Factors associated with syphilis incidence in the HIV-infected in the era of highly active antiretrovirals.

After several years of steady decline, syphilis is reemerging globally as a public health hazard, especially among people living with human immunodeficiency virus (HIV). Syphilis resurgence is observed mainly in men who have sex with men (MSM), yet other transmission groups are affected too. In this manuscript, we study the factors associated with syphilis incidence in the Swiss HIV cohort study in the era of highly effective antiretrovirals. Using parametric interval censored models with fixed and time-varying covariates, we studied the immunological, behavioral, and treatment-related elements associated with syphilis incidence in 3 transmission groups: MSM, heterosexuals, and intravenous drug users. Syphilis incidence has been increasing annually since 2005, with up to 74 incident cases per 1000 person-years in 2013, with MSM being the population with the highest burden (92% of cases). While antiretroviral treatment (ART) in general did not affect syphilis incidence, nevirapine (NVP) was associated with a lower hazard of syphilis incidence (multivariable hazard ratio 0.5, 95% confidence interval 0.2-1.0). We observed that condomless sex and younger age were associated with higher syphilis incidence. Moreover, time-updated CD4, nadir CD4, and CD8 cell counts were not associated with syphilis incidence. Finally, testing frequency higher than the recommended once a year routine testing was associated with a 2-fold higher risk of acquiring syphilis. Condomless sex is the main driver of syphilis resurgence in the Swiss HIV Cohort study; ART and immune reconstitution provide no protection against syphilis. This entails targeted interventions and frequent screening of high-risk populations. There is no known effect of NVP on syphilis; therefore, further clinical, epidemiological, and microbiological investigation is necessary to validate our observation

Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study

An affordable, user-friendly fertility-monitoring tool remains an unmet need. We examine in this study the correlation between pulse rate (PR) and the menstrual phases using wrist-worn PR sensors. 91 healthy, non-pregnant women, between 22–42 years old, were recruited for a prospective-observational clinical trial. Participants measured PR during sleep using wrist-worn bracelets with photoplethysmographic sensors. Ovulation day was estimated with “Clearblue Digital-Ovulation-urine test”. Potential behavioral and nutritional confounders were collected daily. 274 ovulatory cycles were recorded from 91 eligible women, with a mean cycle length of 27.3 days (±2.7). We observed a significant increase in PR during the fertile window compared to the menstrual phase (2.1 beat-per-minute, p < 0.01). Moreover, PR during the mid-luteal phase was also significantly elevated compared to the fertile window (1.8 beat-per-minute, p < 0.01), and the menstrual phase (3.8 beat-per-minute, p < 0.01). PR increase in the ovulatory and mid-luteal phase was robust to adjustment for the collected confounders. There is a significant increase of the fertile-window PR (collected during sleep) compared to the menstrual phase. The aforementioned association was robust to the inter- and intra-person variability of menstrual-cycle length, behavioral, and nutritional profiles. Hence, PR monitoring using wearable sensors could be used as one parameter within a multi-parameter fertility awareness-based method

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study

Background: Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women’s nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women’s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window’s closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. Objective: In this study, we probed what phase-based differences a wearable bracelet could detect in users’ WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. Methods: We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. Results: We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Conclusions: Our contributions highlight the impact of artificial intelligence and machine learning’s integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation

Modern fertility awareness methods: Wrist wearables capture the changes of temperature associated with the menstrual cycle

Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests taking one's daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist-skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant=3.21, S.D.=2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained three-day temperature shift was observed in 357/437 cycles (82%), with the lowest cycle temperature occurring in the fertile window 41% of the time. Most temporal shifts (307/357, 86%) occurred on ovulation day or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, this study suggests that WST could be a promising, convenient parameter for future multi-parameter fertility-awareness methods

The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study

Background: As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation. Objective: This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles. Methods: This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases. Results: Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P<.001) and had a higher true-positive rate (54.9% vs 20.2%) for detecting ovulation; however, it also had a higher false-positive rate (8.8% vs 3.6%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2% for wrist skin temperature and 84.8% for BBT. Both temperatures had low negative predictive values (8.8% for wrist skin temperature and 10.9% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves. Conclusions: For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures. Keywords: BBT; basal body temperature; diagnostic accuracy; fertility; menstruation; mobile phone; oral temperature; ovulation; sensor; thermometer; wearable; wrist skin temperature

Monocyte-derived macrophages exhibit distinct and more restricted HIV-1 integration site repertoire than CD4(+) T cells

The host genetic landscape surrounding integrated HIV-1 has an impact on the fate of the provirus. Studies analysing HIV-1 integration sites in macrophages are scarce. We studied HIV-1 integration site patterns in monocyte-derived macrophages (MDMs) and activated CD4(+) T cells derived from seven antiretroviral therapy (ART)-treated HIV-1-infected individuals whose cells were infected ex vivo with autologous HIV-1 isolated during the acute phase of infection. A total of 1,484 unique HIV-1 integration sites were analysed. Their distribution in the human genome and genetic features, and the effects of HIV-1 integrase polymorphisms on the nucleotide selection specificity at these sites were indistinguishable between the two cell types, and among HIV-1 isolates. However, the repertoires of HIV-1-hosting gene clusters overlapped to a higher extent in MDMs than in CD4(+) T cells. The frequencies of HIV-1 integration events in genes encoding HIV-1-interacting proteins were also different between the two cell types. Lastly, HIV-1-hosting genes linked to clonal expansion of latently HIV-1-infected CD4(+) T cells were over-represented in gene hotspots identified in CD4(+) T cells but not in those identified in MDMs. Taken together, the repertoire of genes targeted by HIV-1 in MDMs is distinct from and more restricted than that of CD4(+) T cells

Antibacterial effects of antiretrovirals, potential implications for microbiome studies in HIV

Background Despite being used by more than 18 million people our understanding of the extent of the effects of antiretrovirals on the human body and other organisms remains incomplete. In addition, the direct effect of antiretrovirals on the gut microbiota of HIV-infected individuals has been largely overlooked in microbiome studies concerned with HIV-infected individuals. Methods Here we tested 25 antiretrovirals on Bacillus subtilis and Escherichia coli using a broth microdilution assay to assess whether these drugs have an antibacterial effect. Results We found that several widely used antiretroviral drugs have in vitro antibacterial activity against both gram-positive and gram-negative commensal bacteria. Efavirenz inhibited the growth of B. subtilis with a minimum inhibitory concentration (MIC) of 16 μg/ml (in all three replicates), while 2‘,3‘-dideoxyinosine and zidovudine inhibited the growth of E. coli with an MIC of 16–32 μg/ml and 0.016–0.125 μg/ml (respectively). Conclusions Given the large and increasing number of individuals on antiretrovirals, and the lifelong nature of HIV treatment, this proof-of-concept report could have several potential implications, including an impact of antiretrovirals on bacterial coinfections, as well as potentials for drug discovery and repositioning. </jats:sec

Parent-offspring regression to estimate the heritability of an HIV-1 trait in a realistic setup

Background: Parent-offspring (PO) regression is a central tool to determine the heritability of phenotypic traits; i.e., the relative extent to which those traits are controlled by genetic factors. The applicability of PO regression to viral traits is unclear because the direction of viral transmission—who is the donor (parent) and who is the recipient (offspring)—is typically unknown and viral phylogenies are sparsely sampled. Methods: We assessed the applicability of PO regression in a realistic setting using Ornstein–Uhlenbeck simulated data on phylogenies built from 11,442 Swiss HIV Cohort Study (SHCS) partial pol sequences and set-point viral load (SPVL) data from 3293 patients. Results: We found that the misidentification of donor and recipient plays a minor role in estimating heritability and showed that sparse sampling does not influence the mean heritability estimated by PO regression. A mixed-effect model approach yielded the same heritability as PO regression but could be extended to clusters of size greater than 2 and allowed for the correction of confounding effects. Finally, we used both methods to estimate SPVL heritability in the SHCS. We employed a wide range of transmission pair criteria to measure heritability and found a strong dependence of the heritability estimates to these criteria. For the most conservative genetic distance criteria, for which heritability estimates are conceptually expected to be closest to true heritability, we found estimates ranging from 32 to 46% across different bootstrap criteria. For less conservative distance criteria, we found estimates ranging down to 8%. All estimates did not change substantially after adjusting for host-demographic factors in the mixed-effect model (±2%). Conclusions: For conservative transmission pair criteria, both PO regression and mixed-effect models are flexible and robust tools to estimate the contribution of viral genetic effects to viral traits under real-world settings. Overall, we find a strong effect of viral genetics on SPVL that is not confounded by host demographics