Common protein networks for various drug regimens of major depression are associated with complement and immunity.

BACKGROUND: Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD. AIM: This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20). METHODS: Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied. RESULTS: Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity. CONCLUSIONS: Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts.

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke.

Objective: Most prognostic indexes for ischemic stroke mortality lack radiologic information. We aimed to create and validate a deep learning-based mortality prediction model using brain diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and clinical factors. Methods: Data from patients with ischemic stroke who admitted to tertiary hospital during acute periods from 2013 to 2019 were collected and split into training (n = 1109), validation (n = 437), and internal test (n = 654). Data from patients from secondary cardiovascular center was used for external test set (n = 507). The algorithm for predicting mortality, based on DWI and ADC (DLP_DWI), was initially trained. Subsequently, important clinical factors were integrated into this model to create the integrated model (DLP_INTG). The performance of DLP_DWI and DLP_INTG was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) and Harrell concordance index (C-index) at one-year mortality. Results: The TD AUC of DLP_DWI was 0.643 in internal test set, and 0.785 in the external dataset. DLP_INTG had a higher performance at predicting one-year mortality than premise score in internal dataset (TD- AUC: 0.859 vs. 0.746; p = 0.046), and in external dataset (TD- AUC: 0.876 vs. 0.808; p = 0.007). DLP_DWI and DLP_INTG exhibited strong discrimination for the high-risk group for one-year mortality. Interpretation: A deep learning model using brain DWI, ADC and the clinical factors was capable of predicting mortality in patients with ischemic stroke.

Serum protein profiling reveals mechanism of activated thrombus formation in patients with stroke and atrial fibrillation.

Stroke is an acute cerebrovascular disease in which blood flow to the brain is suddenly disrupted, causing damage to nerve cells. It involves complex and diverse pathophysiological processes and the treatment strategies are also diverse. The treatment for patients with stroke and atrial fibrillation (AF) is aimed at suppressing thrombus formation and migration. However, information regarding the protein networking involved in different thrombus formation pathways in patients with AF and stroke is insufficient. We performed protein profiling of patients with ischemic stroke with and without AF to investigate the mechanisms of thrombus formation and its pathophysiological association while providing helpful information for treating and managing patients with AF. These two groups were compared to identify the protein networks related to thrombus formation in AF. We observed that patients with ischemic stroke and AF had activated inflammatory responses induced by C-reactive protein, lipopolysaccharide-binding protein, and alpha-1-acid glycoprotein 1. In contrast, thyroid hormones were increased due to a decrease in transthyretin and retinol-binding protein 4 levels. The mechanism underlying enhanced cardiac activity, vasodilation, and the resulting thrombosis pathway were confirmed in AF. These findings will play an essential role in improving the prevention and treatment of AF-related stroke.

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.

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 protein biomarkers for monitoring the effectiveness of drug treatment for major depressive disorder.

Major depressive disorder (MDD) has a high prevalence worldwide. Although the economic burden of depression increases annually, the proportion of patients with MDD receiving treatment did not increase between 2010 and 2018, suggesting an unmet treatment need. The burden of long-term treatment for depression is borne by patients. In this context, biomarkers associated with drug-treatment responses can be used as reference indicators to reduce unnecessary treatment and costs. Changes in biomolecules in response to drug treatment for depression and drug-treatment response markers have been studied extensively. The Hamilton Depression Rating Scale (HAM-D) is mainly used as an indicator of response and remission; however, it is difficult to determine whether the medication contributes to recovery when evaluating the effect of drug treatment for depression based on this assessment. Therefore, it is necessary to monitor the effect of medication compared to normal health conditions. Here, serum protein levels were compared using liquid chromatography-tandem mass spectrometry among a group of patients with depression who did not receive medication, a group of patients receiving medication, and a control group. Eight selected biomarkers, including Apolipoproteins A-I, Complement factor H, Complement C5, Complement C1q subcomponent subunit B, Alpha-2-HS-glycoprotein, Complement C1q subcomponent subunit C, Vitamin D-binding protein and Corticosteroid-binding globulin were distinguished between disease states, and protein levels in the drug-treated group were similar to those in the control group. These markers can be used to monitor the effectiveness of drug treatment.

Serum Protein Profiling Reveals a Decrease in Apolipoprotein A-IV During a Clinical Depressive Mood State.

Purpose: Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Methods: Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography-tandem mass spectrometry. Differentially expressed proteins with a p-value <0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Results: Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. Conclusion: We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.

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.

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.

Feasibility of hemispatial neglect rehabilitation with virtual reality-based visual exploration therapy among patients with stroke: randomised controlled trial.

Background: Hemispatial neglect (HSN) was diagnosed using a virtual reality-based test (FOPR test) that explores the field of perception (FOP) and field of regard (FOR). Here, we developed virtual reality-visual exploration therapy (VR-VET) combining elements from the FOPR test and visual exploration therapy (VET) and examined its efficacy for HSN rehabilitation following stroke. Methods: Eleven participants were randomly assigned to different groups, training with VR-VET first then waiting without VR-VET training (TW), or vice versa (WT). The TW group completed 20 sessions of a VR-VET program using a head-mounted display followed by 4 weeks of waiting, while the WT group completed the opposite regimen. Clinical HSN measurements [line bisection test (LBT), star cancellation test (SCT), Catherine Bergego Scale (CBS), CBS perceptual-attentional (CBS-PA), and CBS motor-explanatory (CBS-ME)] and FOPR tests [response time (RT), success rate (SR), and head movement (HM) for both FOP and FOR] were assessed by blinded face-to-face assessments. Results: Five and six participants were allocated to the TW and WT groups, respectively, and no dropout occurred throughout the study. VR-VET considerably improved LBT scores, FOR variables (FOR-RT, FOR-SR), FOP-LEFT variables (FOP-LEFT-RT, FOP-LEFT-SR), and FOR-LEFT variables (FOR-LEFT-RT, FOR-LEFT-SR) compared to waiting without VR-VET. Additionally, VR-VET extensively improved FOP-SR, CBS, and CBS-PA, where waiting failed to make a significant change. The VR-VET made more improvements in the left hemispace than in the right hemispace in FOP-RT, FOP-SR, FOR-RT, and FOR-SR. Conclusion: The observed improvements in clinical assessments and FOPR tests represent the translatability of these improvements to real-world function and the multi-dimensional effects of VR-VET training. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT03463122, identifier NCT03463122.

Potential Biomarkers to Distinguish Type 1 Myocardial Infarction in Troponin-Elevated Diseases.

Classifying myocardial infarction by subtype is crucial for appropriate patient management. Although troponin is currently the most commonly used biomarker, it is not a specific marker for myocardial infarction and cannot distinguish subtypes. Furthermore, previous studies have confirmed that proteins known as myocardial infarction markers could function to distinguish the type of myocardial infarction. Therefore, we identify a marker that can distinguish type 1 myocardial infarction from other diseases with elevated troponin. We used mass spectrometry to compare type 1 myocardial infarction with other conditions characterized by troponin elevation and identified new candidate markers for disease classification. We then verified these markers, along with those already known to be associated with cardiovascular disease and plaque rupture. We identified α-1 acid glycoprotein 2, corticosteroid-binding globulin, and serotransferrin as potential distinguishing markers. The presence of these markers and other parameters, such as chest pain, electrocardiogram, and troponin levels from the complementary diagnostic processes, could provide valuable information to specifically diagnose type 1 myocardial infarction.

The Effect of Virtual Presence and Self-Efficacy on Nursing Students' Evaluation of Virtual Simulation Training.

The study aims to determine the effect of virtual simulation practice in nursing students using the Virtual Patient Learning System Evaluation (VPLSE) tool. The study uses descriptive research, centering on correlation and regression analysis. 295 nursing students in Grades 3 and 4 who have experienced virtual simulation practice within the past year were included. The main variables of VPLSE comprise four sub-domains: nursing knowledge improvement, clinical competency development, confidence in nursing performance, and nursing care plan application. In addition to the VPLSE, two other tools, measuring virtual presence and self-efficacy, were utilized. The VPLSE was significantly positively correlated with virtual presence and self-efficacy, respectively. The VPLSE subdomain of "clinical competency development" had a strong positive correlation with virtual presence. To increase the effect of virtual simulation education, it is necessary to maintain the advantages of virtual simulation practice, such as freedom from space and time, repeated learning, psychological stability and application of nursing plans.

