Potential molecular patterns for tuberculosis susceptibility in diabetic patients with poor glycaemic control: a pilot study.

Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to developing tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5-8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein-protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.

Untargeted analysis in post-COVID-19 patients reveals dysregulated lipid pathways two years after recovery.

Introduction: Similar to what it has been reported with preceding viral epidemics (such as MERS, SARS, or influenza), SARS-CoV-2 infection is also affecting the human immunometabolism with long-term consequences. Even with underreporting, an accumulated of almost 650 million people have been infected and 620 million recovered since the start of the pandemic; therefore, the impact of these long-term consequences in the world population could be significant. Recently, the World Health Organization recognized the post-COVID syndrome as a new entity, and guidelines are being established to manage and treat this new condition. However, there is still uncertainty about the molecular mechanisms behind the large number of symptoms reported worldwide. Aims and Methods: In this study we aimed to evaluate the clinical and lipidomic profiles (using non-targeted lipidomics) of recovered patients who had a mild and severe COVID-19 infection (acute phase, first epidemic wave); the assessment was made two years after the initial infection. Results: Fatigue (59%) and musculoskeletal (50%) symptoms as the most relevant and persistent. Functional analyses revealed that sterols, bile acids, isoprenoids, and fatty esters were the predicted metabolic pathways affected in both COVID-19 and post-COVID-19 patients. Principal Component Analysis showed differences between study groups. Several species of phosphatidylcholines and sphingomyelins were identified and expressed in higher levels in post-COVID-19 patients compared to controls. The paired analysis (comparing patients with an active infection and 2 years after recovery) show 170 dysregulated features. The relationship of such metabolic dysregulations with the clinical symptoms, point to the importance of developing diagnostic and therapeuthic markers based on cell signaling pathways.

Transcriptome Analysis Identifies Oxidative Stress Injury Biomarkers for Diabetic Nephropathy.

BACKGROUND: The early diagnosis of diabetic nephropathy (DN) is essential for improving the prognosis and effectively manage patients affected with this disease. The standard biomarkers, including albuminuria and glomerular filtration rate, are not very precise. New molecular biomarkers are needed to more accurately identify DN and better predict disease progression. Characteristic DN biomarkers can be identified using transcriptomic analysis. AIM OF THE STUDY: To evaluate the transcriptomic profile of controls (CTRLs, n = 15), patients with prediabetes (PREDM, n = 15), patients with type-2 diabetes mellitus (DM2, n = 15), and patients with DN (n = 15) by microarray analysis to find new biomarkers. RT-PCR was then used to confirm gene biomarkers specific for DN. MATERIALS AND METHODS: Blood samples were used to isolate RNA for microarray expression analysis. 26,803 unique gene sequences and 30,606 LncRNA sequences were evaluated-Selected gene biomarkers for DN were validated using qPCR assays. Sensitivity, specificity, and area under the curve (AUC) were calculated as measures of diagnostic accuracy. RESULTS: The DN transcriptome was composed of 300 induced genes, compared to CTRLs, PREDM, and DM-2 groups. RT-qPCR assays validated that METLL22, PFKL, CCNB1 and CASP2 genes were induced in the DN group compared to CTRLs, PREDM, and DM-2 groups. The ROC analysis for these four genes showed 0.9719, 0.8853, 0.8533 and 0.7748 AUC values, respectively. CONCLUSION: Among induced genes in the DN group, we found that CASP2, PFKL and CCNB1 may potentially be used as biomarkers to diagnose DN. Of these, METLL22 had the highest AUC score, at 0.9719.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusions: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms

ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach.

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Functionalized Allopurinols Targeting Amyloid-Binding Alcohol Dehydrogenase Rescue Aß-Induced Mitochondrial Dysfunction.

Alzheimer's disease (AD) is the most common dementia affecting one in nine people over 65. Only a handful of small-molecule drugs and the anti-ß amyloid (Aß) antibody aducanumab are approved to treat AD. However, they only serve to reduce symptoms of advanced disease. Novel treatments administered early in disease progression before the accumulation of Aß and tau reaches the threshold where neuroinflammation is triggered and irreversible neuronal damage occurs are more likely to provide effective therapy. There is a growing body of evidence implying that mitochondrial dysfunction occurs at an early stage of AD pathology. The mitochondrial enzyme amyloid-binding alcohol dehydrogenase (ABAD) binds to Aß potentiating toxicity. Moreover, ABAD has been shown to be overexpressed in the same areas of the brain most affected by AD. Inhibiting the Aß-ABAD protein-protein interaction without adversely affecting normal enzyme turnover is hypothesized to be a potential treatment strategy for AD. Herein, we conduct structure-activity relationship studies across a series of functionalized allopurinol derivatives to determine their ability to inhibit Aß-mediated reduction of estradiol production from ABAD. The lead compound resulting from these studies possesses potent activity with no toxicity up to 100 µM, and demonstrates an ability to rescue defective mitochondrial metabolism in human SH-SY5Y cells and rescue both defective mitochondrial metabolism and morphology ex vivo in primary 5XFAD AD mouse model neurons.

Topical Administration of Lactiplantibacillus plantarum Accelerates the Healing of Chronic Diabetic Foot Ulcers through Modifications of Infection, Angiogenesis, Macrophage Phenotype and Neutrophil Response.

This work aimed to evaluate the adjuvant treatment to surgical debridement using topical applications of Lactiplantibacillus plantarum ATCC 10241 cultures in complicated diabetic foot ulcers as compared to diabetic foot ulcers receiving surgical wound debridement. A randomised controlled trial was performed involving 22 outpatients with complicated diabetic foot ulcers that either received surgical debridement (SuDe, n = 12) or surgical debridement plus topical applications of L. plantarum cultures (SuDe + Lp, n = 10) every week during a 12 week treatment period. Compared to patients receiving SuDe, patients treated with SuDe + Lp exhibited significantly increased fibroplasia and angiogenesis, as determined by Masson's trichrome staining and the study of CD34 cells, α-smooth muscle actin to semi-quantify vascular area, number of vessels and endothelial cells. In addition, a promotion of the polarisation of macrophages from M1 (CD68) to M2 (CD163) phenotype was observed in SuDe + Lp patients with remarkable differences in the tissue localisation. Bacterial counts were significantly diminished in the SuDe + Lp group compared to the SuDe group. Ex vivo assays, using polymorphonuclears isolated from peripheral blood of patients with diabetes and healthy individuals and challenged with Staphylococcus aureus demonstrated that the addition of L. plantarum supernatants significantly improved the phagocytosis of these cells. L. plantarum-secreted components increased the neutrophils bactericidal activity and regulated the netosis induced by S. aureus. At day 49, the average wound area reduction with SuDe + Lp was 73.5% compared with 45.8% for SuDe (p < 0.05). More patients progressed to closure with SuDe + Lp compared with SuDe treatment, indicating the ability of L. plantarum to accelerate the healing. At day 60, 60% of patients treated with SuDe + Lp achieved 100% of wound area reduction compared with 40% for SuDe. We propose that SuDe + Lp could be an effective adjuvant to surgical debridement when SuDe is not satisfactory for patients with complicated diabetic foot ulcers. The treatment is cheap and easy to apply and the product is easy to obtain.

miR-146a, miR-221, and miR-155 are Involved in Inflammatory Immune Response in Severe COVID-19 Patients.

COVID-19 infection triggered a global public health crisis during the 2020-2022 period, and it is still evolving. This highly transmissible respiratory disease can cause mild symptoms up to severe pneumonia with potentially fatal respiratory failure. In this cross-sectional study, 41 PCR-positive patients for SARS-CoV-2 and 42 healthy controls were recruited during the first wave of the pandemic in Mexico. The plasmatic expression of five circulating miRNAs involved in inflammatory and pathological host immune responses was assessed using RT-qPCR (Reverse Transcription quantitative Polymerase Chain Reaction). Compared with controls, a significant upregulation of miR-146a, miR-155, and miR-221 was observed; miR-146a had a positive correlation with absolute neutrophil count and levels of brain natriuretic propeptide (proBNP), and miR-221 had a positive correlation with ferritin and a negative correlation with total cholesterol. We found here that CDKN1B gen is a shared target of miR-146a, miR-221-3p, and miR-155-5p, paving the way for therapeutic interventions in severe COVID-19 patients. The ROC curve built with adjusted variables (miR-146a, miR-221-3p, miR-155-5p, age, and male sex) to differentiate individuals with severe COVID-19 showed an AUC of 0.95. The dysregulation of circulating miRNAs provides new insights into the underlying immunological mechanisms, and their possible use as biomarkers to discriminate against patients with severe COVID-19. Functional analysis showed that most enriched pathways were significantly associated with processes related to cell proliferation and immune responses (innate and adaptive). Twelve of the predicted gene targets have been validated in plasma/serum, reflecting their potential use as predictive prognosis biomarkers.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach.

