Notes

Distribution associated with posterior malleolus fracture collections in ankle fracture regarding supination-external turn.
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. 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. https://www.selleckchem.com/products/recilisib.html Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.
Cervical cancer is a significant cause of cancer mortality in women, particularly in low-income countries. In regular cervical screening methods, such as colposcopy, an image is taken from the cervix of a patient. The particular image can be used by computer-aided diagnosis (CAD) systems that are trained using artificial intelligence algorithms to predict the possibility of cervical cancer. Artificial intelligence models had been highlighted in a number of cervical cancer studies. However, there are a limited number of studies that investigate the simultaneous use of three colposcopic screening modalities including Greenlight, Hinselmann, and Schiller.

We propose a cervical cancer predictor model which incorporates the result of different classification algorithms and ensemble classifiers. Our approach merges features of different colposcopic images of a patient. The feature vector of each image includes semantic medical features, subjective judgments, and a consensus. The class label of each sample is calculated using an aggregation function on expert judgments and consensuses.

We investigated different aggregation strategies to find the best formula for aggregation function and then we evaluated our method using the quality assessment of digital colposcopies dataset, and our approach performance with 96% of sensitivity and 94% of specificity values yields a significant improvement in the field.

Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods.
Our model can be used as a supportive clinical decision-making strategy by giving more reliable information to the clinical decision makers. Our proposed model also is more applicable in cervical cancer CAD systems compared to the available methods.Anaplastic large cell lymphomas (ALCL) are a rare type of primary breast lymphoma. The association between breast implants and ALCL was first described in 1997. Breast implant associated (BIA)-ALCL arises from the inflammatory T cells surrounding the fibrous capsule, and most tumors are in situ. Here we present the case of a 60-year-old woman with ALCL following bilateral silicone breast prosthesis implantation for aesthetic reason. The patient presented at our observation 7 years following the first surgery reporting a sport trauma in the right thoracic region with breast enlargement and tenderness, complaining breast pain at the palpation of the right breast. Imaging study showed a right fluid collection surrounding the affected breast implant. For this reason, the patient underwent bilateral complete capsulectomy (surgical specimen histologically analyzed and resulted negative for ALCL) and implantation of new breast silicone prosthesis. In 10 months, a progressive relapse of the symptoms with a right peri-implant fluid collection restauration was documented and bilateral surgical removal of breast prostheses with right peri-implant capsular biopsy were performed.
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