Essentially all bacteria secrete nano-sized (~20-200 nm) bacterial extracellular vesicles (bEVs) loaded with proteins, lipids, glycans, and nucleic acids. bEVs facilitate interactions among cells of the same species, different microbial species, and even with cells of multicellular organisms in the context of colonization or infection. Their interactions with host organism immune cell receptors vary depending on the producing bacterial species and are now being harnessed for the development of bEVs as a potential immunotherapeutic platform. Both basic/mechanistic and preclinical therapeutic development studies are thus increasing in number and require implementation of methods for multiparametric analytical characterization as well as in vivo administration in preclinical animal models of disease. We summarize a variety of analytical methods that can be used to calculate bEV dose for preparations made from diverse bacterial sources (including sterility testing, total protein concentration, particle concentration, and lipopolysaccharide concentration). We also describe basic methodology for intravenous administration of bEV preparations via tail vein injection in laboratory mice. Throughout the description of methodology, we highlight potential pitfalls and alternatives to further equip the reader for troubleshooting should challenges arise. Robust and reproducible characterization is a prerequisite of bEV preparation quality control and consistent dosing during preclinical development. This will allow for more streamlined testing of candidate therapeutic bEVs within a given research laboratory, and furthermore facilitate reproducibility of findings across laboratories.

The ubiquitousness of microplastics (<5 mm) has become a pressing environmental concern globally due to the extensive use of plastics. Microplastics have been well-studied in aquatic environments but not well-characterized in soils. Present analytical processes to quantify microplastics accurately in soil samples are quite challenging and require improved and validated analytical steps to eliminate the obscurities and biases. We aimed to develop an effective method for the extraction and quantification of microplastics from soil samples. Different ratios of low-(NaCl) and high-density solutions (ZnCl2/ NaBr) were tested to determine the most efficient combination for density-dependent separation of microplastics from soil. The combination of low- (1:6) and high-density (1:3) solutions {as weight of soil(g)/volume of density solution(ml)} accounted for 95% recovery of the spiked microplastic particles from soil samples. Likewise, different soil-to-solution ratios of H2O2 were tested for the removal of soil organic matter with heating and non-heating steps. Prior removal of organic matter from soil samples achieved a clear supernatant that facilitated 99% recovery of microplastic particles. The validation of individually spiked microplastic particles of small (10-100 μm) and large scale (100-5000 μm) resulted in recovery ranging from 88 to 99%. A validated modified method with prior digestion followed by density-dependent separation was further tested using the field samples with microplastic contamination. The microplastics of different shapes, sizes, colours and polymeric compositions were reported efficiently and well characterized in the field-collected soil samples using this method.

Monitoring and quantifying ATP levels in vivo is essential to understanding its role as a signaling molecule in tumor progression and therapy. Nevertheless, the real-time monitoring and quantitative assessment of lysosomal ATP remains challenging due to the lack of accurate tools in deep tissues. In this study, based on the crosslinking enhanced emission (CEE) effect, we successfully synthesized red carbon dots (R-CDs) with dual emission properties for efficient quantification of intracellular ATP. The R-CDs emit in the near-infrared range and target lysosomes with rapid detection capabilities, rendering them exceptionally well-suited for directly observing and analyzing the dynamics of lysosomal ATP through live cell imaging techniques. Importantly, R-CDs have proven their efficacy in real-time monitoring of drug stimulus-induced fluctuations in endogenous lysosomal ATP concentration and have also been employed for quantifying and distinguishing lysosomal ATP levels among normal and cancer cell lines. These noteworthy findings emphasize the versatility of the R-CD as a valuable imaging tool for elucidating the functional role of lysosomal ATP in drug screening and cancer diagnostics and hold the promise of becoming a reference tool for deepening our understanding of drug mechanisms of action.

The spontaneous generation of hydrogen peroxide (H2O2) within atmospheric microdroplets, such as raindrops and aerosols, plays a crucial role in various environmental processes including pollutant degradation and oxidative stress. However, quantifying hydroxyl radicals (•OH), essential for H2O2 formation, remains challenging due to their short lifespan and low concentration. This study addresses this gap by presenting a highly sensitive and selective surface-enhanced Raman scattering (SERS) nanosensor specifically designed for quantifying •OH within water microdroplets. Utilizing a phthalhydrazide (Phth) probe, the SERS technique enables rapid, interference-free detection of •OH at nanomolar concentrations. It achieves a linear detection range from 2 nM to 2 μM and a limit of detection as low as 0.34 nM. Importantly, the SERS sensor demonstrates robustness and accuracy within water microdroplets, paving the way for comprehensive mechanistic studies of H2O2 generation in the atmosphere. This innovative approach not only offers a powerful tool for environmental research but also holds potential for advancing our understanding of atmospheric H2O2 formation and its impact on air quality and pollutant degradation.

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.

This study tackles the growing global concern about municipal waste management, particularly in cities like the Grand Guayaquil Metropolitan Area (GGA). Through realistic field studies on in situ household waste generation and geographic information system (GIS) tools, this work offers a framework to predict the quantities and types of recyclable household waste for any metropolitan area in Latin America. Over 4 weeks, students collected, sorted and weighed recyclable waste types, including plastic, paper, metal, glass and fabric, from 776 sampled household of the GGA. ArcGIS survey tool identified household locations and allowed to survey different socio-demographic features. With the help of ArcGIS interpolation method, the total household waste generation for GGA was predicted, and the classification of the different types of recyclable waste was also spatially distributed for the study area. The report identified notable trends in plastic waste, specifically polyethylene terephthalate waste\'s steady prevalence and 42% growth rate, emphasizing the importance of enhanced recycling techniques. Spatial density maps showed a heterogeneous waste distribution across the GGA, emphasizing locations with higher waste output. This study demonstrates that improving recyclable waste collection can be accomplished with a moderately cheap expenditure by collaborating with academia to overcome knowledge gaps. This strategy provides opportunities to mitigate the environmental impacts of poor waste management.

BACKGROUND: Subjective surgeon interpretation of near-infrared perfusion video is limited by low inter-observer agreement and poor correlation to clinical outcomes. In contrast, quantification of indocyanine green fluorescence video (Q-ICG) correlates with histologic level of perfusion as well as clinical outcomes. Measuring dye volume over time, however, has limitations, such as it is not on-demand, has poor spatial resolution, and is not easily repeatable. Laser speckle contrast imaging quantification (Q-LSCI) is a real-time, dye-free alternative, but further validation is needed. We hypothesize that Q-LSCI will distinguish ischemic tissue and correlate over a range of perfusion levels equivalent to Q-ICG.
METHODS: Nine sections of intestine in three swine were devascularized. Pairs of indocyanine green fluorescence imaging and laser speckle contrast imaging video were quantified within perfused, watershed, and ischemic regions. Q-ICG used normalized peak inflow slope. Q-LSCI methods were laser speckle perfusion units (LSPU), the base unit of laser speckle imaging, relative perfusion units (RPU), a previously described methodology which utilizes an internal control, and zero-lag normalized cross-correlation (X-Corr), to investigate if the signal deviations convey accurate perfusion information. We determine the ability to distinguish ischemic regions and correlation to Q-ICG over a perfusion gradient.
RESULTS: All modalities distinguished ischemic from perfused regions of interest; Q-ICG values of 0.028 and 0.155 (p < 0.001); RPU values of 0.15 and 0.68 (p < 0.001); and X-corr values of 0.73 and 0.24 (p < 0.001). Over a range of perfusion levels, RPU had the best correlation with Q-ICG (r = 0.79, p < 0.001) compared with LSPU (r = 0.74, p < 0.001) and X-Corr (r = 0.46, p < 0.001).
CONCLUSIONS: These results demonstrate that Q-LSCI discriminates ischemic from perfused tissue and represents similar perfusion information over a broad range of perfusion levels comparable to clinically validated Q-ICG. This suggests that Q-LSCI might offer clinically predictive real-time dye-free quantification of tissue perfusion. Further work should include validation in histologic studies and human clinical trials.

BACKGROUND: Multivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model.
RESULTS: We describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value.
CONCLUSIONS: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

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.