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Fulltext Evaluation of retinal nerve fiber layer thickness and optic nerve functions in fellow eye of neuromyelitis optica with unilateral optic neuritis

Ong Chin Feng W, Wan Hitam WH

Taiwan J Ophthalmol, 2020 06 20;10(3):189-196.
PMID: 33110750 DOI: 10.4103/tjo.tjo_22_20

Purpose: Peripapillary retinal nerve fibre layer (RNFL) thickness might be useful in monitoring ongoing subclinical structural damage especially in eyes with no history of optic neuritis (ON) in neuromyelitis optica (NMO).
Objective: To evaluate the peripapillary RNFL thickness and optic nerve functions in fellow eye of NMO with unilateral optic neuritis.
Materials and Methods: A comparative cross-sectional study was conducted in 2 tertiary hospitals from August 2017 to May 2019. RNFL thickness and optic nerve functions were evaluated. Statistical analysis was performed using Statistical Package for Social Science version 24.
Results: A total of 26 NMO patients and 26 controls were involved in this study. The median age (IQR) of NMO patients was 32.5 (12) years old. The RNFL thickness was significantly reduced in NMO patients with non-ON eyes as compared to control group. Best corrected visual acuity between the 2 groups were comparable (0.20 vs 0.00, p=0.071). Contrast sensitivity was also reduced in NMO patients (non-ON eyes) at all 5 spatial frequencies. In NMO group, 34.6% have normal colour vision. The mean deviation (MD) of Humphrey visual field (HVF) was higher in NMO group (p<0.001). There was a moderate correlation between RNFL thickness and contrast sensitivity. Weak correlation was found between the RNFL thickness with visual acuity and mean deviation of visual field test.
Conclusion: Our study showed that the fellow eye of NMO patients with unilateral ON revealed a significant reduction in RNFL thickness and all the optic nerve functions have subtle early changes that signify a subclinical retinal damage.
Fulltext The effect of 12-week core strength training on dynamic balance, agility, and dribbling skill in adolescent basketball players

Feng W, Wang F, Han Y, Li G

Heliyon, 2024 Mar 30;10(6):e27544.
PMID: 38533080 DOI: 10.1016/j.heliyon.2024.e27544

PURPOSE: The purpose of the study was to examine the impact of core strength training on the dynamic balance, agility, and dribbling ability of adolescent basketball players.
METHODS: A randomized controlled between-subjects design was employed. Forty-four male adolescent basketball players (aged 14.41 ± 3.22 years) were randomly divided into two groups: the core strength training (CST) group and the conventional training (CT) group. The CST program included 1-h sessions, three times/week for 12 weeks. In contrast, the CT group provided a thorough physical training program that targeted general conditioning rather than focusing solely on core strength. Three measurements were used to evaluate performance in players: the Star Excursion Balance Test, the Illinois Agility Test, and the Dribbling Test conducted at T0 (week 0), T1 (week 6), and T2 (week 12), respectively.
RESULTS: Compared to the CT group, the CST group showed a greater improvement (p 0.05).
CONCLUSION: The 12-week CST program significantly improved dynamic balance, agility, and dribbling skills in adolescent basketball players, demonstrating its potential as a valuable training component. Future research should explore CST's impact on other sport-specific elements and its applicability to female players.
UPLC/Q-TOF-MS-based Metabolomics Study of the Antiosteoporosis Effects of Vaccarin in Ovariectomized Mice

Song F, Xie T, Liu X, Chin B, Luo X, Liao S, et al.

Planta Med, 2023 Feb;89(2):218-230.
PMID: 36100252 DOI: 10.1055/a-1942-5428

Osteoporosis is a systemic and metabolic bone disease that usually occurs in postmenopausal women, which mainly manifests as bone loss and increased bone fragility that both facilitate fracture. However, few drugs for osteoporosis have shown good efficacy and limited side effects. Vaccarin has demonstrated its antiosteoporosis effects by inhibiting the formation and osteolytic activities of osteoclasts in our previous investigation. In this study, multivariate statistical analysis and ultrahigh-performance liquid chromatography and quadrupole time-of-flight tandem mass spectrometry were used to analyze the serum metabolites of ovariectomized mice treated with or without vaccarin. As a result, 9 serum metabolites were identified as biomarkers. The metabolic levels of 3 crucial biomarkers, namely, lysophosphatidylcholine [22 : 6, (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)], 1-linoleoylglycerophosphocholine and 1-palmitoyl-Sn-glycero-3-phosphocholine, that were correlated with glycerophospholipid metabolism increased and then decreased significantly after vaccarin treatment. Molecular docking analysis and osteoclasts differentiation experiment further revealed that vaccarin may bind with phospholipase A2 and downregulated its activity to reduce the osteoclastogenesis. Therefore, the occurrence of osteoporosis is closely related with glycerophospholipid metabolism disorders, and vaccarin exerts antiosteoporosis effects by reducing the levels of glycerophospholipid metabolites.
Fulltext Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy

Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, et al.

JAMA Neurol, 2022 Oct 01;79(10):986-996.
PMID: 36036923 DOI: 10.1001/jamaneurol.2022.2514

IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.
OBJECTIVE: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.
EXPOSURES: One of 7 antiseizure medications.
MAIN OUTCOMES AND MEASURES: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.
RESULTS: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.
CONCLUSIONS AND RELEVANCE: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.

Autophagy, 2021 Jan;17(1):1-382.
PMID: 33634751 DOI: 10.1080/15548627.2020.1797280

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.