Differential Effect of 4H-Benzo[d] [1, 3]oxazines on the Proliferation of Breast Cancer Cell Lines.

BACKGROUND: A family of 4H-benzo[d][1,3]oxazines were obtained from a group of N-(2-alkynyl)aryl benzamides precursors via gold(I) catalysed chemoselective 6-exo-dig C-O cyclization. METHOD: The precursors and oxazines obtained were studied in breast cancer cell lines MCF-7, CAMA-1, HCC1954 and SKBR-3 with differential biological activity showing various degrees of inhibition with a notable effect for those that had an aryl substituted at C-2 of the molecules. 4H-benzo[d][1,3]oxazines showed an IC50 rating from 0.30 to 157.4 µM in MCF-7, 0.16 to 139 in CAMA-1, 0.09 to 93.08 in SKBR-3, and 0.51 to 157.2 in HCC1954 cells. RESULTS: We observed that etoposide is similar to benzoxazines while taxol effect is more potent. Four cell lines responded to benzoxazines while SKBR-3 cell line responded to precursors and benzoxazines. Compounds 16, 24, 25 and 26 have the potent effect in cell proliferation inhibition in the 4 cell lines tested and correlated with oxidant activity suggesting a possible mechanism by ROS generation. CONCLUSION: These compounds represent possible drug candidates for the treatment of breast cancer. However, further trials are needed to elucidate its full effect on cellular and molecular features of cancer.

miRNAs as Interconnectors between Obesity and Cancer.

Obesity and cancer are a concern of global interest. It is proven that obesity may trigger the development or progression of some types of cancer; however, the connection by non-coding RNAs has not been totally explored. In the present review, we discuss miRNAs and lncRNAs dysregulation involved in obesity and some cancers, shedding light on how these conditions may exacerbate one another through the dysregulation of ncRNAs. lncRNAs have been reported as regulating microRNAs. An in silico investigation of lncRNA and miRNA interplay is presented. Our investigation revealed 44 upregulated and 49 downregulated lncRNAs in obesity and cancer, respectively. miR-375, miR-494-3p, miR-1908, and miR-196 were found interacting with 1, 4, 4 and 4 lncRNAs, respectively, which are involved in PPARγ cell signaling regulation. Additionally, miR-130 was found to be downregulated in obesity and reported as modulating 5 lncRNAs controlling PPARγ cell signaling. Similarly, miR-128-3p and miR-143 were found to be downregulated in obesity and cancer, interacting with 5 and 4 lncRNAs, respectively, associated with MAPK cell signaling modulation. The delicate balance between miRNA and lncRNA expression emerges as a critical determinant in the development of obesity-associated cancers, presenting these molecules as promising biomarkers. However, additional and deeper studies are needed to reach solid conclusions about obesity and cancer connection by ncRNAs.

The Urinary Metabolome of Newborns with Perinatal Complications.

Maternal pathological conditions such as infections and chronic diseases, along with unexpected events during labor, can lead to life-threatening perinatal outcomes. These outcomes can have irreversible consequences throughout an individual's entire life. Urinary metabolomics can provide valuable insights into early physiological adaptations in healthy newborns, as well as metabolic disturbances in premature infants or infants with birth complications. In the present study, we measured 180 metabolites and metabolite ratios in the urine of 13 healthy (hospital-discharged) and 38 critically ill newborns (admitted to the neonatal intensive care unit (NICU)). We used an in-house-developed targeted tandem mass spectrometry (MS/MS)-based metabolomic assay (TMIC Mega) combining liquid chromatography (LC-MS/MS) and flow injection analysis (FIA-MS/MS) to quantitatively analyze up to 26 classes of compounds. Average urinary concentrations (and ranges) for 167 different metabolites from 38 critically ill NICU newborns during their first 24 h of life were determined. Similar sets of urinary values were determined for the 13 healthy newborns. These reference data have been uploaded to the Human Metabolome Database. Urinary concentrations and ranges of 37 metabolites are reported for the first time for newborns. Significant differences were found in the urinary levels of 44 metabolites between healthy newborns and those admitted at the NICU. Metabolites such as acylcarnitines, amino acids and derivatives, biogenic amines, sugars, and organic acids are dysregulated in newborns with bronchopulmonary dysplasia (BPD), asphyxia, or newborns exposed to SARS-CoV-2 during the intrauterine period. Urine can serve as a valuable source of information for understanding metabolic alterations associated with life-threatening perinatal outcomes.

Proliferation, Migration and Invasion of Breast Cancer Cell Lines Are Inhibited by 1,5-Disubstituted Tetrazol-1,2,3-triazole Hybrids through Interaction with p53.

The anticarcinogenic potential of a series of 1,5-disubstituted tetrazole-1,2,3-triazole hybrids (T-THs) was evaluated in the breast cancer (BC)-derived cell lines MCF-7 (ER+, PR+, and HER2-), CAMA-1 (ER+, PR+/-, and HER2-), SKBR-3 (ER+, PR+, and HER2+), and HCC1954 (ER+, PR+, and HER2+). The T-THs 7f, 7l, and 7g inhibited the proliferation of MCF-7 and CAMA-1, HCC1954, and SKBR-3 cells, respectively. The compounds with stronger effect in terms of migration and invasion inhibition were 7o, 7b, 7n, and 7k for the CAMA-1, MCF-7, HCC1954, and SKBR-3 cells respectively. Interestingly, these T-THs were the compounds with a fluorine present in their structures. To discover a possible target protein, a molecular docking analysis was performed for p53, p38, p58, and JNK1. The T-THs presented a higher affinity for p53, followed by JNK1, p58, and lastly p38. The best-predicted affinity for p53 showed interactions between the T-THs and both the DNA fragment and the protein. These results provide an opportunity for these compounds to be studied as potential drug candidates for breast cancer treatment.

The plasma metabolome of long COVID patients two years after infection.

One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as "long COVID") which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Synthesis of novel fluoro phenyl triazoles via click chemistry with or without microwave irradiation and their evaluation as anti-proliferative agents in SiHa cells.

AIMS: Perform the synthesis of novel fluoro phenyl triazoles via click chemistry with or without microwave irradiation and their evaluation as anti-proliferative agents in SiHa cells Background: Triazoles are heterocyclic compounds containing a five-member ring with two carbon and three nitrogen atoms. They are of great importance since many of them have shown to have biological activity as antifungal, antiviral, antibacterial, anti-HIV, anti-tuberculosis, vasodilator, and anticancer agents. OBJECTIVE: Synthesize novel fluoro phenyl triazoles via click chemistry and evaluate their anti-proliferative activity Method: First, several fluorophenyl azides were prepared. Reacting these aryl azides with phenylacetylene in the presence of Cu(I) catalyst, the corresponding fluoro phenyl triazoles were obtained by two methodologies, stirring at room temperature and under microwave irradiation at 40 ºC. In addition, their antiproliferative activity was evaluated in cervical cancer SiHa cells Result: Fluoro phenyl triazoles were obtained within minutes by means of microwave irradiation. The compound 3f, containing two fluorine atoms next to the carbon connected to the triazole ring, was the most potent among the fluoro phenyl triazoles tested in this study. Interestingly, the addition of a fluorine atom to the phenyl triazole structure in a specific site increases its antiproliferative effect as compared to parent phenyl triazole 3a without a fluorine atom. CONCLUSION: Several fluoro phenyl triazoles were obtained by reacting fluoro phenyl azides with phenylacetylene in the presence of copper sulphate, sodium ascorbate and phenanthroline. Preparation of these triazoles with MW irradiation represents a better methodology since they are obtained within minutes and higher yields of cleaner compounds are obtained. In terms of biological studies, the proximity between fluorine atom and triazole ring increases its biological activity.

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.

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.

Gold(I)-Catalyzed Synthesis of 4H-Benzo[d][1,3]oxazines and Biological Evaluation of Activity in Breast Cancer Cells.

The first gold(I)-catalyzed cycloisomerization procedure applied to the synthesis of substituted 4H-benzo[d][1,3]oxazines has been developed starting from N-(2-alkynyl)aryl benzamides. The chemoselective oxygen cyclization via the 6-exo-dig pathway yielded the observed heterocycles in modest to good chemical yields under very mild reaction conditions. The obtained oxazines were assayed on the breast cancer (BC)-derived cell lines MCF-7 and HCC1954 with differential biological activity. The newly synthesized 4H-benzo[d][1,3]oxazine compounds showed several degrees of cell proliferation inhibition with a remarkable effect for those compounds having a substituted aryl at C-2 of the molecules. The 4H-benzo[d][1,3]oxazines showed an IC50 ranking from 3.1 to 95 µM in MCF-7 and HCC1954 cells. These compounds represent potential drug candidates for BC treatment. However, additional assays are needed to elucidate their complete effect over the cellular and molecular hallmarks of cancer.

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.

A One-Pot Six-Component Reaction for the Synthesis of 1,5-Disubstituted Tetrazol-1,2,3-Triazole Hybrids and Their Cytotoxic Activity against the MCF-7 Cell Line.

A high-order multicomponent reaction involving a six-component reaction to obtain the novel linked 1,5-disubstituted tetrazole-1,2,3-triazole hybrids in low to moderate yield is described. This one-pot reaction is carried out under a cascade process consisting of three sequential reactions: Ugi-azide, bimolecular nucleophilic substitution (SN2), and copper-catalyzed alkyne-azide reaction (CuAAC), with high atom and step-economy due the formation of six new bonds (one C-C, four C-N, and one N-N). Thus, the protocol developed offers operational simplicity, mild reaction conditions, and structural diversity. Finally, to evaluate the antitumoral potential of the synthetized molecules, a proliferation study was performed in the breast cancer (BC) derived cell line MCF-7. The hybrid compounds showed several degrees of cell proliferation inhibition with a remarkable effect in those compounds with cyclohexane and halogens in their structures. These compounds represent potential drug candidates for breast cancer treatment. However, additionally assays are needed to elucidate their complete effect over the cellular hallmarks of cancer.

