Moderate Precision Research Articles

Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.

Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health challenge in tropical regions. Therefore, the development of technological tools such as machine learning (ML) offers an opportunity to innovate strategies for improving the diagnosis of this complex disease. To validate the utility of different ML models for the histopathological diagnosis of Hansen disease. An observational study was conducted where 55 H&E-stained tissue slides from leprosy patients and 51 healthy skin controls were analyzed using microphotographs captured at various magnifications. These images were categorized based on histopathological findings and processed using the Cross-Industry Standard Process for Data Mining methodology for designing and training ML models. Five types of ML models were evaluated using standard metrics such as accuracy, sensitivity, and specificity, emphasizing data normalization as a fundamental step in optimizing model performance. The artificial neural network (ANN) model demonstrated an accuracy of 70%, sensitivity of 74%, and specificity of 65%, demonstrating its ability to identify leprosy cases with moderate precision. The receiver operating characteristic curve of the ANN model showed an area under the curve of 0.71. Conversely, models such as decision trees, logistic regression, and random forests showed similar accuracy results but with slightly lower sensitivity, potentially indicating a higher risk of false negatives in detecting leprosy-positive cases. The ANN model emerges as a promising alternative for leprosy detection. However, further refinement of these models is necessary to enhance their adaptability across different clinical settings and participation in patient care.

Impact and consequences of the error of estimated GFR in patients with heart failure

Heart failure is a highly prevalent disease, which courses with frequent readmissions, mainly by Acute Heart Failure (AHF). Reduced renal function is associated with increased mortality in patients with HF. Therefore, an accurate and precise evaluation of renal function in patients with HF is crucial. The error of estimated GFR (eGFR) is wide and common, showing a ± 30% variability compared to measured GFR (mGFR). However, there is no evidence on the error of formulas in reflecting real renal function and particularly the consequences of this error in patients with AHF. This is a prospective study comparing the impact of mGFR versus eGFR in the onset of cardiovascular (CV) outcomes in patients with AHF. This was tested with cox survival analysis. Measured GFR was determined by the plasma clearance of iohexol-dbs and eGFR by Cockroft-Gould, MDRD, CKD-EPI creatinine, CKD-EPI cystatin-C and CKD-EPI creatinine + cystatin-C equations formulas. Also the agreement between mGFR and eGFR was analyzed. A total of 90 patients were included. Average age was 66 (± 12 years) and 52 (58%) were male. Of them 53 patients (59%) had a cardiovascular event during follow-up, 22 fatal (41%). The agreement between mGFR and eGFR indicated moderate precision and accuracy (concordance correlation coefficient of 0.77; CI = 0.73–0.82). In multiple cox survival analysis, mGFR was significantly associated with cardiovascular events together with NTproBNP, BMI, LVEF and previous coronary artery disease (p = 0.037; HR = 0.98, 95% CI = 0.95–0.99). Estimated GFR by formulas was not significant. In patients with AHF the error of formulas is large, frequent and random, also, mGFR and not eGFR predicted future CV events. The error of eGFR may have clinical consequences in specific subpopulations.

Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke

BackgroundA study was undertaken to assess the effectiveness of open-source large language models (LLMs) in extracting clinical data from unstructured mechanical thrombectomy reports in patients with ischemic stroke caused by...

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity-reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.

Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.

Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL ≥ 30% were incorrectly classified as "unsuccessful procedures". The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.

Design of Low Noise Low Power Instrumentation Amplifier for Biomedical Applications

Advancements in integrated circuit (IC) technology and circuit design are enabling devices to replace complex biological applications. Despite this progress, achieving necessary functionality at low power levels is crucial for fully integrated systems. Biomedical devices like pacemakers, which require ongoing battery charging, operate at nanowatt power levels over several years. To ensure long-term pacemaker survival, an Analog Front End (AFE) with low to moderate power consumption and high precision is essential. The front-end Instrumentation Amplifier (INA) is vital for signal acquisition, significantly improving the effectiveness of ECG signal collection systems.The primary aim of this research is to develop an integrated ECG instrumentation amplifier (INA) that can deliver top-notch performance while keeping noise and power consumption to a minimum. This amplifier will enable seamless monitoring of the ECG signal without interruptions. Using an AC connected Common Mode Feedback system [1]. in conjunction with an Operational Trans-conductance Amplifier (OTA) that uses a Chopper stabilization technique, the study's band pass filtering response within the ECG signal frequency range of 0-100Hz is obtained as anticipated. CMOS 45 nm technology is used throughout the entire process using Mentor Graphics EDA Tools

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile.

Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles. The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility. Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19, and COPS4 as hub genes. In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing "explainable" diagnostic models.

Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb

The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4–0.64, RMSEP = 0.70–1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50–0.94) and biases (−0.86–0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.

Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.

Learnable fusion mechanisms for multimodal object detection in autonomous vehicles

AbstractPerception systems in autonomous vehicles need to accurately detect and classify objects within their surrounding environments. Numerous types of sensors are deployed on these vehicles, and the combination of such multimodal data streams can significantly boost performance. The authors introduce a novel sensor fusion framework using deep convolutional neural networks. The framework employs both camera and LiDAR sensors in a multimodal, multiview configuration. The authors leverage both data types by introducing two new innovative fusion mechanisms: element‐wise multiplication and multimodal factorised bilinear pooling. The methods improve the bird's eye view moderate average precision score by +4.97% and +8.35% on the KITTI dataset when compared to traditional fusion operators like element‐wise addition and feature map concatenation. An in‐depth analysis of key design choices impacting performance, such as data augmentation, multi‐task learning, and convolutional architecture design is offered. The study aims to pave the way for the development of more robust multimodal machine vision systems. The authors conclude the paper with qualitative results, discussing both successful and problematic cases, along with potential ways to mitigate the latter.

Establishing language and ethnic equivalence for health-related quality of life item banks and testing their efficiency via computerised adaptive testing simulations.

We aimed to (1) establish linguistic and ethnic equivalence (i.e. lack of bias) for the items in the English and Chinese versions of the Singapore Health and Well Being (SHAWS) Physical Functioning (PF), Positive Mindset (PM) and Social Relationship (SR) item banks (IBs); and (2) evaluate the preliminary efficiency of these IBs using Computer Adaptive Testing (CAT) simulations. In this cross-sectional study, 671, 670, and 672 subjects answered 55, 48 and 30 items of the PF, PM, and SR IBs, respectively. Rasch analysis was conducted to assess each IB's psychometric properties, particularly the presence of differential item functioning (DIF) for language and ethnicity. A set of performance criteria related to removing items that displayed notable DIF were employed. CAT simulations determined the mean number of items for high, moderate, and moderate-low measurement precisions (stopping rule: SEM 0.300, 0.387. 0.521, respectively). Half of subjects were >50 years old (40.9% PF, 42.1% PM, 41.4% SR), Chinese (50.7% PF, 51.0% PM, 50.6% SR) and female (50.0% PF. 49.4% PM, 52.8% SR) respectively. Rasch analysis revealed 4 items with DIF for the PF IB, 9 items with DIF for the PM IB and 2 items with DIF for the SR IB. In CAT simulations, the mean number of items administered was 8.5, 21.6 and 14.5 for the PF, PM and SR IBs, respectively (SEM 0.300), 5.1, 13.0, 8.0 for PF, PM and SR IBs, respectively (SEM 0.387) and 3.1, 5.3 and 4.1 for PF, PM and SR IBs, respectively (SEM 0.521). The PF, PM and SR IBs to measure health-related quality of life revealed minimal DIF for language and ethnicity after remedial efforts. CAT simulations demonstrated that these IBs were efficient, especially when the stopping rule was set at moderate precision, and support the implementation of the SHAWS IBs into routine clinical care.

Automated hematological cell count using sysmex XN-1000V in Testudo hermanni: Agreement with manual count

Mediterranean area represents the main habitat of Testudo hermanni. Clinical signs of disease of these tortoises are non-specific, making the hematology results crucial in revealing underlying pathological conditions. However, accurate automated identification of blood cell populations is hampered by the presence of nucleated erythrocytes (NRBC) and thrombocytes (Thr), necessitating manual methods such as counting chambers. The aim of the study was to assess the performance of the novel automated hematology analyzer Sysmex XN-1000 V, which includes a a specific channel (WNR) for counting NRBC, in accurately identify and quantify the different blood cell populations of Testudo hermanni. Additionally, its agreement with manual counts was evaluated. Fifty heparinized blood samples were initially counted using the Neubauer improved chamber and then analysed twice with Sysmex XN-1000 V. Thirteen out of 50 samples were instrumentally counted again after 48 h to assess the inter-assay precision. All WNR scattergrams were re-analysed using an ad hoc gate panel to differentiate two populations: NRBCs (weak fluorescence signal) and WBC + Thr (high fluorescence signal). Sysmex XN-1000 V demonstrated optimal intra- and inter-assay precision for NRBCs (CV 0.98% ± 1.96; 1.31% ± 2.98) and moderate precision for WBC + Thr (CV 9.24% ± 16.61; 12.69% ± 10.35). No proportional nor constant errors were observed between the methods for both the populations. The instrumental NRBC counts were consistently slightly lower, while WBC + Thr counts were slightly higher compared to manual counts. These findings suggest that Sysmex XN-1000 V can be used for analyzing cell populations in heparinized blood of Testudo hermanni. However, specific instrumental reference intervals are suggested.

Assessment of the diagnostic performance of intradermal tuberculin test and post-mortem inspection for the diagnosis of bovine tuberculosis according to WOAH guidelines

Bovine tuberculosis (bTB) constitutes a global challenge for public and animal health with still some deficiencies regarding its diagnosis. This study aimed to estimate the accuracy of the single intradermal tuberculin test (SIT) and post-mortem inspection for different diagnostic objectives following WOAH guidelines. Tissue samples from 59 microbiological culture/PCR-positive and 58 microbiological culture/PCR-negative cattle were evaluated. The diagnostic sensitivity and specificity, the positive and negative probability indices as well as the positive and negative predictive values (PPV and NPV) of each technique were estimated for different pretest probabilities. The SIT with strict interpretation demonstrated moderate precision in confirming the absence of infection in populations historically free of bTB, with a 12.1% rate of false positives, but also detecting positive animals in the early stage of the eradication programs, with a 13.6% rate of false negatives. The diagnostic performance for ruling out bTB was notably high (NPV > 90%) in animals with a pre-test probability (PTP) below 42%. Post-mortem inspection constituted an interesting alternative tool to confirm suspected and positive cases for SIT, particularly in areas with bTB prevalence exceeding 19%, where implementing SIT and eradication measures may be impractical. In these areas, the likelihood that animals with tuberculosis-like lesions are affected by the disease surpasses 90%. Similarly, in herds with a PTP below 25%, the absence of bTB could be confidently ruled out with over 90% certainty. These findings highlight the effectiveness of SIT and post-mortem inspection as valuable techniques for current eradication programs and controlling bTB in high-prevalence areas where molecular techniques may not be feasible.

Metagenomic assembly is the main bottleneck in the identification of mobile genetic elements.

Antimicrobial resistance genes (ARG) are commonly found on acquired mobile genetic elements (MGEs) such as plasmids or transposons. Understanding the spread of resistance genes associated with mobile elements (mARGs) across different hosts and environments requires linking ARGs to the existing mobile reservoir within bacterial communities. However, reconstructing mARGs in metagenomic data from diverse ecosystems poses computational challenges, including genome fragment reconstruction (assembly), high-throughput annotation of MGEs, and identification of their association with ARGs. Recently, several bioinformatics tools have been developed to identify assembled fragments of plasmids, phages, and insertion sequence (IS) elements in metagenomic data. These methods can help in understanding the dissemination of mARGs. To streamline the process of identifying mARGs in multiple samples, we combined these tools in an automated high-throughput open-source pipeline, MetaMobilePicker, that identifies ARGs associated with plasmids, IS elements and phages, starting from short metagenomic sequencing reads. This pipeline was used to identify these three elements on a simplified simulated metagenome dataset, comprising whole genome sequences from seven clinically relevant bacterial species containing 55 ARGs, nine plasmids and five phages. The results demonstrated moderate precision for the identification of plasmids (0.57) and phages (0.71), and moderate sensitivity of identification of IS elements (0.58) and ARGs (0.70). In this study, we aim to assess the main causes of this moderate performance of the MGE prediction tools in a comprehensive manner. We conducted a systematic benchmark, considering metagenomic read coverage, contig length cutoffs and investigating the performance of the classification algorithms. Our analysis revealed that the metagenomic assembly process is the primary bottleneck when linking ARGs to identified MGEs in short-read metagenomics sequencing experiments rather than ARGs and MGEs identification by the different tools.

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

AbstractRecently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC‐Juniata‐hydroDL2) that mimics the classical Muskingum‐Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach‐scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real‐world data, the trained differentiable routing model produced more accurate long‐term routing results for both the training gage and untrained inner gages for larger subbasins (&gt;2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national‐scale hydrologic flood simulations.

THE MINI-MENTAL STATE EXAMINATION IN A CHINESE POPULATION: RELIABILITY, VALIDITY, AND MEASUREMENT INVARIANCE

Abstract The Mini-Mental State Examination (MMSE) is a widely used screening tool for cognitive impairment and dementia in many cultures and regions. However, the properties of the MMSE in the Chinese context have not been evaluated with large population-representative samples. It is also unclear whether item properties of the MMSE differ across age and education subgroups. We study the psychometric properties of the MMSE using data from 8974 respondents aged 60 years or older in the 2018 Chinese Health and Retirement Longitudinal Study (CHARLS). Using item response theory (IRT) for the ordinal data, we confirm a three-dimensional factor structure for the MMSE in CHARLS in accordance with the underlying theoretical framework and establish acceptable model-data fit (SRMSR = 0.036). Total scores and subscale scores of the MMSE correlated positively with each other and correlated negatively with ADL (r = -0.19), IADL (-0.30), and CESD scores (-0.25). The reliability of the total MMSE scores was estimated at 0.78, indicating moderate measurement precision in the study population. Multiple group IRT analysis found that item characteristics and item functioning of 5 items differed by age group and 14 items differed by educational level. This analysis provided evidence on violations to measurement invariance of MMSE items, pointing to a need to account for item-level differences in measurement properties primarily with respect to education level when using the MMSE in CHARLS and, more generally, in Chinese populations. In addition, revised cognitive measures are advised to be used in CHARLS to improve the precision of measurement.

A Machine Learning Algorithm using Clinical and Demographic Data for All-Cause Preterm Birth Prediction.

Preterm birth remains the predominant cause of perinatal mortality throughout the United States and the world, with well-documented racial and socioeconomic disparities. To develop and validate a predictive algorithm for all-cause preterm birth using clinical, demographic, and laboratory data using machine learning. We performed a cohort study of pregnant individuals delivering at a single institution using prospectively collected information on clinical conditions, patient demographics, laboratory data, and health care utilization. Our primary outcome was all-cause preterm birth before 37 weeks. The dataset was randomly divided into a derivation cohort (70%) and a separate validation cohort (30%). Predictor variables were selected amongst 33 that had been previously identified in the literature (directed machine learning). In the derivation cohort, both statistical (logistic regression) and machine learning (XG-Boost) models were used to derive the best fit (C-Statistic) and then validated using the validation cohort. We measured model discrimination with the C-Statistic and assessed the model performance and calibration of the model to determine whether the model provided clinical decision-making benefits. The cohort includes a total of 12,440 deliveries among 12,071 individuals. Preterm birth occurred in 2,037 births (16.4%). The derivation cohort consisted of 8,708 (70%) and the validation cohort consisted of 3,732 (30%). XG-Boost was chosen due to the robustness of the model and the ability to deal with missing data and collinearity between predictor variables. The top five predictor variables identified as drivers of preterm birth, by feature importance metric, were multiple gestation, number of emergency department visits in the year prior to the index pregnancy, initial unknown body mass index, gravidity, and prior preterm delivery. Test performance characteristics were similar between the two populations (derivation cohort area under the curve [AUC] = 0.70 vs. validation cohort AUC = 0.63). Clinical, demographic, and laboratory information can be useful to predict all-cause preterm birth with moderate precision. · Machine learning can be used to create models to predict preterm birth.. · In our model, all-cause preterm birth can be predicted with moderate precision.. · Clinical, demographic, and laboratory information can be useful to predict all-cause preterm birth..

Evaluating the performance of ChatGPT in responding to questions about endoscopic procedures for patients

Background and aimsWe aimed to evaluate the precision, medical accuracy, superfluous content, and consistency of ChatGPT's responses to commonly asked questions about endoscopic procedures and its capability to provide emotional support, comparing its performance with the Generative Pre-trained Transformer 4 (GPT-4) model. MethodsA set of 113 questions related to esophagogastroduodenoscopy (EGD), colonoscopy, endoscopic ultrasound (EUS), and endoscopic retrograde cholangiopancreatography (ERCP) were curated from professional societies and institutional web pages. Responses from ChatGPT were generated and subsequently graded by board-certified gastroenterologists and advanced endoscopists. The emotional support efficacy of ChatGPT and GPT-4 was also assessed by a board-certified psychiatrist (LSM). ResultsChatGPT exhibited moderate precision in answering questions about EGD (57.9% comprehensive), colonoscopy (47.6% comprehensive), EUS (48.1% comprehensive), and ERCP (44.4% comprehensive). Medical accuracy was highest for EGD (52.6% fully accurate) and lowest for EUS (40.7% fully accurate). Concerning superfluous content, responses were predominantly concise for EGD and colonoscopy, with ERCP and EUS showing increased extraneous content. Reproducibility scores varied across domains, ranging from 50.34% (for EUS) to 68.6% (for EGD). GPT-4 outperformed ChatGPT in emotional support, though both models exhibited satisfactory performance. ConclusionChatGPT delivers moderately precise and medically accurate answers related to common endoscopic procedures with varying levels of extraneous content. It holds promise as a supplementary information resource for both patients and healthcare professionals.

An open source 3D printed autosampler for capillary electrophoresis

BackgroundIn-house built capillary electrophoresis (CE) systems represent a significant share of laboratory instrumentation. In most of these instruments, sample injection is effected manually with low to moderate precision and requires skilled operators. Although few automated samplers have been previously developed, typically only one sample at a time can be injected. If a series of samples is to be analyzed, manual intervention is required. In the present work, we developed and constructed a fully automated, open source, CE autosampler, able to handle up to 14 different samples that can be used as a modular component of any in-house built CE instrument. ResultsAn inexpensive, 3D printed, open source, autosampler for CE was developed. The autosampler consists of two parts: an injection unit with carousel containing sample and electrolyte vials and a flushing unit, containing a miniature pressure/vacuum pump. The autosampler is operated by an Arduino Mega microcontroller and an Arduino code written in the laboratory. The injection sequence is entered through a keypad and LCD display by the user. The instrument can operate autonomously for extended periods of time. It was used for fully automated analysis and/or calibration of up to 14 samples with excellent injection repeatability reaching less than 2.7% RSD for peak areas. The sampler performance was tested with two independently built CE instruments, a CE system with contactless conductivity detection (C4D) and a CE system with laser induced fluorescence (LIF) detector. Significance and noveltyA novel, 3D printed, Arduino-based autosampler for CE was developed. The autosampler allows autonomous hydrodynamic injection of up to 14 different samples with fully programmable injection sequence, including capillary flushing and high voltage and data acquisition control. It provides the missing instrumental sampling setup for laboratory made CE instruments. It can be simply constructed based on the open-source blueprints in any laboratory and be a useful and time-saving add-on to any modular CE instrument.

Comparisons of Insulin Resistance- and Steatosis-Based Scores in Monitoring Metabolic Associated Fatty Liver Disease Treatment Response

Background: Quantitative measurements of liver fat contents (LFCs) by magnetic resonance imaging derived-proton density fat fraction (MRI-PDFF) are accurate but limited by availability, convenience, and expense in the surveillance of metabolic associated fatty liver (MAFLD). Insulin resistance (IR) and steatosis-associated serum indices are useful in screening for MAFLD, but their value in monitoring MAFLD with or without chronic hepatitis B virus (CHB) infection remains unclear and we aimed to evaluate these scores in predicting changes in LFC. Methods: We conducted a prospective study between January 2015 and December 2021 with 620 consecutive participants with MAFLD (212 participants with CHB) who received a 24-week lifestyle intervention. The homeostasis model assessment of IR (HOMA-IR), HOMA2 index, glucose-insulin ratio, quantitative insulin sensitivity check index, fasting insulin resistance index, fatty liver index (FLI), hepatic steatosis index (HSI), liver fat score (LFS), visceral adiposity index, and triglycerides * glucose were calculated. Results: When using endpoints such as LFS improvements of ≥5% or 10% or escalations of ≥5%, LFS had the highest area under the curve (AUC) values at all endpoints for MAFLD alone (0.756, 95% CI: 0.707–0.805; 0.761, 95% CI: 0.705–0.818; 0.807, 95% CI: 0.713–0.901, all p < 0.05, respectively). With CHB, the FLI (AUC = 0.750) and HIS (AUC = 0.770) exhibited the highest AUCs between the former two outcomes, respectively, but no score could predict LFC escalation of ≥5%. Conclusion: Among IR and steatosis scores, changes in LFC through lifestyle interventions can be captured with LFS possessing moderate precision but not in those with CHB.