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Rationale: Malignant phyllodes tumors are rare breast neoplasms that are associated with a 6.2% to 25% incidence rate of distant metastasis; the lung is the most common metastatic site. To date, there is no consensus regarding the treatment of metastatic malignant phyllodes breast tumors. Patient concern: A 48-year-old woman was admitted into the breast clinic for a rapidly growing right breast tumor that was first noticed 1 month prior. Diagnosis: Core needle biopsy revealed a malignant phyllodes tumor. A chest computed tomography and positron emission tomography/CT showed metastatic lymph nodes that appeared to have spread to the right axilla, as well as multiple solitary pulmonary nodules in the right lung. Fine needle aspiration on the axillary lymph node confirmed metastasis. Interventions: A right mastectomy with axillary lymph node dissection was conducted and a thoracoabdominal flap and a split thickness skin graft were performed for the skin defect. Palliative chemotherapy with doxorubicin plus ifosfamide was performed. Outcomes: An examination conducted 3 years postsurgery showed no signs of recurrence, and the patient's overall health status was satisfactory. Lessons: As standard treatment guidelines for metastatic malignant phyllodes tumors are lacking, we opted for the aforementioned aggressive treatments that resulted in complete remission of the lung metastasis. Therefore, aggressive treatment, whenever possible, is warranted.

The timely diagnosis and therapeutic monitoring of human renal cell carcinoma (RCC) is limited by the lack of specific biomarkers. To identify candidate RCC biomarkers, we used 2-DE gel electrophoresis with mass spectrometry and 2-DE spot intensity-based ROC analysis to analyze 18 sets of paired normal and RCC tumor tissue including conventional, papillary, and chromophobe subtypes. Validation was performed with RCC patient plasma samples and confirmed by clustergram, shRNA, and immunohistochemistry assays. Cardinal candidates were evaluated by ELISA. The leading candidate biomarker that was upregulated in RCC samples according to the clustergram and validation analysis was nicotinamide N-methyltransferase (NNMT) (13/15, P < 0.0001). Other upregulated candidate biomarkers that were identified by this method include ferritin, hNSE, NM23, secretagogin, and L-plastin. The upregulation of NNMT in RCC was confirmed by immunoblotting and immunohistochemistry. Analysis of fractionated membrane-associated proteins identified CAP-G, mitofillin, tubulin alpha, RBBP7, and HSP27. Of these, RBBP7 and HSP27 were highly expressed in the chromophobe subtype of RCC (3/3) but were absent from conventional RCC (0/3). The triple combination of the NNMT, FTL, and hNSE biomarkers had the highest predictive capacity of 0.993, while NNMT was the single, most powerful candidate diagnostic biomarker for all types of RCC.

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.

Background/Aim: It has been reported that adipose tissue contain progenitor cells with angiogenic potential and that therapy based on adipose tissuederived progenitor cells administration may constitute a promising cell therapy in patients with ischemic disease. In this study we evaluated the effect of culture-expanded mesenchymal stem cells (MSC) derived from adipose tissue on neovascularization and blood flow in an animal model of limb ischemia in immunodeficient mice. Methods: MSC were cultured from human adipose tissue by collagenase digestion. Hindlimb ischemia was created by ligating the proximal femoral artery of male nude mice. Human adipose tissue stromal cells (hADSC) were transplanted one day or 7 days after ligation. Results: During culture expansion of hADSC CD34 expression was downregulated. The laser Doppler perfusion index was significantly higher in the CD34(-), Flk-1(-), CD31(-) ADSC-transplanted group than in the control group, even when cells were transplanted 7days after hindlimb ischemia. Histological examination showed that hADSC transplantation recovered muscle injury and increased vascular density, compared with the control group. The effect of hADSC was correlated with the number of transplanted cells, but not with the ratio of CD34 expression. In vitro, hADSC can form vessel-like structure and express von Willibrand Factor. Conditioned media from hADSC increased proliferation and inhibited apoptotic cell death in of human aortic endothelial cells. Conclusion: This study showed that hADSC can be an ideal source for therapeutic angiogenesis in ischemic disease.

In this paper, we propose a new face recognition method under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the exemplars which are synthesized from photometric stereo images of training data and the linear combination of those exemplars are used to represent the new face. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute Face Database.

Facial appearance changes induced by lighting variation cause serious performance degradation in face recognition. Current face recognition systems encounter the difficulty to recognize faces under arbitrary illuminations. In this paper, we propose a new face recognition method under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the exemplars which are synthesized from photometric stereo images of training data and the linear combination of those exemplars are used to represent the new face. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute Face Database.