An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

A Longitudinal MRI-Based Artificial Intelligence System to Predict Pathological Complete Response After Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study.

BACKGROUND: Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer. OBJECTIVE: To develop and validate a deep learning model based on the comparison of paired MRI before and after neoadjuvant chemoradiotherapy to predict pathological complete response. DESIGN: By capturing the changes from MRI before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation. Its performance was independently tested in an internal and 3 external validation sets, and its prognostic value was also evaluated. SETTINGS: Multicenter study. PATIENTS: We retrospectively enrolled 1201 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy before total mesorectal excision. Patients had been treated at 1 of 4 hospitals in China between January 2013 and December 2020. MAIN OUTCOME MEASURES: The main outcome was the accuracy of predicting pathological complete response, measured as the area under receiver operating curve for the training and validation data sets. RESULTS: DeepRP-RC achieved high performance in predicting pathological complete response after neoadjuvant chemoradiotherapy, with area under the curve values of 0.969 (0.942-0.996), 0.946 (0.915-0.977), 0.943 (0.888-0.998), and 0.919 (0.840-0.997) for the internal and 3 external validation sets, respectively. DeepRP-RC performed similarly well in the subgroups defined by receipt of radiotherapy, tumor location, T/N stages before and after neoadjuvant chemoradiotherapy, and age. Compared with experienced radiologists, the model showed substantially higher performance in pathological complete response prediction. The model was also highly accurate in identifying the patients with poor response. Furthermore, the model was significantly associated with disease-free survival independent of clinicopathological variables. LIMITATIONS: This study was limited by its retrospective design and absence of multiethnic data. CONCLUSIONS: DeepRP-RC could be an accurate preoperative tool for pathological complete response prediction in rectal cancer after neoadjuvant chemoradiotherapy. UN SISTEMA DE IA BASADO EN RESONANCIA MAGNTICA LONGITUDINAL PARA PREDECIR LA RESPUESTA PATOLGICA COMPLETA DESPUS DE LA TERAPIA NEOADYUVANTE EN EL CNCER DE RECTO UN ESTUDIO DE VALIDACIN MULTICNTRICO: ANTECEDENTES:La predicción precisa de la respuesta a la quimiorradioterapia neoadyuvante es fundamental para las decisiones de tratamiento posteriores para los pacientes con cáncer de recto localmente avanzado.OBJETIVO:Desarrollar y validar un modelo de aprendizaje profundo basado en la comparación de resonancias magnéticas pareadas antes y después de la quimiorradioterapia neoadyuvante para predecir la respuesta patológica completa.DISEÑO:Al capturar los cambios de las imágenes de resonancia magnética antes y después de la quimiorradioterapia neoadyuvante en 638 pacientes, entrenamos un modelo de aprendizaje profundo multitarea para la predicción de respuesta (DeepRP-RC) que también permitió la segmentación simultánea. Su rendimiento se probó de forma independiente en un conjunto de validación interna y tres externas, y también se evaluó su valor pronóstico.ESCENARIO:Estudio multicéntrico.PACIENTES:Volvimos a incluir retrospectivamente a 1201 pacientes diagnosticados con cáncer de recto localmente avanzado y sometidos a quimiorradioterapia neoadyuvante antes de la escisión total del mesorrecto. Eran de cuatro hospitales en China en el período entre enero de 2013 y diciembre de 2020.PRINCIPALES MEDIDAS DE RESULTADO:Los principales resultados fueron la precisión de la predicción de la respuesta patológica completa, medida como el área bajo la curva operativa del receptor para los conjuntos de datos de entrenamiento y validación.RESULTADOS:DeepRP-RC logró un alto rendimiento en la predicción de la respuesta patológica completa después de la quimiorradioterapia neoadyuvante, con valores de área bajo la curva de 0,969 (0,942-0,996), 0,946 (0,915-0,977), 0,943 (0,888-0,998), y 0,919 (0,840-0,997) para los conjuntos de validación interna y las tres externas, respectivamente. DeepRP-RC se desempeñó de manera similar en los subgrupos definidos por la recepción de radioterapia, la ubicación del tumor, los estadios T/N antes y después de la quimiorradioterapia neoadyuvante y la edad. En comparación con los radiólogos experimentados, el modelo mostró un rendimiento sustancialmente mayor en la predicción de la respuesta patológica completa. El modelo también fue muy preciso en la identificación de los pacientes con mala respuesta. Además, el modelo se asoció significativamente con la supervivencia libre de enfermedad independientemente de las variables clinicopatológicas.LIMITACIONES:Este estudio estuvo limitado por el diseño retrospectivo y la ausencia de datos multiétnicos.CONCLUSIONES:DeepRP-RC podría servir como una herramienta preoperatoria precisa para la predicción de la respuesta patológica completa en el cáncer de recto después de la quimiorradioterapia neoadyuvante. (Traducción-Dr. Felipe Bellolio ).

