Amount Of Sample Research Articles

Data augmentation using a 1D-CNN model with MFCC/MFMC features for speech emotion recognition

Speech emotion recognition (SER) is attractive in several domains, such as automated translation, call centres, intelligent healthcare, and human–computer interaction. Deep learning models for emotion identification need considerable labelled data, which is only sometimes available in the SER industry. A database needs enough speech samples, good features, and a better classifier to identify emotions efficiently. This study uses data augmentation to enhance the amount of input voice samples and address the data shortage issue. The database capacity increases by adding white noise to the speech signals by data augmentation. In this work, the Mel-frequency Cepstral Coefficient (MFCC) and Mel-frequency Magnitude Coefficient (MFMC) features, along with a one-dimensional convolutional neural network (1D-CNN), are used to classify speech emotions. The datasets utilized to estimate the model's enactment were AESDD, CAFE, EmoDB, IEMOCAP, and MESD. The data augmentation with the 1D-CNN (MFMC) model performed best, with an average accuracy of 99.2% for AESDD, 99.5% for CAFE, 97.5% for EmoDB, 92.4% for IEMOCAP and 96.9% for the MESD database. The proposed 1D-CNN (MFMC) with data augmentation outperforms the 1D-CNN (MFCC) without data augmentation in emotion recognition.

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor

A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humor (VH) is an avascular tissue between the lens and the retina, mainly composed by a collagen fibers network, hyaluronic acid, and hyalocytes. Recently, it has received interest in forensic medicine, being easy to collect and susceptible to low microbiological contamination and putrefaction. Based on this evidence and thanks to the ability of Attenuated Total Reflectance – Fourier Transform InfraRed (ATR–FTIR) spectroscopy to perform fast analyses on a minimal sample amount, in this study, a new analytical approach to reliably estimate PMI is proposed combining ATR-FTIR analysis of VH human samples with multivariate statistical procedures, such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), for discriminant classification. Regression procedures, including Partial Least Squares Regression (PLSR), were performed: extremely positive results were obtained, and the most discriminant spectral features were highlighted (peaks at 1665, 1630, 1585, 1400, 1220, 1200, 1120, 854, 835, and 740 cm−1) and associated to PMI classes (average accuracy over 80 %). Specific and reliable markers able to correlate the macromolecular composition of VH with the PMI were identified, revealing a post-mortem protein degradation and amino acids deamination (decrease of proteins and increase of free amino acids and NH3), an increase of lactate, which diffuses from the retina to the VH, and changes in the hyaluronic acid component.

Determination of drugs of abuse and metabolites in plasma microsamples by LC-MS/MS after microQuEChERS extraction.

Aim: Identifying drugs of abuse and their metabolites in plasma is vital in both forensic and clinical toxicology. While the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method offers an efficient approach to sample preparation, its application is complex due to the wide-ranging properties of target analytes and the challenges posed by biological matrix interferences. This study aims to develop a microQuEChERS approach for the quantification of 14 drugs of abuse and metabolites utilizing minimal sample and solvent volumes.Methods: The microQuEChERS method involved using 10μl plasma samples, 25mg of a salt mixture and 150μl of acetonitrile. Extracts were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), with a 7.5min run. The assay was validated according to bioanalytical guidelines.Results: The accuracy was 96.8-112.4%. The within-assay precision was within 2.0-8.9% and the between-assay precision was within 3.2-8.2%. Matrix effects were found to range from -5.7 to 13.5%. The extraction yield was higher than 74.7%.Conclusion: This study described a microQuEChERS sample preparation approach for determining drugs of abuse and metabolites using plasma microsamples and LC-MS/MS. The approach is efficient, environmentally friendly and suitable for scenarios with limited amounts of biological samples.

Magnetic particle spray mass spectrometry for the determinationof beta-blockers in plasma samples.

