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Traumatic brain injury (TBI) poses a major health challenge, with tens of millions of new cases reported globally every year. Brain damage resulting from TBI can vary significantly due to factors including injury severity, injury mechanism and exposure to repeated injury events. Therefore, there is need for robust blood biomarkers. Serum from Sprague Dawley rats was collected at several timepoints within 24 h of mild single or repeat closed head impacts. Serum samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) in positive and negative ion modes. Known lipid species were identified through matching to in-house tandem MS databases. Lipid biomarkers have a unique potential to serve as objective molecular measures of injury response as they may be liberated to circulation more readily than larger protein markers. Machine learning and feature selection approaches were used to construct lipid panels capable of distinguishing serum from injured and uninjured rats. The best multivariate lipid panels had over 90% cross-validated sensitivity, selectivity, and accuracy. These mapped onto sphingolipid signaling, autophagy, necroptosis and glycerophospholipid metabolism pathways, with Benjamini adjusted p-values less than 0.05. The novel lipid biomarker candidates identified provide insight into the metabolic pathways altered within 24 h of mild TBI. [ABSTRACT FROM AUTHOR]

The article explores the validity of using rapid mass spectrometry with novel support vector machine (SVM) computational method to profile relative metabolites levels in sera from 50 healthy women with benign conditions and 44 women diagnosed with serous papillary ovarian cancer in Atlanta, Georgia. Using leave-one-out cross validation (LOOCV), the method showed an overall accuracy of 98.9%. Significant differences in amine, eicosanoid, amino acid and TTP metabolisms were found between cancer and noncancer groups.

Since its inception, Direct Analysis in Real Time (DART) has seen utility in a wide range of applications including chemical reaction monitoring, pharmaceutical screening, and forensic mass spectrometry. Despite the growing interest in DART applications, there has been limited research into the fundamental physiochemical phenomena affecting sampling, ionization, and atmospheric ion transport. Presented here are the first experimentally validated finite element method simulations of an ambient DART-type metastable-induced chemical ionization source. It was found that complex coupled fluid dynamics, heat transfer, and electrostatic phenomena within the sampling region determine the variability in ion transmission efficiencies affecting the overall sensitivity of analysis. Particle tracing plots of a circular acetaminophen tablet placed in various positions and orientations yielded insight into optimal sample placement and evidence for sweet spots conducive to better ion transport. Experiments in a wide range of electric field conditions were performed, finding that under optimum sample placement, sensitivity could be improved by as much as 128% if ion mobility contributions were minimized. [ABSTRACT FROM AUTHOR]

Since its inception, Direct Analysis in Real Time (DART) has seen utility in a wide range of applications including chemical reaction monitoring, pharmaceutical screening, and forensic mass spectrometry. Despite the growing interest in DART applications, there has been limited research into the fundamental physiochemical phenomena affecting sampling, ionization, and atmospheric ion transport. Presented here are the first experimentally validated finite element method simulations of an ambient DART-type metastable-induced chemical ionization source. It was found that complex coupled fluid dynamics, heat transfer, and electrostatic phenomena within the sampling region determine the variability in ion transmission efficiencies affecting the overall sensitivity of analysis. Particle tracing plots of a circular acetaminophen tablet placed in various positions and orientations yielded insight into optimal sample placement and evidence for sweet spots conducive to better ion transport. Experiments in a wide range of electric field conditions were performed, finding that under optimum sample placement, sensitivity could be improved by as much as 128% if ion mobility contributions were minimized. [ABSTRACT FROM AUTHOR]

Simple Summary: Every year, hundreds of thousands of cases of renal carcinoma (RCC) are reported worldwide. Accurate staging of the disease is important for treatment and prognosis purposes; however, contemporary methods such as computerized tomography (CT) and biopsies are expensive and prone to sampling errors, respectively. As such, a non-invasive diagnostic assay for staging would be beneficial. This study aims to investigate urine metabolites as potential biomarkers to stage RCC using machine learning techniques to mine the complex datasets produced. We present a 24-metabolite panel that discriminates between early stage and advanced stage RCC with 87% accuracy in our study cohort. Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease. [ABSTRACT FROM AUTHOR]

Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents. [ABSTRACT FROM AUTHOR]

The human metabolome provides a window into the mechanisms and biomarkers of various diseases. However, because of limited availability, many sample types are still difficult to study by metabolomic analyses. Here, we present a mass spectrometry (MS)-based metabolomics strategy that only consumes sub-nanoliter sample volumes. The approach consists of combining a customized metabolomics workflow with a pulsed MS ion generation method, known as triboelectric nanogenerator inductive nanoelectrospray ionization (TENGi nanoESI) MS. Samples tested with this approach include exhaled breath condensate collected from cystic fibrosis patients as well as in vitro-cultured human mesenchymal stromal cells. Both test samples are only available in minimum amounts. Experiments show that picoliter-volume spray pulses suffice to generate high-quality spectral fingerprints, which increase the information density produced per unit sample volume. This TENGi nanoESI strategy has the potential to fill in the gap in metabolomics where liquid chromatography-MS-based analyses cannot be applied. Our method opens up avenues for future investigations into understanding metabolic changes caused by diseases or external stimuli. The analysis of metabolites offers promises in biomarker discovery. Here the authors demonstrate the metabolomics analysis of sub-nanoliter samples using triboelectric nanogenerator inductive nanoelectrospray ionization, which they apply to exhaled breath condensate from cystic fibrosis patients and mesenchymal stromal cells. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing high resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.99 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation and oxidative stress are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event. [ABSTRACT FROM AUTHOR]

Treatment for medulloblastoma (MB) — the most common malignant pediatric brain tumor — includes prophylactic radiation administered to the entire brain and spine due to the high incidence of metastasis to the central nervous system. However, the majority of long-term survivors are left with permanent and debilitating neurocognitive impairments as a result of this therapy, while the remaining 30-40% of patients relapse with terminal metastatic disease. Development of more effective targeted therapies has been hindered by our lack of understanding of the underlying mechanisms regulating the metastatic process in this disease. To understand the mechanism by which MB metastasis occurs, three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) experiments were performed on whole brains from a mouse model of human medulloblastoma. Analyzing the tumor and surrounding normal brain in its entirety enabled the detection of low abundance, spatially-heterogeneous lipids associated with tumor development. Boundaries of metastasizing and non-metastasizing primary tumors were readily defined, leading to the identification of lipids associated with medulloblastoma metastasis, including phosphatidic acids, phosphatidylethanolamines, phosphatidylserines, and phosphoinositides. These lipids provide a greater insight into the metastatic process and may ultimately lead to the discovery of biomarkers and novel targets for the diagnosis and treatment of metastasizing MB in humans. [ABSTRACT FROM AUTHOR]

Electrospray ionization (ESI) is one of the most popular methods to generate ions for mass spectrometry (MS). When compared with other ionization techniques, it can generate ions from liquid-phase samples without additives, retaining covalent and non-covalent interactions of the molecules of interest. When hyphenated to liquid chromatography, it greatly expands the versatility of MS analysis of complex mixtures. However, despite the extensive growth in the application of ESI, the technique still suffers from some drawbacks when powered by direct current (DC) power supplies. Triboelectric nanogenerators promise to be a new power source for the generation of ions by ESI, improving on the analytical capabilities of traditional DC ESI. In this review we highlight the fundamentals of ESI driven by DC power supplies, its contrasting qualities to triboelectric nanogenerator power supplies, and its applications to three distinct fields of research: forensics, metabolomics, and protein structure analysis. [Display omitted] • Triboelectric nanogenerators improve analytical characteristics of electrospray ionization. • Improved sample economy through picolitre consumption and increased sensitivity. • Nascent technology with demonstrated forensic, metabolomic, and protein structural biology mass spectrometry applications. • Affordable materials and flexible design space. [ABSTRACT FROM AUTHOR]

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

The prebiotic origins of biopolymers and metabolic co‐factors are key questions in Origins of Life studies. In a simple warm‐little‐pond model, using a drying phase to produce a urea‐enriched solution, we present a prebiotic synthetic path for the simultaneous formation of neopterins and tetrahydroneopterins, along with purine nucleosides. We show that, in the presence of ribose and in a formylating environment consisting of urea, ammonium formate, and water (UAFW), the formation of neopterins from pyrimidine precursors is robust, while the simultaneous formation of guanosine requires a significantly higher ribose concentration. Furthermore, these reactions provide a tetrahydropterin–pterin redox pair. This model suggests a prebiotic link in the origin of purine nucleosides and pterin cofactors that provides a possible deep prebiotic temporal connection for the emergence of nucleic acids and metabolic cofactors. [ABSTRACT FROM AUTHOR]

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures. [ABSTRACT FROM AUTHOR]

Simple Summary: The underlying mechanisms associated with ovarian cancer progression remain largely unknown, making it one of the most lethal cancers. To understand the disease pathogenesis, our study involved longitudinal serum metabolomics profiling of a triple-mutant mouse model of ovarian cancer that captured the dynamic metabolic response from disease onset until mouse death. These experiments were complemented with spatial lipidomic profiling of the entire reproductive system of the triple-mutant mice, enabling us to visualize the tissue heterogeneity and lipid alterations within tumors. A combined longitudinal and spatial map of metabolomic alterations associated with ovarian cancer progression is presented, serving as a comprehensive guide towards understanding the disease origin and progression. The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis. [ABSTRACT FROM AUTHOR]

