Naveen Kalra

The Clinicopathological Profile of Indian Patients with Nonalcoholic Fatty Liver Disease (NAFLD) is Different from That in the West

There are limited data on nonalcoholic fatty liver disease (NAFLD) from India. The clinicopathological profile of Indian patients with NAFLD may be different from that of Western patients. One hundred NAFLD patients with increased liver enzymes were prospectively evaluated for clinical presentation, associated diseases, overweight/obesity, central obesity (n=54), presence of diabetes mellitus, lipid abnormalities, insulin resistance (n=39), metabolic syndrome (n=54), serum iron, serum ferritin, and transferrin saturation (n=60), and HFE gene mutations (n=30). Risk factors for the grade and stage of the disease on histology were studied in 38 biopsy-proven patients. Patients were treated with lifestyle modifications and ursodeoxycholic acid (UDCA). Seventeen nonresponder patients were treated with metformin. The majority of patients were males (n=70). Twenty percent of patients were overweight, 68% had obesity, and 78% had central obesity. Abnormal cholesterol, HDL, and triglycerides were present in 36%, 66%, and 53% of patients, respectively. Twelve percent of patients had diabetes mellitus and 16% patients had various associated diseases. All 22 (100%) patients studied by ITT and all but 1 (98%) studied by HOMA-IR were found to have reduced insulin sensitivity and 50% were found to have metabolic syndrome by the modified ATP III criteria. Two (3%) patients were found to have high serum iron, 4 (7%) patients had high ferritin, 5 (8%) patients had increased transferrin saturation, and 4 (13%) patients were found to be heterozygotes for H63D HFE gene mutation. Twenty patients of 38 (53%) had histological evidence of NASH (class 3=6, class 4=14). The other 18 (47%) qualified for class I (n=1) or class II (n=17) NAFLD. Four (10.5%) patients had bridging fibrosis and none had evidence of cirrhosis liver. Seventy-four (74%) patients achieved a biochemical response to lifestyle modification and UDCA. All 17 patients treated with metformin had a reduction in ALT level and 10 (59%) of them had normalization of their enzymes. We conclude that the clinicopathological profile of NAFLD in Indian patients is different from that in the West.

Flyash as a soil conditioner and fertilizer

Abstract Field experiments were conducted in villages around the National Capital Power Project, Ghaziabad, Uttar Pradesh and IARI Farm, New Delhi to evaluate the effects of flyash incorporation on soil properties and the growth and yield of wheat ( Triticum aestivum L. ), mustard ( Brassica juncea L. ), rice ( Oryza sativa L. ) and maize (Zea mays L.). Flyash application levels (up to 50 t/ha) were decided on the basis of an ash/dust fall range of 5–12 t/ha/y in villages adjoining the thermal power station. The grain yield of maize increased in flyash-treated plots with the addition of ash up to a maximum addition of 10 t/ha. Dusting crop canopies with ash decreased the yield in proportion to the amount applied. The yield of wheat increased up to an addition of ash of 20 t/ha, and declined thereafter, but was still higher than the yield when no flyash was added. Paddy yield when 10 t/ha of ash was added was similar to that with no flyash, whereas mustard showed improvements in seed yield with flyash addition at 10 t/ha level. Flyash-treated plots had a marginally higher uptake of Zn, Cu, Fe, Mn and Cd. Flyash addition to soil resulted in lower bulk density, although the differences compared with non-treated plots were not significant. The addition of flyash also reduced the hydraulic conductivity and improved moisture retention at field capacity and wilting point, but no changes in available water were observed. These changes in soil properties might have been due to modifications in macro- and micro-pore size distribution and which may also have contributed to the increased crop yields in light- and medium-textured soils. However, the effects of ash addition on soil health and crop productivity need to be established with long-term studies.

MDCT in the Staging of Gallbladder Carcinoma

OBJECTIVE The purpose of our study was to determine the utility of dual-phase MDCT with 3D reconstruction in the staging and resectability of gallbladder carcinoma. SUBJECTS AND METHODS Twenty-seven consecutive patients with suspected gallbladder carcinoma on clinical examination and routine sonography were prospectively analyzed with dual-phase MDCT. Of these patients, only 20 who underwent a laparotomy for extended cholecystectomy or a palliative surgery were included in the study. Three-dimensional volume-rendered reconstruction was used for evaluation of the vascular invasion and anatomy. The staging and resectability as determined on CT were compared with preoperative findings. RESULTS On the basis of the CT findings, eight tumors were resectable and 12 were unresectable. On surgery, 11 tumors were found to be resectable and the remaining were unresectable. Overstaging by CT occurred in three patients due to overassessment of duodenal infiltration. CT had a sensitivity of 72.7%, a specificity of 100%, and an accuracy of 85% for determining resectability of gallbladder carcinoma. For the diagnosis of hepatic and vascular invasion by the tumor, there was 100% correlation between CT and surgery. Vascular variations were found in six of the 11 patients who underwent radical cholecystectomy. CONCLUSION Dual-phase MDCT with 3D reconstruction is a comprehensive imaging technique for staging gallbladder carcinoma and determining the vascular road map before surgery.

Impacts of Climate Change on Agriculture

Climate change has emerged as the most prominent of the global environment issues and there is a need to evaluate its impact on agriculture. Crop simulation models help greatly in this regard. Crop models such as WTGROWS, INFOCROP, ORYZA and DSSAT have been widely used for land use planning, agri-production estimates, impact of climate change and environmental impact analysis. Vulnerable regions under future scenarios of climate change and adaptation strategies (agronomic and input management) have been evolved for many important crops by using simulation techniques. One of the simple empirical techniques for evaluating the impact of future climate change is through historic analysis of the response of crops to inter-seasonal climatic variability. The impact of temperature rise is different for crops grown under variable production environments. Interactions exist for changes in temperature, carbon dioxide concentration, solar radiation and rainfall on growth and yield of crops. Adaptation strategies through the adoption of agronomic management options (such as altered date of sowing, scheduling of water and nutrients) can sustain agricultural productivity under climate change. The rapid changes in land use and land cover have to be included for impact analysis. Linking of the socioeconomic aspects needs to be strengthened.

Neural network based focal liver lesion diagnosis using ultrasound images.

Present study proposes a computer-aided diagnostic system to assist radiologists in identifying focal liver lesions in B-mode ultrasound images. The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, Hepatocellular carcinoma and Metastases, along with Normal liver. The study is performed with 111 real ultrasound images comprising of 65 typical and 46 atypical images, which were taken from 88 subjects. These images are first enhanced and then regions of interest are segmented into 800 non-overlapping segmented regions-of-interest. Subsequently 208-texture based features are extracted from each segmented region-of-interest. A two step neural network classifier is designed for classification of five liver image categories. In the first step, a neural network classifier gives classification among five liver image categories. If neural network decision is for more than one class as obtained from the first step, binary neural network classifiers are used in the second step for crisp classification between two classes. Test results of two-step neural network classifier showed correct decisions of 432 out of 500 segmented regions-of-interest in test set with classification accuracy of 86.4%. The classifier has given correct diagnosis of 90.3% (308/340) in the tested segmented regions-of-interest from typical cases and 77.5% (124/160) in tested segmented regions-of-interest from atypical cases.

Enhancement of the ultrasound images by modified anisotropic diffusion method

Speckle is a primary factor which degrades the contrast resolution and masks the meaningful texture information present in an ultrasound image. Its presence severely hampers the interpretation and analysis of ultrasound images. When speckle reduction technique is applied for visual enhancement of ultrasound images, it is to be kept in mind that blurring associated with speckle reduction should be less and fine details are properly enhanced. With these points in consideration, the modified speckle reduction anisotropic diffusion (MSRAD) method is proposed in the present study to improve the visual quality of the ultrasound images. In the proposed MSRAD method, the four neighboring pixel template in speckle reduction anisotropic diffusion (SRAD) method of Yu and Acton (IEEE Trans Image Process 11:1260–1270, 2002) have been replaced by a new template of larger number of neighboring pixels to calculate the diffusion term. To enhance visual quality of ultrasound images, nonquadratic regularization (Yu and Yadegar, Proceedings of the IEEE international conference on image processing, 2006) is incorporated with MSRAD method and accordingly changes in parameter settings have been made. The performance of MSRAD method was evaluated using clinical ultrasound images, interpretation by the medical experts and results of MSRAD method by subjective and objective criteria.

Analysis of yield trends of the rice-wheat system in north-western India.

The north-western region of India is extremely important for food security and has contributed substantially to the countrys past agricultural growth. This has been possible largely because the region is endowed with good natural resources such as soils and water, and is relatively well developed in respect of markets and infrastructure. Rice and wheat are now commonly grown in double-cropping rotation and their average productivity varies from 2 to 5 t/ha. This needs to be increased substantially to meet growing demands due to the increasing population, urbanization and income growth. Concerns have been expressed lately that the rice–wheat system is causing environmental degradation in the region, and that there is a stagnation/decline in its productivity threatening food security. In this paper, the authors analyse the historical trends in yields of rice and wheat crops using regional statistics, long-term fertility experiments, other conventional field experiments and crop simulation models. Rice yields showed a very modest decline in many districts in the region, as well as in field experiments. The simulated yields showed a similar decline. Wheat yields of normally sown crops showed an increasing trend in most districts due to a greater application of fertilizers. Long-term experiments conducted elsewhere in the Indo-Gangetic plains also showed a large decline in rice yields and a small or no decline in wheat yields during the same period. The simulation results indicated that the rate of decline was related to the initial yield of crops. A significant annual yield decline was shown only when yield levels were high. It is concluded that the evidence of a yield decline in north-western India is not very strong at present. The yield trends may be partly related to the gradual change in weather conditions during the last two decades in selected research centres located in and around urban areas. The relationship of these trends to changes in nutrient use efficiencies, water use, insects and disease prevalence is discussed. Results indicate that there are only limited management options for increasing yields of rice and wheat crops in north-western India.

Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound

A computer aided diagnostic system to characterise normal and cirrhotic liver by multiresolution texture descriptors is proposed in this paper. The study is carried out in 120 segmented regions of interest extracted from 31 clinically acquired B-mode liver ultrasound images. Mean and standard deviation multiresolution texture descriptors derived by using 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform are considered for analysis and exhaustive search with J3 criterion of class separability is used for feature selection. The performance of subset of five most discriminative texture descriptors obtained from 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform is compared by using a support vector machine classifier. It is observed that only five mean multiresolution texture descriptors obtained from 2D-Gabor wavelet transform at selective scale and orientations provide highest classification accuracy of 98.33% and sensitivity of 100% by using a support vector machine classifier. The promising results indicate that the selective frequency and orientation properties of Gabor filters are extremely useful for providing multiscale texture description.

A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound

Abstract A comparative study of three computer-aided classification (CAC) systems for characterization of focal hepatic lesions (FHLs), such as cyst, hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET), along with normal (NOR) liver tissue is carried out in the present work. In order to develop efficient CAC systems a comprehensive and representative dataset consisting of B-mode ultrasound images with (1) typical and atypical cases of cyst, HEM and MET lesions, (2) small and large HCC lesions and (3) NOR liver cases have been used for designing K-nearest neighbour (KNN), probabilistic neural network (PNN) and a back propagation neural network (BPNN) classifiers. For differential diagnosis between atypical FHLs, expert radiologists often visualize the textural characteristics of regions inside and outside the lesion. Accordingly in the present work, texture features and texture ratio features are computed from regions inside and outside the lesions. A feature set consisting of 208 texture features (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for dimensionality reduction; it is observed that maximum accuracy of 87.7% is obtained for a PCA-BPNN-based CAC system in comparison to 86.1% and 85% as obtained by PCA-PNN and PCA-KNN-based CAC systems. The sensitivity of the proposed PCA-BPNN based CAC system for NOR, Cyst, HEM, HCC and MET cases is 82.5%, 96%, 93.3%, 90% and 82.2%, respectively. The sensitivity values with respect to typical, atypical, small HCC and large HCC cases are 85.9%, 88.1%, 100% and 87%, respectively. Keeping in view the comprehensive and representative dataset used for designing the classifier, the results obtained by the proposed PCA-BPNN-based CAC system are quite encouraging and indicate its usefulness to assist experienced radiologists for interpretation and diagnosis of FHLs.

Prediction of Cirrhosis Based on Singular Value Decomposition of Gray Level Co-occurence Marix and aNneural Network Classifier

In this present work, a technique fordiscrimination between normal and cirrhotic liversegmented regions of interest (SROIs) based on singularvalue decomposition (SVD) of GLCM matrix is reported.Thirty four B-mode ultrasound images taken from 22normal volunteers and 12 patients suffering from livercirrhosis were collected from Department of Radiodiagnosisand Imaging, PGIMER, Chandigarh, India. Firstly, the graylevel co-occurrence matrix (GLCM) texture features arecomputed for 121 SROIs (82 normal SROIs, 39 cirrhoticSROIs) and classification is done using a neural network(NN) classifier. The classification accuracy of 95.86% isachieved without feature selection. Secondly, featureselection is carried out by two different approaches. Inapproach 1, standard correlation based feature selection(CFS) is used to find the optimal subset of GLCM texturefeatures which provides best discrimination between normaland cirrhotic SROIs. It has been observed that CFS method,results in an optimal subset of 7 GLCM texture features{angular second moment (ASM), Contrast, Variance, SumAverage, Entropy, Difference Entropy and InformationMeasures of Correlation-1}. In approach 2, the potential ofsingular values obtained by singular value decomposition(SVD) of GLCMs for discrimination between normal andcirrhotic SROIs is investigated. It has been observed thatonly first 2 singular values can provide effectivediscrimination between normal and cirrhotic liver SROIs.In the classification stage a neural network (NN) classifier isused. The classification accuracy of 95.04% is obtained inboth cases. From the comparison it is concluded that onlyfirst two singular values obtained by SVD decomposition ofthe GLCMs and a NN classifier can be used to build acomputationally efficient computer aided diagnostic (CAD)system for predicting liver cirrhosis.