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Data on airborne microorganisms, particularly in Southeast Asia, are more limited compared to chemical data. This study is the first to examine the community and diversity of microorganisms on PM 2.5 in an urban area of Northern Thailand during both smoke haze and non-smoke haze periods of 2020. This study evaluated the composition of airborne bacteria and fungi and analyzed their association with the chemical composition of PM 2.5 and meteorological variables. Significantly higher concentrations of PM 2.5 and more chemical compounds were observed during the smoke haze period compared to the non-smoke haze period. Increased PM 2.5 concentrations significantly altered both bacterial and fungal communities. The diversity and richness of airborne bacteria increased, whereas those of fungi decreased. The level of PM 2.5 concentration (the carrier), the chemical composition of PM 2.5 (the resources for survival), and the local meteorological conditions (relative humidity (RH)) were associated with the differences in bacterial and fungal populations. In addition, air originating from the west of the receptor site, influenced by both terrestrial and marine air mass routes, contributed to higher bacterial diversity and richness during the smoke haze period. In contrast, fungal diversity and richness were greater when the air came from the southwest, following a marine route. However, the primary health concern is pathogens, which were present in both periods (such as Clostridium, Aspergillus, and Cladosporium) and were especially abundant during smoke haze periods. This study highlights those airborne microorganisms, along with the particles and their chemical composition, are important components that can impact health, including that of humans, animals, and the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Kratom, Mitragyna speciosa, is one of the most popular herbs in the West and Southeast Asia. A number of previous works have focused on bioactive alkaloids in this plant; however, non-alkaloids have never been investigated for their biological activities. Antiviral and virucidal assays of a methanol leaf extract of Kratom, M. speciosa, revealed that a crude extract displayed virucidal activity against the SARS-CoV-2. Activity-guided isolation of a methanol leaf extract of Kratom led to the identification of B-type procyanidin condensed tannins of (-)-epicatechin as virucidal compounds against SARS-CoV-2. The fraction containing condensed tannins exhibited virucidal activity with an EC 50 value of 8.38 μg/mL and a selectivity index (SI) value >23.86. LC-MS/MS analysis and MALDI-TOF MS identified the structure of the virucidal compounds in Kratom as B-type procyanidin condensed tannins, while gel permeation chromatograph (GPC) revealed weight average molecular weight of 238,946 Da for high molecular-weight condensed tannins. In addition to alkaloids, (-)-epicatechin was found as a major component in the leaves of M. speciosa, but it did not have virucidal activity. Macromolecules of (-)-epicatechin, i.e., procyanidin condensed tannins, showed potent virucidal activity against SARS-CoV-2, suggesting that the high molecular weights of these polyphenols are important for virucidal activity., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Short-chain fatty acids (SCFAs) are involved in important physiological processes such as gut health and immune response, and changes in SCFA levels can be indicative of disease. Despite the importance of SCFAs in human health and disease, reference values for fecal and plasma SCFA concentrations in healthy individuals are scarce. To address this gap in current knowledge, we developed a simple and reliable derivatization-free GC-TOFMS method for quantifying fecal and plasma SCFAs in healthy individuals. We targeted six linear- and seven branched-SCFAs, obtaining method recoveries of 73-88% and 83-134% in fecal and plasma matrices, respectively. The developed methods are simpler, faster, and more sensitive than previously published methods and are well suited for large-scale studies. Analysis of samples from 157 medically confirmed healthy individuals showed that the total SCFAs in the feces and plasma were 34.1 ± 15.3 µmol/g and 60.0 ± 45.9 µM, respectively. In fecal samples, acetic acid (Ace), propionic acid (Pro), and butanoic acid (But) were all significant, collectively accounting for 89% of the total SCFAs, whereas the only major SCFA in plasma samples was Ace, constituting of 93% of the total plasma SCFAs. There were no statistically significant differences in the total fecal and plasma SCFA concentrations between sexes or among age groups. The data revealed, however, a positive correlation for several nutrients, such as carbohydrate, fat, iron from vegetables, and water, to most of the targeted SCFAs. This is the first large-scale study to report SCFA reference intervals in the plasma and feces of healthy individuals, and thereby delivers valuable data for microbiome, metabolomics, and biomarker research., Competing Interests: The authors declare that they have no competing interest with the contents of this article., (© 2024 The Authors.)

The collision cross-sections (CCS) measurement using ion mobility spectrometry (IMS) in combination with mass spectrometry (MS) offers a great opportunity to increase confidence in metabolite identification. However, owing to the lack of sensitivity and resolution, IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites (VLMs ≤ 250 Da). Here, we describe an analytical method using ultrahigh-performance liquid chromatography (UPLC) coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples. The experimental CCS values, along with mass spectral properties, were reported for the 174 metabolites. The experimental data included the mass-to-charge ratio ( m / z ), retention time (RT), tandem MS (MS/MS) spectra, and CCS values. Among the studied metabolites, 263 traveling wave ion mobility spectrometry (TWIMS)-derived CCS values ( TW CCS N2 ) were reported for the first time, and more than 70% of these were CCS values of VLMs. The TW CCS N2 values were highly repeatable, with inter-day variations of <1% relative standard deviation (RSD). The developed method revealed excellent TW CCS N2 accuracy with a CCS difference (ΔCCS) within ±2% of the reported drift tube IMS (DTIMS) and TWIMS CCS values. The complexity of the urine matrix did not affect the precision of the method, as evidenced by ΔCCS within ±1.92%. According to the Metabolomics Standards Initiative, 55 urinary metabolites were identified with a confidence level of 1. Among these 55 metabolites, 53 (96%) were VLMs. The larger number of confirmed compounds found in this study was a result of the addition of TW CCS N2 values, which clearly increased metabolite identification confidence., Competing Interests: The authors declare that there are no conflicts of interest., (© 2023 The Author(s).)

The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio ( m / z ) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR&#95;CCSPrediction under the GNU General Public License (GPL) version 3.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)