A mitochondrial EglN1-AMPKα axis drives breast cancer progression by enhancing metabolic adaptation to hypoxic stress.

Mitochondria play essential roles in cancer cell adaptation to hypoxia, but the underlying mechanisms remain elusive. Through mitochondrial proteomic profiling, we here find that the prolyl hydroxylase EglN1 (PHD2) accumulates on mitochondria under hypoxia. EglN1 substrate-binding region in the ß2ß3 loop is responsible for its mitochondrial translocation and contributes to breast tumor growth. Furthermore, we identify AMP-activated protein kinase alpha (AMPKα) as an EglN1 substrate on mitochondria. The EglN1-AMPKα interaction is essential for their mutual mitochondrial translocation. After EglN1 prolyl-hydroxylates AMPKα under normoxia, they rapidly dissociate following prolyl-hydroxylation, leading to their immediate release from mitochondria. In contrast, hypoxia results in constant EglN1-AMPKα interaction and their accumulation on mitochondria, leading to the formation of a Ca2+ /calmodulin-dependent protein kinase 2 (CaMKK2)-EglN1-AMPKα complex to activate AMPKα phosphorylation, ensuring metabolic homeostasis and breast tumor growth. Our findings identify EglN1 as an oxygen-sensitive metabolic checkpoint signaling hypoxic stress to mitochondria through its ß2ß3 loop region, suggesting a potential therapeutic target for breast cancer.

A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection.

The purpose of this study is to develop a lightweight and easily deployable deep learning system for fully automated content-based brain MRI sorting and artifacts detection. 22092 MRI volumes from 4076 patients between 2017 and 2021 were involved in this retrospective study. The dataset mainly contains 4 common contrast (T1-weighted (T1w), contrast-enhanced T1-weighted (T1c), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR)) in three perspectives (axial, coronal, and sagittal), and magnetic resonance angiography (MRA), as well as three typical artifacts (motion, aliasing, and metal artifacts). In the proposed architecture, a pre-trained EfficientNetB0 with the fully connected layers removed was used as the feature extractor and a multilayer perceptron (MLP) module with four hidden layers was used as the classifier. Precision, recall, F1_Score, accuracy, the number of trainable parameters, and float-point of operations (FLOPs) were calculated to evaluate the performance of the proposed model. The proposed model was also compared with four other existing CNN-based models in terms of classification performance and model size. The overall precision, recall, F1_Score, and accuracy of the proposed model were 0.983, 0.926, 0.950, and 0.991, respectively. The performance of the proposed model was outperformed the other four CNN-based models. The number of trainable parameters and FLOPs were the smallest among the investigated models. Our proposed model can accurately sort head MRI scans and identify artifacts with minimum computational resources and can be used as a tool to support big medical imaging data research and facilitate large-scale database management.

Mechanical and medical imaging properties of 3D-printed materials as tissue equivalent materials.

Three materials of polylactic acid (PLA), polyamide 12 (PA12), and light curing resin (LCR) were used to construct phantom using 3D printing technology. The mechanical and medical imaging properties of the three materials, such as elastic modulus, density, effective atomic number, X-ray attenuation coefficient, computed tomography (CT) number, and acoustic properties, were investigated. The results showed that the elastic modulus for PLA was 1.98 × 103  MPa, for PA12 was 848 MPa, for LCR was 1.18×103  MPa, and that of three materials was close to some bones. In the range of 40∼120 kV, the X-ray attenuation coefficient of three materials decreased with increasing tube voltage. The CT number for PLA, PA12, and LCR was 144, -88, and 312 Hounsfield units at 120 kV tube voltage, respectively. The density and the effective atomic number product (ρ*Zeff ) were computed from three materials and decreased in the order of LCR, PLA, and PA12. The acoustic properties of materials were also studied. The speeds of sound of three materials were similar with those of some soft tissues.

Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid-enhanced magnetic resonance imaging.

