Oral Health-Related Quality of Life Among Narcotic and Stimulant Users Referred to Maintenance Methadone Therapy Centers in Ahvaz City: Iran.

Introduction: We aimed to assess quality of life related to oral health in narcotic or stimulant users those were referred to maintenance methadone therapy (MMT) centers in Ahvaz City, Iran. Methods: It was a cross-sectional study based on exploratory approach which has conducted on 187 narcotic and stimulant users in Ahvaz city; during 15th May till September 2020. Data was selected by available non-random sampling method. The data collection tools included the demographic variables and the standard OHIP-14 questionnaires. All tests were used as descriptive statistics, Kolmogorov-Smirnov tests, independent t-test, one-way analysis of variance. P-values of less than 0.05 was considered significant. Results: The mean and standard deviation of the participants' age was 36.03 ± 8.98 years. The quality-of-life scores related to oral health were totally 34.89 ± 6.50 as well as 37.37 and 33.96 in narcotic and stimulant users, respectively. The total quality of life related to OHIP-14 did not have a significant relationship with variables of age, life companions, level of education, number of children, economic status, employment status, insurance status, underlying disease, toothbrush use status, last dentist visit, and number of missing teeth (P > 0.05). However, a significant difference was found between the quality of life related to oral health based on the type of substance used (narcotic or stimulant), so that the mean quality of life related to oral health was higher in narcotic than stimulant users (P < 0.05). Conclusion: Quality of life related to OHIP-14 was more unfavorable in stimulant users than narcotic users. So, policy makers and authorities are required to focus their interventions and research programs to improve health-related quality of life in users, especially stimulant.

An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence.

OBJECTIVE: The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs). SUBJECTS AND PARTICIPANTS: A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models. METHODS: We developed an unsupervised machine learning model to identify clusters with similar VF values. We annotated the clusters based on their respective mean deviation (MD). We computed optimal MD thresholds that discriminate clusters with the highest accuracy based on Bayes minimum error principle. We evaluated the accuracy of the staging system and validated findings based on an independent validation dataset. RESULTS: The unsupervised k -means algorithm discovered 4 clusters with 6784, 4034, 1541, and 872 VFs and average MDs of 0.0 dB (±1.4: SD), -4.8 dB (±1.9), -12.2 dB (±2.9), and -23.0 dB (±3.8), respectively. The supervised Bayes minimum error classifier identified optimal MD thresholds of -2.2, -8.0, and -17.3 dB for discriminating normal eyes and eyes at the early, moderate, and advanced stages of glaucoma. The accuracy of the glaucoma staging system was 94%, based on identified MD thresholds with respect to the initial k -means clusters. CONCLUSIONS: We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning. This glaucoma staging system is unbiased, objective, easy-to-use, and consistent, which makes it highly suitable for use in glaucoma research and for day-to-day clinical practice.

Prevalence of Cryptosporidium and Giardia in vegetables in Iran: a nineteen-years meta-analysis review.

Cryptosporidium and Giardia are two major protozoa reported from vegetables and environment. The prevalence of these parasites supposes to be different regarding the climate zones. This review aimed to evaluate the prevalence of Cryptosporidium and Giardia in vegetables according to the major climate zones in Iran. The results showed pooled prevalence 7% (95% CI: 2%, 14%) and 4% (95% CI: 3%, 6%) for Cryptosporidium spp., and Giardia spp., respectively. The prevalence of Giardia spp. in mountain, desert and semi-desert, and Mediterranean regions was 4% (95% CI: 2%, 6%), 5% (95% CI: 3%, 8%) and 7% (95% CI: 1%, 18%), respectively. Cryptosporidium spp. was reported 8% (95% CI: 0%, 65%), 6% (95% CI: 0%, 18%) and 4% (95% CI: 0%, 77%) from mountain, desert and semi-desert, and Mediterranean climate zones, respectively. This review suggests the higher prevalence of Giardia and Cryptosporidium in Mediterranean and mountain regions, respectively.

Automatic switching between noise classification and speech enhancement for hearing aid devices.

This paper presents a voice activity detector (VAD) for automatic switching between a noise classifier and a speech enhancer as part of the signal processing pipeline of hearing aid devices. The developed VAD consists of a computationally efficient feature extractor and a random forest classifier. Previously used signal features as well as two newly introduced signal features are extracted and fed into the classifier to perform automatic switching. This switching approach is compared to two popular VADs. The results obtained indicate the introduced approach outperforms these existing approaches in terms of both detection rate and processing time.

A multi-band environment-adaptive approach to noise suppression for cochlear implants.

This paper presents an improved environment-adaptive noise suppression solution for the cochlear implants speech processing pipeline. This improvement is achieved by using a multi-band data-driven approach in place of a previously developed single-band data-driven approach. Seven commonly encountered noisy environments of street, car, restaurant, mall, bus, pub and train are considered to quantify the improvement. The results obtained indicate about 10% improvement in speech quality measures.

Fast opposite weight learning rules with application in breast cancer diagnosis.

Classification of breast abnormalities such as masses is a challenging task for radiologists. Computer-aided Diagnosis (CADx) technology may enhance the performance of radiologists by assisting them in classifying patterns into benign and malignant categories. Although Neural Networks (NN) such as Multilayer Perceptron (MLP) have drawbacks, namely long training times, a considerable number of CADx systems employ NN-based classifiers. The reason being that they provide high accuracy when they are appropriately trained. In this paper, we introduce three novel learning rules called Opposite Weight Back Propagation per Pattern (OWBPP), Opposite Weight Back Propagation per Epoch (OWBPE), and Opposite Weight Back Propagation per Pattern in Initialization (OWBPI) to accelerate the training procedure of an MLP classifier. We then develop CADx systems for the diagnosis of breast masses employing the traditional Back Propagation (BP), OWBPP, OWBPE and OWBPI algorithms on MLP classifiers. We quantitatively analyze the accuracy and convergence rate of each system. The results suggest that the convergence rate of the proposed OWBPE algorithm is more than 4 times faster than that of the traditional BP. Moreover, the CADx systems which use OWBPE classifier on average yield an area under Receiver Operating Characteristic (ROC), i.e. Az, of 0.928, a False Negative Rate (FNR) of 9.9% and a False Positive Rate (FPR) of 11.94%.

Classification of benign and malignant masses based on Zernike moments.

In mammography diagnosis systems, high False Negative Rate (FNR) has always been a significant problem since a false negative answer may lead to a patient's death. This paper is directed towards the development of a novel Computer-aided Diagnosis (CADx) system for the diagnosis of breast masses. It aims at intensifying the performance of CADx algorithms as well as reducing the FNR by utilizing Zernike moments as descriptors of shape and margin characteristics. The input Regions of Interest (ROIs) are segmented manually and further subjected to a number of preprocessing stages. The outcomes of preprocessing stage are two processed images containing co-scaled translated masses. Besides, one of these images represents the shape characteristics of the mass, while the other describes the margin characteristics. Two groups of Zernike moments have been extracted from the preprocessed images and applied to the feature selection stage. Each group includes 32 moments with different orders and iterations. Considering the performance of the overall CADx system, the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier, employing both generic Back Propagation (BP) and Opposition-based Learning (OBL) algorithms. The Receiver Operational Characteristics (ROC) curve and the performance of resulting CADx systems are analyzed for each group of features. The designed systems yield Az=0.976, representing fair sensitivity, and Az=0.975 demonstrating fair specificity. The best achieved FNR and FPR are 0.0% and 5.5%, respectively.