Development of an algorithm combining blood-based biomarkers, fecal immunochemical test, and age for population-based colorectal cancer screening.

BACKGROUND AND AIMS: Implementation of screening modalities have reduced the burden of colorectal cancer (CRC), but high false positive rates pose a major problem for colonoscopy capacity. We aimed to create a tailored screening algorithm that expands the fecal immunochemical test (FIT) with a blood specimen and current age to improve selection of individuals for diagnostic colonoscopy. METHODS: In this prospective multi-center study, eight blood-based biomarkers (CEA, Ferritin, hsCRP, HE4, Cyfra21-1, Hepsin, IL-8 and OPG) were investigated in 1,977 FIT positive individuals from the Danish national CRC screening program undergoing follow-up colonoscopy. Specimens were analyzed on ARCHITECT i2000®, ARCHITECT c8000® or Luminex xMAP® machines. FIT analyses and blood-based biomarker data were combined with clinical data (i.e., age and colonoscopy findings) in a cross-validated logistic regression model (algorithm) benchmarked against a model solely using the FIT result (FIT model) applying different cutoffs for FIT positivity. RESULTS: The cohort included individuals with CRC (n = 240), adenomas (n = 938) or no neoplastic lesions (n = 799). The cross-validated algorithm combining the eight biomarkers, quantitative FIT result and age performed superior to the FIT model in discriminating CRC versus non-CRC individuals (AUC 0.77 versus 0.67, p < 0.001). When discriminating individuals with either CRC or high- or medium-risk adenomas versus low-risk adenomas or clean colorectum, the AUCs were 0.68 versus 0.64 for the algorithm and FIT model, respectively. CONCLUSIONS: The algorithm presented here can improve patient allocation to colonoscopy, reducing colonoscopy burden without compromising cancer and adenomas detection rates or vice versa.

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

BACKGROUND: Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts. METHODS: To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. RESULTS: GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. CONCLUSIONS: GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test, Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden.

BACKGROUND: Fecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT+) screening population and thereby reduce the colonoscopy burden. MATERIALS AND METHODS: From the Danish National Colorectal Cancer Screening Program, 4048 FIT+ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling. RESULTS: The discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs. CONCLUSION: A screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

Pre-analytical considerations in the development of a prototype SARS-CoV-2 antigen ARCHITECT automated immunoassay.

OBJECTIVES: To evaluate pre-analytical challenges related to high-volume central laboratory SARS-CoV-2 antigen testing with a prototype qualitative SARS-CoV-2 antigen immunoassay run on the automated Abbott ARCHITECT instrument. METHODS: Contrived positive and negative specimens and de-identified nasal and nasopharyngeal specimens in transport media were used to evaluate specimen and reagent on-board stability, assay analytical performance and interference, and clinical performance. RESULTS: TCID50/mL values were similar for specimens in various transport media. Inactivated positive clinical specimens and viral lysate (USA-WA1/2020) were positive on the prototype immunoassay. Within-laboratory imprecision was ≤0.10 SD (<1.00 S/C) with a ≤10% CV (≥1.00 S/C). Assay reagents were stable on board the instrument for 14 days. No high-dose hook effect was observed with a SARS-CoV-2 stock of Ct 13.0 (RLU>1.0 × 106). No interference was observed from mucin, whole blood, 12 drugs, and more than 20 cross-reactants. While specimen stability was limited at room temperature for specimens with or without viral inactivation, a single freeze/thaw cycle or long-term storage (>30 days) at -20 °C did not adversely impact specimen stability or assay performance. Specificity of the prototype SARS-CoV-2 antigen immunoassay was ≥98.5% and sensitivity was ≥89.5% across two ARCHITECT instruments. Assay sensitivity was inversely correlated with Ct and was similar to that reported for the Roche Elecsys® SARS-CoV-2 Ag immunoassay. CONCLUSIONS: The prototype SARS-CoV-2 antigen ARCHITECT immunoassay is sensitive and specific for detection of SARS-CoV-2 in nasal and nasopharyngeal specimens. Endogenous proteases in mucus may degrade the target antigen, which limits specimen storage and transport times and complicates assay workflow.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Development of exploratory algorithms to aid in risk of malignancy prediction of indeterminate pulmonary nodules.

