Introduction: One way to improve the quality of life of the patients with thalassemia major is to enable them through education. The present research aimed to explore the effects of an educational intervention through mobile phones on self-care behaviors of the patients with thalassemia major Methods: In this quasi experimental study, which was done from May to January in 2017, 91 patients were enrolled who were suffering from thalassemia major.The census sampling method was performed with random allocation of interventional and control groups. Educational intervention was only applied to the intervention group. The study instrument was a questionnaire which was filled out by the patients before and two months after the educational intervention. For data analysis, statistical tests including independent samples t-test, paired-samples t-test, Mann-Whitney test and Wilcoxon test, were used through SPSS v.13 software. Results: Prior to the intervention, the mean scores of knowledge, attitude and self-care behaviors were not significantly different between the intervention and control groups. After the intervention, , however, there was a statistically significant increase in the aforementioned scores in the intervention group (P˂0.05), but there was no statistically significant increase in scores of the control group(P>0.05). Conclusion: The present findings showed the positive effect of the mobile-phone mediated education on knowledge, attitude and self-care behaviors of the patients with thalassemia major. Therefore, the use of mobile phone is recommended as an effective way of transferring instructional material as related to self-care to patients with thalassemia major particularly when access to them is limited.

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson’s Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

The SNCA gene, encoding alpha-synuclein, is implicated in the pathogenesis of Parkinson’s disease (PD), with several single-nucleotide polymorphisms (SNPs) linked to increased risk. This study systematically evaluated the association between common SNCA polymorphisms and PD through a meta-analysis of cohort and case–control studies published before 20 November 2023. Eligible studies were identified via comprehensive searches of PubMed, Scopus, and Web of Science, and pooled odds ratios with 95% confidence intervals were calculated under allelic, dominant, and recessive models. Heterogeneity and publication bias were assessed, and subgroup and sensitivity analyses were performed. Twenty-seven studies were included. SNP rs11931074 showed consistent associations with PD across all models, with low heterogeneity and no evidence of publication bias. rs356219 and rs356165 were also significantly associated with PD, although regional differences contributed to heterogeneity. In contrast, rs2583988 showed marginal significance in the allelic model, which was lost after sensitivity analyses. No associations were found under dominant or recessive models for this SNP. These findings confirm rs11931074 as a robust PD risk variant and support the roles of rs356219 and rs356165 while suggesting weaker evidence for rs2583988. Large, multi-ethnic studies are warranted to elucidate underlying mechanisms and support precision medicine in PD.

Background: Gestational hypertension (GH) and preeclampsia (PE) are two important complications of pregnancy. Considering the U-shaped association between thyroidstimulating hormone (TSH) and hypertensive disorders of pregnancy in some reports, we decided to investigate the effect of levothyroxine treatment on GH and PE in pregnant women with subclinical hypothyroidism (SGH), overt hypothyroidism (OH), and autoimmune thyroid diseases. Methods: Google Scholar and databases, such as ProQuest, Medline, Cochrane Library, ScienceDirect, and Scopus were searched electronically for clinical trials and observational studies using the following search terms: (("levothyroxine" OR "LT4" OR "thyroxine supplementation") AND ("subclinical hypothyroidism" OR "SCH" OR "thyroid peroxidase antibodies" OR "autoimmune thyroid disease") AND ("pregnancy outcomes" OR "preeclampsia" OR "gestational hypertension" OR "PIH")). Further, we investigated the impact of levothyroxine on the incidence of GH and/or PE compared with control or placebo groups from April 4 to November 1, 2022. Results: After treatment with levothyroxine, the odd ratios (ORs) of GH and PE in subclinical [OR = 1.03, 95% CI: (0.85, 1.25), I² = 35.25%, P =0.78, OR = 1.02, 95% CI: (0.66,1.58), I² = 46.86%, P =0.94, respectively] and overt hypothyroidism [OR=1.10, 95% CI: (0.70,1.71), I² =38.44%, P =0.69, OR=1.32, 95% CI: (0.83, 2.09), I² =0.00%, P =0.24, respectively] were not different from controls. Furthermore, this result was observed in studies that recruited women with SCH and OH [OR=1.12, 95% CI: (0.58, 2.14), I²=92.74%, P =0.74, OR=0.51, 95% CI: (0.15, 1.72), I² =97.30%, P =0.28, respectively]. Also, these studies compared thyroperoxidase antibodies between TPOAb-positive participants with TPOAb-negative controls (OR=1.01, 95% CI: (0.80, 1.28), I² =0.00%, P =0.90). However, LT4 reduced the risk of GH in TPOAb+ women compared with untreated TPOAb+ (OR=0.43, 95% CI: (0.30, 0.62), I² =0.00%, P =0.00). Conclusion: After LT4 therapy, the incidence rates of GH and PE in any form of hypothyroidism were not significantly different from controls. However, the reduction of GH in women with TPOAb+ who used levothyroxine needs further consideration.

Background: Cognitive assessments for patients with neurocognitive disorders are mostly measured by the Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) as screening tools. These cognitive scores are usually left-skewed and the results of the association analysis might not be robust. This study aims to study the distribution of the cognitive outcomes and to discuss potential solutions. Materials and Methods: In this retrospective cohort study of individuals with subjective cognitive decline or mild cognitive impairment, the inverse-transformed cognitive outcomes are modelled using different statistical distributions. The robustness of the proposed models are checked under different scenarios: with intercept-only, models with covariates, and with and without bootstrapping. Results: The main results were based on the VCAT score and validated via the MoCA score. The findings suggested that the inverse transformation method improved the modelling the cognitive scores compared to the conventional methods using the original cognitive scores. The association of the baseline characteristics (age, gender, and years of education) and the cognitive outcomes were reported as estimates and 95% confidence intervals. Bootstrap methods improved the estimate precision and the bootstrapped standard errors of the estimates were more robust. Cognitive outcomes were widely analysed using linear regression models with the default normal distribution as a conventional method. We compared the results of our suggested models with the normal distribution under various scenarios. Goodness-of-fit measurements were compared between the proposed models and conventional methods. Conclusions: The findings support the use of the inverse transformation method to model the cognitive outcomes instead of the original cognitive scores for early-stage neurocognitive disorders where the cognitive outcomes are left-skewed.

Background Recent advancements in plasma pTau217 have shown its potential as a reliable biomarker for detecting brain amyloid pathology (Aβ) in Alzheimer’s disease (AD). However, its application in real-world settings remains limited, particularly in diverse populations such as those in Asia. While plasma pTau217 has demonstrated high accuracy in research cohorts, most studies have been conducted in Western populations. We evaluated the performance of plasma pTau217 and pTau181 with longitudinal cognition in a real-world tertiary memory clinic setting. Methods Plasma ptau217 was measured using ALZpath SIMOA assay in 291 participants [68 healthy controls (HC), 100 mild cognitive impairment (MCI), 86 AD, 37 non-AD dementia] from National Neuroscience Institute, Singapore. Performances of pTau217, derived reference ranges (low-, intermediate-, high-) for amyloid positivity and associations with cognitive decline over 4 years was evaluated. Results Plasma pTau217 [(area under the curve (AUC) = 0.942)] outperformed pTau181 (AUC= 0.909) in differentiating AD from other disease groups. Amongst patients with known amyloid status (n=40), pTau217 identified Aβ+ with AUC= 0.961 vs pTau181 with AUC= 0.917. Using a two cut-point approach, a 95% sensitivity and specificity for Aβ+ corresponded to pTau217 >0.66 pg/ml and <0.38 pg/ml, respectively. With these two cut-points, patients in the total cohort were stratified into low-, intermediate-, and high-risk groups for Aβ+. The high-risk group exhibited the steepest annual decline in both MoCA (-2.382) and MMSE (-1.580) scores; while the low-risk group demonstrated relatively stable cognitive performance over time. By applying a k-means clustering analysis, we identified three clusters of participants representing stable, slow, and fast progressors. Fast progressors exhibit the most pronounced cognitive decline and the highest pTau217 levels. DISCUSSION Plasma pTau217 demonstrates high diagnostic accuracy and effectively predicts cognitive decline even in individuals without known Aβ+ status, potentially reducing reliance on amyloid PET confirmation. Our findings reinforce the clinical utility of plasma pTau217 as a prognostic biomarker for AD progression, offering a practical, non-invasive tool for assessing cognitive decline in real-world Southeast Asian memory clinics.