The discovery and reliable detection of markers for neurodegenerative diseases have been complicated by the inaccessibility of the diseased tissue- such as the inability to biopsy or test tissue from the central nervous system directly. RNAs originating from hard to access tissues, such as neurons within the brain and spinal cord, have the potential to get to the periphery where they can be detected non-invasively. The formation and extracellular release of microvesicles and RNA binding proteins have been found to carry RNA from cells of the central nervous system to the periphery and protect the RNA from degradation. Extracellular miRNAs detectable in peripheral circulation can provide information about cellular changes associated with human health and disease. In order to associate miRNA signals present in cell-free peripheral biofluids with neurodegenerative disease status of patients with Alzheimer's and Parkinson's diseases, we assessed the miRNA content in cerebrospinal fluid and serum from postmortem subjects with full neuropathology evaluations. We profiled the miRNA content from 69 patients with Alzheimer's disease, 67 with Parkinson's disease and 78 neurologically normal controls using next generation small RNA sequencing (NGS). We report the average abundance of each detected miRNA in cerebrospinal fluid and in serum and describe 13 novel miRNAs that were identified. We correlated changes in miRNA expression with aspects of disease severity such as Braak stage, dementia status, plaque and tangle densities, and the presence and severity of Lewy body pathology. Many of the differentially expressed miRNAs detected in peripheral cell-free cerebrospinal fluid and serum were previously reported in the literature to be deregulated in brain tissue from patients with neurodegenerative disease. These data indicate that extracellular miRNAs detectable in the cerebrospinal fluid and serum are reflective of cell-based changes in pathology and can be used to assess disease progression and therapeutic efficacy.

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

Acute malnutrition accounts for an immense disease burden and is implicated as a key, underlying cause of child mortality in low resource settings. Child wasting, defined as weight-for-length more than 2 standard deviations below international standards, is a leading indicator to measure the Sustainable Development Goal target to end malnutrition by 2030. Prevailing methods to measure wasting rely on cross-sectional surveys that are unable to measure onset, recovery, and persistence - key features of wasting epidemiology that could inform preventive interventions and disease burden estimates. Here, we show through an analysis of 18 longitudinal cohorts that child wasting is a highly dynamic process of incident onset and recovery, and that peak incidence is between birth and 3 months - far earlier than peak prevalence at 12-15 months. By age 24 months the proportion of children who had ever experienced a wasting episode (33%) was more than 5-fold higher than prevalence (6%), suggesting that the wasting burden is likely far higher than cross-sectional surveys suggest. Seasonally driven changes in population mean weight-for-length were large (>0.5 z in some cohorts) and were synchronous with rainfall across diverse settings, creating potential for seasonally targeted interventions. Our results motivate a new focus on extending preventive interventions for child wasting to pregnant and lactating mothers, and for preventive and therapeutic interventions to include children below age 6 months in addition to current targets of ages 6-59 months.

Recent evidence suggests that microRNAs, small, non-coding RNA molecules that regulate gene expression, may play a role in the regulation of metabolic disorders, including nonalcoholic fatty liver disease (NAFLD). To identify miRNAs that mediate NAFLD-related fibrosis, we used high-throughput sequencing to assess miRNAs obtained from liver biopsies of 15 individuals without NAFLD fibrosis (F0) and 15 individuals with severe NAFLD fibrosis or cirrhosis (F3-4), matched for age, sex, BMI, T2D status, HbA1c, and use of diabetes medications. We used DESeq2 and Kruskal-Wallis test to identify miRNAs that were differentially expressed between NAFLD patients with or without fibrosis, adjusting for multiple testing using Bonferroni correction. We identified a total of 75 miRNAs showing statistically significant evidence (adjusted P-value <0.05) for differential expression between the two groups, including 30 upregulated and 45 downregulated miRNAs. Quantitative reverse-transcription PCR analysis of selected miRNAs identified by sequencing validated nine out of 11 of the top differentially expressed miRNAs. We performed functional enrichment analysis of dysregulated miRNAs and identified several potential gene targets related to NAFLD-related fibrosis including hepatic fibrosis, hepatic stellate cell activation, TGFB signaling, and apoptosis signaling. We identified FOXO3 and FBXW7 as potential targets of miR-182, and found that levels of FOXO3, but not FBXW7, were significantly decreased in fibrotic samples. These findings support a role for hepatic miRNAs in the pathogenesis of NAFLD-related fibrosis and yield possible new insight into the molecular mechanisms underlying the initiation and progression of liver fibrosis and cirrhosis.

Extracellular RNAs (exRNAs) have been identified in all tested biofluids and have been associated with a variety of extracellular vesicles, ribonucleoprotein complexes and lipoprotein complexes. Much of the interest in exRNAs lies in the fact that they may serve as signalling molecules between cells, their potential to serve as biomarkers for prediction and diagnosis of disease and the possibility that exRNAs or the extracellular particles that carry them might be used for therapeutic purposes. Among the most significant bottlenecks to progress in this field is the lack of robust and standardized methods for collection and processing of biofluids, separation of different types of exRNA-containing particles and isolation and analysis of exRNAs. The Sample and Assay Standards Working Group of the Extracellular RNA Communication Consortium is a group of laboratories funded by the U.S. National Institutes of Health to develop such methods. In our first joint endeavour, we held a series of conference calls and in-person meetings to survey the methods used among our members, placed them in the context of the current literature and used our findings to identify areas in which the identification of robust methodologies would promote rapid advancements in the exRNA field.