Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Supplementary value and diagnostic performance of computed tomography scout view in the detection of thoracolumbar spine injuries

PURPOSE

Assessing the diagnostic performance and supplementary value of whole-body computed tomography scout view (SV) images in the detection of thoracolumbar spine injuries in early resuscitation phase and identifying frequent image quality confounders.

METHODS

In this retrospective database analysis at a tertiary emergency center, three blinded senior experts independently assessed SV to detect thoracolumbar spine injuries. The findings were categorized according to the AO Spine classification system. Confounders impacting SV image quality were identified. The suspected injury level and severity, along with the confidence level, were indicated. Diagnostic performance was estimated using the caret package in R programming language.

RESULTS

We assessed images of 199 patients, encompassing 1592 vertebrae (T10-L5), and identified 56 spinal injuries (3.5%). Among the 199 cases, 39 (19.6%) exhibited at least one injury in the thoracolumbar spine, with 12 (6.0%) of them displaying multiple spinal injuries. The pooled sensitivity, specificity, and accuracy were 47%, 99%, and 97%, respectively. All experts correctly identified the most severe injury of AO type C. The most common image confounders were medical equipment (44.6%), hand position (37.6%), and bowel gas (37.5%).

CONCLUSION

SV examination holds potential as a valuable supplementary tool for thoracolumbar spinal injury detection when CT reconstructions are not yet available. Our data show high specificity and accuracy but moderate sensitivity. While not sufficient for standalone screening, reviewing SV images expedites spinal screening in mass casualty incidents. Addressing modifiable factors like medical equipment or hand positioning can enhance SV image quality and assessment.

Concordance between transbronchial lung cryobiopsy and surgical lung biopsy for interstitial lung disease in the same patients

BACKGROUND

The diagnostic accuracy and safety of transbronchial lung cryobiopsy (TBLC) via a flexible bronchoscope under sedation compared with that of surgical lung biopsy (SLB) in the same patients is unknown.

METHODS

Retrospectively the data of fifty-two patients with interstitial lung diseases (median age: 63.5 years; 21 auto-antibody positive) who underwent TBLC followed by SLB (median time from TBLC to SLB: 57 days) was collected. The samples from TBLC and SLB were randomly labelled to mask the relationship between the two samples. Diagnosis was made independently by pathologists, radiologists, and pulmonary physicians in a stepwise manner, and a final diagnosis was made at multidisciplinary discussion (MDD). In each diagnostic step the specific diagnosis, the diagnostic confidence level, idiopathic pulmonary fibrosis (IPF) diagnostic guideline criteria, and treatment strategy were recorded.

RESULTS

Without clinical and radiological information, the agreement between the histological diagnoses by TBLC and SLB was 42.3% (kappa [κ] = 0.23, 95% confidence interval [CI]: 0.08-0.39). However, the agreement between the TBLC-MDD and SLB-MDD diagnoses and IPF/non-IPF diagnosis using the two biopsy methods was 65.4% (κ = 0.57, 95% CI: 0.42-0.73) and 90.4% (47/52), respectively. Out of 38 (73.1%) cases diagnosed with high or definite confidence at TBLC-MDD, 29 had concordant SLB-MDD diagnoses (agreement: 76.3%, κ = 0.71, 95% CI: 0.55-0.87), and the agreement for IPF/non-IPF diagnoses was 97.4% (37/38). By adding the pathological diagnosis, the inter-observer agreement of clinical diagnosis improved from κ = 0.22 to κ = 0.42 for TBLC and from κ = 0.27 to κ = 0.38 for SLB, and the prevalence of high or definite diagnostic confidence improved from 23.0% to 73.0% and from 17.3% to 73.0%, respectively. Of all 383 TBLC performed during the same period, pneumothorax occurred in 5.0% of cases, and no severe bleeding, acute exacerbation of interstitial lung disease, or fatal event was observed.

CONCLUSIONS

TBLC via a flexible bronchoscope under deep sedation is safely performed, and the TBLC-MDD diagnosis with a high or definite confidence level is concordant with the SLB-MDD diagnosis in the same patients.

