Batessharp8832

Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model.

Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. effort, which has the potential to further assist clinical decisions and optimize MV settings.

This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.Uncoupling protein 2 (UCP2) is an integral membrane protein that belongs to the family of mitochondrial anion carrier proteins. The absence of human UCP2 structure, lack of understanding of Cl- ion transport mechanism in the UCP2 and the associated biological functions motivated us to model the protein and investigate its structural and dynamical properties in a realistic mitochondrial lipid membrane system. The lipid-protein and protein-protein interactions were probed since they were found to be responsible for the conformational changes of the transmembrane (TM) helices which are involved in facilitating Cl- ion transport. Here, we employed multiscale molecular dynamics simulations including unbiased and biased MD for the investigation of the transport pathway in hUCP2 and interactions of the ion with TM helices within a membrane environment. We initially validated the hUCP2 model in the lipid membrane and then explored the transport pathway of Cl- ion and its interaction with positive residues of TM2 helix that have been reported to play a major role in the Cl- ion transport along with other TM helices of the protein. The simulation results suggest that the TM2 helix plays an important role in the formation of a stable ion channel due to the presence of arginine residues, in particular Arg88 which was found to be a key residue to maintain the channel pore through which the movement of Cl- ions occurs. Based on the results, it can be said that the study provides an atomic-level description of the Cl- ion transport mechanism in hUCP2 embedded in the mitochondrial lipid membrane.

Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, current OD assessment scores merely describe the number and the severity of each OD, without evaluating the duration of organ injury. The objective of this study is to develop and validate a machine learning model based on the Sequential Organ Failure Assessment (SOFA) score for the prediction of mortality in critically ill patients.

Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development. The MIMIC-IV and Nanjing Jinling Hospital Surgical ICU database were used as external test set A and set B, respectively. The outcome of interest was in-ICU mortality. A modified SOFA model incorporating time-dimension (T-SOFA) was stepwise developed to predict ICU mortality using extreme gradient boosting (XGBoost), support vector machine, random forest and logistic regression algorithms. Time-dimensional features were calculated based on six coignificantly improve the prediction performance of the original SOFA score and is of potential for identifying high-risk patients in future clinical application.

The impacts of electronic medical record implementation on nurses, the largest healthcare workforce, have not been comprehensively examined. Negative impacts on nurses have implications for quality of patient care delivery and workforce retention.

To investigate changes in nurses' well-being, intention to stay, burnout, work engagement, satisfaction, motivation and experience using technology pre- and post-implementation of an organisation-wide electronic medical record in Victoria, Australia.

The natural experiment comprised an electronic medical record system implementation across six hospitals of a large tertiary healthcare organisation. Cross-sectional surveys were collected pre-electronic medical record implementation prior to the SARS-CoV-2 pandemic in 2019, and 18-months post-electronic medical record implementation during the pandemic in 2020, and findings compared.

A total of 942 surveys were analysed (550 pre-electronic medical record (response rate 15.52%) and 392 post-electronic medical reth negative changes in nurses' well-being, intention to stay, burnout, work engagement and satisfaction.

Implementation of an electronic medical record immediately followed by the SARS-CoV-2 pandemic was associated with negative changes in nurses' well-being, intention to stay, burnout, work engagement and satisfaction.

One-to-one online consultation is a common type of online health consultation. In choosing doctors for these consultations, patients rely on online reviews. Yet the deviation between online doctor reviews and the true quality of doctor-provided online services calls the usefulness of online doctor reviews into question, and the methods for reducing this deviation via doctor-patient communication remain unclear.

The purpose of this study is to test the effects of interactive factors on online doctor review deviation and to further explore deviation across doctor specialties in the context of one-to-one online health consultations.

We collect our data from a well-known Chinese online health consultation platform. The dataset includes 60,693 one-to-one online health consultation communication flows and corresponding online doctor reviews. We construct an online doctor review deviation matrix and use logistic regression and multinomial logistic regression models to examine the effects of interactive factors reviews.

Interaction frequency, message delivery methods, and medical information can influence the deviation of online doctor reviews. Furthermore, the effects of voice messages vary across doctor specialties. This study offers theoretical and practical implications for the design of online health consultation platforms and the usage of online doctor reviews.

