Single Run Research Articles

Automated Sampling System for Monitoring 85Kr in Air

Radioactive krypton-85 (85Kr) gas, a chemically inert and non-proliferation indicator, is derived from fission products. Its detection relies on the Budesamt für Strahlenschutz–Institute of Atmospheric Radioactivity (BfS-IAR) method, which necessitated impurity removal using soda lime, silica gel, and liquid nitrogen for cryogenic adsorption. This manual process requires frequent replacements, posing challenges for its automation. To address this, we developed a prototype krypton sampling system as an interim research product for the fully automated remote monitoring of covert nuclear activity. The system incorporates a hollow fiber membrane for impurity removal, a computer-controlled multi-position valve for sampling, and an electric cooler for adsorption. The impurity removal modules demonstrated high efficiencies, removing H2O and CO2 at 99.8% and 97.8% rates, respectively. Further, the custom-made sampling system can process 16 samples in a single run without analyst intervention. We conducted experiments to verify the automatic krypton sampling capability. The activity concentration of 85Kr in ambient air was measured using the BfS-IAR processing and detection system. The system exhibited a recovery rate of ~7.8% for krypton in 1000 L air, demonstrating good continuous remote monitoring capability. This study promotes the development of an automated analysis system for the detection of 85Kr in ambient air.

Neural ADMIXTURE for rapid genomic clustering

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by computing multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.

"Single Knot-Single Running Suture" Vesicourethral Anastomosis with Posterior Musculofascial Reconstruction during Robot-Assisted Radical Prostatectomy: A Step-by-Step Guide of Surgical Technique.

Background: Our aim is to describe Gallucci's (VV-G) technique for vesicourethral anastomosis and posterior musculofascial reconstruction (PMFR) during robot-assisted radical prostatectomy (RARP) and to assess early urinary continence recovery and perioperative outcomes. VV-G consists of a "single knot-single running suture" vesicourethral anastomosis with PMFR. Methods: Between September 2019 and October 2021, we prospectively compared VV-G vs. conventional Van Velthoven anastomosis (VV-STD) during RARP. We excluded patients with urinary incontinence, pelvic radiotherapy, and urethral and BPH surgery prior to RARP. Social continence (SC) recovery, perioperative complications, and length of hospital stay (LOS) were compared between VV-G vs. VV-STD. SC was defined as 0-1 pad/die. We applied 1:1 propensity score matching (PSM) adjusting for different covariates (age, Charlson Comorbidity Index, BMI, prostate volume, nerve-sparing and lymph node dissection). Results: From 166 patients, 1:1 PSM resulted in two equally sized groups of 40 patients each with no residual differences (all p ≥ 0.2). VV-G yielded higher 3-month SC rates than VV-STD (97.5 vs. 55.0%, p < 0.001). A tiny difference was still recorded at one-year follow-up (97.5 vs. 80.0%, p = 0.029, HR: 2.90, 95% CI: 1.74-4.85, p < 0.001). Conversely, we observed no differences in any perioperative complications (15.0 vs. 22.5%, OR: 0.61, 95% CI 0.19-1.88, p = 0.4) and LOS (3 vs. 4 days, Δ: -0.69 ± 0.61, p = 0.1). Conclusions: VV-G significantly improved early SC recovery without increasing perioperative morbidity. In our opinion, VV-G represents an easy-to-learn and easy-to-teach technique due to its single-suture, single-knot, and symmetrical design.

Set up and validation of a sensitive method to quantify prostaglandins, prostaglandin-glycerol esters and prostaglandin-ethanolamides, as well as their respective precursors

Arachidonic acid-derived prostaglandins are widely studied for their role in inflammation. However, besides arachidonic acid, other arachidonic moiety-containing lipids can be metabolized by COX-2. Indeed, the endocannabinoids 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (anandamide, AEA) can follow the same biochemical pathways than arachidonic acid leading to the formation of prostaglandin-glycerol esters (PG-G) and prostaglandin-ethanolamides (or prostamides, PG-EA), respectively.The data reported so far support the interest of these bioactive lipids in inflammatory conditions. However, there is only a handful of methods described for their quantification in biological matrices. Moreover, given the shared biochemical pathways for arachidonic acid, 2-AG and AEA, a method allowing for the quantification of these precursors and the corresponding prostaglandin derivatives appears as largely needed. Thus, we report here the development and validation of a single run UPLC-MS/MS quantification method allowing the quantification of these endocannabinoids-derived mediators together with the classical prostaglandin. Moreover, we applied the method to the quantification of these lipids in vitro (using lipopolysaccharides-activated J774 macrophage cells) and in vivo in several tissues from DSS-induced colitis mice. This femtomole-range method should improve the understanding of the interaction between these lipid mediators and inflammation.

