Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression.

Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.

Microtubule organization within mitotic spindles revealed by serial block face scanning electron microscopy and image analysis.

Serial block face scanning electron microscopy (SBF-SEM) is a powerful method to analyze cells in 3D. Here, working at the resolution limit of the method, we describe a correlative light-SBF-SEM workflow to resolve microtubules of the mitotic spindle in human cells. We present four examples of uses for this workflow that are not practical by light microscopy and/or transmission electron microscopy. First, distinguishing closely associated microtubules within K-fibers; second, resolving bridging fibers in the mitotic spindle; third, visualizing membranes in mitotic cells, relative to the spindle apparatus; and fourth, volumetric analysis of kinetochores. Our workflow also includes new computational tools for exploring the spatial arrangement of microtubules within the mitotic spindle. We use these tools to show that microtubule order in mitotic spindles is sensitive to the level of TACC3 on the spindle.

Modelling the penumbra in Computed Tomography1.

BACKGROUND: In computed tomography (CT), the spot geometry is one of the main sources of error in CT images. Since X-rays do not arise from a point source, artefacts are produced. In particular there is a penumbra effect, leading to poorly defined edges within a reconstructed volume. Penumbra models can be simulated given a fixed spot geometry and the known experimental setup. OBJECTIVE: This paper proposes to use a penumbra model, derived from Beer's law, both to confirm spot geometry from penumbra data, and to quantify blurring in the image. METHODS: Two models for the spot geometry are considered; one consists of a single Gaussian spot, the other is a mixture model consisting of a Gaussian spot together with a larger uniform spot. RESULTS: The model consisting of a single Gaussian spot has a poor fit at the boundary. The mixture model (which adds a larger uniform spot) exhibits a much improved fit. The parameters corresponding to the uniform spot are similar across all powers, and further experiments suggest that the uniform spot produces only soft X-rays of relatively low-energy. CONCLUSIONS: Thus, the precision of radiographs can be estimated from the penumbra effect in the image. The use of a thin copper filter reduces the size of the effective penumbra.

Development and quality appraisal of a new English breast screening linked data set as part of the age, test threshold, and frequency of mammography screening (ATHENA-M) study.

OBJECTIVES: To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality. METHODS: Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness. RESULTS: Screening records were extracted from 76/79 English breast screening centres, 3/79 were not possible due to software issues. Data linkage was successful from 1997 after introduction of a universal identifier for women (NHS number). Prior to 1997 outcome data are incomplete due to linkage issues, reducing validity. Between January 1, 1997 and March 31, 2018, a total of 11 262 730 women were offered screening of whom 9 371 973 attended at least one appointment, with 139 million person-years of follow-up (a median of 12.4 person years for each woman included) with 73 810 breast cancer deaths and 1 111 139 any-cause deaths. Comparability to reference data sets and internal validity were demonstrated. Data completeness was high for core screening variables (>99%) and main cancer outcomes (>95%). CONCLUSIONS: The ATHENA-M project has created a large high-quality and representative data set of individual women's screening trajectories and outcomes in England from 1997 to 2018, data before 1997 are lower quality. ADVANCES IN KNOWLEDGE: This is the most complete data set of English breast screening records and outcomes constructed to date, which can be used to evaluate and optimize screening.

A Systematic Review and Meta-Analysis of Cerebrospinal Fluid Amyloid and Tau Levels Identifies Mild Cognitive Impairment Patients Progressing to Alzheimer's Disease.

Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) were evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD). A systematic review of the literature in PubMed and Web of Science (April 2021) was performed by a single researcher to identify studies reporting immunologically-based (xMAP or ELISA) measures of CSF analytes Aß(1-42) and/or P-tau and/or T-tau in clinical studies with at least two timepoints and a statement of diagnostic criteria. Of 1137 screened publications, 22 met the inclusion criteria for CSF Aß(1-42) measures, 20 studies included T-tau, and 17 included P-tau. Six meta-analyses were conducted to compare the analytes for healthy controls (HC) versus progressive MCI (MCI_AD) and for non-progressive MCI (Stable_MCI) versus MCI_AD; effect sizes were determined using random effects models. The heterogeneity of effect sizes across studies was confirmed with very high significance (p < 0.0001) for all meta-analyses except HC versus MCI_AD T-tau (p < 0.05) and P-tau (non-significant). Standard mean difference (SMD) was highly significant (p < 0.0001) for all comparisons (Stable_MCI versus MCI_AD: SMD [95%-CI] Aß(1-42) = 1.19 [0.96,1.42]; T-tau = −1.03 [−1.24,−0.82]; P-tau = −1.03 [−1.47,−0.59]; HC versus MCI_AD: SMD Aß(1-42) = 1.73 [1.39,2.07]; T-tau = −1.13 [−1.33,−0.93]; P-tau = −1.10 [−1.23,−0.96]). The follow-up interval in longitudinal evaluations was a critical factor in clinical study design, and the Aß(1−42)/P-tau ratio most robustly differentiated progressive from non-progressive MCI. The value of amyloid-beta and tau as markers of patient outcome are supported by these findings.

Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study.

BACKGROUND: Telemonitoring of circadian and sleep cycles could identify shift workers at increased risk of poor health, including cancer and cardiovascular diseases, thus supporting personalized prevention. METHODS: The Circadiem cross-sectional study aimed at determining early warning signals of risk of health alteration in hospital nightshifters (NS) versus dayshifters (DS, alternating morning and afternoon shifts). Circadian rhythmicity in activity, sleep, and temperature was telemonitored on work and free days for one week. Participants wore a bluetooth low energy thoracic accelerometry and temperature sensor that was wirelessly connected to a GPRS gateway and a health data hub server. Hidden Markov modelling of activity quantified Rhythm Index, rest quality (probability, p1-1, of remaining at rest), and rest duration. Spectral analyses determined periods in body surface temperature and accelerometry. Parameters were compared and predictors of circadian and sleep disruption were identified by multivariate analyses using information criteria-based model selection. Clusters of individual shift work response profiles were recognized. FINDINGS: Of 140 per-protocol participants (133 females), there were 63 NS and 77 DS. Both groups had similar median rest amount, yet NS had significantly worse median rest-activity Rhythm Index (0·38 [IQR, 0·29-0·47] vs. 0·69 [0·60-0·77], p<0·0001) and rest quality p1-1 (0·94 [0·94-0·95] vs 0·96 [0·94-0·97], p<0·0001) over the whole study week. Only 48% of the NS displayed a circadian period in temperature, as compared to 70% of the DS (p=0·026). Poor p1-1 was associated with nightshift work on both work (p<0·0001) and free days (p=0·0098). The number of years of past night work exposure predicted poor rest-activity Rhythm Index jointly with shift type, age and chronotype on workdays (p= 0·0074), and singly on free days (p=0·0005). INTERPRETATION: A dedicated analysis toolbox of streamed data from a wearable device identified circadian and sleep rhythm markers, that constitute surrogate candidate endpoints of poor health risk in shift-workers. FUNDING: French Agency for Food, Environmental and Occupational Health & Safety (EST-2014/1/064), University of Warwick, Medical Research Council (United Kingdom, MR/M013170), Cancer Research UK(C53561/A19933).

Data management challenges for artificial intelligence in plant and agricultural research.

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

RET-mediated gene expression pattern is affected by isoform but not oncogenic mutation.

The inherited cancer syndrome multiple endocrine neoplasia type 2 (MEN 2) is caused by mutations of the RET receptor tyrosine kinase and is characterized by medullary thyroid carcinoma. MEN 2 subtypes have distinct mutational spectrums and vary in severity. The most severe disease subtype, MEN 2B, is associated with a specific RET mutation (M918T) that has been predicted to alter downstream signaling and target gene expression patterns. We used gene expression microarray analysis to identify target genes modulated by RET. We compared two oncogenic RET mutants, associated with MEN 2A (2ARET) or MEN 2B (2BRET) disease subtypes, that are predicted to have distinct downstream target genes. We showed that overall, 2ARET and 2BRET modulated genes with similar functional ontologies. Further, when we validated our microarray data by quantitative real time PCR, we did not detect major differences in gene expression associated with these mutants when differences in receptor activity levels were considered. We did, however, detect differences in gene expression induced by two RET COOH-terminal isoforms, RET9 and RET51, irrespective of the RET form present (wildtype, 2ARET, or 2BRET). Our data suggest that similar transcriptional programs contribute to all forms of MEN 2 but that differences in target gene expression may contribute to developmental pattern differences observed between RET isoforms.

Regional segmentation strategy for DTI analysis of human corpus callosum indicates motor function deficit in mild cognitive impairment.

BACKGROUND: The corpus callosum is the largest white matter tract in the human brain, involved in inter-hemispheric transfer and integration of lateralised visual, sensory-motor, language, and cognitive information. Microstructural alterations are implicated in ageing as well as various neurological conditions. NEW METHOD: Cross-sectional diffusion-weighted images of 107 healthy adults were used to create a linear regression model of the ageing corpus callosum and its sub-regions to evaluate the impact of analysis by sub-region, and to test for deviations from healthy ageing parameters in 28 subjects with mild cognitive impairment (MCI). Alterations in diffusion properties including fractional anisotropy, mean, radial and axial diffusivities were investigated as a function of age. RESULTS: Changes in DTI parameters showed age-dependent regional differences, likely arising from axonal diameter variation across cross-sectional regions of interest in the corpus callosum. Patterns suggestive of degeneration with healthy ageing were observed in all regions. Diffusion parameters in sub-regions projecting to pre-motor, primary, and supplementary motor areas of the brain differed for MCI versus healthy controls, and MCI subjects were more likely than healthy controls to experience a reduction in motor skills. COMPARISON WITH EXISTING METHODS: Statistical analyses of the corpus callosum by five manually-defined sub-regions, instead of a single manually-defined region of interest, revealed region-specific changes in microstructure in healthy ageing and MCI, and accounted for clinically-evaluated differences in motor skills between cohorts. CONCLUSION: This method will support future studies of corpus callosum, enabling identification and measurement of white matter changes that are undetectable with the single ROI approach.

Tele-Monitoring of Cancer Patients' Rhythms during Daily Life Identifies Actionable Determinants of Circadian and Sleep Disruption.

The dichotomy index (I < O), a quantitative estimate of the circadian regulation of daytime activity and sleep, predicted overall cancer survival and emergency hospitalization, supporting its integration in a mHealth platform. Modifiable causes of I < O deterioration below 97.5%-(I < O)low-were sought in 25 gastrointestinal cancer patients and 33 age- and sex-stratified controls. Rest-activity and temperature were tele-monitored with a wireless chest sensor, while daily activities, meals, and sleep were self-reported for one week. Salivary cortisol rhythm and dim light melatonin onset (DLMO) were determined. Circadian parameters were estimated using Hidden Markov modelling, and spectral analysis. Actionable predictors of (I < O)low were identified through correlation and regression analyses. Median compliance with protocol exceeded 95%. Circadian disruption-(I < O)low-was identified in 13 (52%) patients and four (12%) controls (p = 0.002). Cancer patients with (I < O)low had lower median activity counts, worse fragmented sleep, and an abnormal or no circadian temperature rhythm compared to patients with I < O exceeding 97.5%-(I < O)high-(p < 0.012). Six (I < O)low patients had newly-diagnosed sleep conditions. Altered circadian coordination of rest-activity and chest surface temperature, physical inactivity, and irregular sleep were identified as modifiable determinants of (I < O)low. Circadian rhythm and sleep tele-monitoring results support the design of specific interventions to improve outcomes within a patient-centered systems approach to health care.

An assessment of candidate genes to assist prognosis in gastric cancer.

