Muscular Dystrophy Canada

As recent hydrocarbon discoveries rekindle exploration activities in the Black Sea Basin (BSB), efforts to understand the geodynamic processes that led to the formation and evolution of the basin have started to play a significant role in understanding the structural trends formed during rifting. The debate on whether the basin rifted open as one east-west oriented basin, or as two separate basins named the Eastern and Western Black Sea Basins, has been discussed in numerous models. Evidence for the two-basin hypothesis focuses on the basin's semi-parallel ridge and depression architecture, which trends NW-SE in the east and W-E in the west. Conversely, the single-basin model is supported by the correspondence between the regional structure and geodynamic rifting models, specifically those involving an asymmetrical rift pivoting on an eastern hinge caused by slab roll-back of the subducting plate located in the south of the basin.To address existing tectonic uncertainties, we established a new structural framework for the BSB by reinterpreting 24 long-offset 2D seismic lines. These structural constraints enabled the development of two 2D computational models, allowing us to simulate the distinct kinematic evolution of the basin's western and eastern sections. Our 2D sectioned models show that rift velocities vary significantly in the east-west direction. This contradicts previous analog models showing that the formation of the BSB was related to a simple asymmetrical rift with constantly increasing velocities towards the west from a hinge point located at the eastern margin of the basin. The complex velocity changes throughout the rift axis suggest an uneven movement throughout the subduction zone that drives the back-arc rift. Ultimately, proposing a new complex kinematic history during the evolution of the rift and alternating rift velocities throughout the rift axis, provide a better understanding of the timing of all tectonic events and the final ridge depression geometry observed throughout the BSB.

End-inspiratory pause (EIP) is traditionally discouraged in mechanically ventilated patients with chronic obstructive pulmonary disease (COPD) because it shortens expiratory time (TE) and may worsen hyperinflation. Previous data in hypoxemic patients suggest that, at equal total inspiratory time (TI), shorter insufflations followed by EIP can improve CO₂ elimination. Whether this strategy is effective and safe in obstructive lung disease remains unknown. To evaluate whether a ventilation with high inspiratory flow and EIP reduces arterial carbon dioxide (PaCO₂) without increasing hyperinflation in mechanically ventilated patients with COPD undergoing controlled hypoventilation. Prospective, single-center, randomized, cross-over physiological study including sedated, intubated adult patients with acute exacerbation of COPD, PaCO₂ ≥45 mmHg, and no spontaneous respiratory effort. Patients were randomized to two 30-minute ventilation strategies in volume-controlled mode: conventional ventilation without EIP (VentNO−PAUSE) and ventilation with EIP (VentPAUSE), in which inspiratory flow was increased and 40–50% of TI was replaced by an EIP. Total TI and TE were identical in both strategies. Arterial blood gases, respiratory mechanics, and hemodynamics were recorded. VentPAUSE significantly reduced PaCO₂ (58.5 ± 9.4 to 52.6 ± 10.4 mmHg; P < 0.001) and increased arterial pH (7.31 ± 0.09 to 7.34 ± 0.08; P < 0.001). Ventilatory ratio decreased (P < 0.001), indicating improved ventilatory efficiency. Oxygenation improved (PaO₂ and PaO₂/FiO₂; P < 0.05), while the alveolar–to–arterial oxygen difference was unchanged (p = 0.813). Total PEEP and auto-PEEP were not affected (P > 0.999). PaCO₂ reduction was greater in patients with higher respiratory system elastance. In sedated, mechanically ventilated patients with COPD, a strategy using high inspiratory flow with a short inspiratory pause reduces PaCO₂ and improves oxygenation without increasing auto-PEEP.

Days alive and at home (DAH) is a validated outcome measure that captures health care transitions between time spent at home vs various nonhome care settings, offering a more nuanced patient-centered understanding of recovery. We aimed to (1) characterize long-term recovery trajectories for adults with moderate-to-severe traumatic brain injury (msTBI) using DAH and (2) develop and internally validate a clinical prediction model for favorable vs unfavorable recovery. This multicenter retrospective population-based cohort study was performed in Ontario, Canada; we identified adults hospitalized with isolated msTBI between 2009 and 2021 that survived beyond 72 hours postadmission. DAH was calculated in 30-day intervals from index admission through 3 years postinjury. Latent class mixed modeling identified unique recovery trajectories. Sociodemographic, clinical, and injury variables were then evaluated as candidate predictors. Logistic regression, penalized regression, and machine learning approaches were compared for discrimination of favorable (early, intermediate, or late recovery) vs unfavorable (delayed deterioration or poor recovery) trajectories. Model performance was evaluated in a held-out test set using area under the receiver operating characteristic curve (AUC) with 95% CIs. There were 3,004 adults included in the cohort with an overall male predominance (n = 2,284, 76.1%), and the average age was 49.6 years (SD 21.4). Latent class mixed modeling revealed 5 distinct DAH trajectories: early recovery (38.6%), intermediate recovery (27.5%), and late recovery (3.2%) groups were characterized by return to the community at varying time intervals; by contrast, delayed deterioration (3.4%) and poor recovery (27.3%) groups experienced limited time at home at final follow-up. Patients in the unfavorable recovery trajectory groups were older, frailer, and more likely to sustain fall-related injuries. Radiographic predictors of poor outcome included subdural hematoma, large intraparenchymal hematoma, diffuse axonal injury, and cerebral edema. Using various demographic, injury and radiographic characteristics, a prediction model (DAH-TBI calculator) could discriminate trajectories with an AUC of 0.812 (95% CI 782-0.842) on a held-out internal validation data set. Our findings emphasize a longer period of postinjury outcome observation may be necessary to capture societal reintegration after msTBI. We developed and internally validated the DAH-TBI calculator to predict long-term postinjury outcomes.

Rare neuromuscular disorders impose a significant burden on patients, caregivers, and the health care system, yet, effective disease-modifying therapies remain limited. Antisense oligonucleotides (ASOs) have emerged as a promising therapeutic strategy, enabling targeted modulation of gene expression through mechanisms such as exon skipping, exon inclusion, and transcript degradation. However, the clinical efficacy of currently approved ASO therapies is often suboptimal. This limitation reflects not only poor target tissue uptake and delivery barriers, but also suboptimal design of ASO sequences and chemical modification patterns, which can compromise potency, safety, and translational robustness. Recent advances in machine learning have led to the development of ASO optimization platforms such as eSkipFinder and ASOptimizer, which aim to predict effective ASO sequences and chemistries for specific RNA targets. While these tools show considerable promise, their broader applicability remains limited due to a lack of comprehensive validation and the absence of integrated safety considerations. Further refinement and validation are necessary to improve their translational utility. Nevertheless, such platforms represent a critical advancement toward accelerating ASO development. By improving design precision, reducing reliance on extensive preclinical screening, and enabling researchers with limited ASO experience to generate optimized candidates, machine learning is poised to accelerate the development and clinical translation of ASO therapies for rare neuromuscular disorders.

IntroductionAcute hypoxaemic respiratory failure is a common reason for intensive care unit (ICU) admission. Non-invasive respiratory support strategies such as high-flow nasal oxygen (HFNO) and helmet non-invasive ventilation may reduce the need for invasive mechanical ventilation and death. The High-flow nasal Oxygen with or without alternating helmet Non-invasive ventilation for Oxygenation sUpport in acute Respiratory failure pilot trial is designed to compare helmet non-invasive ventilation combined with HFNO vs HFNO alone in patients with acute hypoxaemic respiratory failure and to determine the feasibility of a larger randomised controlled trial.Methods and analysisThis is a pragmatic, open-label, multicentre randomised controlled pilot trial enrolling 200 critically ill adults with acute hypoxaemic respiratory failure across 12 Canadian ICUs. Participants are randomised 1 to 1 to receive either helmet non-invasive ventilation plus HFNO or HFNO alone for at least 48 hours. The primary aim is to assess feasibility metrics including recruitment rate, protocol adherence and fidelity to pre-specified intubation criteria. Secondary outcomes include rates of intubation, all-cause mortality, ventilator-free days, ICU length of stay and quality of life at 6 months. Primary and secondary outcomes will be analysed using Bayesian methods.Ethics and disseminationEthics approval has been obtained at all participating centres. Findings will inform the feasibility and design of a future full-scale trial and be disseminated through peer review publications and conference presentations.Trial registration numberClinicalTrials.gov Identifier: NCT05078034.

