Biological Samples Research Articles

Can the cyanide metabolite, 2-aminothiazoline-4-carboxylic acid, be used for forensic verification of cyanide poisoning?

Forensic verification of cyanide (CN) poisoning by direct CN analysis in postmortem blood is challenging due to instability of CN in biological samples. CN metabolites, thiocyanate (SCN-) and 2-aminothiazoline-4-carboxylic acid (ATCA), have been proposed as more stable biomarkers, yet it is unclear if either is appropriate for this purpose. In this study, we evaluated the behavior of CN biomarkers in postmortem swine and postmortem blood to determine which serves as the best biomarker of CN exposure. CN, SCN-, and ATCA were measured in postmortem swine (N = 8) stored at 4°C and postmortem blood stored at 25°C (room temperature, RT) and 37°C (typical human body temperature, HBT). Following CN poisoning, the concentration of each CN biomarker increased well above the baseline. In postmortem swine, CN concentrations declined rapidly (t1/2 = 34.3h) versus SCN- (t1/2 = 359h, 15days) and ATCA (t1/2 = 544h, 23days). CN instability in postmortem blood increased at RT (t1/2 = 10.7h) and HBT (t1/2 = 6.6h). SCN- and ATCA were more stable than CN at all storage conditions. In postmortem swine, the t1/2s of SCN- and ATCA were 15 and 23days, respectively. While boththe t1/2s of SCN- and ATCA were relatively lengthy, endogenous levels of SCN- were muchmore variable than ATCA. While there are still questions to be answered, ATCA was the most adept forensic marker of CN poisoning (i.e., ATCA producedthe longest half-life, the largest increase above baseline levels, and most stable background concentrations).

High-speed light-sheet fluorescent microscopy with an adaptive three-dimensional region-of-signal identification method

Light-sheet fluorescent microscopy (LSFM) is a crucial 3D imaging technique for tissues with advantages in high 3D spatial resolution, effective optical sectioning, and quick imaging. Prior to imaging, the sample's imaging depth, imaging region, and region of interest (ROI) division should all be established in accordance with the sample's outermost dimensions. However, because biological samples are inherently irregular a traditional imaging flow that images ROI by ROI, slice by slice, would waste a great deal of time and data storage space by additionally recording non-signal regions. In order to solve this problem, we create a high-speed LSFM with an adaptive three-dimensional region-of-signal identification method (RSI). We immediately perform crude imaging for each ROI before the software analyzes the longitudinal distribution and, ultimately, uses dimensionality reduction, binarization, filtering, and redundancy expansion to acquire the signal region result. By imaging two mouse spinal cord segments with a total of 170 ROIs and 180 ROIs on two channels, respectively, the high-speed imaging capability is validated. Our method reduced the overall imaging time and raw data size by more than treble and 14 times, respectively, without losing signal. With a programmable imaging range based on the real signal region, this method offers an efficient tool for flexible light-sheet microscopy. It can also be applied to other microscopy techniques to speed up imaging.

Shotgun metagenomic investigation of foodborne pathogens and antimicrobial resistance genes in artisanal fermented meat products from the Mediterranean area

In this pilot study, we compared the metagenomic profiles of different types of artisanal fermented meat products collected in Italy, Greece, Portugal, and Morocco to investigate their taxonomic profile, also in relation to the presence of foodborne pathogens and antimicrobial resistance genes. In addition, technical replicates of the same biological sample were tested to estimate the reproducibility of shotgun metagenomics. The taxonomic analysis showed a high level of variability between different fermented meat products at both the phylum and genus levels. Staphylococcus aureus was identified with the highest abundance in Italian fermented meat; Escherichia coli in fermented meat from Morocco; Salmonella enterica in fermented meat from Greece; Klebsiella pneumoniae and Yersinia enterocolitica in fermented meat from Portugal. The fungi Aspergillus, Neosartoria, Emericella, Penicillum and Debaryomyces showed a negative correlation with Lactococcus, Enterococcus, Streptococcus, Leuconostoc and Lactobacillus. The resistome analysis indicated that genes conferring resistance to aminoglycoside, macrolide, and tetracycline were widely spread in all the samples. Our results showed that the reproducibility between technical replicates tested by shotgun metagenomic was very high under the same conditions of analysis (either DNA extraction, library preparation, sequencing analysis, and bioinformatic analysis), considering both the degree of overlapping and the pairwise correlation.

Biological Potential and Therapeutic Effectiveness of Phytoproduct 'Fargesin' in Medicine: Focus on the Potential of an Active Phytochemical of Magnolia fargesii.

Flos Magnoliae is one of the important medicinal plants in different traditional medicine, including Chinese herbal medicine. Lignans and neolignans, including tetrahydrofurofuran, tetrahydrofuran, and aryltetralin, are present in the Flos Magnoliae species. A wide range of pharmacological activity of Flos Magnoliae has been reported in medicine. Fargesin has been isolated from Magnolia fargesii and is a lignan-class phytochemical. Fargesin has numerous pharmacological activities in medicine, including its effectiveness on lipid and glucose metabolism, oxidative stress, myocardial apoptosis, etc. In the present work, we have summarized the detailed scientific information of fargesin concerning its medicinal properties and pharmacological activities. Numerous biological and chemical aspects of fargesin are discussed here, including the detailed pharmacological activities and analytical aspects of fargesin. In this review, we have also compiled analytical data on fargesin based on available scientific literature. Ethnopharmacological information on fargesin was gathered by a literature survey on Pubmed, Science Direct, Google, and Scopus using the terms fargesin, Flos Magnoliae, phytochemical, and herbal medicine. The present review paper compiled the scientific data on fargesin in medicine for its pharmacological activities and analytical aspects in a very concise manner with proper citations. The present work signified the biological importance of fargesin in medicine due to its significant impact on bone disorders, lung injury, colon cancer, atherosclerosis, neurological disorders, ischemia, sars-cov-2, allergy, lipid and glucose metabolism, melanin synthesis, and different classes of enzymes. Furthermore, fargesin also has anti-inflammatory, antihypertensive, antiprotozoal, antimycobacterial, and antifeedant activity. However, analytical methods used for the separation, identification and isolation of fargesin in different biological and non-biological samples were also covered in the present review. The present work revealed the pharmacological activities and analytical aspects of fargesin in medicine and other allied health sectors.

Dosimetric Considerations for 177Lu-DOTATATE Therapy in a Patient With Chronic Renal Failure Under Hemodialysis

Abstract This case report explores the use of 177Lu-DOTATATE in a hemodialysis patient. For the first time, this study assesses the average dose received by the bone marrow, the primary organ at risk, using an original double estimation method through independently acquired imaging and biological samples counting data. Despite elevated doses, the absorbed doses to the bone marrow (0.662–0.740 Gy) were within safe limits. Radiation protection measurements for staff were also compliant. This work supports that effective early dialysis and systematic personalized dosimetry are crucial for hemodialysis patients undergoing 177Lu-PRRT due to their variability (residual excretion, treatment history, etc).

Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis-Mass Spectrometry.

Proteomics analysis of mass-limited samples has become increasingly important for understanding biological systems in physiologically relevant contexts such as patient samples, multicellular organoids, spheroids, and single cells. However, relatively low sensitivity in top-down proteomics methods makes their application to mass-limited samples challenging. Capillary electrophoresis (CE) has emerged as an ideal separation method for mass-limited samples due to its high separation resolution, ultralow detection limit, and minimal sample volume requirements. Recently, we developed "spray-capillary", an electrospray ionization (ESI)-assisted device, that is capable of quantitative ultralow-volume sampling (e.g., pL-nL level). Here, we developed a spray-capillary-CE-MS platform for ultrasensitive top-down proteomics analysis of intact proteins in mass-limited complex biological samples. Specifically, to improve the sensitivity of the spray-capillary platform, we incorporated a polyethylenimine (PEI)-coated capillary and optimized the spray-capillary inner diameter. Under optimized conditions, we successfully detected over 200 proteoforms from 50 pg of E. coli lysate. To our knowledge, the spray-capillary CE-MS platform developed here represents one of the most sensitive detection methods for top-down proteomics. Furthermore, in a proof-of-principle experiment, we detected 261 ± 65 and 174 ± 45 intact proteoforms from fewer than 50 HeLa and OVCAR-8 cells, respectively, by coupling nanodroplet-based sample preparation with our optimized CE-MS platform. Overall, our results demonstrate the capability of the modified spray-capillary CE-MS platform to perform top-down proteomics analysis on picogram amounts of samples. This advancement presents the possibility of meaningful top-down proteomics analysis of mass-limited samples down to the level of single mammalian cells.

