Trends and hotspots of energy-based imaging in thoracic disease: a bibliometric analysis.

OBJECTIVE: To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention. METHODS: The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords. RESULTS: A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the "European Radiology" (181 articles, h-index = 46), followed by the "European Journal of Radiology" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest. CONCLUSION: Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future. CRITICAL RELEVANCE STATEMENT: This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice. KEY POINTS: Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

RNA modification gene WDR4 facilitates tumor progression and immunotherapy resistance in breast cancer.

INTRODUCTION: Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking. OBJECTIVES: This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients. METHODS: Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining. RESULTS: This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification. CONCLUSION: Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Effect of calcium sources on enzyme-induced carbonate precipitation to solidify desert aeolian sand.

Enzyme-induced carbonate precipitation (EICP) is a promising technique for soil reinforcement. To select a suitable calcium source and a suitable solution amount for aeolian sand stabilization using EICP, specimens treated with different solution amounts (1.5, 2, 2.5, 3, and 3.5 L/m2). Surface strength, crust thickness, calcium carbonate content (CCC) and water vapor adsorption tests were performed to evaluate the effect of two calcium sources (calcium acetate and calcium chloride) on aeolian sand solidification. The plant suitability of solidified sand was investigated by the sea buckthorn growth test. The suitable calcium source was then used for the laboratory wind tunnel test and the field test to examine the erosion resistance of solidified sand. The results demonstrated that Ca(CH3COO)2-treated specimens exhibited higher strength than CaCl2-treated specimens at the same EICP solution amount, and the water vapor equilibrium adsorption mass of Ca(CH3COO)2-treated specimens was less, indicating that Ca(CH3COO)2-solidified sand was more effective and had better long-term stability. In addition, plants grown in Ca(CH3COO)2-treated sand had greater seedling emergence percentage and higher average height, which indicated that calcium acetate is a more suitable calcium source for EICP treatment. Furthermore, the surface strength and crust thickness of solidified sand increased with increasing the solution amount. For sand treated with 3 L/m2 of solution, the excessive strength and thickness of the crust made plants growth difficult, and the performance of sand treated with more than 2 L/m2 of solution significantly improved. Thus, the solution amount of 2-3 L/m2 is suggested for engineering applications. The sand solidified using EICP in the field could effectively mitigate wind erosion and facilitate the growth of native plants. Therefore, EICP can be combined with vegetative method to achieve long-term wind erosion control in the future.

Biomineralization for Reducing and Controlling Sand-Dust Storms.

The sand-dust weather and sand-dust storms have become a serious environmental disaster worldwide. It is an important challenge to develop technologies for desert sand solidification in order to prevent and control sand-dust weather. The biomineralization technology for solidifying desert sands has been a novel method for reinforced soils in recent years. The biomineralization solidification sand field tests are completed at the Wuma Highway solidification section in the Tengger Desert. The superiority of the biomineralization for solidifying sands is verified by measuring the water storage capacity of different reinforcement zones including bare sand zone, plant zone, biomineralization solidifying sand zone, and biomineralization combined plant solidifying sand zone. Simultaneously, the molecular dynamics calculation analysis is used to verify the role of biomineralization solidifying sands in preventing sand-dust storms. All results demonstrate that the biomineralization solidification sand method is effective for controlling and preventing sandstorm disasters.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. METHODS: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features. RESULTS: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. CONCLUSION: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

circLIFR-007 reduces liver metastasis via promoting hnRNPA1 nuclear export and YAP phosphorylation in breast cancer.

Cancer metastasis is the major cause of death in patients with breast cancer (BC). The liver is a common site of breast cancer metastasis, and the 5-year survival rate of patients with breast cancer liver metastases (BCLMs) is only about 8.5 %. CircRNAs are involved in a variety of cancer-related pathological behaviors, and their unique structure and resistance to RNA degradation enable them to serve as ideal diagnostic biomarkers and therapeutic targets. Therefore, it is important to investigate the role and molecular mechanism of circRNAs in cancer metastasis. CircLIFR-007 was identified as a critical circular RNA in BC metastasis by circRNAs microarray and qRT-PCR experiment. Cell function assays were performed to explore the effect of circLIFR-007 in breast cancer cells. Experiments in vivo validated the function of circLIFR-007. Several molecular assays were performed to investigate the underlying mechanisms. We found that circLIFR-007 acted as a negative controller in breast cancer liver metastasis. CircLIFR-007 upregulates the phosphorylation level of YAP by exporting hnRNPA1 to promote the combination between hnRNPA1 and YAP in the cytoplasm. Overexpression of circLIFR-007 suppressed the expression of liver metastasis-related proteins, SREBF1 and SNAI1, which were regulated by transcription factor YAP. Functionally, circLIFR-007 inhibits the proliferation and metastasis of breast cancer cells both in vivo and in vitro.

Identifying New Subtypes of Multiple System Atrophy Using Cluster Analysis.

Background: Multiple system atrophy (MSA) is a disease with diverse symptoms and the commonly used classifications, MSA-P and MSA-C, do not cover all the different symptoms seen in MSA patients. Additionally, these classifications do not provide information about how the disease progresses over time or the expected outcome for patients. Objective: To explore clinical subtypes of MSA with a natural disease course through a data-driven approach to assist in the diagnosis and treatment of MSA. Methods: We followed 122 cases of MSA collected from 3 hospitals for 3 years. Demographic characteristics, age of onset, clinical signs, scale assessment scores, and auxiliary examination were collected. Age at onset; time from onset to assisted ambulation; and UMSARS I, II, and IV, COMPASS-31, ICARS, and UPDRS III scores were selected as clustering elements. K-means, partitioning around medoids, and self-organizing maps were used to analyze the clusters. Results: The results of all three clustering methods supported the classification of three MSA subtypes: The aggressive progression subtype (MSA-AP), characterized by mid-to-late onset, rapid progression and severe clinical symptoms; the typical subtype (MSA-T), characterized by mid-to-late onset, moderate progression and moderate severity of clinical symptoms; and the early-onset slow progression subtype (MSA-ESP), characterized by early-to-mid onset, slow progression and mild clinical symptoms. Conclusions: We divided MSA into three subtypes and summarized the characteristics of each subtype. According to the clustering results, MSA patients were divided into three completely different types according to the severity of symptoms, the speed of disease progression, and the age of onset.

Morphological and regional spontaneous functional aberrations in the brain associated with Crohn's disease: a systematic review and coordinate-based meta-analyses.

Crohn's disease is an acknowledged "brain-gut" disorder with unclear physiopathology. This study aims to identify potential neuroimaging biomarkers of Crohn's disease. Gray matter volume, cortical thickness, amplitude of low-frequency fluctuations, and regional homogeneity were selected as indices of interest and subjected to analyses using both activation likelihood estimation and seed-based d mapping with permutation of subject images. In comparison to healthy controls, Crohn's disease patients in remission exhibited decreased gray matter volume in the medial frontal gyrus and concurrently increased regional homogeneity. Furthermore, gray matter volume reduction in the medial superior frontal gyrus and anterior cingulate/paracingulate gyri, decreased regional homogeneity in the median cingulate/paracingulate gyri, superior frontal gyrus, paracentral lobule, and insula were observed. The gray matter changes of medial frontal gyrus were confirmed through both methods: decreased gray matter volume of medial frontal gyrus and medial superior frontal gyrus were identified by activation likelihood estimation and seed-based d mapping with permutation of subject images, respectively. The meta-regression analyses showed a positive correlation between regional homogeneity alterations and patient age in the supplementary motor area and a negative correlation between gray matter volume changes and patients' anxiety scores in the medial superior frontal gyrus. These anomalies may be associated with clinical manifestations including abdominal pain, psychiatric disorders, and possibly reflective of compensatory mechanisms.

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review.

OBJECTIVES: To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies. METHODS: This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies. RESULTS: Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall. CONCLUSION: The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.

PIWI-interacting RNA-YBX1 inhibits proliferation and metastasis by the MAPK signaling pathway via YBX1 in triple-negative breast cancer.

Breast cancer is the second leading cause of death in women worldwide, with triple-negative breast cancer (TNBC) having the worst prognosis. Although there are numerous studies on TNBC, there is no effective treatment for it, and it is still a major problem today. Studies on PIWI-interacting RNAs (piRNAs) are increasing and investigating the mechanism of piRNAs in the proliferation and metastasis of TNBC may lead to new potential treatment targets. Here, we identified a novel piRNA, piR-YBX1, which was downregulated in TNBC compared to matched normal breast tissue. Overexpression of piR-YBX1 significantly inhibited the proliferation, migration, invasion ability of TNBC cells both in vivo and in vitro. Mechanistically, piR-YBX1 could bind directly to mRNA of Y-box binding protein 1 (YBX1) and overexpression of piR-YBX1 downregulated YBX1 in both mRNA and protein levels, while the function of piR-YBX1 could be partly rescued by overexpression of YBX1. In addition, YBX1 could bind to RAF1 which is the key molecule in the MAPK signaling pathway, and overexpression of piR-YBX1 inhibited the p-MEK and p-ERK1/2, which can be reverted by YBX1. In conclusion, our findings discovered that the piR-YBX1/YBX1/MAPK axis suppresses the proliferation and metastasis of TNBC and therefore piR-YBX1 has the potential to be an effective therapeutic agent for breast cancer.

Transcutaneous electrical acupoint stimulation attenuated neuroinflammation and oxidative stress by activating SIRT1-induced signaling pathway in MCAO/R rat models.

BACKGROUND: Silent information regulator 1 (SIRT1) plays a beneficial role in cerebral ischemic injury. Previous reports have demonstrated that transcutaneous electrical acupoint stimulation (TEAS) exerts a beneficial effect on ischemic stroke; however, whether SIRT1 participates in the underlying mechanism for the neuroprotective effects of TEAS against ischemic brain damage has not been confirmed. METHODS: The rat models of middle cerebral artery occlusion/reperfusion (MCAO/R) were utilized in the current experiment. After MCAO/R surgery, rats in TEAS, EC and EX group received TEAS intervention with or without the injection of EX527, the SIRT1 inhibitor. Neurological deficit scores, infarct volume, hematoxylin eosin (HE) staining and apoptotic cell number were measured. The results of RNA sequencing were analyzed to determine the differential expression changes of genes among sham, MCAO and TEAS groups, in order to investigate the possible pathological processes involved in cerebral ischemia and explore the protective mechanisms of TEAS. Moreover, oxidative stress markers including MDA, SOD, GSH and GSH-Px were measured with assay kits. The levels of the proinflammatory cytokines, such as IL-6, IL-1ß and TNF-α, were detected by ELISA assay, and Iba-1 (the microglia marker protein) positive cells was measured by immunofluorescence (IF). Western blot and IF were utilized to examine the levels of key molecules in SIRT1/FOXO3a and SIRT1/BRCC3/NLRP3 signaling pathways. RESULTS: TEAS significantly decreased brain infarcted size and apoptotic neuronal number, and alleviated neurological deficit scores and morphological injury by activating SIRT1. The results of RNA-seq and bioinformatic analysis revealed that oxidative stress and inflammation were the key pathological mechanisms, and TEAS alleviated oxidative injury and inflammatory reactions following ischemic stroke. Then, further investigation indicated that TEAS notably attenuated neuronal apoptosis, neuroinflammation and oxidative stress damage in the hippocampus of rats with MCAO/R surgery. Moreover, TEAS intervention in the MCAO/R model significantly elevated the expressions of SIRT1, FOXO3a, CAT, BRCC3, NLRP3 in the hippocampus. Furthermore, EX527, as the inhibitor of SIRT1, obviously abolished the anti-oxidative stress and anti-neuroinflammatory roles of TEAS, as well as reversed the TEAS-mediated elevation of SIRT1, FOXO3a, CAT and reduction of BRCC3 and NLRP3 mediated by following MCAO/R surgery. CONCLUSIONS: In summary, these findings clearly suggested that TEAS attenuated brain damage by suppressing apoptosis, oxidative stress and neuroinflammation through modulating SIRT1/FOXO3a and SIRT1/BRCC3/NLRP3 signaling pathways following ischemic stroke, which can be a promising treatment for stroke patients.

Transcutaneous electrical acupoint stimulation alleviates cerebral ischemic injury through the TLR4/MyD88/NF-κ B pathway.

This study was to explore whether transcutaneous electrical acupoint stimulation (TEAS) treatment could mediate inflammation, apoptosis, and pyroptosis of neuronal cells and microglia activation through the TLR4/MyD88/NF-κB pathway in the early stage of ischemic stroke. TEAS treatment at Baihui (GV20) and Hegu (LI4) acupoints of the affected limb was administered at 24, 48, and 72 h following middle cerebral artery occlusion/reperfusion (MCAO/R), with lasting for 30 min each time. Neurological impairment scores were assessed 2 h and 72 h after ischemia/reperfusion (I/R). TTC staining was used to evaluate the volume of brain infarction. The histopathologic changes of hippocampus were detected by H&E staining. WB analysis was performed to assess the levels of TLR4, MyD88, p-NF-κB p65/NF-κB p65, and inflammation, apoptosis, pyroptosis-related proteins. TLR4 expression was measured using immunohistochemistry. The expression of inflammation-related proteins was also measured using ELISA. Immunofluorescence was used to detect the expression level of Iba1. Our findings demonstrated that TEAS intervention after I/R improved neurological function, reduced the volume of brain infarction, and mitigated pathological damage. Moreover, TEAS reduced the levels of TLR4, MyD88, p-NF-κB p65/NF-κB p65, TNF-α, IL-6, Bax, NLRP3, cleaved caspase-1/pro caspase-1, IL-1ß, IL-18, GSDMD, and Iba1 while enhancing Bcl-2 expression. Moreover, the protective effects of TEAS could be counteracted by lipopolysaccharide (LPS, a TLR4 agonist). In conclusion, TEAS can reduce cerebral damage and suppress inflammation, cell death, and microglia activation after ischemic stroke via inhibiting the TLR4/MyD88/NF-κB pathway.