Bias caused by missing or incomplete information on confounding factors constitutes an important challenge in observational studies. The incorporation of external data on more detailed confounding information into the main study data may help remove confounding bias. This work was motivated by a study of the association between chronic obstructive pulmonary disease and herpes zoster. Analyses were based on administrative databases in which information on important confounders—cigarette smoking and alcohol consumption—was lacking. We consider adjusting for the confounding bias arising from missing confounders by incorporating a validation sample with data on smoking and alcohol consumption obtained from a small-scale National Health Interview Survey study. We propose a 2-stage calibration (TSC) method, which summarizes the confounding information through propensity scores and combines the analysis results from the main and the validation study samples, where the propensity score adjustment from the main sample is crude and that from the validation sample is more precise. Unlike the existing methods, the validity of the TSC approach does not rely on any specific measurement error model. When applying the TSC method to the motivating study above, the odds ratio of herpes zoster associated with chronic obstructive pulmonary disease is 1.91 (95% confidence interval: 1.62, 2.26) after adjustment for cumulative smoking and alcohol consumption.

Background and Purpose— The aim of this study is to estimate the risk of stroke in a 3-year period after pelvic inflammatory disease (PID) using a nationwide population-based study. Methods— Our study cohort consisted of all patients with a diagnosis of PID (N=64 515) between 2004 and 2005 with a control cohort (1:2) of age-matched controls (N=129 030). Each patient was tracked from hospitalization until the end of 2006. Cox regressions were performed to compute the 3-year stroke-free survival rates after adjusting for possible confounding factors. Results— We found that women with PID were more likely to have strokes than the control population. After adjusting for potential confounding factors, the adjusted hazard ratio of stroke was 1.63 (95% CI, 1.45–1.85) for PID patients as compared to the general population cohort. Sensitivity analysis using a bootstrap approach further ensured the validity of the results of our study. Conclusions— We concluded that patients with PID have an association with stroke. Further research is necessary to investigate the pathophysiology between PID and stroke.

Most of the existing findings on the association between diabetes mellitus and colorectal cancer were generated from studies in Western societies. However, significant differences in cancer incidence and cancer-prone lifestyles are apparent between Asian and Western countries. This study aims to estimate the risks of colorectal cancer in the diabetic population in Taiwan by conducting a large-scale, controlled cohort study. From Taiwan's Longitudinal Health Insurance Database 2005 (LHID2005), a total of 37 001 diabetic patients were identified. We also obtained data for four controls per patient, matched for sex, age and year of first entry into the LHID2005. All patients were followed up from the date of entry into the LHID2005 until they developed colorectal cancer or to the end of 2006, whichever was earlier. We used Cox's regression models to assess the risk of developing colorectal cancer, with adjustment for sex, age, comorbid disorders, and socioeconomic characteristics. We identified 37 001 diabetic patients and 148 004 controls. The adjusted hazard ratio for colorectal cancer in diabetes mellitus patients was 2.1 (95% confidence interval, 1.82–2.42) compared with controls. The risk was significant to both men and women. The adjusted hazard ratios for colorectal cancer were 2.03 (95% confidence interval, 1.68–2.47) in male diabetes mellitus patients and 2.17 (95% confidence interval, 1.77–2.67) in female diabetes mellitus patients. Our findings based on a large population-based cohort study provide evidence that diabetes mellitus may increase the risk of colorectal cancer in Asians.

We aim to clarify the relationship between low skeletal muscle mass and varying levels of adiposity and to identify the types of physical function impairments associated with sarcopenic obesity (SO). This study examined cross-sectional data from the National Health and Nutrition Examination Survey with whole-body dual-energy X-ray absorptiometry (DXA) scans. The data included age, gender, DXA-assessed body composition, and physical functional activity with performing daily tasks by questionnaire. We subdivided the data by body composition into a non-SO group and a SO group (ASMI 0–49.99% and FMI of 50–100%), after which the SO data were subdivided into three classes. A higher class indicated higher adiposity and lower muscle mass. The physical function impairment of the two groups was compared. Our study examined 7161 individuals, of which 4907 did not have SO and 2254 had SO, and their data were further divided into three classes (i.e., class I, 826 individuals; class II, 1300 individuals; and class III, 128 individuals). Significant differences in demographics and DXA parameters were identified between the non-SO and SO groups (P < 0.001); the individuals with SO were older, included more women, and exhibited high adiposity and less lean muscle mass. The individuals with class III SO exhibited greater differences and reported more difficulty in performing daily activities. The individuals with class III SO exhibited the most severe physical function impairment. Our study highlights the considerable difficulties encountered by individuals with SO in performing daily activities. Given this finding, customized rehabilitation strategies should be implemented to improve the quality of life of individuals with SO.

Prompt diagnosis of tuberculosis (TB) enables timely treatment, limiting spread and improving public health for this disease. Currently, a rapid, sensitive, accurate, and cost-effective detection of TB still remains a challenge. For this purpose, we engaged a transmission skill and an attenuated total reflectance (ATR) technique coupled with Fourier-transform infrared spectrometry (FTIR) to study the IR spectra of the plasma samples from TB patients (n = 10) and healthy individuals (n = 10). To ensure high-quality spectral data, spectra were collected in both transmission and ATR modes, with each measurement consisting of 256 scans at a resolution of 8 cm^-1. For the transmission mode, measurements were repeated five times per sample, while ATR-FTIR measurements were repeated three times per sample. These parameters were carefully optimized through rigorous testing to achieve the highest possible signal-to-noise ratio for patient sample analysis. Using this method, we obtained a total of 100 spectra from 20 samples in the transmission mode and 60 spectra in the ATR-FTIR mode, ensuring sufficient data for robust spectral analysis. Further, we applied machine learning techniques to analyze and classify the IR spectra; by this means, we differentiated those spectra between TB patients and healthy ones. In this work, we modified the transmission-FTIR setup to improve the absorption sensitivity by focusing the IR light on the interface of the sample; while, we used a high-refractive-index crystal ZnSe as a medium to reflect the signals in ATR scheme. Routinely, we compared the spectra obtained from both methods; in their second derivative curves, we notified that there had distinct spectral differences in protein and lipid regions (3500-3000, 2900-2800, and 1700-1500 cm^-1) between TB and healthy groups. Using three machine learning classifiers-Logistic Regression (LR), Random Forest (RF), and XGBoost (Xg)-we found that the Xg achieved an accuracy of 0.749, precision of 0.703, recall of 0.901, F1 score of 0.790, and an AUC of the ROC curve of 0.82 for absorption spectra in the 3500-2700 cm^-1 region; additionally, the machine learning practice showed that ATR data possessed performance parameters of ∼ 80% in accuracy. We randomly assigned participants (rather than individual scans) to 80% training and 20% test sets to train and validate three machine learning models (LR, RF, and Xg). Based on the results, we concluded that the absorption spectroscopic method demonstrated its superior performance in TB diagnosis. Thus, we have showed that absorption-FTIR spectroscopy is a valuable tool for sorting the TB disease from patients. The spectral IR analysis of plasmas can complement clinical evidence and provides a rapid and accurate diagnosis of TB in clinic.

A near-infrared paper-based analytical device (NIR-PAD) for glucose detection in whole blood was based on iridium(III) metal complexes embedded in a three-dimensional (3D) enzyme gel. These complexes emit NIR luminescence, can avoid interference from the color of blood, and increase the sensitivity of sensing glucose. The glucose reaction behaviors of another two different iridium(III) and platinum(II) complexes were also tested. When the glucose solution was added to the device, the oxidation of glucose by glucose oxidase caused oxygen consumption and increased the intensity of the phosphorescence emission. To the best of our knowledge, this is the first time that data have been treated with the programming language “R”, which uses Tukey’s test to identify the outliers in the data and calculate a median for establishing a calibration curve, in order to improve the accuracy of NIR-PADs for sensing glucose. Compared with other published devices, NIR-PADs exhibit a wider linear range (1–30 mM, [relative emission intensity] = 0.0250[glucose] + 0.0451, and R² = 0.9984), a low detection limit (0.7 mM), a short response time (<2 s), and a small sample volume (2 μL). Finally, blood specimens were obtained from 19 patients enrolled in Taipei Veterans General Hospital under an approved IRB protocol (Taiwan; 2017-12-002CC). The sensors exhibited remarkable characteristics for glucose detection in comparison with other methods, including the clinical method in hospitals as well as those without blood sample pretreatment or a dilution factor. The above results confirm that NIR-PAD sensors can be put to practical use for glucose detection.