PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery.

PURPOSE: Endoscopic pituitary surgery entails navigating through the nasal cavity and sphenoid sinus to access the sella using an endoscope. This procedure is intricate due to the proximity of crucial anatomical structures (e.g. carotid arteries and optic nerves) to pituitary tumours, and any unintended damage can lead to severe complications including blindness and death. Intraoperative guidance during this surgery could support improved localization of the critical structures leading to reducing the risk of complications. METHODS: A deep learning network PitSurgRT is proposed for real-time localization of critical structures in endoscopic pituitary surgery. The network uses high-resolution net (HRNet) as a backbone with a multi-head for jointly localizing critical anatomical structures while segmenting larger structures simultaneously. Moreover, the trained model is optimized and accelerated by using TensorRT. Finally, the model predictions are shown to neurosurgeons, to test their guidance capabilities. RESULTS: Compared with the state-of-the-art method, our model significantly reduces the mean error in landmark detection of the critical structures from 138.76 to 54.40 pixels in a 1280 × 720-pixel image. Furthermore, the semantic segmentation of the most critical structure, sella, is improved by 4.39% IoU. The inference speed of the accelerated model achieves 298 frames per second with floating-point-16 precision. In the study of 15 neurosurgeons, 88.67% of predictions are considered accurate enough for real-time guidance. CONCLUSION: The results from the quantitative evaluation, real-time acceleration, and neurosurgeon study demonstrate the proposed method is highly promising in providing real-time intraoperative guidance of the critical anatomical structures in endoscopic pituitary surgery.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

The structural repertoire of Fusarium oxysporum f. sp. lycopersici effectors revealed by experimental and computational studies.

Plant pathogens secrete proteins, known as effectors, that function in the apoplast or inside plant cells to promote virulence. Effector recognition by cell-surface or cytosolic receptors results in the activation of defence pathways and plant immunity. Despite their importance, our general understanding of fungal effector function and recognition by immunity receptors remains poor. One complication often associated with effectors is their high sequence diversity and lack of identifiable sequence motifs precluding prediction of structure or function. In recent years, several studies have demonstrated that fungal effectors can be grouped into structural classes, despite significant sequence variation and existence across taxonomic groups. Using protein X-ray crystallography, we identify a new structural class of effectors hidden within the secreted in xylem (SIX) effectors from Fusarium oxysporum f. sp. lycopersici (Fol). The recognised effectors Avr1 (SIX4) and Avr3 (SIX1) represent the founding members of the Fol dual-domain (FOLD) effector class, with members containing two distinct domains. Using AlphaFold2, we predicted the full SIX effector repertoire of Fol and show that SIX6 and SIX13 are also FOLD effectors, which we validated experimentally for SIX6. Based on structural prediction and comparisons, we show that FOLD effectors are present within three divisions of fungi and are expanded in pathogens and symbionts. Further structural comparisons demonstrate that Fol secretes effectors that adopt a limited number of structural folds during infection of tomato. This analysis also revealed a structural relationship between transcriptionally co-regulated effector pairs. We make use of the Avr1 structure to understand its recognition by the I receptor, which leads to disease resistance in tomato. This study represents an important advance in our understanding of Fol-tomato, and by extension plant-fungal interactions, which will assist in the development of novel control and engineering strategies to combat plant pathogens.

Point-of-care lung ultrasound in detecting pneumonia: A systematic review.

Purpose: Limited evidence exists to assess the sensitivity, specificity, and accuracy of point-of-care lung ultrasound (LUS) across all age groups. This review aimed to investigate the benefits of point-of-care LUS for the early diagnosis of pneumonia compared to traditional chest X-rays (CXR) in a subgroup analysis including pediatric, adult, and geriatric populations. Material and Methods: This systematic review examined systematic reviews, meta-analyses, and original research from 2017 to 2021, comparing point-of-care LUS and CXR in diagnosing pneumonia among adults, pediatrics and geriatrics. Studies lacking direct comparison or exploring diseases other than pneumonia, case reports, and those examining pneumonia secondary to COVID-19 variants were excluded. The search utilized PubMed, Google Scholar, and Cochrane databases with specific search strings. The study selection, conducted by two independent investigators, demonstrated an agreement by the Kappa index, ensuring reliable article selection. The QUADAS-2 tool assessed the selected studies for quality, highlighting risk of bias and applicability concerns across key domains. Statistical analysis using Stata Version 16 determined pooled sensitivity and specificity via a bivariate model, emphasizing LUS and CXR diagnostic capabilities. Additionally, RevMan 5.4.1 facilitated the calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), offering insights into diagnostic accuracy. Results: The search, conducted across PubMed, Google Scholar, and Cochrane Library databases by two independent investigators, initially identified 1045 articles. Following screening processes, 12 studies comprised a sample size of 2897. LUS demonstrated a likelihood ratio of 5.09, a specificity of 81.91%, and a sensitivity of 92.13% in detecting pneumonia in pediatric, adult, and geriatric patients, with a p-value of 0.0002 and a 95% confidence interval, indicating diagnostic accuracy ranging from 84.07% to 96.29% when compared directly to CXR. Conclusion: Our review supports that LUS can play a valuable role in detecting pneumonia early with high sensitivity, specificity, and diagnostic accuracy across diverse patient demographics, including pediatric, adult, and geriatric populations. Since it overcomes most of the limitations of CXR and other diagnostic modalities, it can be utilized as a diagnostic tool for pneumonia for all age groups as it is a safe, readily available, and cost-effective modality that can be utilized in an emergency department, intensive care units, wards, and clinics by trained respiratory care professionals.