Magnetic particle spray mass spectrometry (MPS-MS), an innovative ambient ionization technique proposed by our research group, was employed to determine beta-blockers in human plasma samples. A dispersive solid phase extraction of atenolol, metoprolol, labetalol, propranolol, nadolol, and pindolol was carried out using magnetic molecularly imprinted polymer (M-MIP) particles that were attached to the tip of a metal probe, which was placed in the mass spectrometer inlet. A solvent (1% formic acid in methanol) was dispensed on the particles, and the Taylor cone was formed around them (in high voltage). The analytes were desorbed/ionized and determinedby a triple quadrupole mass spectrometer. M-MIP was synthesized with oxprenolol as a pseudo-template, demonstrating good selectivity to beta-blockers compared withno-analog molecules, with an adsorption process occurring in monolayers, according to isotherm studies. Kinetic experiments indicated chemisorption as the predominant M-MIP/analyte interaction. The analytical curves were linear (R2 > 0.98), and the limit of quantification was 3µg L-1 for all the analytes. Limits of detection ranged from 0.64 to 2.41µg L-1. Precisions (relative standard deviation) and accuracies (relative error) ranged from 3.95 to 21.20% and-17.05 to 18.93%, respectively. MPS-MS proved to be a simple, sensitive, and advantageous technique comparedwithconventional approaches. The analyses were fast, requiring no chromatographic separation and without ionic suppression. The method is aligned with green chemistry principles, requiring minimal sample, solvent, and sorbent amounts. MPS-MS successfully integrates sample preparation and ambient ionization mass spectrometry and holds great potential for application with other sorbents, samples, and analytes.

6-Methoxy-2-Naphthoate as a Standard Chromophore for Chiroptical Studies by Fluorescence-Detected Exciton-Coupled Circular Dichroism.

This study investigated the applicability of fluorescent chromophores for exciton-coupled circular dichroism (ECCD) exploiting fluorescence-detected circular dichroism (FDCD). FDCD had been previously reported useful in allowing the sensitive detection of ECCD in favorable conditions. However, fluorescence detection may prevent applications of the combined method especially when solutions are polarized in emission. Even without polarization of emission, FDCD deviates from circular dichroism (CD) in some cases when the fluorophore of interest interacts with nonfluorescent chromophore. Herein, it was confirmed that employing 6-methoxy-2-naphthoate always yielded interpretable exciton-coupled FDCD spectra even when coupling with nonfluorescent p-substituted benzoates. The 6-methoxy-2-naphthoate chromophore (6-MN) is prescribed in special cases when only a small amount of sample is available for determining the absolute stereochemistry by the CD exciton chirality method observed by FDCD.

Spectroscopic techniques and chemometrics for ink analysis: Document analysis application

This study represents a methodology to determine the falsified documents via a combined method of spectroscopic techniques and chemometrics. The techniques of UV–Vis and FTIR-ATR spectroscopies and principal component analysis methods have been applied to black, blue, and red ballpoint pens available in the Türkiye market. The analysis results of the applied spectroscopic techniques were found to be consistent with each other for the examined eleven ink samples from three different brands, and it was observed that FTIR-ATR spectroscopy had higher discriminative power. A dried sample preparation technique developed for small amounts of samples for FTIR-ATR measurements and the increased the sensitivity of FTIR-ATR technique was associated with this sample preparation method. Statistical techniques showed that the UV–Vis technique proves useful outcomes in distinguishing inks according to their colors, whereas the FTIR-ATR technique is valuable for identifying ink brands. Additional analysis of the sub-brand of black inks and falsified old-dated check sample analysis validated that our methodology has the potential to classify and identify ballpoint inks in forensic applications. Due to the limited sample quantity available for analysis in real samples, only FTIR-ATR analysis was possible, and the results demonstrated that document forgery could be detected from small parts of samples in the document. When we look at the overall outcome of this study, a comprehensive methodology enriched with statistical calculations has been proposed that can be applied even in a laboratory with limited resources, having only UV–Vis and FTIR-ATR capabilities.

Development of Cs separation methods from large amounts of soil samples to determine the 135Cs/137Cs isotope ratio

Development of Cs separation methods from large amounts of soil samples to determine the 135Cs/137Cs isotope ratio

The Role of Different Roasting Temperatures on the Physico-Chemical Properties, Phenolic Compounds, Fatty Acid and Mineral Contents of Carob (Ceratonia siliqua L.) Fruit Powder

