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We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients. Our analysis of the spatio-temporal structure of synaptic interactions reveals that, after training, excitation and inhibition group together in space and time. Notably, the dynamic pruning approach, which employs DEEP R for connection removal and RigL for reconnection, not only preserves these spatio-temporal patterns but outperforms per-synapse delay learning in sparse networks. Our results demonstrate the potential of combining delay learning with dynamic pruning to develop efficient SNN models for temporal data processing. Moreover, the preservation of spatio-temporal dynamics throughout pruning and rewiring highlights the robustness of these features, providing a solid foundation for future neuromorphic computing applications.

ObjectiveMeta-analysis focusing on the role of first-trimester neutrophil-to-lymphocyte ratio (NLR) in the prediction of preeclampsia.Data sourcesPubMed, Scopus, Web of Science, Cochrane Library, and Embase databases were queried from inception up to December 31, 2022.Study eligibility criteriaThe study included all types of original research that was conducted in humans and values of NLR were measured during the first trimester, among patients who later developed preeclampsia, compared to the values of control groups.Study appraisal and synthesis methodsTwo reviewers independently performed data abstraction and quality appraisal, and disagreements were resolved by consensus and, if necessary, by the opinion of a third reviewer. During the analysis, PRISMA and MOOSE guidelines were followed. All statistical analyses were made with R.ResultsFor the research on the predictive role of NLR values in the first trimester for preeclampsia, a total of 6 studies were selected for analysis, covering 2,469 patients. The meta-analysis revealed a 95% confidence interval (CI) for the effect size of 0.641 to 1.523, with a prediction interval of 0.027 to 2.137.ConclusionBased on the analysis, NLR is a promising biochemical marker for future pieces of research that try to find new screening methods for first-trimester preeclampsia. We encourage other researchers to examine NLR’s predictive value combined with other markers in preeclampsia screening, this way being able to find new and affordable protocols for first-trimester preeclampsia screening.Systematic review registrationidentifier CRD42023392663.

ObjectiveTo review of the efficacy and safety of pravastatin use for prophylaxis and treatment of preeclampsia.DesignSystematic review and meta-analysis of clinical studies evaluating pravastatin for treatment and/or prophylaxis of preeclampsia.Data collectionTwo independent reviewers systematically searched data from PubMed, Scopus, Web of Science, Cochrane, Embase, and clinicaltrials.gov databases, for studies evaluating pravastatin for prevention of pre-eclampsia.ResultsFourteen studies were identified, including 1,570 pregnant women who received either pravastatin or placebo, published between 2003 and 2022. From these studies, 5 studies were identified for inclusion in the meta-analysis to evaluate the role of pravastatin use prior to 20 weeks of gestation, to prevent pre-eclampsia, Pravastatin treatment reduced the incidence of preeclampsia by 61% and premature birth by 45%. Among the newborns, there was a 45% reduction in intrauterine growth retardation (IUGR) in the treated group, as well as a 77% reduction in those receiving neonatal intensive care unit (NICU) admissions.ConclusionProphylactic treatment with pravastatin appears to reduce risk of developing pre-eclampsia as well as potentially lowering risk of IUGR, preterm birth, and NICU admission in neonates.

Patients facing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infections with comorbidities, especially patients whose immune system is weakened have higher chances to face severe outcomes. One of the main reasons behind the suppression of the immune system is iatrogenic, in patients who have autoimmune diseases and/or had an organ transplant. Although there are studies that are examining immunocompromised and/or transplanted patients with COVID-19 infection, furthermore there is a limited number of studies available which are dealing with COVID-19 in pregnant women; however, it is unique and is worth reporting when these factors are coexisting. In this study, we present the case of a 33-year-old Caucasian pregnant woman, who had a kidney transplant in 2009 and contracted the SARS-CoV-2 virus on the 26th gestational week, in 2021. After her infection, superimposed preeclampsia was diagnosed and due to the worsening flowmetric parameters, she gave birth to a premature male newborn with cesarean section. Our kidney transplant patient’s case highlights how COVID-19 disease can lead to preeclampsia and artificial termination of gestation.

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