Notes

Defending people as well as workers throughout residential centers through exposure to COVID-19: comments.
As dental care adapts to the COVID-19 era, it is important to ensure access for all to high-quality urgent dental care. Understanding the reasons for variation will help to optimise the use of antibiotics in the future.With increasing medical complexity of patients, including the widespread use of bisphosphonates and the increased incidence in head and neck cancer diagnosis, there are ever more difficult decisions to be made within restorative dentistry. The most morbid dental complication to avoid with both the use of bisphosphonate and radiotherapy to the jaws is necrosis of the jaws, ideally by avoiding dental extractions, usually for the entirety of the patient's lifetime. If these patients undergo a dental assessment before commencing such medical treatments, there is a window of opportunity for both dental extractions of teeth with a poor/hopeless prognosis and for appropriate emphasis on prevention. Treatment planning at this stage has the goal of avoiding further dental extractions completely in the future. However, not all patients have an ideal care pathway. This article describes a case with an impossible choice to be made with significant time pressure and during the most vulnerable of times in a patient's life. The short-, medium- and long-term challenges posed are discussed.Diabesity is a term used to describe the combined adverse health effects of obesity and diabetes mellitus. The worldwide dual epidemic of obesity and type 2 diabetes is an important public health issue. Projections estimate a sixfold increase in the number of adults with obesity in 40 years and an increase in the number of individuals with diabetes to 642 million by 2040. Increased adiposity is the strongest risk factor for developing diabetes. Early detection of the effects of diabesity on the cardiovascular system would enable the optimal implementation of effective therapies that prevent atherosclerosis progression, cardiac remodelling, and the resulting ischaemic heart disease and heart failure. Beyond conventional imaging techniques, such as echocardiography, CT and cardiac magnetic resonance, novel post-processing tools and techniques provide information on the biological processes that underlie metabolic heart disease. In this Review, we summarize the effects of obesity and diabetes on myocardial structure and function and illustrate the use of state-of-the-art multimodality cardiac imaging to elucidate the pathophysiology of myocardial dysfunction, prognosticate long-term clinical outcomes and potentially guide treatment strategies.Cell death occurs when a pathogen invades a host organism or the organism is subjected to sterile injury. Thus, cell death is often closely associated with the induction of an immune response. Furthermore, cell death can occur as a consequence of the immune response and precedes the tissue renewal and repair responses that are initiated by innate immune cells during resolution of an immune response. Beyond immunity, cell death is required for development, morphogenesis and homeostasis. How can such a ubiquitous event as cell death trigger such a wide range of context-specific effector responses? Dying cells are sensed by innate immune cells using specialized receptors and phagocytosed through a process termed efferocytosis. Here, we outline a general principle whereby signals within the dead cell as well as the environment are integrated by specific efferocytes to define the appropriate effector response.The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an "electronic eye" to phenotype cells directly from microscopy images.
Biological sex factors and sociocultural gender norms affect the physiology and behavior of weight loss. However, most diet intervention studies do not report outcomes by sex, thereby impeding reproducibility. The objectives of this study were to compare 12-month changes in body weight and composition in groups defined by diet and sex, and adherence to a healthy low carbohydrate (HLC) vs. healthy low fat (HLF) diet.

This was a secondary analysis of the DIETFITS trial, in which 609 overweight/obese nondiabetic participants (age, 18-50 years) were randomized to a 12-month HLC (n = 304) or HLF (n = 305) diet. Our first aim concerned comparisons in 12-month changes in weight, fat mass, and lean mass by group with appropriate adjustment for potential confounders. The second aim was to assess whether or not adherence differed by diet-sex group (HLC women n = 179, HLC men n = 125, HLF women n = 167, HLF men n = 138).

12-month changes in weight (p < 0.001) were different by group. HLC produced significantly greater weight loss, as well as greater loss of both fat mass and lean mass, than HLF among men [-2.98 kg (-4.47, -1.50); P < 0.001], but not among women. Men were more adherent to HLC than women (p = 0.02). click here Weight loss estimates within group remained similar after adjusting for adherence, suggesting adherence was not a mediator.

By reporting outcomes by sex significant weight loss differences were identified between HLC and HLF, which were not recognized in the original primary analysis. These findings highlight the need to consider sex in the design, analysis, and reporting of diet trials.
By reporting outcomes by sex significant weight loss differences were identified between HLC and HLF, which were not recognized in the original primary analysis. These findings highlight the need to consider sex in the design, analysis, and reporting of diet trials.
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