Identification of patients with nonischemic dilated cardiomyopathy at risk of malignant ventricular arrhythmias: insights from cardiac magnetic resonance feature tracking.

BACKGROUND: Patients with nonischemic dilated cardiomyopathy (NIDCM) are prone to arrhythmias, and the cause of mortality in these patients is either end-organ dysfunction due to pump failure or malignant arrhythmia-related death. However, the identification of patients with NIDCM at risk of malignant ventricular arrhythmias (VAs) is challenging in clinical practice. The aim of this study was to evaluate whether cardiovascular magnetic resonance feature tracking (CMR-FT) could help in the identification of patients with NIDCM at risk of malignant VAs. METHODS: A total of 263 NIDCM patients who underwent CMR, 24-hour Holter electrocardiography (ECG) and inpatient ECG were retrospectively evaluated. The patients with NIDCM were allocated to two subgroups: NIDCM with VAs and NIDCM without VAs. From CMR-FT, the global peak radial strain (GPRS), global longitudinal strain (GPLS), and global peak circumferential strain (GPCS) were calculated from the left ventricle (LV) model. We investigated the possible predictors of NIDCM combined with VAs by univariate and multivariate logistic regression analyses. RESULTS: The percent LGE (15.51 ± 3.30 vs. 9.62 ± 2.18, P < 0.001) was higher in NIDCM patients with VAs than in NIDCM patients without VAs. Furthermore, the NIDCM patients complicated with VAs had significantly lower GPCS than the NIDCM patients without VAs (- 5.38 (- 7.50, - 4.22) vs.-9.22 (- 10.73, - 8.19), P < 0.01). Subgroup analysis based on LGE negativity showed that NIDCM patients complicated with VAs had significantly lower GPRS, GPCS, and GPLS than NIDCM patients without VAs (P < 0.05 for all). Multivariate analysis showed that both GPCS and %LGE were independent predictors of NIDCM combined with VAs. CONCLUSIONS: CMR global strain can be used to identify NIDCM patients complicated with VAs early, specifically when LGE is not present. GPCS < - 13.19% and %LGE > 10.37% are independent predictors of NIDCM combined with VAs.

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

Abdominal pregnancy secondary to uterine horn pregnancy: a case report.

BACKGROUND: Pregnancy begins with a fertilized ovum that normally attaches to the uterine endometrium. However, an ectopic pregnancy can occur when a fertilized egg implants and grows outside the uterine cavity. Tubal ectopic pregnancy is the most common type (over 95%), with ovarian, abdominal, cervical, broad ligament, and uterine cornual pregnancy being less common. As more cases of ectopic pregnancy are diagnosed and treated in the early stages, the survival rate and fertility retention significantly improve. However, complications of abdominal pregnancy can sometimes be life-threatening and have severe consequences. CASE PRESENTATION: We present a case of intraperitoneal ectopic pregnancy with fetal survival. Ultrasound and magnetic resonance imaging showed a right cornual pregnancy with a secondary abdominal pregnancy. In September 2021, we performed an emergency laparotomy, along with additional procedures such as transurethral ureteroscopy, double J-stent placement, abdominal fetal removal, placentectomy, repair of the right uterine horn, and pelvic adhesiolysis, in the 29th week of pregnancy. During laparotomy, we diagnosed abdominal pregnancy secondary to a rudimentary uterine horn. The mother and her baby were discharged eight days and 41 days, respectively, after surgery. CONCLUSIONS: Abdominal pregnancy is a rare condition. The variable nature of ectopic pregnancy can cause delays in timely diagnosis, resulting in increased morbidity and mortality, especially in areas with inadequate medical and social services. A high index of suspicion, coupled with appropriate imaging studies, can help facilitate its diagnosis in any suspected case.

A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly.

BACKGROUND: The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. METHODS: A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. RESULTS: The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. CONCLUSIONS: Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.

Left circumflex artery pericardia fistula combined with huge pseudoaneurysm: a rare case report.

BACKGROUND: Coronary artery fistula refers to an abnormal communication between a coronary artery and great vessel, a cardiac chamber or other structure. The left circumflex artery (LCX) pericardia fistula combined with huge pseudoaneurysm is extremely rare. CASE PRESENTATION: A 39-year-old young female was admitted into our hospital because of palpitation and shortness of breath. Coronary computed tomography angiography (CCTA) showed a huge pseudoaneurysm located in pericardium. Coronary angiography revealed the LCX pericardia fistula. Then surgical treatment was performed. She was in good condition without complications after surgery. CONCLUSIONS: Coronary artery fistula combined with pseudoaneurysm can be caused by congenital factors. Early surgical treatment can relieve the patient's symptoms and prevent the occurrence of adverse cardiovascular events.

A 55-Day-Old Female Infant Infected With 2019 Novel Coronavirus Disease: Presenting With Pneumonia, Liver Injury, and Heart Damage.

BACKGROUND: Previous studies on the pneumonia outbreak caused by the 2019 novel coronavirus disease (COVID-19) were mainly based on information from adult populations. Limited data are available for children with COVID-19, especially for infected infants. METHODS: We report a 55-day-old case with COVID-19 confirmed in China and describe the identification, diagnosis, clinical course, and treatment of the patient, including the disease progression from day 7 to day 11 of illness. RESULTS: This case highlights that children with COVID-19 can also present with multiple organ damage and rapid disease changes. CONCLUSIONS: When managing such infant patients with COVID-19, frequent and careful clinical monitoring is essential.

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient's clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

miR-29c regulates resistance to paclitaxel in nasopharyngeal cancer by targeting ITGB1.

Paclitaxel (Taxol) is an anticancer taxane drug commonly used in the treatment of nasopharyngeal carcinoma (NPC). However, resistance to paclitaxel is a major difficulty in developing an effective therapy against NPC. MicroRNAs (miRNAs) are known to regulate genes that are involved in drug resistance. We assessed the effects of miR-29c, an miRNA identified in a genome-wide study of Taxol resistance, on genes associated with resistance in NPC cells. We established Taxol resistance in two human NPC cell lines, SUNE-1 and C666-1 (SUNE-1-Taxol and C666-1-Taxol) and found that miR-29c was downregulated and integrin beta-1 (ITGB1) was upregulated in Taxol-resistant NPC cells compared with parental NPC cells. Further investigations using a TUNEL assay and BAX/BCL-2 ratio, found that overexpression of miR-29c and knockdown of ITGB1 can resensitize drug-resistant NPC cells to Taxol and promote apoptosis. In addition, a dual-luciferase reporter assay indicated that ITGB1 is the target of miR-29c. Furthermore, silencing miR-29c markedly increased Taxol-resistant NPC tumor growth in a nude mouse xenograft model while knockdown of ITGB1 reversed this result. Overall, these data demonstrate that miR-29c regulates resistance to Taxol in NPC by targeting ITGB1. Our research indicates that miR-29c may have potential use in Taxol-resistant NPC therapy.

Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection.

OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.

MicroRNA-29c Increases the Chemosensitivity of Pancreatic Cancer Cells by Inhibiting USP22 Mediated Autophagy.

BACKGROUND/AIMS: Pancreatic cancer (PC) is an aggressive malignancy with a poor survival rate. Despite advances in the treatment of PC, the efficacy of therapy is limited by the development of chemoresistance. Here, we examined the role of microRNA-29c (miR-29c) and the involvement of autophagy and apoptosis in the chemoresistance of PC cells in vivo and in vitro. METHODS: We employed qRT-PCR, western blot and immunofluorescence to examine the expression level of miR-29c, USP22 and autophagy relative protein. In addition, we used MTT assay to detect cell proliferation and transwell assay to measure migration and invasiveness. The apoptosis was determined using annexin V-FITC/PI apoptosis detection kit by flow cytometry. Luciferase reporter assays confirmed the relationship between USP22 and miR-29c. RESULTS: miR-29c overexpression in the PC cell line PANC-1 enhanced the effect of gemcitabine on decreasing cell viability and inducing apoptosis and inhibited autophagy, as shown by western blotting, immunofluorescence staining, colony formation assays, and flow cytometry. Ubiquitin specific peptidase (USP)-22, a deubiquitinating enzyme known to induce autophagy and promote PC cell survival, was identified as a direct target of miR-29c. USP22 knockdown experiments indicated that USP22 suppresses gemcitabine-induced apoptosis by promoting autophagy, thereby increasing the chemoresistance of PC cells. Luciferase reporter assays confirmed that USP22 is a direct target of miR-29c. A xenograft mouse model demonstrated that miR-29c increases the chemosensitivity of PC in vivo by downregulating USP22, leading to the inhibition of autophagy and induction of apoptosis. CONCLUSIONS: Taken together, these findings reveal a potential mechanism underlying the chemoresistance of PC cells mediated by the regulation of USP22-mediated autophagy by miR-29c, suggesting potential targets and therapeutic strategies in PC.

Migration Pathways of Thalamic Neurons and Development of Thalamocortical Connections in Humans Revealed by Diffusion MR Tractography.

The thalamus plays an important role in signal relays in the brain, with thalamocortical (TC) neuronal pathways linked to various sensory/cognitive functions. In this study, we aimed to see fetal and postnatal development of the thalamus including neuronal migration to the thalamus and the emergence/maturation of the TC pathways. Pathways from/to the thalami of human postmortem fetuses and in vivo subjects ranging from newborns to adults with no neurological histories were studied using high angular resolution diffusion MR imaging (HARDI) tractography. Pathways likely linked to neuronal migration from the ventricular zone and ganglionic eminence (GE) to the thalami were both successfully detected. Between the ventricular zone and thalami, more tractography pathways were found in anterior compared with posterior regions, which was well in agreement with postnatal observations that the anterior TC segment had more tract count and volume than the posterior segment. Three different pathways likely linked to neuronal migration from the GE to the thalami were detected. No hemispheric asymmetry of the TC pathways was quantitatively observed during development. These results suggest that HARDI tractography is useful to identify multiple differential neuronal migration pathways in human brains, and regional differences in brain development in fetal ages persisted in postnatal development.

SIRT3 functions as a tumor suppressor in hepatocellular carcinoma.

Hepatocellular carcinoma is one of the leading causes for cancer-related mortality worldwide. SIRT3 may function as either oncogene or tumor suppressor in a panel of cancers; however, the role of SIRT3 in hepatocellular carcinoma remains unclear. In this study, we assayed the expression level of SIRT3 in hepatocellular carcinoma tissues by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. A loss-of-function approach was used to examine the effects of SIRT3 on biological activity, including cell proliferative activity and invasive potential. The results demonstrated that the expression levels of SIRT3 protein in hepatocellular carcinoma tissues were significantly downregulated compared with those in adjacent non-cancerous tissues. Furthermore, SIRT3 could decrease cell proliferation and inhibit cell migration/invasion in hepatocellular carcinoma cell line. Taken together, these results elucidated the function of SIRT3 in hepatocellular carcinoma development and suggested that SIRT3 might function as tumor suppressor in hepatocellular carcinoma by targeting PI3K/Akt pathway.

[Mutation analysis of the TRAPPC2 gene in a Chinese family with X-linked spondyloepiphyseal dysplasia tarda].

OBJECTIVE: To identify potential mutation of TRAPPC2 gene in a Chinese family affected with X-linked spondyloepiphyseal dysplasia tarda (X-SEDL), and explore its underlying molecular mechanism. METHODS: Peripheral blood samples were collected from 32 members of the family and 50 healthy adults to extract genomic DNA. DNA sequences of exons 3 to 6 and their exon/intron boundaries were amplified with PCR amplification. Direct bi-directional sequencing analysis was performed on the PCR products. The sequences were aligned to the reference sequences from the GenBank to determine mutation site and type. RESULTS: A nucleotide substitution of the splice-donor in TRAPPC2 intron 3, c.93+5G>A, was detected in the proband, but no sequence change was detected in TRAPPC2 exons 3 to 6. All of the 6 male patients and 8 female carriers from the family were detected to have carried this mutation. The same mutation was not found in the remaining 18 family members with a normal phenotype and 50 healthy controls. CONCLUSION: We have detected a c.93+5G>A mutation in the TRAPPC2 gene in a Chinese family affected with X-SEDL. Our results have expanded the spectrum of TRAPPC2 mutations and is helpful for presymptomatic and prenatal diagnoses of this disease.

HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation.

Accurate medical image segmentation is of great significance for subsequent diagnosis and analysis. The acquisition of multi-scale information plays an important role in segmenting regions of interest of different sizes. With the emergence of Transformers, numerous networks adopted hybrid structures incorporating Transformers and CNNs to learn multi-scale information. However, the majority of research has focused on the design and composition of CNN and Transformer structures, neglecting the inconsistencies in feature learning between Transformer and CNN. This oversight has resulted in the hybrid network's performance not being fully realized. In this work, we proposed a novel hybrid multi-scale segmentation network named HmsU-Net, which effectively fused multi-scale features. Specifically, HmsU-Net employed a parallel design incorporating both CNN and Transformer architectures. To address the inconsistency in feature learning between CNN and Transformer within the same stage, we proposed the multi-scale feature fusion module. For feature fusion across different stages, we introduced the cross-attention module. Comprehensive experiments conducted on various datasets demonstrate that our approach surpasses current state-of-the-art methods.

Correlation study of functional magnetic resonance index and clinicopathological features of rectal cancer.

PURPOSE: To investigate the correlation between DCE-MRI, R2*, IVIM, and clinicopathological features of rectal cancer. METHODS: This was a prospective study, enrolling 42 patients with rectal cancer, 20 of whom underwent rectal mesorectal excision. Dynamic contrast-enhanced magnetic resonance imaging scanning was performed preoperatively in all patients, and additional preoperative scanning of R2* imaging and intravoxel incoherent motion was performed in those who underwent surgery. Artificially delineate the ROI around the tumor. Functional magnetic resonance index parameters Ktrans, Ve, R2*, D, D*, and f were estimated by computer software to analyze postoperative pathological reports of patients undergoing total mesenteric resection. Correlation and significance analyses of imaging metrics and pathologic features were performed by GraphPad Prism 9 to assess statistical significance. RESULTS: DEC-MRI, R2*, and IVIM have certain application values in the distance from the lower margin of the tumor to the anorectal ring, imaging T stage and N stage, tumor markers CEA and CA199, immunohistochemical indexes Ki-76 and P53, lymph node cancer metastasis, and rectal fascia status (P < 0.05). CONCLUSION: DEC-MRI, R2*, and IVIM provide reliable quantitative parameters for preoperative clinicopathological evaluation of patients with rectal cancer.

Low breast density and peritumoral edema on MR predict worse overall survival of breast cancer patients after neoadjuvant chemotherapy.

OBJECTIVES: To investigate the relationship of pre-treatment MR image features (including breast density) and clinical-pathologic characteristics with overall survival (OS) in breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: This retrospective study obtained an approval of the institutional review board and the written informed consents of patients were waived. From October 2013 to April 2019, 130 patients (mean age, 47.6 ± 9.4 years) were included. The univariable and multivariable Cox proportional hazards regression models were applied to analyze factors associated with OS, including MR image parameters and clinical-pathologic characteristics. RESULTS: Among the 130 included patients, 11 (8.5%) patients (mean age, 48.4 ± 11.8 years) died of breast cancer recurrence or distant metastasis. The median follow-up length was 70 months (interquartile range of 60-85 months). According to the Cox regression analysis, older age (hazard ratio [HR] = 1.769, 95% confidence interval [CI]): 1.330, 2.535), higher T stage (HR = 2.490, 95%CI:2.047, 3.029), higher N stage (HR = 1.869, 95%CI:1.507, 2.317), low breast density (HR = 1.693, 95%CI:1.391, 2.060), peritumoral edema (HR = 1.408, 95%CI:1.078, 1.840), axillary lymph nodes invasion (HR = 3.118, 95%CI:2.505, 3.881) on MR were associated with worse OS (all p < 0.05). CONCLUSIONS: Pre-treatment MR features and clinical-pathologic parameters are valuable for predicting long-time OS of breast cancer patients after NAC followed by surgery. Low breast density, peritumoral edema and axillary lymph nodes invasion on pre-treatment MR images were associated with worse prognosis.

SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.

Gastric cancer is a significant contributor to cancer-related fatalities globally. The automated segmentation of gastric tumors has the potential to analyze the medical condition of patients and enhance the likelihood of surgical treatment success. However, the development of an automatic solution is challenged by the heterogeneous intensity distribution of gastric tumors in computed tomography (CT) images, the low-intensity contrast between organs, and the high variability in the stomach shapes and gastric tumors in different patients. To address these challenges, we propose a self-attention backward network (SaB-Net) for gastric tumor segmentation (GTS) in CT images by introducing a self-attention backward layer (SaB-Layer) to feed the self-attention information learned at the deep layer back to the shallow layers. The SaB-Layer efficiently extracts tumor information from CT images and integrates the information into the network, thereby enhancing the network's tumor segmentation ability. We employed datasets from two centers, one for model training and testing and the other for external validation. The model achieved dice scores of 0.8456 on the test set and 0.8068 on the external verification set. Moreover, we validated the model's transfer learning ability on a publicly available liver cancer dataset, achieving results comparable to state-of-the-art liver cancer segmentation models recently developed. SaB-Net has strong potential for assisting in the clinical diagnosis of and therapy for gastric cancer. Our implementation is available at https://github.com/TyrionJ/SaB-Net.