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Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls
Large tolerance for incidence angle in Terahertz parametric detection
A 22 month prospective assessment of neutralizing and IgG antibody levels against SARS-CoV-2 variants following homologous and heterologous BNT162b2 boosting
A graph-based computational approach for modeling physicochemical properties in drug design
Integrated artificial intelligence in healthcare and the patient’s experience of care
Determination of bisphenol A in different types of soft drink samples by MSPE-GC/MS technique and contribution to risk assessment
Clinical characteristics and comparative analysis of PD-1/PD-L1 inhibitor-induced pituitary dysfunction versus other pituitary disorders
The effects of frailty, mental health, and cardiac function on quality of life in patients undergoing transcatheter aortic valve replacement
HIPK4 accelerates cutaneous squamous cell carcinoma progression by phosphorylating TAp63 and inhibiting EFEMP1 expression
A merged fuzzy system and neural network for improving management method and strategy in scientific research and education
Incidence of bone loss in primary teeth: a retrospective analysis of contributing factors
Multiscale wavelet attention convolutional network for facial expression recognition
Study on the impact of new quality productive forces on agricultural green production efficiency
Synergistic strategy of riboflavin and lipoids to bioengineer resin dentin hybrid layer
Experimental analysis of hydraulic fracturing for fracture network formation in coal beds
Abstract Understanding the distribution of multi-scale hydraulic fractures (HFs) is critical to improving coal bed methane (CBM) production. The HFs range from metres, centimetres to millimetres are revealed through coal mining face, X-ray CT, and stereoscope. The hydraulic fracturing curves can be categorised as descending, horizontal, ascending and fluctuating. While descending and horizontal types exhibited more effective hydraulic fracturing compared with ascending and fluctuating types. Macroscopic fractures are predominantly horizontal, vertical, X and T shaped. Closer to the CBM wellbore, the macroscopic fractures are more closely spaced and show greater connectivity with the bedding planes of various coal rock layers. During hydraulic fracturing, the fracturing fluid expands selectively along weak surfaces, such as joints in coal seams, leading to the formation of main fractures. Under high fracturing fluid pressure, HFs can penetrate various maceral specification layers and even propagate through coal gangue. Quartz sand embedded in the coal can trigger millimetre-scale HFs while remaining open. The development of multi-scale HFs is influenced by factors such as coal structure, the roof and floor strength, geo-stress, hydraulic fracturing design parameters, quartz sand and coal fines.
In-silico analysis of potential phytochemicals targeting mitogen activating protein kinase-14 (MAPK14) gene in colorectal cancer
Evaluating the InSignia IFI27 expression assay for detecting viral respiratory infection compared to a traditional gene normalisation assay
Abstract Host gene expression is crucial for understanding disease progression and developing diagnostic biomarkers. Previously, we identified a novel immune biomarker IFI27, validated with routine RT-qPCR methods employed in a research setting, that discriminates between influenza and bacteria in patients with suspected respiratory infection. This study aimed to assess the In Signia method, which employs a novel gene normalization technique to yield a variable transcript analysis (VITA) index. The VITA index measures gene expression relative to a non-transcribed region of DNA, such that it is independent of sample quality or quantity. We compared IFI27 gene expression measured by the In Signia assay to that of the research assay in blood samples collected from patients with respiratory diseases and SARS-CoV-2 vaccinated individuals. The study found a strong correlation and acceptable agreement between traditional ΔCq methods and In Signia for IFI27 levels in the higher range (log(ΔCq)Research > 1), but not for IFI27 expression levels below this range, likely due to the different normalization strategies. Notably the In Signia assay was more sensitive in detecting viral infection among hospital patients. These findings suggest that the In Signia assay, which supports high throughput workflows, may be used for the rapid detection of viral infection in patients with respiratory symptoms.