Mesenchymal stromal cells (MSCs) have shown great promise in the field of regenerative medicine, as many studies have shown that MSCs possess immunomodulatory function. Despite this promise, no MSC therapies have been licensed by the Food and Drug Administration. This lack of successful clinical translation is due in part to MSC heterogeneity and a lack of critical quality attributes. Although MSC indoleamine 2,3-dioxygnease (IDO) activity has been shown to correlate with MSC function, multiple predictive markers may be needed to better predict MSC function. Three MSC lines (two bone marrow-derived, one induced pluripotent stem cell-derived) were expanded to three passages. At the time of harvest for each passage, cell pellets were collected for nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography mass spectrometry (MS), and media were collected for cytokine profiling. Harvested cells were also cryopreserved for assessing function using T-cell proliferation and IDO activity assays. Linear regression was performed on functional data against NMR, MS and cytokines to reduce the number of important features, and partial least squares regression (PLSR) was used to obtain predictive markers of T-cell suppression based on variable importance in projection scores. Significant functional heterogeneity (in terms of T-cell suppression and IDO activity) was observed between the three MSC lines, as were donor-dependent differences based on passage. Omics characterization revealed distinct differences between cell lines using principal component analysis. Cell lines separated along principal component one based on tissue source (bone marrow-derived versus induced pluripotent stem cell-derived) for NMR, MS and cytokine profiles. PLSR modeling of important features predicted MSC functional capacity with NMR (R 2 = 0.86), MS (R 2 = 0.83), cytokines (R 2 = 0.70) and a combination of all features (R 2 = 0.88). The work described here provides a platform for identifying markers for predicting MSC functional capacity using PLSR modeling that could be used as release criteria and guide future manufacturing strategies for MSCs and other cell therapies. [ABSTRACT FROM AUTHOR]

Céline Caillet and co-authors discuss a Collection on use of portable devices for the evaluation of medicine quality and legitimacy. [ABSTRACT FROM AUTHOR]

Background: Medicine quality screening devices hold great promise for post-market surveillance (PMS). However, there is little independent evidence on their field utility and usability to inform policy decisions. This pilot study in the Lao PDR tested six devices' utility and usability in detecting substandard and falsified (SF) medicines. Methodology/principal findings: Observational time and motion studies of the inspections by 16 Lao medicine inspectors of 1) the stock of an Evaluation Pharmacy (EP), constructed to resemble a Lao pharmacy, and 2) a sample set of medicines (SSM); were conducted without and with six devices: four handheld spectrometers (two near infrared: MicroPHAZIR RX, NIR-S-G1 & two Raman: Progeny, Truscan RM); one portable mid-infrared spectrometer (4500a), and single-use paper analytical devices (PAD). User experiences were documented by interviews and focus group discussions. Significantly more samples were wrongly categorised as pass/fail with the PAD compared to the other devices in EP inspections (p<0.05). The numbers of samples wrongly classified in EP inspections were significantly lower than in initial visual inspections without devices for 3/6 devices (NIR-S-G1, MicroPHAZIR RX, 4500a). The NIR-S-G1 had the fastest testing time per sample (median 93.5 sec, p<0.001). The time spent on EP visual inspection was significantly shorter when using a device than for inspections without devices, except with the 4500a, risking missing visual clues of samples being SF. The main user errors were the selection of wrong spectrometer reference libraries and wrong user interpretation of PAD results. Limitations included repeated inspections of the EP by the same inspectors with different devices and the small sample size of SF medicines. Conclusions/significance: This pilot study suggests policy makers wishing to implement portable screening devices in PMS should be aware that overconfidence in devices may cause harm by reducing inspectors' investment in visual inspection. It also provides insight into the advantages/limitations of diverse screening devices in the hands of end-users. Author summary: Substandard and falsified (SF) medicines threaten the lives of millions of people, especially where pharmaceutical legislation and regulation are limited. Screening for SF medicines in supply chains ('post-market surveillance') by medicine inspectors is crucial but currently relies only on subjective visual inspection of medicines in most countries. Many innovative portable screening technologies now exist and could be key additional assets to the current practice, but none have been extensively evaluated for medicine quality post-market surveillance. We assessed the utility and usability of six screening devices in the hands of Lao medicines inspectors in a pharmacy constructed to resemble a Lao pharmacy. Five spectrometers showed promising accuracies to identify falsified medicines, but difficulties to correctly set them up before running tests were observed. Reading and interpreting colour barcodes of the 'paper analytical cards'–a lab-on-a-chip test by inspectors were difficult, leading to lower accuracy than with spectrometers. The study suggests that overconfidence in devices may cause harm by reducing inspectors' investment in visual inspection—a crucial step to identify falsified medicines. Advantages/limitations of the devices are also documented to inform policy. [ABSTRACT FROM AUTHOR]

