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Mexico City emits several thousands of tons of particle matter from transportation and other economic sectors. We collected PM2.5 samples from January to March 2015. The chemical analyses showed that PM2.5 is made up of 39% organic carbon, 12% elemental carbon, 23% metals (Al, Si, S, P, and K), and 5% heavy metals (Pb, Cr, Mn, Zn, and Hg). Ca and Fe were also present in trace concentrations, probably due to the resuspension of soils. Nitrate, sulfate, and ammonium suggest that the southwest of Mexico City, specifically the sampling site, receives more oxidated pollutants from vehicle emissions than other emissions. The chemical analyses show no significant changes in the composition or concentration of particle matter compared to previous studies.

The manufacturing and characterization of nanographite films on substrates form the foundation for advances in materials science. Conductive graphite films are challenging products, as isolating graphite oxide is often necessary. In this study, nanographite suspensions containing non-oxidized graphite flakes were used to fabricate novel thin and ultrathin films via blade coating on industry-standard substrates. Films as thin as 346 nm were successfully fabricated. Moreover, it was possible to induce the orientation of the graphite nanoflakes via blade coating. This orientation led to electrical anisotropy; thus, the electrical behavior of the films in each orthogonal direction differed. After adjusting the coating parameters and the concentration of the nanographite flakes, the electrical conductivity ranged from 0.04 S/cm to 0.33 S/cm. In addition, with such adjustments, the transparency of the films in the visible range varied from 20% to 75%. By establishing a methodology for the tuning of both electrical and optical properties via adjustments in the nanographite suspension and coating parameters, we can fabricate resistant, conductive, and transparent films satisfying certain requirements. The results presented here can be extrapolated to enhance applications, especially for photonics and solar cells, in fields that require electrical conductive materials with high levels of transparency.

Sexual arousal (SA) is a multidimensional experience that includes cognitive, emotional, motivational and physiological components. Texts with erotic content have been used to generate a state of SA. Erotic texts often include aggressive content that has not been evaluated in relation to SA. The aim of this work was to compare cortical functionality in women when reading a sexually explicit text (SET) and a sexually explicit text with aggression (SETA). Twenty-seven women participated. The EEG activity of the frontal, temporal and parietal locations was recorded during the reading of both texts. The participants found the SET to be more pleasant than the SETA. Both texts were identified as triggers of general and SA. While reading the SETA, there was an increase in absolute power in the frontal and parietal locations, a higher intrahemispheric correlation between the left frontal and temporal locations in fast frequency bands and a greater interhemispheric correlation between the frontal locations in the delta and alpha1 bands. These findings indicate that cortical functionality during SA in women differs based on the content and context of the erotic material being read, possibly associated with mechanisms that underlie the processing and incentive value assignment of stimuli with sexual and aggressive connotations.

IntroductionBacteremia is a life-threatening condition that can progress to sepsis and septic shock, leading to significant mortality in the emergency department (ED). The standard diagnostic method, blood culture, is time-consuming and prone to false positives and false negatives. Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. However, these strategies require further identification of new variables to improve their diagnostic accuracy. This study proposes a novel strategy to predict positive blood cultures by assessing sepsis-induced immunosuppression status through endotoxin tolerance assessment.MethodsOptimal assay conditions have been explored and tested in sepsis-suspected patients meeting the Sepsis-3 criteria. Blood samples were collected at ED admission, and endotoxin (lipopolysaccharide, LPS) challenge was performed to evaluate the innate immune response through cytokine profiling.ResultsClinical variables, immune cell population biomarkers, and cytokine levels (tumor necrosis factor [TNFα], IL-1β, IL-6, IL-8, and IL-10) were measured. Patients with positive blood cultures exhibited significantly lower TNFα production after LPS challenge than did those with negative blood cultures. The study also included a validation cohort to confirm that the response was consistent.DiscussionThe results of this study highlight the innate immune system immunosuppression state as a critical parameter for sepsis diagnosis. Notably, the present study identified a reduction in monocyte populations and specific cytokine profiles as potential predictive markers. This study showed that the LPS challenge can be used to effectively distinguish between patients with bloodstream infection leading to sepsis and those whose blood cultures are negative, providinga rapid and reliable diagnostic tool to predict positive blood cultures. The potential applicability of these findings could enhance clinical practice in terms of the accuracy and promptness of sepsis diagnosis in the ED, improving patient outcomes through timely and appropriate treatment.

Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics—conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

This study aimed to assess the impact of the identified phenolic, flavonoid, and fat-soluble vitamin compounds found in Caesalpinia coriaria fruits (EC) aqueous extract on growth performance, helminth parasitic load, feeding behavior, and physiological variables in lambs. The fruits were extracted using water, and three doses of the extract as no extract (EC0 mL/lamb/day), 30 mL of aqueous extract of Caesalpinia coriaria fruits (EC30 mL/lamb/day), or 60 mL of aqueous extract of Caesalpinia coriaria fruits (EC60 mL/lamb/day) were orally administered to 24 growing crossbred lambs weighing 18-20 kg for 60 days. The HPLC analysis revealed that ferulic acid, ellagic acid, syringic acid, quercetin, and hesperidin were the predominant compounds in Caesalpinia coriaria fruits. Additionally, water-soluble vitamin compounds such as thiamine, pantothenic acid, and niacin were detected in the Caesalpinia coriaria fruit extract. The dietary administration of EC improved (P < 0.05) average daily gain and feed and water intake. EC30 lambs spent more time (P < 0.03) eating and less time ruminating. The aqueous extract also showed a dose-dependent reduction (P = 0.048) in fecal parasite egg count, with a steady decrease (P = 0.0001) as the extract was applied. EC0 lambs exhibited the highest (P < 0.0001) respiratory frequency and EC30 the lowest. A strong positive correlation was identified between rectal and abdomen temperature in the morning and afternoon measurements. The presence of phenolic and antioxidants in the aqueous extract of C. coriaria, up to 60 mL, demonstrated beneficial effects, including improved productivity, anti-parasitic activity, and mitigation of body temperature. Thus, farmers without access to synthetic drugs can use this plant extract to treat their animals during fattening to reduce gastrointestinal parasites, improve growth rate, and reduce drug residue in animal products. Further studies need to be carried out on the individual components of Caesalpinia coriaria fruits to ascertain their effect on body temperature/physiology and to know if the effects are individualistic or synergistic.

Brown spider envenomation results in dermonecrosis, characterized by an intense inflammatory reaction. The principal toxins of brown spider venoms are phospholipase-D isoforms, which interact with different cellular membrane components, degrade phospholipids, and generate bioactive mediators leading to harmful effects. The Loxosceles intermedia phospholipase D, LiRecDT1, possesses a loop that modulates the accessibility to the active site and plays a crucial role in substrate. In vitro and in silico analyses were performed to determine aspects of this enzyme's substrate preference. Sphingomyelin d18:1/6:0 was the preferred substrate of LiRecDT1 compared to other Sphingomyelins. Lysophosphatidylcholine 16:0/0:0 was preferred among other lysophosphatidylcholines, but much less than Sphingomyelin d18:1/6:0. In contrast, phosphatidylcholine d18:1/16:0 was not cleaved. Thus, the number of carbon atoms in the substrate plays a vital role in determining the optimal activity of this phospholipase-D. The presence of an amide group at C2 plays a key role in recognition and activity. In silico analyses indicated that a subsite containing the aromatic residues Y228 and W230 appears essential for choline recognition by cation-π interactions. These findings may help to explain why different cells, with different phospholipid fatty acid compositions exhibit distinct susceptibilities to brown spider venoms.

High throughput screening (HTS) technologies allow the biological testing of hundreds of thousands of compounds per day. Typically, a substantial proportion of the initial hits obtained by HTS are artifacts caused by assay interference. Therefore, global and technology-specific in silico models for identifying and predicting compounds interfering with biological assays have been developed. The global models benefit from training on large screening data sets, while the specialized models benefit from training on assay technology-specific experimental data. In this work, we develop and explore strategies for generating better predictors of technology-specific assay interference by utilizing the large bioactivity data matrices global models are trained on and employing partially new compound labeling approaches to maintain the assay technology awareness of specialized models. We demonstrate the utility of the statistically derived interference labels in machine learning using fluorescence-based assay interference as a representative example. Our random forest and multi-layer perceptron classifiers showed improved performance compared to existing models, achieving Matthews correlation coefficients (MCCs) of up to 0.47 on holdout data and up to 0.45 on an external test set. These results demonstrate that accurate assay-specific interference labels can be derived from large bioactivity data matrices, enabling the development of new machine-learning models without the need for further experimental data.

Abstract We introduce a novel technique for designing color filter metasurfaces using a data-driven approach based on deep learning. Our innovative approach employs inverse design principles to identify highly efficient designs that outperform all the configurations in the dataset, which consists of 585 distinct geometries solely. By combining Multi-Valued Artificial Neural Networks and back-propagation optimization, we overcome the limitations of previous approaches, such as poor performance due to extrapolation and undesired local minima. Consequently, we successfully create reliable and highly efficient configurations for metasurface color filters capable of producing exceptionally vivid colors that go beyond the sRGB gamut. Furthermore, our deep learning technique can be extended to design various pixellated metasurface configurations with different functionalities.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene–disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Canine leishmaniosis, caused by the protozoan parasite Leishmania infantum, is a cosmopolitan vector-borne zoonosis, transmitted principally by Phlebotomus perniciosus in Spain and Portugal, where it is considered an endemic disease. Ecoinformatics tools such as ecological niche models (ENM) have been successfully tested to model the distribution of the risk of infection of different parasitosis as they take into account environmental variables vital for their survival. The risk map proposed in this study combines the potential distribution of Ph. perniciosus in the Iberian Peninsula and the calculation of the infection rate of the parasite in the vector to model the risk of contracting the disease in a more realistic way. In fact, this weighting strategy improves the predictive power of the resulting model (R2 = 0.42, p = 0.05). The places with the highest risk of transmission are the southwest and central peninsular area, as well as the Mediterranean coast, the Balearic Islands and the Ebro basin, places where the ideal habitat of Ph. perniciosus and the infection rate is also high. In the case of future projections under climate change scenarios, an increase in the risk of infection by L. infantum can be observed in most of the territory (4.5% in 2040, 71.6% in 2060 and 63% in 2080), mainly in the northern part of the peninsula. The use of ENMs and their weighting with the infection rate in Ph. perniciosus is a useful tool in predicting the risk of infection for L. infantum in dogs for a given area. In this way, a more complete model can be obtained to facilitate prevention and control.

Introduction:Musculoskeletal disorders could be associated with metabolic disorders that are common after kidney transplantation, which could reduce the quality of life of patients. The aim of this study was to assess the prevalence of both musculoskeletal and metabolic disorders in kidney transplant patients.Methods:MEDLINE, CINAHL, Cochrane Library, EMBASE and Web of Science were searched from their inception up to June 2023. DerSimonian and Laird random-effects method was used to calculate pooled prevalence estimates and their 95% confidence intervals (CIs).Results:21,879 kidney transplant recipients from 38 studies were analysed. The overall proportion of kidney transplant patients with musculoskeletal disorders was 27.2% (95% CI: 18.4–36.0), with low muscle strength (64.5%; 95% CI: 43.1–81.3) being the most common disorder. Otherwise, the overall proportion of kidney transplant patients with metabolic disorders was 37.6% (95% CI: 21.9–53.2), with hypovitaminosis D (81.8%; 95% CI: 67.2–90.8) being the most prevalent disorder.Conclusion:The most common musculoskeletal disorders were low muscle strength, femoral osteopenia, and low muscle mass. Hypovitaminosis D, hyperparathyroidism, and hyperuricemia were also the most common metabolic disorders. These disorders could be associated with poorer quality of life in kidney transplant recipients.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier [CRD42023449171].

Animal and human dirofilariosis is a vector-borne zoonotic disease, being one of the most important diseases in Europe. In Serbia, there are extensive studies reporting the presence of Dirofilaria immitis and D. repens, mainly in the north of the country, where the human population is concentrated and where there is a presence of culicid mosquitoes that transmit the disease. Ecological niche modeling (ENM) has proven to be a very good tool to predict the appearance of parasitosis in very diverse areas, with distant orography and climatologies at a local, continental, and global level. Taking these factors into account, the objective of this study was to develop an environmental model for Serbia that reflects the suitability of the ecological niche for the risk of infection with Dirofilaria spp. with which the predictive power of existing studies is improved. A wide set of variables related to the transmission of the parasite were used. The potential number of generations of D. immitis and the ecological niche modeling method (ENM) were used to estimate the potential distribution of suitable habitats for Culex pipiens. The highest probability of infection risk was located in the north of the country, and the lowest in the southern regions, where there is more orographic relief and less human activity. The model was corroborated with the location of D. immitis-infected dogs, with 89.28% of the country having a high probability of infection. In addition, it was observed that the percentage of territory with optimal habitat for Culex spp. will increase significantly between now and 2080. This new model can be used as a tool in the control and prevention of heartworm disease in Serbia, due to its high predictive power, and will serve to alert veterinary and health personnel of the presence of the disease in the animal and human population, respectively.

Triple-negative breast cancer (TNBC), accounting for 15–20% of all breast cancers, has one of the poorest prognoses and survival rates. Metastasis, a critical process in cancer progression, causes most cancer-related deaths, underscoring the need for alternative therapeutic approaches. This study explores the anti-migratory, anti-invasive, anti-tumoral, and antimetastatic effects of copper coordination compounds Casiopeína IIIia (CasIIIia) and Casiopeína IIgly (CasIIgly) on MDA-MB-231 and 4T1 breast carcinoma cell lines in vitro and in vivo. These emerging anticancer agents, mixed chelate copper(II) compounds, induce apoptosis by generating reactive oxygen species (ROS) and causing DNA damage. Whole-transcriptome analysis via gene expression arrays indicated that subtoxic concentrations of CasIIIia upregulate genes involved in metal response mechanisms. Casiopeínas® reduced TNBC cell viability dose-dependently and more efficiently than Cisplatin. At subtoxic concentrations (IC20), they inhibited random and chemotactic migration of MDA-MB-231 and 4T1 cells by 50–60%, similar to Cisplatin, as confirmed by transcriptome analysis. In vivo, CasIIIia and Cisplatin significantly reduced tumor growth, volume, and weight in a syngeneic breast cancer model with 4T1 cells. Furthermore, both compounds significantly decreased metastatic foci in treated mice compared to controls. Thus, CasIIIia and CasIIgly are promising chemotherapeutic candidates against TNBC.

Cardiovascular diseases stand as a prominent global cause of mortality, their intricate origins often entwined with comorbidities and multimorbid conditions. Acknowledging the pivotal roles of age, sex, and social determinants of health in shaping the onset and progression of these diseases, our study delves into the nuanced interplay between life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Leveraging data from a cross-sectional survey encompassing Mexican adults, we unearth a robust association between these variables and the prevalence of comorbidities linked to cardiovascular conditions. To foster a comprehensive understanding of multimorbidity patterns across diverse life-stages, we scrutinize an extensive dataset comprising 47,377 cases diagnosed with cardiovascular ailments at Mexico’s national reference hospital. Extracting sociodemographic details, primary diagnoses prompting hospitalization, and additional conditions identified through ICD-10 codes, we unveil subtle yet significant associations and discuss pertinent specific cases. Our results underscore a noteworthy trend: younger patients of lower socioeconomic status exhibit a heightened likelihood of cardiovascular comorbidities compared to their older counterparts with a higher socioeconomic status. By empowering clinicians to discern non-evident comorbidities, our study aims to refine therapeutic designs. These findings offer profound insights into the intricate interplay among life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Armed with data-supported approaches that account for these factors, clinical practices stand to be enhanced, and public health policies informed, ultimately advancing the prevention and management of cardiovascular disease in Mexico.

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Introducción. Los traumatismos cardíacos son lesiones graves y con elevado índice de letalidad, aspecto que influye en el interés mostrado por los lectores cada vez que aparecen reportados en publicaciones científicas. En ocasiones existe cierto grado de incongruencia a la hora de establecer el origen histórico de sucesos o eventos ocurridos y relacionados con la historia de la medicina. En el caso del trauma cardíaco penetrante han sido descritos diversos orígenes en algunos de los artículos científicos publicados, lo cual puede generar un grado de duda en los lectores. Métodos. Se realizó una revisión de la literatura, médica y no médica, para buscar información que ayudara a esclarecer el verdadero origen histórico de esta entidad. Resultados. El trauma cardíaco penetrante fue descrito por primera vez en la obra griega titulada La Ilíada, escrita por Homero en el siglo VIII A.C., y no en El papiro quirúrgico de Edwin Smith, como varios autores mencionan. Conclusiones. De todos los eventos de trauma cardíaco penetrante descritos, el más irrefutable es el narrado en el canto XIII, donde se cuenta la muerte de Alcátoo, producto de una herida cardíaca ocasionada por una lanza arrojada por Idomeneo durante una batalla.

Introducción: la ascitis refractaria puede ser una complicación frecuente en el síndrome nefrótico (SN), existen casos reportados del uso de un catéter tunelizado de diálisis peritoneal en pacientes con cirrosis o neoplasias abdominales. Se presenta el caso de un paciente con SN en quién se utilizó un catéter para diálisis peritoneal (DP) para manejo de la ascitis refractaria. Objetivo: mostrar que el catéter peritoneal puede ser considerado como una alternativa para el manejo de la ascitis refractaria en pacientes con síndrome nefrótico. Presentación del caso: paciente varón de 19 años, sin antecedentes patológicos, cursó con edema progresivo y alteración de la función renal. Se evidenció síndrome nefrótico con anasarca y evolucionó con empeoramiento de la función renal ingresando a hemodiálisis de soporte. Se realizó biopsia renal: podocitopatía, glomerulopatía colapsante. Se inició tratamiento con corticoterapia, mejorando la función renal hasta suspender la hemodiálisis, pero presentó ascitis refractaria al tratamiento médico, por lo que se realizó paracentesis evacuatoria en reiteradas ocasiones. Se decidió colocación de catéter peritoneal tunelizado para el manejo de la ascitis refractaria. La ascitis fue disminuyendo progresivamente hasta el retiro del catéter peritoneal. Discusión y conclusión: el uso de catéter tunelizado de diálisis peritoneal es una opción de manejo efectiva en casos de síndrome nefrótico con ascitis refractaria.

RESUMEN Introducción: Los nódulos de tiroides son lesiones circunscritas a la glándula, para su diagnóstico se debe tener en cuenta dos objetivos: identificar si es una lesión funcionante y su naturaleza. Objetivo: Identificar las características del nódulo de tiroides en los pacientes del municipio Rafael Freyre de Holguín. Método: Se realizó un estudio de una serie de casos, en el municipio Rafael Freyre, provincia Holguín, de diciembre 2017 a diciembre 2018. El universo quedó constituido por todos los pacientes con diagnóstico de nódulo tiroideo, la muestra se seleccionó por un muestreo probabilístico intencional y quedó conformada por 79 pacientes. Resultados: El nódulo de tiroides predominó entre los 40-49 y 50-59 años con 27 pacientes para un 34,2% en cada uno de estos grupos de edades, con asociación de bocio (55,8%), sin microcalcificaciones (94,2%), de 1–3 cm (86,5%), con márgenes bien definidos (97,1%), sólidos (76%) y dentro de estos los hipoecogénicos (73,4%), en el lóbulo derecho (57,7%), con presencia de nódulo único (70,9%). Prevalecieron las pacientes del sexo femenino (94,9%), con eutiroidismo (78,5%), y el 93,6% se encontraron en la categoría II de Bethesda. Conclusiones: Se encontró una asociación estadísticamente significativa entre la edad y la forma clínica de la enfermedad, prevalecieron los nódulos de tiroides benignos, sin alteración en la función tiroidea y asociados a bocio. Palabras clave: nódulo, tiroides, bocio, edad, función tiroidea, signos ultrasonográficos

Muscle fibres have an important impact on meat quality. There are different types of skeletal muscle fibres, mainly oxidative and glycolytic. The present study showed that dairy breeds and goat kids raised with milk replacer had less oxidative fibres than meat breeds and goat kids raised with their dams. In addition, the muscle fibres measured in this study were smaller in dairy breeds and kids raised with milk replacer than in meat breeds and goat kids raised with the dams.

