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BackgroundDespite the effective scale-up of HIV testing and treatment programs, only 75% of people living with HIV (PLWH) globally know their status, and this rate is lower among men. This highlights the importance of implementing HIV testing and linkage interventions with a high uptake in this population. In a cluster randomized controlled trial conducted in Kenya between 2013 and 2015, we found that assisted partner services (APS) for HIV-exposed partners of newly diagnosed PLWH safely reached more HIV-exposed individuals with HIV testing compared with client referral alone. However, more data are needed to evaluate APS implementation in a real-world setting. ObjectiveThis study aims to evaluate the effectiveness, acceptability, fidelity, and cost of APS when integrated into existing HIV testing services (HTS) in Western Kenya. MethodsOur study team from the University of Washington and PATH is integrating APS into 31 health facilities in Western Kenya. We are enrolling females newly diagnosed with HIV (index clients) who consent to receiving APS, their male sexual partners, and female sexual partners of male sexual partners who tested HIV positive. Female index clients and sexual partners testing HIV positive will be followed up at 6 weeks, 6 months, and 12 months postenrollment to assess linkage to care, antiretroviral therapy initiation, and HIV viral load suppression. We will evaluate the acceptability, fidelity, and cost of real-world implementation of APS via in-depth interviews conducted with national, county, and subcounty-level policy makers responsible for HTS. Facility health staff providing HTS and APS, in addition to staff working with the study project team, will also be interviewed. We will also conduct direct observations of facility infrastructure and clinical procedures and extract data from the facilities and county and national databases. ResultsAs of March 2020, we have recruited 1724 female index clients, 3201 male partners, and 1585 female partners. We have completed study recruitment as well as 6-week (2936/2973, 98.75%), 6-month (1596/1641, 97.25%), and 12-month (725/797, 90.9%) follow-up visits. Preliminary analyses show that facilities scaling up APS identify approximately 12-18 new HIV-positive males for every 100 men contacted and tested. We are currently completing the remaining follow-up interviews and incorporating an HIV self-testing component into the study in response to the COVID-19 pandemic. ConclusionsThe results will help bridge the gap between clinical research findings and real-world practice and provide guidance regarding optimal strategies for APS integration into routine HIV service delivery. International Registered Report Identifier (IRRID)DERR1-10.2196/27262

BackgroundHIV testing guidelines provided by the Centers for Disease Control and Prevention (CDC) are continually changing to reflect advancements in new testing technology. Evaluation of existing and new point-of-care (POC) HIV tests is crucial to inform testing guidelines and provide information to clinicians and other HIV test providers. Characterizing the performance of POC HIV tests using unprocessed specimens can provide estimates for the window period of detection, or the time from HIV acquisition to test positivity, which allows clinicians and other HIV providers to select the appropriate POC HIV tests for persons who may be recently infected with HIV. ObjectiveThis paper describes the protocols and procedures used to evaluate the performance of the newest POC tests and determine their sensitivity during early HIV infection. MethodsProject DETECT is a CDC-funded study that is evaluating POC HIV test performance. Part 1 is a cross-sectional, retrospective study comparing behavioral characteristics and HIV prevalence of the overall population of the Public Health–Seattle & King County (PHSKC) Sexually Transmitted Disease (STD) Clinic to Project DETECT participants enrolled in part 2. Part 2 is a cross-sectional, prospective study evaluating POC HIV tests in real time using unprocessed whole blood and oral fluid specimens. A POC nucleic acid test (NAT) was added to the panel of HIV tests in June 2018. Part 3 is a longitudinal, prospective study evaluating seroconversion sensitivity of POC HIV tests through serial follow-up testing. For comparison, HIV-1 RNA and HIV-1/HIV-2 antigen/antibody tests are also performed for participants enrolled in part 2 or 3. A behavioral survey that collects information about demographics, history of HIV testing, STD history, symptoms of acute HIV infection, substance use, sexual behaviors in the aggregate and with recent partners, and use of pre-exposure prophylaxis and antiretroviral therapy is completed at each part 2 or 3 visit. ResultsBetween September 2015 and March 2019, there were 14,990 Project DETECT–eligible visits (part 1) to the PHSKC STD Clinic resulting in 1819 part 2 Project DETECT study visits. The longitudinal study within Project DETECT (part 3) enrolled 27 participants with discordant POC test results from their part 2 visit, and 10 (37%) were followed until they had fully seroconverted with concordant positive POC test results. Behavioral survey data and HIV test results, sensitivity, and specificity will be presented elsewhere. ConclusionsStudies such as Project DETECT are critical for evaluating POC HIV test devices as well as describing characteristics of persons at risk for HIV acquisition in the United States. HIV tests in development, including POC NATs, will provide new opportunities for HIV testing programs. International Registered Report Identifier (IRRID)RR1-10.2196/16332

BackgroundGroup sex events (GSEs) are common among cisgender men who have sex with men (MSM), pose a unique risk profile for HIV and sexually transmitted disease (STD) transmission, and may be on the rise, in part because of Web-based networking platforms. However, collecting data on GSEs can be challenging, and many gaps exist in our knowledge about GSE participation among MSM. ObjectiveThe objective of this study was to develop survey questions addressing aggregate and partner-specific group sex behaviors to measure prevalence of GSEs and associated risks in persons participating in Project Diagnostic Evaluation To Expand Critical Testing Technologies (DETECT), including MSM seeking HIV and STD testing at a public clinic in Seattle, Washington. MethodsWe developed a computer self-assisted survey that included questions about participant demographics, sexual history, and risk behaviors, including group sex, as a part of Project DETECT, a Centers for Disease Control and Prevention–funded study evaluating point-of-care HIV tests. Aggregate and partner-specific questions asked about participation in all GSEs, threesomes, and four-or-more-somes including questions about number and HIV status of sex partners and condom use during the events. To evaluate question performance, we assessed the discrepancies in reporting between the aggregate and partner-specific questions, quantified question refusal rates, and calculated the additional time required to answer the GSE questions. Information about network density (number of partnerships of overlapping duration) was estimated and compared for MSM who did and did not report GSEs. ResultsAmong 841 visits by 690 MSM who were asked any group sex survey question, participation in a GSE of any type in the past 3 months was reported at 293 visits (293/841, 34.8%). We found that 9.0% (76/841) of MSM in the sample reported ≥1 four-or-more-some in the partner-specific questions but did not report in the aggregate. The proportion of refusals on any given aggregate GSE-related question ranged from 0% (0/273) to 10.6% (15/141) (median 2.6%) and partner-specific questions ranged from 0% (0/143) to 22% (5/23) (median 3.0%), with questions about four-or-more-somes having the highest proportions of refusals. Completing the aggregate group sex questions added 1 to 2 minutes and the partner-specific questions added an additional 2 to 4 minutes per partner to the total survey length. As expected, the partner-specific GSE questions documented higher density of sexual networks that was not captured by asking about total partner counts and overlap of specific partnerships. ConclusionsWe found that the Project DETECT survey was able to obtain nuanced information about GSEs. The question skip patterns and consistency checks were effective, and survey fatigue was minimal. More research is needed on GSEs, and our survey represents a promising data collection tool to help fill gaps in knowledge about the subject.

Craniofacial phenotyping is critical for both syndrome delineation and diagnosis because craniofacial abnormalities occur in 30% of characterized genetic syndromes. Clinical reports, textbooks, and available software tools typically provide two-dimensional, static images and illustrations of the characteristic phenotypes of genetic syndromes. In this work, we provide an interactive web application that provides three-dimensional, dynamic visualizations for the characteristic craniofacial effects of 95 syndromes. Users can visualize syndrome facial appearance estimates quantified from data and easily compare craniofacial phenotypes of different syndromes. Our application also provides a map of morphological similarity between a target syndrome and other syndromes. Finally, users can upload 3D facial scans of individuals and compare them to our syndrome atlas estimates. In summary, we provide an interactive reference for the craniofacial phenotypes of syndromes that allows for precise, individual-specific comparisons of dysmorphology.

Human genetic syndromes are often challenging to diagnose clinically. Facial phenotype is a key diagnostic indicator for hundreds of genetic syndromes and computer-assisted facial phenotyping is a promising approach to assist diagnosis. Most previous approaches to automated face-based syndrome diagnosis have analyzed different datasets of either 2D images or surface mesh-based 3D facial representations, making direct comparisons of performance challenging. In this work, we developed a set of subject-matched 2D and 3D facial representations, which we then analyzed with the aim of comparing the performance of 2D and 3D image-based approaches to computer-assisted syndrome diagnosis. This work represents the most comprehensive subject-matched analyses to date on this topic. In our analyses of 1907 subject faces representing 43 different genetic syndromes, 3D surface-based syndrome classification models significantly outperformed 2D image-based models trained and evaluated on the same subject faces. These results suggest that the clinical adoption of 3D facial scanning technology and continued collection of syndromic 3D facial scan data may substantially improve face-based syndrome diagnosis.

Many genetic syndromes are associated with distinctive facial features. Several computer-assisted methods have been proposed that make use of facial features for syndrome diagnosis. Training supervised classifiers, the most common approach for this purpose, requires large, comprehensive, and difficult to collect databases of syndromic facial images. In this work, we use unsupervised, normalizing flow-based manifold and density estimation models trained entirely on unaffected subjects to detect syndromic 3D faces as statistical outliers. Furthermore, we demonstrate a general, user-friendly, gradient-based interpretability mechanism that enables clinicians and patients to understand model inferences. 3D facial surface scans of 2471 unaffected subjects and 1629 syndromic subjects representing 262 different genetic syndromes were used to train and evaluate the models. The flow-based models outperformed unsupervised comparison methods, with the best model achieving an ROC-AUC of 86.3% on a challenging, age and sex diverse data set. In addition to highlighting the viability of outlier-based syndrome screening tools, our methods generalize and extend previously proposed outlier scores for 3D face-based syndrome detection, resulting in improved performance for unsupervised syndrome detection.

One of the primary difficulties in treating patients with genetic syndromes is diagnosing their condition. Many syndromes are associated with characteristic facial features that can be imaged and utilized by computer-assisted diagnosis systems. In this work, we develop a novel 3D facial surface modeling approach with the objective of maximizing diagnostic model interpretability within a flexible deep learning framework. Therefore, an invertible normalizing flow architecture is introduced to enable both inferential and generative tasks in a unified and efficient manner. The proposed model can be used (1) to infer syndrome diagnosis and other demographic variables given a 3D facial surface scan and (2) to explain model inferences to non-technical users via multiple interpretability mechanisms. The model was trained and evaluated on more than 4700 facial surface scans from subjects with 47 different syndromes. For the challenging task of predicting syndrome diagnosis given a new 3D facial surface scan, age, and sex of a subject, the model achieves a competitive overall top-1 accuracy of 71%, and a mean sensitivity of 43% across all syndrome classes. We believe that invertible models such as the one presented in this work can achieve competitive inferential performance while greatly increasing model interpretability in the domain of medical diagnosis.

