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The pathophysiology of sleep apnea goes beyond anatomic predisposition to airway collapse and includes additional factors such as arousal threshold and loop gain. High loop gain is a prominent feature in central and complex sleep apnea (with a mixture of obstructive and central features) where relative hypocapnia can lead to respiratory instability and periodic breathing. Existing therapies, including continuous positive airway pressure (CPAP) and adaptive servo-ventilators, often inadequately treat sleep apnea with high loop gain features. Enhanced expiratory rebreathing space (EERS) targets prevention of the hypocapnia that triggers central events in sleep by increasing dead space in amounts less than typical tidal volumes. This is accomplished by covering traditional exhalation ports on positive airway pressure masks and adding small additional tubing with distal exhalation and safety valves. This technique reduces carbon dioxide (CO2) blow-off during arousals and the associated large recovery breaths, typically producing a maximal increase in resting CO2 by 1–2 mmHg, thus increasing the CO2 reserve and making it less likely to encounter the hypocapnic apneic threshold. Typically, the amount of EERS is titrated in response to central events and periodic breathing rather than aiming for a goal CO2 level. Ideally CO2 monitoring is used during titration of EERS and the technique is avoided in the setting of baseline hypercapnia. This method has been used in clinical practice at our sleep center for over 15 years, and retrospective data suggests an excellent safety profile and high rates of successful therapy including in patients who have previously failed CPAP therapy. Limitations include decreased effectiveness in the setting of leak and decreased tolerance of the bulkier circuit. EERS represents a simple, affordable modification of existing positive airway pressure modalities for treatment of central and complex sleep apnea. Areas of future study include randomized controlled trials of the technique and study of use of EERS in combination with adaptive ventilation, and pharmacologic adjuncts targeting high loop gain physiology.

Introduction Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity.Methods and analysis The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions.Ethics and dissemination Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP).Trial registration number ACTRN12621001223820.

Collateral lethality occurs when loss of a gene/protein renders cancer cells dependent on its remaining paralog. Combining genome-scale CRISPR/Cas9 loss-of-function screens with RNA sequencing in over 900 cancer cell lines, we found that cancers of nervous system lineage, including adult and pediatric gliomas and neuroblastomas, required the nuclear kinase vaccinia-related kinase 1 (VRK1) for their survival in vivo. VRK1 dependency was inversely correlated with expression of its paralog VRK2. VRK2 knockout sensitized cells to VRK1 loss, and conversely, VRK2 overexpression increased cell fitness in the setting of VRK1 loss. DNA methylation of the VRK2 promoter was associated with low VRK2 expression in human neuroblastomas and adult and pediatric gliomas. Mechanistically, depletion of VRK1 reduced barrier-to-autointegration factor phosphorylation during mitosis, resulting in DNA damage and apoptosis. Together, these studies identify VRK1 as a synthetic lethal target in VRK2 promoter–methylated adult and pediatric gliomas and neuroblastomas.

Objectives:. Families in the neurologic ICU urgently request goals-of-care decision support and shared decision-making tools. We recently developed a goals-of-care decision aid for surrogates of critically ill traumatic brain injury patients using a systematic development process adherent to the International Patient Decision Aid Standards. To widen its applicability, we adapted this decision aid to critically ill patients with intracerebral hemorrhage and large hemispheric acute ischemic stroke. Design:. Prospective observational study. Setting:. Two academic neurologic ICUs. Subjects:. Twenty family members of patients in the neurologic ICU were recruited from July 2018 to October 2018. Interventions:. None. Measurements and Main Results:. We reviewed the existing critically ill traumatic brain injury patients decision aid for content and changed: 1) the essential background information, 2) disease-specific terminology to “hemorrhagic stroke” and “ischemic stroke”, and 3) disease-specific prognosis tailored to individual patients. We conducted acceptability and usability testing using validated scales. All three decision aids contain information from validated, disease-specific outcome prediction models, as recommended by international decision aid standards, including careful emphasis on their uncertainty. We replaced the individualizable icon arrays graphically depicting probabilities of a traumatic brain injury patient’s prognosis with icon arrays visualizing intracerebral hemorrhage and hemispheric acute ischemic stroke prognostic probabilities using high-quality disease-specific data. We selected the Intracerebral Hemorrhage Score with validated 12-month outcomes, and for hemispheric acute ischemic stroke, the 12-month outcomes from landmark hemicraniectomy trials. Twenty family members participated in acceptability and usability testing (n = 11 for the intracerebral hemorrhage decision aid; n = 9 for the acute ischemic stroke decision aid). Median usage time was 22 minutes (interquartile range, 16–26 min). Usability was excellent (median System Usability Scale = 84/100 [interquartile range, 61–93; with > 68 indicating good usability]); 89% of participants graded the decision aid content as good or excellent, and greater than or equal to 90% rated it favorably for information amount, balance, and comprehensibility. Conclusions:. We successfully adapted goals-of-care decision aids for use in surrogates of critically ill patients with intracerebral hemorrhage and hemispheric acute ischemic stroke and found excellent usability and acceptability. A feasibility trial using these decision aids is currently ongoing to further validate their acceptability and test their feasibility for use in busy neurologic ICUs.