Substandard and falsified (SF) antimalarials have devastating consequences including increased morbidity, mortality and economic losses. Portable medicine quality screening devices are increasingly available, but whether their use for the detection of SF antimalarials is cost-effective is not known. We evaluated the cost-effectiveness of introducing such devices in post-market surveillance in pharmacies in Laos, conservatively focusing on their outcome in detecting SF artemisinin-based combination therapies (ACTs). We simulated the deployment of six portable screening devices: two handheld near-infrared [MicroPHAZIR RX, NIR-S-G1], two handheld Raman [Progeny, TruScan RM]; one portable mid-infrared [4500a FTIR] spectrometers, and single-use disposable paper analytical devices [PADs]. We considered two scenarios with high and low levels of SF ACTs. Different sampling strategies in which medicine inspectors would test 1, 2, or 3 sample(s) of each brand of ACT were evaluated. Costs of inspection including device procurement, inspector time, reagents, reference testing, and replacement with genuine ACTs were estimated. Outcomes were measured as disability adjusted life years (DALYs) and incremental cost-effectiveness ratios were estimated for each device compared with a baseline of visual inspections alone. In the scenario with high levels of SF ACTs, all devices were cost-effective with a 1-sample strategy. In the scenario of low levels of SF ACTs, only four devices (MicroPHAZIR RX, 4500a FTIR, NIR-S-G1, and PADs) were cost-effective with a 1-sample strategy. In the multi-way comparative analysis, in both scenarios the NIR-S-G1 testing 2 samples was the most cost-effective option. Routine inspection of ACT quality using portable screening devices is likely to be cost-effective in the Laos context. This work should encourage policy-makers or regulators to further investigate investment in portable screening devices to detect SF medicines and reduce their associated undesired health and economic burdens. Author summary: Distribution of poor quality medicines are an increasing global concern, especially in low- and middle-income countries (LMICs) where the effectiveness of antimicrobials can be a matter of life-or-death for patients with malaria and other potentially fatal infectious diseases. Substandard and falsified antimalarial drugs, including artemisinin-based combination therapies are widely distributed across LMICs. This endangers patients and in the longer term threatens malaria control and elimination campaigns by promoting the development of drug resistance. New field detection devices are increasingly available and could enhance the inspection process with prompt and actionable, real-time results. We conducted a cost-effectiveness analysis of the implementation of six portable devices for medicine quality screening during pharmacy post-market surveillances in Laos. This analysis conservatively focused only on the benefits of these devices in detecting substandard and falsified artemisinin-based combination therapies (ACTs), measured in terms of health outcomes for malaria patients obtaining treatment from pharmacies. Our findings suggest that using these portable devices for routine surveillance of ACT quality is likely to be cost-effective. Policy-makers and regulators might therefore consider investment in these field detection devices to minimise undesired health and economic burdens associated with substandard and falsified medicines. [ABSTRACT FROM AUTHOR]

Background: Post-market surveillance is a key regulatory function to prevent substandard and falsified (SF) medicines from being consumed by patients. Field deployable technologies offer the potential for rapid objective screening for SF medicines. Methods and findings: We evaluated twelve devices: three near infrared spectrometers (MicroPHAZIR RX, NIR-S-G1, Neospectra 2.5), two Raman spectrometers (Progeny, TruScan RM), one mid-infrared spectrometer (4500a), one disposable colorimetric assay (Paper Analytical Devices, PAD), one disposable immunoassay (Rapid Diagnostic Test, RDT), one portable liquid chromatograph (C-Vue), one microfluidic system (PharmaChk), one mass spectrometer (QDa), and one thin layer chromatography kit (GPHF-Minilab). Each device was tested with a series of field collected medicines (FCM) along with simulated medicines (SIM) formulated in a laboratory. The FCM and SIM ranged from samples with good quality active pharmaceutical ingredient (API) concentrations, reduced concentrations of API (80% and 50% of the API), no API, and the wrong API. All the devices had high sensitivities (91.5 to 100.0%) detecting medicines with no API or the wrong API. However, the sensitivities of each device towards samples with 50% and 80% API varied greatly, from 0% to 100%. The infrared and Raman spectrometers had variable sensitivities for detecting samples with 50% and 80% API (from 5.6% to 50.0%). The devices with the ability to quantitate API (C-Vue, PharmaChk, QDa) had sensitivities ranging from 91.7% to 100% to detect all poor quality samples. The specificity was lower for the quantitative C-Vue, PharmaChk, & QDa (50.0% to 91.7%) than for all the other devices in this study (95.5% to 100%). Conclusions: The twelve devices evaluated could detect medicines with the wrong or none of the APIs, consistent with falsified medicines, with high accuracy. However, API quantitation to detect formulations similar to those commonly found in substandards proved more difficult, requiring further technological innovation. Author summary: Criminally falsified or poorly manufactured medicines can lead to patients becoming sicker and losing trust in the health system. Portable tools beyond just documentation checks and visual inspection (the current practices in many low- and middle-income countries) can help pharmacy inspectors with early detection of poor quality medicines. Currently, many tools are available to detect poor quality medicines, but their performances have not been properly assessed and compared. In this study, 12 different devices ranging from disposable single use tests to portable spectrometers were tested in a laboratory. All the tested devices could identify medicines that contained none or the wrong active ingredient(s), a common trait of falsified medicines. Disposable tests required few resources to be implemented, but had difficulties identifying medicines with reduced amounts of active ingredients. Spectrometers used 'out-of-the-box' required minimal consumables had varying degrees of success at detecting medicines with reduced amounts of active ingredients. Finally, instruments with more quantitative abilities, such as benchtop simple chromatographs or mass spectrometers, offered the best sensitivity for detecting medicines with reduced amounts of active ingredients, but required the most resources and training and were deemed to be more suitable for centralized testing. [ABSTRACT FROM AUTHOR]

Substandard and falsified (SF) medicines have important but neglected consequences including increased morbidity and mortality, economic losses, and diminished public confidence in health systems. The overall study assesses and compares device accuracy, describes potential barriers, and evaluates the costs versus the benefits of the implementation of a wide diversity of portable medicine quality screening devices in a public health perspective. Falsified medicines purport to be real, authorized medicines but are deliberately and fraudulently mislabeled with respect to their identity and/or source [[2]]. The term "falsified medicines", adopted by the World Health Assembly in May 2017, references the public health issues of poor quality medicines rather than the term "counterfeit" that refers to trademark infringement. [Extracted from the article]

This Communication describes a new thermal desorption/pyrolysis vacuum-assisted plasma ionization (pyro-VaPI) ion source coupled to ion mobility–mass spectrometry (IM–MS) for insoluble polymer analysis. Pyro-VaPI combines a pyrolysis device, soft ambient plasma ionization, IM, and MS into a single platform for polymer analysis with minimal sample preparation. Nylons, a widely used and well-studied thermoplastic, were chosen to evaluate the pyro-VaPI performance. Six different nylon polymers were studied and characterized. With the application of IM–MS, two different isobars for the protonated cyclic dimers of 6–6, 6–9, 6–10, and 6–12 nylon and two isobars for the cyclic tetramer of nylon-6 were detected at 200 °C. These isobars were observed at different heating times, with the species drifting faster in the IM cell appearing several minutes after the slower drifting species. To the best of our knowledge, these isobaric dimers and tetramers have not been previously reported, indicating that pyro-VaPI IM–MS is a useful tool for the structural characterization of heated or pyrolyzed polymers. [ABSTRACT FROM AUTHOR]

Effective cancer prevention requires the discovery and intervention of a factor critical to cancer development. Here we show that ovarian progesterone is a crucial endogenous factor inducing the development of primary tumors progressing to metastatic ovarian cancer in a mouse model of high-grade serous carcinoma (HGSC), the most common and deadliest ovarian cancer type. Blocking progesterone signaling by the pharmacologic inhibitor mifepristone or by genetic deletion of the progesterone receptor (PR) effectively suppressed HGSC development and its peritoneal metastases. Strikingly, mifepristone treatment profoundly improved mouse survival (~18 human years). Hence, targeting progesterone/PR signaling could offer an effective chemopreventive strategy, particularly in high-risk populations of women carrying a deleterious mutation in the BRCA gene. [ABSTRACT FROM AUTHOR]

Medulloblastoma (MB), the most common malignant pediatric brain tumor, has high propensity to metastasize. Currently, the standard treatment for MB patients includes radiation therapy administered to the entire brain and spine for the purpose of treating or preventing against metastasis. Due to this aggressive treatment, the majority of long-term survivors will be left with permanent and debilitating neurocognitive impairment, for the 30–40% patients that fail to respond to treatment, all will relapse with terminal metastatic disease. An understanding of the underlying biology that drives MB metastasis is lacking, and is critically needed in order to develop targeted therapeutics for its prevention. To examine the metastatic biology of sonic hedgehog (SHH) MB, the human MB subgroup with the worst clinical outcome in children, we first generated a robust SmoA1-Math-GFP mouse model that reliably reproduces human SHH MB whereby metastases can be visualized under fluorescence microscopy. Lipidome alterations associated with metastasis were then investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) under positive ionization mode to primary tumor samples collected from mice without (n = 18) and with (n = 7) metastasis. Thirty-four discriminant lipids associated with SHH MB metastasis were successfully annotated, including ceramides (Cers), sphingomyelins (SMs), triacylglycerols (TGs), diacylglycerols (DGs), phosphatidylcholines (PCs), and phosphatidic acids (PAs). This study provides deeper insights into dysregulations of lipid metabolism associated with SHH MB metastatic progression, and thus serves as a guide toward novel targeted therapies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of a triboelectric nanogenerator (TENG) electrospray ionization source for native mass spectrometry. • TENG hyphenated to ion mobility-mass spectrometry (IM-MS) produced native protein measurements. • First native measurements of several egg-based proteins relevant to cultural heritage. • TENG enables ultra-precious material analysis through picoliter and nanomolar consumption rates. [ABSTRACT FROM AUTHOR]

In this communication, we report on the real-time analysis of organic aerosol particles by Vacuum-assisted Plasma Ionization-Mass Spectrometry (Aero-VaPI-MS) using a home-built VaPI ion source coupled to a Synapt G2-S HDMS ion mobility-mass spectrometry (IM-MS) system. Standards of organic molecules of interest in prebiotic chemistry were used to generate aerosols. Monocaprin and decanoic acid aerosol particles were successfully detected in both the positive and negative ion modes, respectively. A complex aerosol mixture of different sizes of polymers of L-malic acid was also examined through ion mobility (IM) separations, resulting in the detection of polymers of up to eight monomeric units. This noncommercial plasma ion source is proposed as a low cost alternative to other plasma ionization platforms used for aerosol analysis, and a higher-performance alternative to more traditional aerosol mass spectrometers. VaPI provides robust online ionization of organics in aerosols without extensive ion activation, with the coupling to IM-MS providing higher peak capacity and excellent mass accuracy.ᅟ [ABSTRACT FROM AUTHOR]

The rise of peptides with secondary structures and functions would have been a key step in the chemical evolution which led to life. As with modern biology, amino acid sequence would have been a primary determinant of peptide structure and activity in an origins-of-life scenario. It is a commonly held hypothesis that unique functional sequences would have emerged from a diverse soup of proto-peptides, yet there is a lack of experimental data in support of this. Whereas the majority of studies in the field focus on peptides containing only one or two types of amino acids, here we used modern mass spectrometry (MS)-based techniques to separate and sequence de novo proto-peptides containing broader combinations of prebiotically plausible monomers. Using a dry-wet environmental cycling protocol, hundreds of proto-peptide sequences were formed over a mere 4 d of reaction. Sequence homology diagrams were constructed to compare experimental and theoretical sequence spaces of tetrameric proto-peptides. MS-based analyses such as this will be increasingly necessary as origins-of-life researchers move toward systems-level investigations of prebiotic chemistry. [ABSTRACT FROM AUTHOR]

Plant-derived sterols, often referred to as phytosterols, are important constituents of plant membranes where they assist in maintaining phospholipid bilayer stability. Consumption of phytosterols has been suggested to positively affect human health by reducing cholesterol levels in blood via inhibition of its absorption in the small intestine, thus protecting against heart attack and stroke. Sterols are challenging analytes for mass spectrometry, since their low polarity makes them difficult to ionize by both electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI), typically requiring derivatization steps to overcome their low ionization efficiencies. We present a fast and reliable method to characterize the composition of phytosterols in vegetable oils and enriched margarines. The method requires no derivatization steps or sample extraction procedures thanks to the use of transmission mode direct analysis in real time mass spectrometry (TM-DART-MS). [ABSTRACT FROM AUTHOR]

