Uncovering the health implications of abandoned mines through protein profiling of local residents.

Residents in areas with abandoned mines risk significant exposure to abundant heavy metals in the environment. However, current clinical indicators cannot fully reflect the health changes associated with abandoned mine exposure. The aim of this study was to identify biological changes in the residents of abandoned mine areas via proteomic analysis of their blood. Blood samples were collected from abandoned mine and control areas, and mass spectrometry was used for protein profiling. A total of 138 unique or common proteins that were differentially expressed in low-exposure abandoned mine area (LoAMA) or high-exposure abandoned mine area (HiAMA) compared to non-exposure control area (NEA) were analyzed, and identified 4 clusters based on functional similarity. Among the 10 proteins that showed specific change in LoAMA, 4 proteins(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) were cluded in cluster 1(plasma lipoprotein remodeling), and linked to proteins that showed specific change in protein expression in HiAMA. Therefore, it is suggested that 4 proteins are changed at low exposure to an abandoned mine (or initial exposure), and then at high exposure, changes in various proteins involved in linked plasma lipoprotein remodeling are induced, which might triggered by the 4 proteins. Interestingly, in addition to plasma lipoprotein remodeling, proteins involved in other functional networks were changed in the high exposure group. These were all directly or indirectly linked to the 4 biomarkers(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) that changed during low exposure. This suggests their potential utility in identifying areas impacted by abandoned mines. Especially, proteins involved in lipid metabolism and renal function-related diseases in individuals exposed to heavy metals in abandoned mine areas were correlated. Chronic kidney disease is predominantly instigated by cardiovascular disease and is commonly accompanied by dyslipidemia.

Discovery and validation of metabolite markers in bloodstains for bloodstain age estimation.

Bloodstain age estimation involves measuring time-dependent changes in the levels of biomolecules in bloodstains. Although several studies have identified bloodstain metabolites as markers for estimating bloodstain age, none have considered sex, age-related metabolomic differences, or long-time bloodstain age. Therefore, we aimed to identify metabolite markers for estimating the age of bloodstains at weekly intervals within 28 days and validate them through multiple reaction monitoring. Adenosine 5'-monophosphate, choline, and pyroglutamic acid were selected as markers. Seven metabolites were validated, including five previously reported metabolites, ergothioneine, hypoxanthine, L-isoleucine, L-tryptophan, and pyroglutamic acid. Choline and hypoxanthine can be used to differentiate bloodstains between days 0 and 14 after deposition at weekly intervals, whereas L-isoleucine and L-tryptophan can help distinguish bloodstains between 7 days before and 14 days after deposition. Evaluation of the changes in metabolite levels according to sex and age revealed that the average levels of all seven metabolites were higher in women on day 0. Moreover, the level of ergothioneine was significantly higher in elderly individuals than in young individuals at all time points. In this study, we confirmed the potential effectiveness of metabolites in bloodstains as forensic markers and provided a new perspective on metabolomic approaches linked to forensic science.

Effect of environmental conditions on bloodstain metabolite analysis.

Establishing a correlation between environmental variables and chemical change can significantly improve the quality of research in multiple fields. Among various environmental variables, temperature and humidity are closely related to the rate of chemical reactions. This study aimed to confirm changes in metabolite markers that were previously discovered in other temperature and humidity environment conditions and to confirm the possibility that they could act as markers. After blood collection from the subjects and bloodstain preparation, the quantitative values of the bloodstain metabolites were confirmed (when the age of the bloodstain was within a month) under eight environmental conditions (4 °C/30%, 4 °C/60%, 25 °C/30%, 25 °C/60%, 25 °C/90%, 40 °C/30%, 40 °C/60%, and 40 °C/90%). Age-of-bloodstain estimation models were constructed to confirm the applicability of bloodstain metabolites as markers for bloodstain age in various environments. The average concentration of metabolite markers exhibited a decreasing trend with the age of the bloodstain, which transformed into an increasing trend from day 7 onwards. In terms of temperature and humidity, 25 °C and 90%, respectively, showed the most dissimilar metabolite change pattern compared to other conditions. The age-of-bloodstain estimation models developed here have an R-square value of up to 0.92 for each condition and an R-square value of 0.71 when all environmental conditions were combined. The findings herein highlight the immense potential of blood metabolites for field application, confirming the possibility of predicting metabolite changes from the rates of their chemical reactions and validating the importance of metabolites as age-of-bloodstain markers under various environmental conditions.

Bloodstain Metabolite Markers: Discovery and Validation for Estimating Age of Bloodstain within 7 Days.

Metabolomic research using analytical chemistry methods has been carried out in a wide range of research fields. However, research combining forensic science and metabolomics is rare. Determining the age of bloodstains could provide key information regarding when a crime was committed. Currently, validated methods for estimating the age of bloodstains are unavailable. Metabolites are intermediate and final products of chemical reactions. Therefore, they are less likely to be degraded than other components of blood under field conditions. In this study, metabolites in bloodstains were analyzed using liquid chromatography-mass spectrometry to discover and validate metabolic markers for determining the age of bloodstains within a week post-bleeding. Nontargeted analysis of bloodstain metabolites revealed statistically significant differences over time. Quantitative analysis of identified candidates via multiple reaction monitoring confirmed the statistical significance according to the age of bloodstain. Pyroglutamic acid, l-glutamine, acetylcarnitine, and adenosine 5'-monophosphate were selected as the final markers. The content of each marker exhibited a statistically significant and consistent tendency to decrease with the age of bloodstain. Furthermore, the effect of hemolysis was considered according to the blood fraction spots of the four markers. This study is the first to identify and validate metabolite markers that may help determine the age of bloodstains within a week post-bleeding. If applied to crime scenes as indicators of the age of bloodstains, they can be used as innovative and important tools for reconstructing crime scenes, suggesting initial investigative direction. This study highlights the forensic utility of blood metabolites ex vivo.

Discovery of donor age markers from bloodstain by LC-MS/MS using a metabolic approach.

Bloodstains are frequently encountered at crime scenes and they provide important evidence about the incident, such as information about the victim or suspect and the time of death or other events. Efforts have been made to identify the age of the bloodstain's donor through genomic approaches, but there are some limitations, such as the availability of databases and the quality dependence of DNA. There is a need for the development of a tool that can obtain information at once from a small blood sample. The aim of this study is to identify bloodstain metabolite candidates that can be used to determine donor age. We prepared bloodstain samples and analyzed metabolites using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Eighteen molecular features (MFs) were selected as candidates using volcano plots and multivariate analysis. Based on the MS/MS spectrum of the MFs, the following nine metabolites were identified from the METaboliteLINk database: Δ2-cis eicosenoic acid, ergothioneine, adenosine 5'-monophosphate, benzaldehyde, phenacylamine, myristic acid ethyl ester, p-coumaric acid, niacinamide, and N-arachidonoyl-L-alanine. These nine age markers at high or low abundances could be used to estimate the age of a bloodstain's donor. This study was the first to develop metabolite age markers that can be used to analyze crime scene bloodstains.

Validation of the Metabolite Ergothioneine as a Forensic Marker in Bloodstains.

Ergothioneine, which is a naturally occurring metabolite, generally accumulates in tissues and cells subjected to oxidative stress, owing to its structural stability at physiological pH; therefore, it has been attracting attention in various biomedical fields. Ergothioneine has also been suggested as a potential forensic marker, but its applicability has not yet been quantitatively validated. In this study, quantitative analysis of ergothioneine in bloodstains was conducted to estimate the age of bloodstains and that of bloodstain donors. Blood from youth and elderly participants was used to generate bloodstains. After extracting metabolites from the bloodstains under prevalent age conditions, ergothioneine levels were quantified by mass spectrometry via multiple reaction monitoring. The concentration of ergothioneine in day 0 bloodstains (fresh blood), was significantly higher in the elderly group than in the youth group, but it did not differ by sex. Statistically significant differences were observed between the samples from the two age groups on days 0, 5 and 7, and on days 2 and 3 compared with day 0. The findings suggest that ergothioneine can be used to estimate the age of bloodstains and of the donor; it could be useful as a potential marker in reconstructing crime scenes.

Metabolomic profiling of bloodstains on various absorbent and non-absorbent surfaces.

