Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.

BACKGROUND: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. METHODS: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. RESULTS: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. CONCLUSIONS: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.

Remote myocardial fibrosis predicts adverse outcome in patients with myocardial infarction on clinical cardiovascular magnetic resonance imaging.

BACKGROUND: Heart failure (HF) most commonly occurs in patients who have had a myocardial infarction (MI), but factors other than MI size may be deterministic. Fibrosis of myocardium remote from the MI is associated with adverse remodeling. We aimed to 1) investigate the association between remote myocardial fibrosis, measured using cardiovascular magnetic resonance (CMR) extracellular volume fraction (ECV), and HF and death following MI, 2) identify predictors of remote myocardial fibrosis in patients with evidence of MI and determine the relationship with infarct size. METHODS: Multicenter prospective cohort study of 1199 consecutive patients undergoing CMR with evidence of MI on late gadolinium enhancement. Median follow-up was 1133 (895-1442) days. Cox proportional hazards modeling was used to identify factors predictive of the primary outcome, a composite of first hospitalization for HF (HHF) or all-cause mortality, post-CMR. Linear regression modeling was used to identify determinants of remote ECV. RESULTS: Remote myocardial fibrosis was a strong predictor of primary outcome (χ2: 15.6, hazard ratio [HR]: 1.07 per 1% increase in ECV, 95% confidence interval [CI]: 1.04-1.11, p < 0.001) and was separately predictive of both HHF and death. The strongest predictors of remote ECV were diabetes, sex, natriuretic peptides, and body mass index, but, despite extensive phenotyping, the adjusted model R2 was only 0.283. The relationship between infarct size and remote fibrosis was very weak. CONCLUSION: Myocardial fibrosis, measured using CMR ECV, is a strong predictor of HHF and death in patients with evidence of MI. The mechanisms underlying remote myocardial fibrosis formation post-MI remain poorly understood, but factors other than infarct size appear to be important.

Mapping the Clinical Pathway for Patients Undergoing Vestibular Schwannoma Resection.

BACKGROUND: The introduction of the electronic health record (EHR) has improved the collection and storage of patient information, enhancing clinical communication and academic research. However, EHRs are limited by data quality and the time-consuming task of manual data extraction. This study aimed to use process mapping to help identify critical data entry points within the clinical pathway for patients with vestibular schwannoma (VS) ideal for structured data entry and automated data collection to improve patient care and research. METHODS: A 2-stage methodology was used at a neurosurgical unit. Process maps were developed using semi-structured interviews with stakeholders in the management of VS resection. Process maps were then retrospectively validated against EHRs for patients admitted between August 2019 and December 2021, establishing critical data entry points. RESULTS: In the process map development, 20 stakeholders were interviewed. Process maps were validated against EHRs of 36 patients admitted for VS resection. Operative notes, surgical inpatient reviews (including ward rounds), and discharge summaries were available for all patients, representing critical data entry points. Areas for documentation improvement were in the preoperative clinics (30/36; 83.3%), preoperative skull base multidisciplinary team (32/36; 88.9%), postoperative follow-up clinics (32/36; 88.9%), and postoperative skull base multidisciplinary team meeting (29/36; 80.6%). CONCLUSIONS: This is a first use to our knowledge of a 2-stage methodology for process mapping the clinical pathway for patients undergoing VS resection. We identified critical data entry points that can be targeted for structured data entry and for automated data collection tools, positively impacting patient care and research.

Quantum pathways for charged track finding in high-energy collisions.

In high-energy particle collisions, charged track finding is a complex yet crucial endeavor. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilizing a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics.

FLT4 causes developmental disorders of the cardiovascular and lymphovascular systems via pleiotropic molecular mechanisms.

AIMS: Rare, deleterious genetic variants in FLT4 are associated with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. The distinct genetic variants in FLT4 are also an established cause of Milroy disease, the most prevalent form of primary hereditary lymphoedema. The phenotypic features of these two conditions are non-overlapping, implying pleiotropic cellular mechanisms during development. METHODS AND RESULTS: In this study, we show that FLT4 variants identified in patients with TOF, when expressed in primary human endothelial cells, cause aggregation of FLT4 protein in the perinuclear endoplasmic reticulum, activating proteostatic and metabolic signalling, whereas lymphoedema-associated FLT4 variants and wild-type (WT) FLT4 do not. FLT4 TOF variants display characteristic gene expression profiles in key developmental signalling pathways, revealing a role for FLT4 in cardiogenesis distinct from its role in lymphatic development. Inhibition of proteostatic signalling abrogates these effects, identifying potential avenues for therapeutic intervention. Depletion of flt4 in zebrafish caused cardiac phenotypes of reduced heart size and altered heart looping. These phenotypes were rescued with coinjection of WT human FLT4 mRNA, but incompletely or not at all by mRNA harbouring FLT4 TOF variants. CONCLUSION: Taken together, we identify a pathogenic mechanism for FLT4 variants predisposing to TOF that is distinct from the known dominant negative mechanism of Milroy-causative variants. FLT4 variants give rise to conditions of the two circulatory subdivisions of the vascular system via distinct developmental pleiotropic molecular mechanisms.

Mapping patient education encounters in elective surgery: a cohort study and cross-sectional survey.

OBJECTIVE: Develop a process map of when patients learn about their proposed surgery and what resources patients use to educate themselves. DESIGN: A mixed methods design, combining semistructured stakeholder interviews, quantitative validation using electronic healthcare records (EHR) in a retrospective cohort and a cross-sectional patient survey. SETTING: A single surgical centre in the UK. PARTICIPANTS: Fourteen members of the spinal multidisciplinary team were interviewed to develop the process map.This process map was validated using the EHR of 50 patients undergoing elective spine surgery between January and June 2022. Postprocedure, feedback was gathered from 25 patient surveys to identify which resources they used to learn about their spinal procedure. Patients below the age of 18 or who received emergency surgery were excluded. INTERVENTIONS: Elective spine surgery and patient questionnaires given postoperatively either on the ward or in follow-up clinic. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the percentage of the study cohort that was present at encounters on the process map. Key timepoints were defined if >80% of patients were present. The secondary outcome was the percentage of the study cohort that used educational resources listed in the patient questionnaire. RESULTS: There were 342 encounters which occurred across the cohort, with 16 discrete event categories identified. The initial surgical clinic (88%), anaesthetic preoperative assessment (96%) and admission for surgery (100%) were identified as key timepoints. Surveys identified that patients most used verbal information from their surgeon (100%) followed by written information from their surgeon (52%) and the internet (40%) to learn about their surgery. CONCLUSIONS: Process mapping is an effective method of illustrating the patient pathway. The initial surgical clinic, anaesthetic preoperative assessment and surgical admission are key timepoints where patients receive information. This has future implications for guiding patient education interventions to focus at key timepoints.

Assessment of the knowledge, attitude and practices of the informed consent process in oral healthcare among dental students in Makerere University Dental Hospital, Uganda.

INTRODUCTION: Informed consent is an ethical and legal component of healthcare. It ensures patient autonomy and allows patients to make decisions regarding their treatment. In dental care, informed consent is particularly important because most dental procedures are invasive. Since dental students are future dentists, they need to learn about their ethical obligations and accountability through the informed consent process as this is critical to patients' well-being. This study aimed to determine dental students' knowledge, attitudes, and practices of the informed consent process for oral health care in Makerere University Dental Hospital, Uganda. STUDY METHODOLOGY: This was a descriptive cross-sectional study using quantitative methods. It was carried out at Makerere University Dental Hospital and third, fourth, and fifth-year students (n = 102) pursuing a Bachelor of Dental Surgery program took part in the survey. A self-administered structured questionnaire was used to assess their knowledge, attitudes, and practices of informed consent for oral health care. Collected data were entered into Epi-data version 3.1, where it was cleaned, coded, and imported to STATA version 14 software for statistical analysis. RESULTS: About two-thirds 67 (65.7%) of the participants were males. The mean age was 25 (SD = 3.21) years. The majority (90%) of the students had a high level of knowledge of the informed consent process. About (80%) had a positive attitude towards informed consent and (85%) most often practiced the informed consent process. Based on bi-variate analysis, training on informed consent, year of study, age, and sex were significantly associated with the informed consent process. However, there was no significant risk factor associated with informed consent in multiple logistic regression analysis. CONCLUSION: The study findings highlighted high levels of knowledge, positive attitude, and practice of the informed consent process among the clinical dental students. Continuous training is necessary to remind dental students about the importance of informed consent in healthcare, not only for complex procedures.

AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition.

Effector proteins are central to the success of plant pathogens, while immunity in host plants is driven by receptor-mediated recognition of these effectors. Understanding the molecular details of effector-receptor interactions is key for the engineering of novel immune receptors. Here, we experimentally determined the crystal structure of the Puccinia graminis f. sp. tritici (Pgt) effector AvrSr27, which was not accurately predicted using AlphaFold2. We characterised the role of the conserved cysteine residues in AvrSr27 using in vitro biochemical assays and examined Sr27-mediated recognition using transient expression in Nicotiana spp. and wheat protoplasts. The AvrSr27 structure contains a novel ß-strand rich modular fold consisting of two structurally similar domains that bind to Zn2+ ions. The N-terminal domain of AvrSr27 is sufficient for interaction with Sr27 and triggering cell death. We identified two Pgt proteins structurally related to AvrSr27 but with low sequence identity that can also associate with Sr27, albeit more weakly. Though only the full-length proteins, trigger Sr27-dependent cell death in transient expression systems. Collectively, our findings have important implications for utilising protein prediction platforms for effector proteins, and those embarking on bespoke engineering of immunity receptors as solutions to plant disease.

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

PURPOSE: Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources. METHODS: Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. RESULTS: Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). CONCLUSIONS: NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

Cyanobacterial α-carboxysome carbonic anhydrase is allosterically regulated by the Rubisco substrate RuBP.

Cyanobacterial CO2 concentrating mechanisms (CCMs) sequester a globally consequential proportion of carbon into the biosphere. Proteinaceous microcompartments, called carboxysomes, play a critical role in CCM function, housing two enzymes to enhance CO2 fixation: carbonic anhydrase (CA) and Rubisco. Despite its importance, our current understanding of the carboxysomal CAs found in α-cyanobacteria, CsoSCA, remains limited, particularly regarding the regulation of its activity. Here, we present a structural and biochemical study of CsoSCA from the cyanobacterium Cyanobium sp. PCC7001. Our results show that the Cyanobium CsoSCA is allosterically activated by the Rubisco substrate ribulose-1,5-bisphosphate and forms a hexameric trimer of dimers. Comprehensive phylogenetic and mutational analyses are consistent with this regulation appearing exclusively in cyanobacterial α-carboxysome CAs. These findings clarify the biologically relevant oligomeric state of α-carboxysomal CAs and advance our understanding of the regulation of photosynthesis in this globally dominant lineage.

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 as a model system. First, we used quantitative genetics and Quantitative Trait Loci 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.

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.