Computational Pathology Detection of Hypoxia-Induced Morphologic Changes in Breast Cancer.

Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of artificial intelligence in computational histopathology to evaluate hypoxia in breast cancer. Weakly supervised deep learning models can accurately detect morphologic changes associated with hypoxia in routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs).

Anti-NOR90 antibodies and their clinical significance: a multicenter experience in southern Spain.

Introduction: Anti-NOR90 antibodies were initially described in patients with autoimmune diseases based on staining of a nucleolar region known as the nucleolar organizer region (NOR). This study aims to explore the clinical aspects of anti-NOR90 antibodies in patients with systemic autoimmune diseases.

Methods: Observational study of patients with positive anti-NOR90 antibodies using the EUROLINE Systemic Sclerosis profile (IgG) kit (Euroimmun, Germany).

Evaluation of epilepsy lesion visualisation enhancement in low-field MRI using image quality transfer: a preliminary investigation of clinical potential for applications in developing countries.

Purpose: Low-field (LF) MRI scanners are common in many Low- and middle-Income countries, but they provide images with worse spatial resolution and contrast than high-field (HF) scanners. Image Quality Transfer (IQT) is a machine learning framework to enhance images based on high-quality references that has recently adapted to LF MRI. In this study we aim to assess if it can improve lesion visualisation compared to LF MRI scans in children with epilepsy.

A one-step method for generating antimicrobial nanofibre meshes coaxial electrospinning.

Respiratory diseases, including influenza, infectious pneumonia, and severe acute respiratory syndrome (SARS), are a leading cause of morbidity and mortality worldwide. The recent COVID-19 pandemic claimed over 6.9 million lives globally.

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia.

In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the spleen, leading to the presence of distinct red blood cells (RBCs) with altered morphological characteristics. We hypothesized that these RBCs could be detected systematically and at scale in peripheral blood films (PBFs) by harnessing the capabilities of deep learning models.

Specialist hybrid models with asymmetric training for malaria prevalence prediction.

Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical component of the Global Technical Strategy for Malaria from 2016 to 2030. While traditional differential equation models can perform basic forecasting, supervised machine learning algorithms provide more accurate predictions, as demonstrated by a recent study using an elastic net model (REMPS). Nevertheless, current short-term prediction systems do not achieve the required accuracy levels for routine clinical practice.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort.

Objectives: Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity.

Methods: Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection.

Lung ultrasound score severity cut-off points in COVID-19 pneumonia. A systematic review and validating cohort.

Objectives: Our purpose was to establish different cut-off points based on the lung ultrasound score (LUS) to classify COVID-19 pneumonia severity.

Methods: Initially, we conducted a systematic review among previously proposed LUS cut-off points. Then, these results were validated by a single-centre prospective cohort study of adult patients with confirmed SARS-CoV-2 infection.

Development and validation of the Ibadan Simplified Developmental Screening chart.

Background: Developmental assessment remains an integral part of the routine evaluation of the wellbeing of every child. Children in resource-poor countries are not routinely assessed for signs of developmental delay and developmental disorders are frequently overlooked. A major gap exists in the availability of culturally appropriate and cost-effective developmental screening tools in many low and middle income countries (LMICs) with large populations.

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error.

Representational ethical model calibration.

Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence - evidence-based or intuitive - guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epistemic equity arises in the context of any decision guidance, whether traditional or innovative.

Network topological determinants of pathogen spread.

How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its topology. In this paper, we deploy simulations to understand and quantify the impact on disease transmission of a set of topological network features, building a dataset of 9000 interaction graphs using generators of different types of synthetic social networks.

Content aware multi-focus image fusion for high-magnification blood film microscopy.

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image.

Development, deployment and evaluation of digitally enabled, remote, supported rehabilitation for people with long COVID-19 (Living With COVID-19 Recovery): protocol for a mixed-methods study.

Introduction: Long COVID-19 is a distressing, disabling and heterogeneous syndrome often causing severe functional impairment. Predominant symptoms include fatigue, cognitive impairment ('brain fog'), breathlessness and anxiety or depression. These symptoms are amenable to rehabilitation delivered by skilled healthcare professionals, but COVID-19 has put severe strain on healthcare systems.

Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.

Intraoperative frozen section analysis can be used to improve the accuracy of tumour margin estimation during cancer resection surgery through rapid processing and pathological assessment of excised tissue. Its applicability is limited in some cases due to the additional risks associated with prolonged surgery, largely from the time-consuming staining procedure. Our work uses a measurable property of bulk tissue to bypass the staining process: as tumour cells proliferate, they influence the surrounding extra-cellular matrix, and the resulting change in elastic modulus provides a signature of the underlying pathology.

SARS-CoV-2 inhibition using a mucoadhesive, amphiphilic chitosan that may serve as an anti-viral nasal spray.

There are currently no cures for coronavirus infections, making the prevention of infections the only course open at the present time. The COVID-19 pandemic has been difficult to prevent, as the infection is spread by respiratory droplets and thus effective, scalable and safe preventive interventions are urgently needed. We hypothesise that preventing viral entry into mammalian nasal epithelial cells may be one way to limit the spread of COVID-19.

Point of care ultrasound (POCUS) in diagnosis of proximal deep vein thrombosis among COVID-19 hospitalized patients with a high rate of low molecular weight heparin prophylaxis.

Background: Deep vein thrombosis (DVT) and pulmonary embolism (PE) are both complications linked with COVID-19. Lower limb point-of-care clinical ultrasound (POCUS) could detect occult clots, helping decide whom to treat with anticoagulation.

Objectives: To determine proximal DVT prevalence with POCUS screening among hospitalized COVID-19 patients.

Wisdom of crowds detects COVID-19 severity ahead of officially available data.

During the unfolding of a crisis, it is crucial to forecast its severity at an early stage , yet access to reliable data is often challenging early on. The wisdom of crowds has been effective at forecasting in similar scenarios. We investigated whether the initial regional social media reaction to the emerging COVID-19 pandemic in three critically affected countries has significant relations with their observed mortality a month later.

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films.

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution.

Structure-dependent amplification for denoising and background correction in Fourier ptychographic microscopy.

Fourier Ptychographic Microscopy (FPM) allows high resolution imaging using iterative phase retrieval to recover an estimate of the complex object from a series of images captured under oblique illumination. FPM is particularly sensitive to noise and uncorrected background signals as it relies on combining information from brightfield and noisy darkfield (DF) images. In this article we consider the impact of different noise sources in FPM and show that inadequate removal of the DF background signal and associated noise are the predominant cause of artefacts in reconstructed images.

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal "monolithic" models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice.