Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
PLoS One ; 19(3): e0292203, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38446766

RESUMO

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.


Assuntos
COVID-19 , Masculino , Humanos , Feminino , Adulto , COVID-19/diagnóstico , Estudos Retrospectivos , SARS-CoV-2 , Algoritmos , Biofísica
2.
Medicine (Baltimore) ; 101(40): e31024, 2022 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-36221382

RESUMO

Reducing the burden of limited capacity on medical practitioners and public health systems requires a time-dependent characterization of hospitalization rates, such that inferences can be drawn about the underlying causes for hospitalization and patient discharge. The aim of this study was to analyze non-medical risk factors that lead to the discharge of trauma patients. This retrospective cohort study includes trauma patients who were treated in Switzerland between 2011 and 2018. The national Swiss database for quality assurance in surgery (AQC) was reviewed for trauma diagnoses according to the ICD-10 code. Non-medical risk factors include seasonal changes, daily changes, holidays, and number of beds occupied by trauma patients across Switzerland. Individual patient information was aggregated into counts per day of total patients, as well as counts per day of levels of each categorical variable of interest. The ARIMA-modeling was utilized to model the number of discharges per day as a function of auto aggressive function of all previously mentioned risk factors. This study includes 226,708 patients, 118,059 male (age 48.18, standard deviation (SD) 22.34 years) and 108,649 female (age 62.57, SD 22.89 years) trauma patients. The mean length of stay was 7.16 (SD 14.84) days and most patients were discharged home (n = 168,582, 74.8%). A weekly and yearly seasonality trend can be observed in admission trends. The mean number of occupied trauma beds ranges from 3700 to 4000 per day. The number of occupied beds increases on weekdays and decreases on holidays. The number of occupied beds is a positive, independent risk factor for discharge in trauma patients; as the number of occupied beds increases at any given time, so does the risk for discharge. The number of beds occupied represents an independent non-medical risk factor for discharge. Capacity determines triage of hospitalized patients and therefore might increase the risk of premature discharge.


Assuntos
Hospitalização , Alta do Paciente , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Fatores de Risco , Triagem
3.
BMJ Open ; 12(6): e058274, 2022 06 21.
Artigo em Inglês | MEDLINE | ID: mdl-35728900

RESUMO

OBJECTIVES: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN: Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS: A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION: Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results.


Assuntos
COVID-19 , Adulto , COVID-19/diagnóstico , Estudos de Coortes , Humanos , Pessoa de Meia-Idade , Estudos Prospectivos , SARS-CoV-2
4.
eNeuro ; 5(2)2018.
Artigo em Inglês | MEDLINE | ID: mdl-30338291

RESUMO

Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other types of task-relevant information has proven challenging. To determine the extent to which movement and location information is relevant to mPFC neurons, tetrodes were used to record neuronal activity while limb positions, poses (i.e., recurring constellations of limb positions), velocity, and spatial locations were simultaneously recorded with two cameras every 200 ms as rats freely roamed in an experimental enclosure. Regression analyses using generalized linear models revealed that more than half of the individual mPFC neurons were significantly responsive to at least one of the factors, and many were responsive to more than one. On the other hand, each factor accounted for only a very small portion of the total spike count variance of any given neuron (<20% and typically <1%). Machine learning methods were used to analyze ensemble activity and revealed that ensembles were usually superior to the sum of the best neurons in encoding movements and spatial locations. Because movement and location encoding by individual neurons was so weak, it may not be such a concern for single-neuron analyses. Yet because these weak signals were so widely distributed across the population, this information was strongly represented at the ensemble level and should be considered in population analyses.


Assuntos
Eletroencefalografia/métodos , Locomoção/fisiologia , Aprendizado de Máquina , Rede Nervosa/fisiologia , Neurônios/fisiologia , Córtex Pré-Frontal/fisiologia , Percepção Espacial/fisiologia , Animais , Comportamento Animal/fisiologia , Masculino , Ratos , Ratos Long-Evans
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA