Scientific Reports最新文献

This study investigated the occurrence of pesticides, their ecological risks, and potential human health implications in irrigation-drainage canals of Chiang Mai's longan orchards during two phenological stages: flowering and easrly fruit development. In total, 25 pesticides were detected, including four fungicides, seven insecticides, and fourteen herbicides, across three canal sites (upstream, midstream, and downstream). Concentration dynamics varied strongly by phenological window and sampling sites along the canal. Among fungicides, carbendazim and hexaconazole increased during fruit set, whereas kresoxim-methyl and metalaxyl declined. For insecticides, carbosulfan peaked during flowering (> 4,200 ng L^-1) but declined sharply at fruit set, while formothion peaked at fruit set. Among herbicides, 2,4-D, alachlor, and atrazine generally declined, whereas isoproturon and metobromuron increased markedly during fruit set, indicating a shift toward phenylurea use. Spatially, carbosulfan peaked upstream, fungicide and insecticide spikes were greatest midstream, and herbicide increases were most pronounced downstream during fruit set. Carbendazim and carbosulfan consistently dominated and produced high ecological risk quotients (RQ ≥ 1) at all sites, while ethion and formothion contributed episodically to elevated risks. From a human-health perspective, all single-compound pesticide daily intakes (PDIs) were below their reference doses; however, when summed across compounds, the site-specific drinking-water hazard index (HI) exceeded 1, ranging from 7.8 to 38.0 at flowering and from 1.6 to 8.1 at fruit set, with the downstream sampling station and infants showing the largest HIs. Despite being a one-time assessment, these patterns likely mirror common pesticide practices in longan orchards and may emerge in other water-adjacent production areas worldwide.

It is suggested that high error rates in clinical IHC compared to other laboratory disciplines are due to a lack of traceable analytic standards with quantitative controls. Quantitative controls can be achieved by implementing calibrators into IHC workflows, but recently published approaches offer little discussion about what the ideal calibrator should look like. Here, we detail an IHC method that utilizes cellular calibrators to determine the average number of HER2 receptors in tumor cells of breast cancer tissue. Electrochemiluminescent immunoassay and flow cytometry were used to establish nominal HER2 amounts in the cellular calibrators. The quantitated number of HER2 receptors and associated 4B5 scores from commercially sourced breast cancer tumors were compared. The results were discordant, indicating differences in analytical performance. Cells are ideal calibrators for IHC because the antigens they express are structurally and functionally representative of the endogenous analyte being measured. They can also be stained and analyzed identically to tissues, and are easily cultured, expanded, and manipulated. However, novel methods for labeling cells with fixed amounts of proteins are needed. In addition, standards for validation of quantitative IHC methods should be developed and their clinical utility must be demonstrated.

Recent rapid developments in artificial intelligence (AI) technologies are leading to significant changes in social and economic structures. These changes are having a direct impact on young people's transition to the labour market and their expectations regarding employment. Against this backdrop, the relationship between university students' concerns about AI and their anxieties about job searching is emerging as a multidisciplinary field of research. This study aims to evaluate the impact of AI-related anxiety on university students' anxieties about finding employment. This cross-sectional study involved 821 health sciences students from various departments at Turkish universities, including Physical Therapy and Rehabilitation, Pharmacy Services, Medical Secretarial and Documentation, Nursing, Speech and Language Therapy, Midwifery and Health Management. Participants were selected via an online survey distributed by class representatives on WhatsApp. Data were collected using the Artificial Intelligence Anxiety Scale and the Job Search Anxiety Scale, and analysed using Pearson's correlation and multiple linear regression in the SPSS (v26.0) programme. Of these participants, 82.5% are female, and 59.6% are undergraduates. Studies in Turkey have shown that the proportion of female university students is higher than that of male students. This may partly explain the higher proportion of female participants in the sample. The mean scores on the artificial intelligence anxiety scale and the job search anxiety scale were 3.02 ± 0.80 and 3.16 ± 0.60, respectively. The study revealed a moderate, positive, and significant correlation (r = 0.233, p < 0.001) between artificial intelligence anxiety and job search anxiety. The study revealed that, even when socio-demographic variables were controlled, artificial intelligence anxiety remained a significant predictor of job search anxiety (β = 0.234, p < 0.001). Additionally, it was found that demographic variables such as income status (p = 0.011, β = - 0.088) and educational attainment (p = 0.001, β = 0.208) significantly affected job search anxiety. The findings show that concerns about artificial intelligence are closely linked to students' expectations regarding employment after graduation. It has therefore been concluded that artificial intelligence anxiety is not merely an attitude toward technology but an important psychological factor shaping students' perceptions of their professional futures and fears of unemployment. The research results suggest that enhancing educational programmes and career counselling services to boost artificial intelligence literacy at the university level could play a significant role in alleviating students' anxiety about job hunting.