UK guidelines for the investigation and management of eosinophilia in returning travellers and migrants.

Eosinophilia is a common finding in returning travellers, migrants and other travelling groups. In this setting it often indicates an underlying helminth infection. Infections associated with eosinophilia are frequently either asymptomatic or associated with non-specific symptoms but some can cause severe disease.

Prompt-guided and multimodal landscape scenicness assessments with vision-language models.

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of VLMs enable new opportunities for the annotation and analysis of images. We test the potential of VLMs for landscape scenicness prediction, i.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical Information in Deep Learning.

Global fine particulate matter (PM) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical PM concentrations over 1998-2019. We develop a loss function that incorporates geophysical estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors.

Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder.

The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imaging. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images.

Assessment and management of secondary bacterial infections complicating Mpox (Monkeypox) using a telemedicine service. A prospective cohort study.

Introduction: During spring 2022, an outbreak of Monkeypox (mpox) emerged as an infection of concern in Europe. Due to the overlapping clinical features of mpox and bacterial infections, diagnosis of concomitant bacterial infection is challenging. In this prospective cohort study, we report the incidence, severity, and progression of patients with secondary bacterial infection complicating mpox infection.

Efficient Training on Alzheimer's Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification.

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer's disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance.

BlpC-mediated selfish program leads to rapid loss of Streptococcus pneumoniae clonal diversity during infection.

Successful colonization of a host requires bacterial adaptation through genetic and population changes that are incompletely defined. Using chromosomal barcoding and high-throughput sequencing, we investigate the population dynamics of Streptococcus pneumoniae during infant mouse colonization. Within 1 day post inoculation, diversity was reduced >35-fold with expansion of a single clonal lineage.

Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.

Background: Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the following insertion and before the commencement of tube feeds. Convolutional neural networks (CNNs) have shown encouraging results in assisting the tube positioning assessment.

Mammalian Orthoreovirus Reassortment Proceeds with Little Constraint on Segment Mixing.

Segmentation of viral genomes provides the potential for genetic exchange within coinfected cells. However, for this potential to be realized, coinfecting genomes must mix during the viral life cycle. The efficiency of reassortment, in turn, dictates its potential to drive evolution.

Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features.

Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today.

Alzheimer's Disease Classification Using 2D Convolutional Neural Networks.

Alzheimer's disease (AD) is a non-treatable and non-reversible disease that affects about 6% of people who are 65 and older. Brain magnetic resonance imaging (MRI) is a pseudo-3D imaging technology that is widely used for AD diagnosis. Convolutional neural networks with 3D kernels (3D CNNs) are often the default choice for deep learning based MRI analysis.

Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging.

A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples.

Role of chromosome 1q copy number variation in hepatocellular carcinoma.

Chromosome 1q often has been observed to be amplified in hepatocellular carcinoma. This review summarizes literature reports of multiple genes that have been proposed as possible 1q amplification drivers. These largely fall within 1q21-1q23.

Weakly-Supervised Self-Training for Breast Cancer Localization.

The use of deep learning methods has dramatically increased the state-of-the-art performance in image object localization. However, commonly used supervised learning methods require large training datasets with pixel-level or bounding box annotations. Obtaining such fine-grained annotations is extremely costly, especially in the medical imaging domain.

Dynamic Image for 3D MRI Image Alzheimer's Disease Classification.

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN.

Collective interactions augment influenza A virus replication in a host-dependent manner.

Infection with a single influenza A virus (IAV) is only rarely sufficient to initiate productive infection. Instead, multiple viral genomes are often required in a given cell. Here, we show that the reliance of IAV on multiple infection can form an important species barrier.

β-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline.

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer's disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD.

Inconsistent Performance of Deep Learning Models on Mammogram Classification.

Objectives: Performance of recently developed deep learning models for image classification surpasses that of radiologists. However, there are questions about model performance consistency and generalization in unseen external data. The purpose of this study is to determine whether the high performance of deep learning on mammograms can be transferred to external data with a different data distribution.

Dysregulation of M segment gene expression contributes to influenza A virus host restriction.

The M segment of the 2009 pandemic influenza A virus (IAV) has been implicated in its emergence into human populations. To elucidate the genetic contributions of the M segment to host adaptation, and the underlying mechanisms, we examined a panel of isogenic viruses that carry avian- or human-derived M segments. Avian, but not human, M segments restricted viral growth and transmission in mammalian model systems, and the restricted growth correlated with increased expression of M2 relative to M1.

Incomplete influenza A virus genomes occur frequently but are readily complemented during localized viral spread.

Segmentation of viral genomes into multiple RNAs creates the potential for replication of incomplete viral genomes (IVGs). Here we use a single-cell approach to quantify influenza A virus IVGs and examine their fitness implications. We find that each segment of influenza A/Panama/2007/99 (H3N2) virus has a 58% probability of being replicated in a cell infected with a single virion.

Automatic Hand Skeletal Shape Estimation From Radiographs.

Rheumatoid arthritis (RA) is an autoimmune disease whose common manifestation involves the slow destruction of joint tissue, a damage that is visible in a radiograph. Over time, this damage causes pain and loss of functioning, which depends, to some extent, on the spatial deformation induced by the joint damage. Building an accurate model of the current deformation and predicting potential future deformations are the important components of treatment planning.

Survey of Australian psychiatrists' and psychiatry trainees' knowledge about and attitudes towards medicinal cannabinoids.

Objective:: To assess Australian psychiatrists' and psychiatry trainees' knowledge about and attitudes towards medicinal cannabinoids, given the recent relaxation of cannabinoid-prescribing laws in Australia.

Method:: All Australian members of the Royal Australian and New Zealand College of Psychiatrists were invited to participate in an anonymous, 64-item online questionnaire, through Royal Australian and New Zealand College of Psychiatrists' newsletters. The questionnaire ran for a 10-week period from March to May 2017.

Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks.

Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis.

Polypharmacology Within the Full Kinome: a Machine Learning Approach.

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies.