Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use.

Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis.

Detection of signs of disease in external photographs of the eyes via deep learning.

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites.

Task-specific employment of sensory signals underlies rapid task switching.

Much of our flexible behavior is dependent on responding efficiently to relevant information while discarding irrelevant information. Little is known, however, about how neural pathways governing sensory-motor associations can rapidly switch to accomplish such flexibility. Here, we addressed this question by electrically microstimulating middle temporal (MT) neurons selective for both motion direction and binocular disparity in monkeys switching between direction and depth discrimination tasks.

Predicting the risk of developing diabetic retinopathy using deep learning.

Background: Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years.

Methods: We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting.

Author Correction: Detection of anaemia from retinal fundus images via deep learning.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Detection of anaemia from retinal fundus images via deep learning.

Owing to the invasiveness of diagnostic tests for anaemia and the costs associated with screening for it, the condition is often undetected. Here, we show that anaemia can be detected via machine-learning algorithms trained using retinal fundus images, study participant metadata (including race or ethnicity, age, sex and blood pressure) or the combination of both data types (images and study participant metadata). In a validation dataset of 11,388 study participants from the UK Biobank, the fundus-image-only, metadata-only and combined models predicted haemoglobin concentration (in g dl) with mean absolute error values of 0.

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training.

Real-Time Processing of Two-Photon Calcium Imaging Data Including Lateral Motion Artifact Correction.

Two-photon calcium imaging has been extensively used to record neural activity in the brain. It has been long used solely with analysis, but the recent efforts began to include closed-loop experiments. Closed-loop experiments pose new challenges because they require fast, real-time image processing without iterative parameter tuning.

Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies.

Brain-computer interfaces have seen an increase in popularity due to their potential for direct neuroprosthetic applications for amputees and disabled individuals. Supporting this promise, animals-including humans-can learn even arbitrary mapping between the activity of cortical neurons and movement of prosthetic devices [1-4]. However, the performance of neuroprosthetic device control has been nowhere near that of limb control in healthy individuals, presenting a dire need to improve the performance.

Whisker row deprivation affects the flow of sensory information through rat barrel cortex.

Unlabelled: Whisker trimming causes substantial reorganization of neuronal response properties in barrel cortex. However, little is known about experience-dependent rerouting of sensory processing following sensory deprivation. To address this, we performed in vivo intracellular recordings from layers 2/3 (L2/3), layer 4 (L4), layer 5 regular-spiking (L5RS), and L5 intrinsically bursting (L5IB) neurons and measured their multiwhisker receptive field at the level of spiking activity, membrane potential, and synaptic conductance before and after sensory deprivation.

A leaky-integrator model as a control mechanism underlying flexible decision making during task switching.

The ability to switch between tasks is critical for animals to behave according to context. Although the association between the prefrontal cortex and task switching has been well documented, the ultimate modulation of sensory-motor associations has yet to be determined. Here, we modeled the results of a previous study showing that task switching can be accomplished by communication from distinct populations of sensory neurons.

Apolipoprotein E, especially apolipoprotein E4, increases the oligomerization of amyloid β peptide.

Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder causing dementia. Massive deposition of amyloid β peptide (Aβ) as senile plaques in the brain is the pathological hallmark of AD, but oligomeric, soluble forms of Aβ have been implicated as the synaptotoxic component. The apolipoprotein E ε 4 (apoE ε4) allele is known to be a genetic risk factor for developing AD.