Can ChatGPT outperform a neurosurgical trainee? A prospective comparative study.

PURPOSE: This study aimed to compare the performance of ChatGPT, a large language model (LLM), with human neurosurgical applicants in a neurosurgical national selection interview, to assess the potential of artificial intelligence (AI) and LLMs in healthcare and provide insights into their integration into the field. METHODS: In a prospective comparative study, a set of neurosurgical national selection-style interview questions were asked to eight human participants and ChatGPT in an online interview. All participants were doctors currently practicing in the UK who had applied for a neurosurgical National Training Number. Interviews were recorded, anonymised, and scored by three neurosurgical consultants with experience as interviewers for national selection. Answers provided by ChatGPT were used as a template for a virtual interview. Interview transcripts were subsequently scored by neurosurgical consultants using criteria utilised in real national selection interviews. Overall interview score and subdomain scores were compared between human participants and ChatGPT. RESULTS: For overall score, ChatGPT fell behind six human competitors and did not achieve a mean score higher than any individuals who achieved training positions. Several factors, including factual inaccuracies and deviations from expected structure and style may have contributed to ChatGPT's underperformance. CONCLUSIONS: LLMs such as ChatGPT have huge potential for integration in healthcare. However, this study emphasises the need for further development to address limitations and challenges. While LLMs have not surpassed human performance yet, collaboration between humans and AI systems holds promise for the future of healthcare.

The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring.

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots-providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

Identification of heart failure hospitalization from NHS Digital data: comparison with expert adjudication.

AIMS: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF. METHODS AND RESULTS: Patients experiencing at least one HHF, as determined by NHS Digital data, and age- and sex-matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; and NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Five hundred four patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position {I50: 96.2% [95% confidence interval (CI) 94.1-97.7%]; NICOR: 93.3% [CI 90.8-95.4%]; OIS: 95.6% [CI 93.3-97.2%]} but decreased substantially as the number of diagnosis positions expanded. Sensitivity [40.0% (CI 12.2-73.8%)] and positive predictive value (PPV) [highest with I50: 17.4% (CI 8.1-33.6%)] were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly [36.4% (CI 16.6-62.2%)]. CONCLUSIONS: NHS Digital data were not able to accurately identify HHF and should not be used in isolation for this purpose.

Growth differentiation factor-15 in patients with or at risk of heart failure but before first hospitalisation.

OBJECTIVE: Identification of patients at risk of adverse outcome from heart failure (HF) at an early stage is a priority. Growth differentiation factor (GDF)-15 has emerged as a potentially useful biomarker. This study sought to identify determinants of circulating GDF-15 and evaluate its prognostic value, in patients at risk of HF or with HF but before first hospitalisation. METHODS: Prospective, longitudinal cohort study of 2166 consecutive patients in stage A-C HF undergoing cardiovascular magnetic resonance and measurement of GDF-15. Multivariable linear regression investigated determinants of GDF-15. Cox proportional hazards modelling, Net Reclassification Improvement and decision curve analysis examined its incremental prognostic value. Primary outcome was a composite of first hospitalisation for HF or all-cause mortality. Median follow-up was 1093 (939-1231) days. RESULTS: Major determinants of GDF-15 were age, diabetes and N-terminal pro-B-type natriuretic peptide, although despite extensive phenotyping, only around half of the variability of GDF-15 could be explained (R2 0.51). Log-transformed GDF-15 was the strongest predictor of outcome (HR 2.12, 95% CI 1.71 to 2.63) and resulted in a risk prediction model with higher predictive accuracy (continuous Net Reclassification Improvement 0.26; 95% CI 0.13 to 0.39) and with greater clinical net benefit across the entire range of threshold probabilities. CONCLUSION: In patients at risk of HF, or with HF but before first hospitalisation, GDF-15 provides unique information and is highly predictive of hospitalisation for HF or all-cause mortality, leading to more accurate risk stratification that can improve clinical decision making. TRIAL REGISTRATION NUMBER: NCT02326324.

