Abstract Objective To investigate the diagnostic utility of asymmetrical tonsils in detecting tonsillar malignancy. Data Sources PubMed, Embase, Scopus, and Cochrane Library; from inception until December 17, 2024. Review Methods We included observational studies of adult/pediatric patients undergoing excisional tonsillectomy or incisional tonsillar biopsy that reported at least one diagnostic accuracy outcome for tonsillar asymmetry in predicting malignancy. We pooled estimates using frequentist univariate random‐effects generalized linear mixed models, examined and adjusted for publication bias via visual inspection, Egger's test, and trim‐and‐fill, performed influence and cumulative meta‐analyses, and used a Bayesian bivariate model as a sensitivity analysis. Outcome measures included the following: sensitivity, specificity, positive/negative likelihood ratio (LR+/LR−), and positive/negative predictive value (NPV/PPV) with 95% confidence interval (95% CI). Results Twenty‐nine studies (5178 participants) from 422 records were included. The risk of bias was low‐moderate. The sensitivity and specificity of tonsillar asymmetry as a diagnostic marker for malignancy were 77.2% (95% CI: 68.6%‐84.0%) and 96.4% (95% CI: 91.6%‐98.6%), respectively. The LR− was 0.24 (0.17‐0.34) and LR+ was 21.44 (8.05‐57.0). The NPV and PPV were 99.8% (95% CI: 99.1%‐99.9%) and 4.31% (95% CI: 1.83%‐9.80%), without considering clinical risks. With concomitant high‐risk clinical features such as lymphadenopathy, the PPV (probability of malignancy given asymmetrical tonsils) was 38.5% (30.3%‐47.4%). Without other high‐risk features, the PPV was 0.16% (0.15%‐0.18%). The overall quality of evidence was high. Conclusion Tonsillar asymmetry has a high specificity and moderate sensitivity for tonsillar malignancy. Due to the low prevalence of malignancy, the probability of malignancy is less than 1% if no other suspicious clinical features are present.

Abstract In prognosis studies with time-to-event outcomes, the survivals of groups with high/low biomarker expression are often estimated by the Kaplan–Meier method, and the difference between groups is measured by the hazard ratios (HRs). Since the high/low expressions are usually determined by study-specific cutoff values, synthesizing only HRs for summarizing the prognostic capacity of a biomarker brings heterogeneity in the meta-analysis. The time-dependent summary receiver operating characteristics (SROC) curve was proposed as a cutoff-free summary of the prognostic capacity, extended from the SROC curve in meta-analysis of diagnostic studies. However, estimates of the time-dependent SROC curve may be threatened by reporting bias in that studies with significant outcomes, such as HRs, are more likely to be published and selected in meta-analyses. Under this conjecture, this paper proposes a sensitivity analysis method for quantifying and adjusting reporting bias on the time-dependent SROC curve. We model the publication process determined by the significance of the HRs and introduce a sensitivity analysis method based on the conditional likelihood constrained by some expected proportions of published studies. Simulation studies showed that the proposed method could reduce reporting bias given the correctly-specified marginal selection probability. The proposed method is illustrated on the real-world meta-analysis of Ki67 for breast cancer.

ABSTRACT The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. We apply the proposed method to real-world meta-analyses to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

Sepsis caused byStreptococcus pyogenesis a life-threatening condition. Blood vessel endothelial cells should serve as a barrier to infection, although we recently reported that endothelial cells allow intracellular GAS proliferation due to defective xenophagy.

The coronavirus disease (COVID-19) pandemic has greatly altered peoples' daily lives, and it continues spreading as a crucial concern globally. Knowledge, attitudes, and practices (KAP) toward COVID-19 are related to individuals' adherence to government measures. This study evaluated KAP toward COVID-19 among university students in Japan between May 22 and July 16, 2020, via an online questionnaire, and it further investigated the associated determining KAP factors. Among the eligible respondents (n = 362), 52.8% were female, 79.0% were undergraduate students, 32.9% were students whose major university subjects were biology-related, 35.4% were from the capital region, and 83.7% were Japanese. The overall KAP of university students in Japan was high. All respondents (100%) showed they possessed knowledge on avoiding enclosed spaces, crowded areas, and close situations. Most respondents showed a moderate or higher frequency of washing their hands or wearing masks (both at 96.4%). In addition, 68.5% of respondents showed a positive attitude toward early drug administration. In the logistic regressions, gender, major subjects, education level, nationality, residence, and psychological factors (private self-consciousness and extroversion) were associated with knowledge or attitudes toward COVD-19 (p < 0.05). In the logistic and multiple linear regressions, capital regions, high basic knowledge, high information acquisition, correct information explanations contributed positively to preventative action (p < 0.05). Non-capital regions, male gender, non-bio-backgrounds, high public self-consciousness, high advanced knowledge, incorrect information explanations, and high extroversion contributed negatively to self-restraint (p < 0.05). Moreover, self-restraint was decreasing over time. These findings clarify the Japanese university students' KAP and the related factors in the early period of the COVID-19 pandemic, and they may help university managers, experts, and policymakers control the future spread of COVID-19 and other emerging infections.