BACKGROUND: Gastric cancer (GC) is the fourth commonest cancer worldwide, with the second highest mortality rate. Its poor mortality is linked to delayed presentation. There is a drive towards non-invasive biomarker screening and monitoring of many different types of cancer, although with limited success so far. We aimed to determine if any genes from a 32-gene panel could be used to determine GC prognosis. METHODS: We carried out a retrospective study on the expression of 32 genes, selected for their proven or potential links to GC, on historic formalin fixed paraffin-embedded (FFPE) GC specimens from our unit. Gene expression was measured using quantitative nuclease protection assays (qNPA) technology. Following statistical analysis of the results, immunohistochemical staining for eight genes, both discriminating and non-discriminating, was conducted in seven age and sex matched non-metastatic: metastatic GC pairings. The stained samples were reviewed by two blinded consultant histopathologists. RESULTS: Multivariate Cox analysis of the gene expression data revealed metastatic status, age, sex and five genes appeared to influence GC survival. Genes negatively influencing survival included BCAS1, P53 and HSP90AA1 (relative risks 2.20, 3.73 and 7.53 respectively). Genes conveying survival benefit included CASP3 and TERT (relative risks 0.10 and 0.24 respectively). Immunohistochemical staining of seven age and sex matched non-metastatic: metastatic pairs revealed no association between gene expression and protein expression. CONCLUSIONS: Our study found several genes whose expression may affect GC prognosis. However, immunohistochemical analysis revealed no association between gene expression and protein expression. It remains to be determined whether gene expression or protein expression are reliable means of assessing GC prognosis.

Neuron-specific mRNA complexity responses during hippocampal apoptosis after traumatic brain injury.

In an effort to understand the complexity of genomic responses within selectively vulnerable regions after experimental brain injury, we examined whether single apoptotic neurons from both the CA3 and dentate differed from those in an uninjured brain. The mRNA from individual active caspase 3(+)/terminal deoxynucleotidyl transferase-mediated biotinylated UTP nick end labeling [TUNEL(-)] and active caspase 3(+)/TUNEL(+) pyramidal and granule neurons in brain-injured mice were amplified and compared with those from nonlabeled neurons in uninjured brains. Gene analysis revealed that overall expression of mRNAs increased with activation of caspase 3 and decreased to below uninjured levels with TUNEL reactivity. Cell type specificity of the apoptotic response was observed with both regionally distinct expression of mRNAs and differences in those mRNAs that were maximally regulated. Immunohistochemical analysis for two of the most highly differentially expressed genes (prion and Sos2) demonstrated a correlation between the observed differential gene expression after traumatic brain injury and corresponding protein translation.

Differential expression of cell-cycle regulatory proteins defines distinct classes of follicular lymphoma.

Follicular lymphoma, a relatively common neoplasm of mature B lymphocytes, generally pursues an indolent clinical course. The disease is biologically heterogeneous, however, and aggressive instances associated with short survival are relatively common. Because defects in the regulation of apoptotic cell death are fundamental in follicular lymphoma pathogenesis, we hypothesized that deregulated expression of components of the Rb signaling pathway may promote cell proliferation, thereby complementing antecedent antiapoptotic mutations and producing more aggressive disease. We determined the differential expression of key cell-cycle regulatory proteins in lymphoma cells by incorporating formalin-fixed, paraffin-embedded samples from the initial, diagnostic biopsies from 127 cases of follicular lymphoma into tissue microarrays, histologic sections of which were stained by immunohistochemistry for p53, pRb, p16(INK4a), and cyclin D3. The results were ascertained by visual inspection and then correlated with histopathological and clinical parameters, including overall survival. Our findings show that increased abundance of p53 or p16(INK4a) is associated with reduced overall survival and conventional pathological markers of tumor aggressiveness including high histologic grade. Therefore, subjective quantification of cell-cycle regulatory proteins by immunohistochemistry can identify biologically and clinically distinct subsets of follicular lymphoma cases.