Biomedical subjects

Sanjeev Garg

Publications and source records attributed to Sanjeev Garg.

3 recordsLinked to original sources

PubMed2005

Mononeuritis multiplex secondary to sarcoidosis.

Peripheral nerve involvement is a rare, yet treatable neurological manifestation of sarcoidosis. Most patients respond well to corticosteroids, but relapses are common and the long-term prognosis remains unpredictable. We present a patient with an asymmetrical neurological presentation of previously undiagnosed sarcoidosis. She presented with paresthesias and predominantly distal extremity weakness. Other possible causes of neuropathy were ruled out and she was found to have an elevated serum ACE level. A nerve/lip biopsy demonstrated non-caseating granulomas consistent with sarcoid. Her clinical outcome was favorable after initiating treatment with high dose oral prednisone.

Female↗

PubMed2003

Multistage gene expression profiling in a differentially susceptible mouse colon cancer model.

The DNA alkylating agent, azoxymethane (AOM), induces tumor formation in the distal colon of susceptible mice. Differential susceptibility to this colonotropic carcinogen has been well characterized in A/J (sensitive) and AKR/J (resistant) mice. However, the reasons underlying the differential response to AOM and the molecular mechanisms involved in colon tumor progression remain unclear. To address these issues, we used a cDNA microarray approach to determine time-related changes in gene expression patterns in A/J and AKR/J colons following carcinogen treatment. In the present study, mice were injected intraperitoneally with either AOM (10mg/kg body weight once a week for 6 weeks) or 0.9% NaCl solution (vehicle controls). Total RNA was isolated from the distal colons at 1, 4, and 24 weeks post-AOM exposure. RNA was reverse transcribed and cDNA samples labeled with Cy3 and Cy5 were hybridized to a glass chip containing 4608 mouse cDNA duplicate clones. The resulting mRNA expression levels were analyzed using GLEAMS 3.0, a Unix/Linux-based software program. Genes with more than twofold variations in expression levels were considered significant. Further clustering analysis was performed based on gene expression patterns at different time points using a novel adaptive centroid algorithm (ACA). Of the 4608 genes, 118 clustered into 11 significant groups that displayed similar and distinct expression patterns between the strains following carcinogen treatment. Nine clusters were selected for further analysis based on their divergence in response between A/J and AKR/J and their potential role in tumorigenesis. Overall, our data indicate time- and strain-specific genetic alterations during different stages of colon tumorigenesis following AOM treatment.

Adenocarcinoma↗

PubMedDate not supplied

An adaptive strategy for single- and multi-cluster gene assignment.

Strict assignment of genes to one class, dimensionality reduction, a priori specification of the number of classes, the need for a training set, nonunique solution, and complex learning mechanisms are some of the inadequacies of current clustering algorithms. Existing algorithms cluster genes on the basis of high positive correlations between their expression patterns. However, genes with strong negative correlations can also have similar functions and are most likely to have a role in the same pathways. To address some of these issues, we propose the adaptive centroid algorithm (ACA), which employs an analysis of variance (ANOVA)-based performance criterion. The ACA also uses Euclidian distances, the center-of-mass principle for heterogeneously distributed mass elements, and the given data set to give unique solutions. The proposed approach involves three stages. In the first stage a two-way ANOVA of the gene expression matrix is performed. The two factors in the ANOVA are gene expression and experimental condition. The residual mean squared error (MSE) from the ANOVA is used as a performance criterion in the ACA. Finally, correlated clusters are found based on the Pearson correlation coefficients. To validate the proposed approach, a two-way ANOVA is again performed on the discovered clusters. The results from this last step indicate that MSEs of the clusters are significantly lower compared to that of the fibroblast-serum gene expression matrix. The ACA is employed in this study for single- as well as multi-cluster gene assignments.

Algorithms↗