• Nutrition Research and Practice
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• v.10 no.4
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• pp.411-417
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• 2016

BACKGROUND/OBJECTIVES: Metabolic syndrome (MetS) is a set of interrelated metabolic risk factors that increase the risk of cardiovascular morbidity and mortality. Studies regarding the specificity and sensitivity of serum levels of leptin and uric acid as predictors of MetS are limited. The aim of this study was to evaluate the serum levels of leptin and uric acid in terms of their specificity and sensitivity as predictors of MetS in the studied Jordanian group. SUBJECTS/METHODS: In this cross sectional study, 630 adult subjects (308 men and 322 women) were recruited from the King Hussein Medical Center (Amman, Jordan). The diagnosis of MetS was made according to the 2005 International Diabetes Federation criteria. Receiver operating characteristic curves were used to determine the efficacy of serum levels of leptin and uric acid as predictors of MetS in the studied Jordanian group. RESULTS: Study results showed that for identification of subjects with MetS risk, area under the curve (AUC) for leptin was 0.721 and 0.683 in men and women, respectively. Serum uric acid levels in men showed no significant association with any MetS risk factors and no significant AUC, while uric acid AUC was 0.706 in women. CONCLUSION: Serum leptin levels can be useful biomarkers for evaluation of the risk of MetS independent of baseline obesity in both men and women. On the other hand, serum uric acid levels predicted the risk of MetS only in women.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.10039-10043
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• 2014

Background: In Pakistan thyroid cancer is responsible for 1.2% cases of all malignant tumors. Ultrasonography (US) is helpful in detecting cancerous thyroid nodules on basis of different features like echogenicity, margins, microcalcifications, size, shape and abnormal neck lymph nodes. We therefore aimed to calculate diagnostic accuracy of ultrasound in detection of carcinoma in thyroid nodules taking fine needle aspiration cytology as the reference standard. Materials and Methods: A cross-sectional analytical study was designed to prospectively collect data from December 2010 till December 2012 from the Department of Radiology in Aga Khan University Hospital, Karachi, Pakistan. A total of 100 patients of both genders were enrolled after informed consent via applying non-probability consecutive sampling technique. Patients referred to Radiology department of Aga Khan University to perform thyroid ultrasound followed by fine-needle aspiration cytology of thyroid nodules were included. They were excluded if proven for thyroid malignancy or if their US or FNAC was conducted outside our institution. Results: The subjects comprised 76 (76%) females and 24 males. Mean age was $41.8{\pm}SD$ 12.3 years. Sensitivity and specificity with 95%CI of ultrasound in differentiating malignant thyroid nodule from benign thyroid nodule calculated to be 91.7% (95%CI, 0.72-0.98) and 78.94% (0.68-0.87) respectively. Reported positive predictive value and negative PV were 57.9% (0.41-0.73) and 96.8% (0.88-0.99) and overall accuracy was 82%. Likelihood ratio (LR) positive was computed to be 4.3 and LR negative was 0.1. Conclusions: Ultrasonography has a high diagnostic accuracy in detecting malignancy in thyroid nodules on the basis of features like echogenicity, margins, micro calcifications and shape.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.603-606
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• 2013

Background: Ovarian cancer is the second most common malignancy in Pakistani women, accounting for 4% of all cancers in the female population. The aim of this study was to determine sensitivity, specificity, positive and negative predictive values and 95% confidence intervals for ultrasound in characterization of ovarian masses in patients presenting at public and private tertiary care hospitals in Karachi, Pakistan. Materials and Methods: We adopted a cross-sectional analytical study design to retrospectively collect data from January 2009-11 from medical records of two tertiary care hospitals. Using a non-probability purposive sampling technique, we recruited a sample of 86 women aged between 15 and 85 years fulfilling inclusion criteria with histopathologically proven ovarian masses presenting for an ultrasound examination in our radiology departments. Results: Our retrospective data depicted sensitivity and specificity of ultrasound to be 90.7%, 95%CI (0.77, 0.97) and 91.4%, 95%CI (0.76, 0.98) respectively. Positive predictive value was 93%, 95%CI (0.79, 0.98) and negative predictive value was 89%, 95%CI (0.73, 0.96). A total of 78 ovarian masses were detected, out of which 42 were malignant and 36 were benign. Conclusions: Results of our study further reinforce the conclusion that ultrasound should be used as an initial modality of choice in the workup of every woman suspected of having an ovarian mass. It not only results in decreasing the mortality but also avoids unnecessary surgical interventions.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1447-1452
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• 2012

Aim: To evaluate the differential diagnostic potential of lesion stiffness assessed by the sonoelastographic strain index ratio (SR) and elastographic color scoring system (UE) for breast lesions. Materials and Methods: Three hundred and forty two breast masses (158 benign and 184 malignant) from 325 consecutive patients (mean age 44.2 years; range 16-81)who had been scheduled for a sonographically guided core biopsy were examined proposed by Itoh et al, with scoring 1-3=benign and 4-5=malignant. Strain and area ratios of each lesion were calculated within the same machine. Histological diagnosis was used as the reference standard. The area under the curve (AUC) and cut-off point were obtained by receiver operating curve and the cross table Fischer Test was carried out for assessing diagnostic value. Sensitivity, specificity, PPV, NPV, accuracy and false-discovery rates were compared. Results: The mean strain ratios for benign and malignant lesions were 1.87 and 7.9 respectively. (P<0.0001). When a cutoff point of 3.54 was used, SR had a sensitivity of 94.6%, a specificity 94.3%, a PPV of 95.1%, an NPV of 93.7% and an accuracy of 94.4%. The AUC values were 0.90 for the 5 point scoring system (UE) and 0.96 for the strain index ratio. The overall diagnostic performance was SR method was better (P<0.05). Conclusions: Strain ratio measurement could be another effective predictor in elastography imaging besides 5 the point scoring system for differential diagnosis of breast lesions.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.381-385
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• 2015

Background:Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. Methods: This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. Results: The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ${\geq}50.3ng/ml$ was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Conclusion: Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (${\geq}50.3ng/ml$). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

• Journal of Crop Science and Biotechnology
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• v.10 no.3
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• pp.175-184
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• 2007

Two subspecies, japonica and indica, have been reported in rice, which differ in several ecotypic traits. However, reproductive barriers in hybrid progenies between subspecies have been major obstacles in breeding programs using inter-subspecific hybridization. As the first step to elucidate the reproductive barriers, we developed subspecies-specific(SS) STS markers in this study. A total of 765 STS primers were designed through comparing DNA sequences at every $2{\sim}3$cM interval between japonica and indica rices, which are available at Web DBs such as IRGSP, NCBI, TIGR, and GRAMENE, and tested for subspecies-specificity using 15 indica and 15 japonica varieties of diverse origin. Of them, 67 STS markers were identified as SS STS markers and their subspecies-specificity scores were estimated. The SS markers were dispersed throughout the genome along chromosomes. Of them, 64 SS markers were mapped on an RIL population derived from a Dasanbyeo(indica)/TR22183(japonica) cross. Genomic inclination of RILs was evaluated based on the genotyping with different types of markers. Association test between markers and segregation distortion revealed that segregation distortion might not be the cause of generating SS markers. The SS markers will be applicable to estimate the genomic inclination of varieties or lines and to study the differentiation of indica and japonica, and ultimately to breed true hybrid rice varieties in which desirable characters from both subspecies are recombined.

• Parasites, Hosts and Diseases
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• v.47 no.2
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• pp.153-157
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• 2009

Diagnosis of hydatidosis is based on immunodiagnostic methods along with radiological and ultrasound examinations. The objectives of the present study were to develop a specific and simple antigen-based ELISA method for diagnosis of hydatidosis and compare it with antibody detection method. The subjects in this study included 89 patients in the following groups: surgically confirmed hydatidosis patients (35 cases), control with other parasitic diseases (29 cases), and healthy controls (25 cases). Hyperimmune serum was raised against hydatid cyst fluid in rabbits. Anti-hydatid cyst IgG was purified by affinity chromatography using protein A column and labeled with horseradish peroxidase. Collected sera were assessed for hydatid cyst antigens and antibody by ELISA. Circulating hydatid antigen was found in 9 out of 35 patients with surgically confirmed hydatidosis. A sensitivity of 25.7% and a specificity of 98.0% were calculated for the antigen detection assay. Antibody detection by indirect ELISA, using antigen B, showed that 94.2% of patients (33 cases) have anti-hydatid cyst antibodies in their serum while cross reaction was noted in a few of non-hydatidosis patients. A sensitivity of 94.2% and specificity of 81.6% were found for the antibody detection assay. Findings of this study indicated that antibody detection assay is a sensitive approach for diagnosis of hydatid cyst while antigen detection assay might be a useful approach for assessment of the efficacy of treatment especially after removal of the cyst.

• The Korean Journal of Pain
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• v.33 no.1
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• pp.54-59
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• 2020

Background: The median nerve cross-sectional area (MNCSA) is a useful morphological parameter for the evaluation of carpal tunnel syndrome (CTS). However, there have been limited studies investigating the anatomical basis of median nerve flattening. Thus, to evaluate the connection between median nerve flattening and CTS, we carried out a measurement of the median nerve thickness (MNT). Methods: Both MNCSA and MNT measurement tools were collected from 20 patients with CTS, and from 20 control individuals who underwent carpal tunnel magnetic resonance imaging (CTMRI). We measured the MNCSA and MNT at the level of the hook of hamate on CTMRI. The MNCSA was measured on the transverse angled sections through the whole area. The MNT was measured based on the most compressed MNT. Results: The mean MNCSA was 9.01 ± 1.94 ㎟ in the control group and 6.58 ± 1.75 ㎟ in the CTS group. The mean MNT was 2.18 ± 0.39 mm in the control group and 1.43 ± 0.28 mm in the CTS group. Receiver operating characteristics curve analysis demonstrated that the optimal cut-off value for the MNCSA was 7.72 ㎟, with 75.0% sensitivity, 75.0% specificity, and an area under the curve (AUC) of 0.82 (95% confidence interval [CI], 0.69-0.95). The best cut off-threshold of the MNT was 1.76 mm, with 85% sensitivity, 85% specificity, and an AUC of 0.94 (95% CI, 0.87-1.00). Conclusions: Even though both MNCSA and MNT were significantly associated with CTS, MNT was identified as a more suitable measurement parameter.

• Journal of Biomedical Engineering Research
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• v.40 no.6
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.