scispace - formally typeset
Search or ask a question

How do you get rid of high white blood cells? 

Answers from top 3 papers

More filters
Papers (3)Insight
This device proved effective to retain >99.9% of white blood cells in 100 μl of WB without affecting plasma VL.
The results of extensive experiments show that the proposed method can successfully classify white blood cells.
Our approach presents for the first time a new state of the art application that assists in diagnosing the white blood cells diseases.

See what other people are reading

Why speech used for stress detection?
5 answers
Speech is utilized for stress detection due to its effectiveness in capturing stress-related changes in individuals' voices. Stress, a significant factor affecting mental and physical health, can be detected through speech-based systems that analyze various sound features extracted from voice recordings using advanced technologies like deep learning models and signal processing techniques. These systems offer a non-invasive and contactless approach to stress detection, making them valuable tools for monitoring stress levels and preventing long-term health issues associated with stress. By leveraging speech signals and features like Mel Frequency Cepstral Coefficients (MFCC) and Teager Energy Operator (TEO), accurate stress detection can be achieved with high classification accuracies, showcasing the potential of speech-based methods in this field.
Why has machine learning gained popularity for gait analysis in recent years?
5 answers
Machine learning has gained popularity for gait analysis in recent years due to its effectiveness in diagnosing gait-related diseases, especially in the elderly population. Studies have shown that machine learning models, including LSTM recurrent neural networks and various classification algorithms, can achieve high accuracy rates of over 90% in detecting gait abnormalities and differentiating pathological gait from normal gait patterns. The incorporation of machine learning techniques allows for the efficient analysis of a large number of interrelated parameters involved in gait, which can be challenging to process manually. By leveraging machine learning algorithms, researchers have been able to improve the diagnosis, treatment, and rehabilitation of gait-related conditions, making it a promising solution for enhancing clinical gait analysis and patient care.
What are the most common reasons behind speeding violations?
5 answers
Speeding violations are commonly driven by various factors. Studies have shown that attitudes, beliefs, and motivations play significant roles in predicting drivers' intentions towards speeding. The Theory of Planned Behaviour (TPB) and Driver Behaviour Questionnaire (DBQ) variables have been utilized to understand and predict speeding violations. Additionally, external pressures like time constraints and internal benefits such as thrill-seeking contribute to speeding behaviors. Factors such as age, road alignment, weather conditions, and speed limits have been identified as significant variables influencing speeding violations. Moreover, specific conditions like young age, morning rush hours, clear visibility, and slippery roads have been associated with a higher percentage of speeding violations. Understanding these underlying reasons is crucial for developing effective interventions to promote road safety and reduce speeding violations.
What are the most commonly used tools for detecting breast cancer in medical imaging?
4 answers
The most commonly used tools for detecting breast cancer in medical imaging include Convolutional Neural Networks (CNNs), machine learning algorithms, and microwave imaging (MI) techniques. CNNs have shown significant promise in medical imaging due to their success in image recognition, achieving human-level performance. Machine learning algorithms, particularly deep learning, are widely utilized for interpreting medical images, as they do not require prior knowledge in the field. MI techniques, involving low-power microwave frequencies to assess S-parameters for breast tissue evaluation, have also emerged as a valuable tool for breast cancer detection, offering a non-invasive and potentially more comfortable alternative to traditional methods. These tools collectively contribute to enhancing the accuracy and efficiency of breast cancer detection in medical imaging.
How does hydrogen bonding in proteins play a role in adjuvant hydrogen therapy for cancer treatment?
5 answers
Hydrogen bonding in proteins contributes to adjuvant hydrogen therapy for cancer treatment by enabling the design of protein-based nanosystems. These nanosystems utilize the dynamic characteristics of hydrogen bonds to create drug carriers, adjuvant agents, or therapeutic drugs, maximizing the intrinsic advantages of proteins in cancer therapy. Additionally, hydrogen gas (H2) has emerged as a gas signaling molecule with anti-cancer properties, showing potential in alleviating side effects of conventional chemotherapeutics and inhibiting cancer cell growth. Studies suggest that hydrogen gas can be used to suppress xenograft tumor growth, indicating its broad application in clinical cancer therapy. Overall, the use of hydrogen bonding in proteins and hydrogen gas in cancer treatment showcases promising avenues for improving therapeutic outcomes and reducing negative effects of current cancer therapies.
Therapeutic targets using cell-cell interactions from single cell data for aging?
5 answers
Cell-cell interactions from single-cell data provide valuable insights into identifying therapeutic targets for aging. By analyzing aging-associated biomarkers and gene expression patterns at the single-cell level, researchers can uncover key features of aging and its impact on various cell types. Additionally, the identification of aging-related genes and their detailed biological mechanisms at the single-cell level can aid in understanding the heterogeneous aging process. Furthermore, the analysis of aging patterns in different cancer entities has revealed senescent cell populations that could serve as therapeutic targets. Leveraging single-cell RNA sequencing data enables the study of cell-cell interactions, immune responses, and genetic regulations associated with aging and rejuvenation, offering innovative opportunities for developing targeted anti-aging therapeutics.
Drug targets or drug responses prediction using spatial transcriptomics gene expression in cancer?
5 answers
Spatial transcriptomics gene expression data can aid in predicting drug targets and responses in cancer treatment. By analyzing spatial determinants within tumors, researchers can identify potential drug targets and predict treatment outcomes. Studies have shown that spatial transcriptomics can reveal drug resistance genes like SNAI2, SOX4, and NDRG1, indicating aggressive disease behavior and potential targets for intervention. Additionally, utilizing transcriptome-based precision cancer medicine methodologies like OncoTarget and OncoTreat can predict tumor sensitivity to various oncology drugs, enhancing the understanding of drug responses and aiding in personalized treatment strategies. These approaches offer a promising avenue for improving cancer therapy outcomes by leveraging spatial gene expression data for targeted drug development and treatment prediction.
What are the most common machine learning algorithms used in walking behavior analysis?
4 answers
Machine learning algorithms commonly used in walking behavior analysis include logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. These algorithms have been applied to predict walking behavior within the next 3 hours based on previous steps-per-minute data, showing varying levels of performance. Among these algorithms, the multilayered perceptron (MLP) demonstrated the highest overall performance in terms of accuracy, Mathew correlation coefficient, sensitivity, and specificity in predicting walking behavior. Additionally, machine learning algorithms such as kNN, Random Forest, and boosting have been utilized in classifying emotions during walking sessions, showing promising results for emotion recognition based on human gait patterns.
How to extract neuron?
5 answers
To extract neurons effectively, various techniques and frameworks have been developed. One approach involves utilizing depth-estimation constrained nonnegative matrix factorization to reinforce neuron extraction by accurately estimating depth and compensating for scattering and depth loss. Additionally, a deformable non-negative matrix factorization (dNMF) framework optimizes registration with signal demixing, particularly useful for animals undergoing significant non-rigid motion, enhancing demixing accuracy. Furthermore, a b-spline active contour method is proposed for delineating and tracking neuronal cell bodies, overcoming challenges like optical overlap and movement in recordings of contractile tissues, such as the enteric nervous system, improving cell tracking during deformations and intensity changes. These methods collectively contribute to accurate and efficient neuron extraction for various research and application purposes.
What are the advantages and limitations of using BlazeMetrics PAT and Blaze900 Probes in chemistry research?
5 answers
BlazeMetrics PAT and Blaze900 Probes offer significant advantages in chemistry research. These probes provide potent, selective, and cell-permeable tools for target engagement and modulation, essential for validating drug targets. Additionally, chemical probes, including fluorogenic ones, play a crucial role in understanding complex biological processes like cancer, aiding in the detection of cancer-associated proteins and monitoring drug efficacy. However, it is crucial to note that the quality of chemical probes is paramount, as poor-quality probes can lead to misleading results. To address this, a community-driven wiki resource is being developed to enhance probe quality and best practices in biomedical research. Overall, while BlazeMetrics PAT and Blaze900 Probes offer valuable insights, ensuring probe quality and relevance is essential for accurate research outcomes.
How accurate are surface meteorological data in predicting precipitable water vapor?
5 answers
Surface meteorological data, combined with advanced modeling techniques, show high accuracy in predicting precipitable water vapor (PWV). Various methods like Random Forest Regression (RFR), Extreme Gradient Boosting Regression (XGBR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Long-Short-Term-Memory (LSTM) networkshave been employed. These models exhibit impressive results with low root mean square errors (RMSE) ranging from 0.0098 mm to 2.75 mm, showcasing their efficiency in PWV estimation. The use of GPS-based data and neural networks like RBFNNfurther enhances the accuracy of predictions. Overall, the integration of surface meteorological data with sophisticated modeling approaches proves to be a reliable method for accurately forecasting precipitable water vapor levels.