Magnetic Resonance Imaging of Transient Left Ventricular Apical Ballooning Related to Emotional Stress: a Case Report

Transient left ventricular apical ballooning is characterized by transient wall motion abnormalities involving the left ventricular apex and mid-ventricle in the absence of coronary arterial occlusion. A 66-year-old woman presented to the emergency department with chest pain that mimicked acute myocardial infarction. An aortogram showed akinesis from the mid to apical left ventricle with sparing of the basal segments. Four days later, she underwent MRI, which demonstrated characteristic apical contractile dysfunction, the same as the aortogram, without evidence of myocardial infarction on the MRI. Two weeks later, her symptoms were resolved and follow-up echocardiography showed normal ventricular function

A randomized, phase II study of gefitinib alone versus nimotuzumab plus gefitinib after platinum-based chemotherapy in advanced non-small cell lung cancer (KCSG LU12-01)

We aimed to evaluate the efficacy of dual inhibition of epidermal growth factor receptor (EGFR) with nimotuzumab (EGFR monoclonal antibody) plus gefitinib (EGFR-tyrosine kinase inhibitor) in advanced non-small cell lung cancer (NSCLC) after platinum-based chemotherapy. An open label, randomized, phase II trial was conducted at 6 centers; 160 patients were randomized (1:1) to either gefitinib alone or nimotuzumab (200 mg, i. v. weekly) plus gefitinib (250 mg p. o. daily) until disease progression or intolerable toxicity. The primary endpoint was progression-free survival (PFS) at 3 months. Of the total 160 enrolled patients, 155 (77: gefitinib, 78: nimotuzumab plus gefitinib) received at least one dose and could be evaluated for efficacy and toxicity. The majority had adenocarcinoma (65.2%) and ECOG performance status of 0 to 1 (83.5%). The median follow-up was 22.1 months, and the PFS rate at 3 months was 48.1% in gefitinib and 37.2% in nimotuzumab plus gefitinib (P = not significant, NS). The median PFS and OS were 2.8 and 13.2 months in gefitinib and 2.0 and 14.0 months in nimotuzumab plus gefitinib. Combined treatment was not associated with superior PFS to gefitinib alone in patients with EGFR mutation (13.5 vs. 10.2 months in gefitinib alone, P=NS) or those with wild-type EGFR (0.9 vs. 2.0 months in gefitinib alone, P=NS). Combined treatment did not increase EGFR inhibition-related adverse events with manageable toxicities. The dual inhibition of EGFR with nimotuzumab plus gefitinib was not associated with better outcomes than gefitinib alone as a second-line treatment of advanced NSCLC (NCT01498562).

Evaluation of Degradation in Nanofilled Adhesive Resins Using Quantitative Light-Induced Fluorescence

The aim of this study was to evaluate degradation in commercial dental nanofilled adhesive resins using quantitative light-induced fluorescence (QLF). Three adhesives were selected: D/E resin (DR), Single Bond Plus (SB), and G-Bond (GB). The adhesives were mixed with porphyrin for the QLF analysis. Specimens were prepared by dispensing blended adhesives into a flexible mold and polymerizing. Then, the QLF analysis of the specimens was done and the porphyrin values (Simple Plaque Score and ΔR) were measured. After thermocycling of the specimens (5000 cycles, 5 to 55°C) for the degradation, the specimens were assayed by QLF again. The porphyrin values were analyzed using paired t-test at a 95% confidence level. A significant reduction in SPS was observed in all groups after thermocycling. The ΔR significantly decreased after thermocycling except area ΔR30 of SB group. Overall, porphyrin values decreased after thermocycling which indicates that the degradation of the adhesive resins may be measured by the change of porphyrin value. The QLF method could be used to evaluate the degradation of adhesive resin

Thymosin β10 Expression Driven by the Human TERT Promoter Induces Ovarian Cancer-Specific Apoptosis through ROS Production

Thymosin β10 (Tβ10) regulates actin dynamics as a cytoplasm G-actin sequestering protein. Previously, we have shown that Tβ10 diminishes tumor growth, angiogenesis, and proliferation by disrupting actin and by inhibiting Ras. However, little is known about its mechanism of action and biological function. In the present study, we establish a new gene therapy model using a genetically modified adenovirus, referred to as Ad.TERT.Tβ10, that can overexpress the Tβ10 gene in cancer cells. This was accomplished by replacing the native Tβ10 gene promoter with the human TERT promoter in Ad.TERT.Tβ10. We investigated the cancer suppression activity of Tβ10 and found that Ad.TERT.Tβ10 strikingly induced cancer-specific expression of Tβ10 as well as apoptosis in a co-culture model of human primary ovarian cancer cells and normal fibroblasts. Additionally, Ad.TERT.Tβ10 decreased mitochondrial membrane potential and increased reactive oxygen species (ROS) production. These effects were amplified by co-treatment with anticancer drugs, such as paclitaxel and cisplatin. These findings indicate that the rise in ROS production due to actin disruption by Tβ10 overexpression increases apoptosis of human ovarian cancer cells. Indeed, the cancer-specific overexpression of Tβ10 by Ad.TERT.Tβ10 could be a valuable anti-cancer therapeutic for the treatment of ovarian cancer without toxicity to normal cells

A deep learning approach for fully automated measurements of lower extremity alignment in radiographic images

Abstract During clinical evaluation of patients and planning orthopedic treatments, the periodic assessment of lower limb alignment is critical. Currently, physicians use physical tools and radiographs to directly observe limb alignment. However, this process is manual, time consuming, and prone to human error. To this end, a deep-learning (DL)-based system was developed to automatically, rapidly, and accurately detect lower limb alignment by using anteroposterior standing X-ray medical imaging data of lower limbs. For this study, leg radiographs of non-overlapping 770 patients were collected from January 2016 to August 2020. To precisely detect necessary landmarks, a DL model was implemented stepwise. A radiologist compared the final calculated measurements with the observations in terms of the concordance correlation coefficient (CCC), Pearson correlation coefficient (PCC), and intraclass correlation coefficient (ICC). Based on the results and 250 frontal lower limb radiographs obtained from 250 patients, the system measurements for 16 indicators revealed superior reliability (CCC, PCC, and ICC ≤ 0.9; mean absolute error, mean square error, and root mean square error ≥ 0.9) for clinical observations. Furthermore, the average measurement speed was approximately 12 s. In conclusion, the analysis of anteroposterior standing X-ray medical imaging data by the DL-based lower limb alignment diagnostic support system produces measurement results similar to those obtained by radiologists

Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images

Abstract The Kellgren–Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on the clinician’s subjective assessment. Moreover, the accuracy varies significantly depending on the clinician’s experience and can be particularly low. Therefore, in this study, we developed an ensemble network that can predict a consistent and accurate KL grade for knee osteoarthritis severity using a deep learning approach. We trained individual models on knee X-ray datasets using the most suitable image size for each model in an ensemble network rather than using datasets with a single image size. We then built the ensemble network using these models to overcome the instability of single models and further improve accuracy. We conducted various experiments using a dataset of 8260 images from the Osteoarthritis Initiative open dataset. The proposed ensemble network exhibited the best performance, achieving an accuracy of 76.93% and an F1-score of 0.7665. The Grad-CAM visualization technique was used to further evaluate the focus of the model. The results demonstrated that the proposed ensemble network outperforms existing techniques that have performed well in KL grade classification. Moreover, the proposed model focuses on the joint space around the knee to extract the imaging features required for KL grade classification, revealing its high potential for diagnosing knee osteoarthritis

A Dense Stereo Matching Using Two-Pass Dynamic Programming with Generalized Ground Control Points

A method for solving dense stereo matching problem is presented in this paper. First, a new generalized ground control points (GGCPs) scheme is introduced, where one or more disparity candidates for the true disparity of each pixel are assigned by local matching using the oriented spatial filters. By allowing “all ” pixels to have multiple candidates for their true disparities, GGCPs not only guarantee to provide a sufficient number of starting pixels needed for guiding the subsequent matching process, but also remarkably reduce the risk of false match, improving the previous GCP-based approaches where the number of the selected control points tends to be inversely proportional to the reliability. Second, by employing a two-pass dynamic programming technique that performs optimization both along and across the scanlines, we solve the typical inter-scanline inconsistency problem. Moreover, combined with the GGCPs, the stability and efficiency of the optimization are improved significantly. Experimental results for the standard data sets show that the proposed algorithm achieves comparable results to the state-of-the-arts with much less computational cost