Notes

Arachnoid abnormal growths: utilizing pre-natal image and wish regarding child neurosurgical treatment to better understand their own normal background and diagnosis.
tcplfit2 is written in R and is available from CRAN.
tcplfit2 is written in R and is available from CRAN.Social epidemiology is concerned with how social forces influence population health. Rather than focusing on a single disease (as in cancer or cardiovascular epidemiology) or a single type of exposure (e.g., nutritional epidemiology), social epidemiology encompasses all the social and economic determinants of health, both historical and contemporary. These include features of social and physical environments, the network of relationships in a society, as well as the institutions, politics, policies, norms and cultures that shape all of these forces. This commentary presents the perspective of several editors at the American Journal of Epidemiology with expertise in social epidemiology. We articulate our thinking to encourage submissions to the journal that (i) expand knowledge of emerging and under-researched social determinants of population health; (ii) advance new empirical evidence on the determinants of health inequities and solutions to advance health equity; (iii) generate evidence to inform the translation of research on social determinants of health into public health impact; (iv) contribute to innovation in methods to improve the rigor and relevance of social epidemiology; and (v) encourage critical self-reflection on the direction, challenges, successes, and failures of the field.Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with SARS-CoV-2 and still susceptible as well as contribute to better estimates of the case fatality rate and other measures of COVID-19 severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. This paper develops a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic - and narrower - credible interval than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys.Blueberries have been extensively studied for the health benefits associated with their high phenolic content. The positive impact of blueberry consumption on human health is associated in part with modulation of pro-inflammatory molecular pathways and oxidative stress. Here, we review in vitro studies examining the anti-inflammatory and antioxidant effects of blueberry phytochemicals, discuss the results in terms of relevance to disease and health, and consider how different blueberry components modulate cellular mechanisms. The dampening effects of blueberry-derived molecules on inflammation and oxidative stress in cell models have been demonstrated through down-regulation of the NF-κB pathway and reduction of ROS and lipid peroxidation. The modulatory effects of blueberry phytochemicals on the MAPK pathway and antioxidant system are not as well described, with inconsistent observations reported on immune cells and between models of endothelial, dermal, and ocular inflammation. Although anthocyanins are often reported as the being the main bioactive compound in blueberries, no individual phytochemical has emerged as the primary compound when different fractions are compared; rather, an effect of whole blueberry extracts or synergy between different phenolic and non-phenolic extracts seems apparent. The major molecular mechanisms of blueberry phytochemicals are increasingly defined in cell models, but their relevance in more complex human systems needs further investigation using well-controlled clinical trials, in which systemic exposures to blueberry-associated molecules are measured concurrently with physiologic indices of inflammation and oxidative stress.Maternal childhood adversity and trauma may elicit biological changes that impact the next generation through epigenetic responses measured in DNA methylation (DNAm). These epigenetic associations could be modified by the early postnatal environment through protective factors such as early childhood home visiting (HV) programs that aim to mitigate deleterious intergenerational impacts of adversity. In a cohort of 53 mother-child pairs recruited 2015-2016 in the Pregnancy and Infant Development Study (Cincinnati, Ohio), we examined the association between maternal adverse childhood experiences (ACEs) and neonatal DNAm in the SCG5 gene important in neuroendocrine function. We examined prenatal HV as an effect modifier. Mothers completed the ACE measure prenatally and infant buccal samples were collected at 1-month post-partum. Multivariable linear regression was used to examine the association between maternal ACEs and neonatal DNAm expressed as M-values averaged across 4 Cytosine-phosphate-Guanine dinucleotide sites. Higher maternal ACEs (>3) was associated with a 5.79 percentage point lower offspring DNAm (95% confidence interval -10.44, -1.14), and the association was modified by the number of HVs received during pregnancy. In a population of at-risk mother-child dyads, preliminary evidence suggests that maternal ACEs have a relationship with offspring SCG5 DNAm that differs by the amount of prenatal HV.The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.Aerosolization of SARS-CoV-2 by COVID-19 patients can put healthcare workers and susceptible individuals at risk of infection. Air sampling for SARS-CoV-2 has been conducted in healthcare settings, but methods vary widely and there is need for improvement. The objective of this study was to evaluate the feasibility of using a high-volume filter sampler, BioCapture z720, to detect SARS-CoV-2 in COVID-19 patient rooms in a medical intensive care unit, a dedicated COVID-19 ward, and at nurses' stations. In some locations, the BioSpot-VIVAS, known for high efficiency in the collection of virus-containing bioaerosols, was also operated. The samples were processed for SARS-CoV-2 RNA with multi-plex nested polymerase chain reaction. One of 28 samples collected with the high-volume filter sampler was positive for SARS-CoV-2; all 6 samples collected with BioSpot-VIVAS were negative for SARS-CoV-2. The high-volume filter sampler was more portable and less intrusive in patient rooms than the BioSpot-VIVAS, but limits of detection remain unknown for this device. This study will inform future work to evaluate the reliability of these types of instruments and inform best practices for their use in healthcare environments for SARS-CoV-2 air sampling.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.
Clinical studies reporting on robot-assisted surgery (RAS) in mandibular contouring surgery are lacking.

Our objective was to follow up on the effectiveness and safety of RAS for craniofacial bone surgery over a long-term period.

This small sample, early phase, prospective, randomized controlled study included patients diagnosed with mandibular deformity requiring mandibular contouring surgery. Patients of both genders aged 18-30 years without complicated craniofacial repair defects were enrolled and randomly assigned in a 11 ratio by a permuted-block randomized assignments list generated by the study statistician. The primary outcomes were the positioning accuracy and angle accuracy of the osteotomy plane 1 week after surgery. Surgical auxiliary measurement index, patient satisfaction scale, surgical pain scale, perioperative period, and complications at 1 week, 1 month, and 6 months after surgery were also analyzed.

One patient was lost to follow-up, resulting in a total of 14 patients in the traditional surgery group and 15 in the robot-assisted group (mean [SD] age, 22.65 [3.60] years). Among the primary outcomes, there was a significant difference in the positioning accuracy (2.91mm vs. 1.65mm; P < 0.01) and angle accuracy (13.26º vs. 4.85º; P < 0.01) between the two groups. Secondary outcomes did not significantly differ.

Compared to traditional surgery, robot-assisted mandibular contouring surgery showed improved precision in bone shaving, as well as higher safety.
Compared to traditional surgery, robot-assisted mandibular contouring surgery showed improved precision in bone shaving, as well as higher safety.
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