The increasing incidence of cardiovascular disease (CVD) is a significant global health concern, affecting millions of individuals each year. Accurate diagnosis of acute CVD poses a formidable challenge, as misdiagnosis can significantly decrease patient survival rates. Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs, but they can be influenced by various factors, such as age, sex, and renal function. Soluble ST2 (sST2) is a novel biomarker that is closely associated with different CVDs. Its low reference change value makes it suitable for continuous measurement, unaffected by age, kidney function, and other confounding factors, facilitating risk stratification of CVDs. Furthermore, the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value. This review aims to provide a comprehensive overview of sST2, focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

UNASSIGNED: Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required.
UNASSIGNED: The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms.
UNASSIGNED: NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC.
UNASSIGNED: NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.

Hepatocellular carcinoma (HCC) is a significant contributor to cancer-related deaths in the world. The development and progression of HCC are closely correlated with the abnormal regulation of non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Important biological pathways in cancer biology, such as cell proliferation, death, and metastasis, are impacted by these ncRNAs, which modulate gene expression. The abnormal expression of non-coding RNAs in HCC raises the possibility that they could be applied as new biomarkers for diagnosis, prognosis, and treatment targets. Furthermore, by controlling the expression of cancer-related genes, miRNAs can function as either tumor suppressors or oncogenes. On the other hand, lncRNAs play a role in the advancement of cancer by interacting with other molecules within the cell, which, in turn, affects processes such as chromatin remodeling, transcription, and post-transcriptional processes. The importance of ncRNA-driven regulatory systems in HCC is being highlighted by current research, which sheds light on tumor behavior and therapy response. This research highlights the great potential of ncRNAs to improve patient outcomes in this difficult disease landscape by augmenting the present methods of HCC care through the use of precision medicine approaches.

Neuroblastoma (NB) is the most common extracranial solid pediatric cancer, and is one of the leading causes of cancer-related deaths in children. Despite the current multi-modal treatment regimens, majority of patients with advanced-stage NBs develop therapeutic resistance and relapse, leading to poor disease outcomes. There is a large body of knowledge on pathophysiological role of small extracellular vesicles (EVs) in progression and metastasis of multiple cancer types, however, the importance of EVs in NB was until recently not well understood. Studies emerging in the last few years have demonstrated the involvement of EVs in various aspects of NB pathogenesis. In this review we summarize these recent findings and advances on the role EVs play in NB progression, such as tumor growth, metastasis and therapeutic resistance, that could be helpful for future investigations in NB EV research. We also discuss different strategies for therapeutic targeting of NB-EVs as well as utilization of NB-EVs as potential biomarkers.

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

Background Head and neck squamous cell carcinoma (HNSCC) is a prominent global cancer that manifests across diverse sites such as the oral cavity, oropharynx, and larynx. Human papillomavirus (HPV) infection and genetic alterations contribute to HNSCC development. Objective To investigate the complex role of breast carcinoma amplified sequence (BCAS3) in HNSCC pathogenesis. Methods We used multiple databases to analyze BCAS3 expression in HNSCC using The Cancer Genome Atlas-Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset and validated it in oral squamous cell carcinoma (OSCC) using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The BCAS3 gene and protein networks were analyzed to identify their functional pathways. Results The results revealed significant overexpression of BCAS3 was observed in HNSCC and OSCC tumors. Our study explores BCAS3\'s correlation with clinicopathological features and patient prognosis, suggesting its involvement in tumor aggressiveness. Notably, BCAS3 expression in HPV-positive and HPV-negative HNSCC samples emphasizes the intricate viral interactions. Kaplan-Meier plots demonstrate BCAS3\'s impact on patient survival. Furthermore, BCAS3\'s association between tumor immune infiltration and autophagy was uncovered. Conclusion Our study contributes to the understanding of BCAS3\'s role in HNSCC and suggests its potential as a therapeutic target and diagnostic marker for these malignancies.

Internal swelling reactions (ISRs) are among the most critical deterioration mechanisms affecting infrastructure\'s durability worldwide. While preventative measures for new structures have been extensively explored, effective protocols for diagnosing and prognosing ISR-affected structures, especially at their early stages, are still required. Therefore, through a comprehensive bibliometric analysis, this study focuses on exploring the evolution and current methods for assessing and forecasting ISR damage in concrete structures. For diagnosis, a shift from concrete petrography and non-destructive techniques (NDTs) towards more comprehensive methods (i.e., multi-level assessment) with the stiffness damage test (SDT) and damage rating index (DRI) is observed. Moreover, it identifies the valuable inputs from residual expansion and pore solution analysis as relevant parameters for prognosis. Based on these findings, a structured management framework is proposed aiming to refine the diagnosis and prognosis processes of ISR-affected infrastructure, ultimately contributing to improved long-term structural health and maintenance strategies.

UNASSIGNED: To analyze the significance of lung ultrasound score (LUS) combined with serum inflammatory indexes in different severities of severe pneumonia and its clinical value on prognosis.
UNASSIGNED: 100 patients with severe pneumonia treated in the Gansu Provincial Hospital from June 2017 to June 2021 were selected as the research objects. According to the acute physiology and chronic health (APACHE II) score, they were divided into a low-risk group (28 cases), a medium-risk group (39 cases) and a high-risk group (33 cases). The general clinical data of the patients (age, gender, smoking history, and underlying diseases) were collected, the lung ultrasound score (LUS) of the patients was measured, and the serum inflammatory indicators (IL-6, IL-10, TNF-α, CRP and NLR) levels; Pearson correlation analysis to evaluate the correlation between LUS score, serum inflammatory index levels and disease severity; receiver operating characteristic (ROC) curve analysis to evaluate the prognostic value of the combined diagnosis of LUS score and serum inflammatory index for the severity of severe pneumonia.
UNASSIGNED: With the increase in the severity of severe pneumonia, the LUS score and the level of inflammation in the body continued to increase, and LUS combined with serum inflammatory indexes could distinguish the severity of low-risk, medium-risk and high-risk severe pneumonia and had high diagnostic value. In addition, the combined diagnosis of LUS and serum inflammatory markers is also closely related to the prognosis of patients with severe pneumonia, which can distinguish the prognosis.
UNASSIGNED: LUS combined with serum inflammatory indicators (IL-6, IL-10, TNF-α, CRP and NLR) can differentiate the severity and prognosis of severe pneumonia, which may be a new direction for diagnosing severe pneumonia and guide early clinical intervention.

Bladder carcinoma is globally among the most prevalent cancers and is associated with a high mortality rate at advanced stages. Its detection relies on invasive diagnostic methods that are unpleasant for the patient. Non-invasive molecular biomarkers, such as miRNAs, could serve as alternatives for early detection and prognosis of this malignancy. We designed a computational approach that combines transcriptome profiling, survival analyses, and calculation of diagnostic power in order to isolate miRNA signatures with high diagnostic and prognostic utility. Our analysis of TCGA-BLCA data from 429 patients yielded one miRNA signature, consisting of five upregulated and three downregulated miRNAs with cumulative diagnostic power that outperforms current diagnostic methods. The same miRNAs have a strong prognostic significance since their expression is associated with the overall survival of bladder cancer patients. We evaluated the expression of this signature in 19 solid cancer types, supporting its unique diagnostic utility for bladder carcinoma. We provide computational evidence regarding the functional implications of this miRNA signature in cell cycle regulation, demonstrating its abundance in body fluids, including peripheral blood and urine. Our study characterized a novel miRNA signature with the potential to serve as a non-invasive method for bladder cancer diagnosis and prognosis.

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.