Search

Abstract Virulent Klebsiella oxytoca strains are associated with gut and lung pathologies, yet our understanding of the molecular signals governing pathogenesis remains limited. Here, we characterized a family of K. oxytoca pyrazine and pyrazinone autoinducers and explored their roles in microbial and host signaling. We identified the human mucin capping sugar Neu5Ac as a selective elicitor of leupeptin, a protease inhibitor prevalent in clinical lung isolates of K. oxytoca, and leupeptin-derived pyrazinone biosynthesis. Additionally, we uncovered a separate pyrazine pathway, regulated by general carbohydrate metabolism, derived from a broadly conserved PLP-dependent enzyme. While both pyrazine and pyrazinone signaling induce iron acquisition responses, including enterobactin biosynthesis, pyrazinone signaling enhances yersiniabactin virulence factor production and selectively activates the proinflammatory human histamine receptor H4 (HRH4). Our findings suggest that the availability of specific carbohydrates delineates distinct autoinducer pathways in K. oxytoca that may have differential effects on bacterial virulence and host immune responses.

Summary: Background: Post-traumatic stress disorder (PTSD) and depression are common after mild traumatic brain injury (mTBI), but their biological drivers are uncertain. We therefore explored whether polygenic risk scores (PRS) derived for PTSD and major depressive disorder (MDD) are associated with the development of cognate TBI-related phenotypes. Methods: Meta-analyses were conducted using data from two multicenter, prospective observational cohort studies of patients with mTBI: the CENTER-TBI study (ClinicalTrials.gov ID NCT02210221) in Europe (December 2014–December 2017) and the TRACK-TBI study in the US (March 2014–July 2018). In both cohorts, the most common causes of injury were road traffic accidents and falls. Primary outcomes, specifically probable PTSD and depression, were defined at 6 months post-injury using scores ≥33 on the PTSD Checklist-5 and ≥15 on the Patient Health Questionnaire-9, respectively. We calculated PTSD-PRS and MDD-PRS for patients aged ≥17 years who had a Glasgow Coma Scale score of 13–15 upon hospital arrival and assessed their association with PTSD and depression following TBI. We also evaluated the transferability of the findings in a cohort of African Americans. Findings: Overall, 11.8% (219/1869) and 6.7% (124/1869) patients were classified as having probable PTSD and depression, respectively. The PTSD-PRS was significantly associated with higher adjusted odds of PTSD in both cohorts, with a pooled odds ratio (OR) of 1.55 [95% confidence interval (CI) 1.30–1.84, p

ABSTRACT Introduction The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone. Patient Inclusion Patients with either histological or radiological diagnoses of lung cancer were included in this study. Results The patient cohort comprised 1864 patients diagnosed from 2016 to 2021. A total of 25 variables were considered as potential risk factors. These variables have been identified in previous studies as independent risk factors for bone metastasis. Treatment methods for lung cancer were taken into account during model development. The outcome variable was binary, (presence or absence of bone metastasis) with follow‐up until death or 12‐month survival, whichever is the sooner. Results showed that American Joint Committee on Cancer staging, the use of EGFR inhibitor, age, T‐staging, and lymphovascular invasion were the five input features contributing the most to the model algorithm. High AJCC staging (OR 1.98; p

Abstract Neonatal Encephalopathy (NE) is a major cause of lifelong disability and neurological complications in affected infants. Identifying novel diagnostic biomarkers in this population may assist in predicting MRI injury and differentiate neonates with NE from those with low-cord pH or healthy neonates and may help clinicians make real-time decisions. To compare the microRNA (miRNA) profiles between neonates with NE, healthy controls, and neonates with low cord pH. Moreover, miRNA concentrations were compared to brain injury severity in neonates with NE. This is a retrospective analysis of miRNA profiles from select samples in the biorepository and data registry at the University of Florida Health Gainesville. The Firefly miRNA assay was used to screen a total of 65 neurological miRNA targets in neonates with NE (n = 36), low cord pH (n = 18) and healthy controls (n = 37). Multivariate statistical techniques, including principal component analysis and orthogonal partial least squares discriminant analysis, and miRNA Enrichment Analysis and Annotation were used to identify miRNA markers and their pathobiological relevance. A set of 10 highly influential miRNAs were identified, which were significantly upregulated in the NE group compared to healthy controls. Of these, miR-323a-3p and mir-30e-5p displayed the highest fold change in expression levels. Moreover, miR-34c-5p, miR-491-5p, and miR-346 were significantly higher in the NE group compared to the low cord pH group. Furthermore, several miRNAs were identified that can differentiate between no/mild and moderate/severe injury in the NE group as measured by MRI. MiRNAs represent promising diagnostic and prognostic tools for improving the management of NE.

