16,162 research outputs found

    Circadian Rhythmicity of Mood : An Exploratory Study

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    Human circadian rhythms are widely observed to fluctuate across the 24-hour circadian period, spanning cognitive, behavioral, and physiological domains. Circadian rhythm (CR) systems, particularly the sleep-wake cycle, are widely studied. Dysregulation of the sleep-wake cycle, common in shift work and mood disorders, diminishes mood regulation, resulting in increased negative mood or inappropriate mood responses. Although emotions have been investigated in the context of circadian variability in the sleep-wake cycle, circadian effects on emotional state per se have infrequently been examined. Previous studies suggest an increase in Positive Affect (PA) and decrease in Negative Affect (NA) as the day progresses, while the reverse occurs in the earlier hours of the day. Our study aimed to investigate circadian variation in PA versus NA, and extend these findings to the specific emotional states of Affection and Annoyance. As part of a larger study, thirteen male participants completed affect assessments using the Brief Mood Introspection Scale (BMIS) seven times over a 24-hour period. Primary findings corroborate previous research finding an increase in PA and decrease in NA during the evening, with the reverse occurring in the morning. Future research should include female participants, longitudinal designs, and objective measures of mood, such as cortisol or testosterone levels, in addition to subjective measures. These findings have clinical relevance, particularly for comparing patients\u27 reported mood ratings with expected ratings based on circadian rhythm of mood. Early-morning NA may reflect normal circadian fluctuations, but late-day NA could indicate a severe clinical condition. In summary, this study replicates circadian patterns in PA and NA but finds unique circadian behaviors in Affection and Annoyance, demanding further exploration

    Análisis genómico de la cepa patógena Salmonella enterica serotipo Enteritidis aislada de una granja avícola en Lima: virulencia y resistencia antimicrobiana

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    Las zoonosis originadas por bacterias constituyen el segundo mayor grupo de microorganismos asociados a infecciones epidémicas con un fuerte impacto en la salud pública en todo el mundo. En el Perú, existen reportes recientes de Salmonelosis provocadas por Salmonella enterica serotipo Enteritidis (S. Enteritidis), una bacteria patógena zoonótica transmitida a humanos mediante el consumo o contacto con productos y animales contaminados. Debido a su potencial virulento, el presente trabajo de investigación tiene como finalidad realizar el análisis genómico de una cepa de S. Enteritidis aislada de una granja avícola de Lima (cepa SMVET14), para identificar los factores genéticos involucrados en la virulencia de este patógeno y genes de resistencia a antibióticos en el genoma que le confieran capacidad para generar brotes epidémicos en animales de granja y poblaciones humanas. En este estudio, el genoma de S. Enteritidis cepa SMVET14 se sometió a un análisis in silico el cual comenzó con el análisis de cobertura, calidad y limpieza de los datos generados por el secuenciamiento con BBMap, FASTQC y Trimmomatic, respectivamente. Las lecturas fueron ensambladas usando dos softwares SPAdes y Velvet, y se contrastó la calidad de ambos ensamblajes utilizando QUAST. Se escogió el ensamblaje generado por SPAdes, el cual generó 25 contigs con una cobertura promedio de 81.26, un tamaño del genoma de 4701879 bp (4.71Mb), y un contenido de %GC fue 52.13%. La anotación génica con Prokka mostró 4513 genes de los cuales 4421 fueron secuencias codificantes (CDS) y 94 ARN. La tipificación de la cepa se realizó con MLST y fue asignada al ST11. Por otro lado, el mapeo genómico y el análisis filogenómico mostró que la cepa SMVET14 está estrechamente relacionada con las cepas P125109 y 17927, ambos implicados en brotes epidémicos de Salmonelosis. Asimismo, se identificaron 105 factores de virulencia, siendo principalmente asociados al sistema de secreción tipo III (TTSS) y a adherencia celular. También, se identificaron 6 genes de resistencia a antibióticos que son utilizados en primera línea en el tratamiento en humanos y animales. Ademas, se identificó el plásmido pSEN de 3 59.350 kb característico de esta especie. Por último, con el servidor PHASTER se identificaron 2 secuencias profagos intactos, incluyendo Gifsy_2 y Salmon_118970_sal3. Por lo tanto, se logró la caracterización e identificación de los factores de virulencia y resistencia dentro del genoma de S. Enteritidis cepa SMVET14 con potencial de generar brotes epidémicos.Perú. Universidad Nacional Mayor de San Marcos. Vicerrectorado de Investigación y Posgrado. Programa de Promoción de Tesis de Pregrado. B20100100

    Verification of molecular subtyping of bladder cancer in the GUSTO clinical trial

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    The GUSTO clinical trial (Gene expression subtypes of Urothelial carcinoma: Stratified Treatment and Oncological outcomes) uses molecular subtypes to guide neoadjuvant therapies in participants with muscle‐invasive bladder cancer (MIBC). Before commencing the GUSTO trial, we needed to determine the reliability of a commercial subtyping platform (Decipher Bladder; Veracyte) when performed in an external trial laboratory as this has not been done previously. Here, we report our pre‐trial verification of the TCGA molecular subtyping model using gene expression profiling. Formalin‐fixed paraffin‐embedded tissue blocks of MIBC were used for gene expression subtyping by gene expression microarrays. Intra‐ and inter‐laboratory technical reproducibilities, together with quality control of laboratory and bioinformatics processes, were assessed. Eighteen samples underwent analysis. RNA of sufficient quality and quantity was successfully extracted from all samples. All subtypes were represented in the cohort. Each sample was subtyped twice in our laboratory and once in a separate reference laboratory. No clinically significant discordance in subtype occurred between intra‐ or inter‐laboratory replicates. Examination of sample histopathology showed variability of morphological appearances within and between subtypes. Overall, these results show that molecular subtyping by gene expression profiling is reproducible, robust and suitable for use in the GUSTO clinical trial

    Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

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    Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird’s eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies

    Targeting immune and desmoplastic tumor microenvironment to sensitize gynecological cancer cells to therapy

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    Cancer is a pervasive global threat that manifests with diverse clinical attributes and notable mortality rates, particularly attributable to its metastatic potential in solid cancers. These tumours encompass various types including epithelial cancers like high-grade serous ovarian cancer (HGSC) and mesenchymal cancers like uterine sarcomas (USs). Despite the differing origins of USs and HGSCs, the pivotal concept of the transition between epithelial and mesenchymal states remains remarkably plastic, occurring frequently in these cancers. This plasticity holds immense significance in understanding tumour invasiveness and metastasis. The TME emerges as a crucial influencer as exerting its impact on cancer progression, epithelial-mesenchymal transition (EMT), metastasis, and even chemoresistance. The TME comprises various elements, with the extracellular matrix (ECM) containing structural proteins like collagens, standing out as a key constituent. Moreover, immune cells within the TME, such as lymphocytes and macrophages, actively engage in interactions with both the ECM and cancer cells shaping local responses to kill the cancer cells or support their growth. Understanding the intricate tumour-TME interactions become imperative in formulating effective strategies aimed at modulating the immune response and halting cancer progression. Therefore, a nuanced comprehension of these complexities is crucial in developing strategies to combat cancer effectively. This thesis focuses on identifying TME factors, including ECM components and immune cell interactions in gynaecological cancers for improved precision medicine including immunotherapies and other novel treatments. In Paper I, Uterine sarcomas present distinct immune signatures with prognostic value, independent of tumour type. FOXP3+ cell density and CD8+/FOXP3+ ratio (CFR) correlated with favourable survival in endometrial stromal sarcomas (ESS) and undifferentiated uterine sarcomas (USS). The CFR also highlighted the correlation between CFR high and upregulation of ECM organization pathways. In Paper II conversely, uterine leiomyosarcomas (uLMS) showed distinct behaviours, with lower collagen density and upregulated ECM remodelling enzymes correlating with aggressiveness. MMP-14 and yes-associated protein 1 (YAP) were required for uLMS growth and invasion. In Paper Ⅲ, shifting to HGSC, matrisome, a group of proteins encoded by genes for core ECM proteins 4 (collagens, proteoglycans, and ECM glycoproteins) and ECM-associated proteins (proteins structurally resembling ECM proteins, ECM remodelling enzymes, and secreted factors) in the ECM, showed changes in expression depending on the type of tumour host tissues and after chemotherapy. Collagen VI, among scrutinized proteins, exhibited elevated expression linked to shortened survival in ovarian cancer patients. Mechanistically, collagen VI promoted platinum resistance via the stiffness-dependent β1 integrin-pMLC and YAP/TAZ pathways in HGSC cell lines In summary, this integrated exploration of uterine sarcomas and ovarian cancer provides a comprehensive understating of their TME. The study elucidates diverse immune and molecular features, offering potential prognostic markers and therapeutic targets. The findings underscore the complexity of these gynaecological malignancies, emphasizing the need for tailored approaches in understanding and combating these diseases

    Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

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    Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers

    Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms

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    BackgroundRadiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer.MethodsThe present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays.ResultsThis process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC.ConclusionTo summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC
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