Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo.

The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization.

A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query “Machine Learning” “AND” “Parkinson Disease”.

The obtained publications were divided into 6 categories, based on different application fields “Gait Analysis – Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics.

Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.Substance use and depressive psychiatric symptoms have been associated with migration and mobility. The Mexico-Guatemala border is a key transit point for internal, regional, and international migration flows. However, there is limited knowledge of the role of substance use, migration, and mobility on mental health among migrants at this border. Our paper explores the association of migration and mobility patterns with possible major depressive symptoms among migrants at this key geographic region. We recruited 392 substance-using migrants using modified time-location sampling. Crude and adjusted logistic regression models were developed. We found that 12% of the sample had possible major depressive symptoms. After adjusting for relevant covariates, including gender, income, and perceived homelessness, we found that recent rural-urban and short-term migrants had higher odds of possible major depressive symptoms, whereas international migrants had lower odds. Findings of this paper suggest that although migrants experience hardship and uncertainty, they may respond with complex and nuanced forms of coping and planning.

The purpose of this study was to determine the impact of unilateral versus bilateral cochlear implantation on receptive and expressive spoken language outcomes. Secondary aims were to investigate factors timing of first and second implant placement and reliance on government funded health plans on language outcome.

This was a retrospective chart review of spoken language users with bilateral severe-to-profound hearing loss. A total of 204 children were included, 105 in the bilateral group and 99 in the unilateral group. Multiple regression analyses were completed to investigate factors that influence language outcomes at age 5.

Recipients who received bilateral CIs performed significantly higher on measures of receptive and expressive language than those with unilateral implants.

This study demonstrates that bilateral implantation has a significant impact on receptive and expressive aspects of language development, and should be strongly considered as standard practice for children with bilateral severe to profound hearing loss.

These results indicate that families should be counseled that language outcomes are better with bilateral cochlear implantation than unilateral implantation. Cochlear implant teams should continue to consider the impact of socioeconomic status on outcomes and explore new methods to reduce the impacts and barriers of poverty to language development.

These results indicate that families should be counseled that language outcomes are better with bilateral cochlear implantation than unilateral implantation. selleck chemicals Cochlear implant teams should continue to consider the impact of socioeconomic status on outcomes and explore new methods to reduce the impacts and barriers of poverty to language development.Congenital facial palsy is a rare medical condition that causes paralysis of the facial muscles, lack of facial expression, and an unusual appearance. Findings from developmental psychology suggest that the face plays a central role in the construction of self. Semi-structured interviews were conducted with 14 adults born with congenital facial palsy. Participant’s constructions of self across the life span were explored and a grounded theory of this process was constructed. Theoretical sampling was conducted with two parents of children born with the condition. All participants reported “struggling to make connections,” “experiencing invalidation,” and “struggling to regulate affect,” which lead to “constructing a defective sense of self.” Alternatively, “making validating connections” facilitated the process of “constructing a validated sense of self.” This study provides insight into the unique social and emotional challenges often experienced by those born with congenital facial palsy and highlights the need for early psychosocial intervention.The springback directly affects the forming accuracy and quality of metal bent-tube, and accurate springback prediction is the key to the springback compensation and control. This paper investigates the springback of mandrel-less rotary draw bending (MLRDB) of circular metal tubes, and an innovative method, springback angle prediction considering the interference of cross-sectional distortion (IoCSD-SAP), is proposed. The digit decomposition condition variational auto-encoder generative adversarial network (D2CVAE-GAN) is developed to augment the data samples. Considering the nonlinear interference of the cross-sectional distortion on springback, auxiliary extended radial basis function (AE-RBF) is proposed. It establishes the mapping relationship between the characteristic parameters and cross-sectional distortion. By extracting the information encode of cross-sectional distortion as the condition input, this model realizes the condition prediction of springback angle. Taking MLRDB of 6060-T6 Al-alloy circular tube as a case study, the proposed method, IoCSD-SAP, is verified.