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

The objective of this study was to evaluate the effect of anaerobic fermentation on the in vitro ruminal production of total gas (TG), methane (CH4), carbon monoxide (CO) and hydrogen sulfide (H2S), as well as on the characteristics of ruminal fermentation and CH4 conversion efficiency of whole-plant maize (WPM) from four native maize genotypes (Amarillo, Olotillo, Tampiqueño and Tuxpeño) from Mexico, and a commercial hybrid was used as a control. In all genotypes, the fermented WPM produced the lowest amounts (p ≤ 0.0236) of TG and CH4 from degraded dry matter (DM), and Tampiqueño and Tuxpeño presented the highest production of these gases. In addition, Tuxpeño also presented the highest proportion of CH4 (mL 100 mL−1 TG), and Olotillo presented the lowest proportion of both gases. Something similar occurred in H2S, where the fermented WPM produced the lowest (p ≤ 0.0077) amount per DM degraded, and Amarillo and Tampiqueño presented the highest and lowest production, respectively. However, the fermented WPM presented the highest (p = 0.0128) CO production from degraded DM, and Tuxpeño and Olotillo presented the highest and lowest production, while the rumen pH was lower (p < 0.0001) in the fermented WPM, and Tuxpeño and Olotillo presented the highest and lowest pH, respectively. Furthermore, the fermented plant presented the greatest (p ≤ 0.0055) DM degradation, and the Amarillo and hybrid genotypes presented the highest percentages, while Olotillo presented the lowest. The short-chain fatty acid (SCFA) content and metabolizable energy (ME) did not differ (p ≥ 0.0899) between genotypes and were higher (p = 0.0009) in the fresh WPM. Despite the above, the fermented WPM was more efficient (p ≤ 0.0249), and the Amarillo and hybrid genotypes produced less CH4 per unit of SCFAs, ME and organic matter. In conclusion, the Amarillo genotype equaled the hybrid one, and although the production of CO increased, anaerobic fermentation showed the potential to reduce the rumen production of TG, CH4 and H2S, as well as to improve DM degradability and CH4 conversion efficiency.

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

The composites industry is present in practically all industrial sectors with an annual growth rate of 5%. Its contribution to the priority "light-weighting" driver in the transport sector is key. The efficiency of the industry is made possible by the evolution of manufacturing processes that also improve the performance of the products obtained. For example, out-of-autoclave (OOA) processes can obtain high-performance composites such as those obtained by the autoclave process at lower costs. A key aspect in the development of this type of process is the preforming of continuous fibre reinforcements, which can achieve high fibre percentages while facilitating processing. Manufacturing these preforms currently requires multiple steps, equipment and tooling. TECNALIA's work developing the ADDICOMP technology, an alternative preform manufacturing method using an additive process based on Fused Deposition Modelling (FDM) is detailed in this article. This development is patented by Tecnalia and was conducted in 2 phases: (a) development of continuous fibre filaments coated with polymeric material and printable by FDM and (b) fine-tuning of FDM technology to print filaments with a very high content of continuous fibre.

Abstract Background Subjective well-being (SWB) can be defined as a self-report evaluation that reflects the satisfaction, and emotional level, over several social and personal indicators. Alterations in these indicators could become risk factors (RF) for major depressive disorder (MDD), but this association has not been studied at women’s life stages such as the perimenopause onset, despite its increasing prevalence for depressive symptomatology. Therefore, the aim of this study was to identify if SWB’s alterations determine RF for MDD during the perimenopause. Methods An analytical cross-sectional study was realized in 252 Mexican women with perimenopause’s age range (48 ± 1.7) and menopausal symptomatology, treated on Medical Units belonging to Jalisco’s 13th Health-Region. We applied the INEGI’s Basic Self-Reported Wellbeing Survey (BIARE) that measured 30 SWB’s indicators. To identify MDD’s presence, the Beck’s Depression Inventory-II (BDI-II) was applied. The sample was studied with associative analysis, along with logistic regression models, to determine adjusted odds ratio (aOR) and corresponding 95% confidence interval (95% CI). Results Trough the BDI-II we identified 40.5% women with MDD. When compared with the undepressed group we found lower scores in all the SWB’s indicators, along with significant associations for depressive symptomatology. However, the logistic regression allowed us to identify significant RF when the women specifically reported personal life-dissatisfaction (aOR 9.6, 95% CI 1.90–17.68), emotional imbalances between happiness/sadness (aOR 7.1, 95% CI 1.49–13.57) and concentration/boredom (aOR 6.7, 95% CI 1.43–13.48); free-time dissatisfaction (aOR 5.5, 95% CI 1.17–5.70), public security unconformity (aOR 5.4, 95% CI 2.20–11.3), and sense of purposelessness (aOR 4.2, 95% CI 1.07–19.41). Conclusion The main objective of the study was to determine if SWB’s alterations are RF for depressive symptomatology, finding that social indicators with low scores are associated with MDD by means of aOR -Which were higher when compared to international research studies. Considering this, we suggest that more studies should be implemented, in order to understand and correctly attend the women’s social conditions during their perimenopause transition.