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Background: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.
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Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Recent advances in deep learning technology promise better diagnosis of CAD and improve assessment of CAD plaque buildup. The purpose of this study is to assess the performance of a deep learning algorithm in detecting and classifying coronary atherosclerotic plaques in coronary computed tomographic angiography (CCTA) images. Methods: Between January 2019 and September 2020, CCTA images of 669 consecutive patients with suspected CAD from Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine were included in this study. There were 106 patients included in the retrospective plaque detection analysis, which was evaluated by a deep learning algorithm and four independent physicians with varying clinical experience. Additionally, 563 patients were included in the analysis for plaque classification using the deep learning algorithm, and their results were compared with those of expert radiologists. Plaques were categorized as absent, calcified, non-calcified, or mixed. Results: The deep learning algorithm exhibited higher sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy {92% [95% confidence interval (CI): 89.5-94.1%], 87% (95% CI: 84.2-88.5%), 79% (95% CI: 76.1-82.4%), 95% (95% CI: 93.4-96.3%), and 89% (95% CI: 86.9-90.0%)} compared to physicians with ≤5 years of clinical experience in CAD diagnosis for the detection of coronary plaques. The algorithm's overall sensitivity, specificity, PPV, NPV, accuracy, and Cohen's kappa for plaque classification were 94% (95% CI: 92.3-94.7%), 90% (95% CI: 88.8-90.3%), 70% (95% CI: 68.3-72.1%), 98% (95% CI: 97.8-98.5%), 90% (95% CI: 89.8-91.1%) and 0.74 (95% CI: 0.70-0.78), indicating strong performance. Conclusions: The deep learning algorithm has demonstrated reliable and accurate detection and classification of coronary atherosclerotic plaques in CCTA images. It holds the potential to enhance the diagnostic capabilities of junior radiologists and junior intervention cardiologists in the CAD diagnosis, as well as to streamline the triage of patients with acute coronary symptoms.
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BACKGROUND: As an innovative treatment, stapled transperineal rectovaginal fistula repair (STR) for rectovaginal fistula (RVF) has demonstrated effectiveness in preliminary reports. This study aims to compare STR with rectal mucosal advancement flap repair (RAF), a widely utilized surgical procedure, for the surgical outcome of the low- and mid-level RVF. METHODS: In this retrospective cohort study, patients with low- and mid-level RVF who underwent STR or RAF were included from both the Sixth Affiliated Hospital of Sun Yat-sen University and Xi'an Daxing Hospital. Among the 99 total patients, 77 underwent STR and 22 underwent RAF. Patient demographics, operative data, and outcomes were collected and analyzed. Recurrence rate and associated risk factors were evaluated. RESULTS: There were no statistically significant differences among patients in terms of clinical characteristics like age, BMI, aetiology, and fistula features. During the follow-up period of 20 months (interquartile range 3.0-41.8 months), a total of 28 patients relapsed, with a significantly lower recurrence rate in the STR group (20.8 %) than in the RAF group (54.6 %) (P = 0.005). In the multivariate Cox analysis, STR was an independent protective factor against recurrence (HR: 0.37, 95%CI: 0.17-0.79, P = 0.01). Logistic regression indicated that there was no statistically significant difference between these two procedures in terms of surgical complications (OR: 0.53, 95%CI: 0.19-1.48, P = 0.23). CONCLUSION: For low- and mid-level RVF, STR may be an alternative option for treatment modality that offers a lower recurrence rate, without observed disadvantage in terms of surgical complication rates.
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Fístula Rectovaginal , Recto , Femenino , Humanos , Fístula Rectovaginal/etiología , Fístula Rectovaginal/cirugía , Estudios Retrospectivos , Recto/cirugía , Colgajos Quirúrgicos , Factores de Riesgo , Resultado del TratamientoRESUMEN
To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (Pâ >â .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.
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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Células Neoplásicas Circulantes , Humanos , Nomogramas , Neoplasias Pulmonares/patología , Tomografía Computarizada por Rayos X/métodos , Estudios RetrospectivosRESUMEN
In recent years, certain luminous extragalactic optical transients have been observed to last only a few days1. Their short observed duration implies a different powering mechanism from the most common luminous extragalactic transients (supernovae), whose timescale is weeks2. Some short-duration transients, most notably AT2018cow (ref. 3), show blue optical colours and bright radio and X-ray emission4. Several AT2018cow-like transients have shown hints of a long-lived embedded energy source5, such as X-ray variability6,7, prolonged ultraviolet emission8, a tentative X-ray quasiperiodic oscillation9,10 and large energies coupled to fast (but subrelativistic) radio-emitting ejecta11,12. Here we report observations of minutes-duration optical flares in the aftermath of an AT2018cow-like transient, AT2022tsd (the 'Tasmanian Devil'). The flares occur over a period of months, are highly energetic and are probably nonthermal, implying that they arise from a near-relativistic outflow or jet. Our observations confirm that, in some AT2018cow-like transients, the embedded energy source is a compact object, either a magnetar or an accreting black hole.
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Colonoscopy is the gold standard method for investigating the gastrointestinal tract. Localizing the polyps in colonoscopy images plays a vital role when doing a colonoscopy screening, and it is also quite important for the following treatment, e.g., polyp resection. Many deep learning-based methods have been applied for solving the polyp segmentation issue. However, precisely polyp segmentation is still an open issue. Considering the effectiveness of the Pyramid Pooling Transformer (P2T) in modeling long-range dependencies and capturing robust contextual features, as well as the power of pyramid pooling in extracting features, we propose a pyramid pooling based network for polyp segmentation, namely PPNet. We first adopt the P2T as the encoder for extracting more powerful features. Next, a pyramid feature fusion module (PFFM) combining the channel attention scheme is utilized for learning a global contextual feature, in order to guide the information transition in the decoder branch. Aiming to enhance the effectiveness of PPNet on feature extraction during the decoder stage layer by layer, we introduce the memory-keeping pyramid pooling module (MPPM) into each side branch of the encoder, and transmit the corresponding feature to each lower-level side branch. Experimental results conducted on five public colorectal polyp segmentation datasets are given and discussed. Our method performs better compared with several state-of-the-art polyp extraction networks, which demonstrate the effectiveness of the mechanism of pyramid pooling for colorectal polyp segmentation.
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Pólipos del Colon , Humanos , Pólipos del Colon/diagnóstico por imagen , Colonoscopía , Tracto Gastrointestinal , Procesamiento de Imagen Asistido por ComputadorRESUMEN
Given that chemotherapy as a stand-alone therapeutic strategy may not be sufficient to effectively treat cancer, there is increasing interest in combination of chemotherapy and alternative therapies. Photodynamic therapy has the advantages of high selectivity and low side effects, so the combination of photodynamic therapy and chemotherapy has become one of the most appealing strategies for tumor treatment. In this work, we constructed a nano drug codelivery system (PPDC) to realize the combined treatment of chemotherapy and photodynamic therapy through encapsulating chemotherapeutic drug dihydroartemisinin and photosensitizer chlorin e6 in PEG-PCL. The potentials, particle size and morphology of nanoparticles were characterized by dynamic light scattering and transmission electron microscopy. We also investigated the reactive oxygen species (ROS) generation and drug release ability. The antitumor effect in vitro was investigated by methylthiazolyldiphenyl-tetrazolium bromide assays and cell apoptosis experiments, and the potential cell death mechanisms were explored by ROS detection and Western blot analysis. The in vivo antitumor effect of PPDC was evaluated under the guidance of fluorescence imaging. Our work provides a potential antitumor treatment approach and expands the application of dihydroartemisinin for breast cancer therapy.
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Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.
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Nonalcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by excessive deposition of fatty acids in the liver. Further deterioration leads to nonalcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma, creating a heavy burden on human health and the social economy. Currently, there are no effective and specific drugs for the treatment of NAFLD. Therefore, it is important to further investigate the pathogenesis of NAFLD and explore effective therapeutic targets for the prevention and treatment of the disease. Six-transmembrane epithelial antigen of prostate 3 (STEAP3), a STEAP family protein, is a metalloreductase. Studies have shown that it can participate in the regulation of liver ischemia-reperfusion injury, hepatocellular carcinoma, myocardial hypertrophy, and other diseases. In this study, we found that the expression of STEAP3 is upregulated in NAFLD. Deletion of STEAP3 inhibits the development of NAFLD in vivo and in vitro, whereas its overexpression promotes palmitic acid/oleic acid stimulation-induced lipid deposition in hepatocytes. Mechanistically, it interacts with transforming growth factor beta-activated kinase 1 (TAK1) to regulate the progression of NAFLD by promoting TAK1 phosphorylation and activating the TAK1-c-Jun N-terminal kinase/p38 signaling pathway. Taken together, our results provide further insight into the involvement of STEAP3 in liver pathology.
