The Hair Growth-Promoting Effect of Rumex japonicus

Rumex japonicus Houtt. is traditionally used as a medicinal plant to treat patients suffering from skin disease in Korea. However, the beneficial effect of Rumex japonicus Houtt. on hair growth has not been thoroughly examined. Therefore, the present study aims to investigate the hair growth-promoting effect of Rumex japonicus (RJ) Houtt. root extract using human dermal papilla cells (DPCs), HaCaT cells, and C57BL/6 mice model. RJ induced antiapoptotic and proliferative effects on DPCs and HaCaT cells by increasing Bcl-2/Bax ratio and activating cellular proliferation-related proteins, ERK and Akt. RJ also increased β-catenin via the inhibition of GSK-3β. In C57BL/6 mice model, RJ promoted the anagen induction and maintained its period. Immunohistochemistry analysis demonstrated that RJ upregulated Ki-67 and β-catenin expressions, suggesting that the hair growth effect of RJ may be mediated through the reinforcement of hair cell proliferation. These results provided important insights for the possible mechanism of action of RJ and its potential as therapeutic agent to promote hair growth

Primary Cardiac Angiosarcoma Presenting With Cardiac Tamponade

Primary cardiac angiosarcoma is a very rare disease with a poor prognosis. We report a case of a patient with a primary cardiac angiosarcoma who presented with cardiac tamponade; the angiosarcoma was successfully resected surgically

Clinical Significance of a Large Difference (≥ 2 points) between Biopsy and Post-prostatectomy Pathological Gleason Scores in Patients with Prostate Cancer

We investigated the clinical significance of large difference (≥ 2 points) between biopsy-derived (bGS) and post-prostatectomy Gleason scores (pGS). At 14 medical centers in Korea, 1,582 men who underwent radical prostatectomy for prostate cancer were included. According to the difference between bGS and pGS, the patients were divided into three groups: A (decreased in pGS ≥ 2, n = 30), B (changed in pGS ≤ 1, n = 1,361; control group), and C (increased in pGS ≥ 2, n = 55). We evaluated various clinicopathological factors of prostate cancer and hazards for biochemical failure. Group A showed significantly higher mean maximal percentage of cancer in the positive cores (max%) and pathological T stage than control. In group C, the number of biopsy core was significantly smaller, however, tumor volume and max% were significantly higher and more positive biopsy cores were presented than control. Worse pathological stage and more margin-positive were observed in group A and C than in control. Hazard ratio for biochemical failure was also higher in group A and C (P = 0.001). However, the groups were not independent factors in multivariate analysis. In conclusion, large difference between bGS and pGS shows poor prognosis even in the decreased group. However it is not an independent prognostic factor for biochemical failure

Both F-18 FDG-avidity and Malignant Shape of Cervical Lymph Nodes on PET/CT after Total Thyroidectomy Predict Resistance to High-dose I-131 Therapy in Patients with Papillary Thyroid Cancer

Objective: Resistance of metastatic lymph nodes (LNs) to high dose I-131 therapy is associated with high morbidity in patients with differentiated thyroid cancer. We evaluated the role of F-18 FDG PET/CT in the prediction of resistance to high dose I-131 therapy in patients with papillary thyroid cancer. Methods: The subjects were 307 patients who underwent total or near total thyroidectomy followed by high dose (5.55-6.66 GBq) I-131 therapy. We divided the patients into three subgroups by visual assessment of regional LNs: FDG-avid LNs with a malignant shape on CT (PET/CT-positive group), FDG-avid LNs with a benign shape on CT (PET/CT-intermediate group) and no FDG-avid lesion (PET/CT-negative group). We measured the maximum SUV (SUVmax) of FDG-avid LNs in each patient. The presence or absence of focal increased uptake of I-131 was evaluated by whole body scan (WBS), and was denoted as WBS-positive group or WBS-negative group, respectively. Resistance to therapy was defined as presence of thyroglobulin (Tg) in serum (Tg ≥1.0 ng/ml) 3-6 months after I-131 therapy. Univariate and multivariate analyses were performed to determine the relationship between resistance to I-131 therapy and various clinico-pathologic variables. Results: PET/CT-positive, intermediate, and negative groups included 20 (6.5%), 44 (14.3%) and 243 (79.2%) patients, respectively. The mean SUVmax was significantly higher in the PET/CT-positive group than that of the PET/CT-intermediate group (4.6 vs. 2.7, P <0.001). Univariate analysis revealed that the PET/CT-positive group (P <0.001), T2-4 stage (P <0.001), N1b stage (P = 0.001), lower dose (5.55 GBq) of I-131 (P <0.001), and the WBS-positive group (P = 0.029) were associated with resistance to therapy. In multivariate analysis, the PET/CT-positive group, lower dose of I-131, N1b stage, and T2-4 stage remained significant with odds ratios of 10.07 (P <0.001), 3.82 (P <0.001), 3.58 (P = 0.001), and 2.53 (P = 0.009), respectively. Conclusion: FDG-avidity and malignant shape of cervical LNs on pre-therapy FDG PET/CT were a strong risk factors predicting resistance to high dose I-131 therapy. A lower dose of administered I-131 (5.55 GBq) and more extensive tumors (T2-4 and N1b) were also associated with resistance to high dose I-131 therapy

Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker

Prognostic significance of common preoperative laboratory variables in clear cell renal cell carcinoma

OBJECTIVE: To investigate the prognostic significance of common preoperative laboratory variables evaluated before surgery for clear cell renal cell carcinoma (RCC). PATIENTS AND METHODS: We retrospectively analysed the records of 355 patients who had surgery for clear cell RCC, assessing: clinical factors, including preoperative laboratory measurements, i.e. haemoglobin level, leukocyte count, platelet count, erythrocyte sedimentation rate (ESR), serum calcium, alkaline phosphatase (ALP), albumin, bilirubin, alanine aminotransferase, aspartate aminotransferase, and red blood cells in urine; and pathological factors, with the survival rates after surgery. RESULTS: The presence of metastasis, tumour stage and tumour size, with the ESR and ALP before surgery, were identified as significant prognostic factors for progression-free survival in a multivariate analysis. The same factors were significant independent factors for disease-specific survival, except for ESR and ALP, which were nearly statistically significant. When limited to non-metastatic tumours only, the multivariate analysis showed that ESR and ALP, with tumour stage, grade, size and necrosis, were independent prognostic factors for disease-specific survival. CONCLUSIONS: Along with traditionally accepted prognostic factors, these results suggest that common laboratory variables assessed before surgery, e.g. ESR and ALP, might also be useful in assessing the prognosis for patients with non-metastatic clear cell RCC. Including various laboratory variables in prognostic algorithms for RCC should be considered after further validation in RCCs of various histological subtypes and stages

Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDG PET/CT, EZRIN, and KI67

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data