Long-term impact of exposure to Royal Guard, a pyriproxyfen-based bed net, on pyrethroid-resistant malaria vectors from Cameroon using DNA-based metabolic resistance markers.

Background: Escalating pyrethroid resistance in malaria vectors highlights the urgency of implementing new control tools incorporating non-pyrethroid molecules. Here, using DNA-based metabolic resistance markers, we assessed the efficacy of the dual active ingredients net Royal Guard against pyrethroids-resistant malaria vectors in Cameroon, establishing its long-term impact on mosquitoes' life traits after exposure.

Results: Cone assays revealed low efficacy of Royal Guard against field Anopheles populations.

The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint.

Purpose: The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management.

Participants: All centres ensure accuracy and completeness of the data.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells. However, this requires estimating patient-specific parameters of the model from clinical data, which is a challenging inverse problem due to limited temporal data and the limited time between imaging and diagnosis.

Bessel beam optical coherence microscopy enables multiscale assessment of cerebrovascular network morphology and function.

Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 × 1000 × 360 μm field-of-view at capillary level resolution.

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.

SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

Objectives: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images.

Material And Methods: This local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones.

A new protocol for multispecies bacterial infections in zebrafish and their monitoring through automated image analysis.

The zebrafish Danio rerio has become a popular model host to explore disease pathology caused by infectious agents. A main advantage is its transparency at an early age, which enables live imaging of infection dynamics. While multispecies infections are common in patients, the zebrafish model is rarely used to study them, although the model would be ideal for investigating pathogen-pathogen and pathogen-host interactions.

Substrate promiscuity of key resistance P450s confers clothianidin resistance while increasing chlorfenapyr potency in malaria vectors.

Novel insecticides were recently introduced to counter pyrethroid resistance threats in African malaria vectors. To prolong their effectiveness, potential cross-resistance from promiscuous pyrethroid metabolic resistance mechanisms must be elucidated. Here, we demonstrate that the duplicated P450s CYP6P9a/-b, proficient pyrethroid metabolizers, reduce neonicotinoid efficacy in Anopheles funestus while enhancing the potency of chlorfenapyr.

Association of a rapidly selected 4.3kb transposon-containing structural variation with a P450-based resistance to pyrethroids in the African malaria vector Anopheles funestus.

Deciphering the evolutionary forces controlling insecticide resistance in malaria vectors remains a prerequisite to designing molecular tools to detect and assess resistance impact on control tools. Here, we demonstrate that a 4.3kb transposon-containing structural variation is associated with pyrethroid resistance in central/eastern African populations of the malaria vector Anopheles funestus.

Indoor residual spraying of experimental huts in Cameroon highlights the potential of Fludora® Fusion to control wild pyrethroid-resistant malaria vectors.

Elevated resistance to pyrethroids in major malaria vectors has led to the introduction of novel insecticides including neonicotinoids. There is a fear that efficacy of these new insecticides could be impacted by cross-resistance mechanisms from metabolic resistance to pyrethroids. In this study, after evaluating the resistance to deltamethrin, clothianidin and mixture of clothianidin + deltamethrin in the lab using CDC bottle assays, the efficacy of the new IRS formulation Fludora® Fusion was tested in comparison to clothianidin and deltamethrin applied alone using experimental hut trials against wild free-flying pyrethroid-resistant Anopheles funestus from Elende and field An.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available.

LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt.

Virtual reality-empowered deep-learning analysis of brain cells.

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches.

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.

Exploring the molecular mechanisms of increased intensity of pyrethroid resistance in Central African population of a major malaria vector .

Molecular mechanisms driving the escalation of pyrethroid resistance in the major malaria mosquitoes of Central Africa remain largely uncharacterized, hindering effective management strategies. Here, resistance intensity and the molecular mechanisms driving it were investigated in a population of from northern Cameroon. High levels of pyrethroid and organochloride resistance were observed in population, with no mortality for 1× permethrin; only 11% and 33% mortalities for 5× and 10× permethrin diagnostic concentrations, and <2% mortalities for deltamethrin and DDT, respectively.

Learning continuous shape priors from sparse data with neural implicit functions.

Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization.