Distinct Functional Humoral Immune Responses Are Induced after Live Attenuated and Inactivated Seasonal Influenza Vaccination

Influenza viruses infect 5-30% of the world's population annually, resulting in millions of incidents of hospitalization and thousands of mortalities worldwide every year. Although annual vaccination has significantly reduced hospitalization rates in vulnerable populations, the current vaccines are estimated to offer a wide range of protection from 10 to 60% annually. Such incomplete immunity may be related to both poor antigenic coverage of circulating strains, as well as to the insufficient induction of protective immunity. Beyond the role of hemagglutinin (HA) and neuraminidase (NA), vaccine-induced Abs have the capacity to induce a broader array of Ab effector functions, including Ab-dependent cellular cytotoxicity, that has been implicated in universal immunity against influenza viruses. However, whether different vaccine platforms can induce functional humoral immunity in a distinct manner remains incompletely defined. In this study, we compared vaccine-induced humoral immune responses induced by two seasonal influenza vaccines in Homo sapiens, the i.m. inactivated vaccine (IIV/Fluzone) and the live attenuated mucosal vaccine (LAIV/FluMist). Whereas the inactivated influenza vaccine induced superior Ab titers and FcγR binding capacity to diverse HA and NA Ags, the live attenuated influenza mucosal vaccine induced a more robust functional humoral immune response against both the HA and NA domains. Multivariate Ab analysis further highlighted the significantly different overall functional humoral immune profiles induced by the two vaccines, marked by differences in IgG titers, FcR binding, and both NK cell-recruiting and opsonophagocytic Ab functions. These results highlight the striking differences in Ab Fc-effector profiles induced systemically by two distinct influenza vaccine platforms.

Trophoblast organoids with physiological polarity model placental structure and function

Human trophoblast organoids (TOs) are a three-dimensional ex vivo culture model that can be used to study various aspects of placental development, physiology and pathology. However, standard culturing of TOs does not recapitulate the cellular orientation of chorionic villi in vivo given that the multi-nucleated syncytiotrophoblast (STB) develops largely within the inner facing surfaces of these organoids (STBin). Here, we developed a method to culture TOs under conditions that recapitulate the cellular orientation of chorionic villi in vivo. We show that culturing STBin TOs in suspension with gentle agitation leads to the development of TOs containing the STB on the outer surface (STBout). Using membrane capacitance measurements, we determined that the outermost surface of STBout organoids contain large syncytia comprising >50 nuclei, whereas STBin organoids contain small syncytia (<10 nuclei) and mononuclear cells. The growth of TOs under conditions that mimic the cellular orientation of chorionic villi in vivo thus allows for the study of a variety of aspects of placental biology under physiological conditions.

American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference

STUDY OBJECTIVE

To explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified.

DESIGN

Observational Studies SETTING: UofL health hospital PATIENTS: This study involved 562 hysterectomy surgeries performed on patients (≥ 18 years) between June 2020 and July 2021.

INTERVENTIONS

None MEASUREMENTS: Preoperative and intraoperative data is collected. Three parametric machine learning models, including Bayesian generalized linear model (BGLM), Bayesian neural network (BNN), a newly proposed BNN with multivariate mixed responses (BNNMR), and one nonparametric model, Gaussian Process (GP), were explored to predict patients' diastolic and systolic blood pressures (continuous responses) and patients' hypotensive event (binary response) for the next five minutes. Data was separated into American Society of Anesthesiologists (ASA) physical status class 1- 4 before being read in by four machine learning models. Statistical analysis and models' constructions are performed in Python. Sensitivity, specificity, and the confidence/credible intervals were used to evaluate the prediction performance of each model for each ASA physical status class.

MAIN RESULTS

ASA physical status classes require distinct models to accurately predict intraoperative blood pressures and hypotensive events. Overall, high sensitivity (above 0.85) and low uncertainty can be achieved by all models for ASA class 4 patients. In contrast, models trained without controlling ASA classes yielded lower sensitivity (below 0.5) and larger uncertainty. Particularly, in terms of predicting binary hypotensive event, for ASA physical status class 1, BNNMR yields the highest sensitivity of 1. For classes 2 and 3, BNN has the highest sensitivity of 0.429 and 0.415, respectively. For class 4, BNNMR and GP are tied with the highest sensitivity of 0.857. On the other hand, the sensitivity is just 0.031, 0.429, 0.165 and 0.305 for BNNMR, BNN, GBLM and GP models respectively, when training data is not divided by ASA physical status classes. In terms of predicting systolic blood pressure, the GP regression yields the lowest root mean squared errors (RMSE) of 2.072, 7.539, 9.214 and 0.295 for ASA physical status classes 1, 2, 3 and 4, respectively, but a RMSE of 126.894 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. RMSEs are 2.175, 13.861, 17.560 and 22.426 for classes 1, 2, 3 and 4 respectively for the BGLM. In terms of predicting diastolic blood pressure, the GP regression yields the lowest RMSEs of 2.152, 6.573, 5.371 and 0.831 for ASA physical status classes 1, 2, 3 and 4, respectively; RMSE of 8.084 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. Finally, in terms of the width of the 95% confidence interval of the mean prediction for systolic and diastolic blood pressures, GP regression gives narrower confidence interval with much smaller margin of error across all four ASA physical status classes.