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Carcinoma Hepatocelular , Resistencia a la Insulina , Neoplasias Hepáticas , Enfermedad del Hígado Graso no Alcohólico , Humanos , Masculino , Carcinoma Hepatocelular/patología , Hepatocitos/metabolismo , Hígado/metabolismo , Neoplasias Hepáticas/patología , Enfermedad del Hígado Graso no Alcohólico/metabolismo , Próstata/metabolismoRESUMEN
Dihydroartemisinin (DHA), a widely used antimalarial agent, has clinical potential for the treatment of hepatic carcinoma. Although chemotherapy is indispensable for tumor therapy, it is generally limited by poor solubility, low efficiency, rapid clearance, and side effects. As an emerging treatment method, photothermal therapy (PTT) has many outstanding properties, but suffers from poor photostability of photosensitizer and incomplete ablation. Multimodal therapies could combine the advantages of different therapy methods to improve antitumor efficiency. Hence, we designed a nano-delivery system (ICG&DHA@ZIF-8) using zeolitic imidazolate framework-8 (ZIF-8) with a high porous rate and pH sensitivity property, to co-load DHA and indocyanine green (ICG). Dynamic light scattering and transmission electron microscopy were used to characterize the prepared nanoparticles. The photothermal conversion and drug release performances of ICG&DHA@ZIF-8 were investigated. In vitro antitumor efficacy and cellular uptake were studied. The mechanism of the combination treatment was studied by reactive oxygen species level detection and western blot assays. In vivo antitumor assays were then studied with the guidance of ex vivo imaging. The results showed that the ICG&DHA@ZIF-8 based combination therapy could efficiently kill hepatic carcinoma cells and suppress tumor growth. This research provides a potential nanodrug for the treatment of hepatic carcinoma.
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Background: The myocardial status of patients who undergo percutaneous coronary intervention (PCI) must be evaluated accurately to enable treatment plans to be made for potential complications such as abrupt vessel closure, stent deformation, and myocardial chronic ischemia. This study examined the modality and clinical feasibility of iodine-based extracellular volume (ECV) assessment of the myocardium versus cardiovascular magnetic resonance (CMR) imaging in patients undergoing PCI. Methods: In all, 21 patients who underwent PCI were prospectively enrolled in the study. All patients underwent follow-up cardiac dual-layer spectral detector computed tomography (SDCT) and CMR imaging after PCI. Myocardial ECV was quantified by either computed tomography (ECVCT) or magnetic resonance (ECVMR) using iodine or T1-weighted mapping, respectively. The quality of SDCT and CMR images was independently assessed by two radiologists using a 4-point scale (1= poor and 4= excellent). Any patient with an image quality (IQ) score <2 was excluded. Consistency between radiologists was evaluated using intraclass correlation coefficients (ICC). Correlations between ECVCT and ECVMR values were analyzed using Pearson's test, and consistency was analyzed with Bland-Altman plots. Results: Nineteen of 21 patients completed both cardiac CT and CMR examinations, while three patients were excluded after IQ assessment (two with poor CMR IQ; one with a discontinuous coronary artery on CT images). The mean (±SD) IQ scores for CT and CMR images were 3.81±0.40 and 3.25±0.58, respectively, and interobserver agreement was good (ICC =0.93 and 0.92 for CT and CMR, respectively). The mean (±SD) ECVCT and ECVMR values were 35.93%±9.73% and 33.89%±7.51%, respectively, with good correlation (r=0.79, P<0.001). Bland-Altman analysis showed a difference of 2.04% (95% CI: -9.56%, 13.64%) between the ECVCT and ECVMR values. Conclusions: There is high correlation between iodine-based ECVCT and ECVMR values, which indicates that ECVCT is clinically feasible for evaluating the status of myocardial recovery in patients undergoing PCI.