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Urinary Metabolomic Profile of Neonates Born to Women with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications with potential adverse outcomes for mothers and newborns. Its effects on the newborn appear during the neonatal period or early childhood. Therefore, an early diagnosis is crucial to prevent the development of chronic diseases later in adult life. In this study, the urinary metabolome of babies born to GDM mothers was characterized. In total, 144 neonatal and maternal (second and third trimesters of pregnancy) urinary samples were analyzed using targeted metabolomics, combining liquid chromatographic mass spectrometry (LC-MS/MS) and flow injection analysis mass spectrometry (FIA-MS/MS) techniques. We provide here the neonatal urinary concentration values of 101 metabolites for 26 newborns born to GDM mothers and 22 newborns born to healthy mothers. The univariate analysis of these metabolites revealed statistical differences in 11 metabolites. Multivariate analyses revealed a differential metabolic profile in newborns of GDM mothers characterized by dysregulation of acylcarnitines, amino acids, and polyamine metabolism. Levels of hexadecenoylcarnitine (C16:1) and spermine were also higher in newborns of GDM mothers. The maternal urinary metabolome revealed significant differences in butyric, isobutyric, and uric acid in the second and third trimesters of pregnancy. These metabolic alterations point to the impact of GDM in the neonatal period.

Insecticidal capacity of polyphenolic seed compounds from neem (Azadirachta indica) on Spodoptera frugiperda (J. E. Smith) larvae.

The objective of this study was to evaluate the insecticidal activity of the polyphenolic compounds found in neem on S. frugiperda larvae. Three neem extracts (1:12 (m/v) with 70% ethanol, 1:12 (m/v) with 0% ethanol (only water), and 1:4 (m/v) with 0% ethanol) were employed. Subsequently, the extraction of phytochemical compounds of each extract was performed using ultrasound and microwave technologies simultaneously. The compound characterization was performed by HPLC-mass. In addition, the insecticidal evaluation of the neem extract was performed against S. frugiperda of the second-stage larvae. The extracts were applied by spraying the larvae according to each bioassay. Results showed that the extract obtained with a 1:12 (m/v) relationship and 70% ethanol was effective for the control of S. frugiperda larvae. In this extract, the predominant organic compound families were: methoxyflavones, flavonols, hydroxycoumarins, anthocyanins, methoxycinnamic acid, and alkylflavones. Phytochemical compounds obtained from neem seeds with environmentally friendly solvents and alternative technologies (ultrasound and microwave) have potent insecticidal activity against S. frugiperda larvae.

Immunobiotic Lactobacilli Improve Resistance of Respiratory Epithelial Cells to SARS-CoV-2 Infection.

Previously, we reported that immunomodulatory lactobacilli, nasally administered, beneficially regulated the lung antiviral innate immune response induced by Toll-like receptor 3 (TLR3) activation and improved protection against the respiratory pathogens, influenza virus and respiratory syncytial virus in mice. Here, we assessed the immunomodulatory effects of viable and non-viable Lactiplantibacillus plantarum strains in human respiratory epithelial cells (Calu-3 cells) and the capacity of these immunobiotic lactobacilli to reduce their susceptibility to the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Immunobiotic L. plantarum MPL16 and CRL1506 differentially modulated IFN-ß, IL-6, CXCL8, CCL5 and CXCL10 production and IFNAR2, DDX58, Mx1 and OAS1 expression in Calu-3 cells stimulated with the TLR3 agonist poly(I:C). Furthermore, the MPL16 and CRL1506 strains increased the resistance of Calu-3 cells to the challenge with SARS-CoV-2. L. plantarum MPL16 induced these beneficial effects more efficiently than the CRL1506 strain. Of note, neither non-viable MPL16 and CRL1506 strains nor the non-immunomodulatory strains L. plantarum CRL1905 and MPL18 could modify the resistance of Calu-3 cells to SARS-CoV-2 infection or the immune response to poly(I:C) challenge. To date, the potential beneficial effects of immunomodulatory probiotics on SARS-CoV-2 infection and COVID-19 outcome have been extrapolated from studies carried out in the context of other viral pathogens. To the best of our knowledge, this is the first demonstration of the ability of immunomodulatory lactobacilli to positively influence the replication of the new coronavirus. Further mechanistic studies and in vivo experiments in animal models of SARS-CoV-2 infection are necessary to identify specific strains of beneficial immunobiotic lactobacilli like L. plantarum MPL16 or CRL1506 for the prevention or treatment of the COVID-19.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR-/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models' predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931-0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968-0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736-0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.

The Urinary Metabolome of Healthy Newborns.

The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate's metabolism, or to assess their general health status. For full term-newborns, many reference biochemical values are available for blood, serum, plasma and cerebrospinal fluid. However, there is a surprising lack of information about normal urine concentration values for a large number of important metabolites in neonates. In the present work, we used targeted tandem mass spectrometry (MS/MS)-based metabolomic assays to identify and quantify 136 metabolites of biomedical interest in the urine from 48 healthy, full-term term neonates, collected in the first 24 h of life. In addition to this experimental study, we performed a literature review (covering the past eight years and over 500 papers) to update the references values in the Human Metabolome Database/Urine Metabolome Database (HMDB/UMDB). Notably, 86 of the experimentally measured urinary metabolites are being reported in neonates/infants for the first time and another 20 metabolites are being reported in human urine for the first time ever. Sex differences were found for 15 metabolites. The literature review allowed us to identify another 78 urinary metabolites with concentration data. As a result, reference concentration values and ranges for 378 neonatal urinary metabolites are now publicly accessible via the HMDB.

