Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models.

Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task of performing localization with textual guidance is commonly referred to as phrase grounding.

Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring.

Background: Inaccurate blood pressure (BP) classification results in inappropriate treatment. We tested whether machine learning (ML), using routine clinical data, can serve as a reliable alternative to ambulatory BP monitoring (ABPM) in classifying BP status.

Methods: This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdańsk, and Birmingham, and a fourth independent evaluation cohort.

Compositionally Equivariant Representation Learning.

Deep learning models often need sufficient supervision (i.e., labelled data) in order to be trained effectively.

The role of noise in denoising models for anomaly detection in medical images.

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time.

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters.

Introduction: Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g.

Survey: Leakage and Privacy at Inference Time.

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models.

Small-molecule screen reveals pathways that regulate C4 secretion in stem cell-derived astrocytes.

In the brain, the complement system plays a crucial role in the immune response and in synaptic elimination during normal development and disease. Here, we sought to identify pathways that modulate the production of complement component 4 (C4), recently associated with an increased risk of schizophrenia. To design a disease-relevant assay, we first developed a rapid and robust 3D protocol capable of producing large numbers of astrocytes from pluripotent cells.

Anti-SOD1 Nanobodies That Stabilize Misfolded SOD1 Proteins Also Promote Neurite Outgrowth in Mutant SOD1 Human Neurons.

ALS-linked mutations induce aberrant conformations within the SOD1 protein that are thought to underlie the pathogenic mechanism of SOD1-mediated ALS. Although clinical trials are underway for gene silencing of , these approaches reduce both wild-type and mutated forms of SOD1. Here, we sought to develop anti-SOD1 nanobodies with selectivity for mutant and misfolded forms of human SOD1 over wild-type SOD1.

Amyotrophic Lateral Sclerosis-Associated Persistent Organic Pollutant -Chlordane Causes GABA-Independent Toxicity to Motor Neurons, Providing Evidence toward an Environmental Component of Sporadic Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease characterized by the death of upper and lower motor neurons. While causative genes have been identified, 90% of ALS cases are not inherited and are hypothesized to result from the accumulation of genetic and environmental risk factors. While no specific causative environmental toxin has been identified, previous work has indicated that the presence of the organochlorine pesticide chlordane in the blood is highly correlated with ALS incidence.

Evaluating the Impact on Clinical Task Efficiency of a Natural Language Processing Algorithm for Searching Medical Documents: Prospective Crossover Study.

Background: Information retrieval (IR) from the free text within electronic health records (EHRs) is time consuming and complex. We hypothesize that natural language processing (NLP)-enhanced search functionality for EHRs can make clinical workflows more efficient and reduce cognitive load for clinicians.

Objective: This study aimed to evaluate the efficacy of 3 levels of search functionality (no search, string search, and NLP-enhanced search) in supporting IR for clinical users from the free text of EHR documents in a simulated clinical environment.

Causal machine learning for healthcare and precision medicine.

Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g.

Learning disentangled representations in the imaging domain.

Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare.

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure.

Resolving cell state in iPSC-derived human neural samples with multiplexed fluorescence imaging.

Human induced pluripotent stem cell-derived (iPSC) neural cultures offer clinically relevant models of human diseases, including Amyotrophic Lateral Sclerosis, Alzheimer's, and Autism Spectrum Disorder. In situ characterization of the spatial-temporal evolution of cell state in 3D culture and subsequent 2D dissociated culture models based on protein expression levels and localizations is essential to understanding neural cell differentiation, disease state phenotypes, and sample-to-sample variability. Here, we apply PRobe-based Imaging for Sequential Multiplexing (PRISM) to facilitate multiplexed imaging with facile, rapid exchange of imaging probes to analyze iPSC-derived cortical and motor neuron cultures that are relevant to psychiatric and neurodegenerative disease models, using over ten protein targets.

Free energy calculations of ALS-causing SOD1 mutants reveal common perturbations to stability and dynamics along the maturation pathway.

With over 150 heritable mutations identified as disease-causative, superoxide dismutase 1 (SOD1) has been a main target of amyotrophic lateral sclerosis (ALS) research and therapeutic efforts. However, recent evidence has suggested that neither loss of function nor protein aggregation is responsible for promoting neurotoxicity. Furthermore, there is no clear pattern to the nature or the location of these mutations that could suggest a molecular mechanism behind SOD1-linked ALS.

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated.

Aggregated SOD1 causes selective death of cultured human motor neurons.

Most human neurodegenerative diseases share a phenotype of neuronal protein aggregation. In Amyotrophic Lateral Sclerosis (ALS), the abundant protein superoxide dismutase (SOD1) or the TAR-DNA binding protein TDP-43 can aggregate in motor neurons. Recently, numerous studies have highlighted the ability of aggregates to spread from neuron to neuron in a prion-like fashion.

Material microenvironmental properties couple to induce distinct transcriptional programs in mammalian stem cells.

Variations in a multitude of material microenvironmental properties have been observed across tissues in vivo, and these have profound effects on cell phenotype. Phenomenological experiments have suggested that certain of these features of the physical microenvironment, such as stiffness, could sensitize cells to other features; meanwhile, mechanistic studies have detailed a number of biophysical mechanisms for this sensing. However, the broad molecular consequences of these potentially complex and nonlinear interactions bridging from biophysical sensing to phenotype have not been systematically characterized, limiting the overall understanding and rational deployment of these biophysical cues.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement.

Using intracellular markers to identify a novel set of surface markers for live cell purification from a heterogeneous hIPSC culture.

Human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) can provide sources for midbrain dopaminergic (mDA) neural progenitors (NPCs) for cell therapy to treat Parkinson's disease (PD) patients. However, the well-known line-to-cell line variability in the differentiation capacity of individual cell lines needs to be improved for the success of this therapy. To address this issue, we sought to identify mDA NPC specific cell surface markers for fluorescence activated cell sorting (FACS).

Developing a Dissociative Nanocontainer for Peptide Drug Delivery.

The potency, selectivity, and decreased side effects of bioactive peptides have propelled these agents to the forefront of pharmacological research. Peptides are especially promising for the treatment of neurological disorders and pain. However, delivery of peptide therapeutics often requires invasive techniques, which is a major obstacle to their widespread application.

Tailored delivery of analgesic ziconotide across a blood brain barrier model using viral nanocontainers.

The blood brain barrier (BBB) is often an insurmountable obstacle for a large number of candidate drugs, including peptides, antibiotics, and chemotherapeutic agents. Devising an adroit delivery method to cross the BBB is essential to unlocking widespread application of peptide therapeutics. Presented here is an engineered nanocontainer for delivering peptidic drugs across the BBB encapsulating the analgesic marine snail peptide ziconotide (Prialt®).

Lipophilic toxins in cultivated mussels (Mytilus galloprovincialis) from Baja California, Mexico.

Here, we report different lipophilic toxins (LTs) detected by LC-MS/MS in Mediterranean mussels (Mytilus galloprovincialis) collected through 2012 in Todos Santos Bay, northwest Baja California, Mexico. The concentration of okadaic acid (OA), dinophysistoxin 2 (DTX2), and pectenotoxin 2 (PTX2) reached 500 μg kg(-1) during July and increased to 1647 μg kg(-1) in October. These toxins were associated with the presence of Dinophysis fortii and Dinophysis acuminata and a strong stratification of the water column.