The ACC/AHA Pooled Cohort Equations (PCE) Risk Calculator is widely used in the US for primary prevention of atherosclerotic cardiovascular disease (ASCVD), but may under- or over-estimate risk in some populations. We therefore designed an automated, population-specific ASCVD risk calculator using machine-learning (ML) methods and electronic medical record (EMR) data, and compared its predictive power with that of the PCE calculator.

We collected data from 101,110 unique EMRs of living patients from January 1, 2009 to April 30, 2020. ML techniques were applied to patient datasets that included either only cross-sectional (CS) features, or CS combined with longitudinal (LT) features derived from vital statistics and laboratory values. We compared the utility of the models using a proposed new cost measure (Screened Cases Percentage @ Sensitivity level). All ML models tested achieved better predictive power than the PCE risk calculator. The random forest ML technique (RF) applied on the combination of CS and LT features (RF-LTC) produced the best area under curve (AUC) score of 0.902 (95% confidence interval (CI), 0.895-0.910). To detect 90% of all positive ASCVD cases, the best ML model required screening only 43% of patients, while the PCE risk calculator required screening 69% of patients.

Prediction models built using ML techniques improved ASCVD prediction and reduced the number of screenings required to predict ASCVD when compared with the PCE calculator, alone. Combining LT and CS features in the ML models significantly improved ASCVD prediction compared with using CS features, alone.

Prediction models built using ML techniques improved ASCVD prediction and reduced the number of screenings required to predict ASCVD when compared with the PCE calculator, alone. Combining LT and CS features in the ML models significantly improved ASCVD prediction compared with using CS features, alone.

Stroke caused by giant cell arteritis (GCA) is a rare but devastating condition and early recognition is of critical importance. The features of GCA-related stroke were compared with those of GCA without stroke and atherosclerosis-related or embolic stroke with the aim of more readily diagnosing GCA.

The study group consisted of 19 patients who experienced GCA-related strokes within an inception cohort (1982-2021) of GCA from the internal medicine department, and the control groups each consisted of 541 GCA patients without a stroke and 40 consecutive patients > 50 years of age with usual first ever stroke from the neurology department of a French university hospital. Clinical, laboratory, and imaging findings associated with GCA related-stroke were determined using logistic regression analyses. Early survival curves were estimated using the Kaplan-Meier method and compared using the log rank test.

Amongst 560 patients included in the inception cohort, 19 (3.4%) developed GCA-related stroke. GCA-relaon to the clinician's attention more quickly, thus shortening diagnostic delay.Previous comparative trials showed that virtual reality (VR) therapies achieved larger effects than gold-standard cognitive-behavioral therapy (CBT) on overall auditory verbal hallucinations (AVHs). However, no trial has examined the corresponding underlying electrophysiological mechanisms. We performed a pilot randomized comparative trial evaluating the efficacy of a virtual reality-based computer AT system (CATS) over CBT for schizophrenia (SCZ) patients with treatment-resistant AVHs and explored these potential electrophysiological changes via the visual P300 component. Patients (CATS, n = 32; CBT, n = 33) completed the clinical assessments pre- and post-interventions and at 12-week follow-up. The visual P300 were measured before and after both therapies. The analysis of changes in psychiatric symptoms used linear mixed-effects models, and the P300 response in temporal and time-frequency domains was analyzed with repeated-measures analysis of variance. There was no interaction effect between change in clinical symptoms and treatment group. However, several statistically significant within-group improvements were found for CATS and CBT over time. AVH improved significantly after both treatments, as measured with the Psychotic Symptom Rating Scales-Auditory Hallucinations (PSYRATS-AH) sub-scores. Especially for the CATS group, omnipotence beliefs, anxiety symptoms, self-esteem, and quality of life also remained improved at the 12-week follow-up. Moreover, P300 amplitude had a significant interaction effect and correlation with AVH response. Overall, our analysis did not demonstrate general clinical superiority of CATS over CBT, but CATS improved refractory AVH in SCZ patients, likely by increasing P300 amplitude. fMLP These findings support the continued development of CATS for persistent AVH and suggest further trials to clarify the neurological effects of CATS.