An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.

BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Development and validation of a patient-level model to predict dementia across a network of observational databases.

BACKGROUND: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. METHODS: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. RESULTS: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. CONCLUSIONS: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning.

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Balancing the benefits and risks of China's national salt iodization policy over 30 years using disability-adjusted life years (DALYs): a systematic review and meta-analysis.

Both insufficient and excessive iodine intake can lead to thyroid-related disorders. Although China has made progress in eliminating iodine deficiency over the past few decades, the incidence of thyroid cancer is increasing. Currently, there is a lack of relevant research on the tradeoff between the benefits and risks of salt iodization in China. In this study, we developed a method that combines the total probability algorithm and disease burden to evaluate the appropriate amount of salt iodization. Following the principle of minimizing the comprehensive disease burden and using the metabolic model of human iodine nutrition. Based on the average national iodine level in water, the optimal iodine content in Chinese salt is determined to be 17 mg/kg. However, iodine content in water is not evenly distributed in China. Approximately 3.23% of administrative villages have water iodine concentrations exceeding 80 ug/L, eliminating the need for iodine fortification in salt. Approximately 83.51% of administrative villages need to continue implementing the salt iodization policy, with the optimal iodine content in salt ranging from 15 to 18 mg/kg. In 13.16% of administrative villages, the iodine content in salt is determined based on the local water iodine concentration, ranging from 0 to 15 mg/kg. Our study cracks open a window of insight suggesting that the optimal iodine content for salt is lower than the existing benchmark dictated by the prevailing policy in China. Hence, there is an urgent need to refine and advance the iodine supplementation strategy in salt to pave the way for precision medicine and health-centric iodine supplementation strategies.

Establishment and validation of a prediction model for gestational diabetes.

AIM: To develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application. METHODS: To establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic-lifestyle factor model (DLFM) and a demographic-lifestyle-environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso-logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence. RESULTS: The DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05-4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89-9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27-2.29), a history of hypertension (OR 2.61; 95% CI 1.41-4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08-1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45-2.67) and Q4 mono-ethyl phthalate concentration (OR 1.85; 95% CI 1.26-2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose. CONCLUSIONS: A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Predicting the multivariate zero-inflated counts: A novel model averaging method under Pearson loss.

Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero-inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression model by simultaneously taking heterogeneity and correlation into consideration. Furthermore, a new model averaging prediction method is introduced based on the multiple Pearson residual, and the asymptotical optimality of this model averaging prediction is proved. Simulations and two empirical applications in medicine are used to illustrate the effectiveness of the proposed method.

Methods in regression analysis in surgical oncology research-best practice guidelines.

BACKGROUND: Using real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical oncology research. METHODS: To demonstrate our best practices, we reviewed patients who underwent tissue expander breast reconstruction between 2019 and 2021. We assessed predictive factors that affect BREAST-Q Physical Well-Being of the Chest (PWB-C) scores at 2 weeks with linear regression modeling and overall complications and malrotation with logistic regression modeling. Model fit and performance were assessed. RESULTS: The 1986 patients were included in the analysis. In linear regression, age [ß = 0.18 (95% CI: 0.09, 0.28); p < 0.001], single marital status [ß = 2.6 (0.31, 5.0); p = 0.026], and prepectoral pocket dissection [ß = 4.6 (2.7, 6.5); p < 0.001] were significantly associated with PWB-C at 2 weeks. For logistic regression, BMI [OR = 1.06 (95% CI: 1.04, 1.08); p < 0.001], age [OR = 1.02 (1.01, 1.03); p = 0.002], bilateral reconstruction [OR = 1.39 (1.09, 1.79); p = 0.009], and prepectoral dissection [OR = 1.53 (1.21, 1.94); p < 0.001] were associated with increased likelihood of a complication. CONCLUSION: We provide focused directives for successful application of regression techniques in surgical oncology research. We encourage researchers to select variables with clinical judgment, confirm appropriate model fitting, and consider clinical plausibility for interpretation when utilizing regression models in their research.

How to make calibration less painful-A proposition for an automatic, reliable and time-efficient procedure.

In behavioral and neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli. Often, studies lack a detailed calibration procedure description, data linearity, and quality quantification and omit required control for sex pain differences. This hampers study repetition and interexperimental comparisons. Moreover, typical calibration procedures require a high number of stimulations, which may cause discomfort and stimuli habituation among participants. To overcome those shortcomings, we present an automatic calibration procedure with a novel stimuli estimation method for intraepidermal stimulation. We provide an in-depth data analysis of the collected self-reports from 70 healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t) and pain (T) thresholds and mid-pain stimulation (MP), with those calculated using traditional estimation methods and standard linear regression models. Compared to the other methods, tLRM exhibits higher R2 and requires 36% fewer stimuli applications and has significantly higher t intensity and lower T and MP intensities. Regarding sex differences, t and T were found to be lower for females compared to males, regardless of the estimation method. The proposed tLRM method quantifies the calibration procedure quality, minimizes its duration and invasiveness, and provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for sex in pain studies.

Spatial-temporal distribution of preterm birth in China, 1990-2020: A systematic review and modelling analysis.

BACKGROUND: Little is known about the long-term trends of preterm birth rates in China and their geographic variation by province. OBJECTIVES: To estimate the annual spatial-temporal distribution of preterm birth rates in China by province from 1990 to 2020. DATA SOURCES: We searched PubMed, EMBASE, Web of Science, CNKI, WANFANG and VIP from January 1990 to September 2023. STUDY SELECTION AND DATA EXTRACTION: Studies that provided data on preterm births in China after 1990 were included. Data were extracted following the Guidelines for Accurate and Transparent Health Estimates Reporting. SYNTHESIS: We assessed the quality of each survey using a 9-point checklist. We estimated the annual preterm birth risk by province using Bayesian multilevel logistic regression models considering potential socioeconomic, environmental, and sanitary predictors. RESULTS: Based on 634 survey data from 343 included studies, we found a gradual increase in the preterm birth risk in most provinces in China since 1990, with an average annual increase of 0.7% nationally. However, the preterm birth rates in Inner Mongolia, Hubei, and Fujian Province showed a decline, while those in Sichuan were quite stable since 1990. In 2020, the estimates of preterm birth rates ranged from 2.9% (95% Bayesian credible interval [BCI] 2.1, 3.8) in Inner Mongolia to 8.5% (95% BCI 6.6, 10.9) in Jiangxi, with the national estimate of 5.9% (95% BCI 4.3, 8.1). Specifically, some provinces were identified as high-risk provinces for either consistently high preterm birth rates (e.g. Jiangxi) or relatively large increases (e.g. Shanxi) since 1990. CONCLUSIONS: This study provides annual information on the preterm birth risk in China since 1990 and identifies high-risk provinces to assist in targeted control and intervention for this health issue.

Factors affecting the incidence of pulmonary tuberculosis based on the GTWR model in China, 2004-2021.

Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004-2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.

Nasopharyngeal angiotensin converting enzyme 2 (ACE2) expression as a risk-factor for SARS-CoV-2 transmission in concurrent hospital associated outbreaks.

BACKGROUND: Widespread human-to-human transmission of the severe acute respiratory syndrome coronavirus two (SARS-CoV-2) stems from a strong affinity for the cellular receptor angiotensin converting enzyme two (ACE2). We investigate the relationship between a patient's nasopharyngeal ACE2 transcription and secondary transmission within a series of concurrent hospital associated SARS-CoV-2 outbreaks in British Columbia, Canada. METHODS: Epidemiological case data from the outbreak investigations was merged with public health laboratory records and viral lineage calls, from whole genome sequencing, to reconstruct the concurrent outbreaks using infection tracing transmission network analysis. ACE2 transcription and RNA viral load were measured by quantitative real-time polymerase chain reaction. The transmission network was resolved to calculate the number of potential secondary cases. Bivariate and multivariable analyses using Poisson and Negative Binomial regression models was performed to estimate the association between ACE2 transcription the number of SARS-CoV-2 secondary cases. RESULTS: The infection tracing transmission network provided n = 76 potential transmission events across n = 103 cases. Bivariate comparisons found that on average ACE2 transcription did not differ between patients and healthcare workers (P = 0.86). High ACE2 transcription was observed in 98.6% of transmission events, either the primary or secondary case had above average ACE2. Multivariable analysis found that the association between ACE2 transcription (log2 fold-change) and the number of secondary transmission events differs between patients and healthcare workers. In health care workers Negative Binomial regression estimated that a one-unit change in ACE2 transcription decreases the number of secondary cases (ß = -0.132 (95%CI: -0.255 to -0.0181) adjusting for RNA viral load. Conversely, in patients a one-unit change in ACE2 transcription increases the number of secondary cases (ß = 0.187 (95% CI: 0.0101 to 0.370) adjusting for RNA viral load. Sensitivity analysis found no significant relationship between ACE2 and secondary transmission in health care workers and confirmed the positive association among patients. CONCLUSION: Our study suggests that ACE2 transcription has a positive association with SARS-CoV-2 secondary transmission in admitted inpatients, but not health care workers in concurrent hospital associated outbreaks, and it should be further investigated as a risk-factor for viral transmission.