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Background: To assess the clinical feasibility of using effective atomic number (Zeff) maps derived from non-contrast-enhanced computed tomography (NCECT) scans obtained by dual-layer spectral computed tomography (DLCT) to identify non-calcified atherosclerotic plaques. Methods: A total of 37 patients with 86 non-calcified atherosclerotic plaques confirmed by contrast-enhanced CT (CECT) were enrolled in this retrospective study. Both spectral-based-images (SBI) and conventional images (CI) were reconstructed from NCECT and CECT scans. The presence of plaques on NCECT Zeff maps and CIs were independently assessed by 2 radiologists. In CECT scans, plaques and regions of interest (ROIs) in vessel lumens were assessed with CT attenuation and Zeff values, and the proportion of plaques was determined as Area (plaque)/Area (vessel). The CT and Zeff values for plaques and blood were recorded from both CECT and NCECT scans. Contrast-to-noise ratios (CNRs) of the plaques were calculated and compared using CT attenuation and Zeff values. Finally, interobserver agreement was evaluated. Results: A total of 47 of the 86 (54.7%) plaques were identified on Zeff map images derived from the NCECT scans while only 7 (8.1%) plaques were identified on the CI. There was no significant difference between the mean vessel ROI area measured on CIs and that measured on Zeff map images (502.19 vs. 498.14 mm2; P=0.28), while the mean plaque ROI area was larger (81.45 vs. 75.46 mm2). The observer consensus of vessel and plaque ROI area measurements using both methods was excellent, with interclass correlation coefficients (ICCs) of 0.99 and 0.94, respectively. For the 7 plaques detected both by NCECT CI and Zeff mapping, the CT attenuation and Zeff blood values were both larger than the plaque values [42.00 vs. 25.67 Hounsfield unit (HU); 7.33 vs. 7.19 HU; both P<0.05]; the plaque ROI area measurement on the NCE Zeff map was smaller than that on the CE CI (48.73 vs. 77.76 mm2), but was much larger than that on the NCE CI (18.39 mm2). For all 47 plaques detected by NCE Zeff mapping, the CT attenuation and Zeff values of blood and plaques on the NCECT images showed no significant differences (42.53 vs. 35.14 HU; P=0.18; 7.32 vs. 7.31, P=0.71); however, the CNR of Zeff was significantly higher than the CT attenuation value (1.69 vs. 1.12; P<0.05) derived from the NCECT scans. Inter-reviewer agreement was good (ICC =0.78). Conclusions: Zeff map images derived from NCECT SBI with DLCT provide a potentially feasible approach for identifying non-calcified atherosclerotic plaques, which might be clinically useful for the screening of asymptomatic at-risk patients.
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OBJECTIVES: The aims of the study were to integrate characteristics of computed tomography (CT), texture, and hematological parameters and to establish predictive models for lymph node (LN) metastasis in lung adenocarcinoma. METHODS: A total of 207 lung adenocarcinoma cases with confirmed postoperative pathology and preoperative CT scans between February 2017 and April 2019 were included in this retrospective study. All patients were divided into training and 2 validation cohorts chronologically in the ratio of 3:1:1. The χ2 test or Fisher exact test were used for categorical variables. The Shapiro-Wilk test and Mann-Whitney U test were used for continuous variables. Logistic regression and machine learning algorithm models based on CT characteristics, texture, and hematological parameters were used to predict LN metastasis. The performance of the multivariate models was evaluated using a receiver operating characteristic curve; prediction performance was evaluated in the validation cohorts. Decision curve analysis confirmed its clinical utility. RESULTS: Logistic regression analysis demonstrated that pleural thickening (P = 0.013), percentile 25th (P = 0.033), entropy gray-level co-occurrence matrix 10 (P = 0.019), red blood cell distribution width (P = 0.012), and lymphocyte-to-monocyte ratio (P = 0.049) were independent risk factors associated with LN metastasis. The area under the curve of the predictive model established using the previously mentioned 5 independent risk factors was 0.929 in the receiver operating characteristic analysis. The highest area under the curve was obtained in the training cohort (0.777 using Naive Bayes algorithm). CONCLUSIONS: Integrative predictive models of CT characteristics, texture, and hematological parameters could predict LN metastasis in lung adenocarcinomas. These findings may provide a reference for clinical decision making.
