Impact of age norms and stereotypes on managers' hiring decisions of retirees

Purpose -Our study investigates the role of managers in the re-employment of early retirees and asks what the effect is of managers’ age norms and stereotypes on managers’ employment decisions. Design/methodology/approach- A combination of a factorial study and a survey was conducted. First, information on the age norms and stereotypes was collected. Secondly, profiles of hypothetical retired job applicants were presented to the employers, who were asked to make a specific hiring decision. The information collected during both studies was combined in the analysis and multilevel models were estimated. Findings -The results indicate that higher age norms result in a higher propensity to hire an early retiree. Stereotypes, by contrast, do not influence managers’ decisions. Early retirees’ chances for re-employment are also related to their own circumstances (physical appearance and relevant experience) and organisational forces, as they are hired when organisations face labour force shortages. Research limitation / implications – with the use of vignettes study we deal with hypothetical hiring situation. Originality value- Although the effect of age norms and age stereotypes has been often suggested, not much empirical evidence was presented to support this notion. Our study estimates the effect of age norms and stereotypes on hiring decision. key words: bridge employment; early retirees; age norms; age stereotypes; multilevel models.

Modulation of the peripheral blood transcriptome by the ingestion of probiotic yoghurt and acidified milk in healthy, young men

The metabolic health benefits of fermented milks have already been investigated using clinical biomarkers but the development of transcriptomic analytics in blood offers an alternative approach that may help to sensitively characterise such effects. We aimed to assess the effects of probiotic yoghurt intake, compared to non-fermented, acidified milk intake, on clinical biomarkers and gene expression in peripheral blood. To this end, a randomised, crossover study was conducted in fourteen healthy, young men to test the two dairy products. For a subset of seven subjects, RNA sequencing was used to measure gene expression in blood collected during postprandial tests and after two weeks daily intake. We found that the postprandial response in insulin was different for probiotic yoghurt as compared to that of acidified milk. Moreover changes in several clinical biomarkers were associated with changes in the expression of genes representing six metabolic genesets. Assessment of the postprandial effects of each dairy product on gene expression by geneset enrichment analysis revealed significant, similar modulation of inflammatory and glycolytic genes after both probiotic yoghurt and acidified milk intake, although distinct kinetic characteristics of the modulation differentiated the dairy products. The aryl hydrocarbon receptor was a major contributor to the down-regulation of the inflammatory genesets and was also positively associated with changes in circulating insulin at 2h after yoghurt intake (p = 0.05). Daily intake of the dairy products showed little effect on the fasting blood transcriptome. Probiotic yoghurt and acidified milk appear to affect similar gene pathways during the postprandial phase but differences in the timing and the extent of this modulation may lead to different physiological consequences. The functional relevance of these differences in gene expression is supported by their associations with circulating biomarkers

Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new technologies (the CRAC study)

Major surgical societies advised using non-operative management of appendicitis and suggested against laparoscopy during the COVID-19 pandemic. The hypothesis is that a significant reduction in the number of emergent appendectomies was observed during the pandemic, restricted to complex cases. The study aimed to analyse emergent surgical appendectomies during pandemic on a national basis and compare it to the same period of the previous year. This is a multicentre, retrospective, observational study investigating the outcomes of patients undergoing emergent appendectomy in March-April 2019 vs March-April 2020. The primary outcome was the number of appendectomies performed, classified according to the American Association for the Surgery of Trauma (AAST) score. Secondary outcomes were the type of surgical technique employed (laparoscopic vs open) and the complication rates. One thousand five hundred forty one patients with acute appendicitis underwent surgery during the two study periods. 1337 (86.8%) patients met the inclusion criteria: 546 (40.8%) patients underwent surgery for acute appendicitis in 2020 and 791 (59.2%) in 2019. According to AAST, patients with complicated appendicitis operated in 2019 were 30.3% vs 39.9% in 2020 (p = 0.001). We observed an increase in the number of post-operative complications in 2020 (15.9%) compared to 2019 (9.6%) (p < 0.001). The following determinants increased the likelihood of complication occurrence: undergoing surgery during 2020 (+ 67%), the increase of a unit in the AAST score (+ 26%), surgery performed > 24 h after admission (+ 58%), open surgery (+ 112%) and conversion to open surgery (+ 166%). In Italian hospitals, in March and April 2020, the number of appendectomies has drastically dropped. During the first pandemic wave, patients undergoing surgery were more frequently affected by more severe appendicitis than the previous year's timeframe and experienced a higher number of complications. Trial registration number and date: Research Registry ID 5789, May 7th, 202

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets