No Abstract available.
Ankle* ; Sarcoidosis* ; Tenosynovitis*

No abstract available.
Breast* ; Hamartoma*

No abstract available.
Rabbits*

Sarcopenia is a disease characterized by age-related decline of skeletal muscle mass and function. The molecular mechanisms of the pathophysiology of sarcopenia form a complex network due to the involvement of multiple interconnected signaling pathways. Therefore, signaling receptors are major targets in pharmacological strategies in general. To provide a rationale for pharmacological interventions for sarcopenia, we herein describe several druggable signaling receptors based on their role in skeletal muscle homeostasis and changes in their activity with aging. A brief overview is presented of the efficacy of corresponding drug candidates under clinical trials. Strategies targeting the androgen receptor, vitamin D receptor, Insulin-like growth factor-1 receptor, and ghrelin receptor primarily focus on promoting anabolic action using natural ligands or mimetics. Strategies involving activin receptors and angiotensin receptors focus on inhibiting catabolic action. This review may help to select specific targets or combinations of targets in the future.

Sarcopenia is a disease characterized by age-related decline of skeletal muscle mass and function. The molecular mechanisms of the pathophysiology of sarcopenia form a complex network due to the involvement of multiple interconnected signaling pathways. Therefore, signaling receptors are major targets in pharmacological strategies in general. To provide a rationale for pharmacological interventions for sarcopenia, we herein describe several druggable signaling receptors based on their role in skeletal muscle homeostasis and changes in their activity with aging. A brief overview is presented of the efficacy of corresponding drug candidates under clinical trials. Strategies targeting the androgen receptor, vitamin D receptor, Insulin-like growth factor-1 receptor, and ghrelin receptor primarily focus on promoting anabolic action using natural ligands or mimetics. Strategies involving activin receptors and angiotensin receptors focus on inhibiting catabolic action. This review may help to select specific targets or combinations of targets in the future.

Bifidobacteria is one of the prototypes of probiotics bacteria, normally inhabitating the intestinal tract of humans. To search for a potent immunoregulatory Bifidobacteria strain, we screened the Bifidobacteria strains isolated from the feces of healthy Korean children. The mRNA or protein expression of an anti-inflammatory cytokine, IL-10, from mouse macrophages stimulated with live Bifidobacteria was examined. Of tested strains, Bifidobacteria A28 induced the highest IL-10 gene expression of murine macrophages. To probe immunoregulatory activity of the selected strain on the mice, we evaluated the proportional changes of CD4+CD25+ surface marker in the murine splenocytes. Flow cytometric analysis showed that the overall percentages of CD4+CD25+ cells in A28-treated splenocytes were higher than those of untreated splenocytes. In parallel, IL-10 release from A28-treated mouse peritoneal macrophages and splenocytes was significantly higher than that of untreated control cells. Collectively, the Bifidobacteria A28 strain isolated from the feces of healthy Korean children augments the mRNA or protein expression of IL-10 release from mouse peritoneal macrophages as well as the proportion of CD4+CD25+ cells of naive splenocytes. These provide in vitro scientific clues that Bifidobacteria A28 might be usable for anti-inflammatory disease such as inflammatory bowel disease (IBD).
Animals ; Bacteria ; Child ; Feces ; Gene Expression ; Humans ; Inflammatory Bowel Diseases ; Interleukin-10 ; Macrophages ; Macrophages, Peritoneal ; Mice ; Probiotics ; RNA, Messenger ; Sprains and Strains

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening.Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Thoracic foreign bodies (FBs) are serious and relatively frequent in emergency departments. Thoracic FBs may occur in association with aspiration, ingestion, trauma, or iatrogenic causes. Imaging plays an important role in the identification of FBs and their dimensions, structures, and locations, before the initiation of interventional treatment. To guide proper clinical management, radiologists should be aware of the radiologic presentations and the consequences of thoracic FBs. In this pictorial essay, we reviewed the optimal imaging settings to identify FBs in the thorax, classified thoracic FBs into four types according to their etiology, and reviewed the characteristic imaging features and the possible complications.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the ‘total significant features model’ (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the ‘selected feature model’ (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the ‘selected feature model’ (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.