Veterans with posttraumatic stress disorder (PTSD) often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG) with concurrent collection of electrodermal activity (EDA) and acceleration. Data from a single forehead channel were transformed into a whole-night spectrogram, and sleep stages were classified using a fully automated algorithm. For this study, 71 control subjects and 60 military-related PTSD subjects were analyzed for percentage of time spent in Light, Hi Deep (1-3 Hz), Lo Deep (<1 Hz), and rapid eye movement (REM) sleep stages, as well as sleep efficiency and fragmentation. The results showed a significant tendency for PTSD sleepers to spend a smaller percentage of the night in REM (p < 0.0001) and Lo Deep (p = 0.001) sleep, while spending a larger percentage of the night in Hi Deep (p < 0.0001) sleep. The percentage of combined Hi+Lo Deep sleep did not differ between groups. All sleepers usually showed EDA peaks during Lo, but not Hi, Deep sleep; however, PTSD sleepers were more likely to lack EDA peaks altogether, which usually coincided with a lack of Lo Deep sleep. Linear regressions with all subjects showed that a decreased percentage of REM sleep in PTSD sleepers was accounted for by age, prazosin, SSRIs and SNRIs (p < 0.02), while decreased Lo Deep and increased Hi Deep in the PTSD group could not be accounted for by any factor in this study (p < 0.005). Linear regression models with only the PTSD group showed that decreased REM correlated with self-reported depression, as measured with the Depression, Anxiety, and Stress Scales (DASS; p < 0.00001). DASS anxiety was associated with increased REM time (p < 0.0001). This study shows altered sleep patterns in sleepers with PTSD that can be partially accounted for by age and medication use; however, differences in deep sleep related to PTSD could not be linked to any known factor. With several medications [prazosin, selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs); p < 0.03], as well as SSRIs were associated with less sleep efficiency (b = -3.3 ± 0.95; p = 0.0005) and more sleep fragmentation (b = -1.7 ± 0.51; p = 0.0009). Anti-psychotics were associated with less sleep efficiency (b = -4.9 ± 1.4; p = 0.0004). Sleep efficiency was negatively impacted by SSRIs, antipsychotic medications, and depression (p < 0.008). Increased sleep fragmentation was associated with SSRIs, SNRIs, and anxiety (p < 0.009), while prazosin and antipsychotic medications correlated with decreased sleep fragmentation (p < 0.05).

Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a "human in the loop" methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen's Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach.

Objective

Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders.

Methods

This paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60 903 30 s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture.

Results

Comparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability.

Conclusion

Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA.

Significance

Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.

Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a "smart drop" bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.

PURPOSE:Studies suggest that melatonin may prevent delirium, a condition of acute brain dysfunction occurring in 20%-30% of hospitalized older adults that is associated with increased morbidity and mortality. We examined the effect of melatonin on delirium prevention in hospitalized older adults while measuring sleep parameters as a possible underlying mechanism. METHODS:This was a randomized clinical trial measuring the impact of 3 mg of melatonin nightly on incident delirium and both objective and subjective sleep in inpatients age ≥65 years, admitted to internal medicine wards (non-intensive care units). Delirium incidence was measured by bedside nurses using the confusion assessment method. Objective sleep measurements (nighttime sleep duration, total sleep time per 24 hours, and sleep fragmentation as determined by average sleep bout length) were obtained via actigraphy. Subjective sleep quality was measured using the Richards Campbell Sleep Questionnaire. RESULTS:Delirium occurred in 22.2% (8/36) of subjects who received melatonin vs in 9.1% (3/33) who received placebo (P = .19). Melatonin did not significantly change objective or subjective sleep measurements. Nighttime sleep duration and total sleep time did not differ between subjects who became delirious vs those who did not, but delirious subjects had more sleep fragmentation (sleep bout length 7.0 ± 3.0 vs 9.5 ± 5.3 min; P = .03). CONCLUSIONS:Melatonin given as a nightly dose of 3 mg did not prevent delirium in non-intensive care unit hospitalized patients or improve subjective or objective sleep.

PURPOSE: To evaluate the anti-inflammatory and antioxidative effects of extracorporeal shockwave therapy (ESWT) on prostatitis and explore the mechanism of alleviating pain. MATERIALS AND METHODS: For in vitro testing, RWPE-1 cells were randomly divided into 5 groups: (1) RWPE-1 group (normal control), (2) LPS group (lipopolysaccharide inducing inflammation), (3) 0.1ESWT group (treated by 0.1 mJ/mm² energy level), (4) 0.2ESWT group (treated by 0.2 mJ/mm² energy level), and (5) 0.3ESWT group (treated by 0.3 mJ/mm² energy level). After ESWT was administered, cells and supernatant were collected for ELISA and western blot. For in vivo testing, Sprague-Dawley male rats were randomly divided into 3 groups: (1) normal group, (2) prostatitis group, and (3) ESWT group (n=12 for each). Prostatitis was induced by 17 beta-estradiol and dihydrotestosterone (DHT) administration. Four weeks after ESWT, the pain index was assessed for all groups and prostate tissues were collected for immunohistochemistry, immunofluorescence, apoptosis analysis and, western blot. RESULTS: Our in vitro studies showed that the optimal energy flux density of ESWT was 0.2 mJ/mm². In vivo, ESWT ameliorated discomfort in rats with prostatitis and inflammation symptoms were improved. Compared to normal rats, overexpressed NLRP3 inflammasomes triggered apoptosis in rats with prostatitis and this was improved by ESWT. TLR4-NFκB pathway was overactive after experimental prostatitis, compared to normal and ESWT groups, and prostatitis induced alterations in BAX/BAK pathway were inhibited by ESWT. CONCLUSIONS: ESWT improved CP/CPPS by reducing NLRP3 inflammasome and ameliorated apoptosis via inhibiting BAX/BAK pathway in a rat model. TLR4 may play a key role in bonding NLRP3 inflammasome and BAX/BAK pathways. ESWT might be a promising approach for the treatment of CP/CPPS.

Previous preclinical studies have suggested a close relationship between cerebrovascular disease (CVD) and Alzheimer's disease. However, a direct correlation between CVD and amyloid burden has not yet been shown in humans. If there is a relationship between CVD and amyloid burden, it is possible that the apolipoprotein E4 (APOE4) genotype may have an effect on this relationship because APOE4 is a risk factor for the development of AD. We therefore evaluated the effects of APOE4 on the relationship between white matter hyperintensities (WMH), a marker of CVD, and amyloid burden, measured by 11C-Pittsburgh compound B (PiB) PET. We recruited 53 patients with subcortical vascular cognitive impairments, who had both WMH on MRI and amyloid deposition assessed by PiB PET. Twenty-two of these patients were APOE4 carriers (41.5%). In the APOE4 non-carriers, a significant positive correlation was shown between the volume of WMH and PiB retention (β = 7.0 × 10-3, p = 0.034) while no significant correlation was found in APOE4 carriers (β = -9.0 × 10-3, p = 0.085). Statistical parametric mapping analyses in APOE4 non-carriers showed that WMH were associated with PiB retention in the bilateral medial occipitotemporal gyrus, cuneus, and superior cerebellum. Our results suggested that WMH are correlated with amyloid burden especially in the posterior brain regions in APOE4 non-carriers. However, this correlation was not observed in APOE4 carriers, perhaps because in these subjects the influence of APOE4 overrides the effect of CVD.