Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

Injuries deeper than the platysma are considered as penetrating neck injuries, constituting approximately 5% - 10% of all trauma. Many vital organs are at risk from a penetrating neck injury. These injuries in zone 1 have the highest mortality, because the injuries are close to the vital organs and difficult to access surgically. A 41-year-old male, a car mechanic by profession, presented to the emergency department with a penetrating neck injury on the right side. CT scan demonstrated a metallic foreign body in zone 1 between the right internal jugular vein and the common carotid artery. The patient was asymptomatic, and the foreign body was removed surgically. This case shows a rare presentation of a penetrating neck injury with a foreign body located in zone 1, where no vital internal structure was injured. As of now, no previous case report has been identified on such presentation. Thus, it will provide a valuable addition to the pre-existing literature.
Humans ; Male ; Adult ; Neck Injuries/diagnostic imaging* ; Wounds, Penetrating/diagnostic imaging* ; Foreign Bodies/diagnostic imaging* ; Jugular Veins ; Tomography, X-Ray Computed ; Carotid Artery Injuries/surgery* ; Carotid Artery, Common

OBJECTIVE: To entrap carvacrol (CAR) in bovine serum albumin nanoparticles (BSANPs) to form CAR-loaded BSANPs (CAR@BSANPs) and to explore the anti-cancer effects in breast adenocarcinoma cells (MCF-7 cells) treated with CAR and CAR@BSANPs. METHODS: A desolvation method was used to synthesize BSANPs and CAR@BSANPs. The BSANPs and CAR@BSANPs were characterized by several physicochemical methods, including visual observation, high-resolution field emission scanning electron microscopy, Fourier transform infrared spectroscopy, and high-performance liquid chromatography. MCF-7 cells were used and analyzed after 24 h of exposure to CAR and CAR@BSANPs at half-maximal inhibitory concentration. The anti-proliferative, apoptotic, reactive oxygen species (ROS), and nitric oxide (NO) scavenging activity as well as gene expression analysis were investigated by the cell viability assay, phase-contrast microscopy, 2',7'-dichlorofluorescein-diacetate assay, Griess-Illosvoy colorimetric assay, and quantitative real-time polymerase chain reaction, respectively. RESULTS: CAR and CAR@BSANPs showed anti-proliferative, apoptotic, ROS generation, and NO scavenging effects on MCF-7 cells. Expression profile of B-cell lymphoma 2-like 11 (BCL2L11), vascular endothelial growth factor A (VEGFA), hypoxia inducible factor factor-1α (HIF1A), BCL2L11/apoptosis regulator (BAX), and BCL2L11/Bcl2 homologous antagonist/killer 1 (BAK1) ratios revealed downregulated genes; and BAX, BAK1, and CASP8 were upregulated by CAR and CAR@BSANPs treatment. In vitro anticancer assays of the CAR and CAR@BSANPs showed that CAR@BSANPs demonstrated higher therapeutic efficacy in the MCF-7 cells than CAR. CONCLUSIONS CAR and CAR@BSANPs affect gene expression and may subsequently reduce the growth and proliferation of the MCF-7 cells. Molecular targeting of regulatory genes of the MCF-7 cells with CAR and CAR@BSANPs may be an effective therapeutic strategy against breast cancer.
Humans ; Cymenes ; Nanoparticles/ultrastructure* ; MCF-7 Cells ; Breast Neoplasms/genetics* ; Apoptosis/drug effects* ; Serum Albumin, Bovine/chemistry* ; Monoterpenes/therapeutic use* ; Adenocarcinoma/genetics* ; Cell Proliferation/drug effects* ; Reactive Oxygen Species/metabolism* ; Female ; Cell Survival/drug effects* ; Animals ; Gene Expression Regulation, Neoplastic/drug effects* ; Nitric Oxide/metabolism* ; Cattle

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

A series of comprehensive focus group discussions with active firefighters revealed that inconsistencies in incorporating a rigorous physical training program during work hours contributed significantly to the challenges of maintaining functional fitness, particularly strength. This article outlines a process for identifying the specific exercises required to enhance strength, drawing on the expertise and experience of physical trainers. Modified nominal group technique (mNGT) sessions were conducted to identify and rank exercises for five functional strengths (i.e., pushing, pulling, lifting, carrying, and dragging). A total of six physical trainers from the firefighter academy were interviewed to 1) identify the skeletal muscles involved in the functional movements using a visual aid; 2) generate ideas for exercises via brainstorming; 3) privately rank the displayed list of exercises for each muscle group involved in each functional movement; and 4) design a physical training programme for beginner, intermediate and advanced levels by manipulating loads. Males dominated the expert group (66.7%) and had a mean of 11.50 (SD 5.20) years of experience in physical training. The mNGTs yielded three top-ranking exercises for each functional strength: 1) Pushing: the push-up, sit-up, and jumping jack; 2) pulling: the push-up, jumping jack, and sit-up; 3) lifting: the jumping jack, push-up, and jumping squat; 4) carrying: the push-up, plank, and sit -up; and 5) dragging: the jumping jack, sit-up, and jumping squat. Then each exercise was designed for the beginner, intermediate, and advanced levels by manipulating loads (i.e. additional weight or number of repetitions). This study demonstrated that mNGT is an effective tool for identifying the three top-ranking exercises that address firefighter recruits’ functional strength. The experts chose multi-joint exercises targeting agonist and antagonist skeletal muscle groups and mimicking movements of daily work life. The exercises, corroborated by previous empirical evidence, provide opportunities for common skeletal muscle groupings to be targeted simultaneously to increase cardiovascular adaptations over a shorter period. The physical exercise is now ready to be implemented for piloting purposes among firefighter recruits.