Chronic sleep reduction in adolescents - clinical cut-off scores for the Chronic Sleep Reduction Questionnaire (CSRQ)

The Chronic Sleep Reduction Questionnaire is a validated questionnaire that measures symptoms of prolonged insufficient and/or poor sleep and therefore accounts for individuals’ sleep need and sleep debt. This study extends its psychometric properties by providing cut-off scores, using a matched sample of 298 healthy adolescents (15.38 ± 1.63 years, 37.9% male, mean Chronic Sleep Reduction Questionnaire score: 32.98 ± 6.51) and 298 adolescents with insomnia/delayed sleep–wake phase disorder (15.48 ± 1.62 years; 37.9% male, mean Chronic Sleep Reduction Questionnaire score: 42.59 ± 7.06). We found an area under the curve of 0.84 (95% confidence interval: 0.81–0.87). Cut-off scores for optimal sensitivity, optimal specificity and based on Youden's criterion are provided. These cut-off scores are highly relevant for use of the Chronic Sleep Reduction Questionnaire in future studies and clinical practice

Genome-wide analysis of intracellular pH reveals quantitative control of cell division rate by pHc in Saccharomyces cerevisiae.

BACKGROUND: Because protonation affects the properties of almost all molecules in cells, cytosolic pH (pH(c)) is usually assumed to be constant. In the model organism yeast, however, pH(c )changes in response to the presence of nutrients and varies during growth. Since small changes in pH(c )can lead to major changes in metabolism, signal transduction, and phenotype, we decided to analyze pH(c )control. RESULTS: Introducing a pH-sensitive reporter protein into the yeast deletion collection allowed quantitative genome-wide analysis of pH(c )in live, growing yeast cultures. pH(c )is robust towards gene deletion; no single gene mutation led to a pH(c )of more than 0.3 units lower than that of wild type. Correct pH(c )control required not only vacuolar proton pumps, but also strongly relied on mitochondrial function. Additionally, we identified a striking relationship between pH(c )and growth rate. Careful dissection of cause and consequence revealed that pH(c )quantitatively controls growth rate. Detailed analysis of the genetic basis of this control revealed that the adequate signaling of pH(c )depended on inositol polyphosphates, a set of relatively unknown signaling molecules with exquisitely pH sensitive properties. CONCLUSIONS: While pH(c )is a very dynamic parameter in the normal life of yeast, genetically it is a tightly controlled cellular parameter. The coupling of pH(c )to growth rate is even more robust to genetic alteration. Changes in pH(c )control cell division rate in yeast, possibly as a signal. Such a signaling role of pH(c )is probable, and may be central in development and tumorigenesis

Predicting sepsis-related mortality and ICU admissions from telephone triage information of patients presenting to out-of-hours GP cooperatives with acute infections:A cohort study of linked routine care databases

BackgroundGeneral practitioners (GPs) often assess patients with acute infections. It is challenging for GPs to recognize patients needing immediate hospital referral for sepsis while avoiding unnecessary referrals. This study aimed to predict adverse sepsis-related outcomes from telephone triage information of patients presenting to out-of-hours GP cooperatives.MethodsA retrospective cohort study using linked routine care databases from out-of-hours GP cooperatives, general practices, hospitals and mortality registration. We included adult patients with complaints possibly related to an acute infection, who were assessed (clinic consultation or home visit) by a GP from a GP cooperative between 2017–2019. We used telephone triage information to derive a risk prediction model for sepsis-related adverse outcome (infection-related ICU admission within seven days or infection-related death within 30 days) using logistic regression, random forest, and neural network machine learning techniques. Data from 2017 and 2018 were used for derivation and from 2019 for validation.ResultsWe included 155,486 patients (median age of 51 years; 59% females) in the analyses. The strongest predictors for sepsis-related adverse outcome were age, type of contact (home visit or clinic consultation), patients considered ABCD unstable during triage, and the entry complaints”general malaise”, “shortness of breath” and “fever”. The multivariable logistic regression model resulted in a C-statistic of 0.89 (95% CI 0.88–0.90) with good calibration. Machine learning models performed similarly to the logistic regression model. A “sepsis alert” based on a predicted probability >1% resulted in a sensitivity of 82% and a positive predictive value of 4.5%. However, most events occurred in patients receiving home visits, and model performance was substantially worse in this subgroup (C-statistic 0.70).ConclusionSeveral patient characteristics identified during telephone triage of patients presenting to out-of-hours GP cooperatives were associated with sepsis-related adverse outcomes. Still, on a patient level, predictions were not sufficiently accurate for clinical purposes