Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis.

A comprehensive survey on deep active learning in medical image analysis.

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation.

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive.

Structural diversity in the membrane-bound hIAPP dimer correlated with distinct membrane disruption mechanisms.

Amyloid deposits of the human islet amyloid polypeptide (hIAPP) have been identified in 90% of patients with type II diabetes. Cellular membranes accelerate the hIAPP fibrillation, and the integrity of membranes is also disrupted at the same time, leading to the apoptosis of β cells in pancreas. The molecular mechanism of hIAPP-induced membrane disruption, especially during the initial membrane disruption stage, has not been well understood yet.

MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases.

Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP).

Improving brain tumor segmentation with anatomical prior-informed pre-training.

Introduction: Precise delineation of glioblastoma in multi-parameter magnetic resonance images is pivotal for neurosurgery and subsequent treatment monitoring. Transformer models have shown promise in brain tumor segmentation, but their efficacy heavily depends on a substantial amount of annotated data. To address the scarcity of annotated data and improve model robustness, self-supervised learning methods using masked autoencoders have been devised.

A Structure-Aware Framework of Unsupervised Cross-Modality Domain Adaptation via Frequency and Spatial Knowledge Distillation.

Unsupervised domain adaptation (UDA) aims to train a model on a labeled source domain and adapt it to an unlabeled target domain. In medical image segmentation field, most existing UDA methods rely on adversarial learning to address the domain gap between different image modalities. However, this process is complicated and inefficient.

Molecular epidemiology and phylogenetic analyses of human adenovirus in pediatric patients with acute respiratory infections from Hangzhou during COVID-19 pandemic.

Background: Acute Respiratory Infections (ARIs) are a major cause of morbidity and mortality worldwide. Human Adenovirus (HAdV), responsible for 5%-10% of children's ARIs, is one of the most prevalent pathogens. Our study aimed to analyze the epidemiology and phylogenesis of HAdV in pediatric patients with ARIs in Hangzhou during the COVID-19 pandemic.

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization.

Label correlation transformer for automated chest X-ray diagnosis with reliable interpretability.

Computer-aided diagnosis of chest X-ray (CXR) images can help reduce the huge workload of radiologists and avoid the inter-observer variability in large-scale early disease screening. Recently, most state-of-the-art studies employ deep learning techniques to address this problem through multi-label classification. However, existing methods still suffer from low classification accuracy and poor interpretability for each diagnostic task.

Multi-task learning-based histologic subtype classification of non-small cell lung cancer.

Purpose: In clinical applications, accurate histologic subtype classification of lung cancer is important for determining appropriate treatment plans. The purpose of this paper is to evaluate the role of multi-task learning in the classification of adenocarcinoma and squamous cell carcinoma.

Material And Methods: In this paper, we propose a novel multi-task learning model for histologic subtype classification of non-small cell lung cancer based on computed tomography (CT) images.

Distinct binding interactions trigger opposite conformational modulations on pathogenic and wildtype Huntingtin exon 1 proteins.

Abnormal elongation of the polyglutamine tract transforms exon 1 of the Huntingtin protein (Htt-exon-1) from wildtype to pathogenic form, and causes Huntington's disease. As an intrinsically disordered protein, the structural ensemble of Htt-exon-1 is highly heterogeneous and the detailed conformation of toxic species is still under debate. Ispinesib, a potential small-molecule drug, has been identified to selectively link the pathogenic Htt-exon-1 into the autophagosome to degrade, thus opening an innovative therapeutic direction.

Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis.

Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome. In recent years, computer-automated analysis techniques for histopathological images have been urgently required in clinical practice, and deep learning methods represented by convolutional neural networks have gradually become the mainstream in the field of digital pathology. However, obtaining large numbers of fine-grained annotated data in this field is a very expensive and difficult task, which hinders the further development of traditional supervised algorithms based on large numbers of annotated data.

Alternative splicing patterns reveal prognostic indicator in muscle-invasive bladder cancer.

Background: Bladder cancer is one of the most lethal malignancy in urological system, and 20-25% of bladder cancer patients are muscle invasive with unfavorable prognosis. However, the role of alternative splicing (AS) in muscle-invasive bladder cancer (MIBC) remains to be elucidated.

Methods: Percent spliced in (PSI) data obtained from the Cancer Genome Atlas (TCGA) SpliceSeq database (n = 394) were utilized to evaluate the AS events in MIBC.

Dynamic boundary-insensitive loss for magnetic resonance medical image segmentation.

Purpose: A deep learning method has achieved great success in MR medical image segmentation. One challenge in applying deep learning segmentation models to clinical practice is their poor generalization mainly due to limited labeled training samples, inter-site heterogeneity of different datasets, and ambiguous boundary definition, etc. The objective of this work is to develop a dynamic boundary-insensitive (DBI) loss to address this poor generalization caused by an uncertain boundary.

Genomic Analyses of Metaplastic or Sarcomatoid Carcinomas From Different Organs Revealed Frequent Mutations in KMT2D.

Metaplastic or sarcomatoid carcinomas (MSCs) are rare epithelial malignancies with heterologous histological differentiation that can occur in different organs. The objective of the current study was to identify novel somatically mutated genes in MSCs from different organs. Whole-exome sequencing was performed in 16 paired MSCs originating from the breast ( = 10), esophagus ( = 3), lung ( = 2), and kidney ( = 1).

Multiemissive Room-Temperature Phosphorescent Carbon Dots@ZnAlO Composites by Inorganic Defect Triplet-State Energy Transfer.

Room-temperature phosphorescence (RTP) with carbon dots (CDs) can be exploited further if the mechanism of trap-state-mediated triplet-state energy transfer is understood and controlled. Herein, we developed an in situ calcination method for the preparation of a CDs@ZnAlO composite material that exhibits unique UV and visible light-excitable ultra-broad-band RTP. The ZnAlO matrix can protect the triplet emissions of CDs by the confinement effect and spin-orbit coupling.

Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.

Methods: In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database.

Clinically significant genomic alterations in the Chinese and Western patients with intrahepatic cholangiocarcinoma.

Background: The goal of this study is to disclose the clinically significant genomic alterations in the Chinese and Western patients with intrahepatic cholangiocarcinoma.

Methods: A total of 86 Chinese patients were enrolled in this study. A panel of 579 pan-cancer genes was sequenced for the qualified samples from these patients.

Vertebrae Labeling via End-to-End Integral Regression Localization and Multi-Label Classification Network.

Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use heatmaps to locate the vertebrae's centroid. However, the process of obtaining vertebrae's centroid coordinates using heatmaps is non-differentiable, so it is impossible to train the network to label the vertebrae directly.

SNX29, a new susceptibility gene shared with major mental disorders in Han Chinese population.

Objectives: Environmental and genetic factors play important roles in the development of schizophrenia (SCZ), bipolar disorder (BPD) or major depressive disorder (MDD). Some risk loci are identified with shared genetic effects on major psychiatric disorders. To investigate whether gene played a significant role in these psychiatric disorders in the Han Chinese population.