Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0026986 (myelodysplastic syndrome)
 14,926 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A patient with acute myelomonocytic leukemia (M4 in FAB classification) refractory to various kinds of intensive chemotherapy was intravenously administered low doses (7 or 14 mg/m2) of aclacinomycin-A (ACM-A) daily. This increased mature neutrophils and monocytes and decreased leukemia cells in the peripheral blood. Pelger Huet-like nuclear anomaly, observed in the neutrophils, suggested the leukemic nature of these cells. The in vivo findings were clearly correlated with the in vitro results in which ACM-A could induce myelomonocytic differentiation of the leukemia cells. During the course of the treatment, the patient achieved and maintained good general condition for more than nine months after the initiation of the treatment. In clinical trials, nine patients, five with acute myeloid leukemia (AML) and four with myelodysplastic syndrome (MDS), were treated with low doses of ACM-A. Five patients, three AML and two MDS, responded to the treatment. The results suggest that low doses of ACM-A may induce in vivo differentiation of leukemia cells, and may be a potential therapeutic strategy in the treatment of AML or MDS that is refractory to conventional chemotherapy.
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PMID:Possible differentiation treatment with aclacinomycin A in acute myelomonocytic leukemia refractory to conventional chemotherapy. 158 May 53

In 114 patients with postrepair myelomeningocele MRI of the spine was performed. Tethered cord (89%) and associated malformations (syrinx, lipoma etc.) (33%) were the most important findings. Additional MRI scans of the head (44 patients) revealed numerous further anomalies. Arnold Chiari malformation was found in 76% of the patients (ACM I:32%, ACM II:44%). In the ACM II group compression of lower cranial nerves, brain stem, and cerebellum can lead to considerable neurologic symptoms. Therefore in patients with progressive neurologic dysfunction a complete investigation of the whole spine and brain is necessary. MRI proves to be the diagnostic procedure of choice in patients with dysraphic myelodysplasia.
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PMID:Cerebral and spinal MR-findings in patients with postrepair myelomeningocele. 233 86

Tumor clustering is one of the important techniques for tumor discovery from cancer gene expression profiles, which is useful for the diagnosis and treatment of cancer. While different algorithms have been proposed for tumor clustering, few make use of the expert's knowledge to better the performance of tumor discovery. In this paper, we first view the expert's knowledge as constraints in the process of clustering, and propose a feature selection based semi-supervised cluster ensemble framework (FS-SSCE) for tumor clustering from bio-molecular data. Compared with traditional tumor clustering approaches, the proposed framework FS-SSCE is featured by two properties: (1) The adoption of feature selection techniques to dispel the effect of noisy genes. (2) The employment of the binate constraint based K-means algorithm to take into account the effect of experts' knowledge. Then, a double selection based semi-supervised cluster ensemble framework (DS-SSCE) which not only applies the feature selection technique to perform gene selection on the gene dimension, but also selects an optimal subset of representative clustering solutions in the ensemble and improve the performance of tumor clustering using the normalized cut algorithm. DS-SSCE also introduces a confidence factor into the process of constructing the consensus matrix by considering the prior knowledge of the data set. Finally, we design a modified double selection based semi-supervised cluster ensemble framework (MDS-SSCE) which adopts multiple clustering solution selection strategies and an aggregated solution selection function to choose an optimal subset of clustering solutions. The results in the experiments on cancer gene expression profiles show that (i) FS-SSCE, DS-SSCE and MDS-SSCE are suitable for performing tumor clustering from bio-molecular data. (ii) MDS-SSCE outperforms a number of state-of-the-art tumor clustering approaches on most of the data sets.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Double Selection Based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles. 2635 43

In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM) are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF algorithms, six spectral clustering algorithms, four GMM algorithms, and a standard kmeans algorithm in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMF algorithms and kmeans have similar performance and outperform spectral clustering algorithms. As spectral clustering can detect some hidden manifold structures, the underperformances of spectral methods lead us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as MDS plots also indicate clusters in some datasets have elliptical boundaries, GMM is also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distribution.
IEEE/ACM Trans Comput Biol Bioinform 2020 Sep 21
PMID:Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM. 3295 65