Computational study of diffraction image formation from XFEL irradiated single ribosome molecule

Single particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Current status, challenges and prospects of antifouling materials for oncology applications

Targeted therapy has become crucial to modern translational science, offering a remedy to conventional drug delivery challenges. Conventional drug delivery systems encountered challenges related to solubility, prolonged release, and inadequate drug penetration at the target region, such as a tumor. Several formulations, such as liposomes, polymers, and dendrimers, have been successful in advancing to clinical trials with the goal of improving the drug’s pharmacokinetics and biodistribution. Various stealth coatings, including hydrophilic polymers such as PEG, chitosan, and polyacrylamides, can form a protective layer over nanoparticles, preventing aggregation, opsonization, and immune system detection. As a result, they are classified under the Generally Recognized as Safe (GRAS) category. Serum, a biological sample, has a complex composition. Non-specific adsorption of chemicals onto an electrode can lead to fouling, impacting the sensitivity and accuracy of focused diagnostics and therapies. Various anti-fouling materials and procedures have been developed to minimize the impact of fouling on specific diagnoses and therapies, leading to significant advancements in recent decades. This study provides a detailed analysis of current methodologies using surface modifications that leverage the antifouling properties of polymers, peptides, proteins, and cell membranes for advanced targeted diagnostics and therapy in cancer treatment. In conclusion, we examine the significant obstacles encountered by present technologies and the possible avenues for future study and development.

Fabrication of MXene/chitosan/polyurea nanocomposite decorated on a graphenized substrate for electro-enhanced solid-phase microextraction of diclofenac followed by its determination using differential pulse voltammetry.

A novel solid-phase microextraction fiber based on MXene-chitosan-polyurea (MXene/CS/EPPU) nanocomposite decorated on a graphenized pencil lead fiber (MXene/CS/EPPU/GPLF) was prepared and utilized for electro-enhanced solid-phase microextraction (EE-SPME) of diclofenac (DCF) in biological samples. After extraction and desorption of DCF, it was determined by differential pulse voltammetry (DPV). For this purpose, the working electrode was prepared by deposition of the mentioned MXene/CS/EPPU nanocomposite onto the graphenized pencil lead. The synthesized SPME fiber was characterized using scanning electron microscopy and X-ray diffraction techniques. The effect of various parameters influencing the extraction and the desorption process were investigated, including applied voltage in the extraction and desorption steps, extraction and desorption times, and pH. The developed method exhibited a rather wide linearity in the range 2-1200 ng mL-1 (R2 = 0.985) for the determination of DCF in plasma samples. The limit of detection and the limit of quantification for plasma samples were estimated to be 0.58 and 1.9 ng mL-1 based on the 3Sb/m and 10Sb/m definitions, respectively. The method's accuracy and applicability have been evaluated by the analysis of plasma samples, leading to the relative recoveries in the range 87.0% and 98.0% with the relative standard deviations lower than 3.1%.

Validation of a simple bioanalytical method for the determination of melatonin concentration in human serum using high-performance liquid chromatography with a fluorescent detector

Analysis of melatonin in biological samples is a challenge for the analyst both because of its low concentration and because of the numerous interfering substances. Therefore, chromatography, as a powerful analytical technique used for the separation, detection, and quantification of analytes, can be the method of choice for determining melatonin in biological samples for clinical use and pharmacokinetic research. The method was developed and validated according to the internal documents in accordance with Good Laboratory Practice. The concentrations of melatonin in human serum were determined using a high-performance liquid chromatography method with a fluorescence detector and mirtazapine as an internal standard. Preparation of serum samples was performed by liquid–liquid extraction with ethyl acetate: n-hexane (1:1 v/v). High linearity was exhibited over a concentration range of 25–10,000 pg/mL of melatonin, with a correlation coefficient higher than 0.999 during the course of method validation. Repeatability and intermediate precision show a relative standard deviation lower than 6.6 %, and accuracies were from 90.7 % to 110.3 %. The lower limit of quantitation was 30 pg/mL, and the limit of detection was 7.92 pg/mL. The in-house method is simple, fast, reproducible, and applicable for the determination of melatonin in human serum.

Bifunctional oxidase-peroxidase mimicking Fe–Ce MOF on paper-based analytical devices to intensify luminol chemiluminescence: Application for measuring different sugars with a smartphone readout

This study presents a potent paper-based analytical device (PAD) for quantifying various sugars using an innovative bi-nanozyme made from a 2-dimensional Fe/Ce metal-organic framework (FeCe-BTC). The MOF showed excellent bifunctional peroxidase-oxidase activities, efficiently catalyzing luminol's chemiluminescence (CL) reaction. As a peroxidase-like nanozyme, FeCe-BTC could facilitate the dissociation of hydrogen peroxide (H2O2) into hydroxyl radicals, which then oxidize luminol. Additionally, it was also discovered that when reacting with H2O2, the MOF turns into a mixed-valence MOF, and acts as an oxidase nanozyme. This activity is caused by the generated Ce4+ ions in the structure of MOF that can directly oxidize luminol. The MOF was directly synthesized on the PAD and cascaded with specific natural enzymes to establish simple, rapid, and selective CL sensors for the measurement of different sugars. A cell phone was also used to record light intensities, which were then correlated to the analyte concentration. The designed PAD showed a wide linear range of 0.1–10 mM for glucose, fructose, and sucrose, with detection limits of 0.03, 0.04, and 0.04 mM, respectively. It showed satisfactory results in food and biological samples with recovery values ranging from 95.8 to 102.4 %, which makes it a promising candidate for point-of-care (POC) testing for food control and medicinal purposes.

Cytochrome c electrochemical detection utilizing molecularly imprinted poly(3, 4-ethylenedioxythiophene) on a disposable screen printed carbon electrode

Cytochrome c (cyt c) has been found to play a function in apoptosis in cell-free models. This work presents the creation of molecularly imprinted conducting poly(3, 4-ethylenedioxythiopene) (MIPEDOT) on the surface of a screen printed carbon electrode (SPCE) for cyt c. Cyt c was imprinted by electropolymerization due to the presence of an EDOT monomer hydrophobic functional group on SPCE, using CV to obtain highly selective materials with excellent molecular recognition ability. MIPEDOT was characterized by CV, EIS, and DPV using ferricyanide/ferrocyanide as a redox probe. Further, the characterization of the sensor was accomplished using SEM for surface morphological confirmation. Using CV, the peak current measured at the potential of +1 to −1 V (vs. Ag/AgCl) is linear in the cyt c concentration range from 1 to 1200 pM, showing a remarkably low detection limit of 0.5 pM (sensitivity:0.080 μA pM). Moreover, the applicability of the approach was successfully confirmed with the detection of cyt c in biological samples (human plasma). Similarly, our research has proven a low-cost, simple, and efficient sensing platform for cyt c detection, rendering it a viable tool for the future improvement of reliable and exact non-encroaching cell death detection.

Phase contrast micro-CT with adjustable in-slice spatial resolution at constant magnification

Objective. To report on a micro computed tomography (micro-CT) system capable of x-ray phase contrast imaging and of increasing spatial resolution at constant magnification. Approach. The micro-CT system implements the edge illumination (EI) method, which relies on two absorbing masks with periodically spaced transmitting apertures in the beam path; these split the beam into an array of beamlets and provide sensitivity to the beamlets’ directionality, i.e. refraction. In EI, spatial resolution depends on the width of the beamlets rather than on the source/detector point spread function (PSF), meaning that resolution can be increased by decreasing the mask apertures, without changing the source/detector PSF or the magnification. Main results. We have designed a dedicated mask featuring multiple bands with differently sized apertures and used this to demonstrate that resolution is a tuneable parameter in our system, by showing that increasingly small apertures deliver increasingly detailed images. Phase contrast images of a bar pattern-based resolution phantom and a biological sample (a mouse embryo) were obtained at multiple resolutions. Significance. The new micro-CT system could find application in areas where phase contrast is already known to provide superior image quality, while the added tuneable resolution functionality could enable more sophisticated analyses in these applications, e.g. by scanning samples at multiple scales.

Fourier space aberration correction for high resolution refractive index imaging using incoherent light

An aberration correction method is introduced for 3D phase deconvolution microscopy. Our technique capitalizes on multiple illumination patterns to iteratively extract Fourier space aberrations, utilizing the overlapping information inherent in these patterns. By refining the point spread function based on the retrieved aberration data, we significantly improve the precision of refractive index deconvolution. We validate the effectiveness of our method on both synthetic and biological three-dimensional samples, achieving notable enhancements in resolution and measurement accuracy. The method's reliability in aberration retrieval is further confirmed through controlled experiments with intentionally induced spherical aberrations, underscoring its potential for wide-ranging applications in microscopy and biomedicine.

Trypsin Encapsulation in the Zeolitic Imidazolate Framework for Low-Molecular Weight Protein Analysis with High Selectivity and Efficiency.

Low-molecular weight proteins (LWPs) are important sources of biological information in biomarkers, signaling molecules, and pathology. However, the separation and analysis of LWPs in complex biological samples are challenging, mainly due to their low abundance and the complex sample pretreatment procedure. Herein, trypsin modified by poly(acrylic acid) (PAA) was encapsulated by a zeolitic imidazolate framework (ZIF-L). Mesopores were formed on the ZIF-L with the introduction of PAA. An alternative strategy for separation and pretreatment of LWPs was developed based on the prepared ZIF-L-encapsulated trypsin with adjustable pore size. The mesoporous structure of the prepared materials selectively excluded high-molecular weight proteins from the reaction system, allowing LWPs to enter the pores and react with the internal trypsin, resulting in an improved separation efficiency. The hydrophobicity of the ZIF-L simplified the digestion process by inducing significant structural changes in substrate proteins. In addition, the enzymatic activity was significantly enhanced by the developed encapsulation method that maintained the enzyme conformation, allowed low mass transfer resistance, and possessed a high enzyme-to-substrate ratio. As a result, the ZIF-L-encapsulated trypsin can achieve highly selective separation, valid denaturation, and efficient digestion of LWPs in a short time by simply mixing with substrate proteins, greatly simplifying the separation and pretreatment process of the traditional hydrolysis method. The prepared materials and the developed strategy demonstrated an excellent size-selective assay performance in model protein mixtures, showing great potential in the application of proteomics analysis.

Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.

For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689±0.024-genomic vs. 0.612±0.045-phenomic), but phenomic data were better than genomic data for KW (0.535±0.034-genomic vs. 0.617±0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.

Optical Tweezing Terahertz Probing for a Single Metal Nanoparticle.

Recently, extensive research has been reported on the detection of metal nanoparticles using terahertz waves, due to their potential for efficient and nondestructive detection of chemical and biological samples without labeling. Resonant terahertz nanoantennas can be used to detect a small amount of molecules whose vibrational modes are in the terahertz frequency range with high sensitivity. However, the positioning of target molecules is critical to obtaining a reasonable signal because the field distribution is inhomogeneous over the antenna structure. Here, we combine an optical tweezing technique and terahertz spectroscopy based on nanoplasmonics, resulting in extensive controllable tweezing and sensitive detection at the same time. We observed optical tweezing of a gold nanoparticle and detected it with terahertz waves by using a single bowtie nanoantenna. Furthermore, the calculations confirm that molecular fingerprinting is possible by using our technique. This study will be a prestep of biomolecular detection using gold nanoparticles in terahertz spectroscopy.

Demonstrating the Applicability of Proton Transfer Reaction Mass Spectrometry to Quantify Volatiles Emitted by the Mycoparasitic Fungus Trichoderma atroviride in Real Time: Monitoring of Trichoderma-Based Biopesticides.

The present study aims to explore the potential application of proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) for real-time monitoring of microbial volatile organic compounds (MVOCs). This investigation can be broadly divided into two parts. First, a selection of 14 MVOCs was made based on previous research that characterized the MVOC emissions of Trichoderma atroviride, which is a filamentous fungus widely used as a biocontrol agent. The analysis of gas-phase standards using PTR-ToF-MS allowed for the categorization of these 14 MVOCs into two groups: the first group primarily undergoes nondissociative proton transfer, resulting in the formation of protonated parent ions, while the second group mainly undergoes dissociative proton transfer, leading to the formation of fragment ions. In the second part of this investigation, the emission of MVOCs from samples of T. atroviride was continuously monitored over a period of five days using PTR-ToF-MS. This also included the first quantitative online analysis of 6-amyl-α-pyrone (6-PP), a key MVOC emitted by T. atroviride. The 6-PP emissions of T. atroviride cultures were characterized by a gradual increase over the first two days of cultivation, reaching a plateau-like maximum with volume mixing ratios exceeding 600 ppbv on days three and four. This was followed by a marked decrease, where the 6-PP volume mixing ratios plummeted to below 50 ppbv on day five. This observed sudden decrease in 6-PP emissions coincided with the start of sporulation of the T. atroviride cultures as well as increasing intensities of product ions associated with 1-octen-3-ol and 3-octanone, whereas both these MVOCs were previously associated with sporulation in T. atroviride. The study also presents the observations and discussion of further MVOC emissions from the T. atroviride samples and concludes with a critical assessment of the possible applications and limitations of PTR-ToF-MS for the online monitoring of MVOCs from biological samples in real time.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.