In this study, the role of different roasting temperatures on oil amount, total phenolic contents, total flavonoid quantities, antioxidant capacity, polyphenol, fatty acid and mineral profiles of roasted-carob fruit powders was revealed. The oil and protein amounts of the carob powders were specified to be between 0.40 (90 °C) and 0.85% (control) to 8.40 (120 °C) and 10.27% (control), respectively. Total phenolic and flavonoid quantities of the raw (control—unroasted) and roasted carob powders were defined to be between 781.16 (control) and 903.07 mg GAE/100 g (150 °C) to 663.81 (control) and 1141.90 mg/100 g (150 °C), respectively. In addition, antioxidant activities of the carob powders were measured to be between 9.27 (150 °C) and 10.30 mmoL/kg (control). Gallic acid and 3,4-dihydroxybenzoic acid amounts of the carob powders were specified to be between 45.16 (control) and 120.66 (150 °C) to 7.06 (control) and 20.46 mg/100 g (150 °C), respectively. Palmitic, oleic and linoleic acids were the key fatty acids of carob oils. It is thought that the fatty acid compositions of carob powder oil, which is generally subjected to heat treatment, are negatively affected by high temperatures. Especially, the linoleic and linolenic acid amounts of the oil samples decreased significantly after 90 °C. The highest element was potassium, followed by Ca, P, Mg, S, Fe, Na, Pb, Cu, Zn B and Mn, in descending order.

Comparing occurrence of per- and polyfluoroalkyl substances (PFAS) in municipal biosolids and industrial wastewater sludge: A City of Los Angeles study

Biosolids and sludge are what remain after the liquid fraction of wastewater is separated during wastewater treatment. These high organic content matrices are known to contain organic contaminants, a few of which are the hazardous and environmentally persistent per- and polyfluoroalkyl substances (PFAS). The current study investigates whether sludge from a treatment facility serving mostly industrial establishments and biosolids from a facility serving mostly domestic dwellings retain these ‘forever chemicals’ similarly. Using 31 markers covering different classes of PFAS, the sludge was found to contain higher levels of PFAS (869 ± 791 ng/g; 21 of 31) than biosolids (31 ± 7 ng/g, 11 of 31). The most abundant overall was perfluorooctane sulfonic acid (PFOS), mostly in sludge (range: 71–1300 ng/g versus 0–18 ng/g in biosolids). The large PFAS concentration variability in sludge was seasonal and sinusoidal. Sludge, additionally, contained all long chain PFAS, precursors (mostly surfactant ingredients and their transformation byproducts) and short chain PFAS (perhaps because of higher moisture content). By regression, the sludge is shown to consistently contain twice as much PFAS as biosolids when the same amounts are exposed to increasing levels of PFAS. Factors observed to cause differential PFAS retention between sludge and biosolids were moisture (98.6 % and 72.1 %, respectively), chain length, input quality (industrial versus residential) and functional group. Sulfonic acids for instance are one C atom shorter than carboxylates with similar occurrence in sludge and biosolids. More studies are needed to define the roles that organic carbon of sludge/biosolids, water chemistry, temperature and factors not considered here play in partitioning PFAS between the two matrices with respect to inputs. Existing Koc values could not help in explaining observed trends, but the ratio of biosolids-to-influent concentrations was found to correlate positively with PFAS size. Using influent in the ratio, and not effluent, is novel. SynopsisSludge and biosolids are soil amendments; they contain hazardous and persistent PFAS. Methods of decoupling PFAS from these matrices start with understanding matrix-driven PFAS partitioning as shown here.

Mask-guided BERT for few-shot text classification

Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting. Here we present Mask-BERT, a simple and modular framework to help BERT-based architectures tackle FSL. The proposed approach fundamentally differs from existing FSL strategies such as prompt tuning and meta-learning. The core idea is to selectively apply masks on text inputs and filter out irrelevant information, which guides the model to focus on discriminative tokens that influence prediction results. In addition, to make the text representations from different categories more separable and the text representations from the same category more compact, we introduce a contrastive learning loss function. Experimental results on open-domain and medical-domain datasets demonstrate the effectiveness of Mask-BERT. Code and data are available at: github.com/WenxiongLiao/mask-bert

Rapid Tryptophan Assay as a Screening Procedure for Quality Protein Maize.

Tryptophan is an essential amino acid deficient in cereals, especially maize. However, maize (Zea mays L.) is the main source of protein in some developing countries in Africa and Latin America. In general, the nutritional profile of cereals is poor, because they are deficient in essential amino acids such as tryptophan and lysine due to a relatively higher proportion of alcohol-soluble proteins. Quality protein maize (QPM) has been developed through genetic manipulation for the nutritional enrichment of maize to address these problems. Nevertheless, methods for protein, lysine and tryptophan are time-consuming and require relatively large amounts of samples. Therefore, we have advanced here a simple, cheap, fast, reliable and robust procedure for the determination of protein and tryptophan in the same biuret supernatant, which can also be used for chemical characterization of other cereals. Samples of 50 mg maize ground to pass through a 0.1 mm screen were sonicated for 5 min. in eppendorf vials with 1.5 mL of a biuret reagent each. After centrifugation and protein determination by biuret, 0.2 mL of supernatant was treated with 0.8 mL of a tryptophan reagent. Both total protein and tryptophan can be determined in microplates at 560 nm to speed up the measurements. The main advantage of the new micro-method is the rapid estimation of the nutrient quality of maize samples by a single weighing of a small amount of valuable plant materials.

Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data.

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis.

Multi-step HPLC fractionation enabled in-depth and unbiased characterization of histone PTMs

Histone post-translational modifications (PTMs) are critical epigenetic regulatory factors. Histone PTMs are highly dynamic and complicated, encompassing over 30 structurally diverse modifications across nearly 180 amino acid residues, which generated extensive information regarding histone marks. In proteomics-based characterization of histone PTMs, chemical derivatization and antibody-based affinity enrichment were frequently utilized to improve the identification depth. However, chemical derivatization suffered from the occurrence of side reactions, and antibody-based affinity enrichment focused on specific PTM types of interest. In this research, we developed a multi-step fractionation strategy for comprehensively unbiased detection of histone PTM sites. By combining protein-level fractionation with peptide-level alkaline and acid phase fractionation, we developed the Multidimensional Fractionation based Histone Mark Identification Technology (MudFIT) and increased PTM identification to a total of 264 histone PTM sites. To the best of our knowledge, this strategy achieved the most comprehensive characterization of histone PTM sites in a single proteomics study. Using the same starting amount of sample, MudFIT identified more Kac sites and Kac peptides than those in antibody-based acetylated peptide enrichment. Moreover, in addition to well-studied histone marks, we discovered 36 potential new histone PTM sites including H2BK116bu, H4R45me2, H1K63pr, and uncovered unknown histone PTM types like aminoadipic on lysine and nitrosylation on tyrosine. Our data provided a method and resource for in-depth characterization of histone PTM sites, facilitating further biological understanding of histone marks.

Heat capacity measurements by a Setaram μDSC3 evo microcalorimeter: estimation of deviation in the measurement, advanced data analysis by mathematical gnostics, and prediction by the artificial neural network

AbstractThe aim of the work is to study the variation in the isobaric heat capacity measurement due to changes in the amount of sample and the calibration standard using a Setaram $$\mu$$ μ DSC3 evo microcalorimeter batch cells to provide a guideline toward the selection of the sample amount to minimize heat capacity measurement error in $$\mu$$ μ DSC. Moreover, overall variation, variation due to the sample amount, and variation due to the calibration standard (reference) amount in heat capacity measurement were estimated for different amounts of the sample or/and the calibration standard material. In the present work, heat capacity measurements were taken for [C4mim][Tf2N] (1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide) ionic liquid as a sample material and 1-butanol as a calibration standard. A novel non-statistical approach, mathematical gnostics (MG), was used for data analysis of measured heat capacities data. Moreover, the artificial neural network (ANN) model was developed to predict the deviation in the heat capacity measurement with 99.83% accuracy and 0.9939 R2 score. The Python package PyCpep based on the trained ANN model was developed to predict the deviation in the heat capacity measurement.

Microvolumetric determination of thrombomodulin based on competitive immunoreaction using a portable glucometer.

A new method of reducing the amount of reagent and sample for determination of thrombomodulin (TM) was developed based on competitive immunoreaction using a portable glucometer (PGM). Two types of nanocomposites, TM protein-modified magnetic nanoparticles (MNPs-TM) and TM antibody-/glucose oxidase-modified gold nanoparticles (Ab-GNPs-GOx), were prepared. Their binding product, MNPs-TM-Ab-GNPs-GOx, in the microvolumetric solution was used to catalyze the oxidation of glucose, leading toa decline of the glucose content. The TM-involved competitive immunoreaction had a negative effect on the generation of MNPs-/GNPs-based nanocomposites and inhibited the catalytic oxidation of glucose. The glucose content difference in the microvolumetric solution, which was revealed by a PGM, was in proportion to the logarithm of the TM concentration from 25ngmL-1 to 2.5μgmL-1. The limit of detection was 5.7ngmL-1. Microvolumetric solution and a PGM were used in the measurement, which overcame some deficienciesof classical methods in chemo/biosensing, for example, special instrument, complicated measurement procedure, and high cost.

Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.

Clinical metabolomics: current state and prospects in Russia.

Using analytical technologies it is possible now to measure the entire diversity of molecules even in a small amount of biological samples. Metabolomic technologies simultaneously analyze thousands of low-molecular substances in a single drop of blood. Such analytical performance opens new possibilities for clinical laboratory diagnostics, still relying on the measurement of only a limited number of clinically significant substances. However, there are objective difficulties hampering introduction of metabolomics into clinical practice. The Institute of Biomedical Chemistry (IBMC), consolidating the efforts of leading scientific and medical organizations, has achieved success in this area by developing a clinical blood metabogram (CBM). CBM opens opportunities to obtain overview on the state of the body with the detailed individual metabolic characteristics of the patient. A number of scientific studies have shown that the CBM is an effective tool for monitoring the state of the body, and based on the CBM patterns (signatures), it is possible to diagnose and monitor the treatment of many diseases. Today, the CBM creation determines the current state and prospects of clinical metabolomics in Russia. This article, dedicated to the 80th anniversary of IBMC, is a review of these achievements focused on a discussion of their implementation in clinical practice.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2μL) at 25°C with a higher sensitivity (LOD 1.3fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N=7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.

Recent advances in capillary electrophoresis-mass spectrometry analysis of ‍trace biomolecules

In recent years, various trace bioanalysis methods have been developed, including single-cell transcriptome analysis methods. As the sample volume and amount of biomolecules contained therein are extremely limited, development of new single-cell analysis methods require extremely high-level techniques. It is necessary to design an appropriate analysis system that integrates a highly sensitive detection system and a pretreatment protocol for minimizing sample loss, where separation method is especially important for analyzing diverse mixtures of biomolecules. Among them, capillary electrophoresis (CE) can separate biomolecules in nanoliter-scale solutions with high resolution, making it highly compatible with trace samples such as single cells. By combining with highly sensitive nano-electrospray ionization-mass spectrometry (MS), it is possible to detect nanomolar to sub-nanomolar biomolecules, which can be further improved by using online sample preconcentration methods. These highly sensitive analytical techniques have made it possible to analyze trace amounts of metabolites, proteins, lipids, etc. This review paper summarizes the research on CE-MS trace bioanalysis that has been reported to date, with a focus on single-cell analysis.

Performance evaluation of endoscopic ultrasonography needles: Experimental study.

Background and study aims The relative procedural performance of needles for endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) is unclear. The present study therefore compared six types of 22-gauge FNA/B needles using a bench simulator. Methods Resistance forces during needle puncture and removal, needle tip damage before and after procedure, leakage after puncture of mucinous cyst models, the shape of the puncture surface at the puncture site, amounts of samples extracted, ranges of needle deflection angles, and needle deformation after multiple procedures were compared using six types of needles. Results Maximum resistance forces during puncture and removal were highest for ProCore needles and lowest for Expect needles. None of the needles had damage after puncturing. SharkCore needles showed the highest amount of leakage, whereas FNA needles showed no leakage. The puncture tracts of FNA needles remained in the form of a flap at the puncture site, whereas FNB needles broke off the target material creating a hole. The target material removed was supplemented within the puncture needle. TopGain needles produced significantly larger samples than ProCore, EZShot3 Plus, and Expect needles. FNB needles produced larger and more core samples than FNA needles. EZShot3 Plus needles had the highest range of needle deflection angle using an elevator device and the lowest needle deformation after 20 punctures at full endoscopic angle and a full elevator. Conclusions The performance of the six needles differed in various ways. Understanding the characteristics of each needle may allow for selection of the appropriate needle for each situation.