Temporal trend analysis of acute hepatitis B virus infection in China, 1990-2019.

China faces challenges in meeting the World Health Organization (WHO)'s target of reducing hepatitis B virus (HBV) infections by 95% using 2015 as the baseline. Using Global Burden of Disease (GBD) 2019 data, joinpoint regression models were used to analyse the temporal trends in the crude incidence rates (CIRs) and age-standardized incidence rates (ASIRs) of acute HBV (AHBV) infections in China from 1990 to 2019. The age-period-cohort model was used to estimate the effects of age, period, and birth cohort on AHBV infection risk, while the Bayesian age-period-cohort (BAPC) model was applied to predict the annual number and ASIRs of AHBV infections in China through 2030. The joinpoint regression model revealed that CIRs and ASIRs decreased from 1990 to 2019, with a faster decline occurring among males and females younger than 20 years. According to the age-period-cohort model, age effects showed a steep increase followed by a gradual decline, whereas period effects showed a linear decline, and cohort effects showed a gradual rise followed by a rapid decline. The number of cases of AHBV infections in China was predicted to decline until 2030, but it is unlikely to meet the WHO's target. These findings provide scientific support and guidance for hepatitis B prevention and control.

Incidence and predictors of ventilator-associated pneumonia using a competing risk analysis: a single-center prospective cohort study in Egypt.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a challenging nosocomial problem in low- and middle-income countries (LMICs) that face barriers to healthcare delivery and resource availability. This study aimed to examine the incidence and predictors of VAP in Egypt as an example of an LMIC while considering death as a competing event. METHODS: The study included patients aged ≥ 18 years who underwent mechanical ventilation (MV) in an intensive care unit (ICU) at a tertiary care, university hospital in Egypt between May 2020 and January 2023. We excluded patients who died or were transferred from the ICU within 48 h of admission. We determined the VAP incidence based on clinical suspicion, radiological findings, and positive lower respiratory tract microbiological cultures. The multivariate Fine-Gray subdistribution hazard model was used to examine the predictors of VAP while considering death as a competing event. RESULTS: Overall, 315 patients were included in this analysis. Sixty-two patients (19.7%) developed VAP (17.1 per 1000 ventilator days). The Fine-Gray subdistribution hazard model, after adjustment for potential confounders, revealed that emergency surgery (subdistribution hazard ratio [SHR]: 2.11, 95% confidence interval [CI]: 1.25-3.56), reintubation (SHR: 3.74, 95% CI: 2.23-6.28), blood transfusion (SHR: 2.23, 95% CI: 1.32-3.75), and increased duration of MV (SHR: 1.04, 95% CI: 1.03-1.06) were independent risk factors for VAP development. However, the new use of corticosteroids was not associated with VAP development (SHR: 0.94, 95% CI: 0.56-1.57). Klebsiella pneumoniae was the most common causative microorganism, followed by Pseudomonas aeruginosa. CONCLUSION: The incidence of VAP in Egypt was high, even in the ICU at a university hospital. Emergency surgery, reintubation, blood transfusion, and increased duration of MV were independently associated with VAP. Robust antimicrobial stewardship and infection control strategies are urgently needed in Egypt.

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 µg/m3) and RMSE (5.36 µg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.

Age-dependent development trends (models) of intestinal significant microbiota species and Eimeria oocysts in coccidia-challenged broiler chickens as affected by dietary encapsulated organic acids and anticoccidial drugs.

