Methods for Improving the Germination of Rhodotypos scandens (Thunb.) Makino Seeds through Endocarp Removal.

Rhodotypos scandens (Thunb.) Makino is known to have a seed dispersal that is thick and stony (endocarp + seeds) and has potential as a landscaping tree seed. In several Rosaceae species, seeds are covered with a hard endocarp, making the internal seeds water-impermeable and germination difficult. Here, we analyzed the morphoanatomical traits and germination properties of R. scandens seeds. To identify ideal seed propagation conditions, we immersed R. scandens seeds in sulfuric acid for varying durations and subjected them to phytohormone (gibberellic acid A3 and fluridone) and a cold stratification (CS) (5 °C) treatment after endocarp removal (ER). The R. scandens stony seeds did not increase in mass by ≥25.0%. Following ER, the seed mass increased by ≥50.0% with water absorption when compared to the initial dry mass. Seed surfaces showed damage and cracks through scarification after 1 h of immersion in sulfuric acid, failing to germinate. A combination of ER, phytohormone treatment, and CS improved seed germination compared to ER alone (26.0 ± 5.3%). Overall, R. scandens seeds showed a dispersal with a hard endocarp from the parent plant, and a pre-treatment with ER, phytohormones, and CS was required for effective seed propagation.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS: In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS: In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION: Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

Diabetic sensory neuropathy and insulin resistance are induced by loss of UCHL1 in Drosophila.

Diabetic sensory neuropathy (DSN) is one of the most common complications of type 2 diabetes (T2D), however the molecular mechanistic association between T2D and DSN remains elusive. Here we identify ubiquitin C-terminal hydrolase L1 (UCHL1), a deubiquitinase highly expressed in neurons, as a key molecule underlying T2D and DSN. Genetic ablation of UCHL1 leads to neuronal insulin resistance and T2D-related symptoms in Drosophila. Furthermore, loss of UCHL1 induces DSN-like phenotypes, including numbness to external noxious stimuli and axonal degeneration of sensory neurons in flies' legs. Conversely, UCHL1 overexpression improves DSN-like defects of T2D model flies. UCHL1 governs insulin signaling by deubiquitinating insulin receptor substrate 1 (IRS1) and antagonizes an E3 ligase of IRS1, Cullin 1 (CUL1). Consistent with these results, genetic and pharmacological suppression of CUL1 activity rescues T2D- and DSN-associated phenotypes. Therefore, our findings suggest a complete set of genetic factors explaining T2D and DSN, together with potential remedies for the diseases.

Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: A Multicenter Diagnostic Study.

CONTEXT: It is not clear how to integrate artificial intelligence (AI)-based models into diagnostic workflows. OBJECTIVE: To develop and validate a deep-learning-based AI model (AI-Thyroid) for thyroid cancer diagnosis, and to explore how this improves diagnostic performance. METHODS: The system was trained using 19 711 images of 6163 patients in a tertiary hospital (Ajou University Medical Center; AUMC). It was validated using 11 185 images of 4820 patients in 24 hospitals (test set 1) and 4490 images of 2367 patients in AUMC (test set 2). The clinical implications were determined by comparing the findings of six physicians with different levels of experience (group 1: 4 trainees, and group 2: 2 faculty radiologists) before and after AI-Thyroid assistance. RESULTS: The area under the receiver operating characteristic (AUROC) curve of AI-Thyroid was 0.939. The AUROC, sensitivity, and specificity were 0.922, 87.0%, and 81.5% for test set 1 and 0.938, 89.9%, and 81.6% for test set 2. The AUROCs of AI-Thyroid did not differ significantly according to the prevalence of malignancies (>15.0% vs ≤15.0%, P = .226). In the simulated scenario, AI-Thyroid assistance changed the AUROC, sensitivity, and specificity from 0.854 to 0.945, from 84.2% to 92.7%, and from 72.9% to 86.6% (all P < .001) in group 1, and from 0.914 to 0.939 (P = .022), from 78.6% to 85.5% (P = .053) and from 91.9% to 92.5% (P = .683) in group 2. The interobserver agreement improved from moderate to substantial in both groups. CONCLUSION: AI-Thyroid can improve diagnostic performance and interobserver agreement in thyroid cancer diagnosis, especially in less-experienced physicians.

Dormancy-release and germination improvement of Korean bellflower (Campanula takesimana Nakai), a rare and endemic plant native to the Korean peninsula.

Korean bellflower (Campanula takesimana Nakai) is a rare and perennial herb with medicinal and ornamental values, is endemic to the Ulleung Island of Korea. In this study, we investigated the dormancy-release and germination characteristics of C. takesimana (Campanulaceae) seeds by subjecting them to varying temperatures (5, 10, 15, 20, and 25°C and diurnal/nocturnal temperatures of 15/6, 20/10, and 25/15°C), cold stratification periods (0, 4, 8, or 12 weeks at 5°C), and gibberellic acid (GA3) concentrations (0, 10, 100, or 1,000 mg·L-1 at 15/6°C and 25/15°C) to identify the ideal seed propagation conditions. The seeds were stimulated to germinate (at 25°C, 12-h photoperiod with fluorescent lamps at 40 ± 10 µmol∙m-2∙s-1) after cold stratification. To examine the germination characteristics, the seeds were tested for water imbibition and found to readily absorb water. The seeds exhibited underdeveloped embryos during dispersal, showed final germination of 37.00% ± 4.43 at 25°C and were not influenced by temperature. The seeds subjected to 0, 4, 8, or 12 weeks of cold stratification germinated at a success rate of 22.00% ± 4.76, 87.00% ± 6.80, 79.00% ± 2.52, and 77.00% ± 1.91, respectively. Additionally, the germination characteristics, which were based on final germination, mean germination time, and germination velocity (Timson index), were significantly greater in the seeds pretreated with 1,000 mg·L-1 GA3 at 25/15°C than in seeds pretreated with 0 mg·L-1 GA3. Overall, the seeds broke dormancy with GA3 and short-term cold stratification. Therefore, we concluded that C. takesimana seeds have non-deep, simple, morphophysiological dormancy, and pretreatment with cold stratification and GA3 is required for effective seed propagation.

Novel Cu(II) complexes as DNA-destabilizing agents and their DNA nuclease activity.

Here, we report a series of four novel Cu complexes, namely 2-(piperidin-1-ylmethyl)quinoline copper(II) nitrate, [LACu(NO3)2] (Cu1), 4-(quinolin-2-ylmethyl)morpholine copper(II) nitrate, [LBCu(NO3)2] (Cu2), 4-(quinolin-2-ylmethyl)morpholine copper(II) chloride, [LBCuCl2] (Cu3), and 2-(piperidin-1-ylmethyl)pyridine copper(II) chloride, [LCCu(µ-Cl)Cl]2 (Cu4). X-ray diffraction studies revealed that the geometry around the Cu(II) center could be best described as distorted octahedral in Cu1 and Cu2, whereas Cu3 and Cu4 showed distorted tetrahedral and square pyramidal geometries, respectively. DNA binding studies showed that Cu complexes Cu1-3 containing quinoline interacted via minor groove binding, whereas the Cu4 complex containing pyridine interacted via intercalation. All Cu complexes containing quinoline and pyridine caused destabilization of DNA at specific homogeneous G-C regions. The Cu1-3 complexes as groove binders destabilized the DNA structure much more than the Cu4 complex as an intercalator. Regarding groove binders, the Cu2 complex containing quinoline and morpholine caused the highest distortion and destabilization of the DNA structure, leading to high DNA cleavage efficiency.

The complete chloroplast genomes of Allium sacculiferum, A. thunbergii, and A. taquetii (Amaryllidaceae) from Korea.

The genus Allium comprises some of the most commonly consumed food crops worldwide. The chloroplast genomes of A. sacculiferum, A. thunbergii, and A. taquetii are 152,444, 153,459, and 154,056 bp circular molecular genomes, respectively. The annotation results revealed the presence of 132 (89 protein-coding, 35 tRNA, and eight rRNA), 132 (86 protein-coding, 38 tRNA, and eight rRNA), and 132 (86 protein-coding, 38 tRNA, and eight rRNA) genes with 36.78%, 36.83%, and 36.88% total GC content in each genome, respectively. The chloroplast genome of each Allium species contains an 81,254, 82,473, and 83,068 bp LSC region, an 18,176, 18,006, and 17,958 bp SSC region, and a pair of 26,507, 26,490, and 26,515 bp IR regions, respectively. Phylogenetic analysis based on the 21 complete chloroplast genomes indicates that A. sacculiferum is closely related to A. koreanum; A. thunbergii and A taquetii are closely related to A. hookeri. This study shows that the three Allium species are Korean crop wild relatives that may be used to develop new Allium varieties in the future.

PINK1 and Parkin regulate IP3R-mediated ER calcium release.

Although defects in intracellular calcium homeostasis are known to play a role in the pathogenesis of Parkinson's disease (PD), the underlying molecular mechanisms remain unclear. Here, we show that loss of PTEN-induced kinase 1 (PINK1) and Parkin leads to dysregulation of inositol 1,4,5-trisphosphate receptor (IP3R) activity, robustly increasing ER calcium release. In addition, we identify that CDGSH iron sulfur domain 1 (CISD1, also known as mitoNEET) functions downstream of Parkin to directly control IP3R. Both genetic and pharmacologic suppression of CISD1 and its Drosophila homolog CISD (also known as Dosmit) restore the increased ER calcium release in PINK1 and Parkin null mammalian cells and flies, respectively, demonstrating the evolutionarily conserved regulatory mechanism of intracellular calcium homeostasis by the PINK1-Parkin pathway. More importantly, suppression of CISD in PINK1 and Parkin null flies rescues PD-related phenotypes including defective locomotor activity and dopaminergic neuronal degeneration. Based on these data, we propose that the regulation of ER calcium release by PINK1 and Parkin through CISD1 and IP3R is a feasible target for treating PD pathogenesis.

Fecal microbiota transplantation improves hepatic fibro-inflammation via regulating oxidative stress in experimental NASH.

Nonalcoholic steatohepatitis (NASH) is associated with imbalance of gut microbiome, indicating participation of gut environment in hepatic health status. Therefore, modulating gut environment via fecal microbiota transplantation (FMT) is a promising therapeutic procedure for NASH patients. However, the effect and mechanism of the FMT remains largely unknown. Here, we investigated the gut-liver axis to understand the FMT-mediated hepatic improvement in NASH. Feces from specific pathogen free mice were infused allogeneically into gastrointestinal tract of mice fed with high fat, high cholesterol and fructose (HFHCF), resulting in suppressing hepatic pathogenic events, featured by decreasing inflammatory and fibrotic mediators. The FMT elevated NF-E2-related factor 2 (NRF2), a key transcription factor that regulates antioxidant enzymes, in livers. The HFHCF-induced NASH increased intestinal permeability with abundant Facklamia and Aerococcus, an imbalanced gut environment that was significantly improved by the FMT, characterized with restoration of intestinal barrier function and an enrichment of Clostridium. Notably, the gut environment created by FMT was inferred to produce metabolites from the aromatic biogenic amine degradation pathway, specifically 4-hydroxyphenylacetic acid (4-HPA), which is known to ameliorate liver injury. We suggest that gut-derived molecules, related to hepatic improvement such as 4-HPA are the potential therapeutic agents for preventing and treating NASH.

Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI.

OBJECTIVES: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. METHODS: A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. RESULTS: The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p = .942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81~0.92, and AUPRC, 0.88~0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p < .001), demonstrating the DL's decision. CONCLUSIONS: Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings. CLINICAL RELEVANCE STATEMENT: Our AI triages patients with raw MRI images to clinical referral pathways in brain intra-axial mass-like lesions. We demonstrate that the decision is based on the relative relevance between contrast-enhanced T1-weighted and diffusion-weighted images, providing explainability across multiparametric MRI data. KEY POINTS: • A deep learning (DL) system using multiparametric MRI suggested clinical referral to patients with intra-axial mass-like lesions (IMLLs) similar to radiologists (accuracy 72.3% vs. 72.6%). • In the differentiation of tumourous and non-tumourous conditions, the DL system (AUC, 0.90) performed similar with radiologists (AUC, 0.81-0.92). • The DL's decision basis for differentiating tumours from non-tumours can be quantified using multiparametric heatmaps obtained via the layer-wise relevance propagation method.

Contrast-enhanced CT-based Radiomics for the Differentiation of Anaplastic or Poorly Differentiated Thyroid Carcinoma from Differentiated Thyroid Carcinoma: A Pilot Study.

Differential diagnosis of anaplastic thyroid carcinoma/poorly differentiated thyroid carcinoma (ATC/PDTC) from differentiated thyroid carcinoma (DTC) is crucial in patients with large thyroid malignancies. This study creates a predictive model using radiomics feature analysis to differentiate ATC/PDTC from DTC. We compared the clinicoradiological characteristics and radiomics features extracted from a volume of interest on contrast-enhanced computed tomography (CT) between the groups. Estimations of variable importance were performed via modeling using the random forest quantile classifier. The diagnostic performance of the model with radiomics features alone had the area under the receiver operating characteristic (AUROC) curve value of 0.883. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 81.7%, 93.3%, 97.7%, 64.5%, and 84.6%, respectively, for the differential diagnosis of ATC/PDTC and DTC. The model with both radiomics and clinicoradiological information showed the AUROC of 0.908, with sensitivity, specificity, PPV, NPV, and accuracy of 82.9%, 97.6%, 99.2%, 67.1%, and 86.5% respectively. Distant metastasis, moment, shape, age, and gray-level size zone matrix features were the most useful factors for differential diagnosis. Therefore, we concluded that a radiomics approach based on contrast-enhanced CT features can potentially differentiate ATC/PDTC from DTC in patients with large thyroid malignancies.

Tumor Habitat Analysis Using Longitudinal Physiological MRI to Predict Tumor Recurrence After Stereotactic Radiosurgery for Brain Metastasis.

OBJECTIVE: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. MATERIALS AND METHODS: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. RESULTS: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). CONCLUSION: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

Development of a machine learning-based fine-grained risk stratification system for thyroid nodules using predefined clinicoradiological features.

OBJECTIVE: We constructed and validated a machine learning-based malignancy risk estimation model using predefined clinicoradiological features, and evaluated its clinical utility for the management of thyroid nodules. METHODS: In total, 5708 benign (n = 4597) and malignant (n = 1111) thyroid nodules were collected from 5081 consecutive patients treated in 26 institutions. Seventeen experienced radiologists evaluated nodule characteristics on ultrasonographic images. Eight predictive models were used to stratify the thyroid nodules according to malignancy risk; model performance was assessed via nested 10-fold cross-validation. The best-performing algorithm was externally validated using data for 454 thyroid nodules from a tertiary hospital, then compared to the Thyroid Imaging Reporting and Data System (TIRADS)-based interpretations of radiologists (American College of Radiology, European and Korean TIRADS, and AACE/ACE/AME guidelines). RESULTS: The area under the receiver operating characteristic (AUROC) curves of the algorithms ranged from 0.773 to 0.862. The sensitivities, specificities, positive predictive values, and negative predictive values of the best-performing models were 74.1-76.6%, 80.9-83.4%, 49.2-51.9%, and 93.0-93.5%, respectively. For the external validation set, the ElasticNet values were 83.2%, 89.2%, 81.8%, and 90.1%, respectively. The corresponding TIRADS values were 66.5-85.0%, 61.3-80.8%, 45.9-72.1%, and 81.5-90.3%, respectively. The new model exhibited a significantly higher AUROC and specificity than did the TIRADS risk stratification, although its sensitivity was similar. CONCLUSION: We developed a reliable machine learning-based predictive model that demonstrated enhanced specificity when stratifying thyroid nodules according to malignancy risk. This system will contribute to improved personalized management of thyroid nodules. KEY POINTS: • The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of our model were 0.914, 83.2%, and 89.2%, respectively (derived using the validation dataset). • Compared to the TIRADS values, the AUROC and specificity are significantly higher, while the sensitivity is similar. • An interactive version of our AI algorithm is at http://tirads.cdss.co.kr .

Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.

PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma. METHOD: This retrospective study included 104 patients (59.4 ± 13.1 years; 46 women) who underwent an MRI protocol including 1-mm deep learning-reconstructed and 3-mm routine images for evaluating pituitary adenoma between August 2019 and October 2020. Five readers (24, 9, 2 years, and <1 year of experience) assessed the delineation of pituitary axis (gland and stalk) and the presence of cavernous sinus invasion for using a pairwise design. The signal-to-noise ratio (SNR) was measured. Diagnostic performance as well as image preference data were analysed and compared according to the readers' experience using the McNemar test. RESULTS: For delineation of normal pituitary axis, all readers preferred thin 1-mm DLR MRI over 3-mm MRI (overall superiority, 55.8 %, P <.001), with this preference being greater in the less experienced readers (92.3 % vs. 55.8 % [expert], P <.001). The readers showed higher diagnostic performance for cavernous sinus invasion on 1-mm (AUC, 0.91 and 0.92) than on 3-mm imaging (AUC, 0.87 and 0.88). The SNR of the 1-mm DLR was 1.21-fold higher than that of the routine 3-mm imaging. CONCLUSION: Deep learning reconstruction-based 1-mm imaging demonstrates improved image quality and better delineation of microstructure in the sellar fossa and is preferred by both radiologists and non-radiologist physicians, especially in less experienced readers.

Co-administration of ursodeoxycholic acid with rosuvastatin/ezetimibe in a non-alcoholic fatty liver disease model.

Background: Ursodeoxycholic acid (UDCA), statins, and ezetimibe (EZE) have demonstrated beneficial effects against non-alcoholic fatty liver disease (NAFLD). We investigated the efficacy of the combination of UDCA and the mix of rosuvastatin (RSV)/EZE in the treatment of NAFLD. Methods: NAFLD mouse models were developed by injecting thioacetamide, fasting, and high-carbohydrate refeeding, high-fat diet, and choline-deficient L-amino acid-defined high-fat diet (CDAHFD). Low-dose UDCA (L-UDCA; 15 mg/kg) or high-dose UDCA (H-UDCA; 30 mg/kg) was administered with RSV/EZE. We also employed an in vitro model of NAFLD developed using palmitic acid-treated Hepa1c1c7 cells. Results: Co-administration of RSV/EZE with UDCA significantly decreased the collagen accumulation, serum alanine aminotransferase (ALT) levels, and mRNA levels of fibrosis-related markers than those observed in the vehicle group in thioacetamide-treated mice (all P < 0.01). In addition, in the group fasted and refed with a high-carbohydrate diet, UDCA/RSV/EZE treatment decreased the number of apoptotic cells and serum ALT levels compared with those observed in the vehicle group (all P < 0.05). Subsequently, H-UDCA/RSV/EZE treatment decreased the number of ballooned hepatocytes and stearoyl-CoA desaturase 1 (SCD-1) mRNA levels (P = 0.027) in the liver of high-fat diet-fed mice compared with those observed in the vehicle group. In the CDAHFD-fed mouse model, UDCA/RSV/EZE significantly attenuated collagen accumulation and fibrosis-related markers compared to those observed in the vehicle group (all P < 0.05). In addition, UDCA/RSV/EZE treatment significantly restored cell survival and decreased the protein levels of apoptosis-related markers compared to RSV/EZE treatment in palmitic acid-treated Hepa1c1c7 cells (all P < 0.05). Conclusion: Combination therapy involving UDCA and RSV/EZE may be a novel strategy for potent inhibition of NAFLD progression.

Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study.

OBJECTIVE: Preoperative differential diagnosis of follicular-patterned lesions is challenging. This multicenter cohort study investigated the clinicoradiological characteristics relevant to the differential diagnosis of such lesions. MATERIALS AND METHODS: From June to September 2015, 4787 thyroid nodules (≥ 1.0 cm) with a final diagnosis of benign follicular nodule (BN, n = 4461), follicular adenoma (FA, n = 136), follicular carcinoma (FC, n = 62), or follicular variant of papillary thyroid carcinoma (FVPTC, n = 128) collected from 26 institutions were analyzed. The clinicoradiological characteristics of the lesions were compared among the different histological types using multivariable logistic regression analyses. The relative importance of the characteristics that distinguished histological types was determined using a random forest algorithm. RESULTS: Compared to BN (as the control group), the distinguishing features of follicular-patterned neoplasms (FA, FC, and FVPTC) were patient's age (odds ratio [OR], 0.969 per 1-year increase), lesion diameter (OR, 1.054 per 1-mm increase), presence of solid composition (OR, 2.255), presence of hypoechogenicity (OR, 2.181), and presence of halo (OR, 1.761) (all p < 0.05). Compared to FA (as the control), FC differed with respect to lesion diameter (OR, 1.040 per 1-mm increase) and rim calcifications (OR, 17.054), while FVPTC differed with respect to patient age (OR, 0.966 per 1-year increase), lesion diameter (OR, 0.975 per 1-mm increase), macrocalcifications (OR, 3.647), and non-smooth margins (OR, 2.538) (all p < 0.05). The five important features for the differential diagnosis of follicular-patterned neoplasms (FA, FC, and FVPTC) from BN are maximal lesion diameter, composition, echogenicity, orientation, and patient's age. The most important features distinguishing FC and FVPTC from FA are rim calcifications and macrocalcifications, respectively. CONCLUSION: Although follicular-patterned lesions have overlapping clinical and radiological features, the distinguishing features identified in our large clinical cohort may provide valuable information for preoperative distinction between them and decision-making regarding their management.

PERK prevents hepatic lipotoxicity by activating the p62-ULK1 axis-mediated noncanonical KEAP1-Nrf2 pathway.

Hepatic lipotoxicity is a crucial factor in nonalcoholic steatohepatitis resulting from excessive saturated fatty acid-induced reactive oxygen species (ROS)-mediated cell death, which is associated with the accumulation of endoplasmic reticulum (ER) stress in the liver. The unfolded protein response (UPR) alleviates ER stress by restoring ER protein folding homeostasis. However, whether UPR contributes ROS elimination under lipotoxicity remains unclear. The Kelch like ECH-associated protein 1 (KEAP1)-nuclear factor, erythroid 2 like 2 (Nrf2) pathway provides antioxidant defense against lipotoxic stress by eliminating ROS and can be activated by the p62-Unc-51 like autophagy activating kinase 1 (ULK1) axis. However, the upstream molecular regulator of the p62-ULK1 axis-induced KEAP1-Nrf2 pathway in the same context remains unidentified. Here, we demonstrated that PKR-like ER kinase (PERK), a UPR sensor, directly phosphorylates p62 and ULK1, thereby activating the noncanonical KEAP1-Nrf2 pathway. We also elucidated the molecular mechanism underlying the PERK-mediated p62-ULK1 axis-dependent noncanonical KEAP1-Nrf2 pathway, which could represent a promising therapeutic strategy against hepatic lipotoxicity.

Tumor habitat analysis by magnetic resonance imaging distinguishes tumor progression from radiation necrosis in brain metastases after stereotactic radiosurgery.