Abstract Background Patients with rheumatoid arthritis (RA) demonstrate reduced aerobic capacity, excess cardiovascular risk, mobility limitations and are less physically active than their healthy peers. Physical activity may decrease RA disease activity through its anti-inflammatory effects and psychological and health benefits. To successfully manage RA symptoms and reduce cardiovascular risks associated with RA through increased physical activity (PA), accurate physical activity assessments are critical. Accelerometry is an objective physical activity measure, but not widely used. Validity of the Nurses’ Health Study physical activity questionnaire II (NHSPAQ) has not been determined for estimation of physical activity in RA. This study examined NHSPAQ validity in adults with RA compared to accelerometry-based metabolic equivalents determined (METs) and results of performance tests. We hypothesized NHSPAQ scores would correlate moderately (0.4–0.5) with accelerometer physical activity estimates. Methods Thirty-five adults with RA (mean age [SD] 62 (Williams et. al, Health Qual Life Outcomes 10:28, 2012) years, 28 females (80%) recruited from a hospital-based clinic registry participated in a one-week accelerometry trial. Medical data was compiled. Participants completed the NHSPAQ, a self-paced 20-m walk test, and modified timed step test. Participants wore an accelerometer for 7 consecutive days, then completed a physical activity log and another NHSPAQ. Metabolic equivalents (METs) were derived from NHSPAQ and accelerometers using standardized formulas. NHSPAQ METs were correlated with accelerometer METs and data from performance measures. Results Average disease duration was 21 years (SD = 11), 63% patients took biologics. The average weekly METs reported were 29 (SD = 33) and accelerometer METs were 33 (SD = 22). NHSPAQ METs correlated moderately with accelerometer-derived METs (r = 0.48 95% CI (0.15–0.70). Self-reported PA correlated moderately with Step Test performance (r = 0.50 95% CI (0.18–0.72). Conclusion Patients with RA exhibit low physical activity levels. General fitness measures were moderately correlated with physical activity levels. A moderate significant correlation existed between NHSPAQ and accelerometry METs. These preliminary data suggest the NHSPAQ may be useful to describe physical activity levels in this population.

Programmed Cell Death-1 (PD-1) is an inhibitory immune receptor, which plays critical roles in T cell co-inhibition and exhaustion upon binding to its ligands PD-L1 and PD-L2. We report the crystal structure of the human PD-1 ectodomain and the mapping of the PD-1 binding interface. Mutagenesis studies confirmed the crystallographic interface, and resulted in mutant PD-1 receptors with altered affinity and ligand-specificity. In particular, a high-affinity mutant PD-1 (HA PD-1) exhibited 45 and 30-fold increase in binding to PD-L1 and PD-L2, respectively, due to slower dissociation rates. This mutant (A132L) was used to engineer a soluble chimeric Ig fusion protein for cell-based and in vivo studies. HA PD-1 Ig showed enhanced binding to human dendritic cells, and increased T cell proliferation and cytokine production in a mixed lymphocyte reaction (MLR) assay. Moreover, in an experimental model of murine Lewis lung carcinoma, HA PD-1 Ig treatment synergized with radiation therapy to decrease local and metastatic tumor burden, as well as in the establishment of immunological memory responses. Our studies highlight the value of structural considerations in guiding the design of a high-affinity chimeric PD-1 Ig fusion protein with robust immune modulatory properties, and underscore the power of combination therapies to selectively manipulate the PD-1 pathway for tumor immunotherapy.

