Serum peptidome: diagnostic window into pathogenic processes following occupational exposure to carbon nanomaterials

Background Growing industrial use of carbon nanotubes and nanofibers (CNT/F) warrants consideration of human health outcomes. CNT/F produces pulmonary, cardiovascular, and other toxic effects in animals along with a significant release of bioactive peptides into the circulation, the augmented serum peptidome. While epidemiology among CNT/F workers reports on few acute symptoms, there remains concern over sub-clinical CNT/F effects that may prime for chronic disease, necessitating sensitive health outcome diagnostic markers for longitudinal follow-up. Methods Here, the serum peptidome was assessed for its biomarker potential in detecting sub-symptomatic pathobiology among CNT/F workers using label-free data-independent mass spectrometry. Studies employed a stratified design between High (> 0.5 µg/m3) and Low (< 0.1 µg/m3) inhalable CNT/F exposures in the industrial setting. Peptide biomarker model building and refinement employed linear regression and partial least squared discriminant analyses. Top-ranked peptides were then sequence identified and evaluated for pathological-relevance. Results In total, 41 peptides were found to be highly discriminatory after model building with a strong linear correlation to personal CNT/F exposure. The top-five peptide model offered ideal prediction with high accuracy (Q2 = 0.99916). Unsupervised validation affirmed 43.5% of the serum peptidomic variance was attributable to CNT/F exposure. Peptide sequence identification reveals a predominant association with vascular pathology. ARHGAP21, ADAM15 and PLPP3 peptides suggest heightened cardiovasculature permeability and F13A1, FBN1 and VWDE peptides infer a pro-thrombotic state among High CNT/F workers. Conclusions The serum peptidome affords a diagnostic window into sub-symptomatic pathology among CNT/F exposed workers for longitudinal monitoring of systemic health risks. Graphical abstract

Identifying opportunities for timely diagnosis through patterns of primary care tests in patients with bladder and renal cancer: a longitudinal linked data study

Background: Understanding pre-diagnostic test use could reveal diagnostic windows where more timely evaluation for cancer may be indicated. Aim: To examine pre-diagnostic patterns of results of abnormal blood tests in bladder and renal cancer patients. Design and setting: Retrospective cohort study using primary care and cancer registry data on bladder and renal cancer patients diagnosed between 2012-2015 in England. Method: We examined the rates of patients with a first abnormal result in the year before diagnosis, for “generic” (full blood count components, inflammatory markers, calcium) and “organ-specific” blood tests (creatinine, liver function test components) which may lead to subsequent detection of incidental cancers. We used Poisson regression, to detect the month during which the cohort’s rate of each abnormal test started to increase from baseline, and examined the proportion of patients with a test found in the first half of the window, as these ‘early’ tests might represent opportunities where further evaluation could be initiated. Results: Data from 4,533 bladder and renal cancer patients were analysed. The monthly rate of patients with a first abnormal test increased towards the time of cancer diagnosis. Abnormalities of both generic and organ-specific tests started to increase from 6-8 months pre-diagnosis, with 25-40% of these patients having an abnormal test in the “early half” of the diagnostic window. Conclusion: Population-level signals of bladder and renal cancer can be observed in abnormalities in commonly performed primary care blood tests up to 8 months before diagnosis, indicating the potential for earlier diagnosis in some patients.

Early cognitive and brain development in infants and children with ASD

Autism spectrum disorder (ASD) is characterized by difficulties in social communication and interaction, restrictive, and repetitive behaviors. The exact etiology of the condition is unknown, and the heterogeneity and the late emergence of characteristic symptoms of ASD limits our ability to identify infants and children who may require early intervention. One way to address the complexity of this condition is to examine early cognitive and brain development prior to the consolidation of behavioral symptoms at around 2–3 years. This chapter overviews early brain and cognitive development in ASD-relevant domains, and putative underlying brain mechanisms. Isolating critical features of early development may be used to reduce the diagnostic window and establish effective intervention options.

The microbiome of the nasopharynx

The nasopharyngeal microbiome is a dynamic microbial interface of the aerodigestive tract, and a diagnostic window in the fight against respiratory infections and antimicrobial resistance. As its constituent bacteria, viruses and mycobacteria become better understood and sampling accuracy improves, diagnostics of the nasopharynx could guide more personalized care of infections of surrounding areas including the lungs, ears and sinuses. This review will summarize the current literature from a clinical perspective and highlight its growing importance in diagnostics and infectious disease management.

Accelerating diagnosis of Parkinson’s disease through risk prediction

Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Investigation on the pressure response behavior of two-layer vertical mixed boundary reservoir: field cases in Western Sichuan XC gas field, China

Pressure response behavior of two-layered reservoir with a vertical mixed boundary is easy to be mistaken for that of the radial composite reservoir or dual-pore reservoir. It is difficult to fit the pressure response curve and easy to obtain abnormal parameter values using a misunderstood model. In this paper, we present the interpretation of three different types of pressure responses of vertical mixed boundary reservoir by our proposed models, where the diagnostic window and feature value are captured for different mixed boundary types. Results show that the mixed boundary with closed boundary and infinite-acting boundary induces the fake pressure response of a radial composite reservoir with poor permeability outer zone. The mixed boundary with the main constant-pressure and non-main closed boundary produces a fake pressure response of a dual-porosity reservoir. The diagnostic window of pressure response curves shape can easily capture the mixed boundary type, and the feature value of the feature values of pressure response value can quickly obtain the permeability ration of one layer. Aiming at different representative types of pressure response cases in the western Sichuan XC gas field, China, we innovatively analyze them from a different perspective and get a new understanding of pressure response behavior of vertical mixed boundary, which provides a guideline for the interpretation of layered oil and gas reservoir with the complex boundary in the vertical direction.