ABSTRACTThe study was conducted to investigate the effect of dietary encapsulated organic acids (EOAs) and anticoccidials on the age-dependent development trend of intestinal Lactobacillus, E. coli, coliforms, and Eimeria in Eimeria spp.-infected broiler chickens from reused litter. In total, 525 mixed-sex 1-day-old broiler chickens were used in an uninfected/un-supplemented control plus a 2 (no EOA or 0.1% EOA) × 3 (no anticoccidial, 0.05% maduramicin, and 0.02% diclazuril) factorial arrangement of treatments as a completely randomized design with five replicates of 15 chickens. Results indicated that the cubic model is the best model for explaining the development trends of the intestinal microbial population in uninfected and infected chickens (affected by the EOAs and anticoccidials). Based on the cubic models, the microbial populations had development trends with a decreasing slope from 1-day-old until the early or middle finisher period. EOAs and anticoccidials, especially their simultaneous usage, improved (P < 0.05) the linear and cubic models' slope (affected negatively by Eimeria infection). A polynomial model (order = 6) was determined as the best model for explaining the EOAs and anticoccidial effects on the trend of intestinal Eimeria oocysts in infected chickens. The infection peak (which happened at 25 days) was reduced by EOAs and anticoccidials, especially their simultaneous usage. In conclusion, cubic and polynomial (order = 6) regressions are the best models fitted for explaining the microbiota and Eimeria oocysts trends, respectively. EOAs and anticoccidials, especially their simultaneous usage, had beneficial effects on the microbiota and Eimeria development trends and gastrointestinal health in coccidia-infected broiler chickens. RESEARCH HIGHLIGHTSCubic regression is the best model for explaining intestinal microbiota development.Polynomial regression is the best model for intestinal Eimeria oocysts development.Age-development trends are affected by dietary encapsulated organic acids and anticoccidials.

Acarbose might be associated with reduced risk of gastric cancer in patients with diabetes mellitus: A nationwide population-based cohort study.

BACKGROUND: Several epidemiologic studies have revealed a higher risk of cancer in patients with diabetes mellitus (DM) relative to the general population. To investigate whether the use of acarbose was associated with higher/lower risk of new-onset cancers. METHOD: We conducted a retrospective cohort study, using a population-based National Health Insurance Research Database of Taiwan. Both inpatients and outpatients with newly onset DM diagnosed between 2000 and 2012 were collected. The Adapted Diabetes Complications Severity Index (aDCSI) was used to adjust the severity of DM. The Cox proportional hazards regression model was used to estimate the hazard ratio (HR) of disease. RESULTS: A total of 22 502 patients with newly diagnosed DM were enrolled. The Cox proportional hazards regression model indicating acarbose was neutral for risk for gastroenterological malignancies, when compared to the acarbose non-acarbose users group. However, when gastric cancer was focused, acarbose-user group had significantly lowered HR than non-acarbose users group (p = 0.003). After adjusted for age, sex, cancer-related comorbidity, severity of DM, and co-administered drugs, the HR of gastric cancer risk was 0.43 (95% CI = 0.25-0.74) for acarbose-user patients. CONCLUSION: This long-term population-based study demonstrated that acarbose might be associated with lowered risk of new-onset gastric cancer in diabetic patients after adjusting the severity of DM.

Face recognition ability can be predicted by microstructural properties of white matter: a study of diffusion tensor imaging (DTI).

There is a great individual difference in people's face recognition ability (FRA). This study aimed to reveal the neural mechanism underlying such individual differences. Elastic-net regression models were constructed to predict FRA based on the white matter (WM) microstructural properties. We found that FRA can be accurately predicted by the WM microstructural properties. For the right inferior longitudinal fasciculus (ILF) and bilateral arcuate fasciculus (AF), FRA was correlated negatively to fractional anisotropy (FA), but positively to radial diffusivity (RD). In contrast, for the corpus callosum forceps minor (CFM), FRA was correlated positively to FA, but negatively to RD. Such various patterns of the WM microstructural properties suggested a positive correlation between FRA and fiber diameter for the right ILF and bilateral AF, but a negative correlation between FRA and diameter of the CFM. These findings reflected that FRA was correlated positively to connectivities of the right ILF and bilateral AF, but negatively to those of the CFM. These findings not only confirmed the significant role of the right ILF in face recognition, but also revealed the involvement of the bilateral AF and CFM in face recognition, particularly implying the important role of hemisphere lateralization modulated by transcallosal connectivity in face recognition.