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Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Adenocarcinoma del Pulmón/diagnóstico por imagen , Teorema de Bayes , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Metástasis Linfática/diagnóstico por imagen , Valor Predictivo de las Pruebas , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodosRESUMEN
In order to improve the energy dissipation capacity and to reduce the residual deformation of civil structures simultaneously, this paper puts forwards an innovative self-centering shape memory alloy (SMA) brace that is based on the design concepts of SMA's superelasticity and low friction slip. Seven self-centering SMA brace specimens were tested under cyclic loading, and the hysteresis curves, bond curves, secant stiffness, energy dissipation coefficient, equivalent damping coefficient, and the self-centering capacity ratio of these specimens were investigated, allowing us to provide an evaluation of the effects of the loading rate and initial strain on the seismic performance. The test results show that the self-centering SMA braces have an excellent energy dissipation capacity, bearing capacity, and self-centering capacity, while the steel plates remain elastic, and the SMA in the specimens that are always under tension are able to return to the initial state. The hysteresis curves of all of the specimens are idealized as a flag shape with low residual deformation, and the self-centering capacity ratio reached 89.38%. In addition, both the loading rate and the initial strain were shown to have a great influence on the seismic performance of the self-centering SMA brace. The improved numerical models combined with the Graesser model and Bouc-Wen model in MATLAB were used to simulate the seismic performance of the proposed braces with different loading rates and initial strains, and the numerical results are consistent with the test results under the same conditions, meaning that they can accurately predict the seismic performance of the self-centering SMA brace proposed here.
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Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or r2, and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; r2 = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner (P = .15), sex (P = .051), and section thickness (P = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.
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Angiografía por Tomografía Computarizada , Angiografía Coronaria , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Aprendizaje Profundo , Calcificación Vascular/diagnóstico por imagen , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios RetrospectivosRESUMEN
BACKGROUND: Previous studies have shown that alkannin has anticancer, anti-inflammatory, and antibacterial effects. However, the effect of alkannin in the development of ovarian cancer (OC) remains unknown. Therefore, this study aims to elucidate the function of alkannin in OC progression. METHODS: RT-qPCR and western blot analysis were used to measure mRNA and protein expression. Cell viability and metastasis were detected by the CCK-8 assay, flow cytometry analysis, and transwell assay. RESULTS: Alkannin had no cytotoxicity toward normal ovarian cells, but alkannin can inhibit cell proliferation and induce apoptosis in OC cells. In addition, alkannin inhibited cell migration and invasion and blocked EMT in OC. Besides, upregulation of miR-4461 was found in OC tissues and cells, which was regulated by alkannin. More importantly, miR-4461 can inverse the effects of alkannin on cell viability and metastasis in OC cells. CONCLUSION: Alkannin restrains cell viability, metastasis, and EMT in OC by downregulating miR-4461 expression.
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PURPOSE: To analyze the impact of hyperglycemia on the clinical outcome of COVID-19 in patients with newly diagnosed diabetes (NDD). PATIENTS AND METHODS: We performed a retrospective study of 3114 cases of COVID-19 without pre-existing diabetes, 351 of which had NDD, in Hubei Province, China. The Cox regression model was used to calculate the risk of adverse clinical outcomes comparing the NDD vs non-NDD group before and after propensity score-matched (PSM) analysis. Patients with NDD were further divided into a sustained hyperglycemia group, a fluctuating group, and a remitted group based on their blood glucose levels during hospitalization as well as into hypoglycemic agent users and nonusers. RESULTS: Compared to the non-NDD individuals, individuals with NDD had a significantly increased risk of all-cause mortality (adjusted HR after PSM, 2.65; 95% CI, 1.49-4.72; P = 0.001) and secondary outcomes involving organ damage during the 28-day follow-up period. Subgroup analyses indicated that among individuals with NDD, the individuals with remitted hyperglycemia had the lowest 28-day mortality, whereas those with sustained hyperglycemia had the highest (IRR 24.27; 95% CI, 3.21-183.36; P < 0.001). Moreover, individuals treated with hypoglycemic agents had significantly lower all-cause mortality than those not treated with hypoglycemic agents (IRR 0.08; 95% CI, 0.01-0.56; P < 0.001). CONCLUSION: Our study reinforces the clinical message that NDD is strongly associated with poor outcomes in COVID-19 patients. Furthermore, resolved hyperglycemia in the later phase of the disease and the use of hypoglycemic agents were associated with improved prognosis in patients with NDD.
