Coagulation indices and fibrinogen degradation products as predictive biomarkers for tumor-node-metastasis staging and metastasis in gastric cancer.

Background: Gastric cancer (GC) is a prevalent malignancy with a substantial health burden and high mortality rate, despite advances in prevention, early detection, and treatment. Compared with the global average, Asia, notably China, reports disproportionately high GC incidences. The disease often progresses asymptomatically in the early stages, leading to delayed diagnosis and compromised outcomes.

Learnable color space conversion and fusion for stain normalization in pathology images.

Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant challenges for computer-assisted diagnostic systems, leading to potential underdiagnosis or misdiagnosis, especially when stain differentiation introduces substantial heterogeneity across datasets from different sources. Traditional stain normalization methods, aimed at mitigating these issues, often require labor-intensive selection of appropriate templates, limiting their practicality and automation.

Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T.

Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer-weighted (APTw) images. Brain tumor MRI data of 40 scans were studied.

Tackling heterogeneity in medical federated learning via aligning vision transformers.

Federated learning enables training models on distributed, privacy-sensitive medical imaging data. However, data heterogeneity across participating institutions leads to reduced model performance and fairness issues, especially for underrepresented datasets. To address these challenges, we propose leveraging the multi-head attention mechanism in Vision Transformers to align the representations of heterogeneous data across clients.

Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

Background: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear.

Methods: The cohort comprised 312 consecutive patients with ICH.

Atrial lead perforation early after device implantation-a case report and literature review.

We report three patients with screw-in lead perforation in the right atrial free wall not long after device implantation. All the patients complained of intermittent stabbing chest pain associated with deep breathing during the implantation. The "dry" epicardial puncture was utilized to avoid hemopericardium during lead extraction in the first case.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability.

Age-related changes after intracerebral hemorrhage: a comparative proteomics analysis of perihematomal tissue.

The risk factors and causes of intracerebral hemorrhage (ICH) and the degree of functional recovery after ICH are distinct between young and elderly patients. The increasing incidence of ICH in young adults has become a concern; however, research on the molecules and pathways involved ICH in subjects of different ages is lacking. In this study, tandem mass tag (TMT)-based proteomics was utilized to examine the protein expression profiles of perihematomal tissue from young and aged mice 24 h after collagenase-induced ICH.

RandStainNA++: Enhance Random Stain Augmentation and Normalization Through Foreground and Background Differentiation.

The wide prevalence of staining variations in digital pathology presents a significant obstacle, often undermining the effectiveness of diagnosis and analysis. The current strategies to counteract this issue primarily revolve around Stain Normalization (SN) and Stain Augmentation (SA). Nonetheless, these methodologies come with inherent limitations.

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities.

Background: Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability.

Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training.

Stroke depression: a concept with clinical applicability.

Stroke is a common neurological condition and among the leading causes of death and disability worldwide. Depression is both a risk factor for and complication of stroke, and the two conditions may have a complex reciprocal relationship over time. However, the secondary effects of depression on stroke are often overlooked, resulting in increased morbidity and mortality.

TMT-based quantitative proteomics reveals the protective mechanism of tenuigenin after experimental intracerebral hemorrhage in mice.

Ethnopharmacological Relevance: Tenuigenin (TNG) is an extract obtained from Polygalae Radix. It possesses anti-inflammatory, antioxidant, and neuroprotective properties. However, the potential mechanism of TNG in intracerebral hemorrhage (ICH) has not been well studied.

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification.

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network.

SERPINA3: A novel inflammatory biomarker associated with cerebral small vessel disease burden in ischemic stroke.

Background And Objective: Inflammation has emerged as a prominent risk factor for cerebral small vessel disease (CSVD). However, the specific association between various inflammatory biomarkers and the development of CSVD remains unclear. Serine proteinase inhibitor A3 (SERPINA3), Matrix metalloproteinase-9 (MMP-9), Tissue inhibitor metalloproteinase-1 (TIMP-1), Monocyte Chemoattractant Protein-1 (MCP-1) are several inflammatory biomarkers that are potentially involved in the development of CSVD.

Multi-time point metabolomics reveals key metabolic features from the ultra-early stage of intracerebral hemorrhage in mice.

Despite decades of intensive research, there are still very limited options for the effective treatment of intracerebral hemorrhage (ICH). Recently, mounting evidence has indicated that the ultra-early stage (<3 h), serving as the primary phase of ICH, plays a pivotal role and may even surpass other stages in terms of its significance. Therefore, uncovering the metabolic alterations induced by ICH in the ultra-early stage is of crucial importance.

Development and validation of a copper-related gene prognostic signature in hepatocellular carcinoma.

Reliable biomarkers are in need to predict the prognosis of hepatocellular carcinoma (HCC). Whilst recent evidence has established the critical role of copper homeostasis in tumor growth and progression, no previous studies have dealt with the copper-related genes (CRGs) signature with prognostic potential in HCC. To develop and validate a CRGs prognostic signature for HCC, we retrospectively included 353 and 142 patients as the development and validation cohort, respectively.

Neutrophil percentage to albumin ratio is associated with stroke-associated pneumonia and poor outcome in patients with spontaneous intracerebral hemorrhage.

Background: The purpose of this study was to investigate the diagnostic performance of the neutrophil percentage-to-albumin ratio (NPAR) for predicting stroke-associated pneumonia (SAP) and functional outcome in patients with intracerebral hemorrhage (ICH).

Methods: We analyzed our prospective database of consecutive ICH patients who were admitted to the First Affiliated Hospital of Chongqing Medical University from January 2016 to September 2021. We included subjects with a baseline computed tomography available and a complete NPAR count performed within 6h of onset.

Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation.

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas.

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting.

Mine local homogeneous representation by interaction information clustering with unsupervised learning in histopathology images.

Background And Objective: The success of data-driven deep learning for histopathology images often depends on high-quality training sets and fine-grained annotations. However, as tumors are heterogeneous and annotations are expensive, unsupervised learning approaches are desirable to obtain full automation.

Methods: In this paper, an Interaction Information Clustering (IIC) method is proposed to extract locally homogeneous features in mutually exclusive clusters.

LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia.

Background: Cancer cachexia is a deadly wasting syndrome that accompanies various diseases (including ~ 50% of cancers). Clinical studies have established that cachexia is not a nutritional deficiency and is linked to expression of certain proteins (e.g.

Tumors evade immune cytotoxicity by altering the surface topology of NK cells.

The highly variable response rates to immunotherapies underscore our limited knowledge about how tumors can manipulate immune cells. Here the membrane topology of natural killer (NK) cells from patients with liver cancer showed that intratumoral NK cells have fewer membrane protrusions compared with liver NK cells outside tumors and with peripheral NK cells. Dysregulation of these protrusions prevented intratumoral NK cells from recognizing tumor cells, from forming lytic immunological synapses and from killing tumor cells.

Interactions between driver genes shape the signaling pathway landscape and direct hepatocellular carcinoma therapy.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies, whose initiation and development are driven by alterations in driver genes. In this study, we identified four driver genes (TP53, PTEN, CTNNB1, and KRAS) that show a high frequency of somatic mutations or copy number variations (CNVs) in patients with HCC. Four different spontaneous HCC mouse models were constructed to screen for changes in various kinase signaling pathways.