BACKGROUND: Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.
OBJECTIVE: In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.
UNASSIGNED: The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

BACKGROUND: RNA sequencing (RNA-seq) is widely used for gene expression profiling and quantification. Quantitative RNA sequencing usually requires cell counting and spike-in, which is not always applicable to many samples. Here, we present a novel quantitative RNA sequencing method independent of spike-ins or cell counting, named siqRNA-seq, which can be used to quantitatively profile gene expression by utilizing gDNA as an internal control. Single-stranded library preparation used in siqRNA-seq profiles gDNA and cDNA with equal efficiency.
RESULTS: To quantify mRNA expression levels, siqRNA-seq constructs libraries for total nucleic acid to establish a model for expression quantification. Compared to Relative Quantification RNA-seq, siqRNA-seq is technically reliable and reproducible for expression profiling but also can sequence reads from gDNA which can be used as an internal reference for accurate expression quantification. Applying siqRNA-seq to investigate the effects of actinomycin D on gene expression in HEK293T cells, we show the advantages of siqRNA-seq in accurately identifying differentially expressed genes between samples with distinct global mRNA levels. Furthermore, we analyzed factors influencing the downward trend of gene expression regulated by ActD using siqRNA-seq and found that mRNA with m6A modification exhibited a faster decay rate compared to mRNA without m6A modification. Additionally, applying this technique to the quantitative analysis of seven tumor cell lines revealed a high degree of diversity in total mRNA expression among tumor cell lines.
CONCLUSIONS: Collectively, siqRNA-seq is a spike-in independent quantitative RNA sequencing method, which creatively uses gDNA as an internal reference to absolutely quantify gene expression. We consider that siqRNA-seq provides a convenient and versatile method to quantitatively profile the mRNA landscape in various samples.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson\'s disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

The diterpene ent-copalol is an important precursor to the synthesis of andrographolide and is found only in green chiretta (Andrographis paniculata). De novo biosynthesis of ent-copalol has not been reported, because the catalytic activity of ent-copalyl diphosphate synthase (CPS) is very low in microorganisms. In order to achieve the biosynthesis of ent-copalol, Saccharomyces cerevisiae was selected as the chassis strain, because its endogenous mevalonate pathway and dephosphorylases could provide natural promotion for the synthesis of ent-copalol. The strain capable of synthesizing diterpene geranylgeranyl pyrophosphate was constructed by strengthening the mevalonate pathway genes and weakening the competing pathway. Five full-length ApCPSs were screened by transcriptome sequencing of A. paniculata and ApCPS2 had the best activity and produced ent-CPP exclusively. The peak area of ent-copalol was increased after the ApCPS2 saturation mutation and its configuration was determined by NMR and ESI-MS detection. By appropriately optimizing acetyl-CoA supply and fusion-expressing key enzymes, 35.6 mg/L ent-copalol was generated. In this study, de novo biosynthesis and identification of ent-copalol were achieved and the highest titer ever reported. It provides a platform strain for the further pathway analysis of andrographolide and derivatives and provides a reference for the synthesis of other pharmaceutical intermediates.

BACKGROUND: Allii Macrostemonis Bulbus is also named Xiebai in China. It is an edible vegetable, and also a famous herb for treating coronary heart disease. Allium chinense G. Don (ACGD) and Allium macrostemon Bunge (AMB) are it botanical sources. The aim of this study was to explore the cardioprotective effects, and decipher the visual spatial distribution and absolute content of primary metabolites derived from these two herbs.
METHODS: H9c2 cells were used to perform the hypoxia-reoxygenation (H/R)-induced myocardial injury model. Their protective effects were evaluated by apoptosis levels. Furthermore, matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry imaging approach (MALDI-TOF MSI) was carried out to present the spatial location of primary metabolites including fatty acids, amino acids, carotenoids, and vitamins in these two Allium herbs. Multiple analytical methods were applied to perform quantitative analysis of these primary metabolites in AMB and ACGD bulbs by liquid chromatography tandem mass spectrometry (LC-MS).
RESULTS: First, AMB and ACGD extracts both could increase the cell viability in H9c2 cells, and attenuate H/R-induced injury. They markedly decreased apoptosis, accompanied by activating the BCL-2/BAX pathway. Further, MALDI-TOF MSI-based relative quantification results showed several amino acids, fatty acids, carotenoids, and vitamins were largely rich in the tunics and outside scales of fresh bulbs, while some primary metabolites were abundant in their developing flower buds. Absolute quantification results displayed total contents of amino acids in ACGD bulbs were higher than those in AMB, while total contents of fatty acids and vitamins provides opposite trends in these two Allium herbs. The total contents of carotenoids and trace elements showed no significant differences between AMB and ACGD samples.
CONCLUSIONS: This study would be helpful to understand the myocardial injury protection effects of these two Allium herbs, and the spatial accumulation and quantitative content levels of their main nutrients.

Neonicotinoids, a highly effective class of insecticides used worldwide, have been identified as a major cause of concern for biodiversity. To assess the ecological and environmental consequences of neonicotinoids\' use, reliable analytical methodologies, including calibration approaches, are needed. Here, we compared the performance of internal calibration (IC) using a single concentration of stable isotope-labeled standard (SIL) with classical multipoint external calibration (EC) for the quantification of six neonicotinoids in honey. IC showed acceptable levels of trueness (86.3% - 116.0%) and precision (1.4% - 20.8%), although slight biases were observed at very low concentrations compared to EC. When applied to 32 original honey samples, both approaches showed strong agreement (R2 > 0.998) with proportional biases lower than 5%. These results highlight the possibility of implementing IC to simplify quantification in liquid chromatography-mass spectrometry-based pesticide applications.

Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors.

The ongoing development of bacterial resistance to antibiotics is a global challenge. Research in that field is thus necessary. Analytical techniques are required for such a purpose. From this perspective, the focus was on atomic absorption spectrometry (AAS). Although it is old, AAS often offers unexpected potential. Of course, this should be exploited. The aim was therefore to demonstrate the versatility of the technique in antibacterial research. This is illustrated by various examples of its practical application. AAS can be used, for example, to confirm the identity of antibacterial compounds, for purity controls, or to quantify the antibiotics in pharmaceutical preparations. The latter allowed analysis without laborious sample preparation and without interference from other excipients. In addition, AAS can help elucidate the mode of action or resistance mechanisms. In this context, quantifying the accumulation of the antibiotic drug in the cell of (resistant) bacteria appears to play an important role. The general application of AAS is not limited to metal-containing drugs, but also enables the determination of some organic chemical antibiotics. Altogether, this perspective presents a range of applications for AAS in antibacterial research, intending to raise awareness of the method and may thus contribute to the fight against resistance.

UNASSIGNED: The study aims to develop a finite element model of the pelvic floor and thighs of elderly men to quantitatively assess the impact of different pelvic floor muscle trainings and the urinary and defecation control ability.
UNASSIGNED: A finite element model of the pelvic floor and thighs of elderly men was constructed based on MRI and CT. Material properties of pelvic floor tissues were assigned through literature review, and the relative changes in waistline, retrovesical angle (RVA) and anorectad angulation (ARA) to quantitatively verify the effectiveness of the model. By changing the material properties of muscles, the study analyzed the muscle strengthening or impairment effects of the five types of rehabilitation training for four types of urination and defecation dysfunction. The changes in four outcome indicators, including the retrovesical angle, anorectad angulation, stress, and strain, were compared.
UNASSIGNED: This study indicates that ARA and RVA approached their normal ranges as material properties changed, indicating an enhancement in the urinary and defecation control ability, particularly through targeted exercises for the levator ani muscle, external anal sphincter, and pelvic floor muscles. This study also emphasizes the effectiveness of personalized rehabilitation programs including biofeedback, exercise training, electrical stimulation, magnetic stimulation, and vibration training and advocates for providing optimized rehabilitation training methods for elderly patients.
UNASSIGNED: Based on the results of computational biomechanics, this study provides foundational scientific insights and practical recommendations for rehabilitation training of the elderly\'s urinary and defecation control ability, thereby improving their quality of life. In addition, this study also provides new perspectives and potential applications of finite element analysis in elderly men, particularly in evaluating and designing targeted rehabilitation training.

Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity and other properties. Measuring how incorporation varies across a population of EVs is important for characterising such materials and understanding their function, yet it remains challenging to quantitatively characterise the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterisation platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labelling to antibody-labelling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labelling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterisation tool which complements and expands existing methods.