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OBJECTIVE: To investigate the curative effects of montmorillonite powder combined with dexamethasone on acute radiation enteritis. METHODS: Eighty-six patients with acute radiation enteritis were enrolled in this prospective research, and they were divided into a control group and an intervention group using a random number table, with 43 cases in each group. Patients in both groups received conventional treatment. The control group was treated with montmorillonite powder, and the intervention group was treated with retention enema with dexamethasone based on montmorillonite powder. The grades of mucosal damage, changes in cytokine levels, the efficacy of colonoscopy, and overall curative effects of the two groups before and after treatment were observed. RESULTS: After treatment, the levels of IL-2 and IFN-γ of the two groups were significantly reduced, and the level of IL-10 was significantly increased. The intervention group was significantly better than the control group (all P<0.001). The grades of mucosal damage in the intervention group showed better improvement than that in the control group (P<0.01), and the overall curative effects in the intervention group were significantly better than those in the control group (both P<0.05). CONCLUSION: Montmorillonite powder combined with dexamethasone is effective in treating patients with acute radiation enteritis, which can effectively alleviate mucosal damage, improve inflammation, and promote patient recovery. It has the value of promotion and application.
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Obesity is characterized by the excessive accumulation of the white adipose tissue (WAT), but healthy expansion of WAT via adipocyte hyperplasia can offset the negative metabolic effects of obesity. Thus, identification of novel adipogenesis regulators that promote hyperplasia may lead to effective therapies for obesity-induced metabolic disorders. Using transcriptomic approaches, we identified transmembrane BAX inhibitor motif-containing 1 (TMBIM1) as an inhibitor of adipogenesis. Gain or loss of function of TMBIM1 in preadipocytes inhibited or promoted adipogenesis, respectively. In vivo, in response to caloric excess, adipocyte precursor (AP)-specific Tmbim1 knockout (KO) mice displayed WAT hyperplasia and improved systemic metabolic health, while overexpression of Tmbim1 in transgenic mice showed the opposite effects. Moreover, mature adipocyte-specific Tmbim1 KO did not affect WAT cellularity or nutrient homeostasis. Mechanistically, TMBIM1 binds to and promotes the autoubiquitination and degradation of NEDD4, which is an E3 ligase that stabilizes PPARγ. Our data show that TMBIM1 is a potent repressor of adipogenesis and a potential therapeutic target for obesity-related metabolic disease.
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Adipogénesis , Enfermedades Metabólicas , Adipocitos Blancos/metabolismo , Tejido Adiposo Blanco/metabolismo , Animales , Hiperplasia/metabolismo , Proteínas de la Membrana , Enfermedades Metabólicas/metabolismo , Ratones , Ratones Endogámicos C57BL , Proteínas del Tejido Nervioso , Obesidad/metabolismoRESUMEN
Beta-amyloid (Aß1-42) triggers the phosphorylation of tau protein in Alzheimer's disease (AD), but the relationship between phosphorylated tau (p-tau) and Aß1-42 in the blood is not elucidated. We investigated the association in individuals with AD (n = 62, including amnesic mild cognitive impairment and dementia), Parkinson's disease (n = 30), frontotemporal dementia (n = 25), and cognitively unimpaired controls (n = 41) using immunomagnetic reduction assays to measure plasma Aß1-42 and p-tau181 concentrations. Correlation and regression analyses were performed to examine the relation between plasma levels, demographic factors, and clinical severity. Both plasma Aß1-42 and p-tau concentrations were significantly higher in AD and frontotemporal dementia than in the controls and Parkinson's disease. A significant positive association was found between plasma p-tau and Aß1-42 in controls (r = 0.579, P < 0.001) and AD (r = 0.699, P < 0.001) but not in frontotemporal dementia or Parkinson's disease. Plasma p-tau was significantly associated with clinical severity in the AD in terms of scores of clinical dementia rating (r = 0.288, P = 0.025) and mini-mental state examination (r = -0.253, P = 0.049). Regression analysis showed that plasma Aß1-42 levels explain approximately 47.7% of the plasma p-tau levels in the AD after controlling age, gender, and clinical severity. While in non-AD participants, the clinical dementia rating explained about 47.5% of the plasma p-tau levels. The disease-specific association between plasma Aß1-42 and p-tau levels in AD implies a possible synergic effect in mechanisms involving these two pathological proteins' genesis.