Dairy product consumption and lung cancer risk: A prospective analysis.

BACKGROUND & AIM: Current evidence on prospective associations between dairy product, dairy fat and lactose intakes and lung cancer risk is limited and inconsistent. We conducted a prospective analysis of associations of lung cancer risk with dairy product intakes in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort. METHODS: Pre-diagnostic dairy product intake was assessed through a validated Diet History Questionnaire. All incident lung cancer cases were pathologically verified. Multivariable Cox regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for associations of lung cancer risk with intakes of total, full-fat, low-fat dairy, fermented or non-fermented dairy products; milk fat content preference; and intakes of total and saturated fats and lactose from dairy products. RESULTS: Among 101,709 adults (mean age of 65.5 years), a total of 1583 lung cancer cases were identified during 1,167,239 person-years of follow up. Mean total dairy product intake was 156 g/1000 kilocalories (kcal), including 20 g/1000 kcal from fermented dairy products. Total dairy intake was not associated with lung cancer risk (HR [95% CI] = 1.03 [0.89-1.18]) comparing the highest quartile with the lowest. Fermented dairy intake was inversely associated with lung cancer risk (0.85 [0.72-0.99]). In contrast, there were no statistically significant associations with low-fat, full-fat or non-fermented dairy product intakes. The preference of whole milk when consuming milk as beverage was associated with a higher risk of lung cancer than the preference of <0.5% fat milk (1.24 [1.03-1.49]). Total fat, saturated fat and lactose intakes from dairy products each were not associated with lung cancer risk. CONCLUSIONS: Our results suggest an inverse association of lung cancer risk with fermented dairy intake and a positive association with the whole milk preference in a US population. Future studies exploring underlying molecular mechanisms are warranted.

Hypothesis Testing for Progressive Kernel Estimation and VCM Framework.

Identifying an appropriate radius for unbiased kernel estimation is crucial for the efficiency of radiance estimation. However, determining both the radius and unbiasedness still faces big challenges. In this paper, we first propose a statistical model of photon samples and associated contributions for progressive kernel estimation, under which the kernel estimation is unbiased if the null hypothesis of this statistical model stands. Then, we present a method to decide whether to reject the null hypothesis about the statistical population (i.e., photon samples) by the F-test in the Analysis of Variance. Hereby, we implement a progressive photon mapping (PPM) algorithm, wherein the kernel radius is determined by this hypothesis test for unbiased radiance estimation. Secondly, we propose VCM+, a reinforcement of Vertex Connection and Merging (VCM), and derive its theoretically unbiased formulation. VCM+ combines hypothesis testing-based PPM with bidirectional path tracing (BDPT) via multiple importance sampling (MIS), wherein our kernel radius can leverage the contributions from PPM and BDPT. We test our new algorithms, improved PPM and VCM+, on diverse scenarios with different lighting settings. The experimental results demonstrate that our method can alleviate light leaks and visual blur artifacts of prior radiance estimate algorithms. We also evaluate the asymptotic performance of our approach and observe an overall improvement over the baseline in all testing scenarios.

Landscape of Alternative Splicing Events Related to Prognosis and Immune Infiltration in Glioma: A Data Analysis and Basic Verification.

