes) in these countries. The review assessed the effectiveness of mHealth/DH interventions for increasing uptake of Diphtheria–Tetanus–Pertussis or Pentavalent vaccines (DTP/Pentavalent). As with any multi-dose vaccine, the DTP/Pentavalent vaccine requires multiple doses to ensure its maximum protective benefit, therefore maintaining schedule adherence and ensuring its timely completion is essential. Thus, identifying strategies to support adherence, such as digital appointment reminders, remains a public health priority. Eight electronic databases were searched, alongside selected grey literature sources. A narrative synthesis was conducted with studies grouped by mHealth/DH intervention-type. Included studies were assessed for risk of bias using RoB2 and ROBINS-I, and certainty of evidence was evaluated using the GRADE approach. 14 studies were included, comprising both randomised and non-randomised control trials. However, only 4 out of the 19 SSA countries were represented (Nigeria, Kenya, Burkina Faso and Cote D’Ivoire). All interventions investigated were appointment reminders. Generally, all intervention-types were positively associated with vaccination coverage and timeliness. SMS-based interventions showed modest effects, whereas interventions incorporating voice components (phone calls/voice messages) tended to yield larger effects. The certainty of evidence ranged from very low to moderate depending on the intervention-type and outcome pairing. The findings offer evidence-based insights to guide the development and implementation of mHealth/DH interventions within SSA childhood immunisation programmes. While interventions with voice-based components appear particularly promising, the limited certainty of evidence demonstrates further high-quality, context-specific research is required to draw stronger conclusions.