The absolute number densities of helium atoms in the 2s 3 S 1 metastable state were determined in four plasma-based ambient desorption/ionization sources by atomic absorption spectroscopy. The plasmas included a high-frequency dielectric barrier discharge (HF-DBD), a low temperature plasma (LTP), and two atmospheric-pressure glow discharges, one with AC excitation and the other with DC excitation. Peak densities in the luminous plumes downstream from the discharge capillaries of the HF-DBD and the LTP were 1.39 × 10 12 cm − 3 and 0.011 × 10 12 cm − 3 , respectively. Neither glow discharge produced a visible afterglow, and no metastable atoms were detected downstream from the capillary exits. However, densities of 0.58 × 10 12 cm − 3 and 0.97 × 10 12 cm − 3 were measured in the interelectrode regions of the AC and DC glow discharges, respectively. Time-resolved measurements of metastable atom densities revealed significant random variations in the timing of pulsed absorption signals with respect to the voltage waveforms applied to the discharges. [ABSTRACT FROM AUTHOR]

Highlights: [•] Titan tholin amino acids were measured using multi-dimensional chromatographic analysis. [•] We compared the tholin amino acids with those generated from Miller–Urey (MU) reactions. [•] The amino acids detected in MU reactions are similar to those generated in the Titan simulation. [•] Many Solar System environments may thus provide similar organic compounds. [•] Given reducing conditions, similar materials should be available throughout the universe. [Copyright &y& Elsevier]

Abstract: In this work, we report the molecular characterisation of trypsin I (Try I) from Monterey sardine (Sardinops sagax caerulea). Aspects such as thermodynamic activation parameters, molecular model and cDNA-deduced amino acid sequence allow a more in depth understanding of its activity at low temperatures. The analysis of the thermodynamic activation parameters suggests that this molecule is a cold-adapted protease. From the molecular cloning, we deduced the amino acid sequence and predicted a theoretical structural model of sardine Try I with a classical trypsin fold. Cold-adaptation of this enzyme probably comes from amino acid replacement of key residues to improve flexibility at low temperature, thus increasing k cat. The cold-adaptation of sardine Try I opens a wide range of biotechnological applications for this protease and also it is interesting from the structure function relationship point of view of serine protease proteins. [Copyright &y& Elsevier]

Physical activity, including structured exercise, is associated with favorable health-related chronic disease outcomes. Although there is evidence of various molecular pathways that affect these responses, a comprehensive molecular map of these molecular responses to exercise has not been developed. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is a multicenter study designed to isolate the effects of structured exercise training on the molecular mechanisms underlying the health benefits of exercise and physical activity. MoTrPAC contains both a preclinical and human component. The details of the human studies component of MoTrPAC that include the design and methods are presented here. The human studies contain both an adult and pediatric component. In the adult component, sedentary participants are randomized to 12 wk of Control, Endurance Exercise Training, or Resistance Exercise Training with outcomes measures completed before and following the 12 wk. The adult component also includes recruitment of highly active endurance-trained or resistance-trained participants who only complete measures once. A similar design is used for the pediatric component; however, only endurance exercise is examined. Phenotyping measures include weight, body composition, vital signs, cardiorespiratory fitness, muscular strength, physical activity and diet, and other questionnaires. Participants also complete an acute rest period (adults only) or exercise session (adults, pediatrics) with collection of biospecimens (blood only for pediatrics) to allow for examination of the molecular responses. The design and methods of MoTrPAC may inform other studies. Moreover, MoTrPAC will provide a repository of data that can be used broadly across the scientific community. NEW & NOTEWORTHY The Molecular Transducers of Physical Activity Consortium (MoTrPAC) will be the first large trial to isolate the effects of structured exercise training on the molecular mechanisms underlying the health benefits of exercise and physical activity. By generating a compendium of the molecular responses to exercise, MoTrPAC will lay the foundation for a new era of biomedical research on Precision Exercise Medicine. Presented here is the design, protocols, and procedures for the MoTrPAC human studies.

Mesenchymal stem/stromal cell (MSC) therapies have had limited success so far in clinical trials due in part to heterogeneity in immune-responsive phenotypes. Therefore, techniques to characterize these properties of MSCs are needed during biomanufacturing. Imaging cell shape, or morphology, has been found to be associated with MSC immune responsivity-but a direct relationship between single-cell morphology and function has not been established. We used label-free differential phase contrast imaging and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to evaluate single-cell morphology and explore relationships with lipid metabolic immune response. In interferon gamma (IFN-γ)-stimulated MSCs, we found higher lipid abundances from the ceramide-1-phosphate (C1P), phosphatidylcholine (PC), LysoPC, and triglyceride (TAG) families that are involved in cell immune function. Furthermore, we identified differences in lipid signatures in morphologically defined MSC subpopulations. The use of single-cell optical imaging coupled with single-cell spatial lipidomics could assist in optimizing the MSC production process and improve mechanistic understanding of manufacturing process effects on MSC immune activity and heterogeneity., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis. We tested our approach on two data sets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using Auto-sklearn, surpassed standalone ML algorithms like SVM and k-Nearest Neighbors in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers. The effectiveness of Auto-sklearn is highlighted by its AUC scores of 0.97 for RCC and 0.85 for OC, obtained from the unseen test sets. Importantly, on most of the metrics considered, Auto-sklearn demonstrated a better classification performance, leveraging a mix of algorithms and ensemble techniques. Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1&#95;16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.

