Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under − 0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from − 3 to + 2 °C in extreme cases, with the majority of the differences between − 2 and + 1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject’s temperature above 38 °C ranged from 0 to 0.69. Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject’s temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

We propose a semiparametric functional single index model to study the relationship between a univariate response and multiple functional covariates. The parametric part of the model integrates the functional linear regression model and the sufficient dimension reduction structure. The nonparametric part of the model allows the response-index dependence or the link function to be unspecified. The B-spline method is used to approximate the coefficient function, which leads to a dimension folding type model. A new kernel regression method is developed to handle the dimension folding model, which allows the efficient estimation of both the index vector and the B-spline coefficients. We also establish the asymptotic properties and semiparametric optimality for the estimators.

Controversy remains over the role of risk factors in developing colorectal polyps. The aim of this study was to investigate the relationship of colorectal polyps with risk factors including obesity, age, alcohol, and smoking. We retrospectively assessed colorectal polyps through medical records and patient interviews of 1080 patients who underwent colonoscopy regardless of symptoms. The degree of obesity was determined by body mass index (BMI), and colorectal polyps were divided into hyperplastic and adenomatous polyps. The prevalence of colorectal polyps was 45.3% (489 patients). Of these, the prevalences of hyperplastic polyps and adenomatous polyps were 23.3% and 26.7%, respectively. The most common number, size, and location of colorectal polyps were one (63.8%), 5.0∼9.0 mm (50.1%), and rectosigmoid colon (35.7%), respectively. Age, amount of alcohol, and smoking were significantly higher in the group with polyps than in that without polyps (54.9±11.3 vs. 50.0±13.1 years, 75.8 vs. 39.3 g/week, and 9.3±12.6 vs. 4.6±9.1 pack years, respectively, p=0.001). There were no significant differences in BMI between the groups with (23.2 kg/m 2 ) or without (23.1 kg/m 2 ) polyps. Also, there was no significant relationship between BMI and the size, number, or location of colorectal polyps. Age, alcohol, and smoking were associated with colorectal polyps by one-way ANOVA test but not by multivariate logistic regression analysis. Additionally, BMI had no relationship with the size, number, or location of colorectal polyps. In conclusion, age, alcohol drinking, and smoking may be associated with colorectal polyps, but obesity as assessed by BMI was not.

Background and objectives: Pressure sores are a common medical burden among patients, particularly those who are bedridden or frail. Surgical management of occipital pressure sores poses unique challenges due to limited elasticity and the spherical shape of the scalp. This study aims to evaluate the efficacy and safety of a novel reconstruction method utilizing a local transpositional flap and split-thickness skin graft with negative pressure wound therapy (NPWT) for occipital pressure sore treatment. Material and methods: A retrospective analysis was performed on patients with occipital pressure sores who underwent hybrid reconstructions using a local flap and split-thickness skin graft in conjunction with NPWT. Surgical outcomes, including flap survival rate, graft take percentage, and complications, were assessed. A comparative analysis was performed between the NPWT group and the conventional dressing group. Results: The NPWT group (n = 24) demonstrated a significantly higher mean graft take percentage at postoperative day 14 compared with the conventional dressing group (n = 22) (98.2% vs. 81.2%, p < 0.05). No significant difference in flap survival rate was observed between the two groups. Conclusions: As the aging population continues to grow, occipital pressure sores have gained significant attention as a crucial medical condition. The innovative surgical method incorporating NPWT offers an efficient and safe treatment option for patients with occipital pressure sores, potentially establishing itself as the future gold standard for managing this condition.

High-dimensional gene expression data often exhibit intricate correlation patterns as the result of coordinated genetic regulation. In practice, however, it is difficult to directly measure these coordinated underlying activities. Analysis of breast cancer survival data with gene expressions motivates us to use a two-stage latent factor approach to estimate these unobserved coordinated biological processes. Compared to existing approaches, our proposed procedure has several unique characteristics. In the first stage, an important distinction is that our procedure incorporates prior biological knowledge about gene-pathway membership into the analysis and explicitly model the effects of genetic pathways on the latent factors. Second, to characterize the molecular heterogeneity of breast cancer, our approach provides estimates specific to each cancer subtype. Finally, our proposed framework incorporates sparsity condition due to the fact that genetic networks are often sparse. In the second stage, we investigate the relationship between latent factor activity levels and survival time with censoring using a general dimension reduction model in the survival analysis context. Combining the factor model and sufficient direction model provides an efficient way of analyzing high-dimensional data and reveals some interesting relations in the breast cancer gene expression data.