Image-level supervision and self-training for transformer-based cross-modality tumor segmentation.

Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This issue is particularly problematic due to the limited availability of annotated data, both in the target as well as the source modality, making it difficult to deploy these models on a larger scale. To overcome these challenges, we propose a new semi-supervised training strategy called MoDATTS.

A foundation model for clinical-grade computational pathology and rare cancers detection.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date.

A glycomic workflow for LC-MS/MS analysis of urine glycosaminoglycan biomarkers in mucopolysaccharidoses.

In recent years, several rational designed therapies have been developed for treatment of mucopolysaccharidoses (MPS), a group of inherited metabolic disorders in which glycosaminoglycans (GAGs) are accumulated in various tissues and organs. Thus, improved disease-specific biomarkers for diagnosis and monitoring treatment efficacy are of paramount importance. Specific non-reducing end GAG structures (GAG-NREs) have become promising biomarkers for MPS, as the compositions of the GAG-NREs depend on the nature of the lysosomal enzyme deficiency, thereby creating a specific pattern for each subgroup.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Subtyping of cardiac amyloidosis by mass spectrometry-based proteomics of endomyocardial biopsies.

Background: Cardiac amyloidosis is a severe condition leading to restrictive cardiomyopathy and heart failure. Mass spectrometry-based methods for cardiac amyloid subtyping have become important diagnostic tools but are currently used only in a few reference laboratories. Such methods include laser-capture microdissection to ensure the specific analysis of amyloid deposits.

Towards annotation-efficient segmentation via image-to-image translation.

An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labeling every slice of volumetric scans for every patient, which becomes prohibitive at the scale of datasets required to train neural networks to optimal performance. To address this, we propose a novel semi-supervised framework that allows training any segmentation (encoder-decoder) model using only information readily available in radiological data, namely the presence of a tumor in the image, in addition to a few annotated images.

Expansion of nickel binding- and histidine-rich proteins during gastric adaptation of Helicobacter species.

Acquisition and homeostasis of essential metals during host colonization by bacterial pathogens rely on metal uptake, trafficking, and storage proteins. How these factors have evolved within bacterial pathogens is poorly defined. Urease, a nickel enzyme, is essential for Helicobacter pylori to colonize the acidic stomach.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution.

Dataset for suppressors of amyloid-β toxicity and their functions in recombinant protein production in yeast.

The production of recombinant proteins at high levels often induces stress-related phenotypes by protein misfolding or aggregation. These are similar to those of the yeast Alzheimer's disease (AD) model in which amyloid-β peptides (Aβ42) were accumulated [1], [2]. We have previously identified suppressors of Aβ42 cytotoxicity via the genome-wide synthetic genetic array (SGA) [3] and here we use them as metabolic engineering targets to evaluate their potentiality on recombinant protein production in yeast .

Combining dense elements with attention mechanisms for 3D radiotherapy dose prediction on head and neck cancers.

Purpose: External radiation therapy planning is a highly complex and tedious process as it involves treating large target volumes, prescribing several levels of doses, as well as avoiding irradiating critical structures such as organs at risk close to the tumor target. This requires highly trained dosimetrists and physicists to generate a personalized plan and adapt it as treatment evolves, thus affecting the overall tumor control and patient outcomes. Our aim is to achieve accurate dose predictions for head and neck (H&N) cancer patients on a challenging in-house dataset that reflects realistic variability and to further compare and validate the method on a public dataset.

Optimized sample preparation and data analysis for TMT proteomic analysis of cerebrospinal fluid applied to the identification of Alzheimer's disease biomarkers.

Background: Cerebrospinal fluid (CSF) is an important biofluid for biomarkers of neurodegenerative diseases such as Alzheimer's disease (AD). By employing tandem mass tag (TMT) proteomics, thousands of proteins can be quantified simultaneously in large cohorts, making it a powerful tool for biomarker discovery. However, TMT proteomics in CSF is associated with analytical challenges regarding sample preparation and data processing.

Suppressors of amyloid-β toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism.

High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-β peptides (Aβ42), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of Aβ42 cytotoxicity.

Age and sex effects across the blood proteome after ionizing radiation exposure can bias biomarker screening and risk assessment.

Molecular biomarkers of ionizing radiation (IR) exposure are a promising new tool in various disciplines: they can give necessary information for adaptive treatment planning in cancer radiotherapy, enable risk projection for radiation-induced survivorship diseases, or facilitate triage and intervention in radiation hazard events. However, radiation biomarker discovery has not yet resolved the most basic features of personalized medicine: age and sex. To overcome this critical bias in biomarker identification, we quantitated age and sex effects and assessed their relevance in the radiation response across the blood proteome.

Cytokine responses to LPS in reprogrammed monocytes are associated with the transcription factor PU.1.

Myeloid-derived suppressor cells (MDSCs) are functionally immunosuppressive cells that arise and expand during extensive inflammatory conditions by increased hematopoietic output or reprogramming of immune cells. In sepsis, an increase of circulating MDSCs is associated with adverse outcomes, but unique traits that can be used to identify increased activity of MDSCs are lacking. By using endotoxin tolerance as a model of sepsis-induced monocytic MDSCs (M-MDSC-like cells), this study aims to identify the mediator and transcriptional regulator profile associated with M-MDSC activity.

