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Access status: Open Access ,
The Evolution of Cephalocentesis in Contemporary Obstetric Practice: From Emergency Intervention to Planned Procedure
(S. Karger AG, 2026-01-19) Windrim, Catherine; Kunpalin, Yada; AlRefai, Alyaa; Holloway, Erica; Kelly, Edmond N.; McParland, Peter; McAuliffe, Fionnuala M.; Chitayat, David; Abbasi, Nimrah; Shinar, Shiri; Windrim, Rory; Seaward, Gareth; Keunen, Johannes; Van Mieghem, Tim; Ryan, Greg
Introduction: The aim of the study was to analyze the evolution, indications, and outcomes of cephalocentesis over a 38-year period at two tertiary fetal medicine centers. Methods: A retrospective review of 70 cephalocentesis procedures (1985–2023) was conducted at Mount Sinai Hospital, Toronto, and the National Maternity Hospital, Dublin. Cases were divided into pre-2002 (n = 37) and 2002-onward (n = 33) cohorts in order to evaluate practice evolution. Results: Mean gestational age at diagnosis was 32.7 ± 5.4 weeks with severe hydrocephalus in 95.7% (67/70) and hydranencephaly in 4.3% (3/70) of cases. Pre-2002, 94.6% (35/37) of procedures were performed intrapartum; 2002 onward, this shifted to 66.7% (22/33) pre-labor planned procedures with 84.8% (28/33) using a transabdominal approach. Concurrent fetal analgesia and potassium chloride (KCl) to achieve fetal asystole was introduced in 2002. Vaginal delivery was achieved in 95.7% (67/70) of cases. Perinatal mortality (excluding KCl cases) was 91.8% (45/49). All four survivors (5.8%) demonstrated neurodevelopmental impairment. Conclusion: Cephalocentesis has evolved from an intrapartum intervention to a planned procedure with standardized protocols. Our findings support reserving this procedure for cases where there is no expectation of postnatal survival, with the primary purpose of facilitating vaginal delivery when caesarean section could unnecessarily increase maternal morbidity.
Access status: Open Access ,
Digital Footprints, Green Impact: User Engagement Analysis of a Conference Management Platform at the 41st Annual IFMSS Meeting
(S. Karger AG, 2025-10-23) Windrim, Catherine; Hojabri, Sara F.; Gotha, Lara; Ryan, Greg; Windrim, Rory; Dada, Jazleen
Introduction: International medical conferences face evolving challenges in optimizing scientific exchange and professional networking while minimizing their environmental footprint. Digital platforms offer solutions that can enhance engagement while reducing ecological impact. This study evaluated implementation outcomes of a custom digital platform at the 41st International Fetal Medicine & Surgery Society (IFMSS) meeting. Methods: We conducted a prospective observational study of a custom mobile application platform deployed during IFMSS 2024 (September 22–28, 2024). The system incorporated authenticated user access, real-time session management, networking capabilities, and comprehensive analytics. Primary outcomes included user engagement metrics, scientific content interaction rates, professional networking efficacy, and platform stability. Results: Platform adoption reached 91.9% (339/369 registrants), generating 178,873 discrete interactions. Scientific content engagement included 56,344 abstract/presentation views by 335 unique users. Networking features facilitated 182 new professional connections and 485 direct message exchanges. Search functionality received 4,310 targeted queries, while speaker profiles were examined 780 times. Conclusion: Implementation of a digital conference platform demonstrated significant efficacy in supporting conference objectives with high engagement rates. These findings suggest digital platforms can effectively enhance traditional conference structures, facilitate attendee engagement and interaction, reduce paper waste, and provide a more sustainable option for scientific exchange in subspecialty meetings.