Background: Gender-affirming facial surgery (GFS) is pursued by transgender individuals who desire facial features that better reflect their gender identity. Currently, there are a few objective guidelines to justify and facilitate effective surgical decision making. Objective: To quantify the effect of sex on adult facial size and shape through an analysis of three-dimensional (3D) facial surface images. Materials and Methods: Facial measurements were obtained by registering an atlas facial surface to 3D surface scans of 545 males and 1028 females older than 20 years of age. The differences between male and female faces were analyzed and visualized for a set of predefined surgically relevant facial regions. Results: On average, male faces are 7.3% larger than female faces (Cohens D = 2.17). Sex is associated with significant facial shape differences (p

BackgroundHIV testing is the gateway to HIV treatment and prevention. HIV self-testing (HIVST) has potential to increase testing; however, the potential population-level impact of HIVST on the HIV epidemic and the best strategies for promoting HIVST are unknown. Our aim is to inform public health approaches for promoting HIVST as part of a comprehensive strategy to reduce HIV incidence.MethodsStochastic network-based HIV transmission models were used to estimate how different HIVST strategies would affect HIV incidence in Seattle and Atlanta over 10 years. We included four types of HIV testers and implemented nine replacement and eleven supplementation strategies for HIVST.ResultsReplacement of clinic-based tests with HIVST increased HIV incidence in Seattle and Atlanta. The benefits of supplementary strategies depended on the tester type using HIVST. Targeting non-testers averted the highest number of cases per test. In Seattle 2.2 (95%SI=-77, 100.4) and 4.7 (95%SI=-35.7, 60.1) infections were averted per 1000 HIVST when non-testers used HIVST once or twice per year respectively. In Atlanta the comparable rates were 8.0 (95%SI=-60.3 to 77.7) and 6.7 (95%SI=-37.7, 41.0). Paradoxically, increasing testing among risk-based testers using HIVST increased incidence.ConclusionsThe population-level impact of HIVST depends on who is reached with HIVST, how kits are used, and by characteristics of the underlying epidemic and HIV care infrastructure. Targeted HIVST can be an effective component of a comprehensive HIV testing strategy. More work is needed to understand how to identify and target non-testers for self-testing implementation.

Kv1.3 potassium channels, expressed by proinflammatory central nervous system mononuclear phagocytes (CNS-MPs), are promising therapeutic targets for modulating neuroinflammation in Alzheimer's disease (AD). The molecular characteristics of Kv1.3-high CNS-MPs and their cellular origin from microglia or CNS-infiltrating monocytes are unclear. While Kv1.3 blockade reduces amyloid beta (Aβ) burden in mouse models, the downstream immune effects on molecular profiles of CNS-MPs remain unknown. We show that functional Kv1.3 channels are selectively expressed by a subset of CD11b+CD45+ CNS-MPs acutely isolated from an Aβ mouse model (5xFAD) as well as fresh postmortem human AD brain. Transcriptomic profiling of purified CD11b+Kv1.3+ CNS-MPs, CD11b+CD45int Kv1.3neg microglia, and peripheral monocytes from 5xFAD mice revealed that Kv1.3-high CNS-MPs highly express canonical microglial markers (Tmem119, P2ry12) and are distinct from peripheral Ly6chigh/Ly6clow monocytes. Unlike homeostatic microglia, Kv1.3-high CNS-MPs express relatively lower levels of homeostatic genes, higher levels of CD11c, and increased levels of glutamatergic transcripts, potentially representing phagocytic uptake of neuronal elements. Using irradiation bone marrow CD45.1/CD45.2 chimerism in 5xFAD mice, we show that Kv1.3+ CNS-MPs originate from microglia and not blood-derived monocytes. We show that Kv1.3 channels regulate membrane potential and early signaling events in microglia. Finally, in vivo blockade of Kv1.3 channels in 5xFAD mice by ShK-223 reduced Aβ burden, increased CD11c+ CNS-MPs, and expression of phagocytic genes while suppressing proinflammatory genes (IL1b). Our results confirm the microglial origin and identify unique molecular features of Kv1.3-expressing CNS-MPs. In addition, we provide evidence for CNS immunomodulation by Kv1.3 blockers in AD mouse models resulting in a prophagocytic phenotype.

PURPOSE: Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. METHODS: We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. RESULTS: Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. CONCLUSION: Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of unaffected relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.