Precision Coefficient Research Articles

Disposable Pipette Extraction Followed by Direct MS/MS Analysis of Beta Amyloid Peptides (Aβ38, Aβ40, and Aβ42) in Cerebrospinal Fluid Samples

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by extracellular accumulation of amyloid-β (Aβ) peptides in the brain. This study demonstrates the direct association of the disposable pipette extraction (DPX) with tandem mass spectrometry (MS/MS) for the analysis of Aβ peptides in cerebrospinal fluid (CSF) samples. Different parameters were optimized in order to improve detectability in the MS/MS including mobile phase percentage of ammonium hydroxide, mobile phase flow rate and acquisition mode. Also, this method used an electrospray ionization (ESI) low-flow probe and direct infusion of an organic solution in the MS/MS. The DPX-MS/MS method showed adequate linearity for determining Aβ peptides in CSF-linearity ranged from 0.1 to 1.5 ng mL-1. The coefficients of determination were higher than 0.99; the precision coefficient of variation (CV) ranged from 0.3 to 12.7%; and the accuracy relative standard deviation (RSD) ranged from -13.6 to 13.2%.

Comparison of Precision Coefficients for Streamflow Data Simulation

Comparison of Precision Coefficients for Streamflow Data Simulation

Computational intelligence to detect bearing faults using optimal features from motor current signals

In recent times, there has been a notable growth in research investigations into the fault diagnosis of electrical machines. The effective detection of permanent magnet synchronous motor bearing faults is a significant challenge; however, it is crucial for ensuring safety and cost-effectiveness in industries. The data referring to faults needs to be studied under distinct operating conditions, with effective features. Consequently, a meticulous choice of features is required before fault identification. The study aims to find the fewest and most reliable features in the dataset from Paderborn University so that a simple and accurate way can be found to diagnose faults using current signals. The selection of optimal features is initially performed using three algorithms: the equilibrium optimizer, the emperor penguin optimizer (EPO), and the butterfly optimization method. The k nearest neighbour (kNN) and random forest classifiers are used for classification. The results are performed based on the metrics of sensitivity, specificity, accuracy, precision, F1-score, and Matthew's Correlation Coefficient. The results indicate that machine learning models employing different feature selection techniques exhibit superior performance across different feature dimensions. Specifically, the model utilizing the kNN classifier and features selected through the EPO method achieved the maximum accuracy of 100%. The model's efficacy was also compared to the similar work presented in the literature. The efficacy of the optimal features is experimentally confirmed by analyzing current data from squirrel cage induction motors and has shown a high accuracy of 95.2%.

Prediction of Myocardial Infarction Complications using Gradient Boosting

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, representing a significant public health challenge. Myocardial Infarction (MI), a severe manifestation of CVDs, contributes substantially to these fatalities. Machine learning holds great promise for predicting MI. This study explores the potential of Gradient Boosting (GB) techniques for this purpose, explicitly focusing on CatBoost, LightGBM, XGBoost, and XGBoost Random Forest. The study leverages GB's embedded feature selection, missing-value handling, and hyperparameter tuning capabilities. Performance was evaluated using multiple metrics: Area Under the Curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews Correlation Coefficient (MCC). A probabilistic comparison matrix was used to assess the relative performance of the GB models. The results demonstrate the superiority of CatBoost, achieving a classification accuracy of 94.9%, an AUC of 0.992, a recall of 94.9%, and an MCC of 0.82. The probabilistic comparison further confirms CatBoost's superior performance. These findings contribute to MI prediction, highlighting the predictive potential of the CatBoost algorithm and ultimately aiding the fight against MI to achieve better patient outcomes.

