Preliminary evidence suggests that a ketogenic diet may be effective for bipolar disorder.

To assess the impact of a ketogenic diet in bipolar disorder on clinical, metabolic and magnetic resonance spectroscopy outcomes.

Euthymic individuals with bipolar disorder (N = 27) were recruited to a 6- to 8-week single-arm open pilot study of a modified ketogenic diet. Clinical, metabolic and MRS measures were assessed before and after the intervention.

Of 27 recruited participants, 26 began and 20 completed the ketogenic diet. For participants completing the intervention, mean body weight fell by 4.2 kg (P < 0.001), mean body mass index fell by 1.5 kg/m2 (P < 0.001) and mean systolic blood pressure fell by 7.4 mmHg (P < 0.041). The euthymic participants had average baseline and follow-up assessments consistent with them being in the euthymic range with no statistically significant changes in Affective Lability Scale-18, Beck Depression Inventory and Young Mania Rating Scale. In participants providing reliable daily ecological momentary assessment data (n = 14), there was a positive correlation between daily ketone levels and self-rated mood (r = 0.21, P < 0.001) and energy (r = 0.19 P < 0.001), and an inverse correlation between ketone levels and both impulsivity (r = −0.30, P < 0.001) and anxiety (r = −0.19, P < 0.001). From the MRS measurements, brain glutamate plus glutamine concentration decreased by 11.6% in the anterior cingulate cortex (P = 0.025) and fell by 13.6% in the posterior cingulate cortex (P = <0.001).

These findings suggest that a ketogenic diet may be clinically useful in bipolar disorder, for both mental health and metabolic outcomes. Replication and randomised controlled trials are now warranted.

Machine learning could predict binge behavior and help develop treatments for bulimia nervosa (BN) and alcohol use disorder (AUD). Therefore, this study evaluates person-specific and pooled prediction models for binge eating (BE), alcohol use, and binge drinking (BD) in daily life, and identifies the most important predictors.

A total of 120 patients (BN: 50; AUD: 51; BN/AUD: 19) participated in an experience sampling study, where over a period of 12 months they reported on their eating and drinking behaviors as well as on several other emotional, behavioral, and contextual factors in daily life. The study had a burst-measurement design, where assessments occurred eight times a day on Thursdays, Fridays, and Saturdays in seven bursts of three weeks. Afterwards, person-specific and pooled models were fit with elastic net regularized regression and evaluated with cross-validation. From these models, the variables with the 10% highest estimates were identified.

The person-specific models had a median AUC of 0.61, 0.80, and 0.85 for BE, alcohol use, and BD respectively, while the pooled models had a median AUC of 0.70, 0.90, and 0.93. The most important predictors across the behaviors were craving and time of day. However, predictors concerning social context and affect differed among BE, alcohol use, and BD.

Pooled models outperformed person-specific models and the models for alcohol use and BD outperformed those for BE. Future studies should explore how the performance of these models can be improved and how they can be used to deliver interventions in daily life.

Recent evidence from case reports suggests that a ketogenic diet may be effective for bipolar disorder. However, no clinical trials have been conducted to date.

To assess the recruitment and feasibility of a ketogenic diet intervention in bipolar disorder.

Euthymic individuals with bipolar disorder were recruited to a 6–8 week trial of a modified ketogenic diet, and a range of clinical, economic and functional outcome measures were assessed. Study registration number: ISRCTN61613198.

Of 27 recruited participants, 26 commenced and 20 completed the modified ketogenic diet for 6–8 weeks. The outcomes data-set was 95% complete for daily ketone measures, 95% complete for daily glucose measures and 95% complete for daily ecological momentary assessment of symptoms during the intervention period. Mean daily blood ketone readings were 1.3 mmol/L (s.d. = 0.77, median = 1.1) during the intervention period, and 91% of all readings indicated ketosis, suggesting a high degree of adherence to the diet. Over 91% of daily blood glucose readings were within normal range, with 9% indicating mild hypoglycaemia. Eleven minor adverse events were recorded, including fatigue, constipation, drowsiness and hunger. One serious adverse event was reported (euglycemic ketoacidosis in a participant taking SGLT2-inhibitor medication).

The recruitment and retention of euthymic individuals with bipolar disorder to a 6–8 week ketogenic diet intervention was feasible, with high completion rates for outcome measures. The majority of participants reached and maintained ketosis, and adverse events were generally mild and modifiable. A future randomised controlled trial is now warranted.

Many triage algorithms exist for use in mass-casualty incidents (MCIs) involving pediatric patients. Most of these algorithms have not been validated for reliability across users.

Investigators sought to compare inter-rater reliability (IRR) and agreement among five MCI algorithms used in the pediatric population.

A dataset of 253 pediatric (<14 years of age) trauma activations from a Level I trauma center was used to obtain prehospital information and demographics. Three raters were trained on five MCI triage algorithms: Simple Triage and Rapid Treatment (START) and JumpSTART, as appropriate for age (combined as J-START); Sort Assess Life-Saving Intervention Treatment (SALT); Pediatric Triage Tape (PTT); CareFlight (CF); and Sacco Triage Method (STM). Patient outcomes were collected but not available to raters. Each rater triaged the full set of patients into Green, Yellow, Red, or Black categories with each of the five MCI algorithms. The IRR was reported as weighted kappa scores with 95% confidence intervals (CI). Descriptive statistics were used to describe inter-rater and inter-MCI algorithm agreement.

