Genetic prediction of quantitative traits: a machine learner's guide focused on height

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics

Not Hot, but Sharp: Dissociation of Pinprick and Heat Perception in Snake Eye Appearance Myelopathy

Following a traumatic spinal cord injury, a 53-year-old male developed a central cord syndrome with at-level neuropathic pain. Magnetic resonance imaging revealed a classical “snake eye” appearance myelopathy, with marked hyperintensities at C5-C7. Clinical examination revealed intact pinprick sensation coupled with lost or diminished thermal/heat sensation. This dissociation could be objectively confirmed through multi-modal neurophysiological assessments. Specifically, contact heat evoked potentials were lost at-level, while pinprick evoked potentials were preserved. This pattern corresponds with that seen after surgical commissural myelotomy. To our knowledge, this is the first time such a dissociation has been objectively documented, highlighting the diagnostic potential of multi-modal neurophysiological assessments. In future studies, a comprehensive assessment of different nociceptive modalities may help elucidate the pathophysiology of neuropathic pain

Walking Outcome After Traumatic Paraplegic Spinal Cord Injury: The Function of Which Myotomes Makes a Difference?

BACKGROUND: Accurate prediction of walking function after a traumatic spinal cord injury (SCI) is crucial for an appropriate tailoring and application of therapeutical interventions. Long-term outcome of ambulation is strongly related to residual muscle function acutely after injury and its recovery potential. The identification of the underlying determinants of ambulation, however, remains a challenging task in SCI, a neurological disorder presented with heterogeneous clinical manifestations and recovery trajectories. OBJECTIVES: Stratification of walking function and determination of its most relevant underlying muscle functions based on stratified homogeneous patient subgroups. METHODS: Data from individuals with paraplegic SCI were used to develop a prediction-based stratification model, applying unbiased recursive partitioning conditional inference tree (URP-CTREE). The primary outcome was the 6-minute walk test at 6 months after injury. Standardized neurological assessments ≤15 days after injury were chosen as predictors. Resulting subgroups were incorporated into a subsequent node-specific analysis to attribute the role of individual lower extremity myotomes for the prognosis of walking function. RESULTS: Using URP-CTREE, the study group of 361 SCI patients was divided into 8 homogeneous subgroups. The node specific analysis uncovered that proximal myotomes L2 and L3 were driving factors for the differentiation between walkers and non-walkers. Distal myotomes L4-S1 were revealed to be responsible for the prognostic distinction of indoor and outdoor walkers (with and without aids). CONCLUSION: Stratification of a heterogeneous population with paraplegic SCI into more homogeneous subgroups, combined with the identification of underlying muscle functions prospectively determining the walking outcome, enable potential benefit for application in clinical trials and practice

Sensorimotor plasticity after spinal cord injury: a longitudinal and translational study

Objective The objective was to track and compare the progression of neuroplastic changes in a large animal model and humans with spinal cord injury. Methods A total of 37 individuals with acute traumatic spinal cord injury were followed over time (1, 3, 6, and 12 months post-injury) with repeated neurophysiological assessments. Somatosensory and motor evoked potentials were recorded in the upper extremities above the level of injury. In a reverse-translational approach, similar neurophysiological techniques were examined in a porcine model of thoracic spinal cord injury. Twelve Yucatan mini-pigs underwent a contusive spinal cord injury at T10 and tracked with somatosensory and motor evoked potentials assessments in the fore- and hind limbs pre- (baseline, post-laminectomy) and post-injury (10 min, 3 h, 12 weeks). Results In both humans and pigs, the sensory responses in the cranial coordinates of upper extremities/forelimbs progressively increased from immediately post-injury to later time points. Motor responses in the forelimbs increased immediately after experimental injury in pigs, remaining elevated at 12 weeks. In humans, motor evoked potentials were significantly higher at 1-month (and remained so at 1 year) compared to normative values. Conclusions Despite notable differences between experimental models and the human condition, the brain's response to spinal cord injury is remarkably similar between humans and pigs. Our findings further underscore the utility of this large animal model in translational spinal cord injury research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Lithium treatment extends human lifespan: findings from the UK Biobank

Lithium is a nutritional trace element that is also used pharmacologically for the management of bipolar and related psychiatric disorders. Recent studies have shown that lithium supplementation can extend health and lifespan in different animal models. Moreover, nutritional lithium uptake from drinking water was repeatedly found to be positively correlated with human longevity. By analyzing a large observational aging cohort (UK Biobank, n = 501,461 individuals) along with prescription data derived from the National Health Services (NHS), we here find therapeutic supplementation of lithium linked to decreased mortality (p = 0.0017) of individuals diagnosed with affective disorders. Subsequent multivariate survival analyses reveal lithium to be the strongest factor in regards to increased survival effects (hazard ratio = 0.274 [0.119-0.634 CI 95%, p = 0.0023]), corresponding to 3.641 times lower (95% CI 1.577-8.407) chances of dying at a given age for lithium users compared to users of other anti-psychotic drugs. While these results may further support the use of lithium as a geroprotective supplement, it should be noted that doses applied within the UK Biobank/NHS setting require close supervision by qualified medical professionals.ISSN:1945-458

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review

Background The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination. Objectives The aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra. Data sources Using PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification. Study eligibility criteria Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded. Methods A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted. Results From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets. Conclusions A growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.ISSN:1470-9465ISSN:1198-743