A method for finding numerical solutions to Diophantine equations using Spiral Optimization Algorithm with Clustering (SOAC)

Diophantine equations are equations containing two or more unknowns, such that only the integer solutions are required. To find solutions of these equations numerically, we can be performed by solving an optimization problem using a metaheuristic method. In this paper, the Spiral Optimization Algorithm with Clustering (SOAC) method is proposed to find solutions to Diophantine equations in the form of polynomial, exponential, and also linear and nonlinear systems of equations. In the implementation of the method on solving some existing benchmark problems, the goal of simulation is to find all solutions only in a single run and in a short period of time. Appropriate values of required parameters are selected during the simulation. Results shows satisfactory in solving four problems in polynomial equations, four problems in exponential equations, and three problems in systems of linear and nonlinear equations. In most of cases, the results yield the same with the analytical or numerical solutions in the reference papers, and in some cases the results give more solutions.

Simple plication alleviates physical symptoms in patients with post-gestational rectus diastasis

PurposeTo evaluate our surgery for post-gestational rectus abdominis muscle diastasis using slowly absorbable monofilament suture and eight weeks of abdominal binder in terms of recurrence rate, complications, and effect on patients’ physical and cosmetic complaints.MethodIn a retrospective cohort study, all 44 patients operated between 2014 and 2020 were invited to a follow-up using ultrasound, clinical examination, and questionnaires regarding symptoms before and after surgery.Results89% of invited patients participated, with a median follow-up of 36 months. There was one recurrence caused by severe postoperative nausea and vomiting, which was the most common complication. Most procedures were safe, but two patients experienced Clavien–Dindo grade 3 complications. Patients reported feeling limited or taking precautions after surgery for a median of 8.5 months. Of all included patients, four responded that the operation did not alleviate their primary complaint. The remaining 35 patients (90%) experienced complete or partial alleviation of their primary complaints and would undergo the procedure again if needed.ConclusionPost-gestational diastasis recti can be associated with a large number of physical symptoms and functional complaints and can safely be operated using a single running plication of the anterior rectus fascia with a slowly absorbable suture, with fair cosmetic results, excellent effect on symptoms, few complications and high levels of patient satisfaction. Future research must determine which symptoms and findings should indicate surgery.

Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution.

Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting from increased sample size. Although the cost of running next-generation sequencing instruments has dropped substantially over time, the cost of sample preparation methods has lagged behind. To counter this, researchers have adapted library miniaturization protocols and large sample pools to maximize the number of samples that can be prepared by a certain amount of reagents and sequenced in a single run. However, due to high variability of sample quality, over and underrepresentation of samples in a sequencing run has become a major issue in high-throughput sequencing. This leads to misinterpretation of results due to increased noise, and additional time and cost rerunning underrepresented samples. To overcome this problem, we present a normalization method that uses shallow iSeq sequencing to accurately inform pooling volumes based on read distribution. This method is superior to the widely used fluorometry methods, which cannot specifically target adapter-ligated molecules that contribute to sequencing output. Our normalization method not only quantifies adapter-ligated molecules but also allows normalization of feature space; for example, we can normalize to reads of interest such as non-ribosomal reads. As a result, this normalization method improves the efficiency of high-throughput next-generation sequencing by reducing noise and producing higher average reads per sample with more even sequencing depth. IMPORTANCE High-throughput next generation sequencing (NGS) has significantly contributed to the field of genomics; however, further improvements can maximize the potential of this important tool. Uneven sequencing of samples in a multiplexed run is a common issue that leads to unexpected extra costs or low-quality data. To mitigate this problem, we introduce a normalization method based on read counts rather than library concentration. This method allows for an even distribution of features of interest across samples, improving the statistical power of data sets and preventing the financial loss associated with resequencing libraries. This method optimizes NGS, which already has huge importance across many areas of biology.

BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.

While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE). Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples. We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmixand https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization

This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. A novel VND MO algorithm based on the well-known particle swarm optimization (PSO) algorithm is introduced and thoroughly described in this paper. The novel VNDMOPSO algorithm is validated on a set of 21 benchmark problems with different dimensionality settings and compared with two other state-of-the-art VND MO algorithms. Then, it is applied to solve five different instances of the space robot pathfinding problem formulated as a VND MO problem where two objectives are considered: (1) the minimal distance of the selected path, and (2) the minimal energy cost (expressed as the number of turning points). VNDMOPSO shows at least comparable or better convergence on the benchmark problems and significantly better convergence properties on the VND pathfinding problems compared with other VND MO algorithms.

Multiattribute Monitoring of Aggregates and Charge Variants of Monoclonal Antibody through Native 2D-SEC-MS-WCX-MS.

Monitoring of critical quality attributes such as size and charge-related heterogeneities is essential for biopharmaceutical manufacturers. Size-exclusion chromatography (SEC) is the preferred analytical technique for the quantification of aggregates and fragments in the product, whereas weak-cation exchange chromatography (WCX) is widely used for the characterization of charge variants of biotherapeutic products, in particular monoclonal antibodies (mAbs). Multiattribute monitoring offers the ability to monitor these attributes in a single run flow using two-dimensional liquid chromatography (2D-LC). Typically, in this approach, only the second-dimension samples are directly analyzed through mass spectrometry, as the first dimension has limitations concerning direct coupling with mass spectrometry. In the present study, a novel 2D-SEC-MS/WCX-MS workflow has been proposed, in which chromatography of both dimensions (D1 and D2) was directly coupled with mass spectrometry, through which size-related and charge-related variants of monoclonal antibody mAb A were analyzed simultaneously in their native form. In comparison to stand-alone SEC and WCX methods, this method enables simultaneous analysis of size and charge variants in a single workflow without manual intervention, allowing analysis of low abundant variants. Further, this method has 75% less sample requirement and a shorter analysis time (25 min vs 90 min) when size and charge variants were analyzed individually. The proposed native 2D-LC-MS workflow was used to analyze a stressed sample of mAb A, in which D1 analysis revealed the presence of aggregates (8-20%), which were primarily dimers, whereas D2 analysis showed an increment in acidic variants (9-21%).

A simple Monte Carlo simulation method for geotechnical reliability-based design

ABSTRACT This paper proposes a simple reliability-based design approach from a quantile value perspective. The proposed approach allows the design variable to be efficiently determined without implementing repetitive “trial and error” procedures for a given target reliability index. Only a single run of Monte Carlo simulation is needed to calculate the threshold values of the design variable after the design variable function (DVF) is derived from the limit state function. The threshold values are then sorted in ascending order, and a quantile value is chosen as the desired design variable such that MCS samples with threshold values larger, or smaller than this value are undesired samples. Three illustrative examples are presented following the development of the proposed method and its implementation procedure. Results are validated by calculating the failure probabilities of desired designs using direct MCS, and they are also favorably comparable with those reported in literature. This method can play a complementary role to existing RBD methods due to its advantages of simplicity, efficiency, and robustness.

Microfluidic Immuno-Serolomic Assay Reveals Systems Level Association with COVID-19 Pathology and Vaccine Protection.

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease-19 (COVID-19) has been a global pursuit over the past years. Here, a microfluidic high-plex immuno-serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti-spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. Thisassay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID-infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which canbe associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

Flow-based approach for scalable fabrication of Ag nanostructured substrate as a platform for surface-enhanced Raman scattering

The droplet-based biphasic reaction is an efficient strategy for the fabrication of surface-bound nanostructures. Here, we developed a process of fabricating ordered micro-ring arrays of silver (Ag) nanostructures from surface nanodroplet reaction on a micro-patterned hydrophobic substrate for reproducible detection by surface-enhanced Raman spectroscopy (SERS). Our process consisted of the generation of surface nanodroplet arrays, followed by a biphasic chemical reaction between droplets and the continuous flow of silver nitrate (AgNO3) precursor solution. The parameters in the formation and reaction of the droplet array were well controlled to maintain the uniformity of Ag nanostructures throughout the substrate. By scaling up the process parameters and the size of the microchamber, we were able to produce a SERS substrate with a surface area of ¿60 cm2 in a single run. Such a large area could be sufficient for analyzing more than a thousand samples. We demonstrated the repeatability of SERS measurements using Ag nanostructures by analyzing three environmental (rhodamine 6G, chlorpyrifos, triclosan), a biological (indoxyl sulfate), and a psychoactive drug (tetrahydrocannabinol) compounds. 2D mapping of SERS intensities was also performed for both small and large-scale substrates by collecting data from more than 100 locations on the substrate. Our work demonstrated droplet-based biphasic reaction as a simple approach for the fabrication of SERS substrate with a large area. The technique may help to eliminate the requirement for sophisticated equipment for the fabrication of SERS active substrate.

Antibody and Nanobody Radiolabeling with Copper-64: Solid vs. Liquid Target Approach.

Antibody and nanobody-based copper-64 radiopharmaceuticals are increasingly being proposed as theranostic tools in multiple human diseases. While the production of copper-64 using solid targets has been established for many years, its use is limited due to the complexity of solid target systems, which are available in only a few cyclotrons worldwide. In contrast, liquid targets, available in virtually in all cyclotrons, constitute a practical and reliable alternative. In this study, we discuss the production, purification, and radiolabeling of antibodies and nanobodies using copper-64 obtained from both solid and liquid targets. Copper-64 production from solid targets was performed on a TR-19 cyclotron with an energy of 11.7 MeV, while liquid target production was obtained by bombarding a nickel-64 solution using an IBA Cyclone Kiube cyclotron with 16.9 MeV on target. Copper-64 was purified from both solid and liquid targets and used to radiolabel NODAGA-Nb, NOTA-Nb, and DOTA-Trastuzumab conjugates. Stability studies were conducted on all radioimmunoconjugates in mouse serum, PBS, and DTPA. Irradiation of the solid target yielded 13.5 ± 0.5 GBq with a beam current of 25 ± 1.2 μA and an irradiation time of 6 h. On the other hand, irradiation of the liquid target resulted in 2.8 ± 1.3 GBq at the end of bombardment (EOB) with a beam current of 54.5 ± 7.8 μA and an irradiation time of 4.1 ± 1.3 h. Successful radiolabeling of NODAGA-Nb, NOTA-Nb, and DOTA-Trastuzumab with copper-64 from both solid and liquid targets was achieved. Specific activities (SA) obtained with the solid target were 0.11, 0.19, and 0.33 MBq/μg for NODAGA-Nb, NOTA-Nb, and DOTA-trastuzumab, respectively. For the liquid target, the corresponding SA values were 0.15, 0.12, and 0.30 MBq/μg. Furthermore, all three radiopharmaceuticals demonstrated stability under the testing conditions. While solid targets have the potential to produce significantly higher activity in a single run, the liquid process offers advantages such as speed, ease of automation, and the feasibility of back-to-back production using a medical cyclotron. In this study, successful radiolabeling of antibodies and nanobodies was achieved using both solid and liquid targets approaches. The radiolabeled compounds exhibited high radiochemical purity and specific activity, rendering them suitable for subsequent in vivo pre-clinical imaging studies.

Simultaneous determination of flouride, iodate and iodide by ion chromatography in caustic off-gas scrubber of nuclear recycle facilities by employing dual detectors

ABSTRACT An ion chromatography method was developed for the simultaneous determination of fluoride, iodate and iodide in a caustic scrubber solution used for trapping off-gases released during the dissolution of nuclear fuels. A separation between iodate and fluoride could be realized with 0.6 mM hydroxide, which is too weak to elute other anions including iodide in reasonable time. Studies were carried out at different column temperature, and with the help of Van’t Hoff plots the thermodynamic parameters associated with the column equilibrium were estimated, which helped in understanding the co-elution of iodate and fluoride at higher concentration of eluent. An arrangement of conductivity detector and UV-VIS detector in sequence after the ion exchange column (with 15 mM NaOH as eluent) was used for recording chromatograms for conductivity signal and UV-VIS signal simultaneously. The information from the two chromatograms was utilized to quantify iodate, fluoride and iodide in a single run. The limit of detection for fluoride, iodate and iodide is 7 ppb, 10 ppb and 12 ppb, respectively. The precision of the method is better than 2% (RSD) at 0.5 mg/L. The method was used successfully for the real sample analysis.

The benefits of mixed-mode chromatography columns for separation of peptides and protein digests

In this work, the evaluation and comparison of mixed-mode chromatography and reversed-phase chromatography for separation of peptides and protein digests have been performed. The effects of pH of aqueous part of mobile phase as well as the effects of organic modifier on retention, resolution, and peak shape were investigated on several columns including three mixed-mode columns possessing reversed-phase/anion-exchange mechanism, two reversed-phase octadecyl columns, and one column with mixed-mode reversed-phase/anion-exchange character only in defined pH range. The set of peptides varying in their polarity, length, amino acid sequence, and charge state, namely dipeptides, N-blocked dipeptides, and oligopeptides, was selected to describe the chromatographic behavior under different conditions properly. These measurements showed the potential of mixed-mode chromatography columns for analysis of differently charged peptides in a single run. The applicability of the tested conditions has been verified by the analysis of cytochrome C digested fragments. Two types of samples were analyzed and compared, i.e., commercial cytochrome C digested standard and cytochrome C digested via trypsin spin columns. The obtained results point to the necessity of using mass spectrometry detection because of large number of unknown peaks in cytochrome C digested standard, probably originating from chymotryptic and miscleavage activities.Graphical abstract

Determination of burn rate affecting elements in composite solid propellant and its ingredients by microwave-induced plasma optical emission spectrometry.

A study of trace metallic elements in solid propellant and its ingredients is important and critical as they affect ballistic properties of solid rocket motor during combustion. In the present study, an attempt is made to develop a rapid, single run and cost-effective analytical technique for the routine and direct analysis of multi elements. Trace level metallic impurities (burn rate affecting elements) present in premix, cured propellant slab and propellant ingredients such as ammonium perchlorate, aluminium, aluminium oxide, dioctyladipate, hydroxy terminated polybutadiene and toluene diisoycanate sourced from different suppliers and propellant slag are determined based on a relatively recent analytical technique, microwave-induced plasma optical emission spectrometry (MIP-OES). Analytical wavelengths are selected based on the sensitivity and interference effects. Precision and accuracy of the developed test procedure for the metallic impurities are demonstrated using replicate analyses of certified calibration standards. The limit of quantification and detection of elements studied is determined. Linear regression coefficients are greater than 0.995. The results obtained demonstrate the potential of MIP-OES technique for the determination of metallic impurities of solid propellant ingredients, pre-mix, cured slab and slag of solid propellants with shortened analysis time which achieved lower detection limits, higher accuracy and better repeatability.

"Iridium Signature" Mass Spectrometric Probes: New Tools Integrated in a Liquid Chromatography-Mass Spectrometry Workflow for Routine Profiling of Nitric Oxide and Metabolic Fingerprints in Cells.

Nitric oxide (NO) is a highly reactive signaling molecule involved in diverse biological processes. Simultaneous profiling of NO and associated metabolic fingerprints in a single assay allows more accurate assessments of cell states and offers the possibility to better understand its exact biological roles. Herein, a multiplexing LC-MS workflow was established for simultaneous detection of intracellular NO and various metabolites based on a novel "iridium signature" mass spectrometric probe (Ir-MSP841). This Ir-MSP841 can convert highly liable NO to a stable permanently charged triazole product (Ir-TP852), enabling direct MS detection of NO. This 191/193Ir-signature mass spectrometric probe-based approach is endowed with overwhelming advantages of interference-free, high quantitative accuracy, and great sensitivity (limit of detection down to 0.14 nM). It also reveals good linearity over a wide concentration range 12.5-500 nM and has been successfully employed for exploring the release behaviors of three representative NO donors in cells. Meanwhile, metabolic profiling results reveal that varying the concentrations of NO has distinct effects on various cellular metabolites. This study provides a robust, sensitive, and versatile method for simultaneous detection of NO and numerous metabolites in a single LC-MS run and expands its applications in biomedical research.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture