Validation of laboratory reports is the ultimate step before transmission of results to the clinician. The biologist checks the intrinsic consistency of the data as well as their possible medical value that is liable to lead to other investigations. Such a policy, when performed on all the data, is time-consuming, boring and uncertain. This step may be simplified by the use of a computerized expert system. The computer assisted validation system presented here concerns routine haematology data (Valab-haemato). Like its predecessor devoted to clinical chemistry (Valab-Biochem) it is based on the performance of a powerful inference engine which generates a decision-making tree for each report according to the data. This adaptability gives the system a capacity very close to human reasoning. In its haematology version the system deals with many variables including sex, age, origin of the patient (hospital ward), and the haematological data (blood cell count, differential, reticulocyte count, various information drawn from microscope examination of the blood smear as well as any report concerning the blood sample, erythrocyte sedimentation rate). Previous data are also taken into account, as well as the normal ranges, the values beyond which no result can be automatically validated and the delta-check. Some information definitely prevents validation of the results, others can be validated if they have been previously approved. Whereas the method of reasoning is fixed, all items are changeable in order to adapt the system to the type of activity of the laboratory.(ABSTRACT TRUNCATED AT 250 WORDS)
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Eur J Sport Sci
February 2025
Department of Sport and Health Sciences and Social Work, Oxford Brookes University, Oxford, UK.
Some technical limitations to using the eccentric mode to measure peak eccentric strength of the hamstrings (PTH) were raised. PTH also has limited validity to predict performance or injury risk factor. Therefore, our aim was to compare PTH and other isokinetic variables tested in the eccentric and passive modes.
View Article and Find Full Text PDFJ Anat
January 2025
Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.
Changes in the microstructure of the aortic wall precede the progression of various aortic pathologies, including aneurysms and dissection. Current clinical decisions with regards to surgical planning and/or radiological intervention are guided by geometric features, such as aortic diameter, since clinical imaging lacks tissue microstructural information. The aim of this proof-of-concept work is to investigate a non-invasive imaging method, diffusion tensor imaging (DTI), in ex vivo aortic tissue to gain insights into the microstructure.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation.
View Article and Find Full Text PDFSci Rep
January 2025
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Pathology, Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing, 100142, China.
Delta-like protein (DLL3) is a novel therapeutic target. DLL3 expression in gastroenteropancreatic neuroendocrine tumors (GEP-NECs) is poorly understood, complicating the distinction between well-differentiated neuroendocrine tumors G3 (NET G3) and poorly differentiated NEC. DLL3 immunohistochemistry (IHC) was performed on 248 primary GEP-NECs, correlating with clinicopathological parameters, NE markers, PD-L1, Ki67 index, and prognosis.
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January 2025
Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, 518000, Shenzhen, China.
Advancements in screening technologies employing small organisms have enabled deep profiling of compounds in vivo. However, current strategies for phenotyping of behaving animals, such as zebrafish, typically involve tedious manipulations. Here, we develop and validate a fully automated in vivo screening system (AISS) that integrates microfluidic technology and computer-vision-based control methods to enable rapid evaluation of biological responses of non-anesthetized zebrafish to molecular gradients.
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