Background:The Hypoglycemia Awareness Restoration Program for people with type 1 diabetes and problematic hypoglycemia with severe episodes persisting despite optimal care (HARPdoc) uniquely focusses on addressing cognitive and motivational barriers to hypoglycemia avoidance associated with impaired awareness to hypoglycemia. We aimed to compare perceptions of acceptability, feasibility, and appropriateness of HARPdoc intervention to an existing program, Blood Glucose Awareness Training (BGAT) and understand how these implementation outcomes relate to cognitive and mental health clinical outcomes. Methods:The HARPdoc trial was a hybrid randomized clinical trial delivered in the United Kingdom and United States between July 2018 and December 2019. Implementation outcomes, including perceived acceptability, appropriateness, and feasibility, were measured using published validated surveys. These surveys were completed by the people with diabetes, healthcare professionals, and relatives of participants. Clinical outcomes, including attitudes to awareness, diabetes distress, anxiety, and depression, were measured using validated self-reported questionnaires. We explored differences of perceived implementation outcomes between HARPdoc and BGAT and associations between implementation and clinical outcomes using quantile and linear regression. We also assessed whether the effect of HARPdoc on cognitive and mental health outcomes were mediated by implementation outcomes. Results:HARPdoc was perceived as more appropriate than BGAT at 12 months, with a median difference of 0.75 (95% CI 0,26,1,24) by both those involved in delivering the programs and the HARPdoc participants. All stakeholder groups also perceived HARPdoc intervention as more acceptable (MD 0.50 95% CI 0.13, 0.87), but as feasible (MD 0.00; 95% CI -0.31, 0.31) as BGAT. Each of perceived acceptability, appropriateness, and feasibility were significantly linked to improvements in clinical outcomes (feasibility- anxiety: Mean Difference: -1.07; 95% CI: -2.03, -0.10); feasibility-depression (MD: -5.25, 95% CI -9.09, -1.41)). No evidence of mediation was observed. Conclusions:HARPdoc compared to BGAT was perceived as more appropriate and acceptable and subsequently higher perceived appropriateness, acceptability and feasibility was linked to better cognitive and mental health outcomes. Our findings provide important insights for the development of an implementation blueprint and the expansion of HARPdoc and BGAT programs into routine healthcare services and highlight the need for larger, better-powered hybrid trials.

Following the first report of use of intracytoplasmic sperm injection (ICSI) in 1992 there has been a consistent global increase in the use of ICSI to fertilise human oocytes in vitro, though since 2016 there has been a small reduction in its use in Australia and New Zealand This consensus statement, developed by the subspecialty group in reproductive endocrinology and infertility, reviews the evidence underpinning the use of ICSI. It is concluded that ICSI is an appropriate intervention when there is reduced semen quality or if there is a significant risk of failed fertilisation, though the exact seminal parameters which predict appropriate use of ICSI are uncertain. ICSI is an appropriate intervention when there has been failed or poor fertilisation rates in a previous IVF cycle. ICSI may be used to reduce the risk of recurrent molar pregnancy, but the evidence to support its use after oocyte cryopreservation, in vitro maturation of oocytes (IVM), prior to preimplantation genetic testing (PGT) and when treating HIV sero-discordant couples is lacking, and these are areas requiring further research. ICSI is not necessary in unexplained infertility when a low number of oocytes have been retrieved during the treatment cycle or when the female age is advanced if the semen parameters are normal.

The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification. Current approaches tend to rely on limited multi-modal techniques and treat the multi-label problem as a multiple multi-class problem, overlooking issues related to imbalanced learning and multi-label correlation. This paper introduces the innovative Skin Lesion Classifier, utilizing a Multi-modal Multilabel TransFormer-based model (SkinM2Former). For multi-modal analysis, we introduce the Tri-Modal Cross-attention Transformer (TMCT) that fuses the three image and metadata modalities at various feature levels of a transformer encoder. For multi-label classification, we introduce a multi-head attention (MHA) module to learn multi-label correlations, complemented by an optimisation that handles multi-label and imbalanced learning problems. SkinM2Former achieves a mean average accuracy of 77.27% and a mean diagnostic accuracy of 77.85% on the public Derm7pt dataset, outperforming state-of-the-art (SOTA) methods.

BackgroundMobile health (mHealth) apps are increasingly being used by community members to track symptoms and manage endometriosis. In addition, clinicians use mHealth apps for continued medical education and clinical decision-making and recommend good-quality apps to patients. However, poor-quality apps can spread misinformation or provide recommendations that are not evidence-based. Therefore, a critical evaluation is needed to assess and recommend good-quality endometriosis mHealth apps. ObjectiveThis study aimed to evaluate the quality and provide recommendations for good quality endometriosis mHealth apps for the community and clinicians. MethodsPRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines informed the search of mHealth apps on the Google Play Store and Apple App Store. The search terms included “endometriosis,” “adenomyosis,” and “pelvic pain.” mHealth apps were eligible if they were (1) related to the search terms, (2) were in the English language, and (3) were available free of cost. Only the free content of the eligible mHealth apps was assessed. ENLIGHT, a validated evaluation tool for mobile and web-based interventions, was used to assess the quality across 7 domains such as usability, visual design, user engagement, content, therapeutic persuasiveness, therapeutic alliance, and general subjective evaluation. mHealth apps with a total score of ≥3.5 were classified as “good” according to the ENLIGHT scoring system and are recommended as good-quality mHealth apps for endometriosis care. ResultsIn total, 42 mHealth apps were screened, and 19 were included in the quality assessment. A total of 6 good-quality mHealth apps were identified (QENDO, Bearable, Luna for Health, Matilda Health, Branch Health: Pain Management, and CHARLI Health). These apps provided symptom-tracking functions and self-management support. A total of 17 apps were designed for community use, while 2 apps provided a digital endometriosis classification tool to clinicians. Most mHealth apps scored well (≥3.5) in the domains of usability (16/19, 84.2%), visual design (14/19, 73.7%), user engagement (11/19, 57.9%), and content (15/19, 78.9%). Few eHealth websites scored well on therapeutic persuasiveness (6/19, 31.6%), therapeutic alliance (9/19, 47.4%), and general subjective evaluation (6/19, 31.6%). ConclusionsAlthough time and geographical location can influence the search results, we identified 6 “good-quality” endometriosis mHealth apps that can be recommended to the endometriosis community. mHealth apps designed for community use should evaluate their effectiveness on user’s endometriosis knowledge, self-recommended management strategies, pain self-efficacy, user satisfaction, and user quality of life. Digital technology should be leveraged to develop mHealth apps for clinicians that contribute to continued medical education and assist clinical decision-making in endometriosis management. Factors that enhance usability, visual design, therapeutic persuasiveness, and therapeutic alliance should be incorporated to ensure successful and long-term uptake of mHealth apps. Trial RegistrationPROSPERO CRD42020185475; https://tinyurl.com/384dkkmj

Background: Uptake of evidence-based interventions that prevent cancer is very low. Electronic signposting (eSignposting) using digital messages sent via electronic healthcare systems is increasingly utilised to improve service reach and uptake. However, synthesized knowledge of how best to implement eSignposting to cancer prevention interventions is lacking. We sought to generate a novel programme theory to illuminate what type of electronic signposting works, for whom and in what circumstances. Methods: A realist review, informed by Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES I), was conducted. Medline, EMBASE, CINAHL, Scopus, PsycINFO, ERIC and AMED databases, and grey literature were searched. Studies that contributed information on context and mechanisms of action for eSignposting to cancer prevention interventions for adults in any healthcare setting were included. Studies were assessed for quality based on relevance, richness and rigour. Realist synthesis and input from stakeholders, including patients, was used to aid development of our programme theory. Results: Thirty studies were included and 57 individual context-mechanism-outcome configurations identified. Findings demonstrate that eSignposting can enhance reach and uptake of cancer prevention interventions. eSignposting worked through multiple pathways and was highly context specific. For patients, memorable messages using wording closely tailored to patient characteristics, and use of a popular communication channel (e.g. short message service [SMS] text) improved ‘buy-in’ and usability. For providers, ‘buy-in’ was linked to a good fit with organisational priorities and finances. Establishing compatibility with existing technical systems improved provider usability. Increasing optimism of technology, and ensuring eSignposting messages did not risk existing patient-provider relationships were also key to implementation success. Conclusions: We developed a novel programme theory for the implementation of eSignposting to cancer prevention interventions. Implementation requires careful tailoring to patients and healthcare settings and the inclusion of a broad range of patients and professionals in intervention design. For both patients and providers, promoting ‘buy-in’, ensuring perceived usability, and nurturing positive attitudes towards technology, were key to implementation success. To effectively reduce cancer and cancer disparities, policymakers and healthcare providers need to implement electronic signposting in ways that increase equality-centred patient benefit.

Fertility preservation services must offer information to patients, prior to their visit, so that they have time to read and digest the information, and also have the opportunity to write down any questions they wish to ask at the oncofertility consultation appointment. Appointments must be offered immediately, based on a specifically designed referral form. Each fertility service providing oncology cryopreservation should have a robust map of the patient's journey to include referral, counseling session, medical consultation, informed consent, treatment plan, and follow-up. Consent for fertility preservation should only be obtained after thorough assessment and discussion with the patient. It must contain basic aspects such as duration of storage, right to dispose or choose alternatives, wishes about stored material if death occurs, and need for patient contact on an annual basis. Appropriate legal advice should be sought in the process of establishing oncology cryopreservation services, most importantly related to patient consent. Ethical and legal aspects of fertility cryopreservation must be considered in the provision of care for cancer patients. Cancer patients attending for cryopreservation have significant concerns in relation to the success of the process, time frame to return for cancer treatment, and safety. Two major nonmedical aspects, coping ability and mortality, are best addressed by counselors and highlight the need to offer this type of psychological support to all cancer patients attending for fertility preservation. The role of genetic counseling is to discuss any potential risks of transmission of the disease to the resulting offspring and offer genetic testing when appropriate. A service based on availability of comprehensive information coupled with in-house implications counseling will have a positive impact and improve the overall care of patients attending for fertility cryopreservation and this should be standard care.

ABSTRACTObjectivesThe development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high‐quality data. This study aimed to test the intra‐ and interobserver agreement of a proposed image‐quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development.MethodsA proposed scoring system to quantify TVS image quality was created following a review of the literature. This system involved a score of 1–4 (2 = poor, 3 = suboptimal and 4 = optimal image quality) assigned by a rater for individual ultrasound images. If the image was deemed inaccurate, it was assigned a score of 1, corresponding to ‘reject’. Six professionals, including two radiologists, two sonographers and two sonologists, reviewed 150 images (50 images of the uterus and 100 images of the ovaries) obtained from 50 women, assigning each image a score of 1–4. The review of all images was repeated a second time by each rater after a period of at least 1 week. Mean scores were calculated for each rater. Overall interobserver agreement was assessed using intraclass correlation coefficient (ICC), and interobserver agreement between paired professionals and intraobserver agreement for all professionals were assessed using weighted Cohen's kappa and ICC.ResultsPoor levels of interobserver agreement were obtained between the six raters for all 150 images (ICC, 0.480 (95% CI, 0.363–0.586)), as well as for assessment of the uterine images only (ICC, 0.359 (95% CI, 0.204–0.523)). Moderate agreement was achieved for the ovarian images (ICC, 0.531 (95% CI, 0.417–0.636)). Agreement between the paired sonographers and sonologists was poor for all images (ICC, 0.336 (95% CI, −0.078 to 0.619) and 0.425 (95% CI, 0.014–0.665), respectively), as well as when images were grouped into uterine images (ICC, 0.253 (95% CI, −0.097 to 0.577) and 0.299 (95% CI, −0.094 to 0.606), respectively) and ovarian images (ICC, 0.400 (95% CI, −0.043 to 0.669) and 0.469 (95% CI, 0.088–0.689), respectively). Agreement between the paired radiologists was moderate for all images (ICC, 0.600 (95% CI, 0.487–0.693)) and for their assessment of uterine images (ICC, 0.538 (95% CI, 0.311–0.707)) and ovarian images (ICC, 0.621 (95% CI, 0.483–0.728)). Weak‐to‐moderate intraobserver agreement was seen for each of the raters with weighted Cohen's kappa ranging from 0.533 to 0.718 for all images and from 0.467 to 0.751 for ovarian images. Similarly, for all raters, the ICC indicated moderate‐to‐good intraobserver agreement for all images overall (ICC ranged from 0.636 to 0.825) and for ovarian images (ICC ranged from 0.596 to 0.862). Slightly better intraobserver agreement was seen for uterine images, with weighted Cohen's kappa ranging from 0.568 to 0.808 indicating weak‐to‐strong agreement, and ICC ranging from 0.546 to 0.893 indicating moderate‐to‐good agreement. All measures were statistically significant (P < 0.001).ConclusionThe proposed image quality scoring system was shown to have poor‐to‐moderate interobserver agreement and mostly weak‐to‐moderate levels of intraobserver agreement. More refinement of the scoring system may be needed to improve agreement, although it remains unclear whether quantification of image quality can be achieved, given the highly subjective nature of ultrasound interpretation. Although some AI systems can tolerate labeling noise, most will favor clean (high‐quality) data. As such, innovative data‐labeling strategies are needed. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Background In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests–focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information. Objective This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications. Methods We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis. Results Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology’s utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages. Conclusions We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

BackgroundeHealth websites are increasingly being used by community members to obtain information about endometriosis. Additionally, clinicians can use these websites to enhance their understanding of the condition and refer patients to these websites. However, poor-quality information can adversely impact users. Therefore, a critical evaluation is needed to assess and recommend high-quality endometriosis websites. ObjectiveThis study aimed to evaluate the quality and provide recommendations for high-quality endometriosis eHealth websites for the community and clinicians. MethodsPRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines informed 2 Google searches of international and Australian eHealth websites. The first search string used the terms “endometriosis,” “adenomyosis,” or “pelvic pain,” whereas “Australia” was added to the second search string. Only free eHealth websites in English were included. ENLIGHT, a validated tool, was used to assess the quality across 7 domains such as usability, visual design, user engagement, content, therapeutic persuasiveness, therapeutic alliance, and general subjective evaluation. Websites with a total score of 3.5 or more were classified as “good” according to the ENLIGHT scoring system and are recommended as high-quality eHealth websites for information on endometriosis. ResultsIn total, 117 eHealth websites were screened, and 80 were included in the quality assessment. Four high-quality eHealth websites (ie, those that scored 3.5 or more) were identified (Endometriosis Australia Facebook Page, Endometriosis UK, National Action Plan for Endometriosis on EndoActive, and Adenomyosis by the Medical Republic). These websites provided easily understood, engaging, and accurate information. Adenomyosis by the Medical Republic can be used as a resource in clinical practice. Most eHealth websites scored well, 3.5 or more in the domains of usability (n=76, 95%), visual design (n=64, 80%), and content (n=63, 79%). However, of the 63 websites, only 25 provided references and 26 provided authorship details. Few eHealth websites scored well on user engagement (n=18, 23%), therapeutic persuasiveness (n=2, 3%), and therapeutic alliance (n=22, 28%). In total, 30 (38%) eHealth websites scored well on general subjective evaluation. ConclusionsAlthough geographical location can influence the search results, we identified 4 high-quality endometriosis eHealth websites that can be recommended to the endometriosis community and clinicians. To improve quality, eHealth websites must provide evidence-based information with appropriate referencing and authorship. Factors that enhance usability, visual design, user engagement, therapeutic persuasiveness, and therapeutic alliance can lead to the successful and long-term uptake of eHealth websites. User engagement, therapeutic persuasiveness, and therapeutic alliance can be strengthened by sharing lived experiences and personal stories and by cocreating meaningful content for both the community and clinicians. Reach and discoverability can be improved by leveraging search engine optimization tools. Trial RegistrationPROSPERO CRD42020185475; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=185475&VersionID=2124365

Understanding of molecular mechanisms contributing to the pathophysiology of endometriosis, and upstream drivers of lesion formation, remains limited. Using a C57Bl/6 mouse model in which decidualized endometrial tissue is injected subcutaneously in the abdomen of recipient mice, we generated a comprehensive profile of gene expression in decidualized endometrial tissue (n=4), and in endometriosis-like lesions at Day 7 (n=4) and Day 14 (n=4) of formation. High-throughput mRNA sequencing allowed identification of genes and pathways involved in the initiation and progression of endometriosis-like lesions. We observed distinct patterns of gene expression with substantial differences between the lesions and the decidualized endometrium that remained stable across the two lesion timepoints, and showed similarity to transcriptional changes implicated in human endometriosis lesion formation. Pathway enrichment analysis revealed several immune and inflammatory response-associated canonical pathways, multiple potential upstream regulators, and involvement of genes not previously implicated in endometriosis pathogenesis, including IRF2BP2 and ZBTB10, suggesting novel roles in disease progression. Collectively, the provided data will be a useful resource to inform research on the molecular mechanisms contributing to endometriosis-like lesion development in this mouse model.

RESUMO Este estudo tem como objetivo apresentar os resultados da tradução, validação transcultural e avaliação preliminar de uma ferramenta, originalmente desenvolvida no Reino Unido, para orientar pesquisadores brasileiros na elaboração de projetos e pesquisas de implementação rigorosos e de alta qualidade: ImpRes-BR. Seguindo boas práticas atualmente estabelecidas para validação transcultural de instrumentos e escalas, a ferramenta, juntamente com seu guia de utilização, foi traduzida e retrotraduzida, submetida a um teste piloto com 20 profissionais de saúde e avaliada por um painel de 10 especialistas que atribuíram os valores utilizados para os cálculos do Índice de Validade de Conteúdo ao nível do item (IVC-I) e escala (IVC-E). Nesse processo, além de índices de validade conceitual superiores à 90%, foi observado um IVC-I de pelo menos 0,90 em todos os domínios da ferramenta e seu guia e um IVC-E de 0,98. Estabelecida a validade da ferramenta e seu guia, a mesma foi aplicada em 14 projetos de pesquisa em fase de planejamento ou execução e foi reconhecida enquanto um instrumento potente para autoanálise das equipes na qualificação de seus projetos e fortalecimento destes em relação aos princípios da Ciência de Implementação.

Diagnosis of endometriosis has traditionally relied on laparoscopic surgery, which was considered the 'gold standard' diagnostic tool. This is not ideal as surgery carries risk, is expensive, is difficult to access, and disrupts patients work or education due to the recovery time needed. As such, imaging has been investigated as a potential method for non-invasive diagnosis, with transvaginal ultrasound showing high diagnostic accuracy for ovarian endometriomas and deep endometriosis. The advances in imaging capability led to recent international guidelines suggesting laparoscopy is no longer the 'gold-standard' for diagnosis and encouraging clinicians to utilise medical imaging as part of their diagnostic work-up for endometriosis. Imaging is emerging as not only a tool for planning endometriosis surgery but increasingly as the first approach for initial diagnosis. Given that transvaginal ultrasound is the primary imaging modality for assessment of gynaecological conditions, it is inevitable that sonographers will have a significant future role in endometriosis diagnosis. This moves away from endometriosis diagnosis being the exclusive realm of laparoscopic surgeons and increasingly involves medical imaging specialists. This review article will describe the origins of endometriosis ultrasound and the current capabilities of transvaginal ultrasound in this field. The expectations of sonographers in this evolving space will be explored, as well as recent novel research findings to gain insight into what the future of endometriosis diagnosis with ultrasound may look like.

Summary: Regulatory T (Treg) cell defects are implicated in disorders of embryo implantation and placental development, but the origins of Treg cell dysfunction are unknown. Here, we comprehensively analyzed the phenotypes and transcriptional profile of peripheral blood Treg cells in individuals with early pregnancy failure (EPF). Compared to fertile subjects, EPF subjects had 32% fewer total Treg cells and 54% fewer CD45RA+CCR7+ naive Treg cells among CD4+ T cells, an altered Treg cell phenotype with reduced transcription factor FOXP3 and suppressive marker CTLA4 expression, and lower Treg:Th1 and Treg:Th17 ratios. RNA sequencing demonstrated an aberrant gene expression profile, with upregulation of pro-inflammatory genes including CSF2, IL4, IL17A, IL21, and IFNG in EPF Treg cells. In silico analysis revealed 25% of the Treg cell dysregulated genes are targets of FOXP3. We conclude that EPF is associated with systemic Treg cell defects arising due to disrupted FOXP3 transcriptional control and loss of lineage fidelity.

Objective.Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models.Approach.In this paper, we introduce theHuman-AICollaborativeMulti-modalMulti-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: (1) multi-rater learning to extract a cleaner label from the multiple 'noisy' labels available per training sample; (2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and (3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models.Main results.Presenting results on the multi-rater T1/T2 MRI endometriosis dataset collected for validating the methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods by a large margin.Significance.The HAICOMM methodology offers a novel solution to the long-standing problem of accurately diagnosing endometriosis from MRI images, specifically in relation to the key diagnostic sign of POD obliteration. By leveraging multi-rater, multi-modal, and human-AI collaborative learning, it has the potential to improve the accuracy of endometriosis diagnosis, which could have far-reaching implications for the better management of this challenging medical condition that affects a significant proportion of the female population.