Introduction. Shared Decision-Making may facilitate information exchange, deliberation, and effective decision-making, but no decision aids currently exist for difficult decisions in neurocritical care patients. The International Patient Decision Aid Standards, a framework for the creation of high-quality decision aids (DA), recommends the presentation of numeric outcome and risk estimates. Efforts are underway to create a goals-of-care DA in critically-ill traumatic brain injury (ciTBI) patients. To inform its content, we examined physicians’ perceptions, and use of the IMPACT-model, the most widely validated ciTBI outcome model, and explored physicians’ preferences for communicating prognostic information towards families. Methods. We conducted a qualitative study using semi-structured interviews in 20 attending physicians (neurosurgery,neurocritical care,trauma,palliative care) at 7 U.S. academic medical centers. We used performed qualitative content analysis of transcribed interviews to identify major themes. Results. Only 12 physicians (60%) expressed awareness of the IMPACT-model; two stated that they “barely” knew the model. Seven physicians indicated using the model at least some of the time in clinical practice, although none used it exclusively to derive a patient’s prognosis. Four major themes emerged: the IMPACT-model is intended for research but should not be applied to individual patients; mistrust in the IMPACT-model derivation data; the IMPACT-model is helpful in reducing prognostic variability among physicians; concern that statistical models may mislead families about a patient’s prognosis. Discussion: Our study identified significant variability of the awareness, perception, and use of the IMPACT-model among physicians. While many physicians prefer to avoid conveying numeric prognostic estimates with families using the IMPACT-model, several physicians thought that they “ground” them and reduce prognostic variability among physicians. These findings may factor into the creation and implementation of future ciTBI-related DAs.

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

BACKGROUND:Assays have been developed for cross-sectional HIV incidence estimation using plasma samples. Large scale surveillance programs are planned using dried blood spot (DBS) specimens for incidence assessment. However, limited information exists on the performance of HIV cross-sectional incidence assays using DBS. METHODS:The assays evaluated were: Maxim HIV-1 Limiting Antigen Avidity EIA (LAg-Avidity), Sedia HIV-1 BED-Capture EIA (BED-CEIA), and CDC modified BioRad HIV-1/2 Plus O Avidity-based Assay (CDC-BioRad Avidity) using pre-determined cutoff values. 100 matched HIV-1 positive plasma and DBS samples, with known duration of infection, from the Consortium for the Evaluation and Performance of HIV Incidence Assays repository were tested. All assays were run in duplicate. To examine the degree of variability within and between results for each sample type, both categorical and continuous results were analyzed. Associations were assessed with Bland Altman, R2 values and Cohen's kappa coefficient (ĸ). RESULTS:Intra-assay variability using the same sample type was similar for all assays (R2 0.96 to 1.00). The R2 values comparing DBS and plasma results for LAg-Avidity, BED-CEIA, and CDC-BioRad Avidity were 0.96, 0.94, and 0.84, respectively. The concordance and ĸ values between DBS and plasma for all three assays were >87% and >0.64, respectively. The Bland-Altman analysis showed significant differences between plasma and DBS samples. For all three assays, a higher number of samples were classified as recent infections using DBS samples. CONCLUSIONS:DBS and plasma sample results were highly correlated. However, when compared to plasma, each assay performed somewhat differently in DBS at the lower and higher ends of the dynamic range. DBS samples were more likely to be classified as recently infected by all three assays, which may lead to overestimation of incidence in surveys using performance criteria derived for plasma samples.

To determine the sensitivity, specificity, and field utility of the Cepheid GeneXpert Chlamydia trachomatis (CT) Assay (GeneXpert) for ocular chlamydia infection compared to Roche Amplicor CT assay (Amplicor).In a trachoma-endemic community in Kongwa Tanzania, 144 children ages 0 to 9 were surveyed to assess clinical trachoma and had two ocular swabs taken. One swab was processed at Johns Hopkins University, Baltimore MD, using Amplicor, (Roche Molecular Diagnostics) and the other swab was processed at a field station in Kongwa using the GeneXpert Chlamydia trachomatis/Neisseria gonorrhoeae assay (Cepheid). The sensitivity and specificity of GeneXpert was compared to the Amplicor assay.Of the 144 swabs taken the prevalence of follicular trachoma by clinical exam was 43.7%, and by evidence of infection according to Amplicor was 28.5%. A total of 17 specimens (11.8%) could not be processed by GeneXpert in the field due to lack of sample volume, other specimen issues or electricity failure. The sensitivity of GeneXpert when compared to Amplicor was 100% and the specificity was 95%. The GeneXpert test identified more positives in individuals with clinical trachoma than Amplicor, 55% versus 52%.The GeneXpert test for C. trachomatis performed with high sensitivity and specificity and demonstrated excellent promise as a field test for trachoma control.

An interview with Bekah Stauffacher, the new President/CEO of the Swiss Center of North America. Topics include her background and Swiss heritage, her plans for enhancing the Swiss Center's programs, and her vision for partnering with the Swiss American Historical Society to promote Swiss culture and traditions.

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce here a new R package, exprso, as an intuitive machine learning suite designed specifically for non-expert programmers. Built primarily for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso provides native support for multi-class classification through the 1-vs-all generalization of binary classifiers. In contrast to other machine learning suites, we have prioritized simplicity of use over expansiveness when designing exprso.