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We conducted a rapid, mixed-methods assessment to understand how COVID-19 affected Latinx sexual minority men (LSMM) and transgender women (LTGW). Using a computer-assisted telephone interviewing software, one interviewer called 52 participants (randomly sampled from a larger HIV prevention pilot study aiming to increase HIV knowledge and testing frequency; n = 36 LSMM and n = 16 LTGW) between 04/27/20-05/18/20. We quantified core domains using the Epidemic-Pandemic Impacts Inventory scale and provided important context through open-ended qualitative questions assessing: 1) COVID-19 infection history and experiences with quarantine; 2) Health and healthcare access; 3) Employment and economic impact of COVID-19. Participants reported increases in physical conflict or verbal arguments with a partner (13.5%) or other adult(s) (19.2%) due to stressors associated with the safer-at-home order. Participants also reported increased alcohol consumption (23.1%), problems with sleep (67.3%) and mental health (78.4%). Further, disruptions in access to Pre-Exposure Prophylaxis or PrEP–a daily pill to prevent HIV–occurred (33.3% of 18 participants who reported being on PrEP). Many said they received less medical attention than usual (34.6%), and LTGW reported delays in critical gender-affirming hormones/procedures. Half of the participants lost their jobs (50.0%); many undocumented participants relayed additional financial concerns because they did not qualify for financial assistance. Though no COVID-19 infections were noted, COVID-19 dramatically impacted other aspects of health and overall wellbeing of LSMM and LTGW. Public health responses should address the stressors faced by LSMM and LTGW during the COVID-19 pandemic and the impact on wellbeing.

Nirsevimab is a monoclonal antibody to the RSV fusion protein that has an extended half-life. In this clinical trial, a single dose of nirsevimab resulted in a significantly lower incidence of medically attended RSV-associated lower respiratory tract infection than that with placebo.

In January 2015, an outbreak of undiagnosed human immunodeficiency virus (HIV) infections among persons who inject drugs (PWID) was recognized in rural Indiana. By September 2016, 205 persons in this community of approximately 4400 had received a diagnosis of HIV infection. We report results of new approaches to analyzing epidemiologic and laboratory data to understand transmission during this outbreak. HIV genetic distances were calculated using the polymerase region. Networks were generated using data about reported high-risk contacts, viral genetic similarity, and their most parsimonious combinations. Sample collection dates and recency assay results were used to infer dates of infection. Epidemiologic and laboratory data each generated large and dense networks. Integration of these data revealed subgroups with epidemiologic and genetic commonalities, one of which appeared to contain the earliest infections. Predicted infection dates suggest that transmission began in 2011, underwent explosive growth in mid-2014, and slowed after the declaration of a public health emergency. Results from this phylodynamic analysis suggest that the majority of infections had likely already occurred when the investigation began and that early transmission may have been associated with sexual activity and injection drug use. Early and sustained efforts are needed to detect infections and prevent or interrupt rapid transmission within networks of uninfected PWID.

Pain perception is decreased by shifting attentional focus away from a threatening event. This attentional analgesia engages parallel descending control pathways from anterior cingulate (ACC) to locus coeruleus, and ACC to periaqueductal grey (PAG) – rostral ventromedial medulla (RVM), indicating possible roles for noradrenergic or opioidergic neuromodulators. To determine which pathway modulates nociceptive activity in humans, we used simultaneous whole brain-spinal cord pharmacological-fMRI (N = 39) across three sessions. Noxious thermal forearm stimulation generated somatotopic-activation of dorsal horn (DH) whose activity correlated with pain report and mirrored attentional pain modulation. Activity in an adjacent cluster reported the interaction between task and noxious stimulus. Effective connectivity analysis revealed that ACC interacts with PAG and RVM to modulate spinal cord activity. Blocking endogenous opioids with Naltrexone impairs attentional analgesia and disrupts RVM-spinal and ACC-PAG connectivity. Noradrenergic augmentation with Reboxetine did not alter attentional analgesia. Cognitive pain modulation involves opioidergic ACC-PAG-RVM descending control which suppresses spinal nociceptive activity.

The overall efficacy of adenotonsillectomy (AT) in treatment of obstructive sleep apnea syndrome (OSAS) in children is unknown. Although success rates are likely lower than previously estimated, factors that promote incomplete resolution of OSAS after AT remain undefined. To quantify the effect of demographic and clinical confounders known to impact the success of AT in treating OSAS. A multicenter collaborative retrospective review of all nocturnal polysomnograms performed both preoperatively and postoperatively on otherwise healthy children undergoing AT for the diagnosis of OSAS was conducted at six pediatric sleep centers in the United States and two in Europe. Multivariate generalized linear modeling was used to assess contributions of specific demographic factors on the post-AT obstructive apnea-hypopnea index (AHI). Data from 578 children (mean age, 6.9 +/- 3.8 yr) were analyzed, of which approximately 50% of included children were obese. AT resulted in a significant AHI reduction from 18.2 +/- 21.4 to 4.1 +/- 6.4/hour total sleep time (P < 0.001). Of the 578 children, only 157 (27.2%) had complete resolution of OSAS (i.e., post-AT AHI <1/h total sleep time). Age and body mass index z-score emerged as the two principal factors contributing to post-AT AHI (P < 0.001), with modest contributions by the presence of asthma and magnitude of pre-AT AHI (P < 0.05) among nonobese children. AT leads to significant improvements in indices of sleep-disordered breathing in children. However, residual disease is present in a large proportion of children after AT, particularly among older (>7 yr) or obese children. In addition, the presence of severe OSAS in nonobese children or of chronic asthma warrants post-AT nocturnal polysomnography, in view of the higher risk for residual OSAS.

Selecting an appropriate population pharmacokinetic (PK) model for individual patients in model‐informed precision dosing (MIPD) can be challenging, particularly in the absence of therapeutic drug monitoring (TDM) samples. We developed a machine learning (ML) model to guide individualized PK model selection for a priori MIPD of vancomycin based on routinely recorded patient characteristics. This retrospective analysis included 343,636 vancomycin TDM records, each from a distinct adult patient across 156 healthcare centers, along with a priori predictions from six PK models. A multi‐label classification approach was applied, labeling PK model predictions based on whether they fell within 80%–125% of observed TDM values. Various modeling strategies were evaluated using XGBoost as the base algorithm, with binary relevance selected for the final model. At the prediction stage, PK models were ranked and averaged for each patient based on ML‐predicted probabilities that predictions would fall within 80%–125% of the observed concentration. Selecting the highest ranked PK model for each patient and ML‐based model averaging outperformed all single PK models, body mass index‐based selection, and naive averaging. On a population level, these ML approaches resulted in more accurate predictions, a higher proportion of predictions within 80%–125% of observed vancomycin concentrations, and no systematic bias. Predictive performance declined with lower ML‐assigned rankings, and selecting the lowest‐ranked PK model for each patient resulted in worse performance than the worst‐performing single PK model. By guiding the selection of appropriate models and avoiding less suitable ones, ML approaches for a priori MIPD may improve early dosing decisions. Schematic overview of the workflow used to train and apply the multi‐label classification model for PK model selection and averaging in a priori vancomycin precision dosing.

Accurately characterizing the vertical distribution of aerosols and their cloud-forming properties is crucial for understanding aerosol-cloud interactions and their impact on climate. This study presents a novel technique for retrieving vertical profiles of aerosols, cloud condensation nuclei (CCN), and ice nucleating particles (INP) by combining micropulse lidar, radiosonde, and ground-based aerosol measurements. Herein, the technique is applied to data collected by our team at Texas A&M University during the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) campaign. Ground-based aerosol size distribution and CCN counter data are used to estimate the value of the aerosol hygroscopicity parameter, κ. The derived κ, together with Mie scattering theory and the relative humidity profile from the radiosonde, is used to estimate aerosol size growth and the associated increase in backscatter at each altitude. We then correct the lidar backscatter to dry conditions to produce the dry aerosol backscatter coefficient profile. The dry aerosol backscatter coefficient profile is linearly scaled to collocated surface measurements of aerosols, CCN, and INP to produce corresponding vertical profiles. Combining lidar backscatter profiles with aerosol and cloud nucleation measurements leads to a more realistic representation of vertical distributions of aerosol properties. The method could be readily applied to lidar measurements in future field campaigns.

