A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients.

INTRODUCTION: The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS). MATERIALS AND METHODS: The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch. MAIN RESULTS: A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed. CONCLUSIONS: The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.

Estimating blood pressure trends and the nocturnal dip from photoplethysmography.

OBJECTIVE: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. APPROACH: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long- and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip. MAIN RESULTS: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12 [Formula: see text] 2.20 [Formula: see text] mmHg and a correlation of 0.69 [Formula: see text]. This dip was derived from trend estimates of BP which had an RMSE of 8.22 [Formula: see text] 1.49 mmHg for systolic and 6.55 [Formula: see text] 1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip. SIGNIFICANCE: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24 h measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Performance evaluation of fusing protected fingerprint minutiae templates on the decision level.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

Analysis of CLCN2 as candidate gene for megalencephalic leukoencephalopathy with subcortical cysts.

Mutations in the gene MLC1 are found in approximately 80% of the patients with the inherited childhood white matter disorder megalencephalic leukoencephalopathy with subcortical cysts (MLC). Genetic linkage studies have not led to the identification of another disease gene. We questioned whether mutations in CLCN2, coding for the chloride channel protein 2 (ClC-2), are involved in MLC. Mice lacking this protein develop white matter abnormalities, which are characterized by vacuole formation in the myelin sheaths, strikingly similar to the intramyelinic vacuoles in MLC. Sequence analysis of CLCN2 at genomic DNA and cDNA levels in 18 MLC patients without MLC1 mutations revealed some nucleotide changes, but they were predicted to be nonpathogenic. Further, in electrophysiological experiments, one of the observed amino acid changes was shown to have no effect on the ClC-2-mediated currents. In conclusion, we found no evidence suggesting that the CLCN2 gene is involved in MLC.

Megalencephalic leukoencephalopathy with subcortical cysts: an update and extended mutation analysis of MLC1.

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is an autosomal recessive cerebral white matter disorder in children. This disease is histopathologically characterized by myelin splitting and intramyelinic vacuole formation. MLC is caused by mutations in the gene MLC1, which encodes a novel protein, MLC1. Since the first report, 50 mutations in this gene have been found. Mutations occur throughout the entire coding region and include all different types: 11 splice-site mutations; one nonsense mutation; 24 missense mutations; and 14 deletions and insertions. Until now, six polymorphisms within the coding sequence of MLC1 had been reported. In about 20% of the patients with a typical clinical and MRI picture, no mutations in the MLC1 gene are found. Several of the families, in which no mutations are found, also do not show linkage with the MLC1 locus, which suggests a second gene involved in MLC. The absence of mutations may also be the consequence of performing standard mutation analysis that can miss heterozygous deletions, mutations in the promoter, 3' and 5' untranslated regions (UTRs), and intron mutations, which may influence the amino acid composition of the end product. In this work we describe 13 novel mutations, including those found with extended mutation analysis on MLC patients. This study shows that extended mutation analysis is a valuable tool to identify at least some of the missing mutations. Therefore, we suggest extended mutation analysis for the MLC1 gene, if no mutations are found during standard analysis.

MLC1: a novel protein in distal astroglial processes.

Megaloencephalic leukoencephalopathy with subcortical cysts (MLC) is a progressive cerebral white matter disease in children caused by mutations in the MLC1 gene. This disease is histopathologically characterized by myelin splitting and intramyelinic vacuole formation. MLC1 encodes a novel protein, MLC1, which is mainly expressed in the brain and leukocytes. The function is unknown, although a transport function has been suggested. In this article, we provide experimental data addressing the membrane topology and cellular localization of MLC1. We show that MLC1 contains an even number of transmembrane domains, supporting the possible transport function of MLC1. We demonstrate that MLC1 is specifically expressed in distal astroglial processes in perivascular, subependymal, and subpial regions. This localization suggests a role for MLC1 in a transport process across the blood-brain and brain-cerebrospinal fluid barriers. Astrocyte functions have long been debated. It is becoming increasingly clear that these cells are of fundamental importance in maintaining the structural and functional integrity of neural tissue. Elucidation of the function of MLC1 will contribute to a better understanding of not only the pathophysiology of the disease, but also the role of astrocytes in normal neural tissue.