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Cardiometabolic diseases (CMDs) are interrelated and multifactorial conditions, including arterial hypertension, type 2 diabetes, heart failure, coronary artery disease, and stroke. Due to the burden of cardiovascular morbidity and mortality associated with CMDs' increasing prevalence, there is a critical need for novel diagnostic and therapeutic strategies in their management. In clinical practice, innovative methods such as epicardial adipose tissue evaluation, ventricular-arterial coupling, and exercise tolerance studies could help to elucidate the multifaceted mechanisms associated with CMDs. Similarly, epigenetic changes involving noncoding RNAs, chromatin modulation, and cellular senescence could represent both novel biomarkers and targets for CMDs. Despite the promising data available, significant challenges remain in translating basic research findings into clinical practice, highlighting the need for further investigation into the complex pathophysiology underlying CMDs.

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The characterization of cyanobacteria communities remains challenging, as taxonomy of several cyanobacterial genera is still unresolved, especially within Nostocales taxa. Nostocales cyanobacteria are capable of nitrogen fixation; nitrogenase genes are grouped into operons and are located in the same genetic locus. Structural nitrogenase genes (nifH, nifK and nifD) as well as 16S rRNA have been shown to be adequate genetic markers for distinguishing cyanobacterial genera. However, there is no available information regarding the phylogeny of regulatory genes of the nitrogenase cluster. Aiming to provide a more accurate overview of the evolution of nitrogen fixation, this study analyzed for the first time nifE and nifN genes, which regulate the production of nitrogenase, alongside nifH. Specific primers were designed to amplify nifE and nifN genes, previously not available in literature and phylogenetic analysis was carried out in 13 and 14 TAU-MAC culture collection strains, respectively, of ten Nostocales genera along with other sequences retrieved from cyanobacteria genomes. Phylogenetic analysis showed that these genes seem to follow a common evolutionary pattern with nitrogenase structural genes and 16S rRNA. The classification of cyanobacteria based on these molecular markers seems to distinguish Nostocales strains with common morphological, ecological, and physiological characteristics.

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PURPOSEOF REVIEW: This review examines the challenges of treating gastrointestinal cancer in the aging population, focusing on the importance of frailty assessment. Emphasized are the rise in gastrointestinal cancer incidence in older adults, advances in frailty assessments for patients with gastrointestinal cancer, the development of novel frailty markers, and a summary of recent trials. RECENT FINDINGS: Increasing evidence suggests that the use of a Comprehensive Geriatric Assessment (CGA) to identify frail older adults and individualize cancer care leads to lower toxicity and improved quality of life outcomes. However, the adoption of a full CGA prior to chemotherapy initiation in older cancer patients remains low. Recently, new frailty screening tools have emerged, including assessments designed to specifically predict chemotherapy-related adverse events. Additionally, frailty biomarkers have been developed, such as blood tests like IL-6 and performance tracking through physical activity monitors. The relevance of nutrition and muscle mass is discussed. Highlights from recent trials suggest the feasibility of successfully identifying patients most at risk of serious adverse events. There have been promising developments in identifying novel frailty markers and methods to screen for frailty in the older adult population. Further prospective trials that focus on and address the needs of the geriatric population for early identification of frailty in cancer care, facilitating a more tailored treatment approach. Practicing oncologists should select a frailty assessment to implement into their routine practice and adjust treatment accordingly.

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The corneal epithelium is composed of stratified squamous epithelial cells on the outer surface of the eye, which acts as a protective barrier and is critical for clear and stable vision. Its continuous renewal or wound healing depends on the proliferation and differentiation of limbal stem cells (LSCs), a cell population that resides at the limbus in a highly regulated niche. Dysfunction of LSCs or their niche can cause limbal stem cell deficiency, a disease that is manifested by failed epithelial wound healing or even blindness. Nevertheless, compared to stem cells in other tissues, little is known about the LSCs and their niche. With the advent of single-cell RNA sequencing, our understanding of LSC characteristics and their microenvironment has grown considerably. In this review, we summarized the current findings from single-cell studies in the field of cornea research and focused on important advancements driven by this technology, including the heterogeneity of the LSC population, novel LSC markers and regulation of the LSC niche, which will provide a reference for clinical issues such as corneal epithelial wound healing, ocular surface reconstruction and interventions for related diseases.

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Oxidative stress (OS) is mediated by reactive oxygen species (ROS), which in cardiovascular and other disease states, damage DNA, lipids, proteins, other cellular and extra-cellular components. OS is both initiated by, and triggers inflammation, cardiomyocyte apoptosis, matrix remodeling, myocardial fibrosis, and neurohumoral activation. These have been linked to the development of heart failure (HF). Circulating biomarkers generated by OS offer potential utility in patient management and therapeutic targeting. Novel OS-related biomarkers such as NADPH oxidases (sNox2-dp, Nrf2), advanced glycation end-products (AGE), and myeloperoxidase (MPO), are signaling molecules reflecting pathobiological changes in HF. This review aims to evaluate current OS-related biomarkers and their associations with clinical outcomes and to highlight those with greatest promise in diagnosis, risk stratification and therapeutic targeting in HF.

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Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer death worldwide. The most common lung cancer is non-small cell lung cancer (NSCLC), with an overall 5-year survival rate of around 20% because NSCLC is a metastatic disease. A better understanding of the mechanism underlying lung cancer metastasis is therefore urgently needed. The tumor microenvironment involves different types of stromal cells and functions as key components in the progression of NSCLC. Through epithelial-mesenchymal transition (EMT), in which epithelial cells lose their polarity and acquire mesenchymal potential, cancer cells acquire metastatic abilities, as well as cancer stem-cell-like potential. We previously reported that cancer-associated fibroblasts (CAFs) interact with lung cancer cells to allow for the acquisition of malignancy and treatment resistance by paracrine loops via EMT signals in the tumor microenvironment. Furthermore, CAFs regulate the cytotoxic activity of immune cells via various cytokines and chemokines, creating a microenvironment of immune tolerance. Regulation of CAFs can therefore affect immune responses. Recent research has shown several roles of CAFs in NSCLC tumorigenesis, owing to their heterogeneity, so molecular markers of CAFs should be elucidated to better classify tumor-promoting subtypes and facilitate the establishment of CAF-specific targeted therapies. CAF-targeted cancer treatments may suppress EMT and regulate the niche of cancer stem cells and the immunosuppressive network and thus may prove useful for NSCLC treatment through multiple mechanisms.

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Breast pathology is an ever-expanding database of information which includes markers, or biomarkers, that detect or help treat the disease as prognostic or predictive information. This review focuses on these aspects of biomarkers which are grounded in immunohistochemistry, liquid biopsies and next-generation sequencing.

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Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12­lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.

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Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease. Along with the increasing prevalence of diabetes, DKD is expected to affect a higher number of patients. Despite the major progress in the therapy of DKD and diabetes mellitus (DM), the classic clinical diagnostic tools in DKD remain insufficient, delaying proper diagnosis and therapeutic interventions. We put forward a thesis that there is a need for novel markers that will be early, specific, and non-invasively obtained. The ongoing investigations uncover new molecules that may potentially become new markers of DKD-among those are: soluble α-Klotho and proteases (ADAM10, ADAM17, cathepsin, dipeptidyl peptidase 4, caspase, thrombin, and circulating microRNAs). This review summarizes the current clinical state-of-the-art in the diagnosis of DKD and a selection of potential novel markers, based on up-to-date literature.

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BACKGROUND: An effective diagnostic and prognostic marker based on the gene expression profile of classic Hodgkin lymphoma (cHL) has not yet been developed. The aim of the present study was to investigate potential markers for the diagnosis and prediction of cHL prognosis. METHODS: The gene expression profiles with all available clinical features were downloaded from the Gene Expression Omnibus (GEO) database. Then, multiple machine learning algorithms were applied to develop and validate a diagnostic signature by comparing cHL with normal control. In addition, we identified prognostic genes and built a prognostic model with them to predict the prognosis for 130 patients with cHL which were treated with first-line treatment (ABVD chemotherapy or an ABVD-like regimen). RESULTS: A diagnostic prediction signature was constructed and showed high specificity and sensitivity (training cohort: AUC=0.981,95% CI 0.933-0.998, P<0.001, validation cohort: AUC=0.955,95% CI 0.895-0.986, P<0.001). Additionally, nine prognostic genes (LAMP1, STAT1, MMP9, C1QB, ICAM1, CD274, CCL19, HCK and LILRB2) were screened and a prognostic prediction model was constructed with them, which had been confirmed effectively predicting prognosis (P<0.001). Furthermore, the results of the immune infiltration assessment indicated that the high scale of the fraction of CD8 + T cells, M1 macrophages, resting mast cells associated with an adverse outcome in cHL, and naive B cells related to prolonged survival. In addition, a nomogram that combined the prognostic prediction model and clinical characteristics is also suggested to have a good predictive value for the prognosis of patients. CONCLUSION: The new markers found in this study may be helpful for the diagnosis and prediction of the prognosis of cHL.

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Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

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Acute appendicitis is the most common condition that presents with an acute abdomen needing emergency surgery. Despite this common presentation, correctly diagnosing appendicitis remains a challenge as clinical signs or positive blood results can be absent in 55% of the patients. The reported proportion of missed diagnoses of appendicitis ranges between 20% and 40%. A delay or mis-diagnosis of appendicitis can result in severe complications such as perforation, abscess formation, sepsis, and intra-abdominal adhesions. Literature has shown that patients who had a negative appendectomy suffer post-op complications and infections secondary to hospital stays; there have even been reported cases of fatality. It is therefore crucial that timely and accurate diagnosis of appendicitis is achieved to avoid complications of both non-operating as well as unnecessary surgical intervention. The aim of this review is to systematically report and analyse the latest evidence on the different approaches used in diagnosing appendicitis. We include discussions of clinical scoring systems, laboratory tests, latest innovative bio-markers and radiological imaging.

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INTRODUCTION: Making the diagnosis of secondary CNS involvement in lymphoma can be difficult due to unspecific signs and symptoms, limited accessibility of brain/myelon parenchyma and low sensitivity and/or specifity of imaging and cerebrospinal fluid (CSF) examination currently available. Areas covered: MRI of the total neuroaxis followed by CSF cytomorphology and flow cytometry are methods of choice when CNS lymphoma (CNSL) is suspected. To reduce the numerous pitfalls of these examinations several aspects should be considered. New CSF biomarkers might be of potential diagnostic value. Attempts to standardize response criteria are presented. Expert commentary: Diagnosing CNSL remains challenging. Until diagnostic methods combining high sensitivity with high specifity are routinely introduced, high level of awareness and optimal utilization of examinations currently available are needed to early diagnose this potentially devastating disease.

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