Anatomical variations of the branches from left colic artery and middle colic artery at splenic flexure.

BACKGROUND: Variations of the vasculature at splenic flexure by left colic artery (LCA) and middle colic artery (MCA) remain ambiguous. OBJECTIVES: This study aim to investigate the anatomical variations of the branches from LCA and MCA at splenic flexure area. METHODS: Using ultra-thin CT images (0.5-mm thickness), we traced LCA and MCA till their merging site with paracolic marginal arteries through maximum intensity projection (MIP) reconstruction and computed tomography angiography (3D-CTA). RESULTS: A total of 229 cases were retrospectively enrolled. LCA ascending branch approached upwards till the distal third of the transverse colon in 37.6%, reached the splenic flexure in 37.6%, and reached the lower descending colon in 23.1%, and absent in 1.7% of the cases. Areas supplied by MCA left branch and aMCA were 33.2%, 44.5% and 22.3% in the proximal, middle and distal third of transverse colon of the cases, respectively. The accessory MCA separately originated from the superior mesenteric artery was found in 17.9% of the cases. Mutual correlation was found that, when the LCA ascending branch supplied the distal transverse colon, MCA left branch tended to feed the proximal transverse colon; when the LCA ascending branch supplied the lower part of descending colon, MCA left branch was more likely to feed the distal third of transverse colon. CONCLUSIONS: Vasculature at splenic flexure by LCA and MCA varied at specific pattern. This study could add more anatomical details for vessel management in surgeries for left-sided colon cancer.

Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features.

BACKGROUND: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiveness of CCTA for myocardial ischaemia patients was explored. METHODS: This was a retrospective analysis of the CCTA images of 155 patients with clinically diagnosed coronary heart disease from September 2019 to January 2020, 79 of whom were considered positive (myocardial ischaemia) and 76 negative (normal myocardial blood supply) according to their clinical diagnoses. By using the deep learning model-based CQK software, the myocardium was automatically segmented from the CCTA images and used to extract texture features. All patients were randomly divided into a training cohort and a test cohort at a 7:3 ratio. The Spearman correlation and least absolute shrinkage and selection operator (LASSO) method were used for feature selection. Based on the selected features of the training cohort, a multivariable logistic regression model was established. Finally, the test cohort was used to verify the regression model. RESULTS: A total of 387 features were extracted from the CCTA images of the 155 coronary heart disease patients. After performing dimensionality reduction with the Spearman correlation and LASSO, three texture features were selected. The accuracy, area under the curve, specificity, sensitivity, positive predictive value and negative predictive value of the constructed multivariable logistic regression model with the test cohort were 0.783, 0.875, 0.733, 0.875, 0.650 and 0.769, respectively. CONCLUSION: CCTA imaging texture features of the myocardium have potential as biomarkers for diagnosing myocardial ischaemia.

Serum miR-515-3p, a potential new RNA biomarker, is involved in gastric carcinoma.

OBJECTIVES: microRNAs (miRNAs) have provided a new opportunity for developing diagnostic biomarkers and effective therapeutic targets in gastric cancer (GC). In this study, we aimed to investigate the relationship between miR-515-3p and GC development. EXPERIMENTAL DESIGN: The Gene Expression Omnibus (GEO) database was used for screening genes and miRNA and for 2R analysis. miRNA prediction target genes and screening key genes were analyzed using protein interactions (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A network of miRNA-mRNA interactions was predicated by Cytoscape (v.3.5.1), Institute of Systems Biology & University of California, San Diego & Pasteur institute & University of California, San Francisco. Finally, miR-515-3p levels were detected by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in gastric cells and plasma levels. Then, the association between the expression level of miR-515-3p and the clinicopathological features of patients with GC was further analyzed. OBSERVATIONS AND CONCLUSIONS: We found that miR-515-3p was markedly overexpressed in individuals with GC compared with that in normal gastric cells (NCs) and the surgery group (P < 0.0001). In addition, receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) value of 0.8555 for miR-515-3p. SIGNIFICANCE: Our results present new information to the field of gastric cancer and has done a good job of creating an initial hypothesis using the database as well as validate their initial results. These results suggest that serum miR-515-3p is a novel potential biomarker for the detection of GC.

Combining multi-dimensional data to identify a key signature (gene and miRNA) of cisplatin-resistant gastric cancer.

Gastric cancer (GC) is one of the most lethal malignant tumors; the resistance of this type of tumor is the main source of GC treatment failure. In this study, we used bioinformatics analysis to verify differences in resistant GCs and identify an effective method for reversing drug resistance in GC. Microarray data [gene and microRNA (miRNA)] were analyzed using GEO2R software, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied to further enrich the genetic data. miRNA-gene interactions were determined using Cytoscape (v.3.5.1). Online software was used to analyze protein interactions and predict network structure. The Cancer Genome Atlas (TCGA) database was used to verify the expression levels of genes in GC resistance. miR-604 expression levels were verified by real-time PCR in GC cell lines. We screened 3981 GC resistance-associated genes and 244 miRNAs using bioinformatics methods. Six hub genes were identified and verified in the TCGA database, including five up-regulated genes, POLR2L, POLR2C, POLR2F, APRT, and LMAN2, and a down-regulated gene, NFKB2. The up-regulated genes POLR2L, POLR2C, APRT, and LMAN2 interact with miR-604; therefore, we focused on miR-604, which has low expression in drug-resistant GC. The results of this study indicate that through bioinformatics technologies, we have determined the hub genes and hub miRNAs related to drug resistance in GC. Among them, miR-604 could become a new indicator in the diagnosis of drug-resistant GC and may be used to investigate the pathogenesis of resistance in GC.

Gene delivery of apoptin-derived peptide using an adeno-associated virus vector inhibits glioma and prolongs animal survival.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. We designed an adeno-associated virus (AAV) vector for intracranial delivery of the secreted HSP70-targeted peptide APOPTIN derived from Apoptin to GBM tumors. We applied this therapy to GBM models using human U87MG glioma cells and GBM xenograft models in mice. In U87MG and U251MG cells, conditioned medium from AAV2-apoptin-derived peptide (ADP)-expressing cells induced 83% and 78% cell death. In mice bearing intracranial U87MG tumors treated with AAV2-ADP, treatment resulted in a significant decrease in tumor growth and longer survival in mice bearing orthotopic invasive GBM brain tumors. These data indicate that ssAAV2-ADP injection in the left hemisphere effectively prevented ipsilateral tumor growth but was insufficient to prevent distal tumor growth in the contralateral hemisphere. However, the systemic route is the most effective approach for treating widely dispersed tumors. In summary, systemic delivery of AAV2-ADP is an attractive approach for invasive GBM treatment.

Creation of an apoptin-derived peptide that interacts with SH3 domains and inhibits glioma cell migration and invasion.

Glioblastoma multiforme (GBM) is an aggressive tumor of the central nervous system characterized by high rates of recurrence, morbidity, and mortality. This study investigated the antitumor effects of an apoptin-derived peptide (ADP) on glioma cells and explored the underlying mechanisms. The U251, U87, and C6 glioma cell lines were used in the present study, and the expression of p-Akt, Akt, and MMP-9 was determined through Western blotting, quantitative real-time PCR, and hematoxylin and eosin (HE) staining. Tumor growth was evaluated by magnetic resonance imaging, and cell viability was assessed through an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide MTT assay. Glioma cell metastasis was evaluated using transwell migration, invasion, and scratch-wound assays. An ADP was designed and synthesized based on the results of a domain-based analysis of the structure of apoptin. The ADP inhibited glioma cell viability, invasion and migration, and treatment with the synthesized ADP led to downregulation of p-Akt and MMP-9 and inhibited MMP-9 translation. The ADP also inhibited glioma invasion and migration in vivo, and HE staining showed decreases in the satellite-like invasion of cell masses and apoptotic cell populations after treatment with the ADP. Our findings demonstrate that treatment with an ADP can suppress glioma cell migration and invasion via the PI3K/Akt/MMP-9 signaling pathway and provide a new platform for the development of drugs for treating glioma.

Altered functional brain networks in coronary heart disease: independent component analysis and graph theoretical analysis.

Coronary heart disease (CHD) confers a high risk of cognitive and mental impairments in patients. This study aimed to explore the association of CHD with functional connectivity and topological properties of brain networks. A total of 27 patients with CHD and 44 healthy controls (HCs) participated in this study and underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Intra- and internetwork functional connectivity alterations were explored using independent component analysis in CHD patients. Furthermore, graph theoretical analysis was adopted to assess abnormalities in small-world properties and network efficiency metrics of brain networks. Compared to HCs, CHD patients exhibited increased functional connectivity between the posterior default mode network and posterior visual network, as well as decreased functional connectivity between the left frontoparietal network and auditory network. In terms of graph theoretical analysis, small-world network topology was identified in both CHD patients and HCs. Furthermore, the nodal local efficiency of the left putamen was significantly decreased in CHD patients compared to HCs. This study revealed alterations in brain functional connectivity and topological properties in CHD patients, shedding light on the potential neurological mechanism underlying cognitive and mental impairments in these patients and suggesting unexplored connections between CHD and higher order cognitive processing.

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.

Introduction: Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. Materials and methods: We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. Results: By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. Conclusion: In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.

The efficacy of combination therapy using adeno-associated virus-mediated co-expression of apoptin and interleukin-24 on hepatocellular carcinoma.

Multigene-based combination therapy is an effective practice in cancer gene therapy. Apoptin is a chicken anemia virus-derived, p53-independent, Bcl-2-insensitive apoptotic protein with the ability to specifically induce apoptosis in various human tumor cells. Interleukin-24 (IL-24) displays ubiquitous antitumor property and tumor-specific killing activity. Adeno-associated virus (AAV) is a promising gene delivery vehicle due to its advantage of low pathogenicity and long-term gene expression. In this study, we assessed the efficacy of combination therapy using AAV-mediated co-expression of apoptin and interleukin-24 on hepatocellular carcinoma in vitro and in vivo. Our results showed that AAV-mediated co-expression of IL-24 and apoptin significantly suppressed the growth and induced the apoptosis of HepG2 cells in vitro. Furthermore, AAV-mediated combined treatment of IL-24 and apoptin significantly suppressed tumor growth and induced apoptosis of tumor cells in xenograft nude mice. These data suggest that AAV vectors that co-express apoptin and IL-24 have great potential in cancer gene therapy.

Apoptin selectively induces the apoptosis of tumor cells by suppressing the transcription of HSP70.

Apoptin is a nonstructural viral protein encoded by VP3 gene of chicken anemia virus, which could specially induce apoptosis of tumor cells. However, the mechanism of apoptin-induced apoptosis in tumor cells without any side effects in normal cells has not yet been well characterized. This study aimed to investigate the molecular mechanism underlying the selective antitumor effects of apoptin. HepG2 cells were treated with apoptin or transfected with apoptin expression vector. Heat shock protein 70 (HSP70) expression was examined by Western blot. The binding of apoptin to HSP70 promoter was detected by electrophoretic mobility shift assay, chromatin immunoprecipitation, and luciferase assay. The results showed that apoptin inhibited HSP70 expression in HepG2 cells and apoptin-induced apoptosis of HepG2 cells was dependent on the expression level of HSP70. Furthermore, apoptin promoted HSF1 trimer depolymerization and inhibited HSF1-mediated HSP70 transcription. In addition, apoptin competed with HSF1 to bind heat shock element in HSP70 promoter, leading to reduced HSP70 transcription. Both these mechanisms contribute to the suppression of HSP70 transcription and expression. Our findings provide the first evidence that apoptin induces tumor cell apoptosis by specifically downregulating the expression of HSP70, which helps explain the specific antitumor effects of apoptin.

Intrinsic brain abnormalities in chronic rhinosinusitis associated with mood and cognitive function.