Sentinel-1A Data Analysis for Rice Classification Utilizing Random Forests and Support Vector Machine

Rice is known to be one of the most important crops in India and many other nations, particularly in Asia, therefore accurate rice area estimation has an important role in many activities, ranging from human nutrition to environmental concerns. As a result, the determination of cultivation area remains a hot topic among researchers from numerous disciplines, planners, and decision makers. Using Sentinel-1A SAR (Synthetic Aperture Radar) satellite data, this study attempts to evaluate the effectiveness of random forest (RF) and support vector machines (SVM) algorithms for rice crop classification. According to the findings, rice fields can easily be distinguished from other crops in the research area by using the temporal characteristics of the rice crop as reflected in the VH backscatter patterns. The total precision and Kappa coefficient produced by RF showed 85.7% and 0.74, respectively, when the classification outcomes were compared to the ground reference data. These values were somewhat higher than those obtained by SVM (81.2% overall accuracy and 0.68 Kappa coefficient). The government’s rice area statistics were used to compare the analysis results; the relative difference in rice area for RF and SVM, respectively, came out to be +1.40% and -4.63%. In summary, the RF algorithm is highly recommended for the accurate differentiation of rice fields from neighbouring classes in conditions of identical climate, soil, and topography with similar methods of cultivation. On the other hand, Sentinel-1A SAR provides a valuable data set at cost-free for similar studies.

Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy.

Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system based on deep learning can lead to safer surgeries; however, deep learning models that are useful for detecting and stopping bleeding in LH have not yet been reported. In this study, we aimed to develop a deep learning model to automatically recognize bleeding regions during liver transection in LH. In this retrospective feasibility study, bleeding scenes were randomly selected from LH videos, and the videos were divided into frames at 30 frames per second. Bleeding regions within the images were annotated by pixels, and subsequently, all images were assigned to the training, validation, and test datasets to develop the deep learning model. A convolutional neural network algorithm was used to perform semantic segmentation. After training and validation, the model was evaluated using images from the test dataset. Precision, recall, and Dice coefficients served as the evaluation metrics for the model. In total, 2203 annotated images from 44 LH videos were utilized and divided into 1500, 400, and 303 frames for the training, validation, and test datasets, respectively. The precision, recall, and Dice coefficient values of the model were 0.76, 0.79, and 0.77, respectively. We developed an automatic bleeding recognition model based on semantic segmentation and verified its performance. The proposed model is potentially useful for intraoperative alerting or evaluating surgical skills in the future.

Milk Adulteration Identification using Hyperspectral Imaging and ML

Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and non-destructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: (Solid Not Fat (SNF), density, fat, lactose, conductivity, solids, protein, temperature, and pH level). The research also examines non-distractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397-1003 nm) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterants detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest (ROI) selection for feature extraction, and empirical line method (ELM) to calculate spectral reflectance signature. Machine learning techniques (Logistic Regression (LRs), Decision Tree (DTs), Support Vector Machine (SVMs), and Linear Discriminant Analysis (LDAs)), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration data set. Training, testing, and validation accuracy, precision, recall, F1-score, Kappa, and Matthew's correlation coefficient (MCC) metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment.

Exploring the Application of Machine Learning for Automatic Inbound Email Classification in CRM System at XYZ Company

Customer service has become increasingly crucial in today's business landscape, necessitating companies to provide fast, responsive, and personalized assistance to their clientele. However, amidst the challenges posed by surges in email volume, manual categorization and response strategies often lead to performance declines. To address this, we propose a system leveraging Machine Learning techniques for automated email classification. Our evaluation reveals promising results, with SVM achieving the highest accuracy of 96.59%, followed by XGB (96.02%) and RF (95.27%). These models exhibit commendable precision, recall, F1 scores, and Matthews Correlation Coefficient (MCC), showcasing their effectiveness in improving customer service efficiency and responsiveness. This integration of technology not only enhances operational efficiency but also fosters harmonious customer relationships, ultimately leading to increased loyalty and profitability for companies.

