Modeling the selective growth advantage of genetically variant human pluripotent stem cells to identify opportunities for manufacturing process control

BACKGROUND AIMS

The appearance of genetically variant populations in human pluripotent stem cell (hPSC) cultures represents a concern for research and clinical applications. Genetic variations may alter hPSC differentiation potential or cause phenotype variation in differentiated cells. Further, variants may have properties such as proliferative rate, or response to the culture environment, that differ from wild-type cells. As such, understanding the behavior of these variants in culture, and any potential operational impact on manufacturing processes, will be necessary to control quality of putative hPSC-based products that include a proportion of variant threshold in their quality specification.

METHODS

Here we show a computational model that mathematically describes the growth dynamics between commonly occurring genetically variant hPSCs and their counterpart wild-type cells in culture.

RESULTS

We show that our model is capable of representing the growth behaviors of both wild-type and variant hPSCs in individual and co-culture systems.

CONCLUSIONS

This representation allows us to identify three critical process parameters that drive critical quality attributes when genetically variant cells are present within the system: total culture density, proportion of variant cells within the culture system and variant cell overgrowth. Lastly, we used our model to predict how the variability of these parameters affects the prevalence of both populations in culture.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study

Background and Objectives

Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods

This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk.

Results

There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort.

Discussion

The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

REM Sleep Behavior Disorder and Its Possible Prodromes in General Population: Prevalence, Polysomnography Findings, and Associated Factors

BACKGROUND AND OBJECTIVES

To evaluate the prevalence of REM sleep behavior disorder (RBD) and its possible prodromal conditions, isolated dream enactment behavior (DEB) and isolated REM without atonia (RWA), in a general population sample, and the factors associated with diagnosis and symptom frequency.

METHODS

From a population-based prospective cohort in Korea, 1,075 participants (age 60.1 ± 7.0 years; range 50-80 years; men 53.7%) completed the RBD screening questionnaire (RBDSQ), a structured telephone interview for the presence and characteristics of repeated DEB, and home polysomnography (PSG). RWA was measured on submentalis EMG, including 30-second epoch-based tonic and phasic activity as well as 3-second mini-epoch-based phasic and any EMG activities. Based on the presence of repeated DEB and any EMG activity of ≥22.3%, we categorized the participants into no RBD, isolated RWA, isolated DEB, and RBD groups.

RESULTS

RBD was diagnosed in 20 participants, isolated RWA in 133 participants, and isolated DEB in 48 participants. Sex and DEB frequency-adjusted prevalence of RBD was 1.4% (95% CI 1.0%-1.8%), isolated RWA was 12.5% (95% CI 11.3%-13.6%), and isolated DEB was 3.4% (95% CI 2.7%-4.1%). Total RBDSQ score was higher in the RBD and isolated DEB groups than in the isolated RWA and no RBD group (median 5 [interquartile range (IQR) 4-6] for RBD, median 4 [IQR 3-6] for isolated DEB, median 2 [IQR 1-3] for isolated RWA, and median 2 [IQR 1-4] for no RBD groups, p < 0.001). RBDSQ score of ≥5 had good specificity but poor positive predictive value (PPV) for RBD (specificity 84.1% and PPV 7.7%) and its prodromal conditions (specificity 85.2% and PPV 29.1%). Among the RWA parameters, any EMG activity showed the best association with the RBD and its possible prodromes (area under the curve, 0.917). Three-second mini-epoch-based EMG activity and phasic EMG activity were correlated with the frequency of DEB (standardized Jonckheere-Terpstra statistic [std. J-T static] for trend = 0.488, p < 0.001, and std. J-T static = 3.265, p = 0.001, respectively).

DISCUSSION

This study provides prevalence estimates of RBD and its possible prodromal conditions based on a structured telephone interview and RWA measurement on PSG from the general population.

Between-year and spatial variation in body condition across the breeding cycle in a pelagic seabird, the Red-billed Tropicbird

Body condition in pelagic seabirds impacts key fitness-related traits such as reproductive performance and breeding frequency. Regulation of body condition can be especially important for species with long incubation periods and long individual incubation shifts between foraging trips. Here, we show that body condition of adult Red-billed Tropicbirds (Phaethon aethereus) at St Helena Island, South Atlantic Ocean, exhibited considerable variation between years (2013-2017) and between different stages of the breeding cycle. Females took the first incubation shift following egg laying, after which males and females alternated incubation shifts of varying length, ranging from <1 to 12 days. Body condition declined in both sexes during an incubation shift by an average of 22 g (2.83% of starting mass) per day and over the incubation period; mass loss was significantly greater during longer incubation shifts, later within a shift and later in the total incubation period. There was also significant differences in incubation behaviour and body condition between years; in 2015, coinciding with a moderate coastal warming event along the Angolan-Namibian coastlines, adults on average undertook longer incubation shifts than in other years and had lower body condition. This suggests that substantial between-year prey fluctuations in the Angola Benguela upwelling system may influence prey availability, in turn affecting incubation behaviour and regulation of body condition. Adults rearing chicks showed a significant reduction in body condition when chicks showed the fastest rate of growth. Chick growth rates during 2017 from two localities in the Atlantic Ocean: an oceanic (St Helena) versus neritic (Cabo Verde) population were similar, but chicks from St Helena were overall heavier and larger at fledging. Results from this multi-year study highlight that flexibility and adaptability in body condition regulation will be important for populations of threatened species to optimise resources as global climate change increasingly influences prey availability.

The association between social jetlag and depression is independent of sleep debt

OBJECTIVES

To investigate whether the association between SJLsc (sleep-corrected social jetlag) and depressive mood is significant and independent of sleep debt.

METHODS

Participants from the general adult population were interviewed using structured questionnaires on sleep duration, weekday/weekend sleep schedules, and depressive mood (Patient Health Questionnaire-9). Social jetlag (SJL) was measured by SJLsc and standard SJL (SJLs). SJLs was the absolute difference between mid-sleep time on free days (MSF) and workdays (MSW). For SJLsc, both MSF and MSW were adjusted for average sleep duration across the week according to the direction of sleep debt. Sleep debt was defined by sleep extension on free days. The association of SJL with depression was investigated, and covariates included age, sex, sociodemographic factors, insomnia symptoms, sleep duration, and sleep debt.

RESULTS

A total of 1982 individuals (1089 men; age 43.1 ± 14.4 years) were analyzed. SJL was present in 24.6% measured by SJLsc and 51.0% by SJLs. SJLsc and SJLs were significantly associated with depressive mood (r = 0.06, P = 0.02; r = 0.06, P = 0.01, respectively), independent of sleep debt. Sleep debt was also associated with depression (r = 0.07, P < 0.01).

CONCLUSIONS

By adopting sleep-corrected formula for SJL, this study found that misaligned and insufficient sleep, at levels occurring in routine social life, can negatively affect mood. Minimizing social jetlag and sleep deprivation may promote individual psychological well-being.

Long-term blood pressure patterns in midlife and dementia in later life: Findings from the Framingham Heart Study

INTRODUCTION

Long-term blood pressure (BP) measures, such as visit-to-visit BP variability (BPV) and cumulative BP, are strong indicators of cardiovascular risks. This study modeled up to 20 years of BP patterns representative of midlife by using BPV and cumulative BP, then examined their associations with development of dementia in later life.

METHODS

For 3201 individuals from the Framingham Heart Study, multivariate logistic regression analyses were performed to examine the association between long-term BP patterns during midlife and the development of dementia (ages ≥ 65).

RESULTS

After adjusting for covariates, every quartile increase in midlife cumulative BP was associated with a sequential increase in the risk of developing dementia (e.g., highest quartile of cumulative systolic blood pressure had approximately 2.5-fold increased risk of all-cause dementia). BPV was not significantly associated with dementia.

DISCUSSION

Findings suggest that cumulative BP over the course of midlife predicts risk of dementia in later life. HIGHLIGHTS Long-term blood pressure (BP) patterns are strong indicators of vascular risks. Cumulative BP and BP variability (BPV) were used to reflect BP patterns across midlife. High cumulative BP in midlife is associated with increased dementia risk. Visit-to-visit BPV was not associated with the onset of dementia.

Decoding information about cognitive health from the brainwaves of sleep

Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world's oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species.

Evaluation of inactivation of bovine coronavirus by low-level radiofrequency irradiation

Inactivation of influenza A virus by radiofrequency (RF) energy exposure at levels near Institute of Electrical and Electronics Engineers (IEEE) safety thresholds has been reported. The authors hypothesized that this inactivation was through a structure-resonant energy transfer mechanism. If this hypothesis is confirmed, such a technology could be used to prevent transmission of virus in occupied public spaces where RF irradiation of surfaces could be performed at scale. The present study aims to both replicate and expand the previous work by investigating the neutralization of bovine coronavirus (BCoV), a surrogate of SARS-CoV-2, by RF radiation in 6-12 GHz range. Results showed an appreciable reduction in BCoV infectivity (up to 77%) due to RF exposure to certain frequencies, but failed to generate enough reduction to be considered clinically significant.

Sound and light levels in intensive care units in a large urban hospital in the United States

Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study

PURPOSE

Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals.

METHODS

Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments.

RESULTS

Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor.

CONCLUSIONS

Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

The sleep and wake electroencephalogram over the lifespan

Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.

Dementia detection from brain activity during sleep

STUDY OBJECTIVES

Dementia is a growing cause of disability and loss of independence in the elderly, yet remains largely underdiagnosed. Early detection and classification of dementia can help close this diagnostic gap and improve management of disease progression. Altered oscillations in brain activity during sleep are an early feature of neurodegenerative diseases and be used to identify those on the verge of cognitive decline.

METHODS

Our observational cross-sectional study used a clinical dataset of 10 784 polysomnography from 8044 participants. Sleep macro- and micro-structural features were extracted from the electroencephalogram (EEG). Microstructural features were engineered from spectral band powers, EEG coherence, spindle, and slow oscillations. Participants were classified as dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN) based on clinical diagnosis, Montreal Cognitive Assessment, Mini-Mental State Exam scores, clinical dementia rating, and prescribed medications. We trained logistic regression, support vector machine, and random forest models to classify patients into DEM, MCI, and CN groups.

RESULTS

For discriminating DEM versus CN, the best model achieved an area under receiver operating characteristic curve (AUROC) of 0.78 and area under precision-recall curve (AUPRC) of 0.22. For discriminating MCI versus CN, the best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM or MCI versus CN, the best model achieved an AUROC of 0.76 and AUPRC of 0.32.

CONCLUSIONS

Our dementia classification algorithms show promise for incorporating dementia screening techniques using routine sleep EEG. The findings strengthen the concept of sleep as a window into neurodegenerative diseases.

Obstructive sleep apnoea and long-term risk of incident diabetes in middle-aged and older general population

Obstructive sleep apnoea (OSA) is associated with increased risk of type-2 diabetes. However, results from large population-based prospective cohort studies are rare. The main aim of the present study was to investigate the relative risk (RR) of 8-year incident type-2 diabetes in relation to OSA severity in a prospective cohort study of middle-aged and older adults. A total of 2,918 participants (avg. age of 59 years) of the Korean Genome and Epidemiology Study (KoGES), who underwent homebased overnight polysomnography at baseline examination between year 2011 and 2014, were followed up 4-yearly between 2015–2018 and 2019–2021. A total of 1,697 participants were present in both the follow-ups. After excluding participants who had diabetes at baseline (n=481), a total of 1,216 participants were eligible for the analyses. OSA at baseline were categorized by apnoea–hypopnoea index (AHI) levels as non-OSA (0–4.9 events·h−1), mild OSA (5.0–14.9 events·h−1) and moderate-severe OSA (≥15.0 events·h−1). Incident type-2 diabetes was identified at each follow-ups. Compared to non-OSA, participants with moderate-severe OSA had 1.5 times higher risk of developing type-2 diabetes at the end of 8-year follow-up after adjusting for potential covariates (RR=1.50, 95% confidence interval=1.02–2.21). In the same analytical models for 4-year RR of incident type-2 diabetes, none of the OSA groups were in significantly higher risk compared to non-OSA group. Moderate-severe OSA, a modifiable risk factor, poses a higher risk of incident type-2 diabetes compared to non-OSA group over 8-year period in general middle-aged and older adults.

Evaluation of Viral Inactivation on Dry Surface by High Peak Power Microwave (HPPM) Exposure

Previous research has shown that virus infectivity can be dramatically reduced by radio frequency exposure in the gigahertz (GHz) frequency range. Given the worldwide SARS-CoV-2 pandemic, which has caused over 1 million deaths and has had a profound global economic impact, there is a need for a noninvasive technology that can reduce the transmission of virus among humans. RF is a potential wide area-of-effect viral decontamination technology that could be used in hospital rooms where patients are expelling virus, in grocery and convenience stores where local populations mix, and in first responder settings where rapid medical response spans many potentially infected locations within hours. In this study, we used bovine coronavirus (BCoV) as a surrogate of SARS-CoV-2 and exposed it to high peak power microwave (HPPM) pulses at four narrowband frequencies: 2.8, 5.6, 8.5, and 9.3 GHz. Exposures consisted of 2 µs pulses delivered at 500 Hz, with pulse counts varied by decades between 1 and 10,000. The peak field intensities (i.e. the instantaneous power density of each pulse) ranged between 0.6 and 6.5 MW/m² , depending on the microwave frequency. The HPPM exposures were delivered to plastic coverslips containing BCoV dried on the surface. Hemagglutination (HA) and cytopathic effect analyses were performed 6 days after inoculation of host cells to assess viral infectivity. No change in viral infectivity was seen with increasing dose (pulse number) across the tested frequencies. Under all conditions tested, exposure did not reduce infectivity more than 1.0 log_10. For the conditions studied, high peak power pulsed RF exposures in the 2-10 GHz range appear ineffective as a virucidal approach for hard surface decontamination. © 2023 Bioelectromagnetics Society.

Association of lifestyle with deep learning predicted electrocardiographic age

Background

People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear.

Methods

This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age.

Results

This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age.

Conclusion

In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks

Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.

Association between older subjective age and poor sleep quality: a population-based study

OBJECTIVE

To examine the association of subjective age (SA) with sleep quality in an adult population.

METHODS

In the Korean Sleep and Headache Study, 2,349 participants (49.2% men; 48.1 ± 16.4 years old) were interviewed face-to-face using structured questionnaires between September and December 2018. SA was assessed by asking participants their perceived age in years and then compared with their chronological age (CA). Participants were assigned to three groups: feeling younger, feeling their age, and feeling older. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Association between SA and sleep quality was analyzed with multiple linear regression controlling for demographics, psychosocial, and sleep characteristics.

RESULTS

The group feeling older (n = 404, 17.2%; men, 58.2%; age, 46.5 ± 16.2 years) had worse sleep quality than the groups feeling younger and feeling their age (PSQI score, 4.3 ± 2.7, 3.8 ± 2.4, 3.4 ± 2.1, respectively, p <.001; prevalence of poor sleep quality, 29.0%, 18.4%, 13.5% respectively, p <.001). The association between SA and the PSQI score remained significant after adjusting for confounders (β = 1.05, 95% confidence interval 0.26, 1.83; p <.001). Stratified analyses by sex and CA showed that the association between SA and the PSQI score was significant only in women and in middle-aged and older group (aged 50-79), suggesting that sex and CA modified the association.

CONCLUSION

Age perception was associated with self-reported sleep quality, independent of CA. SA may be a useful marker that complements the conventional assessment of subjective sleep quality.

Sleep medicine: Practice, challenges and new frontiers

Sleep medicine is an ambitious cross-disciplinary challenge, requiring the mutual integration between complementary specialists in order to build a solid framework. Although knowledge in the sleep field is growing impressively thanks to technical and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms. In this review we explore the main novelties and gaps in the field of sleep medicine, assess the commonest sleep disturbances, provide advices for routine clinical practice and offer alternative insights and perspectives on the future of sleep research.

Observed Reductions in the Infectivity of Bioaerosols Containing Bovine Coronavirus Under Repetitively Pulsed RF Exposure

OBJECTIVE

The purpose of the present study is to investigate the inactivation of bioaerosols containing Bovine Coronavirus, BCov, under repetitively pulsed radio frequency (RF) electromagnetic exposure.

METHODS

These experiments were performed in a waveguide containing a flowing aerosol stream and were limited to a single RF waveform: ∼2 μs square envelope, 5.6 GHz, 4.8 kHz repetition rate. Aerosol streams were exposed to RF electric field amplitudes in the range of 41.9 +/- 6.2 kV/m. Under laminar flow conditions, 75% of the total collected aerosol stream spends 0.85 seconds or less in the RF exposure region.

RESULTS

Application of the RF waveform changes mean survival rate of the aerosolized BCov by -0.58 decades (roughly a 74% reduction) and impacted the variance and standard deviation of the experimental results, with the RF exposure data showing an 800% increase in variance and 196% increase in standard deviation over the control results. Experimental results were compared to those from an analytic electromagnetic-heating inactivation model.

CONCLUSION

The comparison indicated the feasibility that the observed reduction in BCov survival rate might be due to a combination of thermal effects and non-thermal electric field effects.

SIGNIFICANCE

Developing better insight into the mechanisms of inactivation is important for understanding the potential limits of efficacy for this method. Additionally, these results contribute an important baseline for the impact of electromagnetic fields on aerosolized pathogens.

Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird

The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (Acrocephalus scirpaceus) over the 2017 summer breeding season in Somerset, the United Kingdom. The feces of adult, juvenile, and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle, whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; (i) from different taxonomic groups, (ii) with different habitat origins (aquatic vs. terrestrial), and (iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments.

Molecular dynamics simulations explore effects of electric field orientations on spike proteins of SARS-CoV-2 virions

Emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its current worldwide spread have caused a pandemic of acute respiratory disease COVID-19. The virus can result in mild to severe, and even to fatal respiratory illness in humans, threatening human health and public safety. The spike (S) protein on the surface of viral membrane is responsible for viral entry into host cells. The discovery of methods to inactivate the entry of SARS-CoV-2 through disruption of the S protein binding to its cognate receptor on the host cell is an active research area. To explore other prevention strategies against the quick spread of the virus and its mutants, non-equilibrium molecular dynamics simulations have been employed to explore the possibility of manipulating the structure–activity of the SARS-CoV-2 spike glycoprotein by applying electric fields (EFs) in both the protein axial directions and in the direction perpendicular to the protein axis. We have found out the application of EFs perpendicular to the protein axis is most effective in denaturing the HR2 domain which plays critical role in viral-host membrane fusion. This finding suggests that varying irradiation angles may be an important consideration in developing EF based non-invasive technologies to inactivate the virus.

Impact of climate change on the elevational and latitudinal distributions of populations of Tipulidae (Diptera) in Wales, United Kingdom

As dominant features of most ecosystems, insects are responsive to changes in climate, both over short temporal scales (e.g. seasonal fluctuations in abundance) and over longer evolutionary scales (e.g. decade-scale changes in patterns of biodiversity). One such taxonomic group that is sensitive to changing climate are the craneflies (Diptera: Tipulidae). Here, we used aggregated biodiversity data to examine elevational and latitudinal distributions of adult Tipulidae between 1976 and 2019 in Wales, UK, and we related these distributions to climatic patterns. Our analyses showed that species with earlier-emerging adults were most affected by weather conditions in the year before observation. Specifically, as temperature increased, observed elevation increased in high-precipitation conditions, remained stable in average-precipitation conditions and decreased in low-precipitation conditions. For species with later-emerging adults, associations were seen between elevation and weather conditions in the year of observation. Observed latitude generally exhibited a negative association with maximum temperature in the year before observation, with observations of Tipulidae trending southwards during the 43-year study period. Our results support consideration of emergence phenology, weather and habitat data when predicting species distributional changes attributable to climate change, which is vital in understanding the selection pressures that species face in a changing environment.

Association of Obstructive Sleep Apnea With White Matter Integrity and Cognitive Performance Over a 4-Year Period in Middle to Late Adulthood

IMPORTANCE

Obstructive sleep apnea (OSA) is associated with cognitive impairment and brain structural alterations, but longitudinal outcomes are understudied.

OBJECTIVE

To examine the associations of OSA with cognition and white matter (WM) integrity over a 4-year period.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study was conducted in a community-based adult population among participants who had both baseline (2011-2014) and 4-year follow-up (2015-2018) polysomnography, diffusion tensor imaging, and cognitive assessment data. Participants with neurological disorders, anomalous findings on brain magnetic resonance imaging, or inadequate quality of the evaluations were excluded. Data were analyzed from March to November 2021.

EXPOSURES

Participants were categorized depending on the presence vs absence of OSA at baseline and follow-up polysomnographic analysis.

MAIN OUTCOMES AND MEASURES

The main outcomes were proportional changes over a 4-year period in neuropsychological performance and WM integrity. The neuropsychological assessment battery included verbal and visual memory, verbal fluency, Digit Symbol-coding, Trail Making Test-A, and Stroop Test. WM integrity was assessed by fractional anisotropy, axial, and radial diffusivity. To examine interactions with age and sex, participants were subgrouped by age older than 60 years vs 60 years or younger and men vs women.

RESULTS

A total of 1998 individuals were assessed for eligibility, and 888 were excluded based on exclusion criteria, leaving 1110 participants (mean [SD] age, 58.0 [6.0] years; 517 [46.6%] men) for analysis, including 458 participants grouped as OSA-free, 72 participants with resolved OSA, 163 participants with incident OSA, and 417 participants with persistent OSA. Incident OSA was associated with altered WM integrity and with concomitant changes in sustained attention compared with participants without OSA (eg, change in Digit Symbol-coding test score, -3.2% [95% CI, -5.2% to -1.2%]). Participants with resolved OSA showed better visual recall at the follow-up (change in Visual Reproduction-immediate recall test, 17.5% [95% CI, 8.9% to 26.1%]; change in Visual Reproduction-delayed recall test, 33.1% [95% CI, 11.3% to 54.9%]), with concordant changes in diffusion parameters at the relevant anatomic areas. In the older group only (age >60 years), persistent OSA was associated with altered WM integrity and cognition (eg, Visual Reproduction-recognition test: β = -24.2 [95% CI, -40.7 to -7.7]). Sex also was associated with modifying the association of OSA with WM integrity of the left posterior internal capsule, the left genu of corpus callosum, and the right middle cerebellar peduncle only in men and with cognition only in women (eg, Visual Reproduction-immediate recall test: β = 33.4 [95% CI, 19.1 to 47.7]).

CONCLUSIONS AND RELEVANCE

These findings suggest that dynamic changes in OSA status were significantly associated with WM integrity and cognition, which varied by age and sex. It is possible that adequate interventions for OSA could better preserve brain health in middle to late adulthood.

Exploring and Expanding Supererogatory Acts: Beyond Duty for a Sustainable Future

Supererogation has gained attention as a means of explaining the voluntary behaviours of individuals and organizations that are done for the benefit of others and which go above what is required of legislation and what may be expected by society. Whilst the emerging literature has made some significant headway in exploring supererogation as an ethical lens for the study of business there remain several important issues that require attention. These comprise, the lack of primary evidence upon which such examinations have been made, attention has been given to only singular pro-social acts of organizations, and the focus has been upon the actions of large organizations. Furthermore, Heyd's (Supererogation, Cambridge University Press, 1982) original taxonomy of six supererogatory acts, comprising Moral Heroism, Beneficence, Volunteering, Favour, Forgiveness and Forbearance, has been considered to be complete and other forms of supererogatory acts have not yet been explored. In order to address these gaps this study poses the research questions: First, it studies how a single, contemporary SME performs multiple supererogatory acts in its attempts to address its social and environmental goals that go beyond CSR. Second, it seeks to gain a deeper theoretical understanding of Heyd's (Supererogation, Cambridge University Press, 1982) taxonomy of six forms of supererogation through the capture of primary data. This research makes a three-year case study examination of a single SME that has been formally recognized for its work in addressing social and environmental issues at local, national and global levels. Primary data are acquired of the supererogatory acts that it performs through a three-year participant observation case study, utilizing 61 interviews and 3 focus groups with internal and external stakeholders. In doing so, it addresses the empirical limitations of the extant research, substantiates each of the forms that supererogatory acts may take, and makes a contribution to the theory of supererogation by identifying a further class of act that is 'Sharing'.

Optimal spindle detection parameters for predicting cognitive performance

STUDY OBJECTIVES

Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition.

METHODS

Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores.

RESULTS

Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings.

CONCLUSIONS

Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.

Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

Systematic design, generation, and application of synthetic datasets for flow cytometry

Application of synthetic datasets in training and validation of analysis tools have led to improvements in many decision-making tasks in a range of domains from computer vision to digital pathology. Synthetic datasets overcome the constraints of real-world datasets, namely difficulties in collection and labelling, expense, time and privacy concerns. In flow cytometry, real cell-based datasets are limited by properties such as size, number of parameters, distance between cell populations and distributions, and are often focused on a narrow range of disease or cell types. Researchers in some cases have designed these desired properties into synthetic datasets, however operators have implemented them in inconsistent approaches and there is a scarcity of publicly available, high-quality synthetic datasets. In this research, we propose a method to systematically design and generate flow cytometry synthetic datasets with highly controlled characteristics. We demonstrate the generation of two-cluster synthetic datasets with specific degrees of separation between cell populations, and of non-normal distributions with increasing levels of skewness and orientations of skew pairs. We apply our synthetic datasets to test the performance of a popular automated cell populations identification software, SPADE3, and define the region where the software performance decreases as the clusters get closer together. Application of the synthetic skewed dataset suggests the software is capable of processing non-normal data. We calculate the classification accuracy of SPADE3 with robustness not achievable with real-world datasets. Our approach aims to advance research towards generation of high-quality synthetic flow cytometry datasets, and to increase their awareness among the community. The synthetic datasets can be utilised in benchmarking studies that critically evaluate cell population identification tools and help illustrate potential digital platform inconsistencies. These datasets have the potential to improve cell characterisation workflows that integrate automated analysis in clinical diagnostics and cell therapy manufacturing.

Severity of Daytime Sleepiness and Parkinsonian-Like Symptoms in Korean Adults Aged 50-64 Years

Background and Purpose

While excessive daytime sleepiness can predate Parkinson’s disease in late-life, its association with parkinsonian-like (P-L) symptoms in middle age are unknown. Since neurodegeneration can appear decades before a diagnosis of Parkinson’s disease, identifying clinical features associated with this early progression is important. The purpose of this study was to determine the association of daytime sleepiness with P-L symptoms in a population-based sample of middle-aged Korean adults.

Methods

During 2013 and 2014, daytime sleepiness and P-L symptoms were assessed in 2,063 males and females aged 50–64 years who were participating in the Korean Genome and Epidemiology Study. The severity of daytime sleepiness was quantified by the score on the Epworth Sleepiness Scale (ESS). Self-reported P-L symptoms included nine motor disorders commonly associated with Parkinson’s disease. Participants with parkinsonism and related conditions are excluded.

Results

The prevalence of excessive daytime sleepiness (ESS score >10) was 7.0%. The frequencies of P-L symptoms ranged from 0.5% (for “trouble buttoning buttons”) to 18.4% (for “handwriting smaller than it once was”). After adjustment for covariates and multiple testing, the relative odds of P-L symptoms comparing the 80th and 20th percentiles of ESS scores was 1.6 (p=0.001) for “voice is softer than it once was,” 2.1 (p<0.001) for “balance when walking is poor,” and 1.5 (p=0.002) for “loss of facial expression.” The prevalence of excessive daytime sleepiness increased from 6.3% to 19.8% when the number of symptoms increased from zero to three (p=0.004).

Conclusions

In Korean adults aged 50–64 years, daytime sleepiness is significantly associated with P-L symptoms. Whether coexisting daytime sleepiness and P-L symptoms predate extrapyramidal and other impairments in later life warrants further investigation.

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Discerning a smile - The intricacies of analysis of post-neck dissection asymmetry

INTRODUCTION

Iatrogenic facial nerve palsy is distressing to the patient and clinician. The deformity is aesthetically displeasing, and can be functionality problematic for oral competence, dental lip trauma and speech. Furthermore such injuries have litigation implications. Marginal mandibular nerve (MMN) palsy causes an obvious asymmetrical smile. MMN is at particular risk during procedures such as rhytidoplasties, mandibular fracture, tumour resection and neck dissections. Cited causes for the high incidence are large anatomical variations, unreliable landmarks, an exposed neural course and tumour grade or nodal involvement dictating requisite nerve sacrifice. An alternative cause for post-operative asymmetry is damage to the cervical branch of the facial nerve or platysmal dysfunction due to its division. The later tends to have a transient course and recovers. Distinction between MMN palsy and palsy of the cervical branch of the facial nerve or platysma division should therefore be made. In 1979 Ellenbogen differentiated between MMN palsy and "Pseudo-paralysis of the mandibular branch of the facial nerve". Despite this, there is paucity in the literature & confusion amongst clinicians in distinguishing between these palsies, and there is little regarding these post-operative sequelae and neck dissections.

METHOD

This article reflects on the surgical anatomy of the MMN and cervical nerve in relation to danger zones during lymphadenectomy. The authors review the anatomy of the smile. Finally, case studies are utilised to evaluate the differences between MMN palsy and its pseudo-palsy to allow clinical differentiation.

CONCLUSION

Here we present a simple method for clinical differentiation between these two prognostically different injuries, allowing appropriate reassurance, ongoing therapy & management.

Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks

OBJECTIVE

Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning.

METHODS

Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings.

RESULTS

For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.417.8 and a r² of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas.

CONCLUSION

Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation.

SIGNIFICANCE

The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.

Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity

STUDY OBJECTIVES

The main cause of death in patients with obesity hypoventilation syndrome (OHS) is cardiac rather than respiratory failure. Here, we investigated autonomic-respiratory coupling and serum cardiac biomarkers in patients with OHS and obstructive sleep apnea (OSA) with comparable body mass index (BMI) and apnea-hypopnea index (AHI).

METHODS

Cardiopulmonary coupling (CPC) and cyclic variation of heart rate (CVHR) analysis was performed on the electrocardiogram signal from the overnight polysomnogram. Cardiac serum biomarkers were obtained in patients with OHS and OSA with a BMI > 40kg/m². Samples were obtained at baseline and after 3 months of positive airway pressure (PAP) therapy in both groups.

RESULTS

Patients with OHS (n=15) and OSA (n=36) were recruited. No group differences in CPC, CVHR and serum biomarkers were observed at baseline and after 3 months of PAP therapy. An improvement in several CPC metrics, including the sleep apnea index, unstable sleep (low frequency coupling and elevated low frequency coupling narrow band [e-LFC_NB]) and CVHR were observed in both groups with PAP use. However, distinct differences in response characteristics were noted. e-LFC_NB coupling correlated with highly sensitive troponin (hs-troponin-T, p<0.05) in the combined cohort. Baseline hs-troponin-T inversely correlated with awake oxygen saturation in the OHS group (p<0.05).

CONCLUSIONS

PAP therapy can significantly improve CPC stability in obese patients with OSA or OHS, with key differences. e-LFC_NB may function as a surrogate biomarker for early subclinical cardiac disease. Low awake oxygen saturation could also increase this biomarker in OHS.

CLINICAL TRIAL REGISTRATION

Registry: Australian New Zealand Clinical Trials Registry; Name: Obesity Hypoventilation Syndrome and Neurocognitive Dysfunction; URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367492; Identifier: ACTRN12615000122550.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

Understanding cell culture dynamics: a tool for defining protocol parameters for improved processes and efficient manufacturing using human embryonic stem cells

Standardization is crucial when culturing cells including human embryonic stem cells (hESCs) which are valuable for therapy development and disease modeling. Inherent issues regarding reproducibility of protocols are problematic as they hinder translation to good manufacturing practice (GMP), thus reducing clinical efficacy and uptake. Pluripotent cultures require standardization to ensure that input material is consistent prior to differentiation, as inconsistency of input cells creates end-product variation. To improve protocols, developers first must understand the cells they are working with and their related culture dynamics. This innovative work highlights key conditions required for optimized and cost-effective bioprocesses compared to generic protocols typically implemented. This entailed investigating conditions affecting growth, metabolism, and phenotype dynamics to ensure cell quality is appropriate for use. Results revealed critical process parameters (CPPs) including feeding regime and seeding density impact critical quality attributes (CQAs) including specific metabolic rate (SMR) and specific growth rate (SGR). This implied that process understanding, and control is essential to maintain key cell characteristics, reduce process variation and retain CQAs. Examination of cell dynamics and CPPs permitted the formation of a defined protocol for culturing H9 hESCs. The authors recommend that H9 seeding densities of 20,000 cells/cm2, four-day cultures or three-day cultures following a recovery passage from cryopreservation and 100% medium exchange after 48 hours are optimal. These parameters gave ~SGR of 0.018 hour-1 ± 1.5x10-3 over three days and cell viabilities ≥95%±0.4, while producing cells which highly expressed pluripotent and proliferation markers, Oct3/4 (>99% positive) and Ki-67 (>99% positive).

Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests

Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.Here, we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof-especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm-petrel (Monteiro's storm-petrel Hydrobates monteiroi and Madeiran storm-petrel Hydrobates castro) during their respective breeding seasons.Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30-s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera-build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviors and interspecies interactions.

Temporal patterns of wildlife roadkill in the UK

Wildlife-vehicle collisions are one of the main causes of mortality for wild mammals and birds in the UK. Here, using a dataset of 54,000+ records collated by a citizen science roadkill recording scheme between 2014–2019, we analyse and present temporal patterns of wildlife roadkill of the 19 most commonly reported taxa in the UK (84% of all reported roadkill). Most taxa (13 out of 19) showed significant and consistent seasonal variations in road mortality and fitted one of two seasonal patterns; bimodal or unimodal: only three species (red fox Vulpes vulpes, European polecat Mustela putorius and Reeves’ muntjac deer Muntiacus reevesi) showed no significant seasonality. Species that increase movement in spring and autumn potentially have bimodal patterns in roadkill due to the increase in mate-searching and juvenile dispersal during these respective time periods (e.g. European badger Meles meles). Unimodal patterns likely represent increased mortality due to a single short pulse in activity associated with breeding (e.g. birds) or foraging (e.g. grey squirrels Sciurus carolinensis in autumn). Importantly, these patterns also indicate periods of increased risk for drivers, potentially posing a greater threat to human welfare. In addition to behaviour-driven annual patterns, abiotic factors (temperature and rainfall) explained some variance in roadkill. Notably, high rainfall was associated with decreased observations of two bird taxa (gulls and Eurasian magpies Pica pica) and European rabbit Oryctolagus cuniculus. By quantifying seasonal patterns in roadkill, we highlight a significant anthropogenic impact on wild species, which is important in relation to conservation, animal welfare, and human safety.

Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness

PURPOSE

Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness.

PATIENTS AND METHODS

A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 ± 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson's correlation coefficient (R) and probability of absolute error less than standard deviation (PAE

RESULTS

We achieved a meaningful R (up to 0.74) and PAE

CONCLUSION

A deep learning method can assess the association of brain network characteristics with OSA severity and daytime sleepiness and specify the relevant brain connectivity.

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Key Words

• convolutional neural network
• daytime sleepiness
• diffusion tensor imaging
• obstructive sleep apnea
• structural brain network

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MESH Headings Collapse

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Affiliation(s)

Min-Hee Lee Institute of Human Genomic Study, College of Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea
Seung Ku Lee Institute of Human Genomic Study, College of Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea
Robert J Thomas Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
Jee-Eun Yoon Department of Neurology, Uijeongbu Eulji Medical Center, Uijeongbu, Republic of Korea
Chang-Ho Yun Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
Chol Shin Institute of Human Genomic Study, College of Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea Department of Pulmonary Sleep and Critical Care Medicine Disorder Center, College of Medicine, Korea University, Ansan, Republic of Korea

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Sleep structure and electroencephalographic spectral power of middle-aged or older adults: Normative values by age and sex in the Korean population

The fine structure of sleep electrocortical activity reflects health and disease. The current study provides normative data for sleep structure and electroencephalography (EEG) spectral power measures derived from overnight polysomnography (PSG) and examines the effect of age and sex among Korean middle-aged and older adults with or without obstructive sleep apnea (OSA). We analysed home PSG data from 1,153 adult participants of an ongoing population-based cohort study, the Korean Genome and Epidemiology Study. Sleep stages were visually scored and spectral power was measured on a single-channel EEG (C4-A1). We computed spectral power for five frequency ranges. The EEG power was reported in relative (%) and log-transformed absolute values (µV² ). With ageing, the proportion of N1 sleep increased, whereas N3 decreased, which is more noticeable in men than in women. The amount of N3 was relatively low in this cohort. With ageing, relative delta power decreased and alpha and sigma power increased for the whole sleep period, which was more pronounced during REM sleep in non-OSA. For men compared with women, relative theta power was lower during REM and sigma and beta were higher during N1 sleep. The differences of relative powers by age and sex in OSA were comparable to those in non-OSA. In a community-based Korean population, we present normative data of sleep structure and spectral power for middle-aged or older adults of a non-Caucasian ethnicity. The values varied with age and sex and were not influenced by sleep apnea.

A longitudinal observational population-based study of brain volume associated with changes in sleep timing from middle to late-life

STUDY OBJECTIVES

Sleep behaviors are related to brain structure and function, but the impact of long-term changes in sleep timing on brain health has not been clearly addressed. The purpose of this study was to examine the association of longitudinal changes in sleep timing from middle to late-life with gray matter volume (GMV), an important marker of brain aging.

METHODS

We enrolled 1798 adults (aged 49-82 years, men 54.6%) who underwent magnetic resonance imaging (MRI) between 2011 and 2014. Midsleep time (MST) on free days corrected for sleep debt on workdays was adopted as a marker of sleep timing. Data on MST were available at the time of MRI assessment and at examinations that were given 9 years earlier (2003-2004). Longitudinal changes in MST over the 9-year period were derived and categorized into quartiles. Subjects in quartile 1 were defined as "advancers" (MST advanced ≥ 1 h) while those in quartile 4 were defined as "delayers" (MST delayed ≥ 0.2 h). Quartiles 2-3 defined a reference group (MST change was considered modest). The relationship of GMV with MST changes over 9 years was investigated.

RESULTS

Nine-year change in MST were significantly associated with GMV. Compared to the reference group, advancers had smaller GMVs in the frontal and temporal regions. A delay in MST was also associated with smaller cerebellar GMV.

CONCLUSIONS

In middle-to-late adulthood, the direction of change in MST is associated with GMV. While advancers and delayers in MST tend to present lower GMV, associations appear to differ across brain regions.

Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

Study Objectives

Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies “expressed/manifest” HLG via a cyclical self-similarity feature in effort-based respiration signals.

Methods

Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings.

Results

Diagnostic CAI based on technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels.

Conclusions

The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

Sleep staging from electrocardiography and respiration with deep learning

STUDY OBJECTIVES

Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.

METHODS

Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.

RESULTS

ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications.

CONCLUSIONS

Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.

HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy

STUDY OBJECTIVES

Age-related comorbidities and immune activation raise concern for advanced brain aging in people living with HIV (PLWH). The brain age index (BAI) is a machine learning model that quantifies deviations in brain activity during sleep relative to healthy individuals of the same age. High BAI was previously found to be associated with neurological, psychiatric, cardiometabolic diseases, and reduced life expectancy among people without HIV. Here, we estimated the effect of HIV infection on BAI by comparing PLWH and HIV-controls.

METHODS

Clinical data and sleep EEGs from 43 PLWH on antiretroviral therapy (HIV+) and 3,155 controls (HIV-) were collected from Massachusetts General Hospital. The effect of HIV infection on BAI, and on individual EEG features, was estimated using causal inference.

RESULTS

The average effect of HIV on BAI was estimated to be +3.35 years (p < 0.01, 95% CI = [0.67, 5.92]) using doubly robust estimation. Compared to HIV- controls, HIV+ participants exhibited a reduction in delta band power during deep sleep and rapid eye movement sleep.

CONCLUSION

We provide causal evidence that HIV contributes to advanced brain aging reflected in sleep EEG. A better understanding is greatly needed of potential therapeutic targets to mitigate the effect of HIV on brain health, potentially including sleep disorders and cardiovascular disease.

Current trends in flow cytometry automated data analysis software

Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.