HIV-1 infection of genetically engineered iPSC-derived central nervous system-engrafted microglia in a humanized mouse model.

IMPORTANCE: Our mouse model is a powerful tool for investigating the genetic mechanisms governing central nervous system (CNS) human immunodeficiency virus type-1 (HIV-1) infection and latency in the CNS at a single-cell level. A major advantage of our model is that it uses induced pluripotent stem cell-derived microglia, which enables human genetics, including gene function and therapeutic gene manipulation, to be explored in vivo, which is more challenging to study with current hematopoietic stem cell-based models for neuroHIV. Our transgenic tracing of xenografted human cells will provide a quantitative medium to develop new molecular and epigenetic strategies for reducing the HIV-1 latent reservoir and to test the impact of therapeutic inflammation-targeting drug interventions on CNS HIV-1 latency.

A microglia-containing cerebral organoid model to study early life immune challenges.

Prenatal infections and activation of the maternal immune system have been proposed to contribute to causing neurodevelopmental disorders (NDDs), chronic conditions often linked to brain abnormalities. Microglia are the resident immune cells of the brain and play a key role in neurodevelopment. Disruption of microglial functions can lead to brain abnormalities and increase the risk of developing NDDs. How the maternal as well as the fetal immune system affect human neurodevelopment and contribute to NDDs remains unclear. An important reason for this knowledge gap is the fact that the impact of exposure to prenatal risk factors has been challenging to study in the human context. Here, we characterized a model of cerebral organoids (CO) with integrated microglia (COiMg). These organoids express typical microglial markers and respond to inflammatory stimuli. The presence of microglia influences cerebral organoid development, including cell density and neural differentiation, and regulates the expression of several ciliated and mesenchymal cell markers. Moreover, COiMg and organoids without microglia show similar but also distinct responses to inflammatory stimuli. Additionally, IFN-γ induced significant transcriptional and structural changes in the cerebral organoids, that appear to be regulated by the presence of microglia. Specifically, interferon-gamma (IFN-γ) was found to alter the expression of genes linked to autism. This model provides a valuable tool to study how inflammatory perturbations and microglial presence affect neurodevelopmental processes.

Awake prone positioning for patients with COVID-19-related respiratory failure: a systematic review and meta-analysis.

The role of awake prone positioning (aPP) in patients with acute hypoxemic respiratory failure is debated. We performed a systematic review and meta-analysis to evaluate the role of aPP in acute respiratory failure related to COronaVIrus Disease-19 (COVID-19). Studies reporting on the clinical course of patients with acute respiratory failure related to COVID-19 treated or not treated by aPP were included in the systematic review and meta-analysis (ProsperoID: CRD42022333211). The primary study outcome was the composite of in-hospital death or orotracheal intubation; the individual components of the primary outcome were secondary study outcomes. The composite of in-hospital death or orotracheal intubation was available for 6 studies (1884 patients), five randomized and one prospective; a significant reduction in the risk of this outcome was observed in patients treated vs. not treated by aPP (33.5% vs. 39.8%; OR 0.73, 95% CI 0.60-0.89; I2 0%). In-hospital death was reported in 34 studies (6808 patients) and occurred in 17.4% vs. 23.5% of patients treated or not treated with aPP (random effect OR 0.60, 95% CI 0.46-0.79; I2 59%); orotracheal intubation was observed in 25.8% vs. 32.7% of patients treated or not treated with aPP (27 studies, 5369 patients; random effect OR 0.85, 95% CI 0.56-1.27; I2 84%). aPP reduces the risk for death or orotracheal intubation in patients with acute respiratory failure related to COVID-19. Further studies should be conducted to confirm the clinical benefit of aPP outside the ICU.Registration Prospero ID: CRD42022333211.

A microglia-containing cerebral organoid model to study early life immune challenges.

Prenatal infections and activation of the maternal immune system have been proposed to contribute to causing neurodevelopmental disorders (NDDs), chronic conditions often linked to brain abnormalities. Microglia are the resident immune cells of the brain and play a key role in neurodevelopment. Disruption of microglial functions can lead to brain abnormalities and increase the risk of developing NDDs. How the maternal as well as the fetal immune system affect human neurodevelopment and contribute to NDDs remains unclear. An important reason for this knowledge gap is the fact that the impact of exposure to prenatal risk factors has been challenging to study in the human context. Here, we characterized a model of cerebral organoids (CO) with integrated microglia (COiMg). These organoids express typical microglial markers and respond to inflammatory stimuli. The presence of microglia influences cerebral organoid development, including cell density and neural differentiation, and regulates the expression of several ciliated mesenchymal cell markers. Moreover, COiMg and organoids without microglia show similar but also distinct responses to inflammatory stimuli. Additionally, IFN-γ induced significant transcriptional and structural changes in the cerebral organoids, that appear to be regulated by the presence of microglia. Specifically, interferon-gamma (IFN-γ) was found to alter the expression of genes linked to autism. This model provides a valuable tool to study how inflammatory perturbations and microglial presence affect neurodevelopmental processes.

The effect of SARS-CoV-2 infection and vaccination on Th17 and regulatory T cells in a pregnancy cohort in NYC.

Disturbances in T-cells, specifically the Th17/Treg balance, have been implicated in adverse pregnancy outcomes. We investigated these two T-cell populations following pre-pregnancy and pregnancy SARS-CoV-2 infection and COVID-19 vaccination in 351 participants from a pregnancy cohort in New York City (Generation C; 2020-2022). SARS-CoV-2 infection status was determined via laboratory or medical diagnosis and COVID-19 vaccination status via survey and electronic medical records data. Peripheral blood mononuclear cells (PBMCs) were collected at routine prenatal visits throughout gestation (median 108 days; IQR 67-191 days) with repeated measures for 104 participants (29.6%). T-cell populations CD4+/CD3+, Th17/CD4+, Treg/CD4+ and the Th17/Treg ratio were quantified using flow cytometry. Results showed that inter-individual differences are a main influencing factor in Th17 and Treg variance, however total variance explained remained small (R2 = 15-39%). Overall, Th17 and Treg populations were not significantly affected by SARS-CoV-2 infection during pregnancy in adjusted linear mixed models (p>0.05), however comparison of repeated measures among SARS-CoV-2 infected participants and non-infected controls suggests a relative increase of the Th17/Treg ratio following infection. In addition, the Th17/Treg ratio was significantly higher after SARS-CoV-2 infection prior to pregnancy (10-138 weeks) compared to controls (ß=0.48, p=0.003). COVID-19 vaccination was not associated with Th17 and Treg cells. Our findings suggest an impact of SARS-CoV-2 infection on the Th17/Treg ratio, likely depending on severity of infection, yet the observed trends and their potential consequences for pregnancy outcomes require further investigation. Our study contributes to growing evidence that COVID-19 vaccination during pregnancy does not lead to an exacerbated immune response.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

SARS-CoV-2 infection, inflammation and birth outcomes in a prospective NYC pregnancy cohort.

Associations between antenatal SARS-CoV-2 infection and pregnancy outcomes have been conflicting and the role of the immune system is currently unclear. This prospective cohort study investigated the interaction of antenatal SARS-CoV-2 infection, changes in cytokine and HS-CRP levels, birthweight and gestational age at birth. 2352 pregnant participants from New York City (2020-2022) were included. Plasma levels of interleukin (IL)-1ß, IL-6, IL-17A and high-sensitivity C-reactive protein (HS-CRP) were quantified in blood specimens obtained across pregnancy. Quantile and linear regression models were conducted to 1) assess the impact of antenatal SARS-CoV-2 infection, overall and by timing of detection of SARS-CoV-2 positivity (< 20 weeks versus ≥ 20 weeks), on birthweight and gestational age at delivery; 2) examine the relationship between SARS-CoV-2 infection and maternal immune changes during pregnancy. All models were adjusted for maternal demographic and obstetric factors and pandemic timing. Birthweight models were additionally adjusted for gestational age at delivery and fetal sex. Immune marker models were also adjusted for gestational age at specimen collection and multiplex assay batch. 371 (15.8%) participants were infected with SARS-CoV-2 during pregnancy, of which 98 (26.4%) were infected at < 20 weeks gestation. Neither SARS-CoV-2 infection in general nor in early or late pregnancy was associated with lower birthweight nor earlier gestational age at delivery. Further, we did not observe cytokine or HS-CRP changes in response to SARS-CoV-2 infection and thus found no evidence to support a potential association between immune dysregulation and the diversity in pregnancy outcomes following infection.

Efficacy and safety of two heparin regimens for prevention of venous thromboembolism in hospitalized patients with COVID-19: a meta-analysis.

Venous thromboembolism (VTE) is common in patients with coronavirus disease-2019 (COVID-19). The optimal heparin regimen remains unknown and should balance thromboembolic and bleeding risks. The aim of this study was to evaluate the efficacy and safety of standard or higher heparin regimens for the prevention of VTE in patients hospitalized due to COVID-19. We performed a systematic literature search; studies reporting on hospitalized patients with COVID-19 who received standard heparin prophylaxis vs. high (intermediate or therapeutic) heparin regimens were included if outcome events were reported by treatment group and more than 10 patients were included. Primary study outcome was in-hospital VTE. Secondary study outcomes were major bleeding (MB), all-cause death, fatal bleeding and fatal pulmonary embolism. Overall, 33 studies (11,387 patients) were included. Venous thromboembolic events occurred in 5.2% and in 8.2% of patients who received heparin prophylaxis with at high-dose or standard-dose, respectively (RR 0.71, 95% CI 0.55-0.90, I2 48.8%). MB was significantly higher in patients who received high- compared to the standard-dose (4.2% vs 2.2%, RR 1.94, 95% CI 1.47-2.56, I2 18.1%). Sub-analyses showed a slight benefit associated with high-dose heparin in patients admitted to non-intensive care unit (ICU) but not in those to ICU. No significant differences were observed for mortality outcomes. Heparin prophylaxis at high-dose reduces the risk of VTE, but increased the risk of MB compared to the standard-dose. No clinical benefit for heparin high-dose was observed for ICU setting, but its role in the non-ICU deserves further evaluation. PROSPERO registration number: CRD42021252550.

Arterial lactate as a risk factor for death in respiratory failure related to coronavirus disease 2019: an observational study.

BACKGROUND: Arterial lactate is a recognized biomarker associated with death in critically ill patients. The prognostic role of arterial lactate in acute respiratory failure due to the novel coronavirus disease 2019 (COVID-19) is unclear. OBJECTIVES: We aimed to investigate the prognostic role of arterial lactate levels at admission in patients with COVID-19-related acute respiratory failure. DESIGN AND METHODS: Cohorts of consecutive patients admitted to nonintensive care units (ICU) at study centers for COVID-19-related respiratory failure were merged into a collaborative database. The prognostic role of lactate levels at admission was assessed for continuous values and values ⩾2.0 mmol/l, and lactate clearance at 24 h through delta-lactate (ΔLac). The study outcome was 30-day in-hospital death. Cox proportional regression model was used to assess independent predictors of the study outcome. RESULTS: At admission, 14.6% of patients had lactate levels ⩾2 mmol/l. In-hospital death at 30 days occurred in 57 out of 206 patients; 22.3% and 56.7% with normal or ⩾ 2 mmol/l lactate at admission, respectively. The median lactate level was 1.0 [interquartile range (IQR) 0.8-1.3] mmol/l and 1.3 (IQR 1.0-2.1) mmol/l in survivors and nonsurvivors, respectively (p-value < 0.001). After adjusting for age, relevant comorbidities, acidemia, and the severity of respiratory failure, lactate ⩾2.0 mmol/l was associated with in-hospital death (HR 2.53, 95% CI 1.29-4.95, p-value 0.0066), while Δ Lac ⩾0 was not (HR 1.37, 95% CI 0.42-4.49). These results were confirmed in patients with a pO2/FiO2-ratio (P/F ratio) ⩽300 mmHg. CONCLUSIONS: In our study, increased arterial lactate at admission was independently associated with in-hospital death at 30 days in non-ICU patients with acute respiratory failure related to COVID-19.

The role of prone positioning in patients with SARS-CoV-2-related respiratory failure in non-intensive care unit.

BACKGROUND: Prone positioning (PP) is an established and commonly used lung recruitment method for intubated patients with severe acute respiratory distress syndrome, with potential benefits in clinical outcome. The role of PP outside the intensive care unit (ICU) setting is debated. OBJECTIVES: We aimed at assessing the role of PP in death and ICU admission in non-intubated patients with acute respiratory failure related to COronaVIrus Disease-19 (COVID-19) pneumonia. DESIGN: This is a retrospective analysis of a collaborative multicenter database obtained by merging local non-interventional cohorts. METHODS: Consecutive adult patients with COVID-19-related respiratory failure were included in a collaborative cohort and classified based on the severity of respiratory failure according to the partial arterial oxygen pressure to fraction of inspired oxygen ratio (PaO2/FiO2) and on clinical severity by the quick Sequential Organ Failure Assessment (qSOFA) score. The primary study outcome was the composite of in-hospital death or ICU admission within 30 days from hospitalization. RESULTS: PP was used in 114 of 536 study patients (21.8%), more commonly in patients with lower PaO2/FiO2 or receiving non-invasive ventilation and less commonly in patients with known comorbidities. A primary study outcome event occurred in 163 patients (30.4%) and in-hospital death in 129 (24.1%). PP was not associated with death or ICU admission (HR 1.17, 95% CI 0.78-1.74) and not with death (HR 1.01, 95% CI 0.61-1.67) at multivariable analysis; PP was an independent predictor of ICU admission (HR 2.64, 95% CI 1.53-4.40). The lack of association between PP and death or ICU admission was confirmed at propensity score-matching analysis. CONCLUSION: PP is used in a non-negligible proportion of non-intubated patients with COVID-19-related severe respiratory failure and is not associated with death but with ICU admission. The role of PP in this setting merits further evaluation in randomized studies.