CONCLUSIONS

Different ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.

A standard workflow for community-driven manual curation of Strongyloides genome annotations

Advances in the functional genomics and bioinformatics toolkits for Strongyloides species have positioned these species as genetically tractable model systems for gastrointestinal parasitic nematodes. As community interest in mechanistic studies of Strongyloides species continues to grow, publicly accessible reference genomes and associated genome annotations are critical resources for researchers. Genome annotations for multiple Strongyloides species are broadly available via the WormBase and WormBase ParaSite online repositories. However, a recent phylogenetic analysis of the receptor-type guanylate cyclase (rGC) gene family in two Strongyloides species highlights the potential for errors in a large percentage of current Strongyloides gene models. Here, we present three examples of gene annotation updates within the Strongyloides rGC gene family; each example illustrates a type of error that may occur frequently within the annotation data for Strongyloides genomes. We also extend our analysis to 405 previously curated Strongyloides genes to confirm that gene model errors are found at high rates across gene families. Finally, we introduce a standard manual curation workflow for assessing gene annotation quality and generating corrections, and we discuss how it may be used to facilitate community-driven curation of parasitic nematode biodata. This article is part of the Theo Murphy meeting issue 'Strongyloides: omics to worm-free populations'.

Cervical precancer and cancer incidence among insured women with and without HIV in South Africa

HIV infection increases the risk of developing cervical cancer; however, longitudinal studies in sub-Saharan Africa comparing cervical cancer rates between women living with HIV (WLWH) and women without HIV are scarce. To address this gap, we compared cervical precancer and cancer incidence rates between WLWH and women without HIV in South Africa using reimbursement claims data from a medical insurance scheme from January 2011 to June 2020. We used Royston-Parmar flexible parametric survival models to estimate cervical precancer and cancer incidence rates as a continuous function of age, stratified by HIV status. Our study population consisted of 518 048 women, with exclusions based on the endpoint of interest. To analyse cervical cancer incidence, we included 517 312 women, of whom 564 developed cervical cancer. WLWH had an ~3-fold higher risk of developing cervical precancer and cancer than women without HIV (adjusted hazard ratio for cervical cancer: 2.99; 95% confidence interval [CI]: 2.40-3.73). For all endpoints of interest, the estimated incidence rates were higher in WLWH than women without HIV. Cervical cancer rates among WLWH increased at early ages and peaked at 49 years (122/100 000 person-years; 95% CI: 100-147), whereas, in women without HIV, incidence rates peaked at 56 years (40/100 000 person-years; 95% CI: 36-45). Cervical precancer rates peaked in women in their 30s. Analyses of age-specific cervical cancer rates by HIV status are essential to inform the design of targeted cervical cancer prevention policies in Southern Africa and other regions with a double burden of HIV and cervical cancer.

Invade or die: behaviours and biochemical mechanisms that drive skin penetration in Strongyloides and other skin-penetrating nematodes

Skin-penetrating nematodes, including the human threadworm Strongyloides stercoralis and hookworms in the genera Necator and Ancylostoma, are gastrointestinal parasites that are a major cause of neglected tropical disease in low-resource settings worldwide. These parasites infect hosts as soil-dwelling infective larvae that navigate towards hosts using host-emitted sensory cues such as odorants and body heat. Upon host contact, they invade the host by penetrating through the skin. The process of skin penetration is critical for successful parasitism but remains poorly understood and understudied. Here, we review current knowledge of skin-penetration behaviour and its underlying mechanisms in the human parasite S. stercoralis, the closely related rat parasite Strongyloides ratti, and other skin-penetrating nematodes such as hookworms. We also highlight important directions for future investigations into this underexplored process and discuss how recent advances in molecular genetic and genomic tools for Strongyloides species will enable mechanistic investigations of skin penetration and other essential parasitic behaviours in future studies. This article is part of the Theo Murphy meeting issue 'Strongyloides: omics to worm-free populations'.