Background: Chronic rhinosinusitis (CRS) poses a risk for developing emotional and cognitive disorders. However, the neural evidence for this association is largely unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) analysis can demonstrate abnormal brain activity and functional connectivity and contribute to explaining the potential pathophysiology of CRS-related mood and cognitive alterations. Methods: Chronic rhinosinusitis patients (CRS, n = 26) and gender- and age-matched healthy control subjects (HCs, n = 38) underwent resting-state functional MRI scanning. The amplitude of low-frequency fluctuations (ALFF) was calculated to observe the intrinsic brain activity. The brain region with altered ALFF was further selected as the seed for functional connectivity (FC) analysis. Correlation analysis was performed between the ALFF/FC and clinical parameters in CRS patients. Results: Compared with HCs, CRS patients exhibited significantly increased ALFF in the left orbital superior frontal cortex and reduced connectivity in the right precuneus using the orbital superior frontal cortex as the seed region. The magnitude of the orbital superior frontal cortex increased with inflammation severity. In addition, ALFF values in the orbital superior frontal cortex were positively correlated with the hospital anxiety and depression scale (HADS) scores. The ROC curves of altered brain regions indicated great accuracy in distinguishing between CRS patients and HCs. Conclusion: In this study, patients with CRS showed increased neural activity in the orbital superior frontal cortex, a critical region in emotional regulation, and this region also indicated hypoconnectivity to the precuneus with a central role in modulating cognition. This study provided preliminary insights into the potential neural mechanism related to mood and cognitive dysfunctions in CRS patients.

Prognostic Role of Mitochondrial Transcription Termination Factor 3 in Thyroid Carcinoma.

Background: Studies have shown that the Mitochondrial Transcription Termination Factor 3 (MTERF3) negatively regulates mitochondrial gene expression and energy metabolism, and plays a significant role in many cancer types. Nevertheless, the expression and prognostic role of MTERF3 in patients with thyroid carcinoma (THCA) is still unclear. Thus, we investigated the expression, clinicopathological significance, and prognostic value of MTERF3 in THCA. Methods: The protein and mRNA expression levels of MTERF3 were, respectively, analyzed using immunohistochemistry (IHC) from THCA tissues and RNA-Seq data downloaded from The Cancer Genome Atlas. In addition, the relationships among the expression of MTERF3, the stemness feature, the extent of immune infiltration, drug sensitivity, the expression of ferroptosis, and N6-methyladenosine (m6A) methylation regulators, were evaluated as prognostic indicators for patients with THCA using the Kaplan-Meier plotter database. Results: The IHC and RNAseq results showed that the protein and mRNA expression levels of MTERF3 in adjacent nontumor tissues were significantly higher than in THCA tissues. The survival analysis indicated that decreased expression of MTERF3 was associated with a poorer prognosis. Furthermore, the expression of MTERF3 not only negatively correlated with the enhancement of the stemness of THCA and the reduction of drug sensitivity but also was implicated in ferroptosis and m6A methylation. Conclusion: The data from this study support the hypothesis that decreased expression of MTERF3 in THCA is associated with a poor prognosis.

Aberrant Spontaneous Brain Activity in Coronary Heart Disease Using Fractional Amplitude of Low-Frequency Fluctuations: A Preliminary Resting-State Functional MRI Study.

Objective: This study is aimed at exploring the spontaneous brain activity changes by measuring the fractional amplitude of low-frequency fluctuations (fALFF) and their relationship with clinical characteristics in patients with coronary heart disease (CHD). Methods: Coronary heart disease patients (n = 25) and age, gender, and education level-matched control subjects (controls, n = 35) were included. The grey matter volume (GMV) and fALFF values were calculated to assess the difference in brain structure and function between the two groups, respectively. Correlation analyses between the fALFF values and clinical characteristics were further assessed in CHD patients. In addition, receiver operating characteristic (ROC) curves were conducted to access the diagnostic ability of the fALFF method. Results: There was no significant difference in GMV between the CHD and control groups. Compared with the control group, patients with CHD showed significantly decreased fALFF in the left precentral/postcentral gyrus and increased fALFF in the right inferior cerebellum. Patients with a history of myocardial infarction (MI) showed significantly decreased fALFF values of the right inferior cerebellum than patients without MI. There was no significant correlation between the fALFF values in specific brain regions and disease duration. Furthermore, the ROC curves of abnormal brain regions showed the perfect accuracy of the fALFF value in distinguishing between CHD patients and controls. Conclusion: CHD demonstrated aberrant neural activity in specific brain regions mainly related to sensorimotor networks and pain processing, which may contribute to understanding the underlying neurological mechanism of CHD.

