Hasil untuk "Diseases of the endocrine glands. Clinical endocrinology"

Amir Hosseinian, MohammadReza Zare Shahneh, Umer Mansoor et al.

Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to rapidly evolving guidelines and nuanced evidence hierarchies. Methods: We evaluated January Mirror, an evidence-grounded clinical reasoning system, against frontier LLMs (GPT-5, GPT-5.2, Gemini-3-Pro) on a 120-question endocrinology board-style examination. Mirror integrates a curated endocrinology and cardiometabolic evidence corpus with a structured reasoning architecture to generate evidence-linked outputs. Mirror operated under a closed-evidence constraint without external retrieval. Comparator LLMs had real-time web access to guidelines and primary literature. Results: Mirror achieved 87.5% accuracy (105/120; 95% CI: 80.4-92.3%), exceeding a human reference of 62.3% and frontier LLMs including GPT-5.2 (74.6%), GPT-5 (74.0%), and Gemini-3-Pro (69.8%). On the 30 most difficult questions (human accuracy less than 50%), Mirror achieved 76.7% accuracy. Top-2 accuracy was 92.5% for Mirror versus 85.25% for GPT-5.2. Conclusions: Mirror provided evidence traceability: 74.2% of outputs cited at least one guideline-tier source, with 100% citation accuracy on manual verification. Curated evidence with explicit provenance can outperform unconstrained web retrieval for subspecialty clinical reasoning and supports auditability for clinical deployment.

Selene S Mak, Laura M Nally, Juanita Montoya et al.

BackgroundNumerous barriers to moderate to vigorous physical activity exist for youths with type 1 diabetes (T1D). The virtual exercise games for youth with T1D (ExerT1D) intervention implement synchronous support of moderate to vigorous physical activity including T1D peers and role models. ObjectiveThis study aims to understand the acceptability of this intervention to participants. MethodsWe conducted postprogram, semistructured, televideo interviews with participating youths to elicit perspectives on the acceptability of the intervention and experience with the program. Two coders independently reviewed and analyzed each transcript using a coding scheme developed inductively by senior researchers. Discrepancies were resolved by team discussion, and multiple codes were grouped together to produce 4 main thematic areas. ResultsAll 15 participants provided interviews (aged 14-19 years; 2 nonbinary, 6 females; median hemoglobin A1c level of 7.8% (IQR 7.4%-11.2%), 5 with a hemoglobin A1c level of ≥10%). Qualitative data revealed four themes: (1) motivation to engage in physical activity (PA)—improving their physical capabilities and stabilizing glucose levels were cited as motivation for PA and challenges of living with T1D were cited as PA barriers; (2) experience with and motivation to manage diabetes while engaging in PA—participants provided details of accommodating the inherent uncertainty or limitations of PA with diabetes and sometimes preparing for PA involved psychological and motivational adjustments while some relayed feelings of avoidance; (3) peer support encouraged engagement with the intervention—participants appreciated the peer aspects of components of ExerT1D and participants’ reflections of the facilitated group experience highlight many benefits of a small-group virtual program; and (4) improvements in PA and diabetes self-management efficacy—all participants credited the program with improving or at least raising awareness of T1D management skills. ConclusionsOur virtual PA intervention using an active video game and discussion component provided adolescents with T1D the confidence and peer support to engage in PA, improved awareness of diabetes-specific tasks to prepare for exercise, and improved understanding of the effect of PA on glucose levels. Engaging youths with a virtual video game intervention is a viable approach to overcome barriers to PA for adolescents with T1D. Trial RegistrationClinicalTrials.gov NCT05163912; https://clinicaltrials.gov/ct2/show/NCT05163912

Manuela Daniela Danu, George Marica, Constantin Suciu et al.

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.

Estela Lorza-Gil, Estela Lorza-Gil, Estela Lorza-Gil et al.

Min Zhang, Ye Bai, Yutong Wang et al.

