Search

Importance: Population-based analyses provided divergent data on the changes in preterm birth rates during the COVID-19 pandemic, and there is a gap of knowledge on the variations in birth characteristics., Objective: To study changes in perinatal care, causes of preterm delivery, and very preterm (VPT; defined as <32 weeks' gestation) birth rates before and during the COVID-19 pandemic., Design, Setting, and Participants: This population-level cohort study used data from the quality assurance registry, which covers all births in Hesse, Germany. Deliveries during the COVID-19 pandemic (2020) were compared with the corresponding grouped prepandemic time intervals (2017 to 2019). Analyses were executed between August 2023 and July 2024., Exposures: Analyses were directed to study differences in preterm births before and during 3 pandemic phases: first (March 14 to May 15, 2020) and second (October 19 to December 31, 2020) lockdowns and a period of less-vigorous restrictions between them (May 16 to October 18, 2020)., Main Outcomes and Measures: Outcomes of interest were variations in preterm birth rates in the context of baseline characteristics and causes of preterm births during vs before the first year of the COVID-19 pandemic., Results: From the total cohort of 184 827 births from 2017 to 2020, 719 stillbirths occurred and 184 108 infants were liveborn. Compared with the prepandemic period, medical care characteristics did not differ during the COVID-19 period. The odds of VPT births were lower during the pandemic period (odds ratio [OR], 0.87; 95% CI, 0.79-0.95) compared with the prepandemic period, with the greatest reduction observed during the second lockdown period (OR, 0.69; 95% CI, 0.55-0.84). Reduction in VPT births was attributed to fewer births in pregnancies among individuals with a history of serious disease (OR, 0.64; 95% CI, 0.50-0.83), pathologic cardiotocography (OR, 0.66; 95% CI, 0.53-0.82), and intrauterine infection (OR, 0.82; 95% CI, 0.72-0.92) while incidences of history of preterm birth, multiple pregnancies, serious or severe psychological distress, and preeclampsia, eclampsia, or hemolysis, elevated liver enzymes, and low platelet count syndrome as cause for preterm delivery remained unchanged., Conclusions and Relevance: In this population-based cohort study on the COVID-19 pandemic and preterm birth rates, the duration of exposure to mitigation measures during pregnancy was associated with accelerated reductions in preterm births. The findings of lower rates of baseline risks and causes of preterm deliveries support efforts to intensify health care prevention programs during pregnancy to reduce the preterm birth burden. These findings of this study put particular focus on hygiene measures to reduce the rate of deliveries for intrauterine infection and highlight the potential of expanding strategies to the different risks and causes of preterm delivery.

Background: Microbiome dysbiosis can have long-lasting effects on our health and induce the development of various diseases. Bronchopulmonary dysplasia (BPD) is a multifactorial disease with pre- and postnatal origins including intra-amniotic infection as main risk factor. Recently, postnatal pathologic lung microbiota colonization was associated with BPD. The objectives of this prospective observational cohort study were to describe differences in bacterial signatures in the amniotic fluid (AF) of intact pregnancies without clinical signs or risk of preterm delivery and AF samples obtained during preterm deliveries and their variations between different BPD disease severity stages., Methods: AF samples were collected under sterile conditions during fetal intervention from intact pregnancies (n = 17) or immediately before preterm delivery < 32 weeks (n = 126). Metabarcoding based approaches were used for the molecular assessment of bacterial 16S rRNA genes to describe bacterial community structure., Results: The absolute amount of 16S rRNA genes was significantly increased in AF of preterm deliveries and detailed profiling revealed a reduced alpha diversity and a significant change in beta diversity with a reduced relative abundance of 16S rRNA genes indicative for Lactobacillus and Acetobacter while Fusobacterium, Pseudomonas, Ureaplasma and Staphylococcus 16S rRNA gene prevailed. Although classification of BPD by disease severity revealed equivalent absolute 16S rRNA gene abundance and alpha and beta diversity in no, mild and moderate/severe BPD groups, for some 16S rRNA genes differences were observed in AF samples. Bacterial signatures of infants with moderate/severe BPD showed predominance of 16S rRNA genes belonging to the Escherichia-Shigella cluster while Ureaplasma and Enterococcus species were enriched in AF samples of infants with mild BPD., Conclusions: Our study identified distinct and diverse intrauterine 16S rRNA gene patterns in preterm infants immediately before birth, differing from the 16S rRNA gene signature of intact pregnancies. The distinct 16S rRNA gene signatures at birth derive from bacteria with varying pathogenicity to the immature lung and are suited to identify preterm infants at risk. Our results emphasize the prenatal impact to the origins of BPD., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Even more than 50 years after its initial description, bronchopulmonary dysplasia (BPD) remains one of the most important and lifelong sequelae following premature birth. Tremendous efforts have been undertaken since then to reduce this ever-increasing disease burden but a therapeutic breakthrough preventing BPD is still not in sight. The inflammatory response provoked in the immature lung is a key driver of distorted lung development and impacts the formation of alveolar, mesenchymal, and vascular structures during a particularly vulnerable time-period. During the last 5 years, new scientific insights have led to an improved pathomechanistic understanding of BPD origins and disease drivers. Within the framework of current scientific progress, concepts involving disruption of the balance of key inflammatory and lung growth promoting pathways by various stimuli, take center stage. Still today, the number of efficient therapeutics available to prevent BPD is limited to a few, well-established pharmacological interventions including postnatal corticosteroids, early caffeine administration, and vitamin A. Recent advances in the clinical care of infants in the neonatal intensive care unit (NICU) have led to improvements in survival without a consistent reduction in the incidence of BPD. Our update provides latest insights from both preclinical models and clinical cohort studies and describes novel approaches to prevent BPD., (© 2022. The Author(s).)

Bronchopulmonary dysplasia (BPD) remains one of the most devastating consequences of preterm birth resulting in life-long restrictions in lung function. Distorted lung development is caused by its inflammatory response which is mainly provoked by mechanical ventilation, oxygen toxicity and bacterial infections. Dysfunction of resident lung mesenchymal stem cells (MSC) represents one key hallmark that drives BPD pathology. Despite all progress in the understanding of pathomechanisms, therapeutics to prevent or treat BPD are to date restricted to a few drugs. The limited therapeutic efficacy of established drugs can be explained by the fact that they fail to concurrently tackle the broad spectrum of disease driving mechanisms and by the huge overlap between distorted signal pathways of lung development and inflammation. The great enthusiasm about MSC based therapies as novel therapeutic for BPD arises from the capacity to inhibit inflammation while simultaneously promoting lung development and repair. Preclinical studies, mainly performed in rodents, raise hopes that there will be finally a broadly acting, efficient therapy at hand to prevent or treat BPD. Our narrative review gives a comprehensive overview on preclinical achievements, results from first early phase clinical studies and challenges to a successful translation into the clinical setting.

Preterm birth poses a global challenge with a continuously increasing disease burden during the last decades. Advances in understanding the etiopathogenesis did not lead to a reduction of prematurely born infants so far. A balanced development of the host microbiome in early life is key for the maturation of the immune system and many other physiological functions. With the tremendous progress in new diagnostic possibilities, the contribution of microbiota changes to preterm birth and the acute and long-term sequelae of prematurity have come into the research focus. This review summarizes the latest advances in the understanding of microbiomes in the amniotic cavity and the female lower genital tract and how changes in microbiota structures contribute to preterm delivery. The exhibition of these highly vulnerable infants to the hostile environment in the neonatal intensive care unit necessarily entails the rapid colonization with a nonbalanced microbiome in a situation where the organism is still very prone and at an early stage of development. The global research efforts to decipher pathologic changes will pave the way to new pre- and postnatal therapeutic concepts.

What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(1-2):327-350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.

Measuring pairwise and higher-order spike correlations is crucial for studying their potential impact on neuronal information processing. In order to avoid misinterpretation of results, the tools used for data analysis need to be carefully calibrated with respect to their sensitivity and robustness. This, in turn, requires surrogate data with statistical properties common to experimental spike trains. Here, we present a novel method to generate correlated non-Poissonian spike trains and study the impact of single-neuron spike statistics on the inference of higher-order correlations. Our method to mimic cooperative neuronal spike activity allows the realization of a large variety of renewal processes with controlled higher-order correlation structure. Based on surrogate data obtained by this procedure we investigate the robustness of the recently proposed method empirical de-Poissonization (Ehm et al., 2007). It assumes Poissonian spiking, which is common also for many other estimation techniques. We observe that some degree of deviation from this assumption can generally be tolerated, that the results are more reliable for small analysis bins, and that the degree of misestimation depends on the detailed spike statistics. As a consequence of these findings we finally propose a strategy to assess the reliability of results for experimental data., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The population activity of random networks of excitatory and inhibitory leaky integrate-and-fire neurons has been studied extensively. In particular, a state of asynchronous activity with low firing rates and low pairwise correlations emerges in sparsely connected networks. We apply linear response theory to evaluate the influence of detailed network structure on neuron dynamics. It turns out that pairwise correlations induced by direct and indirect network connections can be related to the matrix of direct linear interactions. Furthermore, we study the influence of the characteristics of the neuron model. Interpreting the reset as self-inhibition, we examine its influence, via the spectrum of single-neuron activity, on network autocorrelation functions and the overall correlation level. The neuron model also affects the form of interaction kernels and consequently the time-dependent correlation functions. We find that a linear instability of networks with Erdös-Rényi topology coincides with a global transition to a highly correlated network state. Our work shows that recurrent interactions have a profound impact on spike train statistics and provides tools to study the effects of specific network topologies.

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulant suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence.

The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10-100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations.

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.

Leishmaniasis is an anthropozoonosis caused by infection with leishmania parasites with either cutaneous, mucosal or visceral (kala-azar) involvement. While the benign cutaneous form is self-limited death occurs in approximately 80% of children with kala-azar when untreated. The diagnosis of kala-azar should not be missed in children presenting with fever, hepatosplenomegaly and pancytopenia especially with a history of sand fly bites. We report the case of a 13-month-old boy with both cutaneous and visceral involvement.