HIV-1 infection of genetically engineered iPSC-derived central nervous system-engrafted microglia in a humanized mouse model.

The central nervous system (CNS) is a major human immunodeficiency virus type 1 reservoir. Microglia are the primary target cell of HIV-1 infection in the CNS. Current models have not allowed the precise molecular pathways of acute and chronic CNS microglial infection to be tested with in vivo genetic methods. Here, we describe a novel humanized mouse model utilizing human-induced pluripotent stem cell-derived microglia to xenograft into murine hosts. These mice are additionally engrafted with human peripheral blood mononuclear cells that served as a medium to establish a peripheral infection that then spread to the CNS microglia xenograft, modeling a trans-blood-brain barrier route of acute CNS HIV-1 infection with human target cells. The approach is compatible with iPSC genetic engineering, including inserting targeted transgenic reporter cassettes to track the xenografted human cells, enabling the testing of novel treatment and viral tracking strategies in a comparatively simple and cost-effective way vivo model for neuroHIV.

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.

Impact of prenatal COVID-19 vaccination on delivery and neonatal outcomes: Results from a New York City cohort.

Research suggest prenatal vaccination against coronavirus disease-19 (COVID-19) is safe. However, previous studies utilized retrospectively collected data or examined late pregnancy vaccinations. We investigated the associations of COVID-19 vaccination throughout pregnancy with delivery and neonatal outcomes. We included 1,794 mother-neonate dyads enrolled in the Generation C Study with known prenatal COVID-19 vaccination status and complete covariate and outcome data. We used multivariable quantile regressions to estimate the effect of prenatal COVID-19 vaccination on birthweight, delivery gestational age, and blood loss at delivery; and Poisson generalized linear models for Caesarean delivery (CD) and Neonatal Intensive Care Unit (NICU) admission. Using the above methods, we estimated effects of trimester of vaccine initiation on these outcomes. In our sample, 13.7% (n = 250) received at least one prenatal dose of any COVID-19 vaccine. Vaccination was not associated with birthweight (ß = 12.42 g [-90.5, 114.8]), gestational age (ß = 0.2 days [-1.1, 1.5]), blood loss (ß = -50.6 ml [-107.0, 5.8]), the risks of CD (RR = 0.8; [0.6, 1.1]) or NICU admission (RR = 0.9 [0.5, 1.7]). Trimester of vaccine initiation was also not associated with these outcomes. Our findings suggest that there is no associated risk between prenatal COVID-19 vaccination and adverse delivery and neonatal outcomes in a cohort sample from NYC.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Titer Levels in Pregnant Individuals After Infection, Vaccination, or Both.

We examined differences in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody responses in pregnant individuals with natural, vaccine-induced, or combined immunity. Participants had live or nonlive births between 2020 and 2022, were seropositive (SARS-CoV-2 spike protein, anti-S), and had available mRNA vaccination and infection information (n=260). We compared titer levels among three immunity profiles: 1) natural immunity (n=191), 2) vaccine-induced immunity (n=37), and 3) combined immunity (ie, natural and vaccine-induced immunity; n=32). We applied linear regression to compare anti-S titers between the groups, controlling for age, race and ethnicity, and time between vaccination or infection (whichever came last) and sample collection. Anti-S titers were 57.3% and 94.4% lower among those with vaccine-induced and natural immunity, respectively, compared with those with combined immunity ( P <.001, P =.005).

Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics.

During the past 60 years scientific research proposed many techniques to control robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to limited robustness that may be improved with multimodal data. This paper presents the first acquisition setup, acquisition protocol and dataset including sEMG, eye tracking and computer vision to study robotic hand control. A data analysis on healthy controls gives a first idea of the capabilities and constraints of the acquisition procedure that will be applied to amputees in a next step. Different data sources are not fused together in the analysis. Nevertheless, the results support the use of the proposed multimodal data acquisition approach for prosthesis control. The sEMG movement classification results confirm that it is possible to classify several grasps with sEMG alone. sEMG can detect the grasp type and also small differences in the grasped object (accuracy: 95%). The simultaneous recording of eye tracking and scene camera data shows that these sensors allow performing object detection for grasp selection and that several neurocognitive parameters need to be taken into account for this. In conclusion, this work on intact subjects presents an innovative acquisition setup and protocol. The first results in terms of data analysis are promising and set the basis for future work on amputees, aiming to improve the robustness of prostheses with multimodal data.