Background The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people’s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. Methods In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.

Subdural hematoma (SDH) is the most common finding after abusive head trauma (AHT). Hemispheric hypodensity (HH) is a radiological indicator of severe brain damage that encompasses multiple vascular territories, and may develop in the hemisphere(s) underlying the SDH. In some instances where the SDH is predominantly unilateral, the widespread damage is unilateral underlying the SDH. To date, no animal model has successfully replicated this pattern of injury. We combined escalating severities of the injuries and insults commonly associated with HH including SDH, impact, mass effect, seizures, apnea, and hypoventilation to create an experimental model of HH in piglets aged 1 week (comparable to human infants) to 1 month (comparable to human toddlers). Unilateral HH evolved over 24 h when kainic acid was applied ipsilateral to the SDH to induce seizures. Pathological examination revealed a hypoxic-ischemic injury-type pattern with vasogenic edema through much of the cortical ribbon with relative sparing of deep gray matter. The percentage of the hemisphere that was damaged was greater on the ipsilateral versus contralateral side and was positively correlated with SDH area and estimated seizure duration. Further studies are needed to parse out the pathophysiology of this injury and to determine if multiple injuries and insults act synergistically to induce a metabolic mismatch or if the mechanism of trauma induces severe seizures that drive this distinctive pattern of injury.

Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel generalizable deep learning framework, LM-GVP, composed of a protein Language Model (LM) and Graph Neural Network (GNN) to leverage information from both 1D amino acid sequences and 3D structures of proteins. Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and protein functions from Gene Ontology (GO). We also illustrated insights into how a GNN prediction head can inform the fine-tuning of protein LMs to better leverage structural information. We envision that our deep learning framework will be generalizable to many protein property prediction problems to greatly accelerate protein engineering and drug development.

OBJECTIVEThis study aimed to assess the safety and efficacy of MR-guided stereotactic laser ablation (SLA) therapy in the treatment of pediatric brain tumors.METHODSData from 17 North American centers were retrospectively reviewed. Clinical, technical, and radiographic data for pediatric patients treated with SLA for a diagnosis of brain tumor from 2008 to 2016 were collected and analyzed.RESULTSA total of 86 patients (mean age 12.2 ± 4.5 years) with 76 low-grade (I or II) and 10 high-grade (III or IV) tumors were included. Tumor location included lobar (38.4%), deep (45.3%), and cerebellar (16.3%) compartments. The mean follow-up time was 24 months (median 18 months, range 3–72 months). At the last follow-up, the volume of SLA-treated tumors had decreased in 80.6% of patients with follow-up data. Patients with high-grade tumors were more likely to have an unchanged or larger tumor size after SLA treatment than those with low-grade tumors (OR 7.49, p = 0.0364). Subsequent surgery and adjuvant treatment were not required after SLA treatment in 90.4% and 86.7% of patients, respectively. Patients with high-grade tumors were more likely to receive subsequent surgery (OR 2.25, p = 0.4957) and adjuvant treatment (OR 3.77, p = 0.1711) after SLA therapy, without reaching significance. A total of 29 acute complications in 23 patients were reported and included malpositioned catheters (n = 3), intracranial hemorrhages (n = 2), transient neurological deficits (n = 11), permanent neurological deficits (n = 5), symptomatic perilesional edema (n = 2), hydrocephalus (n = 4), and death (n = 2). On long-term follow-up, 3 patients were reported to have worsened neuropsychological test results. Pre-SLA tumor volume, tumor location, number of laser trajectories, and number of lesions created did not result in a significantly increased risk of complications; however, the odds of complications increased by 14% (OR 1.14, p = 0.0159) with every 1-cm3 increase in the volume of the lesion created.CONCLUSIONSSLA is an effective, minimally invasive treatment option for pediatric brain tumors, although it is not without risks. Limiting the volume of the generated thermal lesion may help decrease the incidence of complications.

Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.