A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer's disease.

BACKGROUND: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We assessed whether compensating the circumpapillary retinal nerve fiber layer (cpRNFL) thickness for multiple demographic and anatomical factors as well as the combination of macular layers improves the detection of MCI and AD. METHODS: This cross-sectional study of 62 AD (n = 92 eyes), 108 MCI (n = 158 eyes), and 55 cognitively normal control (n = 86 eyes) participants. Macular ganglion cell complex (mGCC) thickness was extracted. Circumpapillary retinal nerve fiber layer (cpRNFL) measurement was compensated for several ocular factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between the groups. The main outcome measure was OCT thickness measurements. RESULTS: Participants with MCI/AD showed significantly thinner measured and compensated cpRNFL, mGCC, and altered retinal vessel density (p < 0.05). Compensated RNFL outperformed measured RNFL for discrimination of MCI/AD (AUC = 0.74 vs 0.69; p = 0.026). Combining macular and compensated cpRNFL parameters provided the best detection of MCI/AD (AUC = 0.80 vs 0.69; p < 0.001). CONCLUSIONS AND RELEVANCE: Accounting for interindividual variations of ocular anatomical features in cpRNFL measurements and incorporating macular information may improve the identification of high-risk individuals with early cognitive impairment.

Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives.

PURPOSE: Precision oncology, such as next generation sequencing (NGS) molecular analysis and bioinformatics are used to guide targeted therapies. The laboratory turnaround time (TAT) is a key performance indicator of laboratory performance. This study aims to formally apply statistical process control (SPC) methods such as CUSUM and EWMA to a precision medicine programme to analyze the learning curves of NGS and bioinformatics processes. PATIENTS AND METHODS: Trends in NGS and bioinformatics TAT were analyzed using simple regression models with TAT as the dependent variable and chronologically-ordered case number as the independent variable. The M-estimator "robust" regression and negative binomial regression were chosen to serve as sensitivity analyses to each other. Next, two popular statistical process control (SPC) approaches which are CUSUM and EWMA were utilized and the CUSUM log-likelihood ratio (LLR) charts were also generated. All statistical analyses were done in Stata version 16.0 (StataCorp), and nominal P < 0.05 was considered to be statistically significant. RESULTS: A total of 365 patients underwent successful molecular profiling. Both the robust linear model and negative binomial model showed statistically significant reductions in TAT with accumulating experience. The EWMA and CUSUM charts of overall TAT largely corresponded except that the EWMA chart consistently decreased while the CUSUM analyses indicated improvement only after a nadir at the 82nd case. CUSUM analysis found that the bioinformatics team took a lower number of cases (54 cases) to overcome the learning curve compared to the NGS team (85 cases). CONCLUSION: As NGS and bioinformatics lead precision oncology into the forefront of cancer management, characterizing the TAT of NGS and bioinformatics processes improves the timeliness of data output by potentially spotlighting problems early for rectification, thereby improving care delivery.

Gut microbiota changes in children with autism spectrum disorder: a systematic review.

BACKGROUND: As more animal studies start to disentangle pathways linking the gut microbial ecosystem and neurobehavioral traits, human studies have grown rapidly. Many have since investigated the bidirectional communication between the gastrointestinal tract and the central nervous system, specifically on the effects of microbial composition on the brain and development. METHODS: Our review at the initial stage aimed to evaluate literature on gut microbial alterations in pediatric neurobehavioral conditions. We searched five literature databases (Embase, PubMed, PsychInfo, Scopus, and Medline) and found 4489 published work. As the mechanisms linking gut microbiota to these conditions are divergent, the scope of this review was narrowed to focus on describing gut dysbiosis in children with autism spectrum disorder (ASD). RESULTS: Among the final 26 articles, there was a lack of consistency in the reported gut microbiome changes across ASD studies, except for distinguishable patterns, within limits, for Prevotella, Firmicutes at the phylum level, Clostridiales clusters including Clostridium perfringens, and Bifidobacterium species. CONCLUSIONS: These results were inadequate to confirm a global microbiome change in children with ASD and causality could not be inferred to explain the etiology of the behaviors associated with ASD. Mechanistic studies are needed to elucidate the specific role of the gut microbiome in the pathogenesis of ASD.