Early detection of lung cancer allows for earlier stage treatment initiation and improved patient prognosis. This report focuses on utilization of combining patient demographic information with non-invasive biomarkers and their potential ability to predict risk of malignancy of nodules. A pilot study cohort of 141 subjects with IPNs (105 stage I cancer and 36 benign nodules) were collected by RUMC. The demographic variables of gender, age, sex, race, ethnicity, nodule size (mm), and smoking pack years, as well as the plasma levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, hs-CRP, Ferritin, IgG, IgG1, IgG2, IgG3, IgG4, IgE, IgM, IgA, KFLC, and LFLC, were assessed for this cohort. Multivariable analyses of the previously aforementioned biomarkers and demographic variables yielded a reduced algorithm consisting of CA-125, total IgG, IgA, IgM, IgE, LFLC, nodule size, and smoking pack years with improved performance (AUC 0.82, 95 %CI 0.74-0.90) over the same analysis of the demographic variables (age, nodule size, and smoking pack years) alone (AUC 0.70, 95 %CI 0.61-0.78). This reduced algorithm of biomarkers and demographic variables may aid in assessing the risk of IPN malignancy which could be a useful stratification tool in early detection of lung cancer in high-risk subjects.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

BACKGROUND: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

Biomarker Changes in Response to a 12-Week Supplementation of an Oral Nutritional Supplement Enriched with Protein, Vitamin D and HMB in Malnourished Community Dwelling Older Adults with Sarcopenia.

Malnutrition and sarcopenia commonly overlap and contribute to adverse health outcomes. Previously, chronic supplementation with two oral nutritional supplements (ONS), control (CONS) and experimental ONS enriched with protein, vitamin D and ß-hydroxy ß-methylbutyrate (HMB) (EONS), improved muscle strength and quality in malnourished sarcopenic older adults, with EONS demonstrating early strength benefits at 12 weeks. To understand the underlying biological mechanisms contributing to the observed early strength benefits of EONS, we examined serum biomarker changes in response to 12-week supplementation. Serum samples (EONS (n = 90) and CONS (n = 103)) collected at baseline and 12 weeks were analyzed. Biomarkers (n = 243) were measured using multiplexed immunoassay, commercial immunoassays and ELISAs. Sixty markers were excluded with levels below assay detection limits. Sixteen biomarkers significantly changed in response to both interventions including nutritional and metabolic markers. Thirteen biomarkers significantly changed in response to EONS but not CONS. Increases in immunoglobulins, myoglobin, total protein, vitamin E and magnesium were observed with EONS. Inflammation-related ferritin and osteopontin decreased, while soluble receptors for cytokines increased, suggesting decreased inflammation. Sex hormone-binding globulin associated with sarcopenia also decreased with EONS. Biomarkers reflective of multiple biological systems were impacted by nutritional intervention in sarcopenic older adults. Incremental biomarker changes were observed in response to EONS containing HMB that possibly link to improvements in skeletal muscle health.

Early detection of colorectal neoplasia: application of a blood-based serological protein test on subjects undergoing population-based screening.

BACKGROUND: Blood-based biomarkers used for colorectal cancer screening need to be developed and validated in appropriate screening populations. We aimed to develop a cancer-associated protein biomarker test for the detection of colorectal cancer in a screening population. METHODS: Participants from the Danish Colorectal Cancer Screening Program were recruited. Blood samples were collected prior to colonoscopy. The cohort was divided into training and validation sets. We present the results of model development using the training set. Age, sex, and the serological proteins CEA, hsCRP, TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, ferritin and B2M were used to develop a signature test to discriminate between participants with colorectal cancer versus all other findings at colonoscopy. RESULTS: The training set included 4048 FIT-positive participants of whom 242 had a colorectal cancer. The final model for discriminating colorectal cancer versus all other findings at colonoscopy had an AUC of 0.70 (95% CI: 0.66-0.74) and included age, sex, CEA, hsCRP, HE4 and ferritin. CONCLUSION: The performance of the biomarker signature in this FIT-positive screening population did not reflect the positive performance of biomarker signatures seen in symptomatic populations. Additional biomarkers are needed if the serological biomarkers are to be used as a frontline screening test.

Instability of corticotropin during long-term storage - myth or reality?

OBJECTIVES: Corticotropin is notorious for its instability. Whereas several studies have investigated its short-term stability in plasma following venous blood sampling, studies on long-term stability are lacking. Here we investigated the long-term storage stability of corticotropin in ethylenediaminetetraacetic acid containing plasma. METHODS: Specimens from healthy volunteers (neat, spiked) were stored in polypropylene microcentrifuge tubes with socket screw-caps at -20 °C and -70 °C for up to one and a half years. Corticotropin in plasma was measured using an Abbott research only immunoassay. Separately, specimens from patients were collected during diagnostic routine testing and stored in polystyrene tubes with push-caps at -20 °C for up to 6 years. In these samples corticotropin hormone was measured using the Diasorin corticotropin immunoassay. RESULTS: Storage of specimens at -20 °C or -70 °C for up to one and a half years showed minimal changes (<11%) in corticotropin levels, while storage of patient samples at -20 °C for up to 6 years showed a significant (54%) reduction in corticotropin levels. CONCLUSIONS: Corticotropin levels are stable in plasma when stored at -20 °C for one and a half years using the Abbott research only assay, but with longer storage time a significant reduction in corticotropin levels can be expected. Once specimens are stored for future corticotropin measurements, one should consider storage time, storage temperature and assay differences.

Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts.

Wearable inertial sensors may be used to objectively quantify exposure to some physical risk factors associated with musculoskeletal disorders. However, concerns regarding their potential negative effects on user safety and satisfaction remain. This study characterized the self-reported daily discomfort, distraction, and burden associated with wearing inertial sensors on the upper arms, trunk, and dominant wrist of 31 manufacturing workers collected over 15 full work shifts. Results indicated that the workers considered the devices as generally comfortable to wear, not distracting, and not burdensome to use. Exposure to non-neutral postures (discomfort, right arm, beta = 0.02; trunk, beta = -0.01), non-cyclic tasks (distraction, beta = -0.26), and higher body mass indices (discomfort, beta = 0.05; distraction, beta = 0.02) contributed to statistically significant (p < 0.05), albeit practically small increases in undesirable ratings. For instance, for each additional percentage of time working with the right arm elevated ≥60°, self-reported discomfort ratings increased 0.02 cm on a standard 10 cm visual analog scale. Female workers reported less discomfort and distraction while wearing the sensors at work than males (discomfort, beta = -0.93; distraction, beta = -0.3). In general, the low ratings of discomfort, distraction, and burden associated with wearing the devices during work suggests that inertial sensors may be suitable for extended use among manufacturing workers.

Will the real relationship between lean and safety/ergonomics please stand up?

This paper provides a review of studies containing safety and ergonomic outcomes in lean manufacturing (LM) environments over the past 40 years. The aim is to identify effects from specific LM methods on specific safety/ergonomic outcomes, to understand the relationship in greater detail. One hundred and one studies containing one hundred and seventy outcomes were identified. Thirty-seven outcomes pertained to just-in-time (JIT) production, which contained twenty-three negative, eleven neutral, and three positive safety/ergonomic outcomes. Conversely, twenty-six outcomes pertained to 5S and consisted of twenty-four positive, two negative, and no neutral outcomes. The most common negative JIT outcome was stress and mental strain, while the most common positive 5S outcome was a tie between safety performance and hazard exposure. Studies containing other methods were fewer in number with more mixed outcomes. These findings suggest that individual LM methods, especially JIT and 5S, uniquely contribute to the safety/ergonomic outcomes attributed to LM.

Considerations for the transportation of school aged children amid the Coronavirus pandemic.

Close proximity seating and the distinctive anthropometric characteristics of young children introduce unique challenges when implementing control strategies to promote safe transportation on school buses. Though face coverings may become one of the most commonly used controls on mass transportation to reduce the spread of COVID-19, the lack of personal protective equipment specifically designed for young children requires further investigation into control strategies to potentially reduce the spread of COVID-19 among school bus passengers. The purpose of this paper is to identify potential concerns and countermeasures (immediate and long term) to be considered for the safe transportation of children amid the Coronavirus (COVID-19) crisis by taking into consideration the design of school bus cabins and the anthropometric characteristics of children. COVID-19 mitigation strategies concerning cabin design and busing operations are discussed to provide general recommendations for operating fleets while providing as safe and healthy a passenger environment as possible considering both practicality and cost-effectiveness. The risk of virus transmission among school bus passengers may be reduced by adhering to Centers for Disease Control and Prevention (CDC) guidelines, and additional bus specific considerations such as structured loading and unloading criteria, face coverings guidelines, incorporation of a bus monitor, and potential modifications/design changes for existing/future school buses. Several controls being used to protect passengers from virus transmission in other modes of mass transportation could also have the potential for immediate incorporation into school buses.

Seat Assignments With Physical Distancing in Single-Destination Public Transit Settings.

While the importance of physical (social) distancing in reducing the spread of COVID-19 has been well-documented, implementing similar controls in public transit remains an open question. For instance, in the United States, guidance for maximum seating capacity in single-destination public transit settings, such as school buses, is only dependent on the physical distance between passengers. In our estimation, the available models/guidance are suboptimal/inefficient since they do not account for the possibility of passengers being from the same household. This paper discusses and addresses the aforementioned limitation through two types of physical distancing models. First, a mixed-integer programming model is used to assign passengers to seats based on the reported configuration of the vehicle and desired physical distancing requirement. In the second model, we present a heuristic that allows for household grouping. Through several illustrative scenarios, we show that seating assignments can be generated in near real-time, and the household grouping heuristic increases the capacity of the transit vehicles (e.g., airplanes, school buses, and trains) without increasing the risk of infection. A running application and its source code are available to the public to facilitate adoption and to encourage enhancements.

