A systematic review of the social impact of diseases in Nordic countries.

BACKGROUND: We review the literature on the social impacts of diseases, defined as the social consequences of having a disease on the people around the patient, such as spouses, caregivers and offspring. The two objectives of this study are to summarise the social outcomes commonly associated with diseases and to compare the social impact across a range of diseases. METHODS: A systematic review of the social impact of disease in Nordic countries was conducted using PubMed, PsycINFO and Google Scholar (PROSPERO registration number CRD42022291796). All articles that met the inclusion criteria were reviewed. We tabulated all outcomes and diseases studied, and synthesised the evidence based on the perspectives of patients, spouse/caregiver and offspring. RESULTS: A total of 135 studies met the eligibility criteria, covering 76 diseases and 39 outcomes. From the patient's perspective, diseases impact divorce and marriage rates, social functioning, likelihood of committing a crime and being a victim of crime. From the caregiver's perspective, diseases affect their health-related quality of life and physical and psychological health. From the offspring's perspective, diseases impact their development, health and social adversities in later life. Diseases generally had negative social impacts, but there were some diseases associated with positive impacts. CONCLUSIONS: The review provides a useful summary and gross comparison of the social impact of different diseases. The social impact of diseases can be large and significant. Thus, it should be considered when policymakers are setting priorities across disease areas.

Comprehensive assessment of the estrogenic activity of resin composites.

Resin-based dental composites have been developed to restore decayed teeth or modify tooth color due to their excellent physical and chemical properties. Such composites may have intrinsic toxicity due to components released into the mouth during the early stage of polymerization, and afterward as a result of erosion or material decomposition. In addition, resin-based dental composites have potential environmental pollutant by elution of monomers and degradation. Since certain monomers of resin matrices are synthesized from bisphenol A (BPA), which acts as an estrogenic endocrine disruptor, these resin matrices may have estrogenic activity. Therefore, the estrogenic endocrine-disrupting activity of various dental composites should be evaluated. In this study, we evaluated the estrogenic endocrine-disrupting activity of 10 resin composites by using a BRET-based estrogen receptor (ER)α and ERß dimerization assays and ER transactivation assay. BPA, BisDMA, BisGMA, BisEMA, TEGDMA, HMBP, and DMPA mediated ERα dimerization, and BPA, BisDMA, and DMPA also mediated ERß dimerization. Except for UDMA and CQ, all the compounds were identified as estrogen agonists or antagonists. In-depth information for the safe use of dental composites was acquired, and it was confirmed how the component of dental composites acts in the ER signaling pathway. Further studies on the low-dose and long-term release of these compounds are needed to ensure the safe use of these resin-based dental composites.

MiCrowd: Vision-Based Deep Crowd Counting on MCU.

Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data with deep neural networks (DNNs) have been limited. In this paper, we propose MiCrowd, a floating population measurement system with a tiny DNNs running on MCUs since the data have essential value in urban planning and business. Moreover, MiCrowd addresses the following important challenges: (1) privacy issues, (2) communication costs, and (3) extreme resource constraints on MCUs. To tackle those challenges, we designed a lightweight crowd-counting deep neural network, named MiCrowdNet, which enables on-MCU inferences. In addition, our dataset is carefully chosen and completely re-labeled to train MiCrowdNet for counting people from an mobility view. Experiments show the effectiveness of MiCrowdNet and our relabeled dataset for accurate on-device crowd counting.