BackgroundGeneral obesity is a well-established risk factor for gallstone disease (GSD), but whether central obesity contributes additional independent risk remains controversial. We aimed to comprehensively clarify the effect of body fat distribution on GSD.MethodsWe first investigated the observational association of central adiposity, characterized by waist-to-hip ratio (WHR), with GSD risk using data from UK Biobank (N=472,050). We then explored the genetic relationship using summary statistics from the largest genome-wide association study of GSD (ncase=43,639, ncontrol=506,798) as well as WHR, with and without adjusting for body mass index (BMI) (WHR: n=697,734; WHRadjBMI: n=694,649).ResultsObservational analysis demonstrated an increased risk of GSD with one unit increase in WHR (HR=1.18, 95%CI=1.14-1.21). A positive WHR-GSD genetic correlation (rg =0.41, P=1.42×10-52) was observed, driven by yet independent of BMI (WHRadjBMI: rg =0.19, P=6.89×10-16). Cross-trait meta-analysis identified four novel pleiotropic loci underlying WHR and GSD with biological mechanisms outside of BMI. Mendelian randomization confirmed a robust WHR-GSD causal relationship (OR=1.50, 95%CI=1.35-1.65) which attenuated yet remained significant after adjusting for BMI (OR=1.17, 95%CI=1.09-1.26). Furthermore, observational analysis confirmed a positive association between general obesity and GSD, corroborated by a shared genetic basis (rg =0.40, P=2.16×10-43), multiple novel pleiotropic loci (N=11) and a causal relationship (OR=1.67, 95%CI=1.56-1.78).ConclusionBoth observational and genetic analyses consistently provide evidence on an association of central obesity with an increased risk of GSD, independent of general obesity. Our work highlights the need of considering both general and central obesity in the clinical management of GSD.

Paris Kantaras, Niki Mourouti, Theodora Mouratidou et al.

In total, 3274 adults (65.2% females) from six European countries were included in this cross-sectional analysis using data from the baseline assessment of the Feel4Diabetes study. Anthropometric, sociodemographic, dietary and behavioral data were assessed, and the existence of metabolic syndrome (MetS) was recorded. Beverage consumption patterns (BCPs) were derived via principal component analysis. Three BCPs were derived explaining 39.5% of the total variation. BCP1 was labeled as “Alcoholic beverage pattern”, which loaded heavily on high consumption of beer/cider, wine and other spirits; BCP2 was labeled as “High in sugars beverage pattern” that was mainly characterized by high consumption of soft drinks with sugar, juice containing sugar and low consumption of water; and BCP3 was labeled as “Healthy beverage pattern” that was mainly characterized by high consumption of water, tea, fruit juice freshly squeezed or prepacked without sugar and low consumption of soft drinks without sugar. After adjusting for various confounders, BCP2 was positively associated with elevated triglycerides (<i>p</i> = 0.001), elevated blood pressure (<i>p</i> = 0.001) elevated fasting glucose (<i>p</i> = 0.008) and the existence of MetS (<i>p</i> = 0.006), while BCP1 was inversely associated with reduced HDL-C (<i>p</i> = 0.005) and BCP3 was inversely associated with elevated blood pressure (<i>p</i> = 0.047). The establishment of policy actions as well as public health nutritional education can contribute to the promotion of a healthy beverage consumption.

Grace Aprilia Helena, Shoen Kume

Diabetes is an epidemic caused by a multitude of factors. Despite the studies attempting to unravel its mechanism, there is still more to discover about glucose–insulin dynamics. In a recent issue of the Journal of Clinical Investigation, Cheruiyot et al. uncovered a translational regulatory circuit during β‐cell glucose toxicity that inherently affects the translational makeup and protein expression in functioning β‐cells.Journal of Clinical Investigation, Cheruiyot et al. uncovered a translational regulatory circuit during β‐cell glucose toxicity that inherently affects the translational makeup and protein expression in functioning β‐cells. Their multiomics approach might provide a deeper understanding of high glucose and translational regulation of genes involved in β‐cell insulin impairment caused by prolonged high‐glucose exposure.

Moez Dawood, Ben Heavner, Marsha M. Wheeler et al.

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

Jessica L. Ross, Arman Sabbaghi, Run Zhuang et al.

Clinical trials are critical in advancing medical treatments but often suffer from immense time and financial burden. Advances in statistical methodologies and artificial intelligence (AI) present opportunities to address these inefficiencies. Here we introduce Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM) as an advantageous combination of prognostic covariate adjustment (PROCOVA) and Mixed Models for Repeated Measures (MMRM). PROCOVA-MMRM utilizes time-matched prognostic scores generated from AI models to enhance the precision of treatment effect estimators for longitudinal continuous outcomes, enabling reductions in sample size and enrollment times. We first provide a description of the background and implementation of PROCOVA-MMRM, followed by two case study reanalyses where we compare the performance of PROCOVA-MMRM versus the unadjusted MMRM. These reanalyses demonstrate significant improvements in statistical power and precision in clinical indications with unmet medical need, specifically Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS). We also explore the potential for sample size reduction with the prospective implementation of PROCOVA-MMRM, finding that the same or better results could have been achieved with fewer participants in these historical trials if the enhanced precision provided by PROCOVA-MMRM had been prospectively leveraged. We also confirm the robustness of the statistical properties of PROCOVA-MMRM in a variety of realistic simulation scenarios. Altogether, PROCOVA-MMRM represents a rigorous method of incorporating advances in the prediction of time-matched prognostic scores generated by AI into longitudinal analysis, potentially reducing both the cost and time required to bring new treatments to patients while adhering to regulatory standards.

Amelia LM Tan, Rafael S Gonçalves, William Yuan et al.

Objective: Integrating EHR data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The International Classification of Diseases (ICD) is used to annotate clinical diagnoses; the Human Phenotype Ontology (HPO) to annotate phenotypes. Although these ontologies overlap in biomedical entities described, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration. Materials and Methods: We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the UMLS Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset. Results and Discussion: Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped. Conclusion: We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS.

M. G. Pavlova, O. Golounina, T. Morgunova et al.

Autoimmune polyglandular syndrome type 1 (APS-1) is an extremely rare monogenic autosomal recessive disease characterized by development of multiple organ failure with predominant endocrine glands involvement. The challenges of patient management are related to low adherence to the lifelong multicomponent therapy, high risk of complications, including pneumonia, adrenal insufficiency decompensation, necrotic colitis and other acute infectious and inflammatory diseases. Due to the rarity of this disorder, clinicians lack sufficient experience with management of such patients, which could lead to delayed medical care and patient death. Patient A., 28 years old, was followed up for 10 years in the Endocrinology clinic with the diagnosis of “Autoimmune polyglandular syndrome type 1. Mucocutaneous candidiasis. Primary hypoparathyroidism. Primary chronic adrenal insufficiency. Primary hypothyroidism. Chronic gastroduodenitis. Chronic colitis. Autoimmune alopecia.” The onset of the disease with chronic mucocutaneous candidiasis at the age below 1 year had defined the severe course of the disease, including a wide range of consequently occurring autoimmune diseases associated with recurrent episodes of decompensation of hypoparathyroidism and adrenal insufficiency, as well as the development of acute necrotic colitis at the age of 26. As an adult, the patient admitted that he had previously been insufficiently responsible and attentive to his disease and regular medication intake, with resulting episodes of adrenal insufficiency decompensation and occurrence of the symptoms related to serum calcium fluctuations. Due to abnormalities of cellular and humoral immunity, APS-1 patients are at an extremely high risk for a critical course of COVID-associated pneumonia. In 2020, the patient contracted the coronavirus infection complicated by bilateral pneumonia, followed by respiratory failure, bacterial sepsis and acute renal failure. Despite the timely hospitalization, administration of the state-of-the-art antibacterials and antifungals and all the necessary resuscitation measures, it was not possible to save his life. This clinical observation demonstrates the difficulties of therapeutic management of APS-1 patients with an early disease manifestation, who, due to severe genetically determined impaired immunity, are at high risk of death from an intercurrent infection. The combination of several chronic comorbidities and the need to take a large number of replacement treatments require an individual therapeutic approach, as well as psychological and social adaptation of the patients, starting from their childhood and throughout the whole life, taking into account the frequent psychological problems could lead to low treatment adherence. The timely diagnostics of the disease, understanding of pathophysiology and specifics of its course could contribute to increased qualityadjusted life years of APS-1 patients.

