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Digital PCR pertaining to Genotype Quantification: A Case Examine in the Pasta Production Archipelago.
es > 4cm. A risk stratification model for nodules > 4cm may show better diagnostic performance than ACR TI-RADS, which may lead to better preoperative decision-making.
4 cm may show better diagnostic performance than ACR TI-RADS, which may lead to better preoperative decision-making.Most of the countries with elderly populations are currently facing with chronic diseases. In this regard, Internet of Things (IoT) technology offers promising tools for reducing the chronic disease burdens. Despite the presence of fruitful works on the use of IoT for chronic disease management in literature, these are rarely overviewed consistently. The present study provides an overview on the use of IoT for chronic disease management, followed by ranking different chronic diseases based on their priority for using IoT in the developing countries. For this purpose, a structural coding was used to provide a list of technologies adopted so far, and then latent Dirichlet allocation algorithm was applied to find major topics in literature. In order to rank chronic diseases based on their priority for using IoT, a list of common categories of chronic diseases was subjected to fuzzy analytic hierarchy process. The research findings include lists of IoT technologies for chronic disease management and the most-discussed chronic diseases. In addition, with the help of text mining, a total of 18 major topics were extracted from the relevant pieces of literature. The results indicated that the cardiovascular disease and to a slightly lesser extent, diabetes mellitus are of the highest priorities for using IoT in the context of developing countries.
Recently, many studies have been done on the physicochemical properties and biocompatibility of polycaprolactone (PCL) scaffolds containing ceramic reinforcers in the field of bone tissue engineering. In this study, the physical, mechanical and biological properties of electrospined-fabricated PCL scaffolds containing gehlenite (GLN) nanoparticles (NPs) as a novel bioceramic were investigated.

To obtain the appropriate mechanical properties, the solution contains 3%, 5%, 7%, and 10% wt. of GLN NPs were prepared. Fiber morphology was investigated by scanning electron microscopy. In order to evaluate the NPs distribution, Energy Dispersive X-Ray Spectroscopy, X-ray diffraction, and Fourier Transform Infrared Spectroscopy spectroscopy were used. The scaffold hydrophilicity was measured by the water contact angle test. The tensile test was used to check the mechanical strength of the scaffold. The proliferation of MG-63 cells was evaluated by the MTT test. Alkaline phosphatase (ALP) activity of MG-63 cells was also examined.

Average fibers' diameters and porosity of PCL/GLN7% were obtained 150-500 nm and 80%, respectively. An increase in the scaffold hydrophilicity was observed by the addition of GLN NPs. The strength of PCL/GLN7% was higher than the blank PCL scaffold. Cell proliferation of scaffolds containing GLN was higher than the blank PCL scaffold. A significant increase in the secretion of ALP for GLN-loaded scaffolds was seen.

The results showed that PCL/GLN7% composite scaffold could be a good candidate for bone tissue engineering.

The overall results indicate that the scaffold (PCL /GLN7%) has suitable mechanical properties, a great cell compatibility for bone tissue regeneration.
The overall results indicate that the scaffold (PCL /GLN7%) has suitable mechanical properties, a great cell compatibility for bone tissue regeneration.
A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.
Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.
Mass spectrometry is a method for identifying proteins and could be used for distinguishing between proteins in healthy and nonhealthy samples. This study was conducted using mass spectrometry data of ovarian cancer with high resolution. Usually, diagnostic and monitoring tests are done according to sensitivity and specificity rates; thus, the aim of this study is to compare mass spectrometry of healthy and cancerous samples in order to find a set of biomarkers or indicators with a reasonable sensitivity and specificity rates.

Therefore, combination methods were used for choosing the optimum feature set as t-test, entropy, Bhattacharya, and an imperialist competitive algorithm with K-nearest neighbors classifier. The resulting feature from each method was feed to the C5 decision tree with 10-fold cross-validation to classify data.

The most important variables using this method were identified and a set of rules were extracted. Similar to most frequent features, repetitive patterns were not obtained; the generalized rule induction method was used to identify the repetitive patterns.

Finally, the resulting features were introduced as biomarkers and compared with other studies. It was found that the resulting features were very similar to other studies. In the case of the classifier, higher sensitivity and specificity rates with a lower number of features were achieved when compared with other studies.
Finally, the resulting features were introduced as biomarkers and compared with other studies. It was found that the resulting features were very similar to other studies. In the case of the classifier, higher sensitivity and specificity rates with a lower number of features were achieved when compared with other studies.
Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed.

In the proposed work, the improved variational mode decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method.

The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db.

The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.
The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.
Sometimes, women find it difficult to conceive a baby and others use contraceptives that often have side effects. Researchers have already established the importance of measuring basal body temperature (BBT) and the potential of hydrogen (pH).

We have designed and realized a device that allows the simultaneous measurement of the BBT and the pH. We used an Arduino Uno board, a pH sensor, and a temperature sensor. The device communicates with a smartphone, can be integrated into all e-health platforms, and can be used at home. We validated our ovulation detector by a measurement campaign on a group of twenty women. If the pH is >7 and at the same time, the BBT is minimum and <36.5°C, the women is in ovulation phase. If the pH is ≤7 and in the same time, the BBT is between 36.5°C and 37°C, the women are in preovulation or follicular phase. If the pH is ≤7 and in the same time, the BBT is >36.5°C, the women are in postovulation or luteal phase.

We tested the contraceptive aspect of our ovulometer on a set of seven women. We also tested the help of conceiving babies by having intercourse during the ovulation period fixed by our ovulation detector. The results are satisfactory.

In the final version of our device, we displayed just in "fertility period" if the pH is ≥7 and the BBT is <36.5°C else we displayed in "nonfertility period."
In the final version of our device, we displayed just in "fertility period" if the pH is ≥7 and the BBT is less then 36.5°C else we displayed in "nonfertility period."
Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI.

In this paper, we used the Physikalisch-Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T-wave section, integral of the QRS complex, and J-point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed 100 of iterations on a 10-fold and 5-fold cross-validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K-nearest neighbor, and linear support vector machine. Erastin2 clinical trial Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier's performance using the aforementioned trained classifiers.

The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006.

Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.
Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.
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