Geertsenlott5230

Most of the consultations were follow-up's and in a lower percentage, due to reactivation of the underlying disease or another reason. Patients who completed the survey reported not having difficulties and were able to resolve their consultations through virtual care. Doctors involved in this study were totally satisfied with the experience and they felt that teledermatology was a valid resource to continue with their medical training and would choose to use it in the near future.The COVID-19 pandemic presented high mortality from its beginning, without effective treatment for seriously ill patients. Build on the experience in Argentine hemorrhagic fever with convalescent plasma, we incorporated 90 patients with COVID-19, of which 87 were evaluable, into a multicenter study. We collected 397 plasma donations from 278 convalescent donors. Patients received plasma with an IgG concentration of 0.7-0.8 (measured by Abbott chemiluminescence) for every 10 kg of body weight. Survival during the first 28 days was the primary objective; 77% were male, age 54 ± 15.6 y/o (range 27-85), body mass index 29.7 ± 4.4; hypertension 39% and diabetes 20.7%; 19.5% had an immunosuppressive condition, 23% were health workers. Plasma was administered to 55 (63%) on spontaneous breathing with oxygen supplementation (mainly oxygen mask with reservoir bag in 80%), and to 32 patients (37%) on mechanical ventilation. https://www.selleckchem.com/products/onx-0914-pr-957.html The 28-day survival rate was 80%; 91% in patients infused on spontaneous breathing and 63% in those on mechanical ventilation (p = 0.0002). There was a significant improvement in the WHO pneumonia clinical scale at 7 and 14 days, and in PaO2 / FiO2, ferritin and LDH, in the week post-infusion. We observed an episode of circulatory volume overload and a febrile reaction, both mild. Convalescent plasma infusions are feasible, safe, and potentially effective, especially before requiring mechanical ventilation. They are an attractive clinical option for treating severe forms of COVID-19 until other effective therapies become available.The use of non-invasive respiratory support in the context of the COVID-19 pandemic is controversial. The aim of this observational study was to show the experience of the first month since the creation of a Non-invasive Ventilatory Support Unit (NIVSU) at Hospital Fernández. We describe the creation of the NIVSU, the health professional-patient ratio, the type of room, the modified personal protection equipment; diagnostic, monitoring and ventilatory support equipment for treatment, as well as the inclusion criteria and the treatment algorithm. Twenty five (63%) of patients were referred from the Internal Medicine Ward, 10 (25%)) from Shock Room, and 5 (13%) from Emergency Ward. National Early Warning Score, Acute Physiology and Chronic Health Disease Classification System II and Sequential Organ Failure Assessment, were calculated on admission, with a median of 12, 8, and 2 points, respectively. The Lung Ultrasonography Score was taken to quantify lung ultrasound findings. All patients were admitted with a reservoir mask, 80% inspired O2 fraction was estimated for the calculation of arterial O2 pressure/ inspired O2 fraction ratio (Pa/FiO2) at admission. The median of time elapsed from the onset of symptoms referred by the patient to UNIT admission was 13 days. The development of NIVSU prevented a large proportion of patients from being transferred to Intensive Care Unit (ICU) and it could be beneficial in preserving ICUs capacity. These early results suggest that non-invasive treatment may be beneficial for the treatment of severe acute respiratory failure by COVID-19.Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes.

1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models.

The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79.

This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.

This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.Childhood internalizing disorders, like anxiety and depression, are common, impairing, and difficult to detect. Universal childhood mental health screening has been recommended, but new technologies are needed to provide objective detection. Instrumented mood induction tasks, designed to press children for specific behavioral responses, have emerged as means for detecting childhood internalizing psychopathology. In our previous work, we leveraged machine learning to identify digital phenotypes of childhood internalizing psychopathology from movement and voice data collected during negative valence tasks (pressing for anxiety and fear). In this work, we develop a digital phenotype for childhood internalizing disorders based on wearable inertial sensor data recorded from a Positive Valence task during which a child plays with bubbles. We find that a phenotype derived from features that capture reward responsiveness is able to accurately detect children with underlying internalizing psychopathology (AUC=0.81). In so doing, we explore the impact of a variety of feature sets computed from wearable sensors deployed to two body locations on phenotype performance across two phases of the task.