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Young citizens play a key role in providing a continuous blood supply for public health system. This paper examines the prosocial behavior of young non-blood donors and analyses motivations, barriers, anticipated emotions (AEs), and intention to donate of a sample of 1,626 Spanish non-donors belonging to Generation Z and millennials. Among the main conclusions, motivations and barriers are antecedents of the AEs. Particularly, motivations (external and internal) positively influence AEs that enhance blood donation and deter AEs of not donating. Barriers (external and/or external) positively influence AEs that deter blood donation and deter AEs that enhance blood donation. Findings also demonstrate the influence of AEs in predicting intention to donate, and thus in prosocial behavior. As different clusters of young non-donors coexist according to their motivations and barriers, the paper identifies the cluster that would reduce free-riders. Public policy may change citizens' behavior by changing AEs that deter blood donation. [ABSTRACT FROM AUTHOR]

In the recent review published in 2019, Malinovsky and Albert conjectured analytical formulae of the optimal sample sizes for the modified Dorfman and Sterrett group testing schemes and verified the validity of the formulae numerically within a broad range. However, the authors pointed out that they were unable to give a rigorous proof of the stated conjectures. In this note, we affirm their hypotheses analitically thereby filling in the present gap. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease 2019 (COVID‐19, caused by SARS‐Cov‐2) is a big challenge for global health systems and the economy. Rapid and accurate tests are crucial at early stages of this pandemic. Reverse transcription‐quantitative real‐time polymerase chain reaction is the current gold standard method for detection of SARS‐Cov‐2. It is impractical and costly to test individuals in large‐scale population screens, especially in low‐ and middle‐income countries due to their shortage of nucleic acid testing reagents and skilled staff. Accordingly, sample pooling, such as for blood screening for syphilis, is now widely applied to COVID‐19. In this paper, we survey and review several different pooled‐sample testing strategies, based on their group size, prevalence, testing number, and sensitivity, and we discuss their efficiency in terms of reducing cost and saving time while ensuring sensitivity. [ABSTRACT FROM AUTHOR]

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Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around √ x. However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic. [ABSTRACT FROM AUTHOR]

Background: To date, radiographic sign clusters of multidrug-resistant pulmonary tuberculosis (MDR-TB) patients have not been reported. We conducted a study to investigate the classification and prognosis of sign clusters in pulmonary Computed Tomography (CT) images from patients with MDR-TB for the first time by using principal component analysis (PCA). Methods: The clinical data and pulmonary CT findings of 108 patients with MDR-TB in the Liupanshui Third Hospital were collected (from January 2018 to December 2020). PCA was used to analyze the sign clusters on pulmonary CT, and receiver operating characteristic (ROC) analysis was used to analyze the predictive value of the treatment outcome of MDR-TB patients. Results: Six cluster signs of MDR-TB were determined by PCA: nodules, infiltration, consolidation, cavities, destroyed lung and non-active lesions. Nine months after treatment, the area under the ROC curve (AUC) of MDR-TB patients with a cavity sign cluster was 0.818 (95% CI: 0.733–0.886), and the positive predictive value (PPV) and negative predictive value (NPV) of the treatment outcome were 79.6% (95% CI: 65.7–89.8%) and 72.9% (95% CI: 59.7–83.6%), respectively. Conclusion: PCA plays an important role in the classification of sign groups on pulmonary CT images of MDR-TB patients, and the sign clusters obtained from PCA are of great significance in predicting the treatment outcome. [ABSTRACT FROM AUTHOR]