scholarly journals Ursodeoxycholic acid: a systematic review on the chemical and biochemical properties, biosynthesis, sources and pharmacological activities

2021 ◽  
Vol 3 (1) ◽  
pp. 178-185
Author(s):  
Marlin Megalestin Raunsai ◽  
◽  
Elfahmi Elfahmi ◽  
Agus Chahyadi ◽  
Sony Suhandono ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Ursodeoxycholic Acid ◽  
Biochemical Properties ◽  
Future Research ◽  
Biliary Cirrhosis ◽  
Pharmacological Properties ◽  
Secondary Bile Acid ◽  
Wide Range ◽  
Caroli Syndrome ◽  
Bear Bile

Background: Background: Ursodeoxycholic acid (UDCA), a secondary bile acid, is an acidic steroid synthesized from cholesterol in hepatocytes. UDCA is widely used for the treatment of various diseases related to liver injury. The use of UDCA to treat non-liver diseases has also been developed recently, such as neurodegenerative diseases, cancer, and obesity. Due to the important role of UDCA on human health, numerous studies in understanding its chemical and pharmacological properties have been published. Methods: Literature sources were obtained from online databases such as Science Direct, Google Scholar, Scopus and PubMed using keywords relating to the purpose of study. Critical analysis and review were performed for all literatures. Results: UDCA is a steroid compound with pharmacological properties. Seventeen enzymes are involved in its biosynthesis, which has been proposed in four pathways: classic, alternative, the Yamazaki, and 25-hydroxylation pathways. UDCA can be isolated from bovine bile, bear bile or all Ursidae, human, rabbit, cow, rat, hamster, sheep, pig, and plant. UDCA has been used in the treatment of several diseases such as primary biliary cirrhosis, intrahepatic cholestasis of pregnancy, hepatolithiasis associated with Caroli syndrome gallstones, cystic fibrosis, hepatitis C virus, chronic heart failure, neurodegenerative diseases, and obesity, as well as in the prevention of cancer. Conclusion: UDCA has a wide range of the pharmacological properties. Further investigations on its efficacy and safety on humans are required before it could be used for several indications. All genes which are responsible for UDCA biosynthesis have been elucidated. That said, further genetic engineering studies in order to find other prospective sources of UDCA could be a challenge for the future research.

Comparative Effects of High-Tech Visual Scene Displays and Low-Tech Isolated Picture Symbols on Engagement From Students With Multiple Disabilities

2019 ◽  
Vol 50 (4) ◽  
pp. 693-702 ◽  
Author(s):  
Christine Holyfield ◽  
Sydney Brooks ◽  
Allison Schluterman
Keyword(s):  
Language Learning ◽  
Augmentative And Alternative Communication ◽  
Visual Analysis ◽  
Multiple Disabilities ◽  
Visual Scene ◽  
Future Research ◽  
High Tech ◽  
Single Subject ◽  
Wide Range ◽  
The Difference

Purpose Augmentative and alternative communication (AAC) is an intervention approach that can promote communication and language in children with multiple disabilities who are beginning communicators. While a wide range of AAC technologies are available, little is known about the comparative effects of specific technology options. Given that engagement can be low for beginning communicators with multiple disabilities, the current study provides initial information about the comparative effects of 2 AAC technology options—high-tech visual scene displays (VSDs) and low-tech isolated picture symbols—on engagement. Method Three elementary-age beginning communicators with multiple disabilities participated. The study used a single-subject, alternating treatment design with each technology serving as a condition. Participants interacted with their school speech-language pathologists using each of the 2 technologies across 5 sessions in a block randomized order. Results According to visual analysis and nonoverlap of all pairs calculations, all 3 participants demonstrated more engagement with the high-tech VSDs than the low-tech isolated picture symbols as measured by their seconds of gaze toward each technology option. Despite the difference in engagement observed, there was no clear difference across the 2 conditions in engagement toward the communication partner or use of the AAC. Conclusions Clinicians can consider measuring engagement when evaluating AAC technology options for children with multiple disabilities and should consider evaluating high-tech VSDs as 1 technology option for them. Future research must explore the extent to which differences in engagement to particular AAC technologies result in differences in communication and language learning over time as might be expected.

Understanding Limited Use of Amplification in Infants and Children Who Are Hard of Hearing

2015 ◽  
Vol 25 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Ryan W. McCreery ◽  
Elizabeth A. Walker ◽  
Meredith Spratford
Keyword(s):  
Hard Of Hearing ◽  
Hearing Aid ◽  
Management Strategies ◽  
Infants And Children ◽  
Future Research ◽  
Research Needs ◽  
Case Examples ◽  
Hearing Aid Use ◽  
Wide Range ◽  
In Infants And Children

The effectiveness of amplification for infants and children can be mediated by how much the child uses the device. Existing research suggests that establishing hearing aid use can be challenging. A wide range of factors can influence hearing aid use in children, including the child's age, degree of hearing loss, and socioeconomic status. Audiological interventions, including using validated prescriptive approaches and verification, performing on-going training and orientation, and communicating with caregivers about hearing aid use can also increase hearing aid use by infants and children. Case examples are used to highlight the factors that influence hearing aid use. Potential management strategies and future research needs are also discussed.

The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

2009 ◽  
Vol 23 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Suzannah K. Helps ◽  
Samantha J. Broyd ◽  
Christopher J. James ◽  
Anke Karl ◽  
Edmund J. S. Sonuga-Barke
Keyword(s):  
Brain Activity ◽  
Sampling Rate ◽  
Low Frequency ◽  
Future Research ◽  
Adhd Symptoms ◽  
Default Mode ◽  
Very Low Frequency ◽  
Wide Range ◽  
Interference Hypothesis ◽  
Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

Pharmacologically potentials of different substituted coumarin derivatives

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Benzene Ring ◽  
Bioactive Compounds ◽  
Biological Activities ◽  
Pharmacological Properties ◽  
Coumarin Derivatives ◽  
Wide Range ◽  
Anti Oxidant ◽  
Anti Inflammatory ◽  
Coumarin Compounds

Coumarin and its derivatives are widely spread in nature. Coumarin goes to agroup as benzopyrones, which consists of a benzene ring connected to a pyronemoiety. Coumarins displayed a broad range of pharmacologically useful profile.Coumarins are considered as a promising group of bioactive compounds thatexhibited a wide range of biological activities like anti-microbial, anti-viral,antiparasitic, anti-helmintic, analgesic, anti-inflammatory, anti-diabetic, anticancer,anti-oxidant, anti-proliferative, anti-convulsant, and antihypertensiveactivities etc. The coumarin compounds have immense interest due to theirdiverse pharmacological properties. In particular, these biological activities makecoumarin compounds more attractive and testing as novel therapeuticcompounds.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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