Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiologists' overall impression, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), and a deep learning algorithm in differentiating benign and malignant thyroid nodules on ultrasound in children and young adults. METHODS. This retrospective study included 139 patients (median age 17.5 years; 119 female patients, 20 male patients) evaluated from January 1, 2004, to September 18, 2020, who were 21 years old and younger with a thyroid nodule on ultrasound with definitive pathologic results from fine-needle aspiration and/or surgical excision to serve as the reference standard. A single nodule per patient was selected, and one transverse and one longitudinal image each of the nodules were extracted for further evaluation. Three radiologists independently characterized nodules on the basis of their overall impression (benign vs malignant) and ACR TI-RADS. A previously developed deep learning algorithm determined for each nodule a likelihood of malignancy, which was used to derive a risk level. Sensitivities and specificities for malignancy were calculated. Agreement was assessed using Cohen kappa coefficients. RESULTS. For radiologists' overall impression, sensitivity ranged from 32.1% to 75.0% (mean, 58.3%; 95% CI, 49.2-67.3%), and specificity ranged from 63.8% to 93.9% (mean, 79.9%; 95% CI, 73.8-85.7%). For ACR TI-RADS, sensitivity ranged from 82.1% to 87.5% (mean, 85.1%; 95% CI, 77.3-92.1%), and specificity ranged from 47.0% to 54.2% (mean, 50.6%; 95% CI, 41.4-59.8%). The deep learning algorithm had a sensitivity of 87.5% (95% CI, 78.3-95.5%) and specificity of 36.1% (95% CI, 25.6-46.8%). Interobserver agreement among pairwise combinations of readers, expressed as kappa, for overall impression was 0.227-0.472 and for ACR TI-RADS was 0.597-0.643. CONCLUSION. Both ACR TI-RADS and the deep learning algorithm had higher sensitivity albeit lower specificity compared with overall impressions. The deep learning algorithm had similar sensitivity but lower specificity than ACR TI-RADS. Interobserver agreement was higher for ACR TI-RADS than for overall impressions. CLINICAL IMPACT. ACR TI-RADS and the deep learning algorithm may serve as potential alternative strategies for guiding decisions to perform fine-needle aspiration of thyroid nodules in children.

Predicting Successful Placements for Youth in Child Welfare with Machine Learning.

Out-of-home placement decisions have extremely high stakes for the present and future well-being of children in care because some placement types, and multiple placements, are associated with poor outcomes. We propose that a clinical decision support system (CDSS) using existing data about children and their previous placement success could inform future placement decision-making for their peers. The objective of this study was to test the feasibility of developing machine learning models to predict the best level of care placement (i.e., the placement with the highest likelihood of doing well in treatment) based on each youth's behavioral health needs and characteristics. We developed machine learning models to predict the probability of each youth's treatment success in psychiatric residential care (i.e., Psychiatric Residential Treatment Facility [PRTF]) versus any other placement (AUROCs > 0.70) using data collected in standard care at a behavioral health organization. Placement recommendations based on these machine learning models distinguished between youth who did well in residential care versus non-residential care (e.g., 80% of those who received care in the recommended setting with the highest predicted likelihood of success had above average risk-adjusted outcomes). Then we developed and validated machine learning models to predict the probability of each youth's treatment success across specific placement types in a state-wide system, achieving an average AUROC score of greater than 0.75. Machine learning models based on risk-adjusted behavioral health and functional data show promise in predicting positive placement outcomes and informing future placement decisions for youth in care. Related ethical considerations are discussed.

Transcriptional interference by antisense RNA is required for circadian clock function.

Eukaryotic circadian oscillators consist of negative feedback loops that generate endogenous rhythmicities. Natural antisense RNAs are found in a wide range of eukaryotic organisms. Nevertheless, the physiological importance and mode of action of most antisense RNAs are not clear. frequency (frq) encodes a component of the Neurospora core circadian negative feedback loop, which was thought to generate sustained rhythmicity. Transcription of qrf, the long non-coding frq antisense RNA, is induced by light, and its level oscillates in antiphase to frq sense RNA. Here we show that qrf transcription is regulated by both light-dependent and light-independent mechanisms. Light-dependent qrf transcription represses frq expression and regulates clock resetting. Light-independent qrf expression, on the other hand, is required for circadian rhythmicity. frq transcription also inhibits qrf expression and drives the antiphasic rhythm of qrf transcripts. The mutual inhibition of frq and qrf transcription thus forms a double negative feedback loop that is interlocked with the core feedback loop. Genetic and mathematical modelling analyses indicate that such an arrangement is required for robust and sustained circadian rhythmicity. Moreover, our results suggest that antisense transcription inhibits sense expression by mediating chromatin modifications and premature termination of transcription. Taken together, our results establish antisense transcription as an essential feature in a circadian system and shed light on the importance and mechanism of antisense action.

A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites.

MOTIVATION: The position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure ('shape') is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites. RESULTS: We compared a traditional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential score, for accuracy in detecting binding sites for 75 vertebrate transcription factors. The PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significant loss of accuracy for the remaining TFs. AVAILABILITY AND IMPLEMENTATION: The shape-based model is available as an open-source R package at that is archived on the GitHub software repository at https://github.com/ramseylab/regshape/. CONTACT: stephen.ramsey@oregonstate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detection of candidate tumor driver genes using a fully integrated Bayesian approach.

DNA copy number alterations (CNAs), including amplifications and deletions, can result in significant changes in gene expression and are closely related to the development and progression of many diseases, especially cancer. For example, CNA-associated expression changes in certain genes (called candidate tumor driver genes) can alter the expression levels of many downstream genes through transcription regulation and cause cancer. Identification of such candidate tumor driver genes leads to discovery of novel therapeutic targets for personalized treatment of cancers. Several approaches have been developed for this purpose by using both copy number and gene expression data. In this study, we propose a Bayesian approach to identify candidate tumor driver genes, in which the copy number and gene expression data are modeled together, and the dependency between the two data types is modeled through conditional probabilities. The proposed joint modeling approach can identify CNA and differentially expressed genes simultaneously, leading to improved detection of candidate tumor driver genes and comprehensive understanding of underlying biological processes. We evaluated the proposed method in simulation studies, and then applied to a head and neck squamous cell carcinoma data set. Both simulation studies and data application show that the joint modeling approach can significantly improve the performance in identifying candidate tumor driver genes, when compared with other existing approaches.

Blocking CXCR1/2 attenuates experimental periodontitis by suppressing neutrophils recruitment.

Periodontitis (PD) is a common chronic oral inflammatory disease that cause alveolar bone loss. Current strategies for bone regeneration achieve limited results in PD. The aberrant host osteoimmunity to pathogenic bacteria is responsible for the destruction of alveolar bone in PD. We aimed to investigate the distinctive activity of immune cells in PD to create more effective and precise therapeutic approaches for treating PD. In this study, we revealed that neutrophils in the inflamed alveolar bone of PD patients expressed higher levels of CXCR1/2 and had a stronger pro-inflammatory capacity and chemotactic ability than that in healthy individuals. Suppressing the recruitment of neutrophils to inflamed sites with the CXCR1/2 inhibitor reparixin reduced alveolar bone loss in PD mice. In this study, we not only revealed that neutrophils exhibit a heterogeneously stronger pro-inflammatory capacity in the inflamed alveolar bone of PD patients but also provided a precise therapeutic treatment for PD involving the suppression of neutrophil recruitment.

The spatial transcriptomic landscape of human gingiva in health and periodontitis.

The gingiva is a key oral barrier that protects oral tissues from various stimuli. A loss of gingival tissue homeostasis causes periodontitis, one of the most prevalent inflammatory diseases in humans. The human gingiva exists as a complex cell network comprising specialized structures. To understand the tissue-specific pathophysiology of the gingiva, we applied a recently developed spatial enhanced resolution omics-sequencing (Stereo-seq) technique to obtain a spatial transcriptome (ST) atlas of the gingiva in healthy individuals and periodontitis patients. By utilizing Stereo-seq, we identified the major cell types present in the gingiva, which included epithelial cells, fibroblasts, endothelial cells, and immune cells, as well as subgroups of epithelial cells and immune cells. We further observed that inflammation-related signalling pathways, such as the JAK-STAT and NF-κB signalling pathways, were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals. Additionally, we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva. In conclusion, our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

Studies on the inhibition of α-glucosidase by biflavonoids and their interaction mechanisms.

Biflavonoids are a kind of polyphenol compounds with numerous biological functions. However, the potential inhibitory activities of biflavonoids on α-glucosidase are yet unknown. Here, the inhibitory effects of two biflavonoids (amentoflavone and hinokiflavone) on α-glucosidase and their interaction mechanisms were explored using multispectral approaches and molecular docking. The results showed that the inhibitory activities of biflavonoids were much better compared with monoflavonoid (apigenin) and acarbose, and the order of inhibition ability was hinokiflavone > amentoflavone > apigenin > acarbose. These flavonoids were noncompetitive inhibitors of α-glucosidase and showed synergistic inhibition effects with acarbose. Additionally, they could statically quench the intrinsic fluorescence of α-glucosidase, and form the non-covalent complexes with enzyme primarily through hydrogen bonds and van der Waals forces. The binding of flavonoids changed the conformational structure of α-glucosidase, therefore impairing the enzyme activity. The findings suggested that biflavonoids could be considered as potential hypoglycemic functional foods in diabetes therapy.

Segment anything model for medical image analysis: An experimental study.

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model trained on over 1 billion annotations, predominantly for natural images, that is intended to segment user-defined objects of interest in an interactive manner. While the model performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. In our experiments, we generated point and box prompts for SAM using a standard method that simulates interactive segmentation. We report the following findings: (1) SAM's performance based on single prompts highly varies depending on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation performance appears to be better for well-circumscribed objects with prompts with less ambiguity such as the segmentation of organs in computed tomography and poorer in various other scenarios such as the segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4) SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings. (5) When multiple-point prompts are provided iteratively, SAM's performance generally improves only slightly while other methods' performance improves to the level that surpasses SAM's point-based performance. We also provide several illustrations for SAM's performance on all tested datasets, iterative segmentation, and SAM's behavior given prompt ambiguity. We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others. SAM has the potential to make a significant impact in automated medical image segmentation in medical imaging, but appropriate care needs to be applied when using it. Code for evaluation SAM is made publicly available at https://github.com/mazurowski-lab/segment-anything-medical-evaluation.

Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. METHODS: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of radiologists. The algorithm was further tested with 378 nodules imaged with ultrasound machines from different manufacturers and product types than the training cases. Four experienced radiologists were requested to evaluate the nodules for comparison with deep learning. RESULTS: The Area Under Curve (AUC) of the deep learning algorithm and four radiologists were calculated with parametric, binormal estimation. For the deep learning algorithm, the AUC was 0.69 (95% CI: 0.64-0.75). The AUC of radiologists were 0.63 (95% CI: 0.59-0.67), 0.66 (95% CI:0.61-0.71), 0.65 (95% CI: 0.60-0.70), and 0.63 (95%CI: 0.58-0.67). CONCLUSION: In the new testing dataset, the deep learning algorithm achieved similar performances with all four radiologists. The relative performance difference between the algorithm and the radiologists is not significantly affected by the difference of ultrasound scanner.

Discovery of quinazolin-4(3H)-one derivatives as novel AChE inhibitors with anti-inflammatory activities.

A series of quinazolin-4(3H)-one derivatives was designed through scaffold-hopping strategy and synthesized as novel multifunctional anti-AD agents demonstrating both cholinesterase inhibition and anti-inflammatory activities. Their inhibitory activities against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) were evaluated, and the enzyme kinetics study as well as detailed binding mode via molecular docking were performed for selected compounds. MR2938 (B12) displayed promising AChE inhibitory activity with an IC50 value of 5.04 µM and suppressed NO production obviously (IC50 = 3.29 µM). Besides, it was able to decrease the mRNA levels of pro-inflammatory cytokines IL-1ß, TNF-α, IL-6 and CCL2 at 1.25 µM. Further mechanism study suggested that MR2938 suppressed the neuroinflammation through blocking MAPK/JNK and NF-κB signaling pathways. All these results indicate that MR2938 is a good starting point to develop multifunctional anti-AD lead compounds.

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.

Importance: An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. Objectives: To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods. Design, Setting, and Participants: This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021. Main Outcomes and Measures: The overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes. Results: A total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926. Conclusions and Relevance: In this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.

Laboratory study of Se speciation in the sediment and oligochaete Lumbriculus variegatus from an aquatic environment.

Sediments are the major sink for selenium (Se) in aquatic environments. Se speciation in sediments is crucial for its bioavailability and toxicity in benthos, but this is relatively understudied. In this study, the background levels of Se in the river sediments, fish flakes, and Lumbriculus variegatus were also detected. Then, the dynamic changes of selenium speciation and concentrations in sediments were investigated after adding selenite (Se(IV)) and seleno-L-methionine (Se-Met) in the sediments for 90 and 7 days, and the accumulation and depuration of Se(IV) and Se-Met for 7 days in the oligochaete L. variegatus were also explored. Without the presence of worms, the levels of Se(IV) in the sediments were relatively stable within 7 days but showed a decreasing trend during the 90 days of aging. In contrast, Se-Met in the sediments showed a sharp decrease within 3 days of aging. The LC50-96 h values of Se(IV) and Se-Met in L. variegatus were 372.6 and 9.4 µg/g, respectively. Interestingly, the dominant Se species in Se(IV)- or Se-Met-treated L. variegatus was Se-Met, whose level was increased with time in 7 days of exposure. Se was barely depurated from L. variegatus during the 8 days of the depuration period. This study has provided indispensable data on the levels of total Se in the abiotic and biotic matrices and the biodynamics of Se in a representative benthos, which could better understand the ecological risk of Se to the freshwater benthic communities.

Progressive Diversified Augmentation for General Robustness of DNNs: A Unified Approach.

Adversarial images are imperceptible perturbations to mislead deep neural networks (DNNs), which have attracted great attention in recent years. Although several defense strategies achieved encouraging robustness against adversarial samples, most of them still failed to consider the robustness on common corruptions (e.g. noise, blur, and weather/digital effects). To address this problem, we propose a simple yet effective method, named Progressive Diversified Augmentation (PDA), which improves the robustness of DNNs by progressively injecting diverse adversarial noises during training. In other words, DNNs trained with PDA achieve better general robustness against both adversarial attacks and common corruptions than other strategies. In addition, PDA also enjoys the advantages of spending less training time and keeping high standard accuracy on clean examples. Further, we theoretically prove that PDA can control the perturbation bound and guarantee better robustness. Extensive results on CIFAR-10, SVHN, ImageNet, CIFAR-10-C and ImageNet-C have demonstrated that PDA comprehensively outperforms its counterparts on the robustness against adversarial examples and common corruptions as well as clean images. More experiments on the frequency-based perturbations and visualized gradients further prove that PDA achieves general robustness and is more aligned with the human visual system.

Comparative study of inhibition mechanisms of structurally different flavonoid compounds on α-glucosidase and synergistic effect with acarbose.

Flavonoid compounds have anti-diabetic activity, which can control blood glucose levels by inhibiting α-glucosidase activity. In this paper, the inhibition mechanisms between four flavonoid compounds and α-glucosidase were studied by multispectroscopic methods and molecular docking. The results showed that the inhibitory activities of flavonoid compounds were higher than that of acarbose, and the sequence of inhibition effect was scutellarein > nepetin > apigenin > hispidulin > acarbose. Also, the synergistic effects of flavonoid compounds combined with acarbose on inhibiting α-glucosidase activity were observed. The fluorescence results showed that flavonoid compounds combined with α-glucosidase to form a stable complex. And the spectral analysis indicated that the microenvironmental and secondary structure of α-glucosidase were changed. The present study demonstrated that the molecular structure of flavonoid compounds played an important role in the inhibition process, namely, scutellarein with more hydroxyl groups on the A-ring might serve as the most effective α-glucosidase inhibitor.

miR-30a-5p suppresses lung squamous cell carcinoma via ATG5 - mediated autophagy.

Propose: Autophagy plays a complicated role in cancer progression. This study aims at assessing the function of ATG5-induced autophagy in progression of lung squamous cell carcinoma and its upstream mechanism. METHOD: TCGA database of lung squamous cell carcinoma was analyzed to explore the differentially expressed miRNAs and mRNAs and relative prognosis. RT-PCR and Western blot were performed to evaluate autophagy relative gene expression level in human lung squamous cell carcinoma cell Lines. Autophagy flux was observed using transmission electron microscopy and immunofluorescence. Meanwhile, binding relationship of potential target miRNA and mRNAs were also confirmed using Dual-luciferase reporter gene assay. Lung metastatic model was established to evaluated the effect of targeting protein and miRNA. RESULT: High level expression of ATG5 was detected in LUSC patients. Relative experiments confirmed that ATG5 silencing could decrease the autophagy flux in LUSC. In addition, our research revealed that there is a binding sites between hsa-mir-30a-5p and 3'-UTR of ATG5. Mimic miR-30a-5p suppresses ATG5-mediated autophagy in lung squamous cell carcinoma cells. The in vivo experiments confirmed that miR-30a-5p could attenuate lung squamous cell carcinoma progression through the autophagy pathway. CONCLUSION: Accordingly, the in vivo and in vitro study in our research have demonstrated that miR-30a-5p inhibits lung squamous cell carcinoma progression via ATG5-mediated autophagy.

Inhibition Mechanism of α-Amylase/α-Glucosidase by Silibinin, Its Synergism with Acarbose, and the Effect of Milk Proteins.

