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Differentiation of Crohn’s disease (CD) from intestinal tuberculosis (ITB) is a big challenge to gastroenterologists because of their indistinguishable features and insensitive diagnostic tools. A non-invasive biomarker is urgently required to distinguish ITB/CD patients particularly in India, a TB endemic region, where CD frequency is increasing rapidly due to urbanization. Among the three differentially expressed miRNAs obtained from small RNA transcriptomic profiling of ileocaecal/terminal ileal tissue of ITB/CD patients (n = 3), only two down-regulated miRNAs, miR-31-5p, and miR-215-5p showed comparable data in qRT-PCR. Out of which, only miR-215-5p was detectable in the patient’s plasma, but there was no significant difference in expression between ITB/CD. On the other hand, miR-375-3p, the pulmonary TB specific marker was found in higher amount in the plasma of ITB patients than CD while reverse expression was observed in the ileocaecal/terminal ileal tissues of the same patients. Next, using Bioplex pro-human cytokine 48-plex screening panel, only three chemokines, Eotaxin-1/CCL11, SDF-1α/CXCL12, and G-CSF have noted significantly different levels in the serum of ITB/CD patients. ROC analysis has revealed that compared to a single molecule, a combination of miR-375-3p + Eotaxin-1/CCL11 + SDF-1α /CXCL12 + G-CSF showed a better AUC of 0.83, 95% CI (0.69–0.96) with 100% specificity and positive predictive value while sensitivity, negative predictive value, and accuracy were 56%, 69%, and 78% respectively in distinguishing ITB from CD. This study suggests that a combination of plasma markers shows better potential in differentiating ITB from CD than a single marker and this panel of markers may be used for clinical management of ITB/CD patients.

Clustering ensemble, also referred to as consensus clustering, has emerged as a method of combining an ensemble of different clusterings to derive a final clustering that is of better quality and r...

Background: Psychological factors have been prominently implicated in the causation as well as maintenance of irritable bowel syndrome (IBS). Studies comparing psychiatric morbidity in IBS with healthy controls have reported contrasting findings. The current study was undertaken to assess the prevalence of anxiety and depression in patients with IBS in comparison to healthy controls and to explore the relationship, if any, of anxiety and depression with various subtypes of IBS. Materials and Methods: Fifty consecutive patients of IBS (diagnosed as per Rome III criteria) between 18 and 65 years of age and fifty age- and sex-matched healthy controls were assessed for the presence of anxiety and depression using Hamilton Rating Scale for Anxiety (HAMA) and Hamilton Rating Scale for Depression (HAMD), respectively. Results: The patient group scored higher than controls (P < 0.001) in both HAMA and HAMD scores. The HAMA scores were significantly higher (P < 0.001) in the severe IBS group compared to those with moderate IBS. HAMA scores predicted 25.6% (R2 = 0.256) of variance in IBS severity scores. However, there was no significant difference between the two groups in terms of HAMD scores. Conclusion: The high prevalence of psychiatric comorbidities such as anxiety and depression in IBS samples in our study provides evidence in favor of proper screening for these disorders in gastrointestinal clinics. Recognition and treatment for these comorbidities can improve the quality of life as well as overall outcomes.

In this paper a novel approach to document clustering has been introduced by defining a representative-based document similarity model that performs probabilistic segmentation of documents into chunks. The frequently occuring chunks that are considered as representatives of the document set, may represent phrases or stem of true words. The representative based document similarity model, containing a term-document matrix with respect to the representatives, is a compact representation of the vector space model that improves quality of document clustering over traditional methods.

In this paper we propose a novel approach to document clustering by introducing a representative-based document similarity model that treats a document as an ordered sequence of words and partitions it into chunks for gaining valuable proximity information between words. Chunks are subsequences in a document that have low internal entropy and high boundary entropy. A chunk can be a phrase, a word or a part of word. We implement a linear time unsupervised algorithm that segments sequence of words into chunks. Chunks that occur frequently are considered as representatives of the document set. The representative based document similarity model, containing a term-document matrix with respect to the representatives, is a compact representation of the vector space model that improves quality of document clustering over traditional methods.