Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.

Summary The implementation of routine computer-based screening for suicidal ideation and other psychosocial domains through standardized patient reported outcome instruments in two high volume urban HIV clinics is described. Factors associated with an increased risk of self-reported suicidal ideation were determined. Background HIV/AIDS continues to be associated with an under-recognized risk for suicidal ideation, attempted as well as completed suicide. Suicidal ideation represents an important predictor for subsequent attempted and completed suicide. We sought to implement routine screening of suicidal ideation and associated conditions using computerized patient reported outcome (PRO) assessments. Methods Two geographically distinct academic HIV primary care clinics enrolled patients attending scheduled visits from 12/2005 to 2/2009. Touch-screen-based, computerized PRO assessments were implemented into routine clinical care. Substance abuse (ASSIST), alcohol consumption (AUDIT-C), depression (PHQ-9) and anxiety (PHQ-A) were assessed. The PHQ-9 assesses the frequency of suicidal ideation in the preceding two weeks. A response of “nearly every day” triggered an automated page to pre-determined clinic personnel who completed more detailed self-harm assessments. Results Overall 1,216 (UAB= 740; UW= 476) patients completed initial PRO assessment during the study period. Patients were white (53%; n=646), predominantly males (79%; n=959) with a mean age of 44 (± 10). Among surveyed patients, 170 (14%) endorsed some level of suicidal ideation, while 33 (3%) admitted suicidal ideation nearly every day. In multivariable analysis, suicidal ideation risk was lower with advancing age (OR=0.74 per 10 years;95%CI=0.58-0.96) and was increased with current substance abuse (OR=1.88;95%CI=1.03-3.44) and more severe depression (OR=3.91 moderate;95%CI=2.12-7.22; OR=25.55 severe;95%CI=12.73-51.30). Discussion Suicidal ideation was associated with current substance abuse and depression. The use of novel technologies to incorporate routine self-reported screening for suicidal ideation and other health domains allow for timely detection and intervention for this life threatening condition.

The explanatory characteristics of the PRO model correlated best with factors known to be associated with poor ART adherence (substance abuse; depression). The computerized capture of PROs as a part of routine clinical care may prove to be a complementary and potentially transformative health informatics technology for research and patient care.

Context/objective: Examine the relationship of post-traumatic psychological growth (PTG), depression, and personal and injury characteristics in persons with spinal cord injury (SCI). Design: Cross-sectional survey. Setting: Community. Participants: Eight hundred and twenty-four adults with SCI. Interventions: None. Outcome measures: Five items from the Post-traumatic Growth Inventory, reflecting positive change after injury in life priorities, closeness to others, new opportunities being available, stronger faith, and personal strength. Results: Initial structural equation model testing of a conceptual model of personal and injury characteristics, violent etiology, depression, and PTG resulted in a poor fit. Model modifications resulted in an improved fit, but explained only 5% of the variance in PTG. Being female, younger, having less formal education, and less time since injury had significant relationships with PTG, whereas depression, violent etiology, and injury level/severity did not. In each PTG domain, between 54 and 79% of the sample reported at least some positive change after injury. Conclusions: The results of this study, while promising, explained only a small portion of the variance in PTG. A majority of the sample experienced some positive change after injury, with the greatest change in discovering that they were stronger than they thought they were. Comparing means previously reported in a non-SCI sample of those who experienced trauma, positive change after injury was comparable for each PTG item except for new opportunities being available, which was significantly lower for those with SCI. Future directions of research include the development of theoretical models of PTG after SCI.

Hyperinflammatory response caused by infections such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) increases organ failure, intensive care unit admission, and mortality. Cytokine storm in patients with Coronavirus Disease 2019 (COVID-19) drives this pattern of poor clinical outcomes and is dependent upon the activity of the transcription factor complex nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappaB) and its downstream target gene interleukin 6 ( IL6 ) which interacts with IL6 receptor (IL6R) and the IL6 signal transduction protein (IL6ST or gp130) to regulate intracellular inflammatory pathways. In this study, we compare transcriptomic signatures from a variety of drug-treated or genetically suppressed (i.e. knockdown) cell lines in order to identify a mechanism by which antidepressants such as fluoxetine demonstrate non-serotonergic, anti-inflammatory effects. Our results demonstrate a critical role for IL6ST and NF-kappaB Subunit 1 (NFKB1) in fluoxetine’s ability to act as a potential therapy for hyperinflammatory states such as asthma, sepsis, and COVID-19.