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E-students sometimes feel isolated due to the lack of face-to-face communication with teachers. It is important to pay attention to students' learning styles and the virtual education methods used by teachers. This study aimed to investigate the preferences of students at Shiraz University of Medical Sciences (SUMS) regarding virtual education and their learning styles. A descriptive survey research was conducted on postgraduate e-students in 2019-2020. A stratified random sampling was performed among 300 students from four virtual master's programs at SUMS. A total of 180 questionnaires were distributed, and 155 samples completed the questions. The VEPQ questionnaire was used to measure the participants' virtual education preferences, while the VARK standard questionnaire was utilized to determine their learning styles. The data were analyzed using SPSS 24 software via one-sample T-tests, independent T-tests, and ANOVA tests. All students exhibited a unimodal learning style, with their dominant style being reading/writing (P < 0.05). All educational methods received scores higher than the cut-off point (P < 0.01). Students' virtual education preferences were as follows: e-content (5.26 ± 0.74), self-directed projects (4.70 ± 0.90), face-to-face modules (4.67 ± 1.14), online presentations (4.32 ± 0.94), and text interaction (3.98 ± 1.18). There was no significant difference in students' virtual education preferences based on their learning styles (P > 0.05). E-students have different learning styles, but their educational preferences are more influenced by their circumstances. Therefore, the use of multimedia and independent learning is essential for them. It can be stated that "flexibility," "personalization," and "independence" are the three main preferences of e-students that should be considered in educational planning. [ABSTRACT FROM AUTHOR]

Introduction: This study examined the interplay between perceived feedback (PF), subjective wellbeing (SWB), and students’ sense of belonging to school (SBS). School belonging is a key factor for decisions regarding academic studies, and is usually impacted by PF. The current study explored whether SWB mediates the established relationship between PF and SBS. Method: This was achieved by applying a mediation model to PISA 2018 data from 12,058 students in four areas of China. Results: Perceived feedback positively affected students’ SBS (β  =  0.26, p  <  0.01); and that SWB partially mediated this relationship between PF and SBS (β  =  0.47, p  <  0.01). Discussion: The findings of this study have the potential to contribute to the existing literature on educational psychology and well-being. By shedding light on the mediating role of SWB, the research offers practical implications for educators and policymakers seeking to enhance students’ school belonging and other implications for their academic success. Further research can contribute to this promising area, by focusing on subjective wellbeing and its impact on a range of decisions being made by students during a critical phase of their personal and academic development. [ABSTRACT FROM AUTHOR]

The intricate relationship between cancer and bacteria has garnered increasing attention in recent years. While traditional cancer research has primarily focused on tumor cells and genetic mutations, emerging evidence highlights the significant role of microbial communities within the tumor microenvironment in cancer development and progression. This review aims to provide a comprehensive overview of the current understanding of the complex interplay between cancer and bacteria. We explore the diverse ways in which bacteria influence tumorigenesis and tumor behavior, discussing direct interactions between bacteria and tumor cells, their impact on tumor immunity, and the potential modulation of the tumor microenvironment. Additionally, we delve into the mechanisms through which bacterial metabolites and extracellular products May affect cancer pathways. By conducting a thorough analysis of the existing literature, we underscore the multifaceted and intricate relationship between bacteria and cancer. Understanding this complex interplay could pave the way for novel therapeutic approaches and preventive strategies in cancer treatment. [ABSTRACT FROM AUTHOR]

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) accounts for about 10-20% of all breast cancer cases and is associated with an unfavorable prognosis. Until recently, treatment options for TNBC were limited to chemotherapy. A new successful systemic treatment is immunotherapy with immune checkpoint inhibitors, but new tumor-specific biomarkers are needed to improve patient outcomes. Cannabinoids show antitumor activity in most preclinical studies in TNBC models and do not appear to have adverse effects on chemotherapy. Clinical data are needed to evaluate efficacy and safety in humans. Importantly, the endocannabinoid system is linked to the immune system and immunosuppression. Therefore, cannabinoid receptors could be a potential biomarker for immune checkpoint inhibitor therapy or a novel mechanism to reverse resistance to immunotherapy. In this article, we provide an overview of the currently available information on how cannabinoids may influence standard therapy in TNBC. [ABSTRACT FROM AUTHOR]

Macrophages are phagocytic innate immune cells capable of phenotypical switching in response to the local microenvironment. Studies often use either primary macrophages or immortalized cell lines for hypothesis testing, therapeutic assessment, and biomaterial evaluation without carefully considering the potential effects of cell source and tissue of origin, which strongly influence macrophage response. Surprisingly, limited information is available about how, under similar stimuli, immortalized cell lines and primary cells respond in both phenotypical and functional changes. To address this need, in this work, we cultured immortalized macrophage cell lines derived from different origins (i.e., blood, lung, peritoneal) to understand and compare macrophage phenotypical responses, including polarization and plasticity, morphological changes, and phagocytic functionalities, as well as compared primary macrophages extracted from peritoneal and bone marrow to their immortalized cell line counterparts. We found significant differences in baseline expression of different markers (e.g., CD86, MHCII, CD206, and EGR2) amongst different cell lines, which further influence both polarization and repolarization of the cells, in addition to their phagocytic functionality. Additionally, we observed that, while RAW 264.7 cells behave similarly to the primary bone marrow-derived macrophages, there are noticeable phenotypical and functional differences in cell line (IC-21) and primary peritoneal macrophages, highlighting tissue-specific differences in macrophage response amongst cell lines and primary cells. Moving to three-dimensional (3D) culture in well-defined biomaterials, blood-derived primary and cell line macrophages were encapsulated within hydrogel-based synthetic extracellular matrices and their polarization profiles and cell morphologies were compared. Macrophages exhibited less pronounced polarization during 3D culture in these compliant, soft materials compared to two-dimensional (2D) culture on rigid, tissue culture plastic plates. Overall, our findings highlight origin-specific differences in macrophage response, and therefore, careful considerations must be made to identify the appropriate cell source for the application of interest. [ABSTRACT FROM AUTHOR]

Introduction: Yeast culture (YC) enhances ruminant performance, but its functional mechanism remains unclear because of the complex composition of YC and the uncertain substances affecting rumen fermentation. The objective of this study was to determine the composition of effective metabolites in YC by exploring its effects on rumen fermentation in vitro, growth and slaughter performance, serum index, rumen fermentation parameters, rumen microorganisms, and metabolites in lambs. Methods: In Trial 1, various YCs were successfully produced, providing raw materials for identifying effective metabolites. The experiment was divided into 5 treatment groups with 5 replicates in each group: the control group (basal diet without additives) and YC groups were supplemented with 0.625? of four different yeast cultures, respectively (groups A, B, C, and D). Rumen fermentation parameters were determined at 3, 6, 12, and 24 h in vitro. A univariate regression model multiple factor associative effects index (MFAEI; y) was established to correlate the most influential factors on in vitro rumen fermentation with YC metabolites (x). This identified the metabolites promoting rumen fermentation and optimal YC substance levels. In Trial 2, metabolites in YC not positively correlated with MFAEI were excluded, and effective substances were combined with pure chemicals (M group). This experiment validated the effectiveness of YC metabolites in lamb production based on their impact on growth, slaughter performance, serum indices, rumen parameters, microorganisms, and metabolites. Thirty cross-generation rams (Small tail Han-yang? × Australian white sheep?) with good body condition and similar body weight were divided into three treatment groups with 10 replicates in each group: control group, YC group, pure chemicals combination group (M group). Results: Growth performance and serum index were measured on days 30 and 60, and slaughter performance, rumen fermentation parameters, microorganisms, and metabolites were measured on day 60. The M group significantly increased the dressing percentage, and significantly decreased the GR values of lambs (p < 0.05). The concentration of growth hormone (GH), Cortisol, insulin (INS), and rumen VFA in the M group significantly increased (p < 0.05). Discussion: These experiments confirmed that YC or its screened effective metabolites positively impact lamb slaughter performance, rumen fermentation, and microbial metabolism. [ABSTRACT FROM AUTHOR]

Introduction: Professional psychological qualities are crucial for individuals' career development and overall well-being, especially in clinical medical professions. Medical students often face significant work, academic, and doctor-patient communication pressures, which can challenge their mental and emotional health. Measuring and understanding the relationship between medical students' professional psychological qualities and their mental health is of significant practical importance. Methods: This study developed a comprehensive professional psychological qualities scale through a series of qualitative and quantitative studies, consisting of three main components and thirteen secondary dimensions. The scale's reliability was assessed using Cronbach's a coefficients. In Study 2, the scale was administered to 972 medical students to explore their anxiety and depression levels. A simple mediation analysis was conducted to investigate the relationship between professional psychological qualities, anxiety, and depression. Results: The professional psychological qualities scale demonstrated satisfactory reliability, with a total scale a coefficient of 0.947 and subscale a coefficients ranging from 0.895 to 0.933. The mediation analysis revealed that medical students' professional psychological qualities directly negatively impact depression levels and indirectly positively influence them via their effects on anxiety levels, exhibiting an overall masking effect unrelated to depression levels. Discussion: This study addresses the gap in research on the professional psychological qualities of medical students by providing a reliable measurement tool. The findings shed light on the complex mechanisms through which these qualities impact the mental health process. The scale can be used by other researchers to assess medical students' professional psychological qualities and further investigate their relationship with mental health outcomes. [ABSTRACT FROM AUTHOR]

Genetics is a key factor that governs the susceptibility to oxidative stress. In the body, oxidative burden is regulated by the balance between the prooxidant genes that orchestrate processes that produce oxidant species, while the antioxidant genes aid those involved in scavenging these species. Together, the two components aid in maintaining the oxidative balance in the body. Genetic variations can influence the expression and activity of the encoded proteins which can then affect their efficiency in regulating redox processes, thereby increasing the risk of oxidative stress. This review studies single nucleotide polymorphisms (SNPs) that bear relevance to oxidative stress by exploring the variations in the prooxidant genes, such as XDH, CYBA, CYP1A1, PTGS2, NOS, and MAO and antioxidant genes including SOD, CAT, GPX, GSS, GLUL, GSR, GSTM1, GSTM5, GSTP1, TXN and HMOX1. Early identification of individuals at the increased risk of oxidative stress is possible from the assessment of sequence of these genes. Integrating genetic insights into oxidative stress management measures can pave the way for personalized medicine that tailors' healthcare approaches to individual genetic profiles. Effective genetic assessment along with routine quantification of biological markers can improve and monitor treatment strategies, enhancing mitigation approaches that maintain cellular health and promote longevity. [ABSTRACT FROM AUTHOR]

Objective: To construct radiomics models based on MRI at different time points for the early prediction of cystic brain radionecrosis (CBRN) for nasopharyngeal carcinoma (NPC). Methods: A total of 202 injured temporal lobes from 155 NPC patients with radiotherapy-induced temporal lobe injury (RTLI) after intensity modulated radiotherapy (IMRT) were included in the study. All the injured lobes were randomly divided into the training (n = 143) and validation (n = 59) sets. Radiomics models were constructed by using features extracted from T2WI at two different time points: at the end of IMRT (post-IMRT) and the first-detected RTLI (first- RTLI). A delta-radiomics feature was defined as the percentage change in a radiomics feature from post-IMRT to first-RTLI. The radiomics nomogram was constructed by combining clinical risk factors and radiomics signatures using multivariate logistic regression analysis. Predictive performance was evaluated using area under the curve (AUC) from receiver operating characteristic analysis and decision curve analysis (DCA). Results: The post-IMRT, first-RTLI, and delta-radiomics models yielded AUC values of 0.84 (95% CI: 0.76-0.92), 0.86 (95% CI: 0.78-0.94), and 0.77 (95% CI: 0.67-0.87), respectively. The nomogram exhibited the highest AUC of 0.91 (95% CI: 0.85-0.97) and sensitivity of 0.82 compared to any single radiomics model. From the DCA, the nomogram model provided more clinical benefit than the radiomics models or clinical model. Conclusion: The radiomics nomogram model combining clinical factors and radiomics signatures based on MRI at different time points after radiotherapy showed excellent prediction potential for CBRN in patients with NPC. [ABSTRACT FROM AUTHOR]

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus. [ABSTRACT FROM AUTHOR]

Background: In patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy. Methods: The phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination. Results: Between June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUSradiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation. Conclusion: The study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study. [ABSTRACT FROM AUTHOR]

Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant in global aquatic ecosystems with increasing concern for its toxicity to aquatic wildlife through inadvertent exposures. To assess the likely adverse effects of PFOS exposure on aquatic wildlife inhabiting polluted ecosystems, there is a need to identify biomarkers of its exposure and toxicity. We used an integrated systems toxicological framework to identify physiologically relevant biomarkers of PFOS toxicity in fish. An in silico stoichiometric metabolism model of zebrafish (Danio rerio) was used to integrate available (published by other authors) metabolomics and transcriptomics datasets from in vivo toxicological studies with 5 days post fertilized embryo-larval life stage of zebrafish. The experimentally derived omics datasets were used as constraints to parameterize an in silico mathematical model of zebrafish metabolism. In silico simulations using flux balance analysis (FBA) and its extensions showed prominent effects of PFOS exposure on the carnitine shuttle and fatty acid oxidation. Further analysis of metabolites comprising the impacted metabolic reactions indicated carnitine to be the most highly represented cofactor metabolite. Flux simulations also showed a near dose-responsive increase in the pools for fatty acids and acyl-CoAs under PFOS exposure. Taken together, our integrative in silico results showed dyslipidemia effects under PFOS exposure and uniquely identified carnitine as a candidate metabolite biomarker. The verification of this prediction was sought in a subsequent in vivo environmental monitoring study by the authors which showed carnitine to be a modal biomarker of PFOS exposure in wild-caught fish and marine mammals sampled from the northern Gulf of Mexico. Therefore, we highlight the efficacy of FBA to study the properties of large-scale metabolic networks and to identify biomarkers of pollutant exposure in aquatic wildlife. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) impairs sensory-motor functions, with debilitating consequences on postural control and balance, which persist during the chronic stages of recovery. The Timed Up and Go (TUG) test is a reliable, safe, time-efficient, and one of the most widely used clinical measures to assess gait, balance, and fall risk in TBI patients and is extensively used in inpatient and outpatient settings. Although the TUG test has been used extensively due to its ease of performance and excellent reliability, limited research has been published that investigates the relationship between TUG performance and quantitative biomechanical measures of balance. The objective of this paper was to quantify the relationship between biomechanical variables of balance and the TUG scores in individuals with chronic TBI. Regression models were constructed using six biomechanical variables to predict TUG scores. The model that conservatively removed gait speed (i.e., TUG-1/GS) gave the best results, achieving a root-mean-square error of ~±2 s and explaining over 69% of the variability. [ABSTRACT FROM AUTHOR]

The study aims to investigate the multiple mediating roles of core selfevaluation and sense of school belonging in the relationship between fatherchild attachment and academic burnout in college students. A sample of 418 college students completed the father-child attachment scale, the scale of sense of school belonging, core self-evaluation scale, and academic burnout scale. After controlling for variables such as mother--child attachment, gender, age, and grade, the results showed: (1) father-child attachment was significantly and negatively correlated with academic burnout, and positively correlated with core self-evaluation and sense of school belonging; both core self-evaluation and sense of school belonging were significantly and negatively correlated with academic burnout. (2) The multiple mediating model of father-child attachment influencing academic burnout in college students was established. Both core self-evaluation and sense of school belonging played a partial mediating role between father-child attachment and academic burnout. The direct effect of father-child attachment on academic burnout accounts for 33.3% of the total effect. The indirect effects of core self-evaluation and sense of school belonging between father-child attachment and academic burnout account for 50.0 and 16.7% of the total effect, respectively. These findings identify the internal mechanisms through which father-child attachment affects academic burnout in college students from personal traits and interpersonal perspectives. [ABSTRACT FROM AUTHOR]

Purpose: To identify the clinical and genetic variables associated with rim enhancement of pancreatic ductal adenocarcinoma (PDAC) and to develop a dynamic contrast-enhanced (DCE) MRI-based radiomics model for predicting the genetic status from next-generation sequencing (NGS) Materials and methods: Patients with PDAC, who underwent pretreatment pancreatic DCE-MRI between November 2019 and July 2021, were eligible in this prospective study. Two radiologists evaluated presence of rim enhancement in PDAC, a known radiological prognostic indicator, on DCE MRI. NGS was conducted for the tissue from the lesion. The Mann-Whitney U and Chi-square tests were employed to identify clinical and genetic variables associated with rim enhancement in PDAC. For continuous variables predicting rim enhancement, the cutoff value was set based on the Youden's index from the receiver operating characteristic (ROC) curve. Radiomics features were extracted from a volume-of-interest of PDAC on four DCE maps (Ktrans, Kep, Ve, and iAUC). A random forest (RF) model was constructed using 10 selected radiomics features from a pool of 392 original features. This model aimed to predict the status of significant NGS variables associated with rim enhancement. The performance of the model was validated using test set. Results: A total of 55 patients (32 men; median age 71 years) were randomly assigned to the training (n = 41) and test (n = 14) sets. In the training set, KRAS, TP53, CDKN2A, and SMAD4 mutation rates were 92.3%, 61.8%, 14.5%, and 9.1%, respectively. Tumor size and KRAS variant allele frequency (VAF) differed between rim-enhancing (n = 12) and nonrim-enhancing (n = 29) PDACs with a cutoff of 17.22%. The RF model's average AUC from 10-fold cross-validation for predicting KRAS VAF status was 0.698. In the test set comprising 6 tumors with low KRAS VAF and 8 with high KRAS VAF, the RF model's AUC reached 1.000, achieving a sensitivity of 75.0%, specificity of 100% and accuracy of 87.5%. Conclusion: Rim enhancement of PDAC is associated with KRAS VAF derived from NGS-based genetic information. For predicting the KRAS VAF status in PDAC, a radiomics model based on DCE maps showed promising results. [ABSTRACT FROM AUTHOR]

Natural polymers have many uses, and Tragacanth gum is just one of them. Many people are interested in natural gums because of their many attractive characteristics, such as being 'green' bio-based renewable materials, being easily accessible, inexpensive, and structurally diverse. One class of naturally occurring polysaccharides is called gum because of its tendency to create a gel or a thick solution. Among the many plant-based raw materials, these polysaccharide gums are abundant. Hydrogels, which are three-dimensional polymeric webs that can imitate live tissues, have demonstrated remarkable potential as adjustable biomaterials in numerous regenerative techniques due to their high water or biological exudate absorption capacities. Natural polysaccharides, often known as gums, are present in many different types of trees and possess many desirable properties, such as being renewable, biocompatible, biodegradable, non-toxic, and amenable to chemical modification. Many people are curious about certain parts of the food, water, energy, biotech, environmental, and healthcare sectors as of now. Gum, a type of very important and unique food ingredient, has many vital uses in the food business. Cosmetics, coating, photosensitive resin, fertilizer, casting, pharmaceuticals, and tobacco are just a few of the non-food businesses that make use of their strong water-affinity and structural plasticity. There are a lot of benefits to hydrogels made from natural gums as opposed to those made from synthetic sources. Synthesis hydrogel polymers have been the center of interest among these non-food applications because of their extensive use in the pharmaceutical and medical fields. The Tragacanth gum hydrogels used for medication delivery and tissue engineering have been the focus of this study. We also paid close attention to drug delivery, physical-chemical properties, and the extraction of Tragacanth gum. Our research has a wide range of biomedical applications, including tissue engineering for bone, skin, fixation of bone, periodontal, and cartilage. Possible likewise our primary focus. [ABSTRACT FROM AUTHOR]

Objective: To investigate the relationship between serum uric acid (SUA) levels and ischemic stroke in young patients in the Ganzi Tibetan plateau area. Methods: A cross-sectional survey was undertaken from January 2020 to June 2023 involving young patients (age: 15-45 years) diagnosed with ischemic stroke. The survey was conducted at the Department of Internal Medicine of the People's Hospital of Derong County, Ganzi Prefecture. The participants underwent a comprehensive assessment, including questionnaire surveys, physical examinations, laboratory tests, and head computed tomography (CT) examinations. Based on the tertiles of serum uric acid (SUA) levels, the patients were stratified into three groups. Furthermore, stroke severity was classified into mild (1-4 points), moderate (5-15 points), and severe (>15 points) categories using the National Institute of Health Stroke Scale. Results: The severe stroke group exhibited higher levels of age, glucose, systolic blood pressure, serum triglyceride, low-density lipoprotein cholesterol, and serum uric acid (SUA) compared to the mild stroke group (P < 0.05). Furthermore, the likelihood of male sex, advanced age, smoking, and a family history of stroke, diabetes mellitus, and heart disease were significantly elevated in the severe stroke group compared to the moderate stroke group (P < 0.05). Multivariate logistic regression analysis conducted on young adults residing in highland areas revealed a significant association between SUA levels and the risk of stroke. Conclusion: Elevated SUA levels serve as a distinct risk factor for the development of a major stroke in young patients in highland areas. At SUA levels of 320.56 mol/L, the risk of a moderate-to-severe stroke is noticeably elevated. [ABSTRACT FROM AUTHOR]

Introduction: Disturbances of energy metabolism contribute to the clinical manifestations of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Previously, we found that B cells from ME/CFS patients have an increased expression of CD24, a modulator of many cellular functions including those of cell stress. The relative ability of B cells from ME/CFS patients and healthy controls (HC) to respond to rapid changes in energy demand was compared. Methods: CD24, the ectonucleotidases CD39 and CD73, the NAD-degrading enzyme CD38, and mitochondrial mass (MM) were measured following crosslinking of the B cell receptor and costimulation with either T-cell-dependent or Toll-like-receptor-9-dependent agonists. The levels of metabolites consumed/produced were measured using 1H-NMR spectroscopy and analyzed in relation to cell growth and immunophenotype. Results: Proliferating B cells from patients with ME/CFS showed a lower mitochondrial mass and a significantly increased usage of essential amino acids compared with those from HC, with a significantly delayed loss of CD24 and an increased expression of CD38 following stimulation. Discussion: The immunophenotype results suggested the triggering of a stress response in ME/CFS B cells associated with the increased usage of additional substrates to maintain necessary ATP levels. Disturbances in energy metabolism in ME/CFS B cells were thus confirmed in a dynamic in vitro model, providing the basis for further mechanistic investigations. [ABSTRACT FROM AUTHOR]

Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image. Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM. Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the kth parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients. Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications. [ABSTRACT FROM AUTHOR]

The estrogen receptor (ER) is a classical receptor protein that plays a crucial role in mediating multiple signaling pathways in various target organs. It has been shown that ER-targeting therapies inhibit breast cancer cell proliferation, enhance neuronal protection, and promote osteoclast formation. Several drugs have been designed to specifically target ER in ER-positive (ER+) breast cancer, including selective estrogen receptor modulators (SERM) such as Tamoxifen. However, the emergence of drug resistance in ER+ breast cancer and the potential side effects on the endometrium which has high ER expression has posed significant challenges in clinical practice. Recently, novel ER-targeted drugs, namely, selective estrogen receptor degrader (SERD) and selective estrogen receptor covalent antagonist (SERCA) have shown promise in addressing these concerns. This paper provides a comprehensive review of the structural functions of ER and highlights recent advancements in SERD and SERCA-related small molecule drugs, especially focusing on their structural optimization strategies and future optimization directions. Additionally, the therapeutic potential and challenges of novel SERDs and SERCAs in breast cancer and other ER-related diseases have been discussed. [ABSTRACT FROM AUTHOR]

In recent years, the central role of cell bioenergetics in regulating immune cell function and fate has been recognized, giving rise to the interest in immunometabolism, an area of research focused on the interaction between metabolic regulation and immune function. Thus, early metabolic changes associated with the polarization of macrophages into pro-inflammatory or proresolving cells under different stimuli have been characterized. Tumor-associated macrophages are among the most abundant cells in the tumormicroenvironment; however, it exists an unmet need to study the effect of chemotherapeutics on macrophage immunometabolism. Here, we use a systems biology approach that integrates transcriptomics and metabolomics to unveil the immunometabolic effects of trabectedin (TRB) and lurbinectedin (LUR), two DNA-binding agents with proven antitumor activity. Our results show that TRB and LUR activate human macrophages toward a pro-inflammatory phenotype by inducing a specific metabolic rewiring program that includes ROS production, changes in the mitochondrial inner membrane potential, increased pentose phosphate pathway, lactate release, tricarboxylic acids (TCA) cycle, serine and methylglyoxal pathways in human macrophages. Glutamine, aspartate, histidine, and proline intracellular levels are also decreased, whereas oxygen consumption is reduced. The observed immunometabolic changes explain additional antitumor activities of these compounds and open new avenues to design therapeutic interventions that specifically target the immunometabolic landscape in the treatment of cancer. [ABSTRACT FROM AUTHOR]

Aim: This study aims to investigate depression, anxiety, stress, and fear of the COVID-19 pandemic and the associated risk factors among Bangladeshi medical students. It also explored qualitative insights on mental health from medical students during the first wave of the pandemic. Methods: This mixed-methods study was conducted online in Bangladesh from June 2020 to September 2020. Participants were Bangladeshi medical students from the first year to the final year. The quantitative part included a structured online survey. One focus group discussion (FGD) was organized using the Zoom platform to collect qualitative insights from the students. To determine levels of stress, anxiety, and depression, the Bangla-validated version of the Depression, Anxiety, and Stress Scale 21 (DASS-21) was used. A 7-item and Bangla-validated Fear of COVID-19 Scale, also known as FCV-19S, was used to explore the COVID-19-specific fear of the students. A semi-structured topic guide was used for exploring the qualitative insights of medical students’ perceptions of fear of COVID-19, mental health impacts during COVID-19, overall recommendations to support students, and the impact of the pandemic on the future of the medical curriculum. Results: The study reported that 51.20%, 59.40%, and 64% of the 406 respondents had moderate to severe stress, anxiety, and depressive symptoms, respectively, according to the DASS-21. The mean fear score for the COVID-19 scale was 19.4 (SD 6.4). Respondents with family members aged 50 years or older (B = 2.1; CI: 0.3-3.9) and those who had infected family members (B = 1.9; 95% CI: 0.1-3.7) exhibited a higher level of fear of COVID-19. Moreover, depression was associated with a history of having cancer among family members (AOR = 2.9, CI: 1.1-7.5), anxiety was strongly associated with having symptoms of COVID-19 (AOR = 2, CI: 1.3-3.2), and stress was associated with having symptoms of COVID-19 infection among family members (AOR = 1.9, CI: 1.3-3). Altered sleep was a potential risk factor for developing stress, anxiety, and depression symptoms. Manual thematic analysis of qualitative data generated four major themes, including the perception of fear of COVID-19, the perception of mental health impacts during COVID-19, the change in the medical curriculum along with the pandemic, and recommendations from the medical students to support the mental health concerns of medical students during public health crises like this pandemic. Qualitative findings showed that the participants experienced fear of their parents becoming infected by COVID-19, and this fear was more prominent in those who had their loved ones hospitalized. They were also stressed and anxious, with thoughts of death. Their fear also extended to their thoughts on academic progress and the effectiveness of online classes. Conclusion: A substantial proportion of medical students experienced mental health diffculties in Bangladesh. Appropriate interventions should be designed, and adequate support should be provided to the medical students to protect their mental health and wellbeing, considering their potential impact on the future health system in a low-resource setting like Bangladesh. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system (CNS). It is a leading cause of death among patients with intracranial malignant tumors. GBM exhibits intra- and inter-tumor heterogeneity, leading to drug resistance and eventual tumor recurrence. Conventional treatments for GBM include maximum surgical resection of glioma tissue, temozolomide administration, and radiotherapy, but these methods do not effectively halt cancer progression. Therefore, development of novel methods for the treatment of GBM and identification of new therapeutic targets are urgently required. In recent years, studies have shown that drugs related to mitophagy and mitochondrial apoptosis pathways can promote the death of glioblastoma cells by inducing mitochondrial damage, impairing adenosine triphosphate (ATP) synthesis, and depleting large amounts of ATP. Some studies have also shown that modern nano-drug delivery technology targeting mitochondria can achieve better drug release and deeper tissue penetration, suggesting that mitochondria could be a new target for intervention and therapy. The combination of drugs targeting mitochondrial apoptosis and autophagy pathways with nanotechnology is a promising novel approach for treating GBM.This article reviews the current status of drug therapy for GBM, drugs targeting mitophagy and mitochondrial apoptosis pathways, the potential of mitochondria as a new target for GBM treatment, the latest developments pertaining to GBM treatment, and promising directions for future research. [ABSTRACT FROM AUTHOR]

Introduction: Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agriindustrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress--a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress. Methods: The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented. Results and discussion: Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na+, Cl-, and K+, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants. [ABSTRACT FROM AUTHOR]

Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method: In this study, we conducted a computational characterization of the flux distributionandvariationsof thecentralenergymetabolismandkeybranches inapancancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissuescompared to their adjacent normal tissues. A systemsbiologymodel of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissuespecific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. [ABSTRACT FROM AUTHOR]

Objective: Psychological wellbeing and emotion regulation skills of vulnerable adolescents have been severely threatened by the long-term impact of the ongoing COVID-19 pandemic. The aim of this study was to seek out the potentially effective emotion regulation strategies to minimize the mental health risk of adolescents during the COVID-19 post-pandemic era. Methods: A total of 436 high school students aged 16.07 ± 1.08 years were included in this cross-sectional study to complete questionnaires to self-report socio-demographic information, positive and negative affect state, and emotional regulation abilities. Student's t-test and one-way analysis of variance (ANOVA) were used for intergroup comparisons among socio-demographic variables. Pearson's correlation analysis was used for evaluating the association between each emotion regulation strategy and positivity or negativity. Multiple stepwise linear regression analysis was used for the determination of the predictors for adolescents' positivity and negativity. Results: Adolescents' affect was influenced by multiple emotion regulation strategies, including cognitive reappraisal, acceptance and engagement, difficulty in awareness, acceptance, and modification of emotions after adjusting for a range of socio-demographic variables. Conclusion: Overall, our findings highlight the importance of emotional regulation strategies in the modulation of the mental health of the vulnerable youth population in China during the COVID-19 crisis. In view of the continuous, multifaceted influence on adolescents' mental health of the ongoing pandemic, more effort should be made to leverage emotion regulation strategies to benefit their coping abilities. [ABSTRACT FROM AUTHOR]

Background: There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. Materials and methods: A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. Results: The radiomics model mpMRI, which is based on T1WI, T2WI, and CEMRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. Conclusions: The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC. [ABSTRACT FROM AUTHOR]

The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions. [ABSTRACT FROM AUTHOR]

Background: To investigate reliable associations between dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNAsequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCEMRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis. [ABSTRACT FROM AUTHOR]

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets. [ABSTRACT FROM AUTHOR]

Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II–III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic network models are of great Importance In systems biology research, as they are used in metabolic activity dynamics studies and provide the metabolic level representation in multi-omic investigations. Especially for human, accurate metabolic network reconstruction is important in biomedical research and drug discovery. Today, there exist many instances of the human metabolic network as a whole and in its tissue-specific versions. Some are improved updates of models reconstructed from the same research team, while others are combinations of models from various teams, in an effort to include all available information from genome annotation and omic datasets. A major challenge regarding the human stoichiometric models in particular is the standardization of the reconstruction methods, representation formats and model repositories. Stoichiometric model standardization will enable the educated selection of the model that better fits the goals of a study, the direct comparison of results from various flux analysis studies and the identification of model sections that require reconsideration and updating with respect to the annotation of the human genome and proteome. Standardized human metabolic models aligned to the human genome will be a very useful tool in multi-omic studies, enabling the direct and consistent integration of the metabolic with the gene regulation and protein interaction networks. In this work, we provide a thorough overview of the current collection of human metabolic stoichiometric models, describe the current issues regarding their direct comparison and alignment in the context of the various model repositories, exposing the standardization needs, and propose potential solutions. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material: RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods: The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results: Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions: The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future. [ABSTRACT FROM AUTHOR]

Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore, via bioinformatic analyses of big data derived from the public zone, we identified ten potential driver variants that likely urged the local invasion of tumor cells into nearby tissue. Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients. [ABSTRACT FROM AUTHOR]

In the current scenario, cardiovascular disease (CVD) is one of the most life-threatening diseases that has caused high mortality worldwide. Several scientists, researchers, and doctors are now resorting to medicinal plants and their metabolites for the treatment of different diseases, including CVD. The present review focuses on one such family of medicinal plants, called Lamiaceae, which has relieving and preventive action on CVD. Lamiaceae has a cosmopolitan distribution and has great importance in the traditional system of medicine. Lamiaceae members exhibit a wide range of activities like antioxidant, antihyperlipidemic, vasorelaxant, and thrombolytic effect, both in vitro and in vivo –these are mechanisms that contribute to different aspects of CVD including stroke, heart attack, and others. These plants harbour an array of bioactive compounds like phenolic acids, flavonoids, alkaloids, and other phytochemicals responsible for these actions. The review also highlights that these plants are a rich source of essential nutrients and minerals like omega-3 and hence, can serve as essential sources of functional foods—this can have an additional role in the prevention of CVDs. However, limitations still exist, and extensive research needs to be conducted on the Lamiaceae family in the quest to develop new and effective plant-based drugs and functional foods that can be used to treat and prevent cardiovascular diseases worldwide. [ABSTRACT FROM AUTHOR]

The concept of precision oncology entails molecular profiling of tumors to guide therapeutic interventions. Genomic testing through next-generation sequencing (NGS) molecular analysis provides the basis of such highly targeted therapeutics in oncology. As radiomic analysis delivers an array of structural and functional imaging-based biomarkers that depict these molecular mechanisms and correlate with key genetic alterations related to cancers. There is an opportunity to synergize these two big-data approaches to determine the molecular guidance for precision therapeutics. Colorectal cancer is one such disease whose therapeutic management is being guided by genetic and genomic analyses. We review the rationale and utility of radiomics as a combinative strategy for these approaches in the management of colorectal cancer. [ABSTRACT FROM AUTHOR]

Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatory pathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies, with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid ß-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation in metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to explore the value of MRI texture features based on T1WI, T2-FS and diffusion-weighted imaging (DWI) in differentiation of renal changes in patients with stage III type 2 diabetic nephropathy (DN) and normal subjects. Materials and Methods: A retrospective analysis was performed to analyze 44 healthy volunteers (group A) and 40 patients with stage III type 2 diabetic nephropathy (group B) with microalbuminuria. Urinary albumin to creatinine ratio (ACR) <30 mg/g, estimated glomerular filtration rate (eGFR) in the range of 60–120 ml/(min 1.73 m2), and randomly divided into primary cohort and test cohort. Conventional MRI and DWI of kidney were performed using 1.5 T magnetic resonance imaging (MRI). The outline of the renal parenchyma was manually labeled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. Results: There was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, with no significant difference in age. In the final results, the wavelet and Laplacian–Gaussian filtering are used to extract 1,892 image features from the original T1WI image, and the LASSO algorithm is used for selection. One first-order feature and six texture features are selected through 10 cross-validations. In the mass, 1,638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1,241 imaging features were extracted from the original ADC images, and 5 texture features were selected. Using LASSO-Logistic regression analysis, 10 features were selected for modeling, and a combined diagnosis model of diabetic nephropathy based on texture features was established. The average unit cost in the logistic regression model was 0.98, the 95% confidence interval for the predictive efficacy was 0.9486–1.0, specificity 0.97 and precision 0.93, particularly. ROC curves also revealed that the model could distinguish with high sensitivity of at least 92%. Conclusion: In consequence, the texture features based on MR have broad application prospects in the early detection of DN as a relatively simple and noninvasive tool without contrast media administration. [ABSTRACT FROM AUTHOR]

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC. [ABSTRACT FROM AUTHOR]

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with themultiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity. [ABSTRACT FROM AUTHOR]

Unlike other rodents, guinea pigs (Cavia porcellus) have evolutionarily lost their capacity to synthesize vitamin C (ascorbate) de novo and, like several non-human primates and humans, rely on dietary intake and glutathione-dependent recycling to cope with oxidant stress. This is particularly relevant in red blood cell physiology, and especially when modeling blood storage, which exacerbates erythrocyte oxidant stress. Herein we provide a comprehensive metabolomics analysis of fresh and stored guinea pig red blood cell concentrates (n = 20), with weekly sampling from storage day 0 through 42. Results were compared to previously published ZOOMICS studies on red blood cells from three additional species with genetic loss of L-gulonolactone oxidase function, including humans (n = 21), olive baboons (n = 20), and rhesus macaques (n = 20). While metabolic trends were comparable across all species, guinea pig red blood cells demonstrated accelerated alterations of the metabolic markers of the storage lesion that are consistent with oxidative stress. Compared to the other species, guinea pig red blood cells showed aberrant glycolysis, pentose phosphate pathway end product metabolites, purine breakdown products, methylation, glutaminolysis, and markers of membrane lipid remodeling. Consistently, guinea pig red blood cells demonstrated higher end storage hemolysis, and scanning electron microscopy confirmed a higher degree of morphological alterations of their red blood cells, as compared to the other species. Despite a genetic inability to produce ascorbate that is common to the species evaluated, guinea pig red blood cells demonstrate accelerated oxidant stress under standard storage conditions. These data may offer relevant insights into the basal and cold storage metabolism of red blood cells from species that cannot synthesize endogenous ascorbate. [ABSTRACT FROM AUTHOR]

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems. [ABSTRACT FROM AUTHOR]

T lymphocytes (T cells) are divided into two functionally different subgroups the CD4+ T helper cells (Th) and the CD8+ cytotoxic T lymphocytes (CTL). Adequate CD4 and CD8 T cell activation to proliferation, clonal expansion and effector function is crucial for efficient clearance of infection by pathogens. Failure to do so may lead to T cell exhaustion. Upon activation by antigen presenting cells, T cells undergo metabolic reprograming that support effector functions. In this review we will discuss how metabolic reprograming dictates functionality during viral infections using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human immunodeficiency virus (HIV) as examples. Moreover, we will briefly discuss T cell metabolic programs during bacterial infections exemplified by Mycobacterium tuberculosis (MT) infection. [ABSTRACT FROM AUTHOR]