Bloodstains found at crime scenes contain immense information about the crime; thus, studies involving analysis of small molecules in bloodstains have been conducted. However, most of these studies have not accounted for the difference in the results of small molecule analysis due to the surface of bloodstains. To evaluate the "surface effect," we prepared bloodstains on seven surfaces, including both absorbent and non-absorbent surfaces, and performed global small molecule analysis by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). We used three indicators: (1) count recovery rate (%) of molecular features (MFs), (2) the number of MFs extracted from the surface without bloodstains, and (3) difference in abundance recovery rate (%) of MFs, to determine the ranking of the seven surfaces in the order of their similarity with blood. We also confirmed the correlation between each surface and blood through multivariate analysis. We found that the non-absorbent surfaces ranked better than the absorbent surfaces; wooden flooring was ranked as the most efficient surface, followed by stainless, vinyl flooring, glass, tile, filter paper, and mixed cotton. This study will help in the selection of the most efficient surface for collection of bloodstains for small molecule analysis from a crime scene. This is the first study to identify the effects of surface on extraction of global small molecules from bloodstains; it will help forensic scientists in obtaining more accurate information from small molecules present in the bloodstains collected at the field. Graphical abstract.

Estimation of Age of Bloodstains by Mass-Spectrometry: A Metabolomic Approach.

Bloodstains are common evidence in crime scenes, containing significant information, including genetic information. Although efforts have been made to reliably determine the time of incident by analyzing the elapsed time of the bloodstain, there has been limited success. To identify candidate metabolites in bloodstains over time, we prepared bloodstain samples using filter paper and analyzed the metabolites by high-performance liquid chromatography-mass spectrometry (HPLC-MS)/MS over a 21-day period. Using Venn diagrams and by multivariate analysis, we selected 62 candidate molecular features. We found by partial least-squares discriminant analysis (PLS-DA) that the group can be classified with an accuracy of 75.0%, and the R2 and Q2 values were 0.7513 and 0.6998, respectively. Five metabolites were successfully identified based on candidate molecular features. The level of two metabolites, l-tryptophan and ergothioneine, decreased with time. The concentration of candidate metabolites that we propose reliably increased or decreased with time, thus, enabling the measurement of elapsed time of the bloodstain. This study is the first to identify markers used to analyze the elapsed time of bloodstains through metabolomics analysis.

Cadmium-associated protein changes in residents of contaminated areas: Abandoned mine and smelter.

Cadmium (Cd), a serious environmental contaminant, is associated with adverse health effects. However, the specific changes that the human body experiences in response to exposure to varying concentrations of cadmium remain unknown. The high levels of heavy metal contamination, especially Cd, in abandoned mines and smelter sites make them ideal locations to investigate the physiological manifestations of Cd exposure. This study found that individuals inhabiting abandoned mine and smelter areas had higher concentrations of Cd in their urine and blood compared to those living outside these areas (i.e., the controls). Furthermore, proteomic profiling of blood samples from all study groups was performed to identify proteomic biomarkers associated with chronic and severe Cd exposure. This analysis showed statistically significant correlations between urine Cd levels and sixteen proteins. Among these proteins, seven exhibited significantly altered expressions in samples from contaminated areas compared with those from control areas. Therefore, these proteins were selected as potential markers representing Cd-related protein alterations. Multiple reaction monitoring analysis was performed to validate the expression patterns of the proteins and four proteins were found to exhibit consistent trends. The findings show that Cd exposure significantly affects the expression of certain proteins in the human body. Understanding the underlying mechanisms and diseases associated with Cd-induced protein alterations can aid in the development of effective preventive and therapeutic strategies for individuals exposed to Cd-linked pollution.

Discovery and validation of a protein biomarker for the diagnosis and classification of disease severity of major depressive disorder.

BACKGROUND AND AIMS: Diagnosis and classification of disease severity of major depressive disorder (MDD) are determined through a doctor's consultation and questionnaire-based rating scale. This study aimed to identify and validate a serum protein biomarker for diagnosing and classifying the disease severity of MDD. MATERIALS AND METHODS: Based on the Hamilton Depression Rating Scale (HAMD) score, participants were divided into control, mild, moderate, and severe groups. Samples prepared from collected sera were analyzed using non-targeted qualitative and targeted quantitative tools to identify potential biomarkers. RESULTS: Four proteins were selected as biomarker candidates, which showed statistically significant consistent tendencies depending on MDD severity. Among them, tetranectin was the only validated protein in the quantitative analysis that showed the same decreasing tendency as that in the qualitative analysis. Furthermore, tetranectin showed fair discrimination performance between the control and MDD group. CONCLUSIONS: Tetranectin may be a novel potential biomarker for diagnosing and classifying the severity of MDD, though further verification and validation studies of its efficacy are needed.

Internal standard metabolites for estimating origin blood volume of bloodstains.

