Background: Our prior six-year review (n=2165) revealed 24% of patients undergoing posterior decompression surgeries (laminectomy or discectomy) sought emergency department (ED) care within three months post-surgery. We established an integrated Spine Assessment Clinic (SAC) to enhance patient outcomes and minimize unnecessary ED visits through pre-operative education, targeted QI interventions, and early post-operative follow-up. Methods: We reviewed 13 months of posterior decompression data (n=205) following SAC implementation. These patients received individualized, comprehensive pre-operative education and follow-up phone calls within 7 days post-surgery. ED visits within 90 days post-surgery were tracked using provincial databases and compared to our pre-SAC implementation data. Results: Out of 205 patients, 24 (11.6%) accounted for 34 ED visits within 90 days post-op, showing a significant reduction in ED visits from 24% to 11.6%, and decreased overall ED utilization from 42.1% to 16.6% (when accounting for multiple visits by the same patient). Early interventions including wound monitoring, outpatient bloodwork, and prescription adjustments for pain management, helped mitigate ED visits. Patient satisfaction surveys (n=62) indicated 92% were “highly satisfied” and 100% would recommend the SAC. Conclusions: The SAC reduced ED visits after posterior decompression surgery by over 50%, with pre-operative education, focused QI initiatives, and its individualized, proactive approach.

Background: Dravet syndrome and genetic epilepsy with febrile seizures plus (GEFS+) are associated with pathogenic variants in SCN1A. While most such cases are heterozygous, there have been 16 reported homozygous cases. We report two new biallelic cases associated with divergent phenotypes.Methods: We performed a chart review for two patients with different homozygous SCN1A variants and reviewed all previously published biallelic SCN1A pathogenic variants. Results: Our first patient exhibited early afebrile seizures and severe developmental delay, without febrile seizures or status epilepticus. A homozygous c. 1676T>A, (p. Ile559Asn) variant of uncertain significance was identified, carried by asymptomatic parents. The second patient exhibited early, recurrent, and prolonged febrile seizures, moderate developmental delay, and motor dysfunction; a homozygous pathogenic c. 4970G>A, (p. Arg1657His) variant carried by asymptomatic parents was identified.

Of 18 known cases of biallelic SCN1A pathogenic variants, 15/18 (83%) have diagnoses of Dravet or GEFS+. The remaining 3/18 (17%) had pharmacoresponsive epilepsy with prominent GDD. Cognitive phenotypes ranged from intact neurodevelopment to profound developmental delay. Eleven out of 18 cases (61%) had motor concerns. Conclusions: These cases expand the phenotypic spectrum of biallelic SCN1A variants. While some patients present typically for Dravet/GEFS+, others present with developmental delay and controllable epilepsy.

Languages in contact commonly leave an imprint on one other. The most straightforward of these imprints to identify is MAT-borrowing, which results in clearly identifiable lexical items of one language (the donor language) being used in utterances of another language (the recipient language). This stands in contrast with PAT-borrowing, which does not involve any such incorporation of “other language” material but rather results in the reshaping of existing structures of the recipient language on the model of the donor language. This type of language change is therefore arguably more “invisible” to speakers since no easily identifiable “other language” material is present.

This study presents a detailed examination of PAT-borrowing in Guernésiais, the Norman variety spoken in Guernsey (British Channel Islands), which is now at an advanced state of language shift. It also highlights a major difference between MAT- and PAT-borrowing, namely that, whereas MAT-borrowing can only be explained with reference to the dominant language, PAT-borrowing can on occasion admit an internal explanation.

Increased temporal variability in the gut microbiome is associated with intestinal conditions such as ulcerative colitis and Crohn’s disease, leading to the recently established concept of microbial volatility (1). Increased physiological stress has been shown to increase microbial volatility indicating that microbial volatility is susceptible to external interventions(1). Dietary fibre positively affects the gut microbiome, but it is unclear if it impacts microbial volatility. The gut microbiota influences hypertension, and high-fibre intake reduces blood pressure (BP)(2). However, not all individuals exhibit a response to these fibre-based dietary changes, and the reasons for this variability remain unclear. Similarly, it is unknown whether the degree of stability of the gut microbiota consortium could be a determining factor in individual responsiveness to dietary interventions. Here, we aimed to identify: i) whether gut microbiome volatility differs when dietary fibre vs placebo interventions, and ii) whether microbiome volatility discriminates between BP responders and non-responders to a high fibre intervention. Twenty treatment-naive participants with hypertension received either placebo or 40g per day of prebiotic acetylated and butyrylated high amylose maize starch (HAMSAB) supplementation for 3 weeks in a phase II randomised cross-over double-blind placebo-controlled trial(3). Blood pressure was monitored at baseline and each endpoint by 24-hour ambulatory BP monitoring, with those experiencing a reduction between timepoints of ≥ 2 mmHg classified as responders. Baseline stool samples were collected, and the V4 region of the 16S gene was sequenced. Taxonomy was assigned by reference to the SILVA database. Microbial volatility between timepoints (e.g., pre- and post-intervention) was calculated as the Euclidian distance of centred log-ratio transformed genera counts (Aitchison distance). No difference was observed in microbial volatility between individuals when they received the dietary fibre intervention or the placebo (21.5 ± 5.5 vs 20.5 ± 7.7, p = 0.51). There was no significant difference between microbial volatility on the dietary intervention between responders and non-responders (21.8 ± 4.9 vs 20.9 ± 7.2, p = 0.84). There was no association between the change in BP during intervention and microbial volatility during intervention (r2 = −0.09, p = 0.72). These data suggest that temporal volatility of the gut microbiota does not change with fibre intake or contribute to the BP response to dietary fibre intervention trials in people with hypertension.

Objectives/Goals: The overall goal of this project is to determine bacterial transcriptional signatures from clinical sputum and assess their potential to monitor treatment response and predict the outcome of drug therapy in patients with tuberculosis (TB). Methods/Study Population: We are developing a novel transcript capture sequencing (TC-Seq) approach to sequence the mRNA of Mycobacterium tuberculosis (Mtb) and analyze transcriptomes from clinical samples containing minimal amounts of bacterial RNA. This protocol generates single-stranded biotinylated probes from Mtb DNA. Probes are hybridized to and allow enrichment of Mtb-specific mRNA within next-generation RNA sequencing libraries. We will apply TC-Seq to sputum samples collected throughout an 18-month Phase II clinical trial investigating response to TB treatment to compare the transcriptome of Mtb between patients whose treatment results in cure or relapse. Results/Anticipated Results: We have refined a technique to generate biotinylated probes starting from DNA of lab grown Mtb. This protocol achieves robust and unbiased sampling of the Mtb transcriptome from mixed samples containing both human and Mtb RNA. Preliminary sequencing of clinical sputum collected pretreatment has generated 1–4 million Mtb-specific reads, a sequencing depth that allows examination of the entire bacterial transcriptome. We will measure differential gene expression before and during treatment as well as between cure and relapse cases. These results will allow us to characterize bacterial response to treatment and identify bacterial markers that correlate with relapse. Discussion/Significance of Impact: Understanding Mtb activity during treatment will offer new ways to assess the efficacy of different treatment regimens. Crucially, identifying clear bacterial markers that demarcate a cure or relapse outcome will have a significant impact on determining patient eligibility for shorter drug therapy.

The marketing of unhealthy foods has been implicated in poor diet and rising levels of obesity. Rapid developments in the digital food marketing ecosystem and associated research mean that contemporary review of the evidence is warranted. This preregistered (CRD420212337091)1 systematic review and meta-analysis aimed to provide an updated synthesis of the evidence for behavioural and health impacts of food marketing on both children and adults, using the 4Ps framework (Promotion, Product, Price, Place). Ten databases were searched from 2014 to 2021 for primary data articles of quantitative or mixed design, reporting on one or more outcome of interest following food marketing exposure compared with a relevant control. Reviews, abstracts, letters/editorials and qualitative studies were excluded. Eighty-two studies were included in the narrative review and twenty-three in the meta-analyses. Study quality (RoB2/Newcastle–Ottawa scale) was mixed. Studies examined ‘promotion’ (n 55), ‘product’ (n 17), ‘price’ (n 15) and ‘place’ (n 2) (some > 1 category). There is evidence of impacts of food marketing in multiple media and settings on outcomes, including increased purchase intention, purchase requests, purchase, preference, choice, and consumption in children and adults. Meta-analysis demonstrated a significant impact of food marketing on increased choice of unhealthy foods (OR = 2·45 (95 % CI 1·41, 4·27), Z = 3·18, P = 0·002, I2 = 93·1 %) and increased food consumption (standardised mean difference = 0·311 (95 % CI 0·185, 0·437), Z = 4·83, P < 0·001, I2 = 53·0 %). Evidence gaps were identified for the impact of brand-only and outdoor streetscape food marketing, and for data on the extent to which food marketing may contribute to health inequalities which, if available, would support UK and international public health policy development.

This review aims to highlight the relative importance of cardiovascular disease (CVD) lifestyle-associated risk factors among individuals with inflammatory bowel disease (IBD) and examine the effectiveness of lifestyle interventions to improve these CVD risk factors. Adults with IBD are at higher risk of CVD due to systemic and gut inflammation. Besides that, tobacco smoking, dyslipidaemia, hypertension, obesity, physical inactivity and poor diet can also increase CVD risk. Typical IBD behavioural modification including food avoidance and reduced physical activity, as well as frequent corticosteroid use, can further increase CVD risk. We reviewed seven studies and found that there is insufficient evidence to conclude the effects of diet and/or physical activity interventions on CVD risk outcomes among populations with IBD. However, the limited findings suggest that people with IBD can adhere to a healthy diet or Mediterranean diet (for which there is most evidence) and safely participate in moderately intense aerobic and resistance training to potentially improve anthropometric risk factors. This review highlights the need for more robust controlled trials with larger sample sizes to assess and confirm the effects of lifestyle interventions to mitigate modifiable CVD risk factors among the IBD population.

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits.

Protein circular dichroism (CD) and infrared absorbance (IR) spectra are widely used to estimate the secondary structure content of proteins in solution. A range of algorithms have been used for CD analysis (SELCON, CONTIN, CDsstr, SOMSpec) and some of these have been applied to IR data, though IR is more commonly analysed by bandfitting or statistical approaches. In this work we provide a Python version of SELCON3 and explore how to combine CD and IR data to best effect. We used CD data in Δε/amino acid residue and scaled the IR spectra to similar magnitudes. Normalising the IR amide I spectra scaled to a maximum absorbance of 15 gives best general performance. Combining CD and IR improves predictions for both helix and sheet by ~2% and helps identify anomalously large errors for high helix proteins such as haemoglobin when using IR data alone and high sheet proteins when using CD data alone.

There is a growing trend for studies run by academic and nonprofit organizations to have regulatory submission requirements. As a result, there is greater reliance on REDCap, an electronic data capture (EDC) widely used by researchers in these organizations. This paper discusses the development and implementation of the Rapid Validation Process (RVP) developed by the REDCap Consortium, aimed at enhancing regulatory compliance and operational efficiency in response to the dynamic demands of modern clinical research. The RVP introduces a structured validation approach that categorizes REDCap functionalities, develops targeted validation tests, and applies structured and standardized testing syntax. This approach ensures that REDCap can meet regulatory standards while maintaining flexibility to adapt to new challenges. Results from the application of the RVP on recent successive REDCap software version releases illustrate significant improvements in testing efficiency and process optimization, demonstrating the project’s success in setting new benchmarks for EDC system validation. The project’s community-driven responsibility model fosters collaboration and knowledge sharing and enhances the overall resilience and adaptability of REDCap. As REDCap continues to evolve based on feedback from clinical trialists, the RVP ensures that REDCap remains a reliable and compliant tool, ready to meet regulatory and future operational challenges.