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Advanced HPLC Method with Diode Array Detection Using a Phenyl-Bonded Column for Simultaneous Quantitation of Three Sunscreen Filters in a Moisturizing Sunscreen Cream for Acne-Prone Skin

This study introduces a novel, robust, and efficient method for the simultaneous quantitative determination of three sunscreen filters, namely, 4-methylbenzylidene camphor, octyl methoxycinnamate, and avobenzone, in a moisturizing sunscreen cream specifically designed for acne-prone skin. The method employs high-performance liquid chromatography with photodiode-array detection, providing a reliable separation of the analytes. Chromatographic separation was achieved using a Fortis Phenyl analytical column (150.0 × 2.1 mm, 5 μm), with isocratic elution at a flow rate of 0.4 mL/min. The mobile phase was composed of a 57/43 (v/v) mixture of acetonitrile/45 mM aqueous ammonium formate solution, ensuring sufficient resolution and peak symmetry for the target compounds. The method was validated comprehensively for critical performance parameters, including linearity, precision, accuracy, and robustness. Linearity was established across a suitable range for all three analytes, with high correlation coefficients. Precision was confirmed with intra-run and total precision coefficients of variation of ≤4.6%, while accuracy assessments yielded a percent recovery between 98.6 and 100.4, for all quality control levels. Additionally, the method was able to effectively separate the sunscreen filters from other cosmetic ingredients, such as [β-(1.3), (1.6)-D-glucan], low molecular weight (LMW) hyaluronic acid and plant extracts ensuring specificity in complex formulations. This straightforward and time efficient sample preparation process, involving methanol extraction followed by serial dilution, makes the method suitable for routine quality control in cosmetic laboratories. The method was successfully applied to the analysis of two different lots of a commercial sunscreen cream, achieving excellent recovery for all filters, ranging between 94.6% and 99.8%, thus demonstrating its reliability and applicability for the quality control of cosmetics.

Development and Validation of an Improved HPLC-MS/MS Method for Quantifying Total and Unbound Lenalidomide in Human Plasma.

This study aimed to develop a fully validated HPLC-MS/MS method for quantifying total and unbound lenalidomide concentrations in human plasma. Unbound concentrations were measured using plasma ultrafiltrate prepared with Amicon® Centrifugal Filters. Lenalidomide and lenalidomide-d5 (internal standard) were extracted from 50 μL of human plasma using liquid-liquid extraction. Chromatography was conducted with a Halo® C18 column using 0.1% formic acid and methanol (20:80, v/v) as the mobile phase. The mass spectrometer was operated in a positive ion mode with an electrospray ionization interface and multiple reaction monitoring modes. Calibration curves were linear over the range of 5 to 1000 ng/mL (r2 > 0.996) for both the total and unbound lenalidomide. For total lenalidomide concentrations, between-run precision (coefficients of variation) and accuracy were 1.70-7.65% and 94.45-101.10%, respectively. For unbound concentrations, inter-day precision and accuracy were 1.98-10.55% and 93.95-98.48%, respectively. We developed a highly reproducible, sensitive, and efficient bioanalytical method using a smaller volume of plasma sample (50 μL) with a relatively short run time (2.5 min). The proposed analytical method was successfully applied to measure total and unbound lenalidomide concentrations at various time points in multiple myeloma patients with renal impairment.

B-316 Evaluation of an FDA-cleared Chromogranin A Assay on an Automated Analyzer

Abstract Background Chromogranin A (CgA) is a hydrophilic and acidic protein, present in the chromaffin granules of neuroendocrine cells. It is the preferred tumor marker for the monitoring of neuroendocrine tumors (NETS), which are often found in the lungs, appendix, pancreas, and gastrointestinal tract. Previous CgA assays were performed by manual ELISA testing, and were not FDA-approved, leading to a longer, more rigorous validation process and longer run times. The introduction of an automated, FDA-cleared CgA assay allows the laboratory to process patient samples more quickly and reliably, shortening a multiple hour process to minutes. The objective of this study was to evaluate the performance of an automated CgA assay recently cleared by the FDA. Methods We validated the the B·R·A·H·M·S™ Chromogranin A II (CGAII) assay on the automated B·R·A·H·M·S KRYPTOR Compact PLUS (Thermo Fisher Scientific Inc.). The CGAII assay is an immunofluorescent assay, which uses Time-Resolved Amplified Cryptate Emission (TRACE) to quantitate CGA in human serum. Assay performance characteristics were evaluated including measuring range (MR) and maximum dilution, precision, method comparison, stability, reference interval, stability, and carryover. Results The MR for CGAII was established with triplicate analysis of serum at 8 levels, ranging from 25.8 - 2,700 ng/mL. Linearity was demonstrated with a slope of 0.907, intercept of 1.948 and observed error of 2.54 ng/mL. Recovery at each point ranged from 83.3-110.3%. Carryover (<5.0 ng/mL) was not observed at a concentration approximately 2 times the upper limit of the MR. A 1:25 or 1:500 automatic dilution was determined to be acceptable (% bias <16%). Intraday precision coefficient of variation (CV) ranged from 4.1-1.7% (SDs from 2.32-6.64 ng/mL) at concentrations of 56.7 and 393.0 ng/mL. The interday CVs ranged from 6.0-7.1% (SDs from 3.43 - 28.84 ng/mL) at concentrations of 57.5 and 403.8 ng/mL. Twenty samples with concentrations ranging from 56.4-2,907 ng/mL were compared to a reference laboratory utilizing a similar methodology. The correlation coefficient was 0.9934, with a slope of 1.014, and intercept of -32.95. An additional twenty samples with concentrations ranging from 67.0-2,446 ng/mL were compared to a CgA ELISA kit (CGA-ELISA-NG, CISBIO) to determine the bias between the two methodologies. The correlation coefficient was 0.9902, with a slope of 0.845, and intercept of -15.31, and a bias of -19.9%. The serum samples were stable for 3 days when stored at 2-8°C. Recovered CgA concentrations fell outside of the 95% confidence interval after day 3 at 2-8°C. The manufacturer provided reference interval of <187 ng/mL was verified by testing 20 healthy adult samples, all of which fell within this range. Conclusions This assay has been validated and shown to be an accurate and precise method for monitoring CgA. Further, the method was validated on an automated analyzer to allow expeditious turnaround time in a core laboratory.

A-226 Validation of Calculated Glycerol Corrected Triglycerides

Abstract Background Patients with elevated free glycerol can have falsely elevated triglyceride concentrations known as pseudohypertriglyceridemia. Most clinical laboratory triglycerides methods measure the concentration of glycerol released following the enzymatic hydrolysis of triglycerides to quantitate triglycerides. Thus, increased free glycerol can lead to falsely increased triglyceride concentration. Identifying and measuring the interference is necessary to effectively manage patient care interventions. A glycerol-corrected triglyceride assay was discontinued from Roche Diagnostics in 2021. We created a process to determine glycerol-corrected triglycerides by measuring glycerol directly using an assay from Randox Laboratories. Methods Glycerol (Randox Laboratories) and nonglycerol-blanked triglycerides (Roche Diagnostics) were measured in serum using the Roche Cobas c501. Stability (n=10) and expected values (n=206) were determined using serum from healthy volunteers. Linearity (n=5) and precision (5 replicates on 2 runs over 5 days) were determined using residual clinical serum spiked with exogenous glycerol as necessary to span the analytical measuring range. Glycerol corrected triglyceride concentrations were calculated by subtracting glycerol (mmol/L) from triglyceride (mmol/L) and converting to mg/dL (x88.59). Samples previously measured using the Triglyceride/Glycerol Blanked Reagent (n=53, Roche Diagnostics) were used to assess accuracy. (Figure 1) Results Glycerol measures were stable 3 days refrigerated and 30 days frozen. Precision coefficient of variation was 1.0% within run and 7.1% within lab. Expected values were 10 - 109 mcmol/L, and the method was linear between 2 - 3,971 mcmol/L. The average bias between methods was 11.0% with slope of 1.02 and R2>0.97 (Figure 1). Conclusions Glycerol can be accurately measured using the Randox method and the result can be used to generate an accurate glycerol-corrected triglyceride concentration.

Using qPCR and ddPCR to study biodistribution of cell therapy products: a multi-site evaluation

Backgroud aimsRegenerative therapies employing cell therapy products (CTPs) have attracted considerable attention. Biodistribution (BD) evaluation of CTPs is mainly performed to clarify the cell survival time, engraftment, and distribution site. This evaluation is crucial for predicting the efficacy and safety profiles of clinical studies based on non-clinical BD study outcomes. However, no internationally unified method has been established for assessing cell BD after administration. Here, we aimed to standardize the BD assay method used for CTPs, conducting the following evaluations using the same protocol across multiple study facilities: (1) in vitro validation of quantitative polymerase chain reaction (qPCR) and droplet digital PCR (ddPCR) analyses using the primate-specific Alu gene, and (2) in vivo BD studies after the intravenous administration of human mesenchymal stem cells (hMSCs) to immunodeficient mice, commonly used in non-clinical tumorigenicity studies. MethodsQuality control samples were prepared and analyzed by adding a fixed number of human-derived cells to several mouse tissues. The respective quantitative performances of the qPCR and ddPCR methods were compared for accuracy and precision. hMSCs were intravenously administered to immunodeficient mice, and tissues were collected at 1, 4, and 24 h after administration. ResultsBoth methods demonstrated an accuracy (relative error) generally within ±50% and a precision (coefficient of variation) generally less than 50%. While differences in calibration curve ranges were observed between qPCR and ddPCR, no significant differences in quantification were found among the assay facilities. The BD of hMSCs in mice was evaluated at seven facilities (qPCR at three facilities; ddPCR at four facilities), revealing similar tissue distribution profiles in all facilities, with the lungs showing the highest cell distribution among the tissues tested. ConclusionsQuantitative evaluation of qPCR and ddPCR using Alu sequences was conducted, demonstrating that the test method can be adapted for BD evaluation.

A general strategy for developing eco-friendly, harsh condition adaptive, and friction tunable supramolecular gel lubricants via hydrogen bonding interaction

As green tribology and controllable lubrication technology progress, traditional lubricants fail to meet the evolving requirements of environmental sustainability, adaptability to extreme conditions, and precise friction coefficient control. In this work, a general strategy is proposed to obtain eco-friendly, harsh condition adaptive, and friction tunable polyol supramolecular gel lubricants. Molecular dynamics simulations reveal that the formation of supramolecular gels is induced by the hydrogen bond interaction between PVA, MXene, and polyols. Rheological tests demonstrate the excellent shear thinning and creep recovery properties of the prepared supramolecular gel at both room and low temperatures, positioning it as a promising new lubricant. Tribological tests further show that the supramolecular gel achieved a low friction coefficient of 0.056 and a wear rate as low as 1.88 × 10−9 mm3 N−1 m−1. Moreover, the tribological properties of various polyol supramolecular gels were compared and the viscosity-controlled friction lubrication behavior was observed. This research not only presents a novel strategy for developing environmentally friendly supramolecular gel lubricants that are adaptable to harsh conditions and tunable in friction, but also offers practical solutions to address challenges such as environmental pollution, lubrication failures in extreme environments, and precise friction control.

Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence.

Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical complexity and individual variability of the quadratus lumborum muscle (QLM) on ultrasound images, nerve blocks heavily rely on anesthesiologist experience. Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collected 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) network. Q-VUM precisely segments various tissues, including the QLM, the external oblique muscle, the internal oblique muscle, the transversus abdominis muscle (collectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM. Our model demonstrated robust performance, achieving mean intersection over union (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coefficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exhibited stronger segmentation performance than did the common deep learning segmentation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we proposed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time. This model aids anesthesiologists in precisely locating the nerve block site, thereby reducing potential complications and enhancing the effectiveness of nerve block procedures.