Danang Dwi Cahyadi, Tomoko Warita, Nanami Irie et al.

ABSTRACTNormalization is a crucial step in gene expression analysis to avoid misinterpretation. Reverse transcription-quantitative polymerase chain reaction was used to measure the expression of 10 candidate housekeeping genes in non-differentiated (ND) and differentiated (DI) 3T3-L1 cells on days 5 and 10. We used geNorm, NormFinder, BestKeeper, RefFinder, and the ∆Ct method to evaluate expression stability. The findings revealed that (1) the expression levels of the reference genes changed over time, even in non-differentiating cells, and (2) peptidylprolyl isomerase A (Ppia) and TATA box-binding protein (Tbp) were stable reference genes for 10 days in both undifferentiated and differentiated 3T3-L1 cells. Notably, the expression of known reference genes in non-differentiating cells was altered throughout the experiment.

Kewei Wang, Kewei Wang, Rong Zhang et al.

Background and aimsWnt/β-catenin signaling plays an important role in regulating hepatic metabolism. This study is to explore the molecular mechanisms underlying the potential crosstalk between Wnt/β-catenin and mTOR signaling in hepatic steatosis.MethodsTransgenic mice (overexpress Wnt1 in hepatocytes, Wnt+) mice and wild-type littermates were given high fat diet (HFD) for 12 weeks to induce hepatic steatosis. Mouse hepatocytes cells (AML12) and those transfected to cause constitutive β-catenin stabilization (S33Y) were treated with oleic acid for lipid accumulation.ResultsWnt+ mice developed more hepatic steatosis in response to HFD. Immunoblot shows a significant increase in the expression of fatty acid synthesis-related genes (SREBP-1 and its downstream targets ACC, AceCS1, and FASN) and a decrease in fatty acid oxidation gene (MCAD) in Wnt+ mice livers under HFD. Wnt+ mice also revealed increased Akt signaling and its downstream target gene mTOR in response to HFD. In vitro, increased lipid accumulation was detected in S33Y cells in response to oleic acid compared to AML12 cells reinforcing the in vivo findings. mTOR inhibition by rapamycin led to a down-regulation of fatty acid synthesis in S33Y cells. In addition, β-catenin has a physical interaction with mTOR as verified by co-immunoprecipitation in hepatocytes.ConclusionsTaken together, our results demonstrate that β-catenin stabilization through Wnt signaling serves a central role in lipid metabolism in the steatotic liver through up-regulation of fatty acid synthesis via Akt/mTOR signaling. These findings suggest hepatic Wnt signaling may represent a therapeutic strategy in hepatic steatosis.

Francesca Mancuso, Iva Arato, Catia Bellucci et al.

IntroductionAmong substances released into the environment by anthropogenic activities, the heavy metal cadmium (Cd) is known to induce severe testicular injury causing male subfertility/infertility. Zinc (Zn) is another heavy metal that, unlike Cd, is physiologically present in the testis, being essential for spermatogenesis. We aimed to examine the possibility that 50 µM ZnCl2 could counteract the toxic effects induced by Cd in an in vitro model of porcine prepubertal Sertoli cells (SCs) exposed to both subtoxic (5 μM) and toxic (10 μM) concentrations of CdCl2 for 48 h.Materials and MethodsApoptosis, cell cycle, and cell functionality were assessed. The gene expression of Nrf2 and its downstream antioxidant enzymes, ERK1/2, and AKT kinase signaling pathways were evaluated.Materials and ResultsWe found that Zn, in co-treatment with subtoxic and toxic Cd concentration, increased the number of metabolically active SCs compared to Cd exposure alone but restored SC functionality only in co-treatment with subtoxic Cd concentration with respect to subtoxic Cd alone. Exposure of Cd disrupted cell cycle in SCs, and Zn co-treatment was not able to counteract this effect. Cd alone induced SC death through apoptosis and necrosis in a dose-dependent manner, and co-treatment with Zn increased the pro-apoptotic effect of Cd. Subtoxic and toxic Cd exposures activated the Nrf2 signaling pathway by increasing gene expression of Nrf2 and its downstream genes (SOD, HO-1, and GSHPx). Zn co-treatment with subtoxic Cd attenuated upregulation on the Nrf2 system, while with toxic Cd, the effect was more erratic. Studying ERK1/2 and AKT pathways as a target, we found that the phosphorylation ratio of p-ERK1/2 and p-AKT was upregulated by both subtoxic and toxic Cd exposure alone and in co-treatment with Zn.DiscussionOur results suggest that Zn could counteract Cd effects by increasing the number of metabolically active SCs, fully or partially restoring their functionality by modulating Nrf2, ERK1/2, and AKT pathways. Our SC model could be useful to study the effects of early Cd exposure on immature testis, evaluating the possible protective effects of Zn.

Omid Rohanian, Mohammadmahdi Nouriborji, Hannah Jauncey et al.

Specialised pre-trained language models are becoming more frequent in NLP since they can potentially outperform models trained on generic texts. BioBERT and BioClinicalBERT are two examples of such models that have shown promise in medical NLP tasks. Many of these models are overparametrised and resource-intensive, but thanks to techniques like Knowledge Distillation (KD), it is possible to create smaller versions that perform almost as well as their larger counterparts. In this work, we specifically focus on development of compact language models for processing clinical texts (i.e. progress notes, discharge summaries etc). We developed a number of efficient lightweight clinical transformers using knowledge distillation and continual learning, with the number of parameters ranging from 15 million to 65 million. These models performed comparably to larger models such as BioBERT and ClinicalBioBERT and significantly outperformed other compact models trained on general or biomedical data. Our extensive evaluation was done across several standard datasets and covered a wide range of clinical text-mining tasks, including Natural Language Inference, Relation Extraction, Named Entity Recognition, and Sequence Classification. To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks. The models and code used in this study can be found on our Huggingface profile at https://huggingface.co/nlpie and Github page at https://github.com/nlpie-research/Lightweight-Clinical-Transformers, respectively, promoting reproducibility of our results.

Malikeh Ehghaghi, Marija Stanojevic, Ali Akram et al.

Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks. Developing accurate automated speaker verification (ASV) models is crucial to verify the identity of enrolled individuals and remove duplicates, but the size and quality of data influence ASV performance. However, there has been limited investigation into the factors that can affect ASV capabilities in clinical environments. In this paper, we bridge the gap by conducting analysis of how participant demographic characteristics, audio quality criteria, and severity level of Alzheimer's disease (AD) impact the performance of ASV utilizing a dataset of speech recordings from 659 participants with varying levels of AD, obtained through multiple speech tasks. Our results indicate that ASV performance: 1) is slightly better on male speakers than on female speakers; 2) degrades for individuals who are above 70 years old; 3) is comparatively better for non-native English speakers than for native English speakers; 4) is negatively affected by clinician interference, noisy background, and unclear participant speech; 5) tends to decrease with an increase in the severity level of AD. Our study finds that voice biometrics raise fairness concerns as certain subgroups exhibit different ASV performances owing to their inherent voice characteristics. Moreover, the performance of ASV is influenced by the quality of speech recordings, which underscores the importance of improving the data collection settings in clinical trials.

