METHODS: Seven- to eight-week old female BALB/c mice were fed either normal (ND) or HP diet and were sensitized with ovalbumin intraperitoneally followed by intranasal challenge. Allergic sensitization was tested by measuring anti-ovalbumin (OVA) IgE, IgG1, and IgG2a antibodies. Cytokine levels were tested by multiplex ELISA in splenocyte supernatants after stimulation. Airway inflammation was tested by measuring total and differential cell counts in bronchoalveolar lavage fluid and by measuring bronchial mucus production, goblet cell hyperplasia and perivascular and peribronchial inflammation severity scores by histologic examination.
RESULTS: Mice fed HP diet had a significant increase in weight and higher levels of OVA-specific IgE and IgG1 antibodies compared to the ND group (P-values 0.002, 0.007 and <0.001, respectively). In addition, they showed a selective Th2 bias in cultured splenocyte supernatants compared to the ND group as demonstrated by higher IL-4 and IL-6 levels (P-values <0.001 and 0.011, respectively) and higher ratios of Th2 to Th1 cytokines. However, the level of airway inflammation was comparable between both groups.
CONCLUSIONS: HP diet increases the risk of allergic sensitization though increase in Th2 cytokines. Efforts should be made to define the upper limit of protein in the diet that does not predispose to allergic sensitization. The effect of diet on health should remain a focus of research for the establishment of optimal health and resilience.
METHODS: A total of 160 serum samples (Discovery, n = 60 and Validation, n = 100) of obese and lean individuals with stable Body Mass Index (BMI) values were retrieved from The Malaysian Cohort biobank. Metabolic profiles were obtained using LC-MS/Q-TOF in dual-polarity mode. Metabolites were identified using a molecular feature and chemical formula algorithm, followed by a differential analysis using MetaboAnalyst 5.0. Validation of potential metabolites was conducted by assessing their presence through collision-induced dissociation (CID) using a targeted tandem MS approach.
RESULTS: A total of 85 significantly differentially expressed metabolites (p-value <0.05; -1.5 < FC > 1.5) were identified between the lean and the obese individuals, with the lipid class being the most prominent. A stepwise logistic regression revealed three metabolites associated with increased risk of obesity (14-methylheptadecanoic acid, 4'-apo-beta,psi-caroten-4'al and 6E,9E-octadecadienoic acid), and three with lower risk of obesity (19:0(11Me), 7,8-Dihydro-3b,6a-dihydroxy-alpha-ionol 9-[apiosyl-(1->6)-glucoside] and 4Z-Decenyl acetate). The model exhibited outstanding performance with an AUC value of 0.95. The predictive model underwent evaluation across four machine learning algorithms consistently demonstrated the highest predictive accuracy of 0.821, aligning with the findings from the classical logistic regression statistical model. Notably, the presence of 4'-apo-beta,psi-caroten-4'-al showed a statistically significant difference between the lean and obese individuals among the metabolites included in the model.
CONCLUSIONS: Our findings highlight the significance of lipids in obesity-related metabolic alterations, providing insights into the pathophysiological mechanisms contributing to obesity. This underscores their potential as biomarkers for metabolic dysregulation associated with obesity.
METHODS: A thorough search was conducted using the PubMed/MEDLINE and Web of Science online databases following the PICOS and PRISMA 2020 guidelines. All included studies were deemed to be of high quality according to the Newcastle-Ottawa Scale (NOS).
RESULTS: A total of 31 out of 3268 articles were included in the present study. The main outcome is the proportion of individuals with cognitive deficits, particularly in the EF domain, as detected by neuropsychological assessments. The present study also revealed that EF deficits in long COVID patients are correlated with disruptions in the frontal and cerebellar regions, affecting processes such as nonverbal reasoning, executive aspects of language, and recall. This consistent disturbance also emphasised the correlation between EF deficits and brain alterations in patients with long COVID.
CONCLUSION: The present study highlights the importance of evaluating EF deficits in long COVID patients. This insight has the potential to improve future treatments and interventions.
METHODS: The search was conducted in accordance with the PRISMA guidelines and utilized the following databases: Scopus, Web of Science, ProQuest, and Google Scholar. Inclusion and exclusion criteria: population, research methods, keywords, and time limit were described for this study. This article predominantly includes cross-sectional studies, so we have used the AXIS risk assessment methodology.
RESULTS: The study included ten articles, seven of which (70%) were quantitative. Three key findings emerged from this review: first, the studies on self-efficacy were more noteworthy than the studies on burnout. Second, female teachers were more expressive in their digital teaching, while male teachers had higher levels of self-efficacy in their digital teaching. Finally, the study explored various factors affecting self-efficacy and burnout in relation to digital teaching. The study demonstrated that professional development has a higher impact on physical education teachers' self-efficacy, and in turn, self-efficacy reduces burnout. Additionally, burnout had a significant impact on professional development.
CONCLUSION: This study describes the limitations of risk assessment and uses the AXIS tool to assess the methodological quality of this review report instead of using the risk of bias tool. The use of digital teaching methods can increase self-efficacy and alleviate burnout among physical education teachers. This review analyses the effects of digital technology, self-efficacy, and burnout on the career progression of physical education instructors and examines the implications for future developments.
METHODS: Relevant CEA studies of RARP were identified by searching the PubMed, Embase, Scopus, International Health Technology Assessment database, Tufts CEA Registry, and Centre for Reviews and Dissemination databases from January 2005 to October 2023. To be included, studies must compare costs, and quality-adjusted life years (QALYs) of RARP versus ORP or LRP, and report the incremental cost per QALY gained. Study characteristics, economic model, costs, and outcomes were extracted. INBs were calculated in 2022 US dollars adjusted for purchasing power parity. A pooled analysis was performed using a random-effect model stratified by country income level. Heterogeneity was assessed using the Q test and I2 statistic.
KEY FINDINGS AND LIMITATIONS: Thirteen studies with 17 comparisons, ten from high-income (HICs) and three from middle-income (MICs) countries, were included. Ten and five studies compared RARP with ORP and LRP, respectively. From a payer perspective, RARP was cost effective but not statistically significant compared with LRP in HICs (pooled INB: $7507.83 [-$1193.03 to $16 208.69], I2 = 81.15%) and not cost effective in MICs (%; -$4499.39 [-$16 500 to $7526.87], I2 = 17.15%). RARP showed no statistically significant cost effectiveness over ORP in both HICs ($3322.38 [-$1864.39 to $8509.15], I2 = 90.89%) and MICs ($2222.60 [-$2960.64 to $7405.83], I2 = 58.92%).
CONCLUSIONS AND CLINICAL IMPLICATIONS: RARP is cost effective compared with LRP in HICs but lacks statistical significance. When compared with ORP, RARP is not cost effective in HICs and MICs. Our findings may support decision-making for prostate cancer treatment options in countries with different health care systems, especially those with limited resources.
PATIENT SUMMARY: Our systematic review and meta-analysis provide important information regarding robotic-assisted radical prostatectomy (RARP) compared with open (ORP) and laparoscopic (LRP) radical prostatectomy. In high-income countries, RARP is generally cost effective compared with LRP, but not with ORP, while in middle-income countries, RARP is not cost effective compared with LRP or ORP. The findings of this review can support decision-making for prostate cancer treatment options.
METHODS: A majority of 151 responders (82.1%) were female and 58.3% had comorbid illnesses. Notably, 90.07% of respondents were non-adherence to their prescription, with significant differences by occupation and aids in medication. This study's machine learning models perform better with recursive feature elimination for feature selection. Key variables included occupation, presence of other diseases, religion, income, medication aid, marital status, and number of medications taken per day. These variables were used to build predictive models for medication adherence.
RESULTS: Results from machine learning algorithms showed varied performance. Support vector machine, gradient boosting, and random forest models performed best with AUC values of 0.907, 0.775, and 0.632 utilizing all variables. When using selected variables, random forest (AUC = 0.883), gradient boosting (AUC = 0.872), and Bagging (AUC = 0.860) performed best. Model interpretation using SHapley Additive exPlanations analysis identified occupation as the most important variable affecting medication adherence. The study also found that unemployment, concomitant disease, income, medication aid type, marital status, and daily medication count are connected with non-adherence.
CONCLUSION: The findings underscore the multifaceted nature of medication adherence in arthritis, highlighting the need for personalized approaches to improve adherence rates.
METHODS: Data of various forms, sources and levels of Se were analyzed using a meta-analysis approach in terms of their effects on production, antioxidant activity and egg Se deposition of laying hens by using 81 peer-reviewed publications.
RESULTS: Overall, laying hens' performance and egg quality attributes were not affected by Se supplementation, except for minor changes in egg weight and eggshell thickness in response to higher Se levels in diets. Noticeable effects were found on antioxidant activities where organic Se outperformed the inorganic form. Strong linear relationships between Se levels in the diet and Se content of whole egg, egg yolk and egg albumen were found where Se in the form of selenomethionine (SM) exhibited a stronger relationship with Se content in whole egg (R2 = 0.954), egg yolk (R2 = 0.972) and egg albumen (R2 = 0.926) than other forms of organic Se and inorganic Se (sodium selenite). Also observed was a Se preferential deposition in egg yolk compared with egg albumen especially for SM, indicating a higher bioavailability and deposition rate of SM than other Se sources.
CONCLUSION: Various forms of Se could be safely supplemented to diets at high doses of up to 5 mg kg-1 without adversely affecting hens' performance while enhancing antioxidant status. Supplementation with SM could be the most effective strategy to improve egg Se status among other forms of Se which may be beneficial for consumers. © 2025 Society of Chemical Industry.
MATERIALS AND METHODS: We conducted a systematic review and meta-analysis of AID-URAI versus AID-RAI. We systematically searched PubMed, Scopus, ProQuest, Web of Science, Cochrane Library, Clinicaltrial.gov, and medRxiv for articles up to 30 October 2024. Percent time-in-range (TIR; 3.9-10 mmol/L), time-below-range (TBR; 3.9- and 3.0-mmol/L), and time-above-range (TAR; >10.0- and 13.9-mmol/L) were extracted. This study was registered in the PROSPERO (CRD42024602279).
RESULTS: Sixteen randomized controlled trials (664 participants) were included in this study. AID-URAI were associated with an increased percentage of TIR, but not clinically significant (pooled mean difference {MD} = 1.07% [95% confidence interval {CI}: 0.11 to 2.02]; I2 = 0%; p = 0.029; high certainty). The favourable effect was consistent in AID systems incorporating automated bolus correction, adults, study duration >4 weeks, and FIASP subgroups. AID-URAI has a 0.35% lower percentage of TBR (<3.9 mmol/L) compared with AID-RAI. There were no significant differences in the risk of diabetic ketoacidosis and severe hypoglycemia between the two groups.
CONCLUSIONS: AID-URAI slightly improves the percentage of TIR and has a good safety profile without increasing the risk of diabetic ketoacidosis and severe hypoglycemia.
MATERIALS AND METHODS: The incorporation of Tualang honey into hydroxyapatite was assessed using Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) and field emission scanning electron-energy dispersive X-ray analysis microscopy (FESEM-EDX). The effect of hydroxyapatite combined with Tualang honey on cell viability was determined by WST-1 assay and wound healing was assessed by scratch assay.
RESULTS: The incorporation of Tualang honey into hydroxyapatite altered the functional groups, structure, size, morphology, and components of the crystal as evidenced by FTIR, XRD and FESEM-EDX analysis. High concentrations of pamidronic acid inhibit oral fibroblast viability and wound healing. Low and high concentrations of hydroxyapatite demonstrate non-toxicity towards fibroblast cells. Furthermore, hydroxyapatite reversed the action of pamidronic acid on the cells; it increased fibroblast viability but did not close the wound. Tualang honey promotes fibroblast viability and wound closure. However, the addition of Tualang honey is unable to overcome the inhibitory effects of pamidronic acid on fibroblasts. The addition of Tualang honey and hydroxyapatite improved the cell viability and accelerated wound closure of fibroblast exposed to pamidronic acid.
CONCLUSION: These findings demonstrated that the combination treatment protects oral fibroblasts by preventing bisphosphonate toxicity.
METHODS: This study presents an approach using a 1-dimensional (1D) of airway pressure data as an input to the convolutional long short-term memory neural network (CNN-LSTM) with a classifier method to classify AB types into three categories: 1) reverse Triggering (RT); 2) premature cycling (PC); and 3) normal breathing (NB), which cover normal breathing and 2 primary forms of AB. Three types of classifier are integrated with the CNN-LSTM model which are random forest (RF), support vector machine (SVM) and logistic regression (LR). Clinical data inputs include measured airway pressure from 7 MV patients in IIUM Hospital ICU under informed consent with a total of 4500 breaths. Model performance is first assessed in a k-fold cross-validation assessing accuracy in comparison to the proposed CNN-LSTM integrated with each type of classifier. Then, confusion matrices are used to summarize classification performance for the CNN without classifier, CNN-LSTM without classifier, and CNN-LSTM with each of the 3 classifiers (RF, SVM, LR).
RESULTS AND DISCUSSION: The 1D CNN-LSTM with classifier method achieves 100 % accuracy using 5-fold cross validation. The confusion matrix results showed that the combined CNN-LSTM model with classifier performed better, demostrating higher accuracy, sensitivity, specificity, and F1 score, all exceeding 83.5 % across all three breathing categories. The CNN model without classifier and CNN-LSTM model without classifier displayed comparatively lower performance, with average values of F1 score below 71.8 % for all three breathing categories.
CONCLUSION: The results validate the effectiveness of the CNN-LSTM neural network model with classifier in accurately detecting and classifying the different categories of AB and NB. Overall, this model-based approach has the potential to precisely classify the type of AB and differentiate normal breathing. With this developed model, a better MV management can be provided at the bedside, and these results justify prospective clinical testing.