Major depressive disorder (MDD) contributes to suicide risk. Treating MDD effectively is considered a key suicide prevention intervention. Yet many patients with MDD do not respond to their initial medication and require a ‘next-step’. The relationship between next-step treatments and suicidal thoughts and behaviors is uncharted.

The VA Augmentation and Switching Treatments for Depression trial randomized 1522 participants to one of three next-step treatments: Switching to Bupropion, combining with Bupropion, and augmenting with Aripiprazole. In this secondary analysis, features associated with lifetime suicidal ideation (SI) and attempts (SA) at baseline and current SI during treatment were explored.

Compared to those with SI only, those with lifetime SI + SA were more likely to be female, divorced, or separated, unemployed; and to have experienced more childhood adversity. They had a more severe depressive episode and were more likely to respond to ‘next-step’ treatment. The prevalence of SI decreased from 46.5% (694/1492) at baseline to 21.1% (315/1492) at end-of-treatment. SI during treatment was associated with baseline SI; low positive mental health, more anxiety, greater severity and longer duration of current MDD episode; being male and White; and treatment with S-BUP or C-BUP as compared to A-ARI.

SI declines for most patients during next-step medication treatments. But about 1 in 5 experienced emergent or worsening SI during treatment, so vigilance for suicide risk through the entire 12-week acute treatment period is necessary. Treatment selection may affect the risk of SI.

Face masks reduce disease transmission by protecting the wearer from inhaled pathogens and reducing the emission of infectious aerosols. Although methods quantifying efficiency for wearer protection are established, current methods for assessing face mask containment efficiency rely on measurement of a low concentration of aerosols emitted from an infected or noninfected individual.

A small port enabled the introduction of 0.05 µm sodium chloride particles at a constant rate behind the mask worn by a study participant. A condensation particle counter monitored ambient particle numbers 60 cm in front of the participant over 3-minute periods of rest, speaking, and coughing. The containment efficiency (%) for each mask and procedure was calculated as follows: 100 × (1 − average ambient concentration with face covering worn/average ambient concentration with a sham face covering in place). The protection efficiency (%) was also measured using previously published methods. The probability of transmission (%) from infected to uninfected (a function of both the containment efficiency and the protection efficiency) was calculated as follows: {1 − (containment efficiency/100)}×{1 − (protection efficiency/100)}×100.

The average containment efficiencies for each mask over all procedures and repeated measures were 94.6%, 60.9%, 38.8%, and 43.2%, respectively, for the N95 mask, the KN95 mask, the procedure face mask, and the gaiter. The corresponding protection efficiencies for each mask were 99.0%, 63.7%, 45.3%, and 24.2%, respectively. For example, the transmission probability for 1 infected and 1 uninfected individual in close proximity was ∼14.2% for KN95 masks, compared to 36%–39% when only 1 individual wore a KN95 mask.

Overall, we detected a good correlation between the protection and containment that a face covering afforded to a wearer.

Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.

We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.

Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.

WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Obsessive–compulsive symptoms (OCS) are commonly associated with clozapine treatment but are frequently overlooked by clinicians despite their potential impact on patients' quality of life. In this study, we explored whether OCS severity impacted subjective wellbeing and general functioning, independently of depressive and psychotic symptoms.

We used anonymised electronic healthcare records from a large cohort of patients who were treated with clozapine and assessed annually for OCS, wellbeing, general functioning, and psychopathology using standardised scales as part of routine clinical practice. We used statistical mixed linear model techniques to evaluate the longitudinal influence of OCS severity on wellbeing and general functioning.

A total of 184 patients were included, with 527 face-to-face assessments and 64.7% evaluated three or more times. Different linear mixed models demonstrated that OCS in patients treated with clozapine were associated with significantly worse wellbeing scores, independently of depression and psychotic symptoms, but OCS did not impair general functioning. Obsessional thinking and hoarding behaviour, but not compulsions, were significantly associated with the impact on wellbeing, which may be attributable to the ego-syntonic nature of the compulsions.

Given the frequent occurrence of OCS and their negative impact on wellbeing, we encourage clinicians to routinely assess and treat OCS in patients who are taking clozapine.

We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements.

A synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.

How do we limit global warming to 1.5 °C and why is it crucial? See highlights of latest climate science.

The degree to which suicide risk aggregates in US families is unknown. The authors aimed to determine the familial risk of suicide in Utah, and tested whether familial risk varies based on the characteristics of the suicides and their relatives.

A population-based sample of 12 160 suicides from 1904 to 2014 were identified from the Utah Population Database and matched 1:5 to controls based on sex and age using at-risk sampling. All first through third- and fifth-degree relatives of suicide probands and controls were identified (N = 13 480 122). The familial risk of suicide was estimated based on hazard ratios (HR) from an unsupervised Cox regression model in a unified framework. Moderation by sex of the proband or relative and age of the proband at time of suicide (<25 v. ⩾25 years) was examined.

Significantly elevated HRs were observed in first- (HR 3.45; 95% CI 3.12–3.82) through fifth-degree relatives (HR 1.07; 95% CI 1.02–1.12) of suicide probands. Among first-degree relatives of female suicide probands, the HR of suicide was 6.99 (95% CI 3.99–12.25) in mothers, 6.39 in sisters (95% CI 3.78–10.82), and 5.65 (95% CI 3.38–9.44) in daughters. The HR in first-degree relatives of suicide probands under 25 years at death was 4.29 (95% CI 3.49–5.26).

Elevated familial suicide risk in relatives of female and younger suicide probands suggests that there are unique risk groups to which prevention efforts should be directed – namely suicidal young adults and women with a strong family history of suicide.

The objective of this study was to investigate changes in serum biomarkers of acute brain injury, including white matter and astrocyte injury during chronic foetal hypoxaemia. We have previously shown histopathological changes in myelination and neuronal density in fetuses with chronic foetal hypoxaemia at a level consistent with CHD.

Mid-gestation foetal sheep (110 ± 3 days gestation) were cannulated and attached to a pumpless, low-resistance oxygenator circuit, and incubated in a sterile fluid environment mimicking the intrauterine environment. Fetuses were maintained with an oxygen delivery of 20–25 ml/kg/min (normoxemia) or 14–16 ml/kg/min (hypoxaemia). Myelin Basic Protein and Glial Fibrillary Acidic Protein serum levels in the two groups were assessed by ELISA at baseline and at 7, 14, and 21 days of support.

Based on overlapping 95% confidence intervals, there were no statistically significant differences in either Myelin Basic Protein or Glial Fibrillary Acidic Protein serum levels between the normoxemic and hypoxemic groups, at any time point. No statistically significant correlations were observed between oxygen delivery and levels of Myelin Basic Protein and Glial Fibrillary Acidic Protein.

Chronic foetal hypoxaemia during mid-gestation is not associated with elevated serum levels of acute white matter (Myelin Basic Protein) or astrocyte injury (Glial Fibrillary Acidic Protein), in this model. In conjunction with our previously reported findings, our data support the hypothesis that the brain dysmaturity with impaired myelination found in fetuses with chronic hypoxaemia is caused by disruption of normal developmental pathways rather than by direct cellular injury.