Deep learning can detect elbow disease in dogs screened for elbow dysplasia.

Medical image analysis based on deep learning is a rapidly advancing field in veterinary diagnostics. The aim of this retrospective diagnostic accuracy study was to develop and assess a convolutional neural network (CNN, EfficientNet) to evaluate elbow radiographs from dogs screened for elbow dysplasia. An auto-cropping tool based on the deep learning model RetinaNet was developed for radiograph preprocessing to crop the radiographs to the region of interest around the elbow joint.

Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation.

Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the quality and assessing the uncertainty of CNN-generated contours of head and neck cancers with PET/CT as input.

Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

[This corrects the article DOI: 10.3389/fmed.2023.

Seroprevalence of Dengue and Chikungunya Virus Infections in Children Living in Sub-Saharan Africa: Systematic Review and Meta-Analysis.

Dengue and chikungunya viruses are frequent causes of malarial-like febrile illness in children. The rapid increase in virus transmission by mosquitoes is a global health concern. This is the first systematic review and meta-analysis of the childhood prevalence of dengue and chikungunya in Sub-Saharan Africa (SSA).

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.

Background: Radiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.

Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging.

Background And Purpose: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency.

MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts.

Background: Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties that may be valuable for tumor delineation. We explored MRI-based automatic segmentation of rectal cancer using a deep learning (DL) approach.

Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

Background: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency.

A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers.

Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) this is complicated by the complex anatomy of the head and neck region and the proximity of target volumes to organs at risk. The purpose of this study was to compare and evaluate conventional PET thresholding methods, six classical machine learning algorithms and a 2D U-Net convolutional neural network (CNN) for automatic gross tumor volume (GTV) segmentation of HNC in PET/CT images.

Extensive Antibiotic and Antimalarial Prescription Rate among Children with Acute Febrile Diseases in the Lake Victoria Region, Tanzania.

Objectives: Acute mosquito-borne febrile diseases pose a threat to children in the Sub-Saharan-Africa with ∼272 000 children dying worldwide from malaria in 2018. Although the awareness for malaria in this area has increased due to improved health education, the apparent decline of actual malaria cases has not affected clinical practice significantly. This study collected clinical and epidemiologic data of children presenting with acute febrile diseases in order delineate their diagnostic and therapeutic management.

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

Purpose: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and neck cancer (HNC). CNN models were compared to manual GTV delineations made by experienced specialists.

Patterns of viral pathogens causing upper respiratory tract infections among symptomatic children in Mwanza, Tanzania.

Upper-respiratory tract infections (URTI) are the leading causes of childhood morbidities. This study investigated etiologies and patterns of URTI among children in Mwanza, Tanzania. A cross-sectional study involving 339 children was conducted between October-2017 and February-2018.

High antibiotic prescription rates in hospitalized children with human metapneumovirus infection in comparison to RSV infection emphasize the value of point-of-care diagnostics.

Background: Respiratory infections are the main causes for hospitalization in children and a common reason for the initiation of antibiotic treatment. Rapid antigen detection tests and point-of-care mPCR-based assays provide a fast detection of viral pathogens. Nonetheless, the prescription rate of antibiotics for respiratory infections is exceedingly high.

High dietary quality of non-toxic cyanobacteria for a benthic grazer and its implications for the control of cyanobacterial biofilms.

Background: Mass occurrences of cyanobacteria frequently cause detrimental effects to the functioning of aquatic ecosystems. Consequently, attempts haven been made to control cyanobacterial blooms through naturally co-occurring herbivores. Control of cyanobacteria through herbivores often appears to be constrained by their low dietary quality, rather than by the possession of toxins, as also non-toxic cyanobacteria are hardly consumed by many herbivores.

Consumer species richness and nutrients interact in determining producer diversity.

While it is crucial to understand the factors that determine the biodiversity of primary producer communities, the relative importance of bottom-up and top-down control factors is still poorly understood. Using freshwater benthic algal communities in the laboratory as a model system, we find an unimodal relationship between nutrient availability and producer diversity, and that increasing number of consumer species increases producer diversity, but overall grazing decreases algal biodiversity. Interestingly, these two factors interact strongly in determining producer diversity, as an increase in nutrient supply diminishes the positive effect of consumer species richness on producer biodiversity.

The best of both worlds: A combined approach for analyzing microalgal diversity via metabarcoding and morphology-based methods.

An increasing number of studies use next generation sequencing (NGS) to analyze complex communities, but is the method sensitive enough when it comes to identification and quantification of species? We compared NGS with morphology-based identification methods in an analysis of microalgal (periphyton) communities. We conducted a mesocosm experiment in which we allowed two benthic grazer species to feed upon benthic biofilms, which resulted in altered periphyton communities. Morphology-based identification and 454 (Roche) pyrosequencing of the V4 region in the small ribosomal unit (18S) rDNA gene were used to investigate the community change caused by grazing.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.

Background: Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers.

The Effect of Diet Mixing on a Nonselective Herbivore.

The balanced-diet hypothesis states that a diverse prey community is beneficial to consumers due to resource complementarity among the prey species. Nonselective consumer species cannot differentiate between prey items and are therefore not able to actively regulate their diet intake. We thus wanted to test whether the balanced-diet hypothesis is applicable to nonselective consumers.

The neural correlates of infant and adult goal prediction: evidence for semantic processing systems.

The sequential nature of action ensures that an individual can anticipate the conclusion of an observed action via the use of semantic rules. The semantic processing of language and action has been linked to the N400 component of the event-related potential (ERP). The authors developed an ERP paradigm in which infants and adults observed simple sequences of actions.