Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards), 2013. Scientific Opinion on the public health hazards to be covered by inspection of meat (bovine animals).

A risk ranking process identified Salmonella spp. and pathogenic verocytotoxin-producing Escherichia coli (VTEC) as current high-priority biological hazards for meat inspection of bovine animals. As these hazards are not detected by traditional meat inspection, a meat safety assurance system for the farm-to-chilled carcass continuum using a risk-based approach was proposed. Key elements of the system are risk-categorisation of slaughter animals for high-priority biological hazards based on improved food chain information, as well as risk-categorisation of slaughterhouses according to their capability to control those hazards. Omission of palpation and incision during post-mortem inspection for animals subjected to routine slaughter may decrease spreading and cross-contamination with the high-priority biological hazards. For chemical hazards, dioxins and dioxin-like polychlorinated biphenyls were ranked as being of high potential concern; all other substances were ranked as of medium or lower concern. Monitoring programmes for chemical hazards should be more flexible and based on the risk of occurrence, taking into account the completeness and quality of the food chain information supplied and the ranking of chemical substances, which should be regularly updated to include new hazards. Control programmes across the food chain, national residue control programmes, feed control and monitoring of environmental contaminants should be better integrated. Meat inspection is a valuable tool for surveillance and monitoring of animal health and welfare conditions. Omission of palpation and incision would reduce detection effectiveness for bovine tuberculosis and would have a negative impact on the overall surveillance system especially in officially tuberculosis free countries. The detection effectiveness for bovine cysticercosis, already low with the current meat inspection system, would result in a further decrease, if palpation and incision are removed. Extended use of food chain information could compensate for some, but not all, the information on animal health and welfare lost if only visual post-mortem inspection is applied

Custom design of protein particles as multifunctional biomaterials

Assembled protein particles, as emerging biomaterials, have broad applications ranging from vaccines and drug delivery to biocatalysis and particle tracking, but to date these require trial-and-error rational design experimentation and/or intensive computational methods to generate. Here, the authors describe an easy-to-implement engineering strategy to generate customized protein particles as multifunctional biomaterials. They utilize protein–peptide modules to generate functional nanoparticles whose assembly and size is controlled by the addition of mild stimuli. The protein assembling method is versatile, as exemplified through particle formation with 7 distinct protein modules, using a variety of assembly conditions tailored by the chemistries of 3 peptide partners. They have generated customized protein particles using enzymes, binding and reporter proteins, and their functions and utilities are demonstrated using biocatalysis, sensing, and labelling applications, respectively. Furthermore, co-assembly with two functional proteins within one particle has been successfully achieved and demonstrated. Physical insights into the kinetics and molecular mechanisms of particle formation are revealed by small angle X-ray scattering and mass photometry, providing fundamental knowledge to guide design and manufacture these interesting biomaterials in future. Their protein assembling strategy is a reliable method for fabricating a protein particle to deliver new functionalities on-demand