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Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.
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BACKGROUND: The long-term impact of deep brain stimulation (DBS) on Parkinson's disease (PD) is difficult to assess and has not yet been rigorously evaluated in comparison to its natural history. OBJECTIVE: Comparison of key disability milestones (recurrent falls, psychosis, dementia, and institutionalization) and death in patients with PD with versus without DBS. METHODS: We collected retrospective information from clinical notes of patients with PD at our center that were implanted with subthalamic DBS >8 years ago (1999-2010) and a control group of PD patients without DBS similar in age at onset, age at baseline, sex distribution, and number of comorbidities at baseline (extracted from a registry study performed in 2004). Cox regression models were used to calculate hazard ratios, adjusted for potential baseline confounding variables (age, sex, disease duration, disease severity, and number of comorbidities). RESULTS: A total of 74 DBS-treated and 61 control patients with PD were included. For a median observational period of 14 years, patients treated with DBS were at lower risk of experiencing recurrent falls (hazard ratio = 0.57; 95% confidence interval, 0.37-0.90; P = 0.015) and psychosis (hazard ratio = 0.26; 95% confidence interval, 0.12-0.59; P = 0.001) compared with control patients. There was no significant difference in risk for dementia, institutionalization, or death. Disease progression as assessed by Hoehn and Yahr scores was not slower in DBS-treated patients. CONCLUSIONS: Treatment with chronic subthalamic DBS was associated with lower risk for recurrent falls and psychotic symptoms, effects that may be mediated through improved motor symptom control and reduction in dopaminergic therapies, respectively. There was no evidence for DBS effects on underlying disease progression.
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BACKGROUND: Putaminal diffusivity in brain magnetic resonance diffusion-weighted imaging (DWI) is increased in patients with the parkinsonian variant of multiple system atrophy (MSA-P) compared to Parkinson disease (PD) patients. PURPOSE: We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of DWI to distinguish MSA-P from PD. METHODS: Studies on DWI were identified through a systematic PubMed and Clarivate Analytics® Web of Science® Core Collection search. Papers were selected based on stringent inclusion criteria; minimum requirement was the inclusion of MSA-P and PD patients and documented true positive, true negative, false positive and false negative rates or overall sample size and reported sensitivity and specificity. Meta-analysis was performed using the hierarchical summary receiver operating characteristics curve approach. RESULTS: The database search yielded 1678 results of which 9 studies were deemed relevant. Diagnostic accuracy of putaminal diffusivity measurements were reported in all of these 9 studies, whereas results of other regions of interest were only reported irregularly. Therefore, a meta-analysis could only be performed for putaminal diffusivity measurements: 127 patients with MSA-P, 262 patients with PD and 70 healthy controls were included in the quantitative synthesis. The meta-analysis showed an overall sensitivity of 90% (95% confidence interval (CI): 76.7%-95.8%) and an overall specificity of 93% (95% CI: 80.0%-97.7%) to distinguish MSA-P from PD based on putaminal diffusivity. CONCLUSION: Putaminal diffusivity yields high sensitivity and specificity to distinguish clinically diagnosed patients with MSA-P from PD. The confidence intervals indicate substantial variability. Further multicenter studies with harmonized protocols are warranted particularly in early disease stages when clinical diagnosis is less certain.