Access status: Open Access ,
Designing an Ecological Restoration Solution with Novel Materials for a Circular Economy
(2026-05-28) Zhu, Xingyu; Krigstin, Sally
This study applies a circular economy framework to develop biodegradable substrates for ecological restoration using industrial byproducts. Recovered paper fiber was utilized as the primary matrix, with Biobinder® included as a binding component, and biochar and inactivated yeast incorporated as amendments. Eight formulations were evaluated under controlled laboratory conditions, with physical assessments focusing on water absorption under continuous immersion. Germination and root penetration were first observed using white clover (Trifolium repens). Based on these observations, three representative substrates were selected for further growth measurements (seedling height at days 15/30 and root length at day 30). Results showed that Biobinder® based substrates maintained strong structure despite lower water absorption and later became friable enough to support root penetration. While roots in pulp-only formulations tended to grow along the surface, biochar addition facilitated downward root development. Within the Biobinder® and biochar base substrate, the addition of inactivated yeast was found to significantly increase root length by day 30. Overall, the formulation combining Biobinder®, biochar, and yeast demonstrated the best performance. Despite limitations regarding short test duration and the lack of nutrient dynamic data, this study suggests that selected industrial byproducts have the potential to be transformed into functional restoration substrates. These findings provide experimental support for the development of low-cost, sustainable materials for large-scale ecological restoration.
Access status: Open Access ,
Minimal-Data Peptide Design and Accessible Protein Language Models for Protein Engineering
(2025-10) Bayat, Pouriya; Pardee, Keith; Pharmaceutical Sciences
Protein engineering is increasingly driven by machine learning, yet two practical barriers still limit broad adoption: (i) the scarcity of experimentally labelled data for new protein targets and (ii) the steep hardware demands of modern protein-language models (pLMs). This dissertation tackles both challenges through two core contributions. First, I introduce Minimal-Data → Maximal-Insight (MDMI), a structure-aware peptide-discovery pipeline that needs only a single round (~100 variants) of experimental screening. MDMI couples AlphaFold-Multimer complex prediction with hybrid statistical/physics scoring (SPServer + PyRosetta) to train a predictor, which then steers a genetic algorithm through sequence space to find novel sequences. As a case study, I applied MDMI to the split-GFP system, where the interaction between a 16-residue GFP11 peptide and its target GFP1-10 fragment reconstitutes fluorescence. MDMI successfully designed GFP11 peptides with over 50% sequence divergence from the wild type while preserving function, demonstrating the MDMI’s capacity to uncover non-obvious, high-diversity variants in data-limited scenarios. By decoupling peptide engineering from large training sets, MDMI offers an accessible strategy for laboratories with limited throughput.Second, I present Quantized Low-Rank Adaptation (QLoRA) for protein language models (pLMs), combining 4-bit weight quantization with low-rank adapters to enable efficient fine-tuning on lower cost and accessible GPUs. Across several pLMs (8 million–3 billion parameters) QLoRA cuts required training memory by an average 46.7%, and up to 90 % for the largest models, while retaining ≥ 90 % of baseline performance on regression tasks (i.e. fluorescence and stability datasets), secondary-structure classification, and de novo protein generation. Together, these methods establish a resource-conscious paradigm: MDMI extracts maximal design power from minimal data, and QLoRA delivers advanced pLMs to laboratories without specialized hardware. By uniting minimal-data modelling with hardware-efficient learning, this work broadens access to next-generation protein engineering and paves the way for rapid, distributed innovation in therapeutics, diagnostics, and biomanufacturing.
Access status: Open Access ,
Star formation across the scales
(2025-10) Khullar, Shivan; Matzner, Christopher; Murray, Norman; Astronomy and Astrophysics
The gas flows that form stars assemble at the scales of a few kiloparsecs and collapse into objects smaller than an astronomical unit. Different theories invoke different physical mechanisms to explain the inefficient nature of the star formation process -- some applicable on the global galactic scales, others on local cloud scales. In this thesis, we extend existing models at the cloud scale, test local models at the galactic scales and validate methods to increase resolution in simulations that bridge the gap in these two scales. First, we perform numerical simulations of self-gravitating turbulent flows in a patch of a molecular cloud. We study the physical origin of the gas density distribution (PDF) and explain the connection between the PDF and the dimensionless star formation rate. We also characterize the dependence of the various moving parts of the PDF on dimensionless cloud parameters. Second, we perform a numerical experiment at the galactic scale to understand the role of stellar feedback in the evolution of giant molecular cloud populations. We find that feedback drives turbulent motions, and test predictions of turbulence-regulated theories of star formation. Third, we test and validate a technique to increase resolution in Lagrangian simulations. We find that simulations where we increase resolution, compared to ones where we do not, exhibit systematic changes in the density field, masses and numbers of accreting stellar particles. However, the chaotic nature of star forming systems ultimately implies that simulations where we increase resolution do not sample a star formation history that might otherwise not exist. This thesis paves the way for future simulations that span the entire dynamical range of the star formation process.