Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system.

BACKGROUND AND OBJECTIVES: The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex. METHODS: We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions. RESULTS: We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases. CONCLUSION: We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include electrocardiogram (ECG), photoplethysmogram (PPG) data, and surface electromyogram (sEMG) data. The deep learning models include Long short-term memory (LSTM) networks, Bidirectional LSTM (Bi-LSTM), attention-based variants of LSTM, CNN-LSTM and Convolutional-LSTM networks. The deep learning models are evaluated for two separate windows which are 32 s and 64 s window. The models' performance is evaluated using mean absolute error (MAE). The 64 s window has more accurate prediction compared to the 32 s window. Our results indicate Bi-LSTM with Bahdanu Attention has the best performance for the bio-signals. LSTM performs best with one of the datasets, yielding an MAE of 0.70 ± 0.02. Bi-LSTM with Bahdanau attention showed best results with two of the three datasets with MAE of 0.51 ± 0.03 for sEMG based data and MAE of 0.24 ± 0.03 with PPG and ECG based data.

Increased Risk of Pancreatitis after Endoscopic Retrograde Cholangiopancreatography Following a Positive Intraoperative Cholangiogram: A Single-Center Experience

Background/Aims@#To determine if patients with a positive intraoperative cholangiogram (IOC) who undergo a subsequent endoscopic retrograde cholangiopancreatography (ERCP) have an increased risk of post-ERCP pancreatitis (PEP) compared to those who undergo ERCP directly for suspected common bile duct stones. @*Methods@#A retrospective case-control study was performed from 2010 to 2016. Cases included inpatients with a positive IOC at cholecystectomy who underwent subsequent ERCP. The control group included age-sex matched cohorts who underwent ERCP for choledocholithiasis. Multivariate logistic regression was used to assess the association between PEP and positive IOC, adjusting for matching variables and additional potential confounders. @*Results@#Of the 116 patients that met the inclusion criteria, there were 91 women (78%) in each group. Nine patients (7.8%) developed PEP in the IOC group, compared to 3 patients in the control group (2.6%). The use of pancreatic duct stents and rectal indomethacin was similar in both groups. After adjusting for age, sex, total bilirubin levels, and any stent placement, patients with a positive IOC had a significantly increased risk of PEP (odds ratio, 4.79; 95% confidence interval, 1.05–21.89; p<0.05). @*Conclusions@#In this single-center case-control study, there was a five-fold increased risk of PEP following a positive IOC compared to an age-sex matched cohort.

A Case-Study of Implementation of Improved Strategies for Prevention of Laboratory-acquired Brucellosis

BACKGROUND: In 2012, the Alaska Section of Epidemiology investigated personnel potentially exposed to a Brucella suis isolate as it transited through three laboratories. METHODS: We summarize the first implementation of the United States Centers for Disease Control and Prevention 2013 revised recommendations for monitoring such exposures: (1) risk classification; (2) antimicrobial postexposure prophylaxis; (3) serologic monitoring; and (4) symptom surveillance. RESULTS: Over 30 people were assessed for exposure and subsequently monitored for development of illness. No cases of laboratory-associated brucellosis occurred. Changes were made to gaps in laboratory biosafety practices that had been identified in the investigation. CONCLUSION: Achieving full compliance for the precise schedule of serologic monitoring was challenging and resource intensive for the laboratory performing testing. More refined exposure assessments could inform decision making for follow-up to maximize likelihood of detecting persons at risk while not overtaxing resources.