Clinical risk associated with COVID-19 among 86000 patients with congenital heart disease.

OBJECTIVE: To determine the magnitude of any excess risk of mortality and hospitalisation due to COVID-19 infection in patients with congenital heart disease (CHD) in the UK healthcare system. METHODS: Matched case-control study within the Clinical Practice Research Datalink study of anonymised general practice records in the National Health Service in England. Patients with CHD were stratified for disease severity according to the European Society of Cardiology guidelines. Presence of a positive COVID-19 test, hospitalisation with a diagnosis of COVID-19 and COVID-19-related mortality were compared in case and control groups. RESULTS: 86 441 patients with CHD and 335 839 controls were studied. Of patients with a positive COVID-19 test, patients with CHD were more likely than controls to be hospitalised (22.4% vs 14.5%; OR=1.77 (95% CI 1.60 to 1.96); p=2.11e-28) and suffer COVID-19-related death (6.1% vs 3.8%; OR=1.60 (95% CI 1.35 to 1.89); p=7.00e-08). The excess risk of COVID-19 hospitalisation and death rose with increasing physiological severity of CHD (presence of pulmonary vascular disease and/or cyanosis), rather than anatomical complexity. CONCLUSIONS: In this study of the COVID-19 pandemic experience, using population health records in over 86000 patients with CHD in England, patients with CHD with COVID-19 were at around 50-75% higher risk of hospitalisation and mortality compared with matched controls with COVID-19. We provide the first primary care-derived estimates for COVID-19 hospitalisation and case-fatality rates in patients with CHD. Some factors predictive of worse COVID-19 outcome in general populations (such as non-white ethnic group), and other CHD-specific comorbidities (such as pulmonary hypertension), influenced outcomes among patients with CHD.

Assessing the activity of different plant-derived molecules and potential biological nitrification inhibitors on a range of soil ammonia- and nitrite-oxidizing strains.

IMPORTANCE: Synthetic nitrification inhibitors are routinely used with nitrogen fertilizers to reduce nitrogen losses from agroecosystems, despite having drawbacks like poor efficiency, cost, and entry into the food chain. Plant-derived BNIs constitute a more environmentally conducive alternative. Knowledge on the activity of BNIs to soil nitrifiers is largely based on bioassays with a single Nitrosomonas europaea strain which does not constitute a dominant member of the community of ammonia-oxidizing microorganisms (AOM) in soil. We determined the activity of several plant-derived molecules reported as having activity, including the recently discovered maize-isolated BNI, zeanone, and its natural analog, 2-methoxy-1,4-naphthoquinone, on a range of ecologically relevant AOM and one nitrite-oxidizing bacterial culture, expanding our knowledge on the intrinsic inhibition potential of BNIs toward AOM and highlighting the necessity for a deeper understanding of the effect of BNIs on the overall soil microbiome integrity before their further use in agricultural settings.

An approach for embedding behavioural science in antimicrobial resistance One Health research.

Antimicrobial Resistance (AMR) is a global health emergency that threatens modern medicine and incurs great cost to human health. The World Health Organization as part of a quadripartite joint initiative with the Food and Agriculture Organization of the United Nations, World Organisation for Animal Health, and United Nations Environment Programme, has recently published a One Health Priority Research Agenda for AMR. In this article we present a multidisciplinary approach, proposed by behavioural science experts, One Health experts and AMR experts to support the implementation of the Priority Research Agenda. We review, using specific examples of complex interventions designed to tackle AMR in which behavioural science has been embedded, five main steps: Define - what behaviours are a priority in each context; Diagnose - What are the barriers and enablers to the behaviours prioritised? Design - what interventions exist and what new or enhanced interventions could work to tackle the barriers identified? and, Implement and Evaluate the intervention(s). The approach presented will be useful for funders and researchers who wish to incorporate methods, frameworks and insights from the behavioural sciences into research plans, proposals and protocols in relation to a multisectoral One Health agenda and produce findings that are more relevant to policymakers.

Effectiveness of communications in enhancing adherence to public health behavioural interventions: a COVID-19 evidence review.