OBJECTIVES: The identification of viable tumor after stereotactic radiosurgery (SRS) is important for future targeted therapy. This study aimed to determine whether tumor habitat on structural and physiologic MRI can distinguish viable tumor from radiation necrosis of brain metastases after SRS. METHOD: Multiparametric contrast-enhanced T1- and T2-weighted imaging, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) were obtained from 52 patients with 69 metastases, showing enlarging enhancing masses after SRS. Voxel-wise clustering identified three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable). Habitat-based predictors for viable tumor or radiation necrosis were identified by logistic regression. Performance was validated using the area under the curve (AUC) of the receiver operating characteristics curve in an independent dataset with 24 patients. RESULTS: None of the physiologic MRI habitats was indicative of viable tumor. Viable tumor was predicted by a high-volume fraction of solid low-enhancing habitat (low T2-weighted and low CE-T1-weighted values; odds ratio [OR] 1.74, p <.001) and a low-volume fraction of nonviable tissue habitat (high T2-weighted and low CE-T1-weighted values; OR 0.55, p <.001). Combined structural MRI habitats yielded good discriminatory ability in both development (AUC 0.85, 95% confidence interval [CI]: 0.77-0.94) and validation sets (AUC 0.86, 95% CI:0.70-0.99), outperforming single ADC (AUC 0.64) and CBV (AUC 0.58) values. The site of progression matched with the solid low-enhancing habitat (72%, 8/11). CONCLUSION: Solid low-enhancing and nonviable tissue habitats on structural MRI can help to localize viable tumor in patients with brain metastases after SRS. KEY POINTS: • Structural MRI habitats helped to differentiate viable tumor from radiation necrosis. • Solid low-enhancing habitat was most helpful to find viable tumor. • Providing spatial information, the site of progression matched with solid low-enhancing habitat.

A GLP-1/GLP-2 receptor dual agonist to treat NASH: Targeting the gut-liver axis and microbiome.

BACKGROUND AND AIMS: Currently there is no Food and Drug Administration-approved drug to treat NAFLD and NASH, the rates of which are increasing worldwide. Although NAFLD/NASH are highly complex and heterogeneous conditions, most pharmacotherapy pipelines focus on a single mechanistic target. Considering the importance of the gut-liver axis in their pathogenesis, we investigated the therapeutic effect of a long-acting dual agonist of glucagon-like peptide (GLP)-1 and GLP-2 receptors in mice with NAFLD/NASH. APPROACH AND RESULTS: C57BL/6J mice were fed a choline-deficient high-fat diet/high fructose and sucrose solution. After 16 weeks, mice were randomly allocated to receive vehicle, GLP1-Fc, GLP2-Fc, or GLP1/2-Fc fusion (GLP1/2-Fc) subcutaneously every 2 days for 4 weeks. Body weight was monitored, insulin/glucose tolerance tests were performed, feces were collected, and microbiome profiles were analyzed. Immobilized cell systems were used to evaluate direct peptide effect. Immunohistochemistry, quantitative PCR, immunoblot analysis, tunnel assay, and biochemical assays were performed to assess drug effects on inflammation, hepatic fibrosis, cell death, and intestinal structures. The mice had well-developed NASH phenotypes. GLP1/2-Fc reduced body weight, glucose levels, hepatic triglyceride levels, and cellular apoptosis. It improved liver fibrosis, insulin sensitivity, and intestinal tight junctions, and increased microvillus height, crypt depth, and goblet cells of intestine compared with a vehicle group. Similar effects of GLP1/2-Fc were found in in vitro cell systems. GLP1/2-Fc also changed microbiome profiles. We applied fecal microbiota transplantation (FMT) gain further insight into the mechanism of GLP1/2-Fc-mediated protection. We confirmed that FMT exerted an additive effect on GLP1-Fc group, including the body weight change, liver weight, hepatic fat accumulation, inflammation, and hepatic fibrosis. CONCLUSIONS: A long-acting dual agonist of GLP-1 and GLP-2 receptors is a promising therapeutic strategy to treat NAFLD/NASH.