Investigation of the effectiveness of atmospheric pressure cold plasma on sciatic nerve injury in rats.

BACKGROUND: The aim of this study was to evaluate the efficacy of atmospheric pressure cold plasma jet and plasma activated medium (PAM) on sciatic nerve injury (SNI). MATERIALS AND METHODS: Rats were divided into 6 groups (n = 10); group 1 (Sham), group 2 (SNI), group 3 (SNI + Atmospheric pressure cold plasma jet 5 min), group 4 (SNI + Atmospheric pressure cold plasma jet 10 min), group 5 (SNI + PAM 5 min), group 6 (SNI + PAM 10 min). On the 1st, 8th, 15th, 22nd days of the study, atmospheric pressure cold plasma jet was applied to rats in groups 3 and 4, and PAM was applied to rats in groups 5 and 6. Hot plate test was applied to all rats on the same days. On day 28, the experiment was terminated and sciatic nerve tissues were removed for histopathologic evaluations. RESULTS: According to the 4-week average of the hot plate tests, a significant relationship was found between group 2 and group 4 and group 6 (p < 0.05). When evaluated within each week, significant differences were found between group 2 and group 4 in week 1, between group 2 and group 5 and group 6 in week 2, between group 2 and group 4 in week 3, and between group 2 and group 4 and group 6 in week 4 (p < 0.05). As a result of histopathologic analysis, except for the control group, the other groups had similar characteristics in terms of axonal degeneration, periaxonal swelling and axon density. CONCLUSIONS: As a result of our study, we found that plasma application showed an improvement in the duration of the hot plate test, but did not show any improvement histopathologically.

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 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.