Birmingham Women's Hospital

The purpose of this review was to provide current evidence of the relationship between fibroid surgery (hysteroscopic, laparoscopic/robotic, open) and the formation of intrauterine adhesions (IUA). A systematic electronic literature search was conducted to provide a survey of the various surgical modalities and their relevant incidence of intrauterine adhesions. Utilizing PRISMA methodology the search identified 23 full text original studies that were included in the analysis. Our analysis identified 2437 cases reported in the international literature whereby the de novo formation of IUAs were systematically assessed with second look hysteroscopy at least 4 weeks and usually up to 3 months after the surgery. In our analysis, out of 1678 hysteroscopic cases the mean incidence was 9.4% for all different techniques, including monopolar or bipolar diathermy, cold loop and radio frequency ablation. The reported numbers for laparoscopic/robotic and open surgery were 399 and 360 cases respectively. The incidence of IUA following endoscopic abdominal surgery was 12.8% and for the traditional open approach (laparotomy) was 18.3%. There is substantial heterogeneity on the reported data regarding risks factors contributing to IUAs; such as fibroid size, location, number as well as uterine cavity breach during abdominal surgery (either open, laparoscopic or robotic). Iatrogenic IUAs following myomectomies are not uncommon and may be implicated in patient morbidity and sub fertility. Further studies are necessary to evaluate the prevention of such sequelae and improve therapeutic outcomes following fibroid surgery.

Pelvic Floor Dysfunctions (PFD) have been strongly associated with vaginal birth and can significantly affect women's quality of life. This study aimed to determine the prevalence of PFDs during the postpartum period and its impact on women's daily activities. A hospital-based retrospective cross-sectional study was conducted among 747 primiparous women at 9-12 months postpartum, who had delivered term live baby from July to September 2021. The different components of PFD were evaluated using a Nepali translated version of PFDI-20 and the impact on sexual function by BFLUTS questionnaire. The primary outcome was women's self-reported occurrence of urinary, prolapse, defecatory and sexual problems. The impact on women's daily activities was secondary outcomes. Data were analyzed by Spearman's Rho correlation coefficient. Among 747 primiparous women, 194 (25.97%) had pelvic floor dysfunction. The mean age was 23.01±3.6 years, with 94 (48.45%) of PFD cases aged 20-24 years. Stress urinary incontinence was reported by 103 (53.09%), pelvic heaviness by 48 (24.74%), straining during defecation by 23 (11.86%), and dyspareunia by 54 (27.84%). Most women 137 (70.61%) reported no impact on daily life, and 18 (2.40%) were aware of pelvic floor muscle training. Pelvic floor dysfunction affected a notable proportion of primiparous women, with urinary, prolapse-related, colorectal, and sexual symptoms. Most affected women were young, and labour characteristics such as spontaneous onset, vaginal delivery, and shorter second stage were common.

Aims: The volume of gastric contents that might be expected at anesthetic induction following a 3-hour breast milk fast has not yet been investigated. The role of gastric ultrasound in quantifying particulate gastric content is also undefined. The primary objective of this study is to assess the gastric residual volume (GRV) measured by gastric suctioning in children who are breast fed 3 hours prior to general anesthesia. The secondary objective is to compare measured and predicted GRV, using a published formula derived from a gastric ultrasound study in infants. Method: An observational prospective study of children aged 37 weeks to 18 months, fed 3 hours prior to general anesthesia was performed at 2 tertiary pediatric centers. Assessment of gastric antrum was performed using gastric ultrasound. The correlation between predicted and measured gastric residual volume was investigated using Pearson’s correlation coefficient. Results: Data from 44 general anesthetics performed in 42 children, with a median age of 9.5 months, and a median fasting time of 180 minutes are reported (range 150–210 minutes). Median GRV was 0 mL kg−1 (IQR 0–0.1). Correlation between predicted and actual gastric residual volume by aspiration was weak (r = 0.42, p = 0.007). Conclusion: For breast milk fasting intervals of 3 hours, GRV was low and comparable to an overnight fast in this small study. The limited feasibility of performing awake gastric ultrasound in this age group, and therefore limitations as a clinical tool are discussed.

