-
Frontiers in Oral Health 2024The use of fluoridated toothpaste (FT) is essential for controlling caries. This analytical cross-sectional study aimed to determine the proportion of students who...
OBJECTIVE
The use of fluoridated toothpaste (FT) is essential for controlling caries. This analytical cross-sectional study aimed to determine the proportion of students who brushed their teeth with fluoridated toothpaste/or do not know the content at least once a day and to determine the factors associated with the knowledge of brushing teeth with FT.
METHODS
An anonymous questionnaire was distributed during the academic year 2019-2020 among 439 high school students. The data collected included sociodemographic characteristics and oral-health-related variables [e.g., brushing teeth, knowledge of the effect of fluoride on caries (KEFC) and dental service utilisation (DSU)]. The dependent variable was the knowledge of using FT when brushing teeth (Yes or do not know). Descriptive, bivariate, and logistic regression analysis were performed.
RESULTS
The response rate was 98% ( = 432) and usable data was 88% ( = 385). The median (IQR) age of the students was 16.00 (1) years, and 190 (47%) were males. Eighty eight percent of the students brushed their teeth with toothpaste daily with no knowledge of toothpaste content and only 86 (21.8%) knew the content of the toothpaste used for brushing their teeth i.e., FT. The multivariable analyses revealed an association of family income and KEFC with brushing teeth with FT [adjusted odds ratio (AOR): 1.98, 95% confidence interval (CI): 1.14-3.43, = 0.015 and AOR = 6.11, 95% CI: 3.45-10.83, < 0.001, respectively].
CONCLUSIONS
While the brushing and use of toothpaste among high school students was common, the knowledge of the content of toothpaste used for brushing teeth was less common and was associated with family income and KEFC.
PubMed: 38966591
DOI: 10.3389/froh.2024.1416718 -
Frontiers in Pharmacology 2024The field of Medicines Development faces a continuous need for educational evolution to match the interdisciplinary and global nature of the pharmaceutical industry.... (Review)
Review
Blended e-learning and certification for medicines development professionals: results of a 7-year collaboration between King's College, London and the GMDP Academy, New York.
INTRODUCTION
The field of Medicines Development faces a continuous need for educational evolution to match the interdisciplinary and global nature of the pharmaceutical industry. This paper discusses the outcomes of a 7-year collaboration between King's College London and the Global Medicines Development Professionals (GMDP) Academy, which aimed to address this need through a blended e-learning program.
METHODS
The collaboration developed a comprehensive curriculum based on the PharmaTrain syllabus, delivered through a combination of asynchronous and synchronous e-learning methods. The program targeted a diverse range of professionals serving in areas related to Medical Affairs.
RESULTS
Over seven annual cohorts, 682 participants from eighty-six countries were enrolled in the program. The program's effectiveness was assessed using Kirkpatrick's model, showing elevated levels of satisfaction (over 4.0 on a five-point scale), suggesting significant gains in competence at the cognitive level and leveraged performance. Notably, 70% of responding alumni reported significant improvement in their functions, corroborated by 30% of their supervisors. The further long-term impact of the program on their respective organization has not been established.
DISCUSSION
The GMDP Academy's program has significantly contributed to life-long learning in Medicines Development, addressing educational gaps and fostering interdisciplinary collaboration. Its success highlights the importance of continuous education in keeping pace with the industry's evolving demands and underscores the potential of blended learning in achieving educational objectives in pharmaceutical medicine.
PubMed: 38966556
DOI: 10.3389/fphar.2024.1417036 -
Frontiers in Pharmacology 2024Obstructive sleep apnea (OSA) has been linked to various pathologies, including arrhythmias such as atrial fibrillation. Specific treatment options for OSA are mainly...
BACKGROUND
Obstructive sleep apnea (OSA) has been linked to various pathologies, including arrhythmias such as atrial fibrillation. Specific treatment options for OSA are mainly limited to symptomatic approaches. We previously showed that increased production of reactive oxygen species (ROS) stimulates late sodium current through the voltage-dependent Na channels via Ca/calmodulin-dependent protein kinase IIδ (CaMKIIδ), thereby increasing the propensity for arrhythmias. However, the impact on atrial intracellular Na homeostasis has never been demonstrated. Moreover, the patients often exhibit a broad range of comorbidities, making it difficult to ascertain the effects of OSA alone.
OBJECTIVE
We analyzed the effects of OSA on ROS production, cytosolic Na level, and rate of spontaneous arrhythmia in atrial cardiomyocytes isolated from an OSA mouse model free from comorbidities.
METHODS
OSA was induced in C57BL/6 wild-type and CaMKIIδ-knockout mice by polytetrafluorethylene (PTFE) injection into the tongue. After 8 weeks, their atrial cardiomyocytes were analyzed for cytosolic and mitochondrial ROS production via laser-scanning confocal microscopy. Quantifications of the cytosolic Na concentration and arrhythmia were performed by epifluorescence microscopy.
RESULTS
PTFE treatment resulted in increased cytosolic and mitochondrial ROS production. Importantly, the cytosolic Na concentration was dramatically increased at various stimulation frequencies in the PTFE-treated mice, while the CaMKIIδ-knockout mice were protected. Accordingly, the rate of spontaneous Ca release events increased in the wild-type PTFE mice while being impeded in the CaMKIIδ-knockout mice.
CONCLUSION
Atrial Na concentration and propensity for spontaneous Ca release events were higher in an OSA mouse model in a CaMKIIδ-dependent manner, which could have therapeutic implications.
PubMed: 38966545
DOI: 10.3389/fphar.2024.1411822 -
Frontiers in Medicine 2024The field of machine learning has been evolving and applied in medical applications. We utilised a public dataset, MIMIC-III, to develop compact models that can...
BACKGROUND
The field of machine learning has been evolving and applied in medical applications. We utilised a public dataset, MIMIC-III, to develop compact models that can accurately predict the outcome of mechanically ventilated patients in the first 24 h of first-time hospital admission.
METHODS
67 predictive features, grouped into 6 categories, were selected for the classification and prediction task. 4 tree-based algorithms (Decision Tree, Bagging, eXtreme Gradient Boosting and Random Forest), and 5 non-tree-based algorithms (Logistic Regression, K-Nearest Neighbour, Linear Discriminant Analysis, Support Vector Machine and Naïve Bayes), were employed to predict the outcome of 18,883 mechanically ventilated patients. 5 scenarios were crafted to mirror the target population as per existing literature. S1.1 reflected an imbalanced situation, with significantly fewer mortality cases than survival ones, and both the training and test sets played similar target class distributions. S1.2 and S2.2 featured balanced classes; however, instances from the majority class were removed from the test set and/or the training set. S1.3 and S 2.3 generated additional instances of the minority class via the Synthetic Minority Over-sampling Technique. Standard evaluation metrics were used to determine the best-performing models for each scenario. With the best performers, Autofeat, an automated feature engineering library, was used to eliminate less important features per scenario.
RESULTS
Tree-based models generally outperformed the non-tree-based ones. Moreover, XGB consistently yielded the highest AUC score (between 0.91 and 0.97), while exhibiting relatively high Sensitivity (between 0.58 and 0.88) on 4 scenarios (1.2, 2.2, 1.3, and 2.3). After reducing a significant number of predictors, the selected calibrated ML models were still able to achieve similar AUC and MCC scores across those scenarios. The calibration curves of the XGB and BG models, both prior to and post dimension reduction in Scenario 2.2, showed better alignment to the perfect calibration line than curves produced from other algorithms.
CONCLUSION
This study demonstrated that dimension-reduced models can perform well and are able to retain the important features for the classification tasks. Deploying a compact machine learning model into production helps reduce costs in terms of computational resources and monitoring changes in input data over time.
PubMed: 38966539
DOI: 10.3389/fmed.2024.1398565 -
Frontiers in Medicine 2024Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction...
INTRODUCTION
Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge.
METHODS
This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures.
RESULTS
Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set.
DISCUSSION
We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.
PubMed: 38966533
DOI: 10.3389/fmed.2024.1414637 -
Frontiers in Medicine 2024Potential uncertainties and overtreatment exist in adjuvant chemotherapy for triple-negative breast cancer (TNBC) patients.
BACKGROUND
Potential uncertainties and overtreatment exist in adjuvant chemotherapy for triple-negative breast cancer (TNBC) patients.
OBJECTIVES
This study aims to explore the performance of deep learning (DL) models in personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy.
METHODS
Patients who received treatment recommended by models were compared to those who did not. Overall survival for treatment according to model recommendations was the primary outcome. To mitigate bias, inverse probability treatment weighting (IPTW) was employed. A mixed-effect multivariate linear regression was employed to visualize the influence of certain baseline features of patients on chemotherapy selection.
RESULTS
A total of 10,070 female TNBC patients met the inclusion criteria. Treatment according to Self-Normalizing Balanced (SNB) individual treatment effect for survival data model recommendations was associated with a survival benefit (IPTW-adjusted hazard ratio: 0.53, 95% CI, 0.32-8.60; IPTW-adjusted risk difference: 12.90, 95% CI, 6.99-19.01; IPTW-adjusted the difference in restricted mean survival time: 5.54, 95% CI, 1.36-8.61), which surpassed other models and the National Comprehensive Cancer Network guidelines. No survival benefit for chemotherapy was seen for patients not recommended to receive this treatment. SNB predicted older patients with larger tumors and more positive lymph nodes are the optimal candidates for chemotherapy.
CONCLUSION
These findings suggest that the SNB model may identify patients with TNBC who could benefit from chemotherapy. This novel analytical approach may provide debiased individual survival information and treatment recommendations. Further research is required to validate these models in clinical settings with more features and outcome measurements.
PubMed: 38966532
DOI: 10.3389/fmed.2024.1418800 -
Frontiers in Medicine 2024Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of...
INTRODUCTION
Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life.
METHODS
This research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson's disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration.
RESULTS
The decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG.
PubMed: 38966531
DOI: 10.3389/fmed.2024.1418684 -
Frontiers in Medicine 2024This meta-analysis evaluates the comparative diagnostic efficacy of Ga-prostate-specific membrane antigen-11 PET (Ga-PSMA-11 PET) and multiparametric MRI (mpMRI) for the...
PURPOSE
This meta-analysis evaluates the comparative diagnostic efficacy of Ga-prostate-specific membrane antigen-11 PET (Ga-PSMA-11 PET) and multiparametric MRI (mpMRI) for the initial lymph node staging of prostate cancer.
METHODS
We searched PubMed and Embase databases through October 2023 for studies that provide a head-to-head comparison of Ga-PSMA-11 PET and mpMRI, using pelvic lymph node dissection as the gold standard. We assessed sensitivity and specificity using the DerSimonian and Laird method, with variance stabilization via the Freeman-Tukey double inverse sine transformation. The quality of included studies was evaluated using the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool.
RESULTS
The meta-analysis incorporated 13 articles, involving a total of 1,527 patients. Ga-PSMA-11 PET demonstrated an overall sensitivity of 0.73 (95% CI: 0.51-0.91) and a specificity of 0.94 (95% CI: 0.88-0.99). In comparison, mpMRI showed a sensitivity of 0.49 (95% CI: 0.30-0.68) and a specificity of 0.94 (95% CI: 0.88-0.99). Although Ga-PSMA-11 PET appeared to be more sensitive than mpMRI, the differences in sensitivity ( = 0.11) and specificity ( = 0.47) were not statistically significant.
CONCLUSION
Our findings indicated that Ga-PSMA-11 PET and mpMRI exhibit similar sensitivity and specificity in the diagnosis of initial lymph node staging of prostate cancer. However, given that most included studies were retrospective, further prospective studies with larger sample sizes are essential to validate these results.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO code is CRD42023495266.
PubMed: 38966530
DOI: 10.3389/fmed.2024.1425134 -
Frontiers in Medicine 2024Several observational studies suggested an association between rheumatoid arthritis (RA) and bronchiectasis. Nevertheless, the presence of a causal relationship between...
BACKGROUND
Several observational studies suggested an association between rheumatoid arthritis (RA) and bronchiectasis. Nevertheless, the presence of a causal relationship between these conditions is yet to be determined. This study aimed to investigate whether genetically predicted RA is associated with the risk of bronchiectasis and vice versa.
METHODS
We obtained RA genome-wide association study (GWAS) data from FinnGen consortium, and bronchiectasis GWAS data from IEU Open GWAS project. Univariate Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW) estimation as the main method. Furthermore, bidirectional and replication MR analysis, multivariate MR (MVMR), Mediation analysis, and sensitivity analyses were conducted to validate the findings.
RESULTS
In the UVMR analysis, the IVW results revealed that RA had an increased risk of bronchiectasis (OR = 1.18, 95% CI = 1.10-1.27; = 2.34 × 10). In the reverse MR analysis, no evidence of a causal effect of bronchiectasis on the risk of RA was detected. Conversely, in the replication MR analysis, RA remained associated with an increased risk of bronchiectasis. Estimates remained consistent in MVMR analyses after adjusting for the prescription of non-steroidal anti-inflammatory drugs (NSAIDs) and glucocorticoids. Immunosuppressants were found to mediate 58% of the effect of the RA on bronchiectasis. Sensitivity analyses confirmed the stability of these associations.
CONCLUSION
This study demonstrated a positive causal relationship between RA and an increased risk of bronchiectasis, offering insights for the early prevention of bronchiectasis in RA patients and shedding new light on the potential role of immunosuppressants as mediators in promoting the effects of RA on bronchiectasis.
PubMed: 38966529
DOI: 10.3389/fmed.2024.1403851 -
Frontiers in Medicine 2024Chronic Obstructive Pulmonary Disease (COPD) is a chronic condition characterized primarily by airflow obstruction, significantly impacting patients' quality of life....
The impact of traditional mind-body exercises on pulmonary function, exercise capacity, and quality of life in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis of randomized controlled trials.
BACKGROUND
Chronic Obstructive Pulmonary Disease (COPD) is a chronic condition characterized primarily by airflow obstruction, significantly impacting patients' quality of life. Traditional mind-body exercises, as a non-pharmacological intervention for COPD, have become a new research focus.
OBJECTIVE
To assess the impact of traditional mind-body exercises (Tai Chi, Qigong, Yoga) on pulmonary function, exercise capacity, and quality of life in COPD patients. Additionally, to identify the most suitable form of traditional mind-body exercise for different indicators.
METHODS
Searches were conducted in databases such as Web of Science, PubMed, EBSCOhost, CNKI, etc., to collect randomized controlled trials (RCTs) evaluating the intervention of traditional mind-body exercises (Tai Chi, Yoga, Qigong) in COPD. The Cochrane evaluation tool was applied for methodological quality assessment of the included literature. Statistical analysis and sensitivity analysis were performed using Revman 5.4 software, while publication bias was assessed using R software.
RESULTS
This study included 23 studies with a total of 1862 participants. Traditional mind-body exercises improved patients' FEV1% index (WMD = 4.61, 95%CI [2.99, 6.23]), 6-min walk distance (SMD = 0.83, 95%CI [0.55, 1.11]), and reduced patients' SGRQ score (SMD = -0.79, 95%CI [-1.20, -0.38]) and CAT score (SMD = -0.79, 95%CI [-1.20, -0.38]). Qigong showed the most significant improvement in FEV1% and 6MWT, while Tai Chi primarily improved 6MWT, and the effect of Yoga was not significant. Sensitivity analysis indicated stable and reliable research conclusions.
CONCLUSION
Traditional mind-body exercises are effective rehabilitation methods for COPD patients, significantly improving pulmonary function, exercise capacity, and quality of life. They are suitable as complementary interventions for standard COPD treatment.
SYSTEMATIC REVIEW REGISTRATION
[https://www.crd.york.ac.uk/prospero/display-record.php?ID=CRD42023495104], identifier [CRD42023495104].
PubMed: 38966524
DOI: 10.3389/fmed.2024.1359347