IgE production plays a crucial role in protective as well as pathogenic type 2 immune responses. Although the cytokine IL-4 is required for the development of IgE-producing plasma cells, the source of IL-4 and cellular requirements for optimal IgE responses remain unclear. Recent evidence suggests that T follicular helper (Tfh) cells are the primary producer of IL-4 in the reactive lymph node during type 2 immune responses. As Tfh cells are also required for the development of plasmablasts derived from germinal center and extrafollicular sources, we hypothesized that this cell subset is essential for the IgE plasmablast response. In this study, we show that during intestinal helminth infection, IL-4 derived from Tfh cells is required for IgE class switching and plasmablast formation. Notably, early IgE class switching did not require germinal center formation. Additionally, Tfh cell-derived IL-4 was required to maintain the Th2 response in the mesenteric lymph nodes of infected mice. Collectively, our results indicate that IL-4-producing Tfh cells are central orchestrators of the type 2 immune response in the reactive lymph nodes during parasitic helminth infection.

Interleukin-17 (IL-17)-producing gd (gd17) T cells are innate-like lymphocytes that contribute to protective anti-microbial responses but are also implicated in pathogenic inflammation at barrier sites. Understanding tissue-specific signals that regulate this subset is important to boost host defense mechanisms, but also to mitigate immunopathology. Here, we demonstrate that prostaglandin E 2 (PGE 2 ), a cyclooxygenase-dependent member of the eicosanoid family, directly enhances cytokine production by circulating and tissue-specific gd17 T cells in vitro. Gain-and loss-of-function in vivo approaches further reveal that although provision of PGE 2 amplifies psoriasiform inflammation, ablation of host mPGES1-dependent PGE 2 synthesis is dispensable for cutaneous gd17 T cell activation. By contrast, loss of endogenous PGE 2 production or depletion of the gut microbiota compromises intestinal gd17 T cell responses and increases disease severity during experimental colitis. Together, our results demonstrate how a lipid mediator can synergize with tissuespecific signals to enhance innate lymphocyte production of IL-17 during barrier inflammation.

BackgroundChlamydia trachomatis(CT) andNeisseria gonorrhoeae(GC) resulted in over 200 million new sexually transmitted infections last year. Self-sampling strategies alone or combined with digital innovations (ie, online, mobile or computing technologies supporting self-sampling) could improve screening methods. Evidence on all outcomes has not yet been synthesised, so we conducted a systematic review and meta-analysis to address this limitation.MethodsWe searched three databases (period: 1 January 2000–6 January 2023) for reports on self-sampling for CT/GC testing. Outcomes considered for inclusion were: accuracy, feasibility, patient-centred and impact (ie, changes in linkage to care, first-time testers, uptake, turnaround time or referrals attributable to self-sampling).We used bivariate regression models to meta-analyse accuracy measures from self-sampled CT/GC tests and obtain pooled sensitivity/specificity estimates. We assessed quality with Cochrane Risk of Bias Tool-2, Newcastle–Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies-2 tool.ResultsWe summarised results from 45 studies reporting self-sampling alone (73.3%; 33 of 45) or combined with digital innovations (26.7%; 12 of 45) conducted in 10 high-income (HICs; n=34) and 8 low/middle-income countries (LMICs; n=11). 95.6% (43 of 45) were observational, while 4.4% (2 of 45) were randomised clinical trials.We noted that pooled sensitivity (n=13) for CT/GC was higher in extragenital self-sampling (>91.6% (86.0%–95.1%)) than in vaginal self-sampling (79.6% (62.1%–90.3%)), while pooled specificity remained high (>99.0% (98.2%–99.5%)).Participants found self-sampling highly acceptable (80.0%–100.0%; n=24), but preference varied (23.1%–83.0%; n=16).Self-sampling reached 51.0%–70.0% (n=3) of first-time testers and resulted in 89.0%–100.0% (n=3) linkages to care. Digital innovations led to 65.0%–92% engagement and 43.8%–57.1% kit return rates (n=3).Quality of studies varied.DiscussionSelf-sampling had mixed sensitivity, reached first-time testers and was accepted with high linkages to care. We recommend self-sampling for CT/GC in HICs but additional evaluations in LMICs. Digital innovations impacted engagement and may reduce disease burden in hard-to-reach populations.PROSPERO registration numberCRD42021262950.

With a prevalence almost twice as high as the national average, people living in South African townships are particularly impacted by the HIV epidemic. Yet, it remains unclear how socioeconomic factors impact the risk of HIV infection within township populations. Our objective was to estimate the extent to which socioeconomic factors (dwelling situation, education, employment status, and monthly income) explain the risk of HIV in South African township populations, after controlling for behavioural and individual risk factors. Using Bayesian logistic regression, we analysed secondary data from a quasi-randomised trial which recruited participants (N = 3095) from townships located across three subdistricts of Cape Town. We controlled for individual factors (age, sex, marital status, testing history, HIV exposure, comorbidities, and tuberculosis infection) and behavioural factors (unprotected sex, sex with multiple partners, with sex workers, with a partner living with HIV, under the influence of alcohol or drugs), and accounted for the uncertainty due to missing data through multiple imputation. We found that residing in informal dwellings and not having post-secondary education increased the odds of HIV (aOR, 89% CrI: 1.34, 1.07–1.68 and 1.82, 1.29–2.61, respectively), after controlling for subdistrict of residence, individual, and behavioural factors. Additionally, our results suggest different pathways for how socioeconomic status (SES) affect HIV infection in males and female participants: while socioeconomic factors associated with lower SES seem to be associated with a decreased likelihood of having recently sough HIV testing among male participants, they are associated with increased sexual risk taking which, among female participants, increase the risk of HIV. Our analyses demonstrate that social determinants of health are at the root of the HIV epidemic and affect the risk of HIV in multiple ways. These findings stress the need for the deployment of programs that specifically address social determinants of health.

Background: Low-risk perception is an important barrier to the utilization of HIV services. In this context, offering an online platform for people to assess their risk of HIV and inform their decision to test can be impactful in increasing testing uptake. Using secondary data from the HIVSmart! quasirandomized trial, we aimed to identify predictors of HIV, develop a risk staging model for South African township populations, and validate it in combination with the HIVSmart! digital self-testing program. Setting: Townships in Cape Town, South Africa. Methods: Using Bayesian predictive projection, we identified predictors of HIV and constructed a risk assessment model that we validated in external data. Results: Our analyses included 3095 participants from the HIVSmart! trial. We identified a model of 5 predictors (being unmarried, HIV testing history, having had sex with a partner living with HIV, dwelling situation, and education) that performed best during external validation (area under the receiver operating characteristic curve, 89% credible intervals: 0.71, 0.68 to 0.72). The sensitivity of our HIV risk staging model was 91.0% (89.1% to 92.7%) and the specificity was 13.2% (8.5% to 19.8%) but increased when combined with a digital HIV self-testing program, the specificity was 91.6% (95.9% to 96.4%) and sensitivity remained similar at 90.9% (89.1% to 92.6%). Conclusions: This is the first validated digital HIV risk assessment tool developed for South African township populations and the first study to evaluate the added value of a risk assessment tool with an app-based HIV self-testing program. Study findings are relevant for application of digital programs to improve utilization of HIV testing services.