Validating local drivers influencing land use cover change in Southwestern Ghana: a mixed-method approach

Addressing undesirable changes associated with the driving forces of land use cover change are critical to sustainable land management, and the future modeling of land use systems in developing countries. The study accentuates local drivers of land use cover change in Southwestern Ghana using a mixed-method approach. The approach aided in identifying key land-use drivers, using different research strategies for comparisons through confidence level analysis and Analytic Hierarchy Process. We used expert interviews, existing literature and geostatistical tools to ascertain the driving forces triggering such unprecedented changes. Landsat imagery 5 MSS, 4 and 5 TM, 7 ETM + and 8 OLI/TIRS were acquired from the United States Geological Survey's website. Land-use analysis revealed a decline in forests (- 82.41%) and areas covered by waterbodies (- 27.39%). A fundamental drift in built-up (+ 1288.36%) and farmlands/shrubs (+ 369.81%) areas were also observed. The contribution rate of change analysis revealed built-environment and increasing population contributed the most to surface temperature and land-use change. A steady increase in surface temperature can be attributed to the undesirable changes associated with land-use systems over the past 50 years. Socio-economic development in Southwestern Ghana is fuelling interest in studies related to land use cover change. Biophysical, cultural and technological factors are considered key drivers despite the "medium-to-very low confidence" in results generated. They could potentially impact climate-sensitive sectors that significantly modify land-use systems from the pessimists' and optimists' perspectives. Standpoints established through this study will enrich basic datasets for further studies at the continental level.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12665-022-10481-y.

Confidence level, challenges, and obstacles faced by orthopedic residents in obtaining informed consent

OBJECTIVES

The objective is to evaluate the opinions of orthopedic residents on current practices, experiences, training, confidence level, difficulties, and challenges faced when obtaining informed consent.

DESIGN

This is a cross-sectional, multi-center, and questionnaire-based study.

SETTING

The study was done in forty-four training centers across Saudi Arabia.

PARTICIPANTS

In total, 313 orthopedic residents participated.

MATERIAL AND METHODS

The web-based questionnaire examined the perceptions of residents regarding practices, experience, training, difficulties, and challenges surrounding the obtention of informed consent, as well as residents' confidence in obtaining informed consent for different orthopedic situations and eight common orthopedic procedures.

RESULTS

Most residents were allowed to obtain consent independently for all emergency, trauma, primary, and revision cases at their institution (92.7%). Only 33.5% of the residents received formal training and teaching on obtaining informed consent, with 67.1% having believed that they needed more training. Only 4.2% of the residents routinely disclosed all essential information of informed consent to patients. Inadequate knowledge (86.3%) and communication barriers (84.7%) were the most reported difficulties. Generally, 77.3% of the residents described their confidence level in obtaining informed consent as good or adequate, and 33.9% were confident to discuss all key components of the informed consent. Residents' confidence level to independently obtain informed consent decreased with procedure complexity. Receiving formal training, senior level (postgraduate year (PGY) 4 and 5), and being frequently involved in obtaining informed consent correlated with increased confidence level.

CONCLUSION

Many residents incompletely disclosed key information upon obtaining informed consent and reported lacking confidence in their ability to perform the procedure in their daily practices. To improve patient care and residents' performance and overcome these difficulties and challenges, institutions should develop effective strategies to standardize the informed consent process, provide formal training for obtaining informed consent, and provide supervision for residents during obtention of informed consent.

A Step-by-Step Guide to Using BioNetFit

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

A simple Bayesian approach to quantifying confidence level of adverse event incidence proportion in small samples

In both clinical development and post-marketing of a new therapy or a new treatment, incidence of an adverse event (AE) is always a concern. When sample sizes are small, large sample-based inferential approaches on an AE incidence proportion in a certain time period no longer apply. In this brief discussion, we introduce a simple Bayesian framework to quantify, in small sample studies and the rare AE case, (1) the confidence level that the incidence proportion of a particular AE p is over or below a threshold, (2) the lower or upper bounds on p with a certain level of confidence, and (3) the minimum required number of patients with an AE before we can be certain that p surpasses a specific threshold, or the maximum allowable number of patients with an AE after which we can no longer be certain that p is below a certain threshold, given a certain confidence level. The method is easy to understand and implement; the interpretation of the results is intuitive. This article also demonstrates the usefulness of simple Bayesian concepts when it comes to answering practical questions.

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.