Investigation of associations between apolipoprotein A5 and C3 gene polymorphisms with plasma triglyceride and lipid levels.

OBJECTIVE: The aim of this study was to determine frequency and associations between APOA5 c.56C>G, -1131T>C, c.553G>T, and APOC3 -482C>T and SstI gene polymorphisms with hypertriglyceridemia. METHODS: Under a case-control study model, 135 hypertriglyceridemic and 178 normotriglyceridemic control participants were recruited. Polymerase chain reaction and restriction fragment length polymorphism methods were utilized for genotyping. Statistical calculations were performed by comparing allele and genotype frequencies between groups. Clinical characteristics were compared between groups and intra-group genotypes. RESULTS: APOC3 gene -482C>T and SstI polymorphic genotypes and allele frequencies were significantly higher in hypertriglyceridemic group (genotype frequencies, p=0.035, p=0.028, respectively). Regression analysis under unadjusted model confirmed that APOC3 -482C>T and SstI polymorphisms were significantly contributing to have hypertriglyceridemia (p=0.02, odds ratio [OR]=1.831 (95% confidence interval [CI] 1.095-3.060); p=0.04, OR=1.812 (1.031-3.183), respectively). APOA5 c.56C>G was in complete linkage disequilibrium with APOA5 c.553G>T polymorphism (D'=1). CONCLUSION: For the first time in a population sample from Turkey, among the five polymorphisms of APOA5 and APOC3 genes investigated, APOC3 -482C>T and SstI polymorphisms were associated with elevated serum TG levels, while APOA5 c.56C>G, -1131T>C, and c.553G>T polymorphisms were not.

Investigation of associations between apolipoprotein A5 and C3 gene polymorphisms with plasma triglyceride and lipid levels

SUMMARY OBJECTIVE: The aim of this study was to determine frequency and associations between APOA5 c.56C>G, −1131T>C, c.553G>T, and APOC3 −482C>T and SstI gene polymorphisms with hypertriglyceridemia. METHODS: Under a case-control study model, 135 hypertriglyceridemic and 178 normotriglyceridemic control participants were recruited. Polymerase chain reaction and restriction fragment length polymorphism methods were utilized for genotyping. Statistical calculations were performed by comparing allele and genotype frequencies between groups. Clinical characteristics were compared between groups and intra-group genotypes. RESULTS: APOC3 gene −482C>T and SstI polymorphic genotypes and allele frequencies were significantly higher in hypertriglyceridemic group (genotype frequencies, p=0.035, p=0.028, respectively). Regression analysis under unadjusted model confirmed that APOC3 −482C>T and SstI polymorphisms were significantly contributing to have hypertriglyceridemia (p=0.02, odds ratio [OR]=1.831 (95% confidence interval [CI] 1.095-3.060); p=0.04, OR=1.812 (1.031-3.183), respectively). APOA5 c.56C>G was in complete linkage disequilibrium with APOA5 c.553G>T polymorphism (D'=1). CONCLUSION: For the first time in a population sample from Turkey, among the five polymorphisms of APOA5 and APOC3 genes investigated, APOC3 −482C>T and SstI polymorphisms were associated with elevated serum TG levels, while APOA5 c.56C>G, −1131T>C, and c.553G>T polymorphisms were not.

A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium.

The aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p ≤ 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.

A study on the correlation between spleen volume estimated via cavalieri principle on computed tomography images with basic hemogram and biochemical blood parameters.

Considering its hematological and immunological functions, spleen is a very important organ. A change occurs in its size as the spleen performs these functions. This study aims to examine the possible relationships between spleen volume and the basic hemogram and biochemical parameters in serum. Multidetector computed tomography images and basic hemogram and biochemical parameters of 74 adult individuals, 34 male and 40 female, who were found to be healthy, were used in the study. Spleen volume was estimated using the Cavalieri method on multidetector computed tomography images and the correlations between the volume value with basic hemogram and biochemistry parameters were researched. While negative significant correlations were found between the estimated spleen volume and lymphocyte percentage (r=-0.224) and platelet level (r=-0.271); positive significant correlations were found between hemoglobin level (r=0.228), hematocrit level (r=0.237), alanine aminotransferase (r=0.345), and erythrocyte level (r=0.375). As a result of this study, a relationship was found between spleen volume and lymphocyte percentage, hematocrit level, erythrocyte level, platelet level, and alanine aminotransferase level in serum. We believe that the results of the study will provide a larger perspective to clinicians in the diagnosis of diseases associated with spleen.

Comparison of pirfenidone and corticosteroid treatments at the COVID-19 pneumonia with the guide of artificial intelligence supported thoracic computed tomography.

AIM: We aimed to investigate the effect of short-term pirfenidone treatment on prolonged COVID-19 pneumonia. METHOD: Hospital files of patients hospitalised with a diagnosis of critical COVID-19 pneumonia from November 2020 to March 2021 were retrospectively reviewed. Chest computed tomography images taken both before treatment and 2 months after treatment, demographic characteristics and laboratory parameters of patients receiving pirfenidone + methylprednisolone (n = 13) and only methylprednisolones (n = 9) were recorded. Pulmonary function tests were performed after the second month of the treatment. CT involvement rates were determined by machine learning. RESULTS: A total of 22 patients, 13 of whom (59.1%) were using methylprednisolone + pirfenidone and 9 of whom (40.9%) were using only methylprednisolone were included. When the blood gas parameters and pulmonary function tests of the patients were compared at the end of the second month, it was found that the FEV1, FEV1%, FVC and FVC% values were statistically significantly higher in the methylprednisolone + pirfenidone group compared with the methylprednisolone group (P = .025, P = .012, P = .026 and P = .017, respectively). When the rates of change in CT scans at diagnosis and second month of treatment were examined, it was found that the involvement rates in the methylprednisolone + pirfenidone group were statistically significantly decreased (P < .001). CONCLUSION: Antifibrotic agents can reduce fibrosis that may develop in the future. These can also help dose reduction and/or non-use strategy for methylprednisolone therapy, which has many side effects. Further large series and randomised controlled studies are needed on this subject.

Classification of DNA damages on segmented comet assay images using convolutional neural network.

BACKGROUND AND OBJECTIVE: Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. METHODS: 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. RESULTS: The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. CONCLUSIONS: This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images.

A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals.

BACKGROUND: Predicting sex is an important problem in forensic medicine. The femur, patella, mandible and calcaneus bones are frequently used in predicting sex. In our study, we aimed to use the artificial neural network (ANN) technique to predict sex by measuring the values of the phalanges of the first and fifth toes and the first and fifth metatarsal bones. METHOD: All bone measurements were conducted on the direct X-ray images of 176 males and 178 females in the age range of 24-60 years. The multilayer perceptron classifier (MLPC) input layer included parameters on the bone length measurements of phalanx proximalis I, phalanx distalis I, metatarsal I, phalanx proximalis V, phalanx medialis V, phalanx distalis V and metatarsal V. The output layer contained two neurons to define the male and female sexes. The present study used an MLPC model that had two hidden layers, and the first and second hidden layers contained 14 and 7 nodes, respectively. RESULTS: The model had an overall accuracy (Acc) of 0.95, specificity (Spe) of 0.97, sensitivity (Sen) of 0.95 and Matthews correlation coefficient (Mcc) of 0.92. While the sex prediction success of our proposed model was higher in women, the results were more specific in men and more sensitive in women (AccMale = 0.93, AccFemale = 0.98, SenMale = 0.93, SpeMale = 0.98, SenFemale = 0.98 and SpeFemale = 0.93). CONCLUSIONS: This study demonstrated that the ANN model for length measurements on small bones is a highly effective instrument for sex prediction.

Sex estimation using sternum part lenghts by means of artificial neural networks.

In addition to the pelvis, cranium and phalanges, the sternum is also used for postmortem sex identification. Bone measurements may be obtained on cadaveric bones. Alternatively, computerized tomography may be used to obtain measurements close to the original ones. Moreover, usage of artificial neural networks (ANNs) in the field of medicine has started to provide new horizons. In this study, we aimed to identify sex by an ANN using lengths of manubrium sterni (MSL), corpus sterni (CSL) and processus xiphoideus (XPL) and sternal angle (SA) from computerized tomography (CT) images brought to an orthogonal plane. This study used the thin-slice thoracic CT images of 422 cases (213 female, 209 male) with an age range of 27-60 years brought to the orthogonal plane. Measurements of MSL, CSL, XPL and SA were analyzed with a multilayer artificial neural network that used stochastic gradient descent (SGD) for optimization and two hidden layers. MSL, CSL and XPL were longer, and SA was wider in men (MSL p = 0.000, CSL p = 0.000, XPL p = 0.000, SA p = 0.02). In the case of the two hidden layers of the network with 20 and 14 neurons in the hidden layers, respectively, learning rate of 0.1 and momentum coefficient of 0.9, the accuracy (Acc) of sex prediction was 0.906. In order to define a more realistic performance of the network, bootstrap was run with the confidence interval of 94%. A sensitivity (Sen) value of 0.91 and a specificity (Spe) value of 0.90 were calculated. The success rates that were achieved in sex identification with measurements on the skeleton using ANN were observed to be higher than those achieved by linear models. Also, sometimes all parts of the bones may not be found or might be deformed. In this case, the number of parameters used for the estimation will be incomplete. The ANN has the strong advantage to be able to estimate despite the missing parameter.

A novel method to identify and grade DNA damage on comet images.

BACKGROUND AND OBJECTIVE: In recent years, development of software programs in medicine field has proceeded in a rapid manner. Comet assay is one of the research methods in medicine field that displays whether DNA has damage. In this study, it is aimed to share experience of dynamic time warping method and decision tree to decide whether DNA has damage and grade DNA damage by means of the software program. METHODS: The application analyzes manually extracted RGB single comet images whose centers are manually marked. The application performs pixel profile analysis at the directions of vertical and horizontal based on the center of comet. The bidirectional pixel profile results are used as inputs of dynamic time warping. Four novel and one conventional measurement parameters some of which are calculated by dynamic time warping are given to decision tree. RESULTS: The decision tree identifies whether DNA has damage and grades DNA damage categorizing four damage levels with accuracy of 99.03% using only two of five measurement parameters. CONCLUSIONS: This paper mainly focuses on features and usage of dynamic time warping and decision tree as a novel method to identify and grade DNA damage on comet images.