Of the 253 patients, 247 had complete triage assignments among the five algorithms and were included in the study. The IRR was excellent for a majority of the algorithms; however, J-START and CF had the highest reliability with a kappa 0.94 or higher (0.9-1.0, 95% CI for overall weighted kappa). The greatest variability was in SALT among Green and Yellow patients. Overall, J-START and CF had the highest inter-rater and inter-MCI algorithm agreements.

The IRR was excellent for a majority of the algorithms. The SALT algorithm, which contains subjective components, had the lowest IRR when applied to this dataset of pediatric trauma patients. Both J-START and CF demonstrated the best overall reliability and agreement.

To describe the cumulative seroprevalence of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) antibodies during the coronavirus disease 2019 (COVID-19) pandemic among employees of a large pediatric healthcare system.

Prospective observational cohort study open to adult employees at the Children’s Hospital of Philadelphia, conducted April 20–December 17, 2020.

Employees were recruited starting with high-risk exposure groups, utilizing e-mails, flyers, and announcements at virtual town hall meetings. At baseline, 1 month, 2 months, and 6 months, participants reported occupational and community exposures and gave a blood sample for SARS-CoV-2 antibody measurement by enzyme-linked immunosorbent assays (ELISAs). A post hoc Cox proportional hazards regression model was performed to identify factors associated with increased risk for seropositivity.

In total, 1,740 employees were enrolled. At 6 months, the cumulative seroprevalence was 5.3%, which was below estimated community point seroprevalence. Seroprevalence was 5.8% among employees who provided direct care and was 3.4% among employees who did not perform direct patient care. Most participants who were seropositive at baseline remained positive at follow-up assessments. In a post hoc analysis, direct patient care (hazard ratio [HR], 1.95; 95% confidence interval [CI], 1.03–3.68), Black race (HR, 2.70; 95% CI, 1.24–5.87), and exposure to a confirmed case in a nonhealthcare setting (HR, 4.32; 95% CI, 2.71–6.88) were associated with statistically significant increased risk for seropositivity.

Employee SARS-CoV-2 seroprevalence rates remained below the point-prevalence rates of the surrounding community. Provision of direct patient care, Black race, and exposure to a confirmed case in a nonhealthcare setting conferred increased risk. These data can inform occupational protection measures to maximize protection of employees within the workplace during future COVID-19 waves or other epidemics.

We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding of Earth's sensitivity to carbon dioxide, finds that permafrost thaw could release more carbon emissions than expected and that the uptake of carbon in tropical ecosystems is weakening. Adverse impacts on human society include increasing water shortages and impacts on mental health. Options for solutions emerge from rethinking economic models, rights-based litigation, strengthened governance systems and a new social contract. The disruption caused by COVID-19 could be seized as an opportunity for positive change, directing economic stimulus towards sustainable investments.

A synthesis is made of ten fields within climate science where there have been significant advances since mid-2019, through an expert elicitation process with broad disciplinary scope. Findings include: (1) a better understanding of equilibrium climate sensitivity; (2) abrupt thaw as an accelerator of carbon release from permafrost; (3) changes to global and regional land carbon sinks; (4) impacts of climate change on water crises, including equity perspectives; (5) adverse effects on mental health from climate change; (6) immediate effects on climate of the COVID-19 pandemic and requirements for recovery packages to deliver on the Paris Agreement; (7) suggested long-term changes to governance and a social contract to address climate change, learning from the current pandemic, (8) updated positive cost–benefit ratio and new perspectives on the potential for green growth in the short- and long-term perspective; (9) urban electrification as a strategy to move towards low-carbon energy systems and (10) rights-based litigation as an increasingly important method to address climate change, with recent clarifications on the legal standing and representation of future generations.

Stronger permafrost thaw, COVID-19 effects and growing mental health impacts among highlights of latest climate science.

Associations of socioenvironmental features like urbanicity and neighborhood deprivation with psychosis are well-established. An enduring question, however, is whether these associations are causal. Genetic confounding could occur due to downward mobility of individuals at high genetic risk for psychiatric problems into disadvantaged environments.

We examined correlations of five indices of genetic risk [polygenic risk scores (PRS) for schizophrenia and depression, maternal psychotic symptoms, family psychiatric history, and zygosity-based latent genetic risk] with multiple area-, neighborhood-, and family-level risks during upbringing. Data were from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative cohort of 2232 British twins born in 1994–1995 and followed to age 18 (93% retention). Socioenvironmental risks included urbanicity, air pollution, neighborhood deprivation, neighborhood crime, neighborhood disorder, social cohesion, residential mobility, family poverty, and a cumulative environmental risk scale. At age 18, participants were privately interviewed about psychotic experiences.

