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Incivility in online commenting sections can create a hostile environment and result in the silencing of vulnerable voices. Accordingly, content websites and social media platforms have an ethical responsibility-one that aligns with their strategic interests-to minimize users' exposure to uncivil content. To this end, platforms invest great effort and budget in automatic and manual filtering mechanisms. Yet, these efforts create a competing ethical quandary, as they often come at the expense of free expression, particularly in cases where comments do not explicitly infringe on stated guidelines but might nevertheless be interpreted as offensive. In this paper, we examine an alternative moderation approach, based on comment reordering as opposed to deletion of uncivil comments. Specifically, we show that exposure to uncivil (vs. civil) comments located in the head or at the bottom of a list of comments increases subsequent commenters' likelihood of posting uncivil comments themselves. Exposure to uncivil comments in the middle of a list, however, does not significantly enhance commenters' likelihood of commenting uncivilly. These results offer new theoretical insight into how incivility is transferred between users in online environments. Our results also suggest a straightforward technological solution for mitigating online incivility, which is more ethical and practical than current industry standards. This involves placing civil comments at the beginning and end of the comment thread, with uncivil comments located in the middle., Competing Interests: Conflict of interestThe authors have no relevant financial or non-financial interests to disclose., (© The Author(s) 2023.)

Introduction: Wearable biosensors have the potential to monitor physiological change associated with opioid overdose among people who use drugs., Methods: We enrolled 16 individuals who reported ≥ 4 daily opioid use events within the previous 30 day. Each was assigned a wearable biosensor that measured respiratory rate (RR) and actigraphy every 15 s for 5 days and also completed a daily interview assessing drug use. We describe the volume of RR data collected, how it varied by participant characteristics and drug use over time using repeated measures one-way ANOVA, episodes of acute respiratory depression (≤5 breaths/minute), and self-reported overdose experiences., Results: We captured 1626.4 h of RR data, an average of 21.7 daily hours/participant over follow-up. Individuals with longer injection careers and those engaging in polydrug use captured significantly fewer total hours of respiratory data over follow-up compared to those with shorter injections careers (94.7 vs. 119.9 h, p = 0.04) and injecting fentanyl exclusively (98.7 vs. 119.5 h, p = 0.008), respectively. There were 385 drug use events reported over follow-up. There were no episodes of acute respiratory depression which corresponded with participant reports of overdose experiences., Discussion: Our preliminary findings suggest that using a wearable biosensor to monitor physiological changes associated with opioid use was feasible. However, more sensitive biosensors that facilitate triangulation of multiple physiological data points and larger studies of longer duration are needed., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Adoptive cell transfer (ACT) using autologous tumor infiltrating lymphocytes (TILs) was previously shown to yield clinical response in metastatic melanoma patients as an advanced line. Unfortunately, there is no reliable marker for predicting who will benefit from the treatment. We analyzed TIL samples from the infusion bags used for treatment of 57 metastatic melanoma patients and compared their microRNA profiles. The discovery cohort included six responding patients and seven patients with progressive disease, as defined by RECIST1.1. High throughput analysis with NanoString nCounter demonstrated significantly higher levels of miR-34a-5p and miR-22-3p among TIL from non-responders. These results were validated in TIL infusion bag samples from an independent cohort of 44 patients, using qRT-PCR of the individual microRNAs. Using classification trees, a data-driven predictive model for response was built, based on the level of expression of these microRNAs. Patients that achieved stable disease were classified with responders, setting apart the patients with progressive disease. Moreover, the expression levels of miR-34a-5p in the infused TIL created distinct survival groups, which strongly supports its role as a potential biomarker for TIL-ACT therapy. Indeed, when tested against autologous melanoma cells, miR Low TIL cultures exhibited significantly higher cytotoxic activity than miR High TIL cultures, and expressed features of terminally exhausted effectors. Finally, overexpression of miR-34a-5p or miR-22-3p in TIL inhibited their cytotoxic ability in vitro. Overall, we show that a two-microRNA signature correlates with failure of TIL-ACT therapy and survival in melanoma patients.

In a Philadelphia neighbourhood where opioid overdoses are frequent, neighbors used a smartphone app to request and give help for a victim of suspected overdose. A one-year study demonstrated the feasibility of this approach, which empowered the local community to save lives and even respond to overdoses faster than emergency medical services.

Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD., (Copyright © 2020 Slobodin, Yahav and Berger.)

Background: Rapid naloxone administration is crucial in reversing an opioid overdose. We investigated whether equipping community members, including people who use opioids (PWUO), with a smartphone application enabling them to signal and respond to suspected overdose would support naloxone administration in advance of Emrgency Medical Services (EMS)., Methods: This observational cohort study of opioid overdose intervention used a dedicated smartphone app, UnityPhilly, activated by volunteers witnessing an overdose to signal other nearby volunteers in Philadelphia (March 2019 - February 2020). Alerted volunteers chose to respond, or declined to respond, or ignored/missed the alert. Witnessing volunteer was connected to 9-1-1 through a semi-automated telephone call. The primary outcome was layperson-initiated overdose reversal before EMS arrival, and a secondary outcome was hospital transfer. This study is registered with ClinicalTrials.gov, NCT03305497., Findings: 112 volunteers, including 57 PWUO and 55 community members, signaled 291 suspected opioid overdose alerts. 89 (30⸱6%) were false alarms. For 202 true alerts, the rate of layperson initiated naloxone use was 36⸱6% (74/202 cases). Most naloxone-use cases occurred in the street (58⸱11% (43/74)) and some in home settings (22⸱98% (17/74)). The first naloxone dose was provided by a nearby volunteer responding to the alert in 29⸱73% (22/74) of cases and by the signaling volunteer in 70⸱27% (52/74) of cases. Successful reversal was reported in 95⸱9% (71/74) of cases. Layperson intervention preceded EMS by 5 min or more in 59⸱5% of cases. Recovery without hospital transport was reported in 52⸱7% (39/74) of cases., Interpretation: Our findings support the benefits of equipping community members, potentially witnessing suspected opioid overdose, with naloxone and an emergency response community smartphone app, alerting EMS and nearby laypersons to provide additional naloxone., Funding: Funding provided by NIH through NIDA, grant number: 5R34DA044758., Competing Interests: All authors report grants from NIH/NIDA during the course of this study., (© 2020 The Author(s).)

This study examines two theoretical explanations for the existence of close ties among continuing care retirement community residents: the attractiveness theory, which suggests that residents who possess certain attributes are more likely to be perceived as appealing to others; and the homophily theory, which argues that individuals are more likely to have close ties with people who share similar attributes. As a variant of the homophily theory, we also examined whether sharing a physical location makes the existence of certain connections more likely. Data from four continuing care retirement communities were used. To test the attractiveness theory, correlations between the number of individuals who named a person as a significant contact (ego's in-degree) and ego attributes were examined. To test the homophily theory, the median value of existing ties was compared against all possible social ties as though they were randomly formed. Finally, to further test the role of the institutional culture against various motivations that drive social ties-attractiveness and homophily-we used link prediction models with random forests. In support of the homophily theory, beyond the institutional culture, the only consistent predictor of the existence of close ties among residents was sharing a wing in the retirement community (geographic proximity). Therefore, we discuss the role of the physical location in the lives of older adults., Competing Interests: The authors have declared that no competing interests exist.

Background and Objectives: The present study describes whole social networks in 4 adult day care centers (ADCCs) and 4 continuing care retirement communities (CCRCs) in Israel., Method: Each respondent received a list of names of all individuals receiving services in the respective ADCC or CCRC and was asked to indicate whom he/she knows from the list. We derived whole social network properties and used hierarchical cluster analysis to group network settings. We further examined the ability of the social network data to classify respondents as members of either an ADCC or a CCRC., Results: Many social network properties were more favorable in CCRCs than in ADCCs. A striking finding of the present study is that one can classify with a relatively high degree of accuracy a respondent as belonging to an ADCC or a CCRC, simply based on his or her social properties (specifically, number of people who know the participant and are known by the participant)., Implications: Despite some similarities between CCRCs and ADCCs, CCRCs likely allow for more inclusive and active social relations. This information should be valuable to administrators and care providers.

Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.