Background: With a notably narrow therapeutic window and wide intra- and interindividual pharmacokinetic (PK) variability, initial weight-based dosing along with routine therapeutic drug monitoring of tacrolimus are employed to optimize its clinical utilization. Both supratherapeutic and subtherapeutic tacrolimus concentrations can result in poor outcomes, thus tacrolimus PK variability is particularly important to consider in the pediatric population given the differences in absorption, distribution, metabolism, and excretion among children of various sizes and at different stages of development. The primary goals of the current study were to develop a population PK (PopPK) model for tacrolimus IV continuous infusion in the pediatric and young adult hematopoietic cell transplant (HCT) population and implement the PopPK model in a clinically available Bayesian forecasting tool. Methods: A retrospective chart review was conducted of 111 pediatric and young adult patients who received IV tacrolimus by continuous infusion early in the post-transplant period during HCT from February 2016 to July 2020 at our institution. PopPK model building was performed in NONMEM. The PopPK model building process included identifying structural and random effects models that best fit the data and then identifying which patient-specific covariates (if any) further improved model fit. Results: A total of 1,648 tacrolimus plasma steady-state trough concentrations were included in the PopPK modeling process. A 2-compartment structural model best fit the data. Allometrically-scaled weight was a covariate that improved estimation of both clearance and volume of distribution. Overall, model predictions only showed moderate bias, with minor under-prediction at lower concentrations and minor over-prediction at higher predicted concentrations. The model was implemented in a Bayesian dosing tool and made available at the point-of-care. Discussion: Novel therapeutic drug monitoring strategies for tacrolimus within the pediatric and young adult HCT population are necessary to reduce toxicity and improve efficacy in clinical practice. The model developed presents clinical utility in optimizing the use of tacrolimus by enabling model-guided, individualized dosing of IV, continuous tacrolimus via a Bayesian forecasting platform.

Road mortality of animals (roadkill) threatens public safety and wildlife populations. As mitigation tools, predictive models of roadkill are becoming more common in the published literature; however, few models generalize across multiple taxa, and thus are less useful for management scenarios that account for multiple target species. Using a dataset of 653 vertebrate roadkills collected from 2 parks in southern Ontario, we constructed generalized linear mixed models to determine the simultaneous risk factors for bird, frog, mammal, five-lined skink (Eumeces fasciatus), snake, toad, and turtle hatchling roadkills from among a set of 8 potential predictor variables. Posted road speed limit was the dominant roadkill predictor (positive coefficient), followed by maximum daily temperature (positive), habitat diversity (positive), and distance from wetlands (negative). All else being equal, as road speed limits increase from 20 km/hr to 50 km/hr, the model predicted the season's mean roadkill probability for a given location to increase from less than 0.1 to 0.75. Conversely, roadkill probability declined from 0.55 to 0.29 as distance from wetland edges increases from 0 km to 1 km. Model diagnostics calculated from randomly resampled cross-validation datasets indicated that the best model formulation had an averaged predictive accuracy of 67.5% and an area under the curve (AUC) of 0.867. The best model also reflected seasonal patterns of animal behavior, including late-summer frog movements and fall turtle hatching events. The best model also compared favorably to single-taxon equivalent models. To reduce the incidence of vertebrate roadkill, we recommend that managers lower road speed limits, especially on roads near diverse habitats and near wetlands, and on warmer days if temporary signage is used.

Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.