<h3></h3> Progressive familial intrahepatic cholestasis (PFIC) is a group of genetic disorders resulting in disrupted bile composition, cholestasis, and pruritus. Maralixibat, an inhibitor of the ileal bile acid transporter, is approved in the EU for the treatment of Progressive Familial Intrahepatic Cholestasis (PFIC) in individuals ≥3 months of age. In the 26-week placebo-controlled MARCH Phase 3 study, MRX at 570 μg/kg BID demonstrated significant improvements in pruritus, serum bile acids (sBA), total bilirubin (TB) and growth in patients across the broadest range of PFIC types studied to date. We report on long-term maintenance of effect of up to 2 years of treatment with MRX in MARCH-ON, an open-label, long-term extension study of MARCH. Long-term maintenance of response was assessed for patients who were originally randomized to receive MRX in MARCH and continued with treatment in MARCH-ON (MRX-MRX group: n=47), and for patients who received placebo (PBO) in the MARCH study and switched to open-label MRX in MARCH-ON (PBO-MRX group: n=41). Assessments included: pruritus, sBA, TB, growth z-scores, and incidence of treatment-emergent adverse events (TEAEs). Baseline (BL) was defined as the start of MRX for each group. For the MRX-MRX group, the median (min, max) time on MRX was 638 days (10, 1135). 13 of 47 patients reached Week 104 at time of analysis. Significant improvements observed in the first 26 weeks of the MARCH study were sustained through Week 104 in MARCH-ON for pruritus (-2.03, p&lt;0.0001), sBA (-166 μmol/L, p=0.003), TB (-27.7 μmol/L, p=0.02), and growth (height z-score: +0.40, p=0.046; weight z-score: +0.52, p=0.01). In the PBO-MRX group, the median time on MRX was 456 days (22, 720). 18 of 41 patients reached Week 52 of MRX treatment at time of analysis. Significant improvements through Week 52 for pruritus (-1.1, p=0.0001), sBA (-71 μmol/L, p=0.03), and growth (height z-score: +0.37, p=0.01; weight z-score: +0.32, p=0.03) were in line with observations from the initial MARCH MRX group. Additionally, numeric reductions in TB (-6.4 μmol/L; p=0.7) were observed. No new safety signals were identified. The most common TEAEs were GI-related with diarrhea (50%) being mostly mild and transient. Significant and sustained improvements in pruritus, sBA, TB, and growth are observed with up to 2 years of MRX treatment across the broadest range of genetic PFIC types studied to date. These data suggest overall improved liver health with MRX treatment which can be maintained long-term.

Occult tethered cord syndrome (OTCS) is poorly documented in dogs. This retrospective multicenter study evaluated the clinical presentation, MRI findings, treatment outcomes, and prognostic factors in 30 dogs diagnosed with OTCS managed surgically (n = 11) or medically (n = 19). Novel clinical severity scoring and neurological grading systems were developed to assess prognostic utility. The median age at clinical onset was 11 months (range 2-65), with a median duration of clinical signs of 13 months (range 1-60). Pain/dysesthesia in the lumbosacral region/tail/pelvic limbs was the most common presenting complaint (97%), followed by pelvic limb gait abnormalities (70%), behavioral changes (67%), impaired physical activity (63%), and urinary/fecal incontinence (17%). Neurological deficits were present in 90% of dogs. MRI findings showed variability in conus medullaris and dural sac termination, with no physiological translocation detected in available dynamic studies. Electrodiagnostic abnormalities were identified in four of nine tested dogs (44%). Clinical severity scores strongly predicted response to medical treatment, with responders having significantly lower scores than non-responders (3.25 ± 2.09 vs. 7.78 ± 3.15, p < 0.001). Higher neurological grades (p = 0.006), presence of behavioral abnormalities (p = 0.045), and worsening clinical evolution prior to referral (p = 0.009) were also associated with poor medical therapy outcomes. Surgical intervention was significantly associated with full recovery (p = 0.015) and discontinuation of medical treatment (p = 0.023) at last follow-up (median: 9 months, range: 2-108). Three surgically treated dogs experienced partial relapse within 6 months, with two undergoing reintervention and improving postoperatively. This study highlights the clinical and MRI characteristics of canine OTCS, introduces novel prognostic factors, and supports surgical detethering as a key intervention for optimizing outcomes. Larger prospective studies are needed to validate these findings, refine the proposed scoring systems, and establish evidence-based guidelines for managing canine OTCS.

Abstract Study question Can an image recognition model, utilising automated deep learning, be trained to diagnose and classify the severity of adenomyosis on ultrasound? Summary answer The automated deep learning model developed in this study can accurately diagnose and classify the severity of adenomyosis from ultrasound images. What is known already Adenomyosis is prevalent across all stages of life, starting from adolescence to perimenopause, however it remains underdiagnosed. The diagnosis and classification of adenomyosis are essential for correlating with symptom severity and counselling about disease prognosis, management options and long-term impact. Diagnosing and classifying the severity is a time-consuming and complex process that depends on the operator’s experience. Machine learning (ML) has an enormous potential to assist healthcare professionals with image classification tasks. It can directly process and automatically learn mid to high-level abstract features acquired from images using a deep architecture model without requiring the manual definition of features. Study design, size, duration This observational cohort study utilised ultrasound images from four assisted conception units in the United Kingdom from February 2023 to February 2024. Labelled images were used as input for training and testing of the model. The complete dataset was manually split into 9:1 ratio; 90% of all images were used as training dataset (80% for training and 10% for validation) and 10% as test dataset. Ethics approval was obtained from the Institutional Review Board (IRB). Participants/materials, setting, methods An automated ML tool (Vertex AI Vision) was trained and tested to diagnose and classify adenomyosis severity. Two investigators, experienced in gynaecological ultrasound scanning, labelled two and three-dimensional images into four categories: normal uterus, mild, moderate, and severe adenomyotic uterus, as per standardised ultrasound criteria. The labelled images were formatted and uploaded on Google Cloud to train the neural network based on pattern recognition. The model’s performance was determined for an internal test dataset. Main results and the role of chance A total of 3,500 ultrasound images were obtained from 3,194 patients, and the internal test dataset consisted of 350 ultrasound images from 292 patients. An overall and per-category score threshold of 0.5 was adopted to provide a balance between reasonable precision and recall during the evaluation. The investigator’s categorization was used as the reference standard to evaluate the performance of the automated machine-learning model. The model’s overall accuracy for diagnosing and classifying adenomyosis, as determined by the area under the precision-recall curve, was 80.24% across all score thresholds. The overall precision was 83.00%, and recall was 65.75% at a confidence threshold of 0.5. The precision and recall were 91% and 78% for normal uterus, 67% and 53% for mild adenomyosis, 86% and 68% for moderate adenomyosis, and 88% and 64% for severe adenomyosis. The algorithm achieved a high accuracy for the internal dataset. The time taken by a trained healthcare professional to label and process 3,500 images into 4 categories was 70 hours. The time taken by an untrained automated ML tool to get trained, process, and label the images into 4 categories was 4 hours, saving 66 hours per healthcare professional. Limitations, reasons for caution Due to the inherent nature of non-invasive investigations for the population attending the assisted conception unit, histological confirmation of adenomyosis was not possible. In addition, the model’s performance has been tested using an internal test dataset and needs further validation using an external test dataset from a different population. Wider implications of the findings This novel study leverages the power of artificial intelligence to develop an automated machine learning model for diagnosing and classifying adenomyosis from ultrasound scans. This tool can potentially enhance the diagnostic capacity and efficiency of healthcare professionals and empower women by providing better care through early and uniform diagnosis. Trial registration number No

Biallelic inactivating variants in ZNF142 underlie a clinically variable neurodevelopmental disorder. ZNF142 is a zinc-finger transcription factor with potential roles on chromatin organization, implying a possible association of ZNF142 loss of function with perturbed genome-wide DNA methylation (DNAm) pattern. We performed EPIC array-based methylation profiling of peripheral blood-derived DNA samples from 27 individuals with biallelic ZNF142 inactivating variants, together with 6 heterozygous carriers and 40 controls. A DNAm signature discovery pipeline was applied by using 440 controls for discovery and validation analyses, and a machine-learning model was trained to classify 8 individuals carrying ZNF142 variants of uncertain clinical significance. Analyses directed to explore the genome-wide DNAm landscape in affected individuals revealed 88 differentially methylated probes constituting the minimal informative set specific to ZNF142 loss of function. This reproducible pattern of DNAm changes involved regulatory regions of a small number of genes. The DNAm signature derived from peripheral blood allowed us to diagnose individuals carrying biallelic inactivating ZNF142 variants when applied to fibroblasts. Our findings provide evidence that biallelic loss-of-function ZNF142 variants result in a specific and robust DNAm signature. The identified DNAm pattern suggests occurrence of a methylation disturbance involving a small number of loci that appears to be shared by different cell lineages.