The effect of low-level laser therapy on the oxidative stress level and quality of life in patients with Hashimoto's thyroiditis.

This study aimed to examine the effects of low-level laser therapy (LLLT) combined with levothyroxine replacement therapy on thyroid function, oxidative stress (OS), and quality of life in patients with Hashimoto's thyroiditis (HT). Forty-six patients diagnosed with HT were randomized to receive active LLLT (n = 23) and sham LLLT (n = 23) twice a week for three weeks. Clinical and laboratory evaluations of the participants were performed before treatment and three months after treatment. Biochemical parameters were taken from the patient file requested by the physician as a routine examination. Malondialdehyde and nitricoxide indicating oxidant stress and superoxide dismutase, catalase, and glutathione, which indicate antioxidant capacity, were used in OS evaluation. The Oxidative Stress Index was calculated by measuring the Total Antioxidant Status and the Total Oxidant Status. At the end of our study, a significant improvement in oxidant and antioxidant biomarker levels showing OS and quality of life was observed in the treatment groups (p < 0.05). There was no change in thyroid function and autoimmunity at the end of the treatment between the two groups (p > 0.05). Improvements in glutathione levels and quality of life were significantly higher in the active treatment group than in the sham-controlled group. LLLT was found to be more effective on OS and quality of life in patients with HT than in patients in the sham-controlled group. It was concluded that LLLT is a safe and effective method that can be used in the treatment of patients with HT.

Recognition of Glaucomatous Fundus Images Using Machine Learning Methods Based on Optic Nerve Head Topographic Features.

PURPOSE: The aim was to compare the effectiveness of clinical discriminant rules and machine learning classifiers in identifying glaucomatous fundus images based on optic disc topographic features. DESIGN: Retrospective case-control study. METHODS: The study used a total of 800 fundus images, half of which were glaucomatous cases and the other half non- glaucomatous cases obtained from an open database and clinical work. The images were randomly divided into training and testing sets with equal numbers of glaucomatous and non-glaucomatous images. An ophthalmologist framed the edge of the optic cup and disc, and the program calculated five features, including the vertical cup-to-disc ratio and the width of the optic rim in four quadrants in pixels, used to create machine learning classifiers. The discriminative ability of these classifiers was compared with clinical discriminant rules. RESULTS: The machine learning classifiers outperformed clinical discriminant rules, with the extreme gradient boosting method showing the best performance in identifying glaucomatous fundus images. Decision tree analysis revealed that the cup-to-disc ratio was the most important feature for identifying glaucoma fundus images. At the same time, the temporal width of the optic rim was the least important feature. CONCLUSIONS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features and integration with an automated program for framing and calculating the required parameters would make it a straightforward and effective approach.

Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy.

BACKGROUND: Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be "suitable" only when both "efficacy" and "safety" are considered. This study presents a model, namely the "ensemble strategy model," to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 "suitable" and "unsuitable" patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman-Pearson, and Stacking were used for model training. The "ensemble strategy concept" was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens. RESULTS: The results of the tenfold cross-validation showed that the average accuracy of the proposed "ensemble strategy model" was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity. CONCLUSIONS: The "ensemble strategy model" can be used as a reference for the determination of vancomycin doses in clinical treatment.

Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods.

In a pineapple exporting factory, manual lines are usually built to screen fruits of non-ripen hitting sounds from millions of undecided fruits for long-haul transportation. However, human workers cannot concentratedly listen and make consistent judgments over long hours. Pineapple screening becomes arbitrary after approximately an hour. We developed a non-destructive screening device aside from the conveyor sorter to classify pineapples automatically. The device makes intelligent judgments by tapping a sound source to the skin of pineapples and analyzing the penetrated sounds by wavelet kernel decomposition and unsupervised machine learning (ML). The sound tapping relies on the well-touch of the skin. We also design several acoustic couplers to adapt the vibrator to the skin and pick high-quality penetrated sounds. A Taguchi experiment design was used to determine the most suitable coupler. We found that our unsupervised ML method achieves 98.56% accuracy and 0.93 F1-score by using a specially designed thorn-board for assisting tapping sound to fruit skin.