Research progress on the application of optical coherence tomography in the field of oncology.

Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the "gold standard" for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin's surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

Near-infrared ultrabroadband luminescence spectra properties of subvalent bismuth in CsI halide crystals.

We observed two ultrabroadband near-infrared (NIR) luminescence bands around 1.2 and 1.5 µm in as-grown bismuth-doped CsI halide crystals, without additional aftertreatment. Dependence of the NIR emission properties on the excitation wavelength and measurement temperature was studied. Two kinds of NIR active centers of subvalent bismuth and color centers were demonstrated to coexist in Bi:CsI crystal. The eye-safe 1.5 µm emission band with an FWHM of 140 nm and lifetime of 213 µs at room temperature makes Bi:CsI crystal promising in the applications of the ultrafast laser and ultrabroadband amplifier.

Facile fabrication and low-temperature bonding of Cu@Sn-Bi core-shell particles for conductive pastes.

The rapid development of flexible wearable electronics arouses huge demand for low-temperature sintering metal inks applied to temperature-sensitive substrates. The high sintering temperature and easy oxidation limited the application of Cu-based pastes. A two-step method involving liquid co-reduction and heat ripening was developed to synthesize Cu@Sn-Bi core-shell particles. The thickness of Sn-Bi shells can be flexibly adjusted via changing the mass ratio of Cu to Sn-Bi. The volume resistivity of printed circuits using Cu@Sn-Bi pastes solidified at 200 °C was as low as 481 µΩ cm, which increased by 11.8% after an aging process at 190 °C for 6 h. The outstanding stability in a harsh environment would attribute to the effective protection of Sn-Bi alloy shells. This work suggests a new pathway toward the low-temperature bonding and anti-oxidation of Cu particles as conductive fillers, which can be widely applied to the additive manufacturing of flexible wearable electronics.

Study on the effect of heat-annealing and irradiation on spectroscopic properties of Bi:alpha-BaB2O4 single crystal.

The absorption, excitation, and ultrabroadband near-infrared luminescence spectra of Bismuth were investigated in H(2)-annealed and gamma-irradiated Bi:alpha-BaB(2)O(4)(alpha-BBO) single crystals, respectively. Energy-level diagrams of the near-infrared luminescent centers were fixed. The electronic transition energies of near-infrared active centers are basically consistent with the multiplets of free Bi(+) ions. The minor difference of the energy-level diagrams of Bi(+) ions in H(2)-annealed and gamma-irradiated Bi:alpha-BaB(2)O(4) crystals can be ascribed to the difference of the local lattice environments. The involved physical and chemical processes were discussed. The effect of Ar-, air-annealing and electron-irradiation on Bi:alpha-BaB(2)O(4) crystal were also investigated.

A predictive analysis approach for paediatric and adult high-grade glioma: miRNAs and network insight.

BACKGROUND: Brain tumours are the most common solid tumour in children and are a cause of mortality in adults. Most cases of brain tumour-related death are attributed to glioblastoma (GBM), with an elevated rate for high-grade glioma (HGG). Showing strong heterogeneity, the lesion location, molecule expression and type of HGG differ between adults and children. However, with regard to pathogenesis, brain tumours are expected to have the same underlying molecular processes. METHODS: In this study, we obtained data from the Gene Expression Omnibus (GEO) database to analyse molecular expression in HGG between adults and children. The same and different mutations were identified in these groups, and the genes involved were compared using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Molecular analysis revealed the same trend of differences between children and adults, which was verified in The Cancer Genome Atlas (TCGA). RESULTS: A total of 12 microarrays including 455 HGG patients were screened. Through a rigorous intersecting process, we identified miR-10a, miR-10b, and miR-139 as having common differences, as well as 6 target genes, such as CDK6, SOX4 and VEGFA, etc. And 12 long noncoding RNAs (lncRNAs). CONCLUSIONS: We identified that these key molecules are involved in development and progression of HGG between adults and children. The findings provide a comprehensive description of the similarities in advanced diseases between adults and children and molecular diagnostic directions for precision small-molecule medicine to treat HGG in different age populations.