ObjectiveTo develop and validate models to predict which endometriosis patients are likely to experience pain reduction following therapeutic laparoscopy using intraoperative findings and patient characteristics.DesignA retrospective secondary data analysis with patient workshops.SettingAnalysis of a UK nationwide, specialist centre, surgical database (British Society for Gynaecological Endoscopy, BSGE) (2013–2019, N=9171) and two research databases, MEDAL (2011–2013, N=667) and LUNA (1998–2005, N=592) for exploratory analyses and external validation.ParticipantsDatabase patients had laparoscopically confirmed (BSGE) or clinically suspected endometriosis (MEDAL, LUNA) and ranged from 16 to 65 years. Patient workshops included UK-wide endometriosis patients from the community, secondary care doctors and endometriosis nurses.Primary and secondary outcome measuresFollowing model development and internal validation, primary outcome measures included model performance statistics for discrimination (C-statistic) and calibration (calibration slope and calibration-in-the-large) for pain-improvement models for each of the five clinically meaningful pain domains. Secondary outcome measures included performance statistics for externally validated models and net benefit (using decision curve analysis).ResultsFollowing internal validation for dyspareunia (pain during sexual intercourse), non-cyclical pelvic pain (NPP), dyschezia (painful defecation) and quality of life our models showed good discrimination ability with C-statistics of 0.768, 0.750, 0.808 and 0.792, respectively. Significant increases in odds of pain relief were associated with trying to conceive for less than 18 months, any treated endometriosis of the ovary or uterosacral ligament or hysterectomy at the time of laparoscopy. For those models for which sufficient data were available to do external validation, dyspareunia and NPP showed good ability to predict pain reduction following surgery with C-statistics of 0.759 and 0.741, respectively, but after external validation only the model for dyspareunia good discriminatory ability (C-statistic=0.718). Despite this, decision curve analysis indicated some net benefit for all externally validated models.ConclusionsClinical prediction models can help identify women who will experience pain reduction after therapeutic laparoscopy, but more work is required to externally validate the current models. Removal of ovarian and utero-sacral ligament endometriosis appears to convey pain relief after surgery, whereas removal of superficial peritoneal endometriosis does not.

The development of improved therapies is complicated by the limited availability of well-characterized human models, especially in rare tumors such as soft-tissue sarcoma (STS). We report the optimization of conditions and clinical factors that correlate with the successful establishment of primary STS cell lines. We focus on leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma, which are adult STS with complex genomics and poor survival rates. We initiated cell lines from 165 fresh STS specimens. Cultures were classified as (i) no/little in vitro growth or (ii) persistent growth. Tumor and clinical characteristics were analyzed to determine their correlation with cell line growth. To determine whether cell lines shared tumor-specific variations (TSV) and mutational signatures with bulk specimens, comparative and Catalogue of Somatic Mutations in Cancer mutational signature analyses were performed on a subset of cases with cell line, tumor, and blood DNAs available. Cell lines were established from 46 specimens (28%). Myxofibrosarcoma specimens yielded more successful cell lines (P < 0.05) than leiomyosarcoma specimens. Primary specimens from treatment-naïve patients and those who presented with metastases demonstrated higher success rates (P < 0.05) compared with treated specimens and those who had only local disease, respectively. Cell line growth was not associated with patient outcomes or specimen grade. Six of the eight cases retained TSVs, including in ATRX or TP53, whereas two did not retain TSVs. Paired samples shared clock-like mutational signatures. Xenograft mouse models were created with a subset of the cell lines. The development and characterization of preclinical STS models will advance our understanding of STS biology.