Novel Simplified Practical Method for One‐Dimensional Large‐Strain Consolidation

ABSTRACTA new simplified practical method for one‐dimensional nonlinear large‐strain consolidation of saturated homogenous soils is proposed. The derivation processes of the proposed method are introduced first, with a modification of Terzaghi's theory from a novel perspective to solve large‐strain consolidation problems. Verification checks of the proposed method with other solutions are then conducted. The proposed method is different from Lekha's solution because Lekha's analytical solution is based on the small strain theory. For linear consolidation, the proposed method shows excellent agreement with the Consolidation Settlement 2 (CS2) model. For nonlinear large‐strain consolidation, the new method is in good agreement with the CS2 model when Cc/Ck ≤ 1. After that, optimization of the proposed nonlinear solution is carried out for Cc/Ck > 1 with a more precise average constant coefficient of consolidation used in the simplified practical method, and good agreement is obtained between the solutions from the proposed method and the CS2 model. Overall, the proposed simplified method provides practical, reliable, and efficient solutions for analyzing linear and nonlinear large‐strain consolidation.

A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.

An Automated pre-Dilution Setup for Von Willebrand Factor Activity Assays.

Accurate quantification of von Willebrand factor ristocetin cofactor activity (VWF:RCo) is critical for the diagnosis and classification of von Willebrand disease, the most common hereditary and acquired bleeding disorder in humans. Moreover, it is important to accurately assess the function of von Willebrand factor (VWF) concentrates within the pharmaceutical industry to provide consistent and high-quality biopharmaceuticals. Although the performance of VWF:RCo assay has been improved by using coagulation analyzers, which are specialized devices for blood and blood plasma samples, scientists still report a high degree of intra- and inter-assay variation in clinical laboratories. Moreover, high, manual sample dilutions are required for VWF:RCo determination of VWF concentrates within the pharmaceutical industry, which are a major source for assay imprecision. For the first time, we present a precise and accurate method to determine VWF:RCo, where all critical pipetting and mixing steps are automated. A pre-dilution setup was established on CyBio FeliX (Analytik-Jena) liquid handling system, and an adapted VWF:RCo method on BCS-XP analyzer (Siemens) is used. The automated pre-dilution method was executed on three different, most frequently used coagulation analyzers and compared to manual pre-dilutions performed by an experienced operator. Comparative sample testing revealed a similar assay precision (coefficient of variation = 5.9% automated, 3.1% manual pre-dilution) and no significant differences between the automated approach and manual dilutions of an expert in this method. While no outliers were generated with the automated procedure, the manual pre-dilution resulted in an error rate of 8.3%. Overall, this operator-independent protocol enables standardization and offers an efficient way of fully automating VWF activity assays, while maintaining the precision and accuracy of an expert analyst. Key features • Automated pre-dilution setup for von Willebrand factor concentrates of various natures. • Combination of a liquid handling system (CyBio FeliX) with a coagulation analyzer (BCS-XP). • Simplifies method transfer to other laboratories. • Basic training for CyBio FeliX and BCS-XP is required. Graphical overview VWF:RCo assay principle and measurement setup. Platelets (yellow ellipsoids) with negative surface charge (- - -) are treated with formaldehyde, which partly denatures the cell surface and thus stabilizes platelets for use as assay reagents. Stabilized platelets (dark-yellow-framed yellow ellipsoids) are then brought in contact with ristocetin A (chemical structure shown; black dots), which binds to the platelet surface and facilitates binding of VWF (green circles). The graphs show an example of quantitative determination of platelet agglutination by measurement of light transmission, where increasing amounts of VWF increase light transmission over time. The photo in the left-bottom corner shows the CyBio FeliX setup for VWF sample dilution and the photo in the right-bottom corner displays the BCS-XP system, which is used for VWF:RCo measurements.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.