Antibody response to three-dose anti-SARS-CoV-2 mRNA-vaccination in treated solid cancer patients.

Solid cancer patients are at higher risk of SARS-CoV-2 infection and severe complications. Moreover, vaccine-induced antibody response is impaired in patients on anticancer treatment. In this retrospective, observational, hypothesis-generating, cohort study, we assessed the antibody response to the third dose of mRNA vaccine in a convenience sample of patients on anticancer treatment, comparing it to that of the primary two-dose cycle. Among 99 patients included, 62.6% were ≥60 years old, 32.3% males, 67.7% with advanced disease. Exactly 40.4% were receiving biological therapy, 16.2% chemotherapy only and 7.1% both treatments. After the third dose, seroconversion rate seems to increase significantly, especially in non-responders to two doses. Heterologous vaccine-type regimen (two-dose mRNA-1273 and subsequent tozinameran or vice versa) results in higher antibody levels. This explorative study suggests that repeated doses of mRNA-vaccines could be associated with a better antibody response in this population. Furthermore, heterologous vaccine-type three-dose vaccination seems more effective in this population. Since this is a hypothesis-generating study, adequately statistically powered studies should validate these results.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Cross-species comparative analysis of single presynapses.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Percutaneous Vertebral Reconstruction (PVR) Technique of Pathological Compression Fractures: An Innovative Combined Treatment of Microwave Ablation, Bilateral Expandable Titanium SpineJack Implants Followed by Vertebroplasty.

(1) Background: to retrospectively evaluate safety and efficacy of combined microwave ablation (MWA) and bilateral expandable titanium SpineJack (SJ) implants followed by vertebroplasty (VP) for the treatment of painful thoracolumbar pathological vertebral compression fracture. (2) Methods: from July 2017 to October 2022, twenty-eight patients (13 women and 15 men; mean age 68 ± 11 years) with a history of primary neoplasm and thirty-six painful vertebral metastases with vertebral compression fracture underwent combined MWA and bilateral expandable titanium SpineJack implants with vertebroplasty. We analyzed safety through complications rate, and efficacy through vertebral height restoration and pain decrease, evaluated using a visual analogue scale (VAS), and Functional Mobility Scale (FMS), and local tumor control. Contrast-enhanced CT scans were performed at 1, 3, and 6 months and a contrast-enhanced spine MRI at 6 months after the procedure. (3) Results: Technical success rate was 100%. No procedure-related major complications or death occurred. Vertebral height restoration was observed in 22 levels (58%), with a mean anterior height restoration of 2.6 mm ± 0.6 and a mean middle height restoration of 4.4 mm ± 0.6 (p < 0.001). Mean VAS score of pain evaluation on the day before treatment was 6.3 ± 1.5 (range 4-9). At the 6-month evaluation, the median VAS score for pain was 0.4 ± 0.6 (range 0-2) with a mean reduction of 93.65% (6.8 ± 0.7 vs. 0.4 ± 0.6; p < 0.000) compared with baseline evaluation. Contrast-enhanced CT scans were performed at 1, 3, and 6 months and a contrast-enhanced spine MRI was performed at 6 months after the procedure, showing no local recurrence, implant displacement, or new fractures in the treated site. (4) Conclusions: combined microwave ablation and bilateral expandable titanium SpineJack implants with vertebroplasty is a safe and effective procedure for the treatment of pathological compressive vertebral fractures. The vertebral stabilization achieved early and persistent pain relief, increasing patient mobility, improving recovery of walking capacity, and providing local tumor control.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Prediction of neuropathologic lesions from clinical data.

INTRODUCTION: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. METHODS: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. RESULTS: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. DISCUSSION: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.

OBJECTIVE: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established. BACKGROUND: Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity. METHODS: Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state. RESULTS: The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery. CONCLUSIONS: Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Safety and Feasibility of Steerable Radiofrequency Ablation in Combination with Cementoplasty for the Treatment of Large Extraspinal Bone Metastases.

Background: Radiofrequency ablation (RFA) and cementoplasty, individually and in concert, has been adopted as palliative interventional strategies to reduce pain caused by bone metastases and prevent skeletal related events. We aim to evaluate the feasibility and safety of a steerable RFA device with an articulating bipolar extensible electrode for the treatment of extraspinal bone metastases. Methods: All data were retrospectively reviewed. All the ablation procedures were performed using a steerable RFA device (STAR, Merit Medical Systems, Inc., South Jordan, UT, USA). The pain was assessed with a VAS score before treatment and at 1-week and 3-, 6-, and 12-month follow-up. The Functional Mobility Scale (FMS) was recorded preoperatively and 1 month after the treatment through a four-point scale (4, bedridden; 3, use of wheelchair; 2, limited painful ambulation; 1, normal ambulation). Technical success was defined as successful intraoperative ablation and cementoplasty without major complications. Results: A statistically significant reduction of the median VAS score before treatment and 1 week after RFA and cementoplasty was observed (p < 0.001). A total of 6/7 patients who used a wheelchair reported normal ambulation 1 month after treatment. All patients with limited painful ambulation reported normal ambulation after the RFA and cementoplasty (p = 0.003). Technical success was achieved in all the combined procedures. Two cement leakages were reported. No local recurrences were observed after 1 year. Conclusions: The combined treatment of RFA with a steerable device and cementoplasty is a safe, feasible, and promising clinical option for the management of painful bone metastases, challenging for morphology and location, resulting in an improvement of the quality of life of patients.

A data-driven health index for neonatal morbidities.

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

Screening Accuracy of Mini Addenbrooke's Cognitive Examination Test for HIV-Associated Neurocognitive Disorders in People Ageing with HIV.

Aging and increased cardiovascular risk are major drivers for HIV-associated neurocognitive disorders (HAND), for which accurate screenings are lacking. Mini-Addenbrooke's Cognitive Examination (MACE) reliably detects vascular and neurodegenerative cognitive decline among HIV-negative patients. We evaluated MACE diagnostic accuracy in detecting HAND in people living with HIV (PLWH) and we compared it with the International HIV Dementia Scale (IHDS). A single-centre double-blind study of diagnostic accuracy on adult outpatient PLWH without neurocognitive confounding was performed. MACE and IHDS were administered in 5 and 10 min by clinicians, followed by the reference standard battery (14 tests) by neuropsychologists. HAND diagnosis was based on the modified version of Frascati's criteria by Gisslén to reduce false positives. Exploratory cut-offs were evaluated for MACE. Diagnostic accuracy and clinical utility parameters were assessed. 231 patients were enrolled. 75.7% men with a median age, education, and length of infection of 54 (48-59), 10 (8-13) and 16 (5-25) years. HAND prevalence was 48.5% (38.9% asymptomatic impairment). Compared to IHDS, MACE sensitivity (89.3% vs 70.5%), specificity (94.1% vs 63.0%), correct classification rate (86.5% vs 66.7%), J index (0.83 vs 0.34), AUROC (0.97 vs 0.79), agreement with the gold standard (k 0.84 vs 0.33) and effect size in distinguishing HAND vs non-HAND (d 2.11 vs 1.15) were higher. Among PLWH aged 65 years and above (n = 37) MACE performance was consistently better than IHDS. The quick and easy-to-perform MACE could possess an accurate and useful screening performance for HAND in otherwise neurocognitively healthy cohorts of PLWH.

Correlation between cerebrospinal fluid and plasma neurofilament light protein in treated HIV infection: results from the COBRA study.

Cerebrospinal fluid (CSF) neurofilament light protein (NfL) is a marker of central nervous system neuro-axonal injury. A novel, ultra-sensitive assay can determine plasma NfL. In untreated people-with-HIV (PWH), CSF and plasma NfL are strongly correlated. We aimed to assess this correlation in PWH on suppressive antiretroviral treatment (ART) and lifestyle-similar HIV-negative individuals enrolled into the COmorBidity in Relation to AIDS (COBRA) study. Differences in paired CSF (sandwich ELISA, UmanDiagnostics) and plasma (Simoa digital immunoassay, Quanterix™) NfL between PWH and HIV-negative participants were tested using Wilcoxon's test; associations were assessed using Pearson's correlation. CSF and plasma NfL, standardised to Z-scores, were included as dependent variables in linear regression models to identify factors independently associated with values in PWH and HIV-negative participants. Overall, 132 PWH (all with plasma HIV RNA < 50 copies/mL) and 79 HIV-negative participants were included. Neither CSF (median 570 vs 568 pg/mL, p = 0.37) nor plasma (median 10.7 vs 9.9 pg/mL, p = 0.15) NfL differed significantly between PWH and HIV-negative participants, respectively. CSF and plasma NfL correlated moderately, with no significant difference by HIV status (PWH: rho = 0.52; HIV-negative participants: rho = 0.47, p (interaction) = 0.63). In multivariable regression analysis, higher CSF NfL Z-score was statistically significantly associated with older age and higher CSF protein, and higher plasma NfL Z-score with older age, higher serum creatinine and lower bodyweight. In conclusion, in PWH on ART, the correlation between CSF and plasma NfL is moderate and similar to that observed in lifestyle-similar HIV-negative individuals. Consideration of renal function and bodyweight may be required when utilising plasma NfL.

The Role of a Navigational Radiofrequency Ablation Device and Concurrent Vertebral Augmentation for Treatment of Difficult-to-Reach Spinal Metastases.

AIMS: The purpose of this study was to assess the effectiveness of a navigational radiofrequency ablation device with concurrent vertebral augmentation in the treatment of posterior vertebral body metastatic lesions, which are technically difficult to access. Primary outcomes of the study were evaluation of pain palliation and radiologic assessment of local tumor control. MATERIALS AND METHODS: Thirty-five patients with 41 vertebral spinal metastases involving the posterior vertebral body underwent computed tomography-guided percutaneous targeted radiofrequency ablation, with a navigational radiofrequency ablation device, associated with vertebral augmentation. Twenty-one patients (60%) had 1 or 2 metastatic lesions (Group A) and fourteen (40%) patients had multiple (>2) vertebral lesions (Group B). Changes in pain severity were evaluated by visual analog scale (VAS). Metastatic lesions were evaluated in terms of radiological local control. RESULTS: The procedure was technically successful in all the treated vertebrae. Among the symptomatic patients, the mean VAS score dropped from 5.7 (95% CI 4.9-6.5) before tRFA and to 0.9 (95% CI 0.4-1.3) after tRFA (p < 0.001). The mean decrease in VAS score between baseline and one week follow up was 4.8 (95% CI 4.2-5.4). VAS decrease over time between one week and one year following radiofrequency ablation was similar, suggesting that pain relief was immediate and durable. Neither patients with 1-2 vertebral metastases, nor those with multiple lesions, showed radiological signs of local progression or recurrence of the tumor in the index vertebrae during a median follow up of 19 months (4-46 months) and 10 months (4-37 months), respectively. CONCLUSION: Treatment of spinal metastases with a navigational radiofrequency ablation device and vertebral augmentation can be used to obtain local tumor control with immediate and durable pain relief, providing effective treatment in the multimodality management of difficult-to-reach spinal metastases.

Non-nucleoside reverse transcriptase inhibitor-based combination antiretroviral therapy is associated with lower cell-associated HIV RNA and DNA levels compared to protease inhibitor-based therapy.

Background: It remains unclear whether combination antiretroviral therapy (ART) regimens differ in their ability to fully suppress human immunodeficiency virus (HIV) replication. Here, we report the results of two cross-sectional studies that compared levels of cell-associated (CA) HIV markers between individuals receiving suppressive ART containing either a non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI). Methods: CA HIV unspliced RNA and total HIV DNA were quantified in two cohorts (n = 100, n = 124) of individuals treated with triple ART regimens consisting of two nucleoside reverse transcriptase inhibitors (NRTIs) plus either an NNRTI or a PI. To compare CA HIV RNA and DNA levels between the regimens, we built multivariable models adjusting for age, gender, current and nadir CD4+ count, plasma viral load zenith, duration of virological suppression, NRTI backbone composition, low-level plasma HIV RNA detectability, and electronically measured adherence to ART. Results: In both cohorts, levels of CA HIV RNA and DNA strongly correlated (rho = 0.70 and rho = 0.54) and both markers were lower in NNRTI-treated than in PI-treated individuals. In the multivariable analysis, CA RNA in both cohorts remained significantly reduced in NNRTI-treated individuals (padj = 0.02 in both cohorts), with a similar but weaker association between the ART regimen and total HIV DNA (padj = 0.048 and padj = 0.10). No differences in CA HIV RNA or DNA levels were observed between individual NNRTIs or individual PIs, but CA HIV RNA was lower in individuals treated with either nevirapine or efavirenz, compared to PI-treated individuals. Conclusions: All current classes of antiretroviral drugs only prevent infection of new cells but do not inhibit HIV RNA transcription in long-lived reservoir cells. Therefore, these differences in CA HIV RNA and DNA levels by treatment regimen suggest that NNRTIs are more potent in suppressing HIV residual replication than PIs, which may result in a smaller viral reservoir size. Funding: This work was supported by ZonMw (09120011910035) and FP7 Health (305522).

Correlation between CSF and blood neurofilament light chain protein: a systematic review and meta-analysis.

OBJECTIVE: To assess the overall pooled correlation coefficient estimate between cerebrospinal fluid (CSF) and blood neurofilament light (NfL) protein. METHODS: We searched Medline, Embase and Web of Science for published articles, from their inception to 9 July 2019, according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Studies reporting the correlation between CSF and blood NfL in humans were included. We conducted a random-effects meta-analysis to calculate the overall pooled correlation coefficient estimate, accounting for correlation technique and assay used. Heterogeneity was assessed using the I2 statistic test. In sensitivity analyses, we calculated the pooled correlation coefficient estimate according to blood NfL assay: single-molecule array digital immunoassay (Simoa), electrochemiluminescence (ECL) assay or ELISA. RESULTS: Data were extracted from 36 articles, including 3961 paired CSF and blood NfL samples. Overall, 26/36 studies measured blood NfL using Simoa, 8/36 ECL, 1/36 ELISA and 1 study reported all three assay results. The overall meta-analysis demonstrated that the pooled correlation coefficient estimate for CSF and blood NfL was r=0.72. Heterogeneity was significant: I2=83%, p<0.01. In sensitivity analyses, the pooled correlation coefficient was similar for studies measuring blood NfL using Simoa and ECL (r=0.69 and r=0.68, respectively) but weaker for ELISA (r=0.35). CONCLUSION: Moderate correlations are demonstrated between CSF and blood NfL, especially when blood NfL was measured using Simoa and ECL. Given its high analytical sensitivity, Simoa is the preferred assay for measuring NfL, especially at low or physiological concentrations, and this meta-analysis supports its use as the current most advanced surrogate measure of CSF NfL. PROSPERO REGISTRATION NUMBER: CRD42019140469.

Associations between plasma nucleoside reverse transcriptase inhibitors concentrations and cognitive function in people with HIV.

OBJECTIVES: To investigate the associations of plasma lamivudine (3TC), abacavir (ABC), emtricitabine (FTC) and tenofovir (TFV) concentrations with cognitive function in a cohort of treated people with HIV (PWH). METHODS: Pharmacokinetics (PK) and cognitive function (Cogstate, six domains) data were obtained from PWH recruited in the POPPY study on either 3TC/ABC or FTC/tenofovir disoproxil fumarate (TDF)-containing regimens. Association between PK parameters (AUC0-24: area under the concentration-time curve over 24 hours, Cmax: maximum concentration and Ctrough: trough concentration) and cognitive scores (standardized into z-scores) were evaluated using rank regression adjusting for potential confounders. RESULTS: Median (IQR) global cognitive z-scores in the 83 PWH on 3TC/ABC and 471 PWH on FTC/TDF were 0.14 (-0.27, 0.38) and 0.09 (-0.28, 0.42), respectively. Higher 3TC AUC0-24 and Ctrough were associated with better global z-scores [rho = 0.29 (p = 0.02) and 0.27 (p = 0.04), respectively], whereas higher 3TC Cmax was associated with poorer z-scores [rho = -0.31 (p<0.01)], independently of ABC concentrations. Associations of ABC PK parameters with global and domain z-scores were non-significant after adjustment for confounders and 3TC concentrations (all p's>0.05). None of the FTC and TFV PK parameters were associated with global or domain cognitive scores. CONCLUSIONS: Whilst we found no evidence of either detrimental or beneficial effects of ABC, FTC and TFV plasma exposure on cognitive function of PWH, higher plasma 3TC exposures were generally associated with better cognitive performance although higher peak concentrations were associated with poorer performance.