Higher genetic risk on all indices was associated with riskier environments during upbringing. For example, participants with higher schizophrenia PRS (OR = 1.19, 95% CI = 1.06–1.33), depression PRS (OR = 1.20, 95% CI = 1.08–1.34), family history (OR = 1.25, 95% CI = 1.11–1.40), and latent genetic risk (OR = 1.21, 95% CI = 1.07–1.38) had accumulated more socioenvironmental risks for schizophrenia by age 18. However, associations between socioenvironmental risks and psychotic experiences mostly remained significant after covariate adjustment for genetic risk.

Genetic risk is correlated with socioenvironmental risk for schizophrenia during upbringing, but the associations between socioenvironmental risk and adolescent psychotic experiences appear, at present, to exist above and beyond this gene-environment correlation.

The importance of timely identification and treatment of psychosis are increasingly the focus of early interventions, with research targeting the initial high-risk period in the months following first-episode hospitalization. However, ongoing psychiatric treatment and service utilization after the symptoms have been stabilized over the initial years following first-episode has received less research attention.

To model the variables predicting continued service utilization with psychiatrists for adolescents following their first-episode psychosis; examine associated temporal patterns in continued psychiatric service utilization.

This study utilized a cohort design to assess adolescents (age 14.4 ± 2.5 years) discharged following their index hospitalization for first-episode psychosis. Bivariate analyses were conducted on predictor variables associated with psychiatric service utilization. All significant predictor variables were included in a logistic regression model.

Variables that were significantly associated with psychiatric service utilization included: diagnosis with a schizophrenia spectrum disorder rather than major mood disorder with psychotic features (OR = 24.0; P = 0.02), a first degree relative with depression (OR = 0.12; P = 0.05), and months since last psychiatric inpatient discharge (OR = 0.92; P = 0.02). Further examination of time since last hospitalization found that all adolescents continued service utilization up to 18 months post-discharge.

Key findings highlight the importance of early diagnosis, that a first degree relative with depression may negatively influence the adolescent's ongoing service utilization, and that 18 months post-discharge may a critical time to review current treatment strategies and collaborate with youth and families to ensure that services continue to meet their needs.

The authors have not supplied their declaration of competing interest.

The Sort, Access, Life-saving interventions, Treatment and/or Triage (SALT) mass-casualty incident (MCI) algorithm is unique in that it includes two subjective questions during the triage process: “Is the victim likely to survive given the resources?” and “Is the injury minor?”

Given this subjectivity, it was hypothesized that as casualties increase, the inter-rater reliability (IRR) of the tool would decline, due to an increase in the number of patients triaged as Minor and Expectant.

A pre-collected dataset of pediatric trauma patients age <14 years from a single Level 1 trauma center was used to generate “patients.” Three trained raters triaged each patient using SALT as if they were in each of the following scenarios: 10, 100, and 1,000 victim MCIs. Cohen’s kappa test was used to evaluate IRR between the raters in each of the scenarios.

A total of 247 patients were available for triage. The kappas were consistently “poor” to “fair:” 0.37 to 0.59 in the 10-victim scenario; 0.13 to 0.36 in the 100-victim scenario; and 0.05 to 0.36 in the 1,000-victim scenario. There was an increasing percentage of subjects triaged Minor as the number of estimated victims increased: 27.8% increase from 10- to 100-victim scenario and 7.0% increase from 100- to 1,000-victim scenario. Expectant triage categorization of patients remained stable as victim numbers increased.

Overall, SALT demonstrated poor IRR in this study of increasing casualty counts while triaging pediatric patients. Increased casualty counts in the scenarios did lead to increased Minor but not Expectant categorizations.

Recent investigations now suggest that cerebrovascular reactivity (CVR) is impaired in Alzheimer’s disease (AD) and may underpin part of the disease’s neurovascular component. However, our understanding of the relationship between the magnitude of CVR, the speed of cerebrovascular response, and the progression of AD is still limited. This is especially true in patients with mild cognitive impairment (MCI), which is recognized as an intermediate stage between normal aging and dementia. The purpose of this study was to investigate AD and MCI patients by mapping repeatable and accurate measures of cerebrovascular function, namely the magnitude and speed of cerebrovascular response (τ) to a vasoactive stimulus in key predilection sites for vascular dysfunction in AD.

Thirty-three subjects (age range: 52–83 years, 20 males) were prospectively recruited. CVR and τ were assessed using blood oxygen level-dependent MRI during a standardized carbon dioxide stimulus. Temporal and parietal cortical regions of interest (ROIs) were generated from anatomical images using the FreeSurfer image analysis suite.

Of 33 subjects recruited, 3 individuals were excluded, leaving 30 subjects for analysis, consisting of 6 individuals with early AD, 11 individuals with MCI, and 13 older healthy controls (HCs). τ was found to be significantly higher in the AD group compared to the HC group in both the temporal (p = 0.03) and parietal cortex (p = 0.01) following a one-way ANCOVA correcting for age and microangiopathy scoring and a Bonferroni post-hoc correction.

The study findings suggest that AD is associated with a slowing of the cerebrovascular response in the temporal and parietal cortices.