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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.

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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)".

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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.

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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.

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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.

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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.

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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.

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Tight regulation of hormone and neurochemical milieu during developmental periods is critical for adequate physiological functions. For instance, activation of peptide systems during early life stress induces morphological changes in the brain resulting in depression and anxiety disorders. Prolactin (PRL) exerts different actions within the brain; it regulates neurogenesis and modulates neuroendocrine functions in the adult. However, PRL effects during early postnatal life are hardly known. Therefore, we examined whether neonatal administration of PRL influences cell survival in the hippocampal dentate gyrus (DG) and in the olfactory bulb (OB) and whether such influence results in behavioral consequences in adulthood. PRL-treated rat pups (13 mg/kg; PND1 to PND14), injected with BrdU at postnatal day 5 (PND5), showed a decrease in the density of DG BrdU/DCX and BrdU/NeuN-positive cells that survive at PND15. Similarly, PRL treatment decreased the density of BrdU+ cells in the OB compared with VEH. Fluorojade B analysis showed no significant changes in the amount of cell death in the DG between the groups. Postnatal PRL administration induced a passive coping strategy in the forced swimming test in male and female adult rats when compared with control and vehicle groups. Corticosterone endogenous levels at PND12 were not affected by PRL or VEH treatment. Altogether, these results suggest that opposed to its effects in the adult, postnatal PRL treatment affects neurogenesis and results in psychopathology later in life. High PRL levels, as observed in neonates under several pathological states, might contribute to detrimental effects on the developing brain.

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Objetivo: Conocer las características epidemiológicas, clínicas y patológicas de los pacientes con linfoma diagnosticados en el Hospital Nacional Cayetano Heredia. Material y métodos: Se realizó un estudio descriptivo, retrospectivo, tipo serie de casos de 433 pacientes portadores de linfoma del año 1998 al 2008. Resultados: El promedio de edad fue de 44,8 años, 60,04% eran de sexo masculino. El lugar de nacimiento y procedencia más frecuente fue Lima con 61,5% y 90% respectivamente. Los síntomas más frecuentes son la baja de peso y la presencia de linfoadenopatías (40,5% y 3,7% respectivamente). El 55,08% de los pacientes se encontraban en estadio clínico I û II. La localización más frecuente fue ganglionar (55,1%). Las localizaciones extraganglionares más frecuentes fueron: gastrointestinal (15,2%) y piel (10,8%). Se halló que el inmunofenotipo B fue de 57,8% de los linfoma no Hodgkin y el patrón histológico más común fue el linfoma de células grandes difuso (35,8%), enfermedad de Hodgkin fue el 14,08%. De los pacientes tratados, 78,12% tuvieron respuesta completa y parcial de la enfermedad. El tratamiento en la enfermedad de Hodgkin fue ABVD con 90,97% de respuesta completa y parcial. Solo se reportó que el 23,45% de los pacientes han fallecido al momento. Conclusiones: Los pacientes con linfoma atendidos en el Hospital Nacional Cayetano Heredia tienen características similares a lo reportado a nivel mundial.

Objective: To determine the epidemiological, clinical and pathological characteristics of lymphoma cases in Cayetano Heredia National Hospital. Material and methods: We conducted a descriptive and retrospective case series including 433 patients with lymphoma diagnosed from 1998 to 2008. Results: Average age of patients was 44.8 years, and 60.04% were male. Most frequent birthplace and living area was Lima, with 61.5% and 90% of all patients, respectively. Most frequent symptoms were weight loss and lymph node enlargement (40.5% and 3,7%, respectively). More than half of all patients (55.08%) were in I-II clinical stages. The most frequent location of the disease was in the lymph nodes (55.1%). Extranodal locations were the gastrointestinal tract (15.2%) and the skin (10.8%). The B immunophenotype corresponded to 57.8% o all non- Hodgkin lymphoma cases, and the most common histological type was diffuse large cell lymphoma (35.8%), and HodgkinÆs disease accounted for 14.08%. Of all treated patients, 78.12% had complete and partial response. Therapy for HodgkinÆs disease was ABVD, with 90.97% of all patients achieving complete and/or partial response. Only 23.45% of all patients were reported as dead. Conclusions: Lymphoma patients seen in Cayetano Heredia National Hospital have similar characteristics compared to was is reported in a worldwide basis.

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