Lalit Gupta

Assessment of brain cognitive functions in patients with vitamin B12 deficiency using resting state functional MRI: A longitudinal study.

INTRODUCTION The resting state functional MRI (rsfMRI) approach is useful to explore the brains functional organization in health and disease conditions. In this study, using rsfMRI the alteration in brain due to vitamin B12 deficiency and reversibility of these alterations following therapy was studied. METHODS Thirteen patients with clinical and biochemical evidence of vitamin B12 deficiency were recruited in this study. Fifteen age and sex matched healthy controls were also included. Patients and controls were clinically evaluated using neuropsychological test (NPT). The analysis was carried out using regional homogeneity (ReHo) and low frequency oscillations (LFO) of BOLD signals in resting state. Six patients were also evaluated with rsfMRI and NPT after 6 weeks replacement therapy. RESULTS ReHo values in patients with vitamin B12 deficiency were significantly lower than controls in the entire cerebrum and the brain networks associated with cognition control, i.e., default mode, cingulo-opercular and fronto-parietal network. There was no significant difference using LFO and it did not show significant correlations with NPT scores. ReHo showed significant correlation with NPT scores. All the 6 patients showed increase in ReHo after replacement therapy. CONCLUSION We conclude that brain networks associated with cognition control are altered in patients with vitamin B12 deficiency, which partially recover following six weeks of replacement therapy. This is the first study to evaluate the rsfMRI in the light of clinical neuropsychological evaluation in patients. rsfMRI may be used as functional biomarker to assess therapeutic response in vitamin B12 deficiency patients.

Automatic doppler signal analysis to assess utero-placental circulation for identifying high risk pregnancies

High risk pregnancy conditions such as preeclampsia, pregnancy induced hypertension, intra-uterine growth restriction and gestational diabetes are associated with defective utero-placental circulation. These conditions, if undetected early during pregnancy, are associated with poor pregnancy outcomes including high morbidity/mortality for the fetus/mother. The current state of the art for monitoring such conditions is via (color) Doppler ultrasound with key clinical parameters being observed in uterine and umbilical arteries being the resistance index (RI), pulsatility index (PI) and AB index and early diastolic notching. High risk conditions in pregnancy manifest as abnormal flow profiles and indices in select fetal/maternal blood vessels. These parameters are gold standard as far as current clinical practice goes but they still suffer from low sensitivity in detecting and predicting the above mentioned high risk conditions at an earlier stage. In this paper, we propose a method based on Doppler signal analysis that automatically identifies the above conditions with higher sensitivity even when the current RI/PI indices are normal.

Doppler based identification of uterine artery and umbilical artery for monitoring pregnancy

In this paper, we present an algorithm to identify umbilical and uterine arteries from a set of four different maternal and fetal arteries using their Doppler signatures. To distinguish these arteries, we use 132 Doppler signals collected from pregnant women with gestational ages between 24 to 40 weeks. Initially we filter them to remove noise; spectrograms are generated to extract good cycles, which are then analyzed to derive independent features that could uniquely represent an artery. A non-linear classification technique using k-NN (k-nearest neighbor) classifier is further applied to identify umbilical and uterine arteries. The proposed algorithm achieves sensitivity and specificity of above 95% and 97% for identification of uterine artery and above 63% and 80% for umbilical artery.

Hybrid SVM - Random Forest classication system for oral cancer screening using LIF spectra

In this paper, a system for oral cancer screening using Laser Induced Fluorescence(LIF) has been developed. A hybrid approach of classification using Support Vector Machine (SVM) and Random Forest (RF) classifiers is proposed. Performance of the classifier is evaluated using several features types such as Wavelet, DFT, LDFT, ILDFT, DCT, LDCT and Slopes features. The most discriminating features are selected using Recursive Feature Elimination(RFE). Analysis of the problem of subset selection from SVM-RFE ranked list is also performed. The hybrid approach has been compared with stand-alone SVM, SVM-RFE and RF classifiers. The proposed technique improves the performance of the classification system significantly. The novelty of the approach lies in the way the most significant features are exstracted in separate modules to arrive at a decision and how the decision are then fused in an intelligent fashion to arrive at a final classification.

Optical Screening of Oral Cancer: Technology for Emerging Markets

Oral cancer is the sixth most common cancer in the world. It is one of the most prevalent cancers in the developing countries of South Asia accounting for one third of the world burden. Sixty percent of the cancers are advanced by the time they are detected. Two methods of optical spectroscopy for detection of oral cancer have been discussed here. These methods are simple, easy to handle and noninvasive. The evaluation of the data is done automatically using pattern recognition techniques, making the screening subjective.

A new feature selection and classification scheme for screening of oral cancer using laser induced fluorescence

Screening for oral cancer in its early stage is of utmost importance for improving the survival rate of oral cancer patients. The current method of visual examination followed by biopsy of suspected cases is subjective with inter and intra personal variations. With a low ratio of oral-cancer experts to patients compounded by the reluctance of patients to undergo biopsy in rural India. The situation cries out for automatic screening device for oral cancer. In this context, optical spectroscopy based on Laser Induced Fluorescence (LIF) has been shown to be a promising technique in distinguishing between cancerous and benign lesions in the mouth. However, it has been observed that it is very difficult to distinguish pre-malignant spectra from malignant and normal spectra recorded in-vivo. Hence, obtaining the most discriminating features from the spectra becomes important. In this article a new method of feature selection is proposed using mean-shift and Recursive Feature Elimination (RFE) techniques to increase discrimination ability of the feature vectors. Performance of the algorithm is evaluated on a in-vivo recorded LIF data set consisting of spectra from normal, malignant and pre-malignant patients. Sensitivity of above 95% and specificity of above 99% towards malignancy are obtained using the proposed method.

Classification method for microarray probe selection using sequence, thermodynamics and secondary structure parameters

Probe design is the most important step for any microarray based assay. Accurate and efficient probe design and selection for the target sequence is critical in generating reliable and useful results. Several different approaches for probe design are reported in literature and an increasing number of bioinformatics tools are available for the same. However, based on the reported low accuracy, determining the hybridization efficiency of the probes is still a big computational challenge. Present study deals with the extraction of various novel features related to sequence composition, thermodynamics and secondary structure that may be essential for designing good probes. A feature selection method has been used to assess the relative importance of all these features. In this paper, we validate the importance of various features currently used for designing an oligonucleotide probe. Finally, a classification methodology is presented that can be used to predict the hybridization quality of a probe.