Background: Glioma is a prevalent primary brain cancer with high invasiveness and typical local diffuse infiltration. Alternative splicing (AS), as a pervasive transcriptional regulatory mechanism, amplifies the coding capacity of the genome and promotes the progression of malignancies. This study was aimed at identifying AS events and novel biomarkers associated with survival for glioma. Methods: RNA splicing patterns were collected from The Cancer Genome Atlas SpliceSeq database, followed by calculating the percentage of splicing index. Expression profiles and related clinical information of glioma were integrated based on the UCSC Xena database. The AS events in glioma were further analyzed, and glioma prognosis-related splicing factors were identified with the use of bioinformatics analysis and laboratory techniques. Further immune infiltration analysis was performed. Results: Altogether, 9028 AS events were discovered. Upon univariate Cox analysis, 425 AS events were found to be related to the survival of patients with glioma, and 42 AS events were further screened to construct the final prognostic model (area under the curve = 0.974). Additionally, decreased expression of the splicing factors including Neuro-Oncological Ventral Antigen 1 (NOVA1), heterogeneous nuclear ribonucleoprotein C (HNRNPC), heterogeneous nuclear ribonucleoprotein L-like protein (HNRNPLL), and RNA-Binding Motif Protein 4 (RBM4) contributed to the poor survival in glioma. The immune infiltration analysis demonstrated that AS events were related to the proportion of immune cells infiltrating in glioma. Conclusions: It is of great value for comprehensive consideration of AS events, splicing networks, and related molecular subtype clusters in revealing the underlying mechanism and immune microenvironment remodeling for glioma, which provides clues for the further verification of related therapeutic targets.

Coffee Consumption and C-Reactive Protein Levels: A Systematic Review and Meta-Analysis.

Coffee contains bioactive compounds with anti-inflammatory properties, and its consumption may reduce c-reactive protein (CRP) levels, a biomarker of chronic inflammation. A previous meta-analysis reported no overall association between blood CRP level and coffee consumption by modeling the coffee consumption in categories, with substantial heterogeneity. However, the coffee cup volume was not considered. We conducted a systematic review and dose-response meta-analysis investigating the association between coffee consumption and CRP levels reported in previous observational studies. A dose-response meta-analysis was conducted by mixed-effects meta-regression models using the volume of coffee consumed as metric. Eleven studies from three continents were identified using the PubMed database, totaling 61,047 participants. Three studies with the largest sample sizes observed a statistically significant association between coffee and CRP levels, which was inverse among European and United States (US) women and Japanese men (1.3%-5.5% decrease in CRP per 100 mL of coffee consumed) and positive among European men (2.2% increase). Other studies showed no statistically significant associations. When all studies were combined in the dose-response meta-analysis, no statistically significant associations were observed among all participants or when stratified by gender or geographic location, reflecting the conflicting associations reported in the included studies. Further studies are warranted to explore these inconsistent associations.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Clinical and genetic mapping of X chromosome in the X-linked dominant inherited Alport's syndrome.

To study the hereditary mode and clinical characteristics and detect mutations of gene COL4A5 encoding type IV collagen a5 chain among family members of an X-linked dominant inherited Alport's syndrome (AS) family of China, we studied all of 38 family members of whom 2 volunteers underwent renal biopsy. Genomic DNA from all members of the AS family was characterized. All of 51 exons of COL4A5 gene were amplified by polymerase chain reaction (PCR) with the primers synthesized according to the published flanking intervening sequences. PCR products were further analyzed by agarose gel electrophoresis and single strand conformation polymorphism (SSCP) analysis. The study subjects revealing polymorphism by SSCP analysis were directly sequenced. Suspected exons were analyzed with reverse sequencing. Six males and 9 females of the family were diagnosed to have AS by clinical manifestations, family history and/or renal biopsy. Four patients died of end-stage renal disease (ESRD), and 1 patient received kidney transplantation. In the rest of the family members renal function remained normal, however, 22 (58%) revealed hematuria, 11/22 (59%) of them also had proteinuria. The hearing loss was detected in 6 (16%) and ocular lesion in 20 (53%) of family members. By PCR-SSCP analysis, 17 PCR products were identified with different mobility of single strand DNA in volunteers and 9 suspected mutations were revealed with DNA sequencing analysis, but all of which could not be proven by bidirectional sequencing analysis. We conclude that the incidence of hematuria and ophthalmopathy is higher in the X-linked dominant inherited AS in this Chinese family, while some patients have isolated hematuria. Bidirectional sequence analysis should be taken to identify mutations of certain genes. No mutations were found on the region of exons of gene COL4A5.