Early-life stress increases granule cell density in the cerebellum of male rats.

In rodents, daily maternal separation for 180 min (MS180) during the first weeks of life affects hippocampal granule cell neurogenesis. Development of the cerebellum granule cell layer also occurs during the first weeks of life. However, whether MS180 affects this neurogenic niche remains unknown. To study this, we evaluated the immediate and long term effect of MS180 on granule cell survival within the cerebellum. Pups were injected twice at an 8-hour interval at PND (postnatal day) 5 with bromodeoxyuridine (BrdU, 50 mg/kg) and were sacrificed ten days later (PND15) or allowed to survive into adulthood (PND60). We observed a higher density of BrdU-positive cells in the cerebellar foliae (p < 0.05) of MS180 pups at PND15. This increase was also observed in both, cerebellar foliae and fissures (p < 0.05) at PND60. Triple immunofluorescence staining against BrdU, NeuN (a marker of mature neurons), and GFAP (a marker of mature glia), revealed that BrdU + cells labeled at PND5 co-localized with NeuN but not with GFAP, indicating that they were mature neurons. MS180 did not affect baseline corticosterone levels at PND15 but significantly increased adult corticosterone levels (p < 0.05). In conclusion, MS180 increased cell survival in the granular layer of cerebellar foliae and fissures and resulted in further integration of the cells into adult circuits. These effects occurred without early alterations of basal corticosterone by MS180. Our results indicate that early-life stress induces a permanent increase in cerebellar neurogenesis.

Body frame size, body image, self-esteem, and health-related quality of life in schoolchildren.

OBJECTIVE: The aim of this study was to explore the relationship between body frame size (BFS) and body image, self-esteem, and health-related quality of life (HRQL) in Mexican schoolchildren. METHODS: This cross-sectional study included children aged 6 to 11 years. Body image, self-esteem, and HRQL were evaluated through interviews. Two frame-size measures, biacromial and bitrochanteric diameters, were collected and summed for categorizing BFS as small, medium, or large. Height and weight were also measured. Spearman's correlations were determined and adjusted by sex, age, and body mass index (BMI). Multiple logistic regression analyses were performed with the psychological measure as the binary dependent variable, the categories of BFS as the independent variable, and sex, age, and BMI as control variables. RESULTS: The correlation between BFS and body image was 0.15 (P < .01) and after BMI adjustment was 0.07 (P > .05). BFS did not correlate with self-esteem nor HRQL (P > .05). Of the children, 79% were dissatisfied with their body image, 20% had a low self-esteem, and 31.8% had a poorly perceived HRQL; there were no differences by BFS. The multivariate analysis showed that a large BFS was not associated with body image dissatisfaction (OR 1.2, 95% CI 0.6-2.3), low self-esteem (OR 1.3, 95% CI 0.7-2.6), or poor HRQL (OR 1.3, 95% CI 0.8-2.2). CONCLUSIONS: BFS was not correlated with body image, self-esteem, or HRQL. A high self-esteem and a good level of HRQL prevailed, but a high proportion of children were dissatisfied with their body image. School interventions should promote an appropriate body image and a healthy lifestyle.

Glycerophospholipid Metabolism Alterations in Patients with Type 2 Diabetes Mellitus and Tuberculosis Comorbidity.

Type-2 Diabetes (T2D) is a predisposing cause for developing tuberculosis (TB) in low- and middle-income countries. TB-T2D comorbidity worsens clinical control and prognosis of the affected individuals. The underlying metabolic alterations for this infectious-metabolic disease are still largely unknown. Possible mediators of the increased susceptibility to TB in diabetic patients are lipids levels, which are altered in individuals with T2D. To evaluate the modulation of glycerophospholipids in patients with TB-T2D, an untargeted lipidomic approach was developed by means of ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization/quadrupole time-of-flight mass spectrometry (ESI-QToF). In addition, tandem mass spectrometry was performed to determine the identity of the differentially expressed metabolites. We found that TB infected individuals with or without T2D share a common glycerophospholipid profile characterized by a decrease in phosphatidylcholines. A total of 14 glycerophospholipids were differentially deregulated in TB and TB-T2D patients and could potentially be considered biomarkers. It is necessary to further validate these identified lipids as biomarkers, focusing on the anticipate diagnosis for TB development in T2D predisposed individuals.