As a natural flavonolignan, silibinin is reported to possess multiple biological activities, while the inhibitory potential of silibinin on carbohydrate-hydrolyzing enzymes is still unclear. Therefore, in this study, the inhibitory effect and underlying mechanism of silibinin against α-amylase/α-glucosidase were investigated. The results indicated that silibinin showed a strong inhibitory efficiency against α-amylase/α-glucosidase in noncompetitive manners and exhibited synergistic inhibition against α-glucosidase with acarbose. However, interestingly, the inhibitory effect of silibinin was significantly hindered in various milk protein-rich environments, but this phenomenon disappeared after simulated gastrointestinal digestion of milk proteins in vitro. Furthermore, silibinin could combine with the inactive site of α-amylase/α-glucosidase and change the microenvironment and secondary structure of the enzymes, thereby influencing the catalytic efficiency of enzymes. This research suggested that silibinin could be used as a novel carbohydrate-hydrolyzing enzyme inhibitor, and milk beverages rich in silibinin had the potential for further application in antidiabetic dietary or medicine.

Protective effects of three structurally similar polyphenolic compounds against oxidative damage and their binding properties to human serum albumin.

Brazilin (Bra), hematoxylin (Hto) and hematein (Hte) are structurally similar polyphenols having rich biological activities, but their antioxidant ability has not been well studied. Here, their protective ability against human serum albumin (HSA) oxidative degradation were investigated using 2,2'-Azobis (2-methylpropionamidine) dihydrochloride (AAPH), NaClO and Fenton like reactions methods. The results indicated that polyphenols inhibited the oxidative injuries of HSA in the order: Hto > Bra > Hte. Additionally, the biological effects of polyphenols were mostly influenced by their binding to protein. Therefore, the structure-affinity relationships of polyphenols binding to HSA were also explored. Fluorescence experiments indicated that polyphenols bound to HSA through static quenching mechanism. Furthermore, some conformational changes of HSA could be observed in the presence of polyphenols. Altogether, molecular structure of polyphenols played a significant role in their protective effect against HSA oxidative damage and binding ability, which provided fundamental insights into their application as health care foods.

Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists.

A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system. PURPOSE: To develop an automated deep learning-based algorithm that jointly uses Posterior-Anterior (PA) and Lateral (LAT) views of knee radiographs to assess knee osteoarthritis severity according to the Kellgren-Lawrence grading system. MATERIALS AND METHODS: We used a dataset of 9739 exams from 2802 patients from Multicenter Osteoarthritis Study (MOST). The dataset was divided into a training set of 2040 patients, a validation set of 259 patients and a test set of 503 patients. A novel deep learning-based method was utilized for assessment of knee OA in two steps: (1) localization of knee joints in the images, (2) classification according to the KL grading system. Our method used both PA and LAT views as the input to the model. The scores generated by the algorithm were compared to the grades provided in the MOST dataset for the entire test set as well as grades provided by 5 radiologists at our institution for a subset of the test set. RESULTS: The model obtained a multi-class accuracy of 71.90% on the entire test set when compared to the ratings provided in the MOST dataset. The quadratic weighted Kappa coefficient for this set was 0.9066. The average quadratic weighted Kappa between all pairs of radiologists from our institution who took part in the study was 0.748. The average quadratic-weighted Kappa between the algorithm and the radiologists at our institution was 0.769. CONCLUSION: The proposed model performed demonstrated equivalency of KL classification to MSK radiologists, but clearly superior reproducibility. Our model also agreed with radiologists at our institution to the same extent as the radiologists with each other. The algorithm could be used to provide reproducible assessment of knee osteoarthritis severity.

Ang II Promotes Cardiac Autophagy and Hypertrophy via Orai1/STIM1.

The pathophysiology of cardiac hypertrophy is complex and multifactorial. Both the store-operated Ca2+ entry (SOCE) and excessive autophagy are the major causative factors for pathological cardiac hypertrophy. However, it is unclear whether these two causative factors are interdependent. In this study, we examined the functional role of SOCE and Orai1 in angiotensin II (Ang II)-induced autophagy and hypertrophy using in vitro neonatal rat cardiomyocytes (NRCMs) and in vivo mouse model, respectively. We show that YM-58483 or SKF-96365 mediated pharmacological inhibition of SOCE, or silencing of Orai1 with Orail-siRNA inhibited Ang II-induced cardiomyocyte autophagy both in vitro and in vivo. Also, the knockdown of Orai1 attenuated Ang II-induced pathological cardiac hypertrophy. Together, these data suggest that Ang II promotes excessive cardiomyocyte autophagy through SOCE/Orai1 which can be the prime contributing factors in cardiac hypertrophy.