A. V. Hajrieva, N. Tarbaeva, N. Volevodz et al.

The study of the genetic aspects of endocrine diseases is based on the aspiration to develop the methods of early diagnosis, treatment and observation of patients. Von Hippel-Lindau syndrome is genetically determined disease characterized by damage of various organs and systems. The article presents a clinical case of treatment of a patient with retinal detachment who was first admitted to the surgical department of the Federal State Budgetary Institution «NMIC of Endocrinology» of the Ministry of Health of Russia with complaints of dry mouth, general weakness. Further examination, revealed pathological changes in the adrenal glands, kidneys, brain, pancreas, spleen, spinal cord. The presented clinical case demonstrates the need for a multidisciplinary approach to the management of patients with von Hippel-Lindau syndrome.

C. Hong, Y. Jeong, H. Kim et al.

Xinmi Liu, Hua Lou, Junwei Zhang et al.

ObjectiveTo investigate the clinical outcomes of Day 7 (D7) frozen-thawed embryo transfer (FET) and to provide a reference value for clinical work.MethodsThis was a retrospective cohort study. Patients undergoing FET cycles in the Reproductive Medicine Center of the Third Affiliated Hospital of Zhengzhou University between December 2015 and January 2021 were included. According to the developmental stage of the embryos at transfer, the embryos were divided into three groups: Day (D) 5, D6 and D7 blastocysts. Group D7 was compared with Groups D5 and D6. Simultaneously, the preimplantation genetic testing (PGT) and non-PGT cycles in Group D7 were analyzed and compared. The main outcomes were the clinical pregnancy, live birth and miscarriage rates. The secondary outcomes were the implantation and euploidy rates.ResultsIn total, 5945, 4094 and 137 FET cycles were included in the D5, D6 and D7 groups, respectively. The clinical pregnancy rate was significantly lower in Group D7 than in Groups D5 (13.9% vs 62.9%, P &lt;0.001) and D6 (13.9% vs 51.4%, P &lt;0.001). Additionally, the live birth rate was significantly lower in Group D7 than in Groups D5 (7.3% vs 50.7%, P &lt;0.001) and D6 (7.3% vs 40.5%, P &lt;0.001). However, the miscarriage rate was significantly higher in Group D7 than in Groups D5 (47.4% vs 18.2%, P =0.001) and D6 (47.4% vs 20.6%, P =0.004). The clinical pregnancy and live birth rates for D7 blastocysts were significantly higher in the PGT group than in the non-PGT group (41.7% vs 13.9%, P=0.012; 33.3% vs 7.3%, P =0.003).ConclusionsD7 blastocyst transfer can yield a live birth rate that is lower than that for D5 and D6 blastocysts but has value for transfer. PGT for D7 blastocysts may reduce the number of ineffective transfers and improve the outcome of D7 blastocyst transfer, which can be performed according to a patient’s situation.

Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji et al.

Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP. In this work, we evaluate a broad set of machine learning techniques ranging from simple RNNs to specialised transformers such as BioBERT on a dataset containing clinical notes along with a set of annotations indicating whether a sample is cancer-related or not. Furthermore, we specifically employ efficient fine-tuning methods from NLP, namely, bottleneck adapters and prompt tuning, to adapt the models to our specialised task. Our evaluations suggest that fine-tuning a frozen BERT model pre-trained on natural language and with bottleneck adapters outperforms all other strategies, including full fine-tuning of the specialised BioBERT model. Based on our findings, we suggest that using bottleneck adapters in low-resource situations with limited access to labelled data or processing capacity could be a viable strategy in biomedical text mining. The code used in the experiments are going to be made available at https://github.com/omidrohanian/bottleneck-adapters.