Development and validation of a cumulative exposure shoulder risk assessment tool based on fatigue failure theory.

OBJECTIVE: To present a new risk assessment tool for shoulder intensive occupational tasks based on fatigue failure theory. METHODS: The tool estimates cumulative damage (CD) based on shoulder moments and loading cycles using an S-N curve derived from in vitro tendon fatigue failure tests. If multiple shoulder tasks are performed, the CD for each is summed. In the validation, 293 workers were evaluated for five separate shoulder outcomes. Logistic regression was used to assess the log CD against five shoulder outcomes adjusted for covariates including age, sex, body mass index (BMI), and plant site. RESULTS: Both crude and adjusted logistic regression results demonstrated strong dose-response associations between the log CD measure and all five shoulder outcomes (continuous ORs ranged from 2.12 to 5.20). CONCLUSIONS: The CD measure of The Shoulder Tool demonstrated dose-response relationships with multiple health outcomes. This provides further support that MSDs may be the result of a fatigue failure process. PRACTITIONER SUMMARY: This study presents a new, easy-to-use risk assessment tool for occupational tasks involving stressful shoulder exertions. The tool is based on fatigue failure theory. The tool was tested against an existing epidemiology study and demonstrated strong relationships to multiple shoulder outcomes. ABBREVIATIONS: MSD: musculoskeletal disorder; NORA: national occupational research agenda; RULA: rapid upper limb assessment; REBA: rapid entire body assessment; S-N: stress-number of cycles; EDL: extensor digitorum longus; DPC: damage per cycle; CD: cumulative damage; UTS: ultimate tensile strength; FTOV: first time office visit; 3DSSPP: 3-dimensional static strength prediction program; AS: visual analogue scale; BMI: body mass index; CI: confidence interval; Nm: newton-metre; LiFFT: lifting fatigue failure tool; DUET: distal upper extremity tool; OMNI-RES: OMNI resistance exercise scale.

Serum biomarkers that predict lean mass loss over bed rest in older adults: An exploratory study.

BACKGROUND: Lean mass (LM) loss during extended bed rest contributes to long term functional decline in older adults. Identifying blood biomarkers that predict a hospitalized individual's risk of losing LM could allow for timely intervention. METHODS: LM from 19 healthy subjects (age 60-76 y, 4 males, 15 females), who were confined to 10 days of complete bed rest, was measured pre- and post-bed rest. One hundred eighty-seven biomarkers from pre-bed rest fasted serum samples were obtained from all evaluable subjects (n = 18), analyzed using multiplexed immunoassay array and pooled. Decision tree analysis was used to identify pre-bed rest markers that predict LM loss over bed rest. RESULTS: Sixty-three markers were excluded due to being below assay detection limits. One pair of markers, Tissue inhibitor of metalloprotease-1 (TIMP1) and tenascin C (TNC), were found to correlate with percent change in total LM over bed rest: [R2 = 0.71, all subjects; R2 = 0.76, females]. Subjects with pre-bed rest TIMP1 ≥ 141 ng/ml had the highest loss of total LM over bed rest, whereas subjects with pre-bed rest TIMP1 < 141 and TNC ≥ 461 ng/ml maintained total LM over bed rest. An additional marker set was found to correlate with percent change in leg LM loss over bed rest: matrix metalloprotease-3 (MMP3) and apolipoprotein A2 (APOA2) [R2 = 0.59, females]. Females with pre-bed rest MMP3 < 6.93 ng/ml had the highest loss of leg LM over bed rest. Whereas females with pre-bed rest MMP3 ≥ 6.93 and ApoA2 < 276 ng/ml, maintained leg lean mass at the end of bed rest. CONCLUSIONS: Panels of blood biomarkers associated with the muscle extracellular matrix may predict the likelihood for LM loss over extended bed rest.

Development of blood-based biomarker tests for early detection of colorectal neoplasia: Influence of blood collection timing and handling procedures.

INTRODUCTION: Blood-based, cancer-associated biomarkers are susceptible to a variety of well-known preanalytical factors. The influence of bowel preparation before a diagnostic colonoscopy on biomarker levels is, however, poorly investigated. The present study assessed the influence of bowel preparation on colorectal cancer-associated biomarkers. In addition, the effect of single versus double centrifugation of plasma biomarkers was assessed. METHODS: Blood samples were collected pre- and post-bowel preparation from 125 subjects scheduled for first time diagnostic colonoscopy due to symptoms attributable to CRC. The samples were separated into serum and EDTA plasma, and analyzed by four independent collaborators for: 1) the proteins AFP, CA19-9, CEA, hs-CRP, CyFra21-1, Ferritin, Galectin-3 and TIMP-1, 2) the proteins BAG4, IL6ST, vWF, CD44 and EGFR, 3) the glycoprotein Galectin-3 ligand, and 4) cell-free DNA (cfDNA). Statistical analysis of biomarker data has been performed using mixed modelling, including repeated measures. RESULTS: The biomarkers generally showed negligible variation between pre- and post-bowel preparation except for CyFra21-1, Ferritin, BAG4 and cfDNA. CyFra21-1 levels were systematically reduced with 29% (95% CI 21-36%) by bowel preparation (p ≤ 0.0001). Ferritin was not significantly different between pre- and post-bowel preparation (p = 0.07), however the estimated difference (increase) was 18%. BAG4 was systematically reduced by 12% (95% CI 1-22%, p = 0.04), while cfDNA showed a significant increase of 28% (95% CI 17-39%, p < 0.0001). Double centrifugation compared to single centrifugation showed reduced vWF (ratio 0.86, p ≤ 0.0001) and CD44 (ratio 0.85, p = 0.016), but increased IL6ST levels (ratio 1.18, p = 0.014). CONCLUSIONS: Results of the present study demonstrated systematic, statistically significant differences between pre-bowel and post-bowel preparation levels for three independent blood-based biomarkers (BAG4, CyFra21-1, cfDNA), illustrating the importance of timing of sample collection for biomarker analyses.

Evaluation of algorithm development approaches: Development of biomarker panels for early detection of colorectal lesions.

INTRODUCTION: Colorectal cancer (CRC) is the third most common cancer in the U.S. Early detection of CRC can substantially increase survival rates. Test compliance may be improved by offering a blood-based test option. METHODS: Endoscopy II trial specimens were tested for AFP, CA19-9, CEA, hs-CRP, CyFra 21-1, Ferritin, Galectin-3, and TIMP-1 levels. These biomarkers, as well as patient demographic information (e.g., age, gender), were included in algorithm development. Six statistical methods were utilized to develop algorithms that would discriminate cancer vs. noncancers. Statistical methods included logistic regression, adaptive index modeling, partial least-squares discriminant analysis, feature vector (weighted and unweighted), and random forest. The performance of these algorithms was compared against benchmark criteria established for stool-based tests. RESULTS: Using several statistical methods, the presence of CRC and high-risk adenomas was detected with an AUCs of at least 0.65-0.76, with a few of models approaching the stool-based tests benchmark performance. Further, common markers were utilized across the different statistical techniques, with model complexities ranging from 3 to 9 markers. CONCLUSIONS: Predictive models identified subjects with CRC and high-risk adenomas with the similar levels of statistical accuracy. Clinical performance differences were minimal across the statistical techniques, although the intuitive interpretations, model complexity, clinical adoption and implementation varied.

Effects of age and obesity on trunk kinetics and kinematics during dominant side one-handed carrying.

The proportions of older and obese people are increasing in both the general and working populations worldwide. Older and obese individuals are more susceptible to work-related musculoskeletal disorders (MSDs) in comparison with healthy, younger individuals. Manual material handling (MMH) is associated with the development of work-related MSDs. Although previous research has suggested that one-handed carrying is a particularly undesirable method of MMH, the effects of one-handed carrying on trunk kinetics and kinematics among older and/or obese people have not been adequately studied. The objective of this study was to examine the effects of age and obesity on trunk angles and moments during dominant side one-handed carrying of various load magnitudes. Twenty (20) participants divided into four groups with respect to age (young and older) and obesity (obese and non-obese) carried different loads (No-load [0 kg], Light [5.67 kg], and Heavy [10.21 kg]) in their dominant hand for approximately 6 m. Three-dimensional (3D) trunk angles and moments approximately about the L4/L5 vertebral segment were calculated using Visual3D. The findings indicated that while carrying a load in the dominant hand plays an important role in changing trunk kinematics and kinetics, the results were not dependent on age and/or obesity category. Absolute moments were greatest among participants in the obese groups; however, these moments were mitigated when normalized to body weight and height (%BW * Ht). Age did not exacerbate the effects of load magnitude on trunk kinetics and kinematics.