The volume of blood leaked from blood vessels may change due to evaporation of water under the natural influence of the external environment. Bloodstains and dried blood spots (DBS), which describes blood dried in the external environment, are similar in their production and their metabolite quantification profiles. In both bloodstain metabolite analysis in the forensic science field and DBS metabolite analysis in the clinical field, it is important to determine the volume of the origin blood as this affects metabolite quantification results. Therefore, the purpose of this study is to discover the internal standard metabolites that have quantitatively proportional relationships with origin blood volume and maintain constant concentrations even as the age of the bloodstain increases. As a result, the concentrations of L-isoleucine and L-phenylalanine increased in proportion to the origin blood volume of the bloodstain. The differences in concentration of L-isoleucine were significant in all volume comparisons except in the comparison between 65 µL and 85 µL. The differences in concentration of L-phenylalanine were significant in all volume comparisons except between 65 µL and 45 µL and between 65 µL and 85 µL. In addition, it was confirmed that both metabolites tended to maintain constant concentrations without being affected by bloodstain age as the volume became smaller. These internal standard metabolites can be used for estimating the origin blood volume of bloodstains during metabolite analysis of bloodstains and DBS and could provide a volume criterion for standardization when comparing metabolite quantification between samples.

Corrigendum: Role of histamine-mediated macrophage differentiation in clearance of metastatic bacterial infection.

[This corrects the article DOI: 10.3389/fimmu.2023.1290191.].

Role of histamine-mediated macrophage differentiation in clearance of metastatic bacterial infection.

Macrophages are highly heterogeneous immune cells with a role in maintaining tissue homeostasis, especially in activating the defense response to bacterial infection. Using flow cytometric and single-cell RNA-sequencing analyses of peritoneal cells, we here show that small peritoneal macrophage and immature macrophage populations are enriched in histamine-deficient (Hdc -/-) mice, characterized by a CD11bmiF4/80loCCR2+MHCIIhi and CD11bloF4/80miTHBS1+IL-1α+ phenotype, respectively. Molecular characterization revealed that immature macrophages represent an abnormally differentiated form of large peritoneal macrophages with strong inflammatory properties. Furthermore, deficiency in histamine signaling resulted in significant impairment of the phagocytic activity of peritoneal macrophage populations, conferring high susceptibility to bacterial infection. Collectively, this study reveals the importance of histamine signaling in macrophage differentiation at the molecular level to maintain tissue homeostasis, offering a potential therapeutic target for bacterial infection-mediated diseases.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach.

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Deep-learning-based automated terminology mapping in OMOP-CDM.

OBJECTIVE: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping. MATERIALS AND METHODS: To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings. RESULTS: The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements. DISCUSSION: The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system. CONCLUSION: Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.

A medical care model using comprehensive geriatric assessment for community-dwelling older Korean adults.

BACKGROUND: The quality of life of older adults deteriorates when they lose their ability to perform activities of daily living. Therefore, the older adults should be assessed to identify risk factors for functional decline and to correct these factors so that they may live as independently as possible in the community. We developed a medical care model using comprehensive geriatric assessment (CGA) for community-dwelling older patients. METHODS: Three hundred and ninety-one older adults who were frail or likely to be frail were selected. CGA was performed before and after the interventions to determine the effect of the interventions. Three interventions-exercise training, nutritional education, and medication reconciliation-were performed for 5.1 ± 0.6 months. RESULTS: A comparison of the results of the first and second assessments revealed that the participants showed improvement in physical function, quality of life, medication, and nutrition. The average gait speed had increased from 0.77 ± 0.17 m/s to 0.89 ± 0.20 m/s (P < 0.001). For health-related quality of life, the average EuroQol-5 dimension-3L score for each domain decreased significantly. The number of patients with polypharmacy decreased from 181(50 %) to 155(43 %) (P = 0.001). The number of patients who were at risk of malnutrition or malnourished decreased from 72(20 %) to 45(12 %) (P < 0.001). The majority of participants were highly satisfied and were willing to participate again. CONCLUSION: Our medical model based on CGA showed a significantly positive effect on the physical function and quality of life of community-dwelling older adults. Our model may be a promising strategy for improving the care of them.

A discovery of screening markers for rheumatoid arthritis by liquid chromatography mass spectrometry: A metabolomic approach.

