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

A review of mechanistic learning in mathematical oncology

Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology. We highlight the synergistic potential of knowledge-driven mechanistic mathematical modeling and data-driven modeling, such as machine and deep learning. We point out similarities and differences regarding model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Then, organizing combinations of knowledge- and data-driven modeling into four categories (sequential, parallel, intrinsic, and extrinsic mechanistic learning), we summarize a variety of approaches at the interface between purely data- and knowledge-driven models. Using examples predominantly from oncology, we discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. We see that mechanistic learning, with its intentional leveraging of the strengths of both knowledge and data-driven modeling, can greatly impact the complex problems of oncology. Given the increasing ubiquity and impact of machine learning, it is critical to incorporate it into the study of mathematical oncology with mechanistic learning providing a path to that end. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input dat

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

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness)

Tracking Changes in Neuropathic Pain After Acute Spinal Cord Injury

Neuropathic pain represents a primary detrimental outcome of spinal cord injury. A major challenge facing effective management is a lack of surrogate measures to examine the physiology and anatomy of neuropathic pain. To this end, we investigated the relationship between psychophysical responses to tonic heat stimulation and neuropathic pain rating after traumatic spinal cord injury. Subjects provided a continuous rating to 2 min of tonic heat at admission to rehabilitation and again at discharge. Adaptation, temporal summation of pain, and modulation profile (i.e., the relationship between adaptation and temporal summation of pain) were extracted from tonic heat curves for each subject. There was no association between any of the tonic heat outcomes and neuropathic pain severity at admission. The degree of adaptation, the degree of temporal summation of pain, and the modulation profile did not change significantly from admission to discharge. However, changes in modulation profiles between admission and discharge were significantly correlated with changes in neuropathic pain severity (p = 0.027; R2 = 0.323). The modulation profile may represent an effective measure to track changes in neuropathic pain severity from early to later stages of spinal cord injury

Exercise and aerobic capacity in individuals with spinal cord injury:A systematic review with meta-analysis and meta-regression

BACKGROUND: A low level of cardiorespiratory fitness [CRF; defined as peak oxygen uptake (V̇O2peak) or peak power output (PPO)] is a widely reported consequence of spinal cord injury (SCI) and a major risk factor associated with chronic disease. However, CRF can be modified by exercise. This systematic review with meta-analysis and meta-regression aimed to assess whether certain SCI characteristics and/or specific exercise considerations are moderators of changes in CRF.METHODS AND FINDINGS: Databases (MEDLINE, EMBASE, CENTRAL, and Web of Science) were searched from inception to March 2023. A primary meta-analysis was conducted including randomised controlled trials (RCTs; exercise interventions lasting &gt;2 weeks relative to control groups). A secondary meta-analysis pooled independent exercise interventions &gt;2 weeks from longitudinal pre-post and RCT studies to explore whether subgroup differences in injury characteristics and/or exercise intervention parameters explained CRF changes. Further analyses included cohort, cross-sectional, and observational study designs. Outcome measures of interest were absolute (AV̇O2peak) or relative V̇O2peak (RV̇O2peak), and/or PPO. Bias/quality was assessed via The Cochrane Risk of Bias 2 and the National Institute of Health Quality Assessment Tools. Certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Random effects models were used in all meta-analyses and meta-regressions. Of 21,020 identified records, 120 studies comprising 29 RCTs, 67 pre-post studies, 11 cohort, 7 cross-sectional, and 6 observational studies were included. The primary meta-analysis revealed significant improvements in AV̇O2peak [0.16 (0.07, 0.25) L/min], RV̇O2peak [2.9 (1.8, 3.9) mL/kg/min], and PPO [9 (5, 14) W] with exercise, relative to controls (p &lt; 0.001). Ninety-six studies (117 independent exercise interventions comprising 1,331 adults with SCI) were included in the secondary, pooled meta-analysis which demonstrated significant increases in AV̇O2peak [0.22 (0.17, 0.26) L/min], RV̇O2peak [2.8 (2.2, 3.3) mL/kg/min], and PPO [11 (9, 13) W] (p &lt; 0.001) following exercise interventions. There were subgroup differences for RV̇O2peak based on exercise modality (p = 0.002) and intervention length (p = 0.01), but there were no differences for AV̇O2peak. There were subgroup differences (p ≤ 0.018) for PPO based on time since injury, neurological level of injury, exercise modality, and frequency. The meta-regression found that studies with a higher mean age of participants were associated with smaller changes in AV̇O2peak and RV̇O2peak (p &lt; 0.10). GRADE indicated a moderate level of certainty in the estimated effect for RV̇O2peak, but low levels for AV̇O2peak and PPO. This review may be limited by the small number of RCTs, which prevented a subgroup analysis within this specific study design.CONCLUSIONS: Our primary meta-analysis confirms that performing exercise &gt;2 weeks results in significant improvements to AV̇O2peak, RV̇O2peak, and PPO in individuals with SCI. The pooled meta-analysis subgroup comparisons identified that exercise interventions lasting up to 12 weeks yield the greatest change in RV̇O2peak. Upper-body aerobic exercise and resistance training also appear the most effective at improving RV̇O2peak and PPO. Furthermore, acutely injured, individuals with paraplegia, exercising for ≥3 sessions/week will likely experience the greatest change in PPO. Ageing seemingly diminishes the adaptive CRF responses to exercise training in individuals with SCI.REGISTRATION: PROSPERO: CRD42018104342.</p

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset

STUDY DESIGN Retrospective data analysis. OBJECTIVES This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs. METHODS We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA). RESULTS Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively. CONCLUSIONS In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution

Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis

Introduction Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19). Methods EMBASE, PubMed/ Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I 2 , Tau2 , and Cochrane Q, sensitivity analyses, and assessed publication bias. Results: 148 studies met the inclusion criteria for the systematic review and meta-analysis with 12’149 patients (5’739 female) and a median age of 47.0 [35.0-64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78- 1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I 2 ; range: 1.5-98.2%). Publication bias was only found in eight studies (Egger’s test: p < 0.05). Conclusions: Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19

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