Good health is extremely important for athletes who engage in strenuous physical activities, such as football. They must develop a healthy body before participating in vigorous activities and ...competitions. Although researchers have presented a wide range of analytical approaches emphasizing athlete health, only a small percentage of completed studies have used neural networks. In this study, we propose a novel technique for predicting football players’ health using wearable technology and recurrent neural networks. The proposed system monitors the players’ health in real-time, making it one of the first applications of wearable sensors for athletes’ conditioning and health. Health prediction results are provided after the time-step data is entered into a recurrent neural network, and subsequent deep features are obtained from that data. Several trials are conducted in this investigation, and the outcomes are determined by the information acquired about the players’ health. The simulation results illustrate the practicality and dependability of the proposed approach. The algorithms developed in this study can serve as the foundation for data-driven monitoring and training.
•Athletes must develop a healthy body before participating in vigorous activities and competitions.•Researchers have presented a wide range of analytical approaches emphasizing athlete health.•We propose a novel technique for predicting players’ health using wearable technology and recurrent neural networks.•The proposed system monitors the players’ health in real time.•The simulation results illustrate the practicality and dependability of the proposed approach.
•Tightly controlled protocol used to systematically vary trunk flexion in walking•Small changes in trunk flexion increase hip and ankle moments•Increasing trunk flexion leads to relatively large ...increases in knee flexor EMG•Knee muscle co-contraction increases with trunk flexion
The head, arms and trunk segment constitute a large proportion of the body’s mass. Therefore, small alterations in trunk inclination may affect lower limb joint moments and muscle activation patterns. Although previous research has investigated the effect of changing frontal plane inclination of the trunk, it is not clear how increasing trunk flexion will impact on the activation of the lower limb muscles.
What is the effect of independently manipulating trunk flexion angle on lower limb kinematics, moments and muscle function?
Gait analysis was carried out on 20 healthy people under four trunk flexion conditions: normal walking (NW), NW-5°, NW+5° and NW+10°. For the latter three conditions, a biofeedback approach was used to tightly control trunk flexion angle. A linear mixed model was used to investigate the effect of changing trunk flexion on joint angles, moments, and knee muscle activation.
There were clear increases in hip and ankle moments as trunk flexion was increased, but no change in knee moments. The results also showed a linear increase in knee flexor muscle activity and a corresponding increase in co-contraction as trunk flexion increased. Interestingly, there was a dramatic change in the profile of hamstring activity. In the medial hamstrings, this change led to a 100% increase in activation during early stance as flexion was increased by 5° from NW.
This is the first study to demonstrate a strong dependence of knee flexor muscle activity on trunk flexion. This is important as people with knee osteoarthritis have been observed to walk with elevated muscle activation and this has been linked to increased joint loads. It is possible that these altered muscle patterns may result from increased trunk flexion during walking.
Fog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server ...machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.
The 4-drug regimen of rifampin, isoniazid, pyrazinamide, and ethambutol is an inexpensive, reliable option for treating patients with drug-susceptible tuberculosis (TB). Its efficacy could be further ...improved by determining the free drug concentrations in plasma, knowing that only the unbound drug can freely penetrate to the tissues. Using an ultrafiltration technique, we determined the protein binding (PB) extent and variability of the first-line anti-TB drugs when given simultaneously to TB patients, representing a real-life case scenario. We used clinical samples routinely received by our laboratory. Plasma proteins were also measured. A protein-free medium was used to determine the nonspecific binding. Plasma samples from 22 patients were included, of which plasma proteins were measured for 18 patients. The median PB was determined for rifampin (88%; range, 72 to 91%), isoniazid (14%; range, 0 to 34%), pyrazinamide (1%; range, 0 to 7%), and ethambutol (12%; range, 4 to 24%). Plasma proteins were not found to be significant predictors for the PB of first-line anti-TB drugs. Rifampin PB was positively correlated with its plasma concentration (
value = 0.0051). Conversely, isoniazid PB was negatively correlated with its plasma concentration (
value = 0.0417). Age was found to have a significant effect on isoniazid PB (
value = 0.0376). No correlations were observed in pyrazinamide or ethambutol. In conclusion, we have determined variable PB of rifampin, isoniazid, pyrazinamide, and ethambutol in patient plasma samples, with median values of 88, 14, 1, and 12%, respectively. In this small study, PB of rifampin and that of isoniazid are dependent on their plasma concentrations.
The mortality rate of patients with a drug-resistant bacterial infection is high, as are the associated treatment costs. To overcome these issues, optimization of the available therapeutic options is ...required. Beta-lactams are time-dependent antibiotics and their efficacy is determined by the amount of time the free concentration remains above the minimum inhibitory concentration. Therefore, the aim of this study was to assess the extent and variability of protein binding for meropenem, cefepime, and piperacillin.
Plasma samples for the analysis of meropenem, cefepime, and piperacillin were collected from patients admitted to a tertiary care hospital as part of the standard care. The bound and unbound drug fractions in the samples were separated by ultrafiltration. Validated liquid chromatography-tandem mass spectrometry assays were used to quantify the total and free plasma concentrations, and the protein binding was determined.
Samples from 95 patients were analyzed. The median (range) age of patients was 56 years (17-87) and the median (range) body mass index was 25.7 kg/m (14.7-74.2). Approximately 59% of the patients were men. The median (range) unbound fraction (fu) was 62.5% (41.6-99.1) for meropenem, 61.4% (51.6-99.2) for cefepime, and 48.3% (39.4-71.3) for piperacillin. In the bivariate analysis, as the total meropenem concentration increased, the fu increased (r = 0.37, P = 0.045). A decrease in piperacillin fu was observed as the albumin concentration increased (r = -0.56, P = 0.005).
The average fu values were lower than those reported in the literature. There was also a large variability in fu; hence, it should be considered when managing patients administered with these drugs through direct measurements of free drug concentrations.
Tuberculosis (TB) and hepatitis C virus (HCV) infection are both major public health problems. Despite high rates of co-infection there is scarce literature addressing the convergence of the two ...diseases. One particularly unexplored area is the potential for simultaneous treatment of TB and HCV which would allow for leveraging an extensive global TB treatment infrastructure to help scale up HCV treatment. We review the drug metabolism of anti-TB and HCV drugs and the known and potential drug-drug interactions between recommended HCV regimens and individual anti-TB drugs. Rifampin is the only anti-TB drug to have been formally studied for potential drug interactions with anti-HCV direct-acting antivirals (DAAs) and existing data precludes these combinations. However, based on known pathways of drug metabolism and enzyme effects, the combination of HCV DAA regimens with all other anti-TB drugs may be feasible. Pharmacokinetic studies are needed next to help move co treatment regimens forward for clinical use among patients coinfected with TB and HCV.
Abstract
Background
Pharmacokinetic data are needed for newly implemented anti-tuberculosis drugs to help optimize their use.
Objectives
To help fill existing knowledge gaps, we evaluated the ...pharmacokinetic parameters of novel and repurposed anti-tuberculosis drugs among patients with drug-resistant pulmonary tuberculosis.
Methods
A prospective cohort study among patients ≥16 years with confirmed pulmonary drug-resistant TB was conducted in Tbilisi, Georgia. Patients receiving bedaquiline, delamanid and/or clofazimine were included. Blood samples were collected 4–6 weeks after drug initiation, and serum concentrations were measured using validated liquid chromatography tandem mass spectrometry assays. A non-compartmental analysis was performed, and the association of exposure parameters with covariates was explored.
Results
Among 99 patients, the average age and weight were 40 years and 65 kg, respectively. The median Cmin was 0.68 mg/L for bedaquiline, 0.17 mg/L for delamanid, and 0.52 mg/L for clofazimine. The median AUC0–24 was 30.6 mg·h/L for bedaquiline, 16.1 mg·h/L for clofazimine, and the AUC0–12 was 2.9 mg·h/L for delamanid. Among the significant covariates associated with drug exposure parameters were weight and sex for bedaquiline, alcohol use for delamanid, and weight for clofazimine.
Conclusions
We found a strong association of weight with bedaquiline and clofazimine exposure parameters, suggesting the need for weight-based dosing for those agents.
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of ...images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.
Limited data are available on factors that are associated with passing rates for the Saudi Pharmacist Licensure Examination (SPLE). The aim of this study is to investigate student characteristics and ...academic performance characteristics that may predict their success on SPLE.
This was a single-institution retrospective cohort study, which included pharmacy graduates from 2019 to 2021. Demographic, academic, and SPLE data were collected for each graduate. Binary logistic regression was used to explore the association between potential predictors and first-time SPLE pass status. A stepwise regression was then performed to develop multiple logistic models.
A total of 494 graduates were included in the study. Females, PharmD graduates, and on-time graduation had higher odds of passing SPLE (P = 0.0065, P = 0.0003, and P < 0.0001, respectively). For each 0.5 increase in GPA, the odds of passing SPLE increase by 3.5 times (OR 3.53; 95 % CI, 2.83–4.42; P < 0.0001). Of the tests taken prior to university admission, the overall high school score, general aptitude test (GAT) score, and qualifying score were significantly associated with higher SPLE first-time pass rates. When multiple logistic regression analysis was performed, GPA and GAT scores were the only significant predictors for higher SPLE first-time pass rates (P < 0.0001 and P = 0.0002, respectively).
The current research has shown that there is an association between higher SPLE first-time pass rates and several factors, most importantly the GPA and GAT score. Further research is needed, as it has the potential to inform the decision when reviewing pharmacy admission criteria.
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