Triboelectric nanogenerators (TENG) are useful devices for converting mechanical motion into electric current using readily available materials. Though the applications for these devices span across many fields, TENG can be leveraged for mass spectrometry (MS) as inexpensive and effective power supplies for pulsed nanoelectrospray ionization (nESI). The inherently discontinuous spray provided by TENG is particularly useful in scenarios where high sample economy is imperative, as in the case of ultraprecious samples. Previous work has shown the utility of TENG MS as a highly sensitive technique capable of yielding quality spectra from only a few microliters of sample at low micromolar concentrations. As the field of miniaturized, fieldable mass spectrometers grows, it remains critical to develop advanced ion sources with similarly small power requirements and footprints. Here, we present a redesigned TENG ion source with a sub-1000 USD material cost, lower power consumption, reduced footprint, and improved capabilities. We validate the performance of this new device for a diverse set of applications, including lipid double bond localization and native protein analysis.

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT., (© 2024. Battelle Memorial Institute.)

Background: Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention., Methods: Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches., Results: We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set., Conclusions: This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women., Impact: Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions., (©2024 The Authors; Published by the American Association for Cancer Research.)

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context., (© 2022 John Wiley & Sons Ltd.)

Traumatic brain injury (TBI) is a significant source of disability in the United States and around the world and may lead to long-lasting cognitive deficits and a decreased quality of life for patients across injury severities. Following the primary injury phase, TBI is characterized by complex secondary cascades that involve altered homeostasis and metabolism, faulty signaling, neuroinflammation, and lipid dysfunction. The objectives of the present study were to (1) assess potential correlations between lipidome and cytokine changes after closed-head mild TBI (mTBI), and (2) examine the reproducibility of our acute lipidomic profiles following TBI. Cortices from 54 Sprague Dawley male and female rats were analyzed by ultra-high-performance liquid chromatography mass spectrometry (LC-MS) in both positive and negative ionization modes and multiplex cytokine analysis after single (smTBI) or repetitive (rmTBI) closed-head impacts, or sham conditions. Tissue age was a variable, given that two cohorts ( n = 26 and n = 28) were initially run a year-and-a-half apart, creating inter-batch variations. We annotated the lipidome datasets using an in-house data dictionary based on exact masses of precursor and fragment ions and removed features with statistically significant differences between sham control batches. Our results indicate that lipids with high-fold change between injury groups moderately correlate with the cytokines eotaxin, IP-10, and TNF-α. Additionally, we show a significant decrease in the pro-inflammatory markers IL-1β and IP-10, TNF-α, and RANTES in the rmTBI samples relative to the sham control. We discuss the major challenges in correlating high dimensional lipidomic data with functional cytokine profiles and the implications for understanding the biological significance of two related but disparate analysis modes in the study of TBI, an inherently heterogeneous neurological disorder.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Traumatic brain injury (TBI) is a major health concern in the United States and globally, contributing to disability and long-term neurological problems. Lipid dysregulation after TBI is underexplored, and a better understanding of lipid turnover and degradation could point to novel biomarker candidates and therapeutic targets. Here, we investigated overlapping lipidome changes in the brain and blood using a data-driven discovery approach to understand lipid alterations in the brain and serum compartments acutely following mild TBI (mTBI) and the potential efflux of brain lipids to peripheral blood. The cortices and sera from male and female Sprague-Dawley rats were analyzed via ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in both positive and negative ion modes following single and repetitive closed head impacts. The overlapping lipids in the data sets were identified with an in-house data dictionary for investigating lipid class changes. MS-based lipid profiling revealed overall increased changes in the serum compartment, while the brain lipids primarily showed decreased changes. Interestingly, there were prominent alterations in the sphingolipid class in the brain and blood compartments after single and repetitive injury, which may suggest efflux of brain sphingolipids into the blood after TBI. Genetic algorithms were used for predictive panel selection to classify injured and control samples with high sensitivity and specificity. These overlapping lipid panels primarily mapped to the glycerophospholipid metabolism pathway with Benjamini-Hochberg adjusted q -values less than 0.05. Collectively, these results detail overlapping lipidome changes following mTBI in the brain and blood compartments, increasing our understanding of TBI-related lipid dysregulation while identifying novel biomarker candidates.