MicroRNAs (miRNAs) are mostly located at cancer-associated genomic regions or in fragile sites, suggesting their important role in the pathogenesis of human cancers. Multiple myeloma (MM) is a cancer of plasma cells, the third most common cancer of the blood after lymphoma and leukaemia. There are several published reports on miRNAs in MM, however most used bone marrow rather than peripheral blood samples. The aim of this study is to characterise miRNA expression in normal and MM patients using peripheral blood samples as it is less invasive and is readily available from patients. Blood samples from 35 MM patients were analysed using the microarray method. We identified up-regulation of 36 miRNAs (57%) and down-regulation of 27 miRNAs (43%). We also identified the CCND2, HMGA2 and IGF1R genes were among the highly predictive target genes (P(CT) > 0.80) for most of the deregulated miRNAs. These genes are known to play important roles in MM as well as other cancers. Five miRNAs (let-7c, miR-16, miR- 449, miR-181a and miR-181b) were found to exhibit similar expression patterns (p < 0.05) in peripheral blood when compared to data obtained by using bone marrow aspirates from MM patients in other studies. In conclusion, our study has demonstrated that miRNAs are also present and differentially expressed in the peripheral blood of MM patients compared to controls and may potentially serve as candidate tumour biomarkers in MM. In particular, let-7c and miR-16 have been shown to be significantly expressed in the bone marrow.

Sequence and phylogenetic analyses of the N, P, M, F, G and L open-reading frames of a Nipah virus isolated from a Pteropus vampyrus illustrated the uniqueness of the genetic signature of this virus compared to all the other Malaysian isolates of Nipah virus from pigs, bat (Pteropus hypomelanus) and humans, as well as the Nipah virus isolated from Pteropus lylei in Cambodia, and that from human in Bangladesh. The Nipah virus of P. vampyrus is more closely related to the Nipah virus isolate from P. lylei, Cambodia than to Nipah virus human isolate of Bangladesh.

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women).CONCLUSIONS: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500
Adult ; Artificial Intelligence ; Cardiovascular Diseases ; Cohort Studies ; Female ; Follow-Up Studies ; Humans ; Insurance, Health ; Learning ; Male ; Mass Screening ; National Health Programs

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). CONCLUSIONS A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500

Objective: To investigate the effects of docosahexenoic acid (DHA) supplementation on infant's growth and BMI during pregnancy. Methods: A total of 1 516 healthy pregnant women delivered their babies in two maternal and child health care hospitals in Beijing and were chosen as the subjects in this cohort study from May to October 2015. Self-developed questionnaires were used to gather general information of the subjects, including age, height, weight, weight gain during pregnancy, delivery mode, DHA supplementation etc., before giving birth. Information on body length, weight, head circumference and BMI at birth and 6 months postnatal, of the infants were recorded. Breast milk was collected to test the fatty acid profiles by using the gas chromatography (GC) method at one to three months postnatally. Results: The overall rate of DHA supplementation was 47.76% among the pregnant women, in which introduction of DHA from the early and second stage of the pregnancy accounted for 49.31% and 39.64% respectively. When DHA supplementation began from the early pregnant stage, the DHA concentration showed an increase in the milk (P<0.05), whereas the supplementation began from the second and third stages did not affect the milk DHA concentration (P>0.05). Higher height and lower BMI were seen in the infants at birth and 6 months in the supplementation group when comparing to the non-supplementary group (P<0.05), with the greatest effects noticed in the earliest supplementation group. Specifically, the head circumference appeared larger from the early pregnant stage in the DHA supplementary group, than that in the non-supplement group (P=0.001). The increment of head circumference was larger than that in the other groups when the infants were 6-month old (P<0.01). Results from the partial regression analysis showed that during pregnancy, there were positive correlations between DHA supplementation and height (r=0.324, r=0.216), head circumference (r=0.221, r=0.302) as well as the increment of head circumference (r=0.276) at birth and 6 months (P<0.05). Whereas, a negative correlation was shown between DHA and the infants' BMI (r=-0.310, r=-0.371) (P<0.05) when supplementation was given during maternal pregnancy. Conclusions: When DHA supplementation program was carried out during maternal pregnancy, it could increase the height and head circumference and inhibit the rapid increase of BMI in the infants BMI. Our findings seemed helpful in promoting brain development and preventing the childhood obesity.
Body Height ; Body Mass Index ; Body Weight ; Child Development/drug effects* ; Cohort Studies ; Dietary Supplements ; Docosahexaenoic Acids/pharmacology* ; Female ; Humans ; Infant ; Infant, Newborn/physiology* ; Maternal-Fetal Exchange ; Parturition ; Pregnancy ; Pregnancy Outcome ; Prenatal Care ; Weight Gain

Objective: To investigate the association between alcohol consumption and lung cancer risk in Chinese males. Methods: Information on alcohol consumption and outcomes were collected on a biennial basis among males in Kailuan Cohort (2006-2015). In addition, electronic databases of hospitals affiliated to Kailuan Community, Insurance Systems of Kailuan Community and Tangshan were also used for supplementary information retrieval. Cox proportional hazards regression models were used to evaluate the hazard ratio (HR) and 95%CI of baseline frequency and type of alcohol consumption associated with lung cancer risk in males. Non-drinkers were used as control group. Results: A total of 101 751 males were included and 913 new lung cancer cases were identified in the Kailuan male cohort study, with a total follow-up time of 808 146.56 person-years and a median follow-up time of 8.88 years by 31 December 2015. After adjusting for potential confounding factors, the HR of former drinkers, occasional drinkers (<1/day) and drinkers (≥1/day) were 1.30 (95%CI: 0.90-1.88), 0.80 (95%CI: 0.64-1.01) and 1.04 (95%CI: 0.85-1.27), respectively, compared with non-drinkers. In addition, drinking beer/red wine (HR=0.91, 95%CI: 0.69-1.20) and white wine (HR=0.99, 95%CI: 0.83-1.19) showed no significant association with lung cancer. The results were similar when stratified analysis were conducted. Conclusion: Our study results don't support the hypothesis that alcohol consumption is significantly associated with the risk of lung cancer in males.
Adult ; Alcohol Drinking/epidemiology* ; China/epidemiology* ; Cohort Studies ; Humans ; Lung Neoplasms/epidemiology* ; Male ; Middle Aged ; Proportional Hazards Models ; Prospective Studies ; Risk Factors