AIM: This study aimed to discover serum metabolite biomarkers for potential use in screening for rheumatoid arthritis (RA). METHODS: The sera from 43 healthy controls (HCs) and 49 RA patients were globally analyzed using high-performance liquid chromatography- tandem mass spectrometry. Molecular features (MFs) from samples were analyzed using volcano plots, partial least squares discriminant analysis, and variable importance in projection scores to select candidates. The spectra of candidate MFs were matched with the METLIN database. We confirmed the association between candidates and RA and analyzed the receiver-operating characteristic (ROC) curves. RESULTS: We selected a total of 57 candidate MFs that had a fold change ≥1.5, P value ≤.05, and over 80% of frequency. Among them, 18 MFs were identified as metabolites with the METLIN database. Six metabolites (dehydroepiandrosterone sulfate, androsterone sulfate, γ-linolenic acid, 9[E],11[E]-conjugated linoleic acid, docosahexaenoic acid, and docosapentaenoic acid [22n-3]) out of the 18 were associated with mechanisms of RA and were selected as final candidates. ROC curve analysis revealed their area under the curve (AUC) values were all above 0.75 and the combined AUC of the six candidates was 0.89. CONCLUSION: Using six candidates as a marker set showed potential in distinguishing RA patients from HCs, based on high AUC values. Therefore, we propose that a marker set of these six candidates has potential clinical application in RA screening.

Risk Stratification Using Multivariable Fractional Polynomials in Diffuse Large B-Cell Lymphoma.

The risk stratification of diffuse large B-cell lymphoma (DLBCL) is crucial. The International Prognostic Index, the most commonly used and the traditional risk stratification system, is composed of fixed and artificially dichotomized attributes. We aimed to develop a novel prognostic model that allows the incorporation of up-to-date attributes comprehensively without information loss. We analyzed 204 patients with primary DLBCL who were uniformly treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) from 2007 to 2012 at Asan Medical Center. Using the multivariable fractional polynomial (MFP) method and bootstrap resampling, we selected the variables of significance and the best fitted functional form in fractional polynomials. Age, serum ß2-microglobulin, serum lactate dehydrogenase, and BCL2 expression were selected as significant variables in predicting overall survival (OS), while age was excluded in predicting 2-years event-free survival. The prognostic score calculated by the MFP model effectively classifies patients into four risk groups with 5-years OS of 89.91% (low risk), 81.21% (low-intermediate risk), 66.40% (high-intermediate risk), and 37.89% (high risk). We suggest a new prognostic model that is simple and flexible. By using the MFP method, we can incorporate various clinicopathologic factors into a risk stratification system without arbitrary dichotomization.

Presoaking dried blood spot with water improves efficiency for small-molecule analysis.

The current method of extracting small molecules from dried blood spots (DBSs) and liquid blood is similar. However, owing to their different physical characteristics, a modification of the extraction process for DBS is required. We propose a modified method involving presoaking in water that results in better extraction efficiency for small-molecule analysis than the conventional protein precipitation method. Using blood and DBSs from eight subjects, the similarities, recovery rates and extraction efficiencies of both methods were compared. Quantitative analysis showed that seven and six out of ten conditions for the modified method group exhibited almost 100% recovery and extraction efficiency rates, respectively, compared with the conventional method group. Taken together, the results suggest that a presoaking step is needed for efficient DBS analysis.

Proteomics Analysis for Verification of Rheumatoid Arthritis Biomarker Candidates Using Multiple Reaction Monitoring.

PURPOSE: Rheumatoid arthritis (RA) is an autoimmune disease in which autoantibodies attack the synovial membrane, causing joint inflammation. Blood tests would offer a powerful, minimally invasive method for early diagnosis of RA. However, no reliable biomarkers for RA are presently available. The aim is to develop biomarkers for RA by multiple reaction monitoring (MRM)-based quantification of candidate biomarkers. EXPERIMENTAL DESIGN: Proteomics approaches are commonly used to identify and verify disease biomarkers. For discovery of biomarkers for RA, SWATH acquisition is performed and selected candidate biomarkers are validated by MRM. Target serum proteins are compared between patients with RA and healthy controls divided into three groups based on rheumatoid factor level. RESULTS: A total of 45 differentially expressed proteins are identified, as determined by SWATH acquisition. Of these, 13 proteins are selected as novel candidate biomarkers. A total of five proteins (transthyretin, gelsolin, angiotensinogen, lipopolysaccharide-binding protein, and protein S100-A9) are shown to have the potential to distinguish patients with RA from healthy controls. CONCLUSIONS AND CLINICAL RELEVANCE: These five proteins may improve the efficiency of diagnosis of RA. MRM can be used to easily diagnose RA by detecting five proteins simultaneously in a single sample with high sensitivity.