Common genetic variants in ADCY5 and gestational glycemic traits.

Two meta-analysis of genome wide association studies identified two variants at adenylate cyclase 5 (ADCY5) associated with type 2 diabetes mellitus, fasting and 2-hour glucose in non-pregnant individuals of European descent. The objective of our study was to explore the role of common variants in ADCY5 on gestational glycemic traits, including plasma glucose, insulin values, β cell function and insulin resistance in the fasted state as well as plasma glucose 1 hour after a 50-gram glucose challenge test among Chinese Han women. Homoeostasis model assessment (HOMA) was used to quantify β cell function (HOMA1-β and HOMA2-β) and insulin resistance (HOMA1-IR and HOMA2-IR). Thirty-five single nucleotide polymorphisms (SNPs) in ADCY5 were genotyped in 929 unrelated Chinese Han women with singleton pregnancies. Three SNPs (rs6797915, rs9856662 and rs9875803) displayed evidence for association with plasma glucose 1 hour after a 50-gram glucose challenge test (P = 0.042, 0.018 and 0.018, respectively), one (rs6777397) displayed evidence for association with HOMA1-β (P = 0.014), and one (rs6762009) displayed evidence for association with HOMA1-IR (P = 0.033). These results provide additional insight into the effects of genetic variation within ADCY5 in glucose metabolism, especially during pregnancy and in non-European descent populations

Heat transfer performance of a device integrating thermosyphon with form-stable phase change materials

peer reviewedPhase change materials (PCM) are applied worldwide as a thermal energy storage technology to reduce energy demands in buildings and solve environmental contamination issues. Form-stable phase change materials (FSPCM), as one branch of PCMs, can be improved by embedding them with thermosyphon, resulting in a better thermal performance. In this paper, a novel thermoplastic elastomer-based FSPCM was developed and tested. A device integrating FSPCM with thermosyphon was created, and the heat transfer mechanism of the unit was studied. The numerical model was established, and experiments were conducted accordingly. The average relative error between the experimental data and the model predictions was <3 %. Furthermore, a parameter study was conducted to investigate the effects of several essential factors. As a result, the evaporator length and PCM thermal conductivity were found to significantly influence the unit's heat transfer rate and overall thermal performance. However, the impact of the latent heat of the FSPCM on the heat transfer rate was negligible, for which it takes 1170 s, 1230 s, and 1394 s to finish changing phase with 136 kJ/kg, 160 kJ/kg, and 200 kJ/kg, respectively. This paper provides insights on the performance of thermosyphon integrated form-stable phase change materials and discusses its relevance for thermal energy storage applications.11. Sustainable cities and communitie

Novel SLCO2A1 mutations cause gender-differentiated pachydermoperiostosis

Primary hypertrophic osteoarthropathy (PHO) is a rare familial disorder with reduced penetrance for females. The genetic mutations associated with PHO have been identified in HPGD and SLCO2A1, which involved in prostaglandin E2 metabolism. Here, we report 5 PHO patients from four non-consanguineous families. Two heterozygous mutations in solute carrier organic anion transporter family member 2A1 (SLCO2A1) were identified in two brothers by whole-exome sequencing. Three heterozygous mutations and one homozygous mutation were identified in other three PHO families by Sanger sequencing. However, there was no mutation in HPGD. These findings confirmed that homozygous or compound heterozygous mutations of SLCO2A1 were the pathogenic cause of PHO. A female individual shared the same mutations in SLCO2A1 with her PHO brother but did not have any typical PHO symptoms. The influence of sex hormones on the pathogenesis of PHO and its implication were discussed

A review on methods for diagnosis of breast cancer cells and tissues.

This research article published by John Wiley & Sons Ltd., 2020Breast cancer has seriously been threatening physical and mental health of women in the world, and its morbidity and mortality also show clearly upward trend in China over time. Through inquiry, we find that survival rate of patients with early-stage breast cancer is significantly higher than those with middle- and late-stage breast cancer, hence, it is essential to conduct research to quickly diagnose breast cancer. Until now, many methods for diagnosing breast cancer have been developed, mainly based on imaging and molecular biotechnology examination. These methods have great contributions in screening and confirmation of breast cancer. In this review article, we introduce and elaborate the advances of these methods, and then conclude some gold standard diagnostic methods for certain breast cancer patients. We lastly discuss how to choose the most suitable diagnostic methods for breast cancer patients. In general, this article not only summarizes application and development of these diagnostic methods, but also provides the guidance for researchers who work on diagnosis of breast cancer

A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging

ObjectiveTo develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions.MethodsOf all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric.ResultsThe proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results.ConclusionThe proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis

Application of machine learning for risky sexual behavior interventions among factory workers in China

IntroductionAssessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models.MethodsThe survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12.ResultsAbout a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%).DiscussionMachine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services

Prioritising actions for improving classroom air quality based on the analytic hierarchy process: case studies in China and the UK

The air quality in classrooms significantly impacts school children’s health and learning performance. It has been reported worldwide that classroom air quality does not meet the required standard and actions are pledged for improvement. However, it poses a challenge for decision-making in terms of prioritising taking-up measures. The aim of this study is to propose a method of identifying the action measures for improving classroom air quality and prioritising them. Case studies in the UK and China were conducted, and the key measures were identified through literature studies, open-ended questionnaire surveys, and workshop discussions, which are classified into three categories: B1, policy; B2, technology; and B3, information sharing. The analytical hierarchy process (AHP) is applied in the prioritisation of the action measures. A total of 138 teachers and parents from China and the UK participated in this case study. The genetic algorithm-optimised Hadamard product (GAOHP) method is applied to justify the consistency ratio (CR) within the required threshold value in order to ensure the consistency of the subjective perception and the accuracy of comparative weights. The results show that item B2, technology, is the most desired measure by both Chinese and British parents and teachers, despite the deviation from the optimal choice in China and the UK. Among the proposed action measures, the UK respondents strongly expected air purifiers with natural ventilation as opposed to their Chinese counterparts preferring to share the real-time status of classroom air quality. Our work will provide strong support for the subsequent selection of indoor air quality improvement strategies for schools