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Foveal (central) and peripheral vision are strongly interconnected to provide an integrated experience of the world around us. Recently, it has been suggested that there is a feedback mechanism that links foveal and peripheral vision. This peripheral-to-foveal feedback differs from other feedback mechanisms in that during visual processing a novel representation of a stimulus is formed in a different cortical region than that of the feedforward representation. The functional role of foveal feedback is not yet completely understood, but some evidence from neuroimaging studies suggests a link with peripheral shape processing. Behavioural and transcranial magnetic stimulation studies show impairment in peripheral shape discrimination when the foveal retinotopic cortex is disrupted post stimulus presentation. This review aims to link these findings to the visual sketchpad hypothesis. According to this hypothesis, foveal retinotopic cortex stores task-relevant information to aid identification of peripherally presented objects. We discuss how the characteristics of foveal feedback support this hypothesis and rule out other possible explanations. We also discuss the possibility that the foveal feedback may be independent of the sensory modality of the stimulation.

Abstract Peripheral object discrimination is hindered by a central dynamic mask presented between 150 and 300 ms after stimulus onset. The mask is thought to interfere with task-relevant feedback coming from higher visual areas to the foveal cortex in V1. Fan et al. (2016) supported this hypothesis by showing that the effect of mask can be further delayed if the task requires mental manipulation of the peripheral target. The main purpose of this study was to better characterize the temporal dynamics of foveal feedback. Specifically, in two experiments we have shown that (1) the effect of foveal noise mask is sufficiently robust to be replicated in an online data collection (2) in addition to a change in sensitivity the mask affects also the criterion, which becomes more conservative; (3) the expected dipper function for sensitivity approximates a quartic with a global minimum at 94 ms, while the best fit for criterion is a quintic with a global maximum at 174 ms; (4) the power spectrum analysis of perceptual oscillations in sensitivity data shows a cyclic effect of mask at 3 and 12 Hz. Overall, our results show that foveal noise affects sensitivity in a cyclic manner, with a global dip emerging earlier than previously found. The noise also affects the response bias, even though with a different temporal profile. We, therefore, suggest that foveal noise acts on two distinct feedback mechanisms, a faster perceptual feedback followed by a slower cognitive feedback.

Abstract Background The World Health Organization (WHO) risk assessment algorithm for vertical transmission of HIV (VT) assumes the availability of maternal viral load (VL) result at delivery and early viral control 4 weeks after initiating antiretroviral treatment (ART). However, in many low-and-middle-income countries, VL is often unavailable and mothers’ ART adherence may be suboptimal. We evaluate the inclusion of the mothers’ self-reported adherence into the established WHO-algorithm to identify infants eligible for enhanced post-natal prophylaxis when mothers’ VL result is not available at delivery. Methods We used data from infants with perinatal HIV infection and their mothers enrolled from May-2018 to May-2020 in Mozambique, South Africa, and Mali. We retrospectively compared the performance of the WHO-algorithm with a modified algorithm which included mothers’ adherence as an additional factor. Infants were considered at high risk if born from mothers without a VL result in the 4 weeks before delivery and with adherence

Background: Possible cardiac impairment after SARS-CoV-2 mRNA vaccination is a common driver of parental vaccine hesitancy. We performed a comprehensive echocardiographic evaluation of biventricular function in vaccinated children with or without previous COVID-19 compared to healthy controls. Methods: We conducted a single-center, prospective, case–control study enrolling children and adolescents aged 5–18 years attending the pediatric clinic of the University Hospital of Padua from April to June 2022. Three months after receiving the primary mRNA vaccination or booster dose, the patients underwent a cardiac assessment, including standard echocardiography and speckle-tracking echocardiography (STE). A pre-pandemic historical cohort of age- and gender-matched healthy children were used as a control. Results: A total of 39 post-VACCINE cases (24, 61% female), mean age 12.6 ± 2.6 years (range 8–17), were enrolled in the study. Ninety percent (N = 35) of patients were previously healthy. No differences in left ventricular diameters, left ventricular ejection fraction (LVEF), and tricuspid annular plane systolic excursion (TAPSE) were observed between cases and controls. Global longitudinal strain (GLS) was in the normal range in all individuals, with no differences between post-VACCINE cases and controls (−21.7 ± 2.3% vs. 21.2 ± 1.8%; p = 0.338). However, GLS was found to be slightly but significantly reduced in post-VACCINE children with a previous COVID-19 compared to naïve-vaccinated individuals (post-VACCINE+COVID-19: −19.9 ± 1.1% vs. post-VACCINE-only: −22.0 ± 2.3%; p = 0.002). Conclusions: We did not observe an impairment in GLS or in other indices of LV structure or function after mRNA COVID-19 vaccination.

BackgroundDespite a successful antiretroviral therapy (ART), adolescents living with perinatally acquired HIV (PHIV) experience signs of B-cell hyperactivation with expansion of ‘namely’ atypical B-cell phenotypes, including double negative (CD27-IgD-) and termed age associated (ABCs) B-cells (T-bet+CD11c+), which may result in reduced cell functionality, including loss of vaccine-induced immunological memory and higher risk of developing B-cells associated tumors. In this context, perinatally HIV infected children (PHIV) deserve particular attention, given their life-long exposure to chronic immune activation.MethodsWe studied 40 PHIV who started treatment by the 2nd year of life and maintained virological suppression for 13.5 years, with 5/40 patients experiencing transient elevation of the HIV-1 load in the plasma (Spike). We applied a multi-disciplinary approach including immunological B and T cell phenotype, plasma proteomics analysis, and serum level of anti-measles antibodies as functional correlates of vaccine-induced immunity.ResultsPhenotypic signs of B cell hyperactivation were elevated in subjects starting ART later (%DN T-bet+CD11c+ p=0.03; %AM T-bet+CD11c+ p=0.02) and were associated with detectable cell-associated HIV-1 RNA (%AM T-bet+CD11c+ p=0.0003) and transient elevation of the plasma viral load (spike). Furthermore, B-cell hyperactivation appeared to be present in individuals with higher frequency of exhausted T-cells, in particular: %CD4 TIGIT+ were associated with %DN (p=0.008), %DN T-bet+CD11c+ (p=0.0002) and %AM T-bet+CD11c+ (p=0.002) and %CD4 PD-1 were associated with %DN (p=0.048), %DN T-bet+CD11c+ (p=0.039) and %AM T-bet+CD11c+ (p=0.006). The proteomic analysis revealed that subjects with expansion of these atypical B-cells and exhausted T-cells had enrichment of proteins involved in immune inflammation and complement activation pathways. Furthermore, we observed that higher levels of ABCs were associated a reduced capacity to maintain vaccine-induced antibody immunity against measles (%B-cells CD19+CD10- T-bet+, p=0.035).ConclusionWe identified that the levels of hyperactivated B cell subsets were strongly affected by time of ART start and associated with clinical, viral, cellular and plasma soluble markers. Furthermore, the expansion of ABCs also had a direct impact on the capacity to develop antibodies response following routine vaccination.

Logistic regression (LR) is the most common prediction model in medicine. In recent years, supervised machine learning (ML) methods have gained popularity. However, there are many concerns about ML utility for small sample sizes. In this study, we aim to compare the performance of 7 algorithms in the prediction of 1-year mortality and clinical progression to AIDS in a small cohort of infants living with HIV from South Africa and Mozambique. The data set (n = 100) was randomly split into 70% training and 30% validation set. Seven algorithms (LR, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), and Elastic Net) were compared. The variables included as predictors were the same across the models including sociodemographic, virologic, immunologic, and maternal status features. For each of the models, a parameter tuning was performed to select the best-performing hyperparameters using 5 times repeated 10-fold cross-validation. A confusion-matrix was built to assess their accuracy, sensitivity, and specificity. RF ranked as the best algorithm in terms of accuracy (82,8%), sensitivity (78%), and AUC (0,73). Regarding specificity and sensitivity, RF showed better performance than the other algorithms in the external validation and the highest AUC. LR showed lower performance compared with RF, SVM, or KNN. The outcome of children living with perinatally acquired HIV can be predicted with considerable accuracy using ML algorithms. Better models would benefit less specialized staff in limited resources countries to improve prompt referral in case of high-risk clinical progression.

SARS-CoV-2 infection may impair behavior and mental health; we explored the psychological impact on parents and children who experienced COVID-19 within their families. A cross-sectional web-based survey was conducted on families attending the COVID-19 Follow-up Clinic at the Department for Women’s and Children’s Health, Padua (Italy). From April 2020 to August 2021, 75 surveys were collected from 66 families (97 parents and 129 children); almost 70% of participants had COVID-19, mostly asymptomatic/mildly symptomatic, and the median time from infection to survey compilation was 164.7 days (SD 56). Most parents (>87%) reported positive relationships with family members either before, during, or after COVID-19. More than one-third of children and adolescents were unable to adapt to isolation. Among 31 pre-school children with a median age of 3 (SD 1.7), a change of one or more functions was reported for 74.2% of cases irrespective of COVID-19 status, particularly a change in circadian rhythm (25%), in relationship with parents (42.8%), and poor emotional control (36%). Among 74 children with a median age of 10.9 years (SD 2.7), 8.1% had a score indicating a disease; however, significant impairment in attention was reported for 16.7%, along with anxiety/depression and problems with conduct in 5.6% and 6.5% of cases, respectively.

Logistic regression (LR) is the most common prediction model in medicine. In recent years, supervised machine learning (ML) methods have gained popularity. However, there are many concerns about ML utility for small sample sizes. In this study, we aim to compare the performance of 7 algorithms in the prediction of 1-year mortality and clinical progression to AIDS in a small cohort of infants living with HIV from South Africa and Mozambique. The data set (n = 100) was randomly split into 70% training and 30% validation set. Seven algorithms (LR, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), and Elastic Net) were compared. The variables included as predictors were the same across the models including sociodemographic, virologic, immunologic, and maternal status features. For each of the models, a parameter tuning was performed to select the best-performing hyperparameters using 5 times repeated 10-fold cross-validation. A confusion-matrix was built to assess their accuracy, sensitivity, and specificity. RF ranked as the best algorithm in terms of accuracy (82,8%), sensitivity (78%), and AUC (0,73). Regarding specificity and sensitivity, RF showed better performance than the other algorithms in the external validation and the highest AUC. LR showed lower performance compared with RF, SVM, or KNN. The outcome of children living with perinatally acquired HIV can be predicted with considerable accuracy using ML algorithms. Better models would benefit less specialized staff in limited resources countries to improve prompt referral in case of high-risk clinical progression. [ABSTRACT FROM AUTHOR]