Abstract Background and Objective Iron deficiency is the most common cause of anemia. We compared the effect of ferric carboxymaltose (FCM), low-dose intravenous (IV) iron (LDI), and iron sucrose on total cost of care in patients with iron-deficiency anemia (IDA) from a US health plan perspective. Methods We conducted a retrospective claims analysis using the IQVIA PharMetrics Plus database. Patients with index (first) claims of FCM and LDI and a medical claim associated with IDA between 1 January 2017 and 31 December 2019 were included. Monthly total healthcare and inpatient and outpatient costs after receiving index IV iron for patients in the treatment cohorts were compared using a generalized linear model with gamma distribution and log-link. Results The overall study cohort included 37,655 FCM, 44,237 LDI, and 27,461 iron sucrose patients. Mean per-patient-per-month numbers of IV iron infusions for FCM, LDI, and iron sucrose were 0.20, 0.34, and 0.37, respectively. Compared with baseline, the FCM group had greater reductions in the number of hospital admissions and smaller increases in the number of outpatient visits in the 12 months post-IV iron therapy than LDI and iron sucrose, translating to significantly lower total healthcare cost (post-index adjusted cost ratio for total cost: 0.96 and 0.92, respectively; both P < 0.0001). Conclusions Higher drug acquisition cost of FCM relative to LDI and iron sucrose was offset by significantly lower inpatient and outpatient costs in the 12 months post-IV iron therapy. These results support the economic value of FCM for patients with IDA receiving IV iron therapy.

Objectives Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called ‘fit notes’) are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19.Design Cohort study.Setting With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database.Participants People 18–64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673).Primary outcome measure Receipt of a sick note in primary care.Results Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia.Conclusions Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact.

With the advancement of Artificial Intelligence (AI) technologies and accumulation of big Earth data, Deep Learning (DL) has become an important method to discover patterns and understand Earth science processes in the past several years. While successful in many Earth science areas, AI/DL applications are often challenging for computing devices. In recent years, Graphics Processing Unit (GPU) devices have been leveraged to speed up AI/DL applications, yet computational performance still poses a major barrier for DL-based Earth science applications. To address these computational challenges, we selected five existing sample Earth science AI applications, revised the DL-based models/algorithms, and tested the performance of multiple GPU computing platforms to support the applications. Application software packages, performance comparisons across different platforms, along with other results, are summarized. This article can help understand how various AI/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing (such as supermicro, GPU clusters, and cloud computing-based) for their AI/ML applications, and to optimize their science applications to better leverage the computing device.

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text. Through a series of experiments on diverse datasets, applying transfer and incremental learning techniques, we demonstrate the effectiveness of our hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Furthermore, our findings emphasize the importance of dataset alignment and balance in transfer learning, as well as the utility of incremental learning in maintaining high detection performance while reducing runtime. This research offers promising avenues for further advancements in fake news detection methodologies, with implications for future research and development in this critical domain.

This paper presents fluid dynamics simulation data associated with two test cases in the related research article [1]. In this article, an efficient bimaterial Riemann problem solver is proposed to accelerate multi-material flow simulations that involve complex thermodynamic equations of state and strong discontinuities across material interfaces. The first test case is a one-dimensional benchmark problem, featuring large density jump (4 orders of magnitude) and drastically different thermodynamics relations across a material interface. The second test case simulates the nucleation of a pear-shaped vapor bubble induced by long-pulsed laser in water. This multiphysics simulation combines laser radiation, phase transition (vaporization), non-spherical bubble expansion, and the emission of acoustic and shock waves. Both test cases are performed using the M2C solver, which solves the three-dimensional Eulerian Navier-Stokes equations, utilizing the accelerated bimaterial Riemann solver. Source codes provided in this paper include the M2C solver and a standalone version of the accelerated Riemann problem solver. These source codes serve as references for researchers seeking to implement the acceleration algorithms introduced in the related research article. Simulation data provided include fluid pressure, velocity, density, laser radiance and bubble dynamics. The input files and the workflow to perform the simulations are also provided. These files, together with the source codes, allow researchers to replicate the simulation results presented in the research article, which can be a starting point for new research in laser-induced cavitation, bubble dynamics, and multiphase flow in general.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

This case describes an atypical cutaneous presentation of metastatic gastric carcinoma in a patient initially presenting with dysphagia and a sclerotic red plaque overlying the anterior neck and chest. Skin biopsy revealed metastatic adenocarcinoma from the upper gastrointestinal tract. Esophagogastroduodenoscopy revealed stage IV metastatic gastric adenocarcinoma. Treatment with chemotherapy was initiated.

Pancreatic cancer is a highly lethal disease with limited treatment options. Hence, there is a considerable medical need for novel treatment strategies. Monoclonal antibodies (mAbs) have significantly improved cancer therapy, primarily due to their ability to stimulate antibody-dependent cellular cytotoxicity (ADCC), which plays a crucial role in their therapeutic efficacy. As a result, significant effort has been focused on improving this critical function by engineering mAbs with Fc regions that have increased affinity for the Fc receptor CD16 expressed on natural killer (NK) cells, the major cell population that mediates ADCC in humans. Here we report on the preclinical characterization of a mAb directed to the target antigen B7-H3 (CD276) containing an Fc part with the amino acid substitutions S239D/I332E to increase affinity for CD16 (B7-H3-SDIE) for the treatment of pancreatic cancer. B7-H3 (CD276) is highly expressed in many tumor entities, whereas expression on healthy tissues is more limited. Our findings confirm high expression of B7-H3 on pancreatic cancer cells. Furthermore, our study shows that B7-H3-SDIE effectively activates NK cells against pancreatic cancer cells in an antigen-dependent manner, as demonstrated by the analysis of NK cell activation, degranulation and cytokine release. The activation of NK cells resulted in significant tumor cell lysis in both short-term and long-term cytotoxicity assays. In conclusion, B7-H3-SDIE constitutes a promising agent for the treatment of pancreatic cancer.

Background & Aims: Pancreatic ductal adenocarcinoma can develop from precursor lesions, including pancreatic intraepithelial neoplasia and intraductal papillary mucinous neoplasm (IPMN). Previous studies indicated that loss of Acvr1b accelerates the Kras-mediated development of papillary IPMN in the mouse pancreas; however, the cell type predominantly affected by these genetic changes remains unclear. Methods: We investigated the contribution of cellular origin by inducing IPMN associated mutations (KRASG12D expression and Acvr1b loss) specifically in acinar (Ptf1aCreER;KrasLSL-G12D;Acvr1bfl/fl mice) or ductal (Sox9CreER;KrasLSL-G12D;Acvr1bfl/fl mice) cells in mice. We then performed magnetic resonance imaging and a thorough histopathologic analysis of their pancreatic tissues. Results: The loss of Acvr1b increased the development of pancreatic intraepithelial neoplasia and IPMN-like lesions when either acinar or ductal cells expressed a Kras mutation. Magnetic resonance imaging, immunohistochemistry, and histology revealed large IPMN-like lesions in these mice that exhibited features of flat, gastric epithelium. In addition, cyst formation in both mouse models was accompanied by chronic pancreatitis. Experimental acute pancreatitis accelerated the development of large mucinous cysts and pancreatic intraepithelial neoplasia when acinar, but not ductal, cells expressed mutant Kras and lost Acvr1b. Conclusions: These findings indicate that loss of Acvr1b in the presence of the Kras oncogene promotes the development of large and small precancerous lesions from both ductal and acinar cells. However, the IPMN-like phenotype was not equivalent to that observed when these mutations were made in all pancreatic cells during development. Our study underscores the significance of the cellular context in the initiation and progression of precursor lesions from exocrine cells.

Holmium:yttrium–aluminum-garnet (Ho:YAG) laser lithotripsy (LL) has been the treatment of choice for kidney stone disease for more than two decades, yet the mechanisms of action are not completely clear. Besides photothermal ablation, recent evidence suggests that cavitation bubble collapse is pivotal in kidney stone dusting when the Ho:YAG laser operates at low pulse energy (Ep) and high frequency (F). In this work, we perform a comprehensive series of experiments and model-based simulations to dissect the complex physical processes in LL. Under clinically relevant dusting settings (Ep = 0.2 J, F = 20 Hz), our results suggest that majority of the irradiated laser energy (>90 %) is dissipated by heat generation in the fluid surrounding the fiber tip and the irradiated stone surface, while only about 1 % may be consumed for photothermal ablation, and less than 0.7 % is converted into the potential energy at the maximum bubble expansion. We reveal that photothermal ablation is confined locally to the laser irradiation spot, whereas cavitation erosion is most pronounced at a fiber tip-stone surface distance about 0.5 mm where multi foci ring-like damage outside the thermal ablation zone is observed. The cavitation erosion is caused by the progressively intensified collapse of jet-induced toroidal bubble near the stone surface (

Objective: The current total cavopulmonary connection Fontan has competing inflows and outflows, creating hemodynamic inefficiencies that contribute to Fontan failure and complicate placement and efficiency of mechanical circulatory support. We propose a novel convergent cavopulmonary connection (CCPC) Fontan design to create a single, converged venous outflow to the pulmonary arteries, thus increasing efficiency and mechanical circulatory support access. We then evaluate the feasibility and hemodynamic performance of the CCPC in various patient sizes using computational fluid dynamic assessments of computer-aided designs. Methods: Cardiac magnetic resonance imaging data from 12 patients with single ventricle (10 total cavopulmonary connection, 2 Glenn) physiology (body surface area, 0.5-2.0 m2) were segmented to create 3-dimensional replicas of all thoracic structures. Surgically feasible CCPC shapes within constraints of anatomy were created using iterative computational fluid dynamic and clinician input. Designs varied based on superior and inferior vena cava conduit sizes, coronal attachment height, coronal entry angle, and axial entry angle of the superior vena cava to the inferior vena cava. CCPC designs were optimized based on efficiency (indexed power loss), risk of arteriovenous malformations (hepatic flow distribution), and risk of flow stasis (% nonphysiologic wall shear stress). Results: All CCPC designs met hemodynamic performance thresholds for indexed power loss and hepatic flow distribution. CCPC designs showed improvements in reducing % nonphysiologic wall shear stress and balancing HFD. Conclusions: CCPC is physiologically and surgically feasible in various patient sizes using validated computational fluid dynamic models. CCPC configuration has analogous indexed power loss, hepatic flow distribution, and % nonphysiologic wall shear stress compared with total cavopulmonary connection, and the single inflow and outflow may ease mechanical circulatory support therapies. Further studies are required for design optimization and mechanical circulatory support institution.

Measurement(s) redistricting, partisanship Technology Type(s) sequential Monte Carlo algorithm Factor Type(s) population deviation • compactness • county splits • racial composition • municipality splits Sample Characteristic - Organism Congressional redistricting Sample Characteristic - Environment year 2020 Sample Characteristic - Location United States

Isolating the neural mechanisms of consciousness is complicated by task report and other irrelevant signals. Here, the authors removed report and eye movement confounds to uncover large scale cortical-subcortical networks specific for human visual consciousness.

Abstract Background Magnetic resonance imaging (MRI) carries prognostic importance after traumatic brain injury (TBI), especially when computed tomography (CT) fails to fully explain the level of unconsciousness. However, in critically ill patients, the risk of deterioration during transfer needs to be balanced against the benefit of detecting prognostically relevant information on MRI. We therefore aimed to assess if day of injury serum protein biomarkers could identify critically ill TBI patients in whom the risks of transfer are compensated by the likelihood of detecting management-altering neuroimaging findings. Methods Data were obtained from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Eligibility criteria included: TBI patients aged ≥ 16 years, Glasgow Coma Score (GCS)

BackgroundThe periodontium is a highly vascularized area of the mouth, and periodontitis initiates negative functional and structural changes in the vasculature. However, mild oral inflammation, including levels experienced by many apparently healthy individuals, has an unclear impact on cardiovascular function. The purpose of this pilot study is to investigate the effects of objectively measured whole mouth oral inflammatory load (OIL) on vascular function in apparently healthy individuals.MethodsIn this cross-sectional and correlational analysis, we recruited 28 young (18–30 years) and systemically healthy participants (16 male, 12 female). Using oral neutrophil counts, a validated measure for OIL, we collected participant's mouth rinse samples and quantified OIL. Blood pressure, arterial stiffness (pulse-wave velocity) and endothelial function (brachial artery flow-mediated dilation) were also measured.ResultsOnly oral neutrophil count significantly predicted flow-mediated dilation % (p = 0.04; R2 = 0.16, β = − 1.05) and those with OIL levels associated with >2.5 × 105 neutrophil counts (n = 8) had a lower flow-mediated dilation % (6.0 ± 2.3%) than those with counts associated with gingival health with less than 2.5 × 105 neutrophil counts (10.0 ± 5.2%, p = 0.05). There were no significant predictors for arterial stiffness.ConclusionWe found that OIL was a predictor of reduced flow-mediated dilation. An impairment in flow-mediated dilation is an indicator of future possible risk of cardiovascular disease—one of the leading causes of death in North America. Therefore, this study provides evidence for the importance of oral health and that OIL may impact endothelial function.

Summary: Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. Methods: In this population-based, observational cohort study we used primary care electronic health record data with linkage to hospital episode statistics data and mortality data within OpenSAFELY, a data analytics platform created, with approval of NHS England, to address urgent COVID-19 research questions. We included adults aged 18 years and over registered with a TPP practice between March 1, 2018, and April 30, 2022. We excluded those with missing age, sex, geographic region, or Index of Multiple Deprivation. We grouped ethnicity (exposure), into five categories: White, Asian, Black, Other, and Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (blood pressure and Hba1c measurements, chronic obstructive pulmonary disease and asthma annual reviews) before and after March 23, 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to diabetes, cardiovascular disease, respiratory disease, and mental health before and after March 23, 2020. Findings: Of 33,510,937 registered with a GP as of 1st January 2020, 19,064,019 were adults, alive and registered for at least 3 months, 3,010,751 met the exclusion criteria and 1,122,912 were missing ethnicity. This resulted in 14,930,356 adults with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to the White ethnic group (Pre-pandemic hazard ratio (HR): 0.50, 95% confidence interval (CI) 0.41, 0.60, Pandemic HR: 0.75, 95% CI: 0.65, 0.87). There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in those of White ethnicity (heart failure risk difference: 5.4). Relatively, ethnic differences narrowed for heart failure admission in those of Asian (Pre-pandemic HR 1.56, 95% CI 1.49, 1.64, Pandemic HR 1.24, 95% CI 1.19, 1.29) and Black ethnicity (Pre-pandemic HR 1.41, 95% CI: 1.30, 1.53, Pandemic HR: 1.16, 95% CI 1.09, 1.25) compared with White ethnicity. For other outcomes the pandemic had minimal impact on ethnic differences. Interpretation: Our study suggests that ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes. Funding: LSHTM COVID-19 Response Grant (DONAT15912).

ABSTRACT This study addresses the challenge of accurately identifying filamentous fungi in medical laboratories using transfer learning with convolutional neural networks (CNNs). The study uses microscopic images from touch-tape slides with lactophenol cotton blue staining, the most common method in clinical settings, to classify fungal genera and identify Aspergillus species. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently. IMPORTANCE This study utilizes transfer learning with CNNs to classify fungal genera and identify Aspergillus species using microscopic images from touch-tape preparation and lactophenol cotton blue staining. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently.