Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT.

Developing a clinical AI model necessitates a significant amount of highly curated and carefully annotated dataset by multiple medical experts, which results in increased development time and costs. Self-supervised learning (SSL) is a method that enables AI models to leverage unlabelled data to acquire domain-specific background knowledge that can enhance their performance on various downstream tasks. In this work, we introduce CypherViT, a cluster-based histo-pathology phenotype representation learning by self-supervised multi-class-token hierarchical Vision Transformer (ViT). CypherViT is a novel backbone that can be integrated into a SSL pipeline, accommodating both coarse and fine-grained feature learning for histopathological images via a hierarchical feature agglomerative attention module with multiple classification (cls) tokens in ViT. Our qualitative analysis showcases that our approach successfully learns semantically meaningful regions of interest that align with morphological phenotypes. To validate the model, we utilize the DINO self-supervised learning (SSL) framework to train CypherViT on a substantial dataset of unlabeled breast cancer histopathological images. This trained model proves to be a generalizable and robust feature extractor for colorectal cancer images. Notably, our model demonstrates promising performance in patch-level tissue phenotyping tasks across four public datasets. The results from our quantitative experiments highlight significant advantages over existing state-of-the-art SSL models and traditional transfer learning methods, such as those relying on ImageNet pre-training.

A concise review of the regulatory, diagnostic, and prognostic implications of HOXB-AS3 in tumors.

Recent studies have reported that HOXB-AS3 (HOXB Cluster Antisense RNA 3) is an intriguing molecule with dual functionality as a long noncoding RNA (lncRNA) and putative coding peptide in tumorigenesis and progression. The significant expression alterations of HOXB-AS3 were detected in diverse cancer types and closely correlated with clinical stage and patient survival. Furthermore, HOXB-AS3 was involved in a spectrum of biological processes in solid tumors and hematological malignancies, such as stemness, lipid metabolism, migration, invasion, and tumor growth. This review comprehensively analyzes its clinical relevance for diagnosis and prognosis across human tumors and summarizes its functional role and regulatory mechanisms in different malignant tumors, including liver cancer, acute myeloid leukemia, ovarian cancer, lung cancer, endometrial carcinoma, colon cancer, and oral squamous cell carcinoma. Overall, HOXB-AS3 emerges as a promising biomarker and novel therapeutic target in multiple human tumors.

HOXC Cluster Antisense RNA 3, a Novel Long Non-Coding RNA as an Oncological Biomarker and Therapeutic Target in Human Malignancies.

HOXC cluster antisense RNA 3 (HOXC-AS3) is a novel long noncoding RNA (lncRNA) that exhibits aberrant expression patterns in various cancer types. Its expression is closely related to clinicopathological features, demonstrating significant clinical relevance across multiple tumors. And HOXC-AS3 plays multifaceted roles in tumor progression, impacting cell proliferation, apoptosis, migration, invasion, epithelial-mesenchymal transition (EMT), autophagy, senescence, tumor growth, and metastasis. In this review, we summarized and comprehensively analyzed the expression and clinical significance of HOXC-AS3 as a diagnostic and prognostic biomarker for malignancies. Additionally, we presented an in-depth update on HOXC-AS3's functions and regulatory mechanisms in cancer pathogenesis. This narrative review underscores the importance of HOXC-AS3 as a promising lncRNA candidate in cancer research and its potential as a predictive biomarker and therapeutic target in clinical applications.

Hotspots, trends, and advice: a 10-year visualization-based analysis of painting therapy from a scientometric perspective.

Purpose: Research on painting therapy is available worldwide and painting therapy is widely applied as a psychological therapy in different fields with diverse clients. As an evidence-based psychotherapy, previous studies have revealed that painting therapy has favorable therapeutic effects. However, limited studies on painting therapy used universal data to assemble in-depth evidence to propose a better recommendation on it for the future use. Large-scale retrospective studies that used bibliometric methodology are lacking. Therefore, this study presented a broad view of painting therapy and provided an intensively analytical insight into the structure of knowledge regarding painting therapy employing bibliometric analysis of articles. CiteSpace software was used to evaluate scientific research on painting therapy globally published from January 2011 to July 2022. Methods: Publications related to painting therapy from 2011 to 2022 were searched using the Web of Science database. This study employed bibliometric techniques to perform co-citation analysis of authors, visualize collaborations between countries/regions as network maps, and analyze keywords and subjects relevant to painting therapy by using CiteSpace software. Results: In total, 871 articles met the inclusion criteria. We found that the number of painting therapy publications generally trended incrementally. The United States and United Kingdom made the most contributions to painting therapy research and had the greatest impact on the practical application in other countries. Arts in Psychotherapy and Frontiers in Psychology occupied key publishing positions in this research field. The application groups were mainly children, adolescents, and females, and Western countries paid high attention to painting therapy. The main areas of application of painting therapy were Alzheimer's disease and other psychosomatic disease fields. Identified research priorities for painting therapy were emotion regulation and mood disorder treatment, personality disorder treatment, personal self-esteem enhancement, and medical humanistic care. Three keywords, "depression," "women," and "recovery," had the strongest citation bursts, which emphasized the research trends. Conclusion: The general trend for painting therapy research is positive. Our findings provide useful information for researchers on painting therapy to determine new directions in relate to popular issues, collaborators, and research frontiers. Painting therapy holds a promising future, and further studies could explore the clinical implications of this therapy in terms of mechanisms and criteria for assessing efficacy.

Role and therapeutic target of P2X2/3 receptors in visceral pain.

Visceral pain (VP) is caused by internal organ disease. VP is involved in nerve conduction and related signaling molecules, but its specific pathogenesis has not yet been fully elucidated. Currently, there are no effective methods for treating VP. The role of P2X2/3 in VP has progressed. After visceral organs are subjected to noxious stimulation, cells release ATP, activate P2X2/3, enhance the sensitivity of peripheral receptors and the plasticity of neurons, enhance sensory information transmission, sensitize the central nervous system, and play an important role in the development of VP. However, antagonists possess the pharmacological effect of relieving pain. Therefore, in this review, we summarize the biological functions of P2X2/3 and discuss the intrinsic link between P2X2/3 and VP. Moreover, we focus on the pharmacological effects of P2X2/3 antagonists on VP therapy and provide a theoretical basis for its targeted therapy.

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy.

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aß and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Selective synthetic augmentation with HistoGAN for improved histopathology image classification.

Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.

Efficacy and safety of Shenfu injection for patients with return of spontaneous circulation after sudden cardiac arrest: Protocol for a systematic review and meta-analysis.

BACKGROUND: Sudden cardiac arrest (SCA) is one of the most common critical illnesses encountered in clinical practice. Shenfu injection (SFI) has received extensive attention as an alternative therapy that can effectively maintain the autonomic circulation function after cardiopulmonary resuscitation. However, the mechanism of SFI is not yet fully understood. In addition, there has been no systematic review or meta-analysis of SFI in the treatment of patients with return of spontaneous circulation after SCA. Herein, we describe the protocol of a proposed study based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines that aims to systematically evaluate the efficacy and safety of SFI in patients with return of spontaneous circulation after SCA. METHODS: Two researchers will search 9 electronic databases (PubMed, Medline, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Chinese VIP Information, Wanfang, and Chinese Biomedical Database) to identify all studies that meet the inclusion criteria and were published before July 2018. After information extraction and methodological quality evaluation, we will use Stata 13.0 software (STATA Corporation, College Station, TX, USA) to synthesize the data. The primary outcomes will be the survival rate and Glasgow Coma Scale. RESULTS: The data synthesis results will objectively illustrate the efficacy and safety of SFI in patients with return of spontaneous circulation after SCA. CONCLUSION: The findings will provide a reference for the use of SFI in the treatment of patients with return of spontaneous circulation after SCA. REGISTRATION: PROSPERO (registration number: CRD42018104230).