OBJECTIVES: To identify image features of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to determine its role in predicting MTM-HCC. METHODS: Patients who underwent preoperative gadoxetic acid-enhanced MRI and with surgery proven HCC were retrospectively included. Imaging features were assessed according to Liver Imaging Reporting and Data System. Quantitative measurements were recorded. Clinical characteristics and imaging findings were compared between MTM-HCCs and non-MTM-HCCs. Predictive factors of MTM-HCC were screened with univariate analyses and then identified with multivariate logistic regression. A regression-based diagnostic model was constructed. ROC analyses were used to determine cutoff values, AUC, and corresponding 95% confidence interval (CI) of findings. The diagnostic performance was validated by 10-fold cross-validation. RESULTS: One hundred and forty-one patients with 37 MTM-HCCs were included. Multivariate analyses identified high platelet count (≥ 163.5 × 103/ul, odds ratio = 3.20; 95% CI: 1.29, 7.96; p = 0.012), low tumor-to-liver ADC ratio (≤ 1.05, odds ratio = 3.05; 95% CI, 1.23 - 7.55; p = 0.016), and necrosis or severe ischemia (odds ratio = 11.61; 95% CI, 3.99 - 33.76, p < 0.001) as independent predictors of MTM-HCC. Necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. The average AUCs were 0.81 (95% CI: 0.71, 0.90) for the regression-based diagnostic model, with a sensitivity of 57% and specificity of 92%. CONCLUSIONS: Necrosis or severe ischemia was a sensitive imaging feature of MTM-HCC. Noninvasive prediction of this subtype can be achieved with good accuracy and excellent specificity when findings were combined. KEY POINTS: • The macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) represents an aggressive subtype of HCC and is associated with poor prognosis. • Imaging features of necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. • A regression-based diagnostic model including high platelet count (≥ 163.5 × 103/ul), low tumor-to-liver ADC ratio (≤ 1.05), and necrosis or severe ischemia can provide noninvasive assessment of MTM-HCC with good accuracy and high specificity.

Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains.

PURPOSE: To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities. MATERIALS AND METHODS: In this study, a novel method based on three-dimensional deep autoencoder network is proposed to automatically detect and segment various brain abnormalities without being trained on any abnormal samples. A total of 578 normal T2w MR volumes without obvious abnormalities were used for model training and validation. The proposed 3D autoencoder was evaluated on two different datasets (BraTs dataset and in-house dataset) containing T2w volumes from patients with glioblastoma, multiple sclerosis and cerebral infarction. Lesions detection and segmentation performance were reported as AUC, precision-recall curve, sensitivity, and Dice score. RESULTS: In anomaly detection, AUCs for three typical lesions were as follows: glioblastoma, 0.844; multiple sclerosis, 0.858; cerebral infarction, 0.807. In anomaly segmentation, the mean Dice for glioblastomas was 0.462. The proposed network also has the ability to generate an anomaly heatmap for visualization purpose. CONCLUSION: Our proposed method was able to automatically detect various brain anomalies such as glioblastoma, multiple sclerosis, and cerebral infarction. This work suggests that unsupervised anomaly detection is a powerful approach to detect arbitrary brain abnormalities without labeled samples. It has the potential to support diagnostic workflow in radiology as an automated tool for computer-aided image analysis.

FDXR drives primary and endocrine-resistant tumor cell growth in ER+ breast cancer via CPT1A-mediated fatty acid oxidation.

Background: The majority of breast cancers (BCs) expressing estrogen receptor (ER) have shown endocrine resistance. Our previous study demonstrated that ferredoxin reductase (FDXR) promoted mitochondrial function and ER+ breast tumorigenesis. But the underlying mechanism is not clear. Methods: Liquid chromatography (LC) tandem mass spectrometry (MS/MS)-based metabolite profiling was utilized to reveal the metabolites regulated by FDXR. RNA microarray was utilized to determine the potential downstream targets of FDXR. Seahorse XF24 analyzer was performed to analyze the FAO-mediated oxygen consumption rate (OCR). Q-PCR and western blotting assays were used to measure expression levels of FDXR and CPT1A. MTS, 2D colony formation and anchorage-independent growth assays were used to evaluate the effects of FDXR or drug treatments on tumor cell growth of primary or endocrine-resistant breast cancer cells. Results: We found that depletion of FDXR inhibited fatty acid oxidation (FAO) by suppressing CPT1A expression. Endocrine treatment increased the expression levels of both FDXR and CPT1A. Further, we showed that depletion of FDXR or FAO inhibitor etomoxir treatment reduced primary and endocrine-resistant breast cancer cell growth. Therapeutically, combining endocrine therapy with FAO inhibitor etomoxir synergistically inhibits primary and endocrine-resistant breast cancer cell growth. Discussion: We reveal that the FDXR-CPT1A-FAO signaling axis is essential for primary and endocrine-resistant breast cancer cell growth, thus providing a potential combinatory therapy against endocrine resistance in ER+ breast cancer.

Relative specificity of acupuncture reinforcing and reducing methods.

OBJECTIVE: To probe the effects of acupuncture reinforcing and reducing methods on the human body. METHODS: Studied acupuncture reinforcing and reducing actions in the ancient literature, based on modern clinical and experimental studies, in combination with experience of acupuncture teaching and clinical treatment. RESULTS: The key to the generation of reinforcing and reducing actions is not the acupuncture reinforcing and reducing methods themselves, but instead the functional state of the patient during the acupuncture. When reinforcing and reducing methods act on the human body, a reinforcing-reducing effect is produced through regulation of Qi, indicating that the production of the reinforcing-reducing effect requires a certain condition. Specifically, acupuncture does not produce the reinforcing-reducing effect under all conditions, but can produce a reinforcing effect in patients with deficiency syndrome and a reducing effect in patients with excess syndrome. CONCLUSION: Reinforcing and reducing methods each have therapeutic effects on both deficiency syndrome and excess syndrome, but a reinforcing method should be selected first for deficiency syndrome and a reducing method should be selected first for excess syndrome.

FGF21 Enhances Therapeutic Efficacy and Reduces Side Effects of Dexamethasone in Treatment of Rheumatoid Arthritis.

In order to investigate efficacy of FGF21 combine dexamethasone (Dex) on rheumatoid arthritis (RA) meanwhile reduce side effects of dexamethasone. We used combination therapy (Dex 15 mg/kg + FGF21 0.25 mg/kg, Dex 15 mg/kg + FGF21 0.5 mg/kg or Dex 15 mg/kg + FGF21 1 mg/kg) and monotherapy (Dex 15 mg/kg or FGF21 1 mg/kg) to treat CIA mice induced by chicken type II collagen, respectively. The effects of treatment were determined by arthritis severity score, histological damage, and cytokine production. The levels of oxidative stress parameters, liver functions, and other blood biochemical indexes were detected to determine FGF21 efficiency to side effects of dexamethasone. Oil red O was performed to detect the effects of FGF21 and dexamethasone on fat accumulation in HepG2 cells. The mechanism of FGF21 improves the side effects of dexamethasone which was analyzed by Western blotting. This combination proved to be therapeutically more effective than dexamethasone or FGF21 used singly. FGF21 regulates oxidative stress and lipid metabolism by upregulating dexamethasone-inhibited SIRT-1 and then activating downstream Nrf-2/HO-1and PGC-1. FGF21 and dexamethasone are highly effective in the treatment of arthritis; meanwhile, FGF21 may overcome the limited therapeutic response and Cushing syndrome associated with dexamethasone.

Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis.

PURPOSE: A meta-analysis was performed to assess the diagnostic performance of diffusion-weighted imaging (DWI) in liver fibrosis (LF) staging. METHODS: We conducted a comprehensive literature search to identify relevant articles. Diagnostic data were extracted for each METAVIR fibrosis stage (F0-F4). A bivariate binomial model was used to combine sensitivities and specificities. Summary receiver operating characteristics (SROC) curves were performed and areas under SROC curve (AUC) were calculated to indicate diagnostic accuracies. Subgroup analyses were performed between different study characteristics. RESULTS: Twelve studies met the inclusion criteria for LF ≥F1, 16 for ≥F2, 18 for ≥F3, and 12 for F4. AUCs of DWI were 0.8554, 0.8770, 0.8836, and 0.8596 for ≥F1, ≥F2, ≥F3, and F4, respectively. Subgroup analyses showed that for LF ≥F2 and ≥F3, maximal b values (b max) ≥ 800 s/mm2 performed significantly better than b max < 800 s/mm2. The diagnostic accuracies of 3.0 T and intravoxel incoherent motion (IVIM)-DWI were significantly higher than those of 1.5 T and conventional DWI for diagnosing liver cirrhosis (F4). CONCLUSIONS: DWI is a reliable noninvasive technique with good diagnostic accuracy for LF staging. Using b max ≥ 800 s/mm2, high-field strength (3.0 T) and IVIM-DWI can optimize the diagnostic performance of DWI.

Preoperative evaluation of the histological grade of hepatocellular carcinoma with diffusion-weighted imaging: a meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance of diffusion-weighted imaging (DWI) in the preoperative prediction of the histological grade of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: A comprehensive literature search was performed in several authoritative databases to identify relevant articles. QUADAS-2 was used to assess the quality of included studies. Data were extracted to calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR). Summary receiver operating characteristic (SROC) curves were derived and areas under the SROC curve (AUC) were computed to indicate the diagnostic accuracy. Heterogeneity test, meta-regression analysis and sensitivity analysis were performed to identify factors and studies contributed to the heterogeneity. RESULTS: A total of 11 studies with 912 HCCs were included in this meta-analysis. The pooled sensitivity, specificity, PLR and NLR with corresponding 95% confidence intervals (CI) were 0.54(0.47-0.61), 0.90(0.87-0.93), 4.88(2.99-7.97) and 0.46(0.27-0.77) for the prediction of well-differentiated HCC (w-HCC), 0.84(0.78-0.89), 0.48(0.43-0.52), 2.29(1.43-3.69) and 0.30(0.22-0.41) for the prediction of poorly-differentiated HCC (p-HCC). The AUC were 0.9311 and 0.8513 in predicting w-HCC and p-HCC, respectively. Results were further evaluated according to the method of image interpretation. Significant heterogeneity was observed. CONCLUSION: DWI had excellent and moderately high diagnostic accuracy for the detection of w-HCC and p-HCC, respectively. Nonetheless, further studies in larger populations and an optimized image acquisition and interpretation are required before DWI-derived parameters can be used as a useful image biomarker for the prediction of the histological grade of HCC.