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.

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.

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.

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.

S-nitrosylated PARIS Leads to the Sequestration of PGC-1α into Insoluble Deposits in Parkinson's Disease Model.

Neuronal accumulation of parkin-interacting substrate (PARIS), a transcriptional repressor of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), has been observed in Parkinson's disease (PD). Herein, we showed that PARIS can be S-nitrosylated at cysteine 265 (C265), and S-nitrosylated PARIS (SNO-PARIS) translocates to the insoluble fraction, leading to the sequestration of PGC-1α into insoluble deposits. The mislocalization of PGC-1α in the insoluble fraction was observed in S-nitrosocysteine-treated PARIS knockout (KO) cells overexpressing PARIS WT but not S-nitrosylation deficient C265S mutant, indicating that insolubility of PGC-1α is SNO-PARIS-dependent. In the sporadic PD model, α-synuclein preformed fibrils (α-syn PFFs)-injected mice, we found an increase in PARIS, SNO-PARIS, and insoluble sequestration of PGC-1α in substantia nigra (SN), resulting in the reduction of mitochondrial DNA copy number and ATP concentration that were restored by N(ω)-nitro-L-arginine methyl ester, a nitric oxide synthase (NOS) inhibitor. To assess the dopaminergic (DA) neuronal toxicity by SNO-PARIS, lentiviral PARIS WT, C265S, and S-nitrosylation mimic C265W was injected into the SN of either PBS- or α-syn PFFs-injected mice. PARIS WT and C265S caused DA neuronal death to a comparable extent, whereas C265W caused more severe DA neuronal loss in PBS-injected mice. Interestingly, there was synergistic DA loss in both lenti-PARIS WT and α-syn PFFs-injected mice, indicating that SNO-PARIS by α-syn PFFs contributes to the DA toxicity in vivo. Moreover, α-syn PFFs-mediated increment of PARIS, SNO-PARIS, DA toxicity, and behavioral deficits were completely nullified in neuronal NOS KO mice, suggesting that modulation of NO can be a therapeutic for α-syn PFFs-mediated neurodegeneration.

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.

In vivo electrical conductivity measurement of muscle, cartilage, and peripheral nerve around knee joint using MR-electrical properties tomography.

This study aimed to investigate whether in vivo MR-electrical properties tomography (MR-EPT) is feasible in musculoskeletal tissues by evaluating the conductivity of muscle, cartilage, and peripheral nerve around the knee joint, and to explore whether these measurements change after exercise. This prospective study was approved by the institutional review board. On February 2020, ten healthy volunteers provided written informed consent and underwent MRI of the right knee using a three-dimensional balanced steady-state free precession (bSSFP) sequence. To test the effect of loading, the subjects performed 60 squatting exercises after baseline MRI, immediately followed by post-exercise MRI with the same sequences. After reconstruction of conductivity map based on the bSSFP sequence, conductivity of muscles, cartilages, and nerves were measured. Measurements between the baseline and post-exercise MRI were compared using the paired t-test. Test-retest reliability for baseline conductivity was evaluated using the intraclass correlation coefficient. The baseline and post-exercise conductivity values (mean ± standard deviation) [S/m] of muscles, cartilages, and nerves were 1.73 ± 0.40 and 1.82 ± 0.50 (p = 0.048), 2.29 ± 0.47 and 2.51 ± 0.37 (p = 0.006), and 2.35 ± 0.57 and 2.36 ± 0.57 (p = 0.927), respectively. Intraclass correlation coefficient for the baseline conductivity of muscles, cartilages, and nerves were 0.89, 0.67, and 0.89, respectively. In conclusion, in vivo conductivity measurement of musculoskeletal tissues is feasible using MR-EPT. Conductivity of muscles and cartilages significantly changed with an overall increase after exercise.