Health communication has relevance for virtually every aspect of health and well-being, including disease prevention. This review explored the effectiveness of communications in enhancing the adoption of or adherence to behavioural interventions (non-pharmaceutical interventions (NPIs)) related to COVID-19. The review takes the UK as a case study and focuses on self-reported behaviours (e.g. social distancing). It also reviews the psychosocial determinants of adherence. Searches were conducted using PubMed, Scopus, CINAL, ASSIA and iCite databases. Eleven thousand five hundred records were identified and 13 were included in the final sample. Included studies suggest that NPI adoption or adherence was generally high, and communication had significant impacts, with key themes including clarity and consistency, trust and control. Based on the evidence in this review, features of effective communication in the context of NPI adoption or adherence are (i) information should be conveyed clearly and conflicting (mixed) messages should be avoided; (ii) information should be conveyed by trusted sources (e.g. health authorities) and (iii) communication should strike a balance between being authoritative but avoiding language seen as controlling (e.g. 'you must'). Future research should prioritize quantitative, experimental and longitudinal study designs, that focus specifically on communication as an intervention, and which measure behaviour. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

The Necrotrophic Pathogen Parastagonospora nodorum Is a Master Manipulator of Wheat Defense.

Parastagonospora nodorum is a necrotrophic pathogen of wheat that is particularly destructive in major wheat-growing regions of the United States, northern Europe, Australia, and South America. P. nodorum secretes necrotrophic effectors that target wheat susceptibility genes to induce programmed cell death (PCD), resulting in increased colonization of host tissue and, ultimately, sporulation to complete its pathogenic life cycle. Intensive research over the last two decades has led to the functional characterization of five proteinaceous necrotrophic effectors, SnTox1, SnToxA, SnTox267, SnTox3, and SnTox5, and three wheat susceptibility genes, Tsn1, Snn1, and Snn3D-1. Functional characterization has revealed that these effectors, in addition to inducing PCD, have additional roles in pathogenesis, including chitin binding that results in protection from wheat chitinases, blocking defense response signaling, and facilitating plant colonization. There are still large gaps in our understanding of how this necrotrophic pathogen is successfully manipulating wheat defense to complete its life cycle. This review summarizes our current knowledge, identifies knowledge gaps, and provides a summary of well-developed tools and resources currently available to study the P. nodorum-wheat interaction, which has become a model for necrotrophic specialist interactions. Further functional characterization of the effectors involved in this interaction and work toward a complete understanding of how P. nodorum manipulates wheat defense will provide fundamental knowledge about this and other necrotrophic interactions. Additionally, a broader understanding of this interaction will contribute to the successful management of Septoria nodorum blotch disease on wheat. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Benchtop simulation of the retrosigmoid approach: Validation of a surgical simulator and development of a task-specific outcome measure score.

Background: Neurosurgical training is changing globally. Reduced working hours and training opportunities, increased patient safety expectations, and the impact of COVID-19 have reduced operative exposure. Benchtop simulators enable trainees to develop surgical skills in a controlled environment. We aim to validate a high-fidelity simulator model (RetrosigmoidBox, UpSurgeOn) for the retrosigmoid approach to the cerebellopontine angle (CPA). Methods: Novice and expert Neurosurgeons and Ear, Nose, and Throat surgeons performed a surgical task using the model - identification of the trigeminal nerve. Experts completed a post-task questionnaire examining face and content validity. Construct validity was assessed through scoring of operative videos employing Objective Structured Assessment of Technical Skills (OSATS) and a novel Task-Specific Outcome Measure score. Results: Fifteen novice and five expert participants were recruited. Forty percent of experts agreed or strongly agreed that the brain tissue looked real. Experts unanimously agreed that the RetrosigmoidBox was appropriate for teaching. Statistically significant differences were noted in task performance between novices and experts, demonstrating construct validity. Median total OSATS score was 14/25 (IQR 10-19) for novices and 22/25 (IQR 20-22) for experts (p < 0.05). Median Task-Specific Outcome Measure score was 10/20 (IQR 7-17) for novices compared to 19/20 (IQR 18.5-19.5) for experts (p < 0.05). Conclusion: The RetrosigmoidBox benchtop simulator has a high degree of content and construct validity and moderate face validity. The changing landscape of neurosurgical training mean that simulators are likely to become increasingly important in the delivery of high-quality education. We demonstrate the validity of a Task-Specific Outcome Measure score for performance assessment of a simulated approach to the CPA.

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Genomic testing in patients with renal disease.

Inherited kidney disease accounts for a significant proportion of chronic kidney disease and end-stage renal failure. There is increasing evidence that genetic testing for inherited kidney disease should be integrated into clinical care pathways at the earliest opportunity so that patients and their families can maximally benefit from carefully tailored care. Despite increased availability of genetic testing, the proportion of patients with renal disease undergoing genetic investigations remains low. This article introduces key concepts of genetic and genomic testing to the renal physician and addresses some common barriers to the wider integration of genetic testing in routine clinical practice to fully capitalise on recent advances in genomic medicine and improve patient outcomes.