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep learning offers an advantage over traditional radiomics for medical image processing by learning salient features from training data originating from multiple datasets. However, while their large capacity allows to combine high-level medical imaging data for outcome prediction, they lack generalization to be used across institutions.

Changes in the Proteome in the Development of Chronic Human Papillomavirus Infection-A Prospective Study in HIV Positive and HIV Negative Rwandan Women.

Background: Effects on the proteome when a high risk (HR)-HPV infection occurs, when it is cleared and when it becomes chronic were investigated. Moreover, biomarker panels that could identify cervical risk lesions were assessed.

Methods: Cytology, HPV screening and proteomics were performed on cervical samples from Rwandan HIV+ and HIV- women at baseline, at 9 months, at 18 months and at 24 months.

Proteoglycan profiling of human, rat and mouse insulin-secreting cells.

Proteoglycans (PGs) are proteins with glycosaminoglycan (GAG) chains, such as chondroitin sulfate (CS) or heparan sulfate (HS), attached to serine residues. We have earlier shown that prohormones can carry CS, constituting a novel class of PGs. The mapping of GAG modifications of proteins in endocrine cells may thus assist us in delineating possible roles of PGs in endocrine cellular physiology.

Domain Mapping of Chondroitin/Dermatan Sulfate Glycosaminoglycans Enables Structural Characterization of Proteoglycans.

Of all posttranslational modifications known, glycosaminoglycans (GAGs) remain one of the most challenging to study, and despite the recent years of advancement in MS technologies and bioinformatics, detailed knowledge about the complete structures of GAGs as part of proteoglycans (PGs) is limited. To address this issue, we have developed a protocol to study PG-derived GAGs. Chondroitin/dermatan sulfate conjugates from the rat insulinoma cell line, INS-1832/13, known to produce primarily the PG chromogranin-A, were enriched by anion-exchange chromatography after pronase digestion.

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast.

In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities.

Telomeric double-strand DNA-binding proteins DTN-1 and DTN-2 ensure germline immortality in .

Telomeres are nucleoprotein complexes at the ends of chromosomes and are indispensable for the protection and lengthening of terminal DNA. Despite the evolutionarily conserved roles of telomeres, the telomeric double-strand DNA (dsDNA)-binding proteins have evolved rapidly. Here, we identified double-strand telomeric DNA-binding proteins (DTN-1 and DTN-2) in as non-canonical telomeric dsDNA-binding proteins.

The BRCA2-MEILB2-BRME1 complex governs meiotic recombination and impairs the mitotic BRCA2-RAD51 function in cancer cells.

Breast cancer susceptibility gene II (BRCA2) is central in homologous recombination (HR). In meiosis, BRCA2 binds to MEILB2 to localize to DNA double-strand breaks (DSBs). Here, we identify BRCA2 and MEILB2-associating protein 1 (BRME1), which functions as a stabilizer of MEILB2 by binding to an α-helical N-terminus of MEILB2 and preventing MEILB2 self-association.

Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast.

Cells maintain reserves in their metabolic and translational capacities as a strategy to quickly respond to changing environments. Here we quantify these reserves by stepwise reducing nitrogen availability in yeast steady-state chemostat cultures, imposing severe restrictions on total cellular protein and transcript content. Combining multi-omics analysis with metabolic modeling, we find that seven metabolic superpathways maintain >50% metabolic capacity in reserve, with glucose metabolism maintaining >80% reserve capacity.

Absolute yeast mitochondrial proteome quantification reveals trade-off between biosynthesis and energy generation during diauxic shift.

constitutes a popular eukaryal model for research on mitochondrial physiology. Being Crabtree-positive, this yeast has evolved the ability to ferment glucose to ethanol and respire ethanol once glucose is consumed. Its transition phase from fermentative to respiratory metabolism, known as the diauxic shift, is reflected by dramatic rearrangements of mitochondrial function and structure.

Glycosaminoglycan Domain Mapping of Cellular Chondroitin/Dermatan Sulfates.

Glycosaminoglycans (GAGs) are polysaccharides produced by most mammalian cells and involved in a variety of biological processes. However, due to the size and complexity of GAGs, detailed knowledge about the structure and expression of GAGs by cells, the glycosaminoglycome, is lacking. Here we report a straightforward and versatile approach for structural domain mapping of complex mixtures of GAGs, GAGDoMa.

Lipid droplet-associated kinase STK25 regulates peroxisomal activity and metabolic stress response in steatotic liver.

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are emerging as leading causes of liver disease worldwide and have been recognized as one of the major unmet medical needs of the 21st century. Our recent translational studies in mouse models, human cell lines, and well-characterized patient cohorts have identified serine/threonine kinase (STK)25 as a protein that coats intrahepatocellular lipid droplets (LDs) and critically regulates liver lipid homeostasis and progression of NAFLD/NASH. Here, we studied the mechanism-of-action of STK25 in steatotic liver by relative quantification of the hepatic LD-associated phosphoproteome from high-fat diet-fed knockout mice compared with their wild-type littermates.