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).

Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study.

(1) Background: Latin America has been harshly hit by SARS-CoV-2, but reporting from this region is still incomplete. This study aimed at identifying and comparing clinical characteristics of patients with COVID-19 at different stages of disease severity. (2) Methods: Cross-sectional multicentric study. Individuals with nasopharyngeal PCR were categorized into four groups: (1) negative, (2) positive, not hospitalized, (3) positive, hospitalized with/without supplementary oxygen, and (4) positive, intubated. Clinical and laboratory data were compared, using group 1 as the reference. Multivariate multinomial logistic regression was used to compare adjusted odds ratios. (3) Results: Nine variables remained in the model, explaining 76% of the variability. Men had increased odds, from 1.90 (95%CI 0.87-4.15) in the comparison of 2 vs. 1, to 3.66 (1.12-11.9) in 4 vs. 1. Diabetes and obesity were strong predictors. For diabetes, the odds for groups 2, 3, and 4 were 1.56 (0.29-8.16), 12.8 (2.50-65.8), and 16.1 (2.87-90.2); for obesity, these were 0.79 (0.31-2.05), 3.38 (1.04-10.9), and 4.10 (1.16-14.4), respectively. Fever, myalgia/arthralgia, cough, dyspnea, and neutrophilia were associated with the more severe COVID-19 group. Anosmia/dysgeusia were more likely to occur in group 2 (25.5; 2.51-259). (4) Conclusion: The results point to relevant differences in clinical and laboratory features of COVID-19 by level of severity that can be used in medical practice.

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.

UVB Inhibits Proliferation, Cell Cycle and Induces Apoptosis via p53, E2F1 and Microtubules System in Cervical Cancer Cell Lines.

Ultraviolet (UV) exposure has been linked to skin damage and carcinogenesis, but recently UVB has been proposed as a therapeutic approach for cancer. Herein, we investigated the cellular and molecular effects of UVB in immortal and tumorigenic HPV positive and negative cells. Cells were irradiated with 220.5 to 1102.5 J/m2 of UVB and cell proliferation was evaluated by crystal violet, while cell cycle arrest and apoptosis analysis were performed through flow cytometry. UVB effect on cells was recorded at 661.5 J/m2 and it was exacerbated at 1102.5 J/m2. All cell lines were affected by proliferation inhibition, cell cycle ablation and apoptosis induction, with different degrees depending on tumorigenesis level or HPV type. Analysis of the well-known UV-responsive p53, E2F1 and microtubules system proteins was performed in SiHa cells in response to UVB through Western-blotting assays. E2F1 and the Microtubule-associated protein 2 (MAP2) expression decrease correlated with cellular processes alteration while p53 and Microtubule-associated Protein 1S (MAP1S) expression switch was observed since 882 J/m2, suggesting they were required under more severe cellular damage. However, expression transition of α-Tubulin3C and ß-Tubulin was abruptly noticed until 1102.5 J/m2 and particularly, γ-Tubulin protein expression remained without alteration. This study provides insights into the effect of UVB in cervical cancer cell lines.

Placental exosomes isolated from urine of patients with gestational diabetes exhibit a differential profile expression of microRNAs across gestation.

Placenta­derived exosomes play an important role in cellular communication both in the mother and the fetus. Their concentration and composition are altered in several pregnancy disorders, such as gestational diabetes mellitus (GDM). The isolation and characterization of placental exosomes from serum, plasma and tissues from patients with GDM have been previously described; however, to the best of our knowledge, to date, there is no study available on placental exosomes isolated from urine of patients with GDM. In the present study, placental exosomes were purified from urine the 1st, 2nd and 3rd trimester of gestation. Placental exosomes were characterized by transmission electron microscopy in cryogenic mode and by western blot analysis, confirming the presence of exosomal vesicles. The expression profile of five microRNAs (miR­516­5p, miR­517­3p, miR­518­5p, miR­222­3p and miR­16­5p) was determined by RT­qPCR. In healthy pregnant women, the expression of the miRNAs increased across gestation, apart from miR­516­5p, which was not expressed at the 2nd trimester. All the miRNAs examined were downregulated in patients with GDM at the 3rd trimester of gestation. The downregulated miRNAs affected several metabolic pathways closely associated with the pathophysiology of GDM. This provides further evidence of the regulatory role of miRNAs in the GDM. This also suggests that the of urinary exosomes may be an excellent source of biomarkers and therapeutic targets.