Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project

Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients.

Correlation of serum electrolytes with serial miRNA in advanced stage non-small cell lung cancer (NSCLC) in Indonesia

Objective This study aims to evaluate the correlation between electrolytes and serial miRNAs from our previous study. We want to prove that there is the molecular basis that underlying electrolytes disturbances as the predictive indicator to the outcome in NSCLC patients. Results There were positive correlation between potassium level with miR-34 (p = 0.008, r = 0.366), miR-148 (p = 0.004, r = 0.394) and miR-155 (p = 0.031, r = 0.300).

Thrusts control the thermal maturity of accreted sediments

Thermal maturity assessments of hydrocarbon-generation potential and thermal history rarely consider how structures developing during subduction influence the trajectories of accreted sediments. Our thermomechanical models of subduction support that thrusts evolving under variable sedimentation rates and décollement strengths fundamentally influence the trajectory, temperature, and thermal maturity of accreting sediments. This is particularly true for the frontal thrust, which pervasively partitions sediments along a low and a high maturity path. Notably, our findings imply that interpretations of the distribution of thermal maturity cannot be detached from accounts of the length and frequency of thrusts and their controlling factors. Taking these factors into consideration, our approach reduces former inconsistencies between predicted and factual thermal maturity distributions in accretionary wedges and provides a first-order predictive indicator for thermal maturity distribution based on known fault architectures.

An Ultrasound-Based Radiomics Nomogram for Differentiation of Triple-Negative Breast Cancer From Fibroadenoma

Background: To develop a radiomics nomogram that incorporates the radiomics features and ultrasound (US) conventional features and clinical findings to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.Methods: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs which underwent preoperative US examination were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The regions of interest (ROIs) of all lesions were delineated based on preoperative US examination subsequently radiomics features extracted. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).Results: The radiomics signature, composing of twelve selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models alone (p < 0.05). The calibration curve and decision curve analysis demonstrated the better clinical utility of the combined radiomics nomogram.Conclusions: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models alone.Advances in knowledge: Recent advances in radiomics-based US identified increasingly gaining ground in oncology to improve diagnosis, assessment of therapeutic response, and disease prediction. We revealed that the Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models alone.

Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project (Preprint)

UNSTRUCTURED Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients.

A predictive indicator using lender composition for loan evaluation in P2P lending

Most loan evaluation methods in peer-to-peer (P2P) lending mainly exploit the borrowers’ credit information. However, the present study presents the maturity-based lender composition score, which exploits the investment capability of a group of lenders who fund the same loan, to enhance the P2P loan evaluation. More specifically, we extract lenders’ profiles in terms of performance, risk, and experience by quantifying their investment history and develop our loan evaluation indicator by aggregating the profiles of lenders in the composition. To measure the ability of a lender for continuous improvement in P2P investment, we introduce lender maturity to capture this evolvement and incorporate it into the aggregation process. Our empirical study demonstrates that the maturity-based lender composition score can serve as an effective indicator for identifying loan quality and be included in other commonly used loan evaluation models for accuracy improvement.