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Frontiers in Pediatrics 2024The aim of the study was to explore the factors influencing the availability of medications for children, and establish a machine learning model to provide an empirical...
OBJECTIVE
The aim of the study was to explore the factors influencing the availability of medications for children, and establish a machine learning model to provide an empirical basis for the subsequent formulation and improvement of relevant policies.
METHODS
Design: Cross-sectional survey. Setting: 12 provinces, China. Medical doctors from 25 public hospitals were enrolled. All data were randomly divided into a training set and a validation set at a ratio of 7:3. Three prediction models, namely random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost), were developed and compared. The receiver operating characteristic curve (ROC) and the associated area under the curve (AUC) were used to evaluate the three models. A nomogram and clinical impact curve (CIC) for availability of medication were developed.
RESULTS
Fifteen of 29 factors in the database that were most likely to be selected were considered to establish the prediction model. The XGBoost model (AUC = 0.915) demonstrated better performance than the RF model (AUC = 0.902) and the LR model (AUC = 0.890). According to the Shapley additive explanation values, the five factors that most significantly affected the availability of medications for children in the XGboost model were as follows: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor. The CIC was used to assess the practical applicability of the factor prediction nomogram.
CONCLUSIONS
The XGBoost model can be used to establish a prediction model to screen the factors associated with the availability of medications for children. The most important contributing factors to the models were the following: the relatively small number of specialized dosage forms for children; unaffordable medications for children; public education on the accessibility and safety of medication for children; uneven distribution of medical resources, leading to insufficient access to medication for children; and years of service as a doctor.
PubMed: 38957774
DOI: 10.3389/fped.2024.1341199 -
International Journal of Chronic... 2024Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between...
BACKGROUND
Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between pyroptosis-related genes (PRGs) and COPD diagnosis in the context of immune infiltration, ultimately proposing a PRG-based diagnostic model for predicting COPD outcomes.
METHODS
Clinical data and PRGs of COPD patients were sourced from the GEO database. The "ConsensusClusterPlus" package was employed to generate molecular subtypes derived from PRGs that were identified through differential expression analysis and LASSO Cox analysis. A diagnostic signature including eight genes (CASP4, CASP5, ELANE, GPX4, NLRP1, GSDME, NOD1and IL18) was also constructed. Immune cell infiltration calculated by the ESTIMATE score, Stroma scores and Immune scores were also compared on the basis of pyroptosis-related molecular subtypes and the risk signature. We finally used qRT - PCR to detect the expression levels of eight genes in COPD patient and normal.
RESULTS
The diagnostic model, anchored on eight PRGs, underwent validation with an independent experimental cohort. The area under the receiver operating characteristic (ROC) curves (AUC) for the diagnostic model showcased values of 0.809, 0.765, and 0.956 for the GSE76925, GSE8545, and GSE5058 datasets, respectively. Distinct expression patterns and clinical attributes of PRGs were observed between the comparative groups, with functional analysis underscoring a disparity in immune-related functions between them.
CONCLUSION
In this study, we developed a potential as diagnostic biomarkers for COPD and have a significant role in modulating the immune response. Such insights pave the way for novel diagnostic and therapeutic strategies for COPD.
Topics: Humans; Pulmonary Disease, Chronic Obstructive; Pyroptosis; Databases, Genetic; Predictive Value of Tests; Gene Expression Profiling; Lung; Male; Female; Middle Aged; Genetic Markers; Case-Control Studies; Transcriptome; Aged; Reproducibility of Results; Genetic Predisposition to Disease; Prognosis
PubMed: 38957709
DOI: 10.2147/COPD.S438686 -
Frontiers in Human Neuroscience 2024Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because...
Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.
PubMed: 38957693
DOI: 10.3389/fnhum.2024.1371631 -
Public Health Reviews 2024We evaluated studies that used the World Health Organization's (WHO) AirQ and AirQ+ tools for air pollution (AP) health risk assessment (HRA) and provided best practice... (Review)
Review
OBJECTIVES
We evaluated studies that used the World Health Organization's (WHO) AirQ and AirQ+ tools for air pollution (AP) health risk assessment (HRA) and provided best practice suggestions for future assessments.
METHODS
We performed a comprehensive review of studies using WHO's AirQ and AirQ+ tools, searching several databases for relevant articles, reports, and theses from inception to Dec 31, 2022.
RESULTS
We identified 286 studies that met our criteria. The studies were conducted in 69 countries, with most (57%) in Iran, followed by Italy and India (∼8% each). We found that many studies inadequately report air pollution exposure data, its quality, and validity. The decisions concerning the analysed population size, health outcomes of interest, baseline incidence, concentration-response functions, relative risk values, and counterfactual values are often not justified, sufficiently. Many studies lack an uncertainty assessment.
CONCLUSION
Our review found a number of common shortcomings in the published assessments. We suggest better practices and urge future studies to focus on the quality of input data, its reporting, and associated uncertainties.
PubMed: 38957684
DOI: 10.3389/phrs.2024.1606969 -
International Journal of Clinical and... 2024The adaptation and validation of measures to assess Sexual Distress (SD) are crucial for the diagnosis and treatment of sexual dysfunction. This study aimed to adapt and...
BACKGROUND/OBJECTIVE
The adaptation and validation of measures to assess Sexual Distress (SD) are crucial for the diagnosis and treatment of sexual dysfunction. This study aimed to adapt and validate the Spanish Sexual Distress Scale (SDS) in a Colombian sample and provide a percentile ranking score for a comprehensive understanding of sexual distress among the population.
METHOD
Five hundred ninety-six people from Colombia (50.08 % women; 49.92 % men) aged 18-60 participated in the study. Exploratory and confirmatory factorial analyses and a convergent validity analysis were performed.
RESULTS
The SDS showed a high internal consistency (Ω = .95, α = .94) and a unidimensional model. Significative correlations were found between the SDS and related measures with sexual functioning, further supporting its convergent validity.
CONCLUSIONS
The SDS is a valid and reliable measure to evaluate SD in Colombians, with implications for clinical practice and sexual health research. More investigations are needed to address the limitations, strengthen the validity and reliability of the scale, and develop specific interventions based on its results.
PubMed: 38957682
DOI: 10.1016/j.ijchp.2024.100469 -
Dermatology Reports Jun 2024Translating and validating the Greek version of the Patient Oriented Eczema Measure (POEM) was our goal. A parallel backtranslation process was used to translate POEM. A...
Translating and validating the Greek version of the Patient Oriented Eczema Measure (POEM) was our goal. A parallel backtranslation process was used to translate POEM. A total of fifty-nine adult atopic dermatitis patients were enlisted to assess validity and reliability. Through patient interviews with physicians, a questionnaire comprising demographics, POEM, and the dermatology life quality index (DLQI) was filled out. 3-7 days after the first visit, a second POEM completion was conducted. The POEM items conducted with study participants demonstrated a good level of internal consistency (Cronbach's alpha = 0.88), and no overall floor and ceiling effects were found. There was a significant correlation between the DLQI and POEM scores (Spearman rho =0.71; p<0.001). The POEM score between interviews showed an average intraclass correlation coefficient (95% confidence interval) of 0.89 (0.80, 0.94), indicating good to excellent test-retest reliability. Patient-reported outcome measures are becoming more and more common in Greece, so it's critical to have access to Greek translations of validated instruments that are frequently used in literature.
PubMed: 38957632
DOI: 10.4081/dr.2023.9689 -
Annals of Gastroenterological Surgery Jul 2024The existing predictive risk models for the surgical outcome of acute diffused peritonitis (ADP) need renovation by adding relevant variables such as ADP's definition or...
AIM
The existing predictive risk models for the surgical outcome of acute diffused peritonitis (ADP) need renovation by adding relevant variables such as ADP's definition or causative etiology to pursue outstanding data collection reflecting the real world. We aimed to revise the risk models predicting mortality and morbidities of ADP using the latest Japanese Nationwide Clinical Database (NCD) variable set.
METHODS
Clinical dataset of ADP patients who underwent surgery, and registered in the NCD between 2016 and 2019, were used to develop a risk model for surgical outcomes. The primary outcome was perioperative mortality.
RESULTS
After data cleanup, 45 379 surgical cases for ADP were derived for analysis. The perioperative and 30-day mortality were 10.6% and 7.2%, respectively. The prediction models have been created for the mortality and 10 morbidities associated with the mortality. The top five relevant predictors for perioperative mortality were age >80, advanced cancer with multiple metastases, platelet count of <50 000/mL, serum albumin of <2.0 g/dL, and unknown ADP site. The C-indices of perioperative and 30-day mortality were 0.859 and 0.857, respectively. The predicted value calculated with the risk models for mortality was highly fitted with the actual probability from the lower to the higher risk groups.
CONCLUSIONS
Risk models for postoperative mortality and morbidities with good predictive performance and reliability were revised and validated using the recent real-world clinical dataset. These models help to predict ADP surgical outcomes accurately and are available for clinical settings.
PubMed: 38957554
DOI: 10.1002/ags3.12800 -
Frontiers in Neuroinformatics 2024Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and...
Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.
PubMed: 38957548
DOI: 10.3389/fninf.2024.1384720 -
Frontiers in Immunology 2024Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy...
INTRODUCTION
Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched.
METHODS
In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance.
RESULTS
Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed , , and as potential contributors to primary resistance, with , and emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC.
DISCUSSION
These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.
Topics: Humans; Small Cell Lung Carcinoma; Lung Neoplasms; Drug Resistance, Neoplasm; Biomarkers, Tumor; Transcriptome; Female; Male; Gene Expression Profiling; Middle Aged; Gene Expression Regulation, Neoplastic; Aged; Antineoplastic Combined Chemotherapy Protocols; Etoposide
PubMed: 38957470
DOI: 10.3389/fimmu.2024.1338162 -
Frontiers in Endocrinology 2024This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of...
OBJECTIVE
This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.
METHODS
242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.
RESULTS
The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.
CONCLUSION
The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.
Topics: Humans; Pancreatic Neoplasms; Machine Learning; Female; Male; Retrospective Studies; Ultrasonography; Middle Aged; Aged; Adult; Nomograms; Radiomics
PubMed: 38957447
DOI: 10.3389/fendo.2024.1381822