Rehabilitation and related professions have grown rapidly in China during this decade. Quality of life becomes a top priority after acute care service has been delivered. Communication is a unique and essential skill of humans and is one of the main factors influencing quality of life. People share their ideas with each other and advocate for their rights by communicating. The purpose of this paper is to introduce the general concepts of Augmentative and Alternative Communication (AAC). The paper includes a brief explanation of AAC, types of AAC, targeted populations benefitted by AAC, principles of a language-based service delivery, and a discussion of interdisciplinary team member roles.>Rehabilitation and related professions have grown rapidly in China during this decade. Quality of life becomes a top priority >after acute care service has been delivered. Communication is a unique and essential skill of humans and is one of the main factors influencing >quality of life. People share their ideas with each other and advocate for their rights by communicating. The purpose of this paper is to introduce>the general concepts of Augmentative and Alternative Communication (AAC). The paper includes a brief explanation of AAC, types>of AAC, targeted populations benefitted by AAC, principles of a language-based service delivery, and a discussion of interdisciplinary team>member roles

An augmentative and alternative communication (AAC) assessment is a complex, multidisciplinary process. Historic AAC assessment models have focused on treatment providing for basic communication needs more than on consideration of optimizing communication and maximizing an individual's potential. This paper presents an AAC assessment model based on the goal of interactive communication and a comprehensive evaluation of cognitive, linguistic, sensory and motor abilities that is evidence-based. We describe each component and assessment step within an evidence-based framework. The purpose is to offer a systematic, principled approach to selecting AAC assessment procedures in rehabilitation, speech language pathology and other related fields in order to strengthen their confidence in providing best possible services to clients who require AAC.

BACKGROUND/OBJECTIVES: Docosahexaenoic acid (DHA), an n-3 long chain polyunsaturated fatty acid (LCPUFA), is acquired by dietary intake or the in vivo conversion of α-linolenic acid. Many enzymes participating in LCPUFA synthesis are regulated by peroxisome proliferator-activated receptor alpha (PPARα). Therefore, it was hypothesized that the tissue accretion of endogenously synthesized DHA could be modified by PPARα. MATERIALS/METHODS: The tissue DHA concentrations and mRNA levels of genes participating in DHA biosynthesis were compared among PPARα homozygous (KO), heterozygous (HZ), and wild type (WT) mice (Exp I), and between WT mice treated with clofibrate (PPARα agonist) or those not treated (Exp II). In ExpII, the expression levels of the proteins associated with DHA function in the brain cortex and retina were also measured. An n3-PUFA depleted/replenished regimen was applied to mitigate the confounding effects of maternal DHA. RESULTS: PPARα ablation reduced the hepatic Acox, Fads1, and Fads2 mRNA levels, as well as the DHA concentration in the liver, but not in the brain cortex. In contrast, PPARα activation increased hepatic Acox, Fads1, Fads2 and Elovl5 mRNA levels, but reduced the DHA concentrations in the liver, retina, and phospholipid of brain cortex, and decreased mRNA and protein levels of the brain-derived neurotrophic factor in brain cortex. CONCLUSIONS: LCPUFA enzyme expression was altered by PPARα. Either PPARα deficiency or activation-decreased tissue DHA concentration is a stimulus for further studies to determine the functional significance.
Animals ; Brain ; Brain-Derived Neurotrophic Factor ; Clofibrate ; Docosahexaenoic Acids ; Fatty Acid Desaturases ; Liver ; Mice ; Peroxisomes ; PPAR alpha ; Retina ; RNA, Messenger

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.