Exploring the capabilities of ChatGPT in women’s health: obstetrics and gynaecology

Artificial Intelligence (AI) is transforming healthcare, with Large Language Models (LLMs) like ChatGPT offering novel capabilities. This study evaluates ChatGPT’s performance in interpreting and responding to the UK Royal College of Obstetricians and Gynaecologists MRCOG Part One and Two examinations – international benchmarks for assessing knowledge and clinical reasoning in Obstetrics and Gynaecology. We analysed ChatGPT’s domain-specific accuracy, the impact of linguistic complexity, and its self-assessment confidence. A dataset of 1824 MRCOG questions was curated, ensuring minimal prior exposure to ChatGPT. ChatGPT’s responses were compared to known correct answers, and linguistic complexity was assessed using token counts and Type-Token ratios. Confidence scores were assigned by ChatGPT and analysed for self-assessment accuracy. ChatGPT achieved 72.2% accuracy on Part One and 50.4% on Part Two, performing better on Single Best Answer (SBA) than Extended Matching (EMQ) Questions. The findings highlight the potential and significant limitations of ChatGPT in clinical decision-making in women’s health

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine- learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health- care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93 ·7%) in the internal-validation dataset and 0 ·95 (0·92–0·98, sensitivity 97 ·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy. Funding The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre

"On the Spot": travelling artists and Abolitionism, 1770-1830

Until recently the visual culture of Atlantic slavery has rarely been critically scrutinised. Yet in the first decades of the nineteenth century slavery was frequently represented by European travelling artists, often in the most graphic, sometimes voyeuristic, detail. This paper examines the work of several itinerant artists, in particular Augustus Earle (1793-1838) and Agostino Brunias (1730–1796), whose very mobility along the edges of empire was part of a much larger circulatory system of exchange (people, goods and ideas) and diplomacy that characterised Europe’s Age of Expansion. It focuses on the role of the travelling artist, and visual culture more generally, in the development of British abolitionism between 1770 and 1830. It discusses the broad circulation of slave imagery within European culture and argues for greater recognition of the role of such imagery in the abolitionist debates that divided Britain. Furthermore, it suggests that the epistemological authority conferred on the travelling artist—the quintessential eyewitness—was key to the rhetorical power of his (rarely her) images. Artists such as Earle viewed the New World as a boundless source of fresh material that could potentially propel them to fame and fortune. Johann Moritz Rugendas (1802-1858), on the other hand, was conscious of contributing to a global scientific mission, a Humboldtian imperative that by the 1820s propelled him and others to travel beyond the traditional itinerary of the Grand Tour. Some artists were implicated in the very fabric of slavery itself, particularly those in the British West Indies such as William Clark (working 1820s) and Richard Bridgens (1785-1846); others, particularly those in Brazil, expressed strong abolitionist sentiments. Fuelled by evangelical zeal to record all aspects of the New World, these artists recognised the importance of representing the harsh realities of slave life. Unlike those in the metropole who depicted slavery (most often in caustic satirical drawings), many travelling artists believed strongly in the evidential value of their images, a value attributed to their global mobility. The paper examines the varied and complex means by which visual culture played a significant and often overlooked role in the political struggles that beset the period

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy. Funding The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

BACKGROUND: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. METHODS: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. FINDINGS: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91-0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92-0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93-0·96, sensitivity 88·2%, specificity 95·3%). INTERPRETATION: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy. FUNDING: The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre

Reduction of Friction by Recombinant Human Proteoglycan 4 in IL-1α Stimulated Bovine Cartilage Explants

A boundary lubricant attaches and protects sliding bearing surfaces by preventing interlocking asperity-asperity contact. Proteoglycan-4 (PRG4) is a boundary lubricant found in the synovial fluid that provides chondroprotection to articular surfaces. Inflammation of the diarthrodial joint modulates local PRG4 concentration. Thus, we measured the effects of inflammation, with Interkeukin-1α (IL-1α) incubation, upon boundary lubrication and PRG4 expression in bovine cartilage explants. We further aimed to determine whether the addition of exogenous human recombinant PRG4 (rhPRG4) could mitigate the effects of inflammation on boundary lubrication and PRG4 expression in vitro. Cartilage explants, following a 7-day incubation with IL-1α, were tested in a disc-on-disc configuration using either rhPRG4 or saline (PBS control) as a lubricant. Following mechanical testing, explants were studied immunohistochemically or underwent RNA extraction for RT-PCR. We found that static coefficient of friction (COF) significantly decreased to 0.14 ±0.065 from 0.21 ±0.059 (p=0.014) in IL-1α stimulated explants lubricated with rhPRG4, as compared to PBS. PRG4 expression was significantly up regulated from 30.8 ± 19 copies in control explants lubricated with PBS to 3330 ± 1760 copies in control explants lubricated with rhPRG4 (p\u3c0.001). Explants stimulated with IL-1α displayed no increase in PRG4 expression upon lubrication with rhPRG4, but with PBS as the lubricant, IL-1α stimulation significantly increased PRG4 expression compared to the control condition from 30.8 ± 19 copies to 401 ± 340 copies (p=0.015). Overall, these data suggest that exogenous rhPRG4 may provide a therapeutic option for reducing friction in transient inflammatory conditions and increasing PRG4 expression

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN