Biomarker discovery study design consistent with the receiver-operator characteristic.

Monoclonal antibodies are produced by the cell fusion method, whereby immune murine spleen cells are fused with a murine myeloma to produce large quantities of specific homogeneous antibodies. These have obvious advantages over conventional antisera particularly for in vitro diagnostic procedures. The use of monoclonal antibodies to human T cells, leukaemias and solid tumours are reviewed. In these areas, monoclonal antibodies are now being used for the diagnosis of disease activity, for the monitoring of graft rejection, and for the in vivo treatment of leukaemia and graft rejection in man. Murine monoclonal antibodies--examples of genetic engineering--represent a new and early phase of the use of molecular biological techniques for the production of reagents of use in clinical medicine.

Parkinson’s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of Parkinson’s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing—a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms—extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)—were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, F1 score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying Parkinson’s disease using RNA-seq data.

Prostate-specific Antigen: Its Usefulness in Clinical Medicine

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

Identification of a miRNA based-signature associated with acute coronary syndrome: Evidence from the FLORINF study

Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. In this study we compare feature selection methods on public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Surprisingly, complex wrapper and embedded methods generally do not outperform simple univariate feature selection methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results.

BackgroundIn recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.ResultsThree experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.ConclusionsWe developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common and serious complication after coronary artery bypass grafting (CABG), leading to increased morbidity and healthcare utilization. Although systemic inflammation is a well-established driver of POAF pathogenesis, no composite preoperative inflammatory biomarker has been validated for risk stratification in this population. This study aimed to evaluate the novel Inflammatory Burden Index (IBI)-the first composite biomarker combining acute-phase (C-reactive protein, CRP) and chronic cellular (neutrophil-to-lymphocyte ratio, NLR) inflammation-as a preoperative predictor of POAF after CABG. Methods: In this large retrospective cohort study, we included 3481 consecutive patients who underwent isolated CABG at a high-volume cardiac center between 2019 and 2024. Preoperative IBI was calculated as CRP (mg/dL) × NLR. The primary outcome was new-onset POAF within the first 7 postoperative days, confirmed by continuous telemetry on 12-lead ECG. Predictive performance was assessed using multivariable logistic regression, receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and internal validation via bootstrapping (1000 resamples). Results: POAF developed in 866 patients (24.9%). Patients with POAF exhibited significantly higher preoperative IBI levels (39.4 ± 18.6 vs. 26.3 ± 16.7, p < 0.01). In multivariable analysis adjusted for age, hypertension, left atrial diameter, and other clinical covariates, IBI emerged as a strong independent predictor of POAF (adjusted OR 1.041, 95% CI 1.036-1.046, p < 0.01). The IBI alone demonstrated moderate-to-good discriminative performance (AUC 0.72, 95% CI 0.70-0.74), significantly outperforming the Systemic Immune/Inflammation Index (SII; AUC 0.61, DeLong test p < 0.001) and providing superior reclassification (NRI 0.150, IDI 0.032) and model fit (lower AIC). Combining IBI with established clinical risk factors further improved predictive accuracy (combined AUC 0.74, specificity 72.4%). Tertile-based stratification revealed a clear graded relationship with POAF incidence (low IBI: 16.6%, medium: 21.3%, high: 35.1%; p = 0.02). Notably, the medium IBI stratum (11.18-25.44) displayed the highest discriminative power (AUC 0.87, 95% CI 0.85-0.88), with bootstrap validation confirming model stability (minimal bias, robust 95% CI). Conclusions: This study establishes the preoperative Inflammatory Burden Index (IBI) as the first validated composite inflammatory biomarker independently associated with POAF following CABG. Its superior performance over existing indices (SII), graded risk stratification, and peak accuracy in the moderate inflammation window highlight its potential for personalized preoperative risk assessment and targeted perioperative intervention strategies.

To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer. A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models. The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857). The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status. This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures. • Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer. • Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.

Angiotensin-converting enzyme inhibitors: Are there credible mechanisms for beneficial effects in diabetic neuropathy?

Cannabis sativa finds application in the medical field as an opioid (medical marijuana) and as a recreational drug despite the fact that it is a prohibited drug substance in many countries. Currently, there are reports on the increasing trends in the number of deaths associated with the use of marijuana. Regardless of these dangers, some countries across the world have legalized the sale and use of marijuana for recreational and medicinal purposes. For these reasons, this review seeks to explore the emerging trends through which marijuana is consumed and the chemicals produced in the course of its use. Furthermore, the use of Cannabis sativa as medicine especially in the management of emerging diseases such a Corona virus disease-2019 (COVID-19) and the perceived harms have been reviewed. Relevant literature was identified from database search published between the period 2012 and 2020 in PubMed, Crossref, Google scholar, Cochrane and Web of Science. The articles were considered relevant if they addressed marijuana (Cannabis sativa) use, its impacts on users and non-users, carcinogenicity, medicinal value and COVID-19 management. A number of methods by which marijuana is used have been identified with each method producing different results among users. The chemicals released during smoking of marijuana and their carcinogenic potential and health effects are reported in this work. Further, Cannabis sativa has found significant use in the management of cancer and human immunodeficiency virus (HIV/ AIDS) to alleviate pain and improve appetite, respectively. Although marijuana is regarded as a prohibited and toxic drug substance, there is limited documentation in literature that extensively reports on its toxicological mechanisms especially its role as a cancer causing agent. On the contrary, most scientific studies have reported its use for medical reasons. Accordingly, there is need for further research on cannabis application as a medicinal drug, investigation on its possibility in the managing of the severe acute respiratory syndrome corona virus-2 (SARS-Cov-2) and its epidemiological concerns. Campaigns for the legalization of cannabis for use in clinical medicine are fundamentally recommended but must take into account possible toxicological concerns, health ethics and psychotic problems.

Ultrasound elastography is a method of instrumental diagnosing which allows rapidly and in a non-invasive way estimating the elasticity and histological properties of the tissue. The article presents a description of this method: the physical basis of the method functioning, the methodology of examination, the clinical significance in diagnosing fibrosis in patients with chronic liver diseases from the standpoint of evidence-based medicine, the advantages and disadvantages of the method, the factors which may distort the result, and the prospects of its use in clinical medicine. The possibility to use elastometry as a screening method and its economic significance are considered. In particular, the results of the studies conducted at Republic Clinical Hospital of the Republic of Tatarstan are presented. The average values of liver stiffness were calculated in patients with hepatitis of various etiologies and liver cirrhosis. The correlation was evaluated between the average liver stiffness and the Child-Pugh and FIB 4 indices, laboratory parameters (hemoglobin, total bilirubin, total protein, percentage of Quick’s prothrombin) and the portal vein diameter.

Methylated-DNA sequencing technologies are producing vast amounts of methylome data from cancer samples, from which cancer-associated differentially methylated CpG sites (cDMCs) are continuously identified and filed. The inclusion of as many cDMCs as possible helps improve the accuracy of cancer diagnosis and sometimes identify cancer subtypes. However, the lack of an established method for the analysis of 100s of cDMCs practically impedes their robust use in clinical medicine. Here, we tested the availability of targeted bisulfite-PCR-sequencing (TBPseq) technology for the assessment of methylation levels of a myriad of CpGs scattered over the genome. In randomly selected 46 cancer cell lines, multiplexed PCR yielded a variety of amplicons harboring 246 CpGs residing at promoters of 97 cancer-associated genes, all of which were sequenced in the same flow cell. Clustering analysis of the TBPseq-assessed methylation levels of target CpGs showed that the lung and liver cancer cell lines correlated relatively strongly with each other while they weakly correlated with colon cancer cells. CpGs at the LIFR gene promoter, which are known to be hypermethylated in colon cancers, indeed were heavily methylated in the tested colon cancer cells. Moreover, the LIFR promoter hypermethylation was found in colon cancer cells only, but not in biliary tract, liver, lung, and stomach cancers cell lines. A meta-analysis with public cancer methylome data verified the colon cancer specificity of LIFR promoter methylation. These results demonstrate that our TBPseq-based methylation assessment could be considered an effective, accurate, and competitive method to simultaneously examine a large number of target cDMCs and patient samples.

A considerable number of physiological functions are known to show a gradual decline with increasing age. However, the effects of ageing differ widely between organ systems. It is believed that basal metabolic rate (BMR) falls dramatically with age. These observations, largely based on cross-sectional surveys, are discussed in light of our present understanding of the biology of ageing. This paper reviews both the longitudinal and cross-sectional studies of BMR and presents evidence that the fall in BMR with ageing may be less dramatic than previously perceived. Indeed, some subjects may show an increase in BMR with ageing. The mechanism of changes in BMR during ageing will be discussed. Organ weight changes appear to have a profound impact on BMR. The use of BMR to predict total energy expenditure in the 'old elderly' (>75 y) is unlikely to be of any practical use due to wide intra- and inter-individual variation in BMR. This wide intra- and inter-individual variation in BMR is due to illness, disease and other metabolic disorders seen in the elderly. Finally, the importance of measuring BMR in elderly populations for its use in clinical medicine will be discussed.

“Seeing” with sound instead of light seemed far-fetched a generation ago. However, after more than twenty years of successful ultrasound use in clinical medicine (1–10), many persons have seen acoustically-produced images of the organs in their bodies and are accustomed to the concept of acoustical imaging. An example of such imaging is shown in Fig. 1. The ultrasonic instruments in our hospitals are the most generally known of all acoustical imaging systems, but, in addition to medical diagnosis, there are many other applications for sonic images. For example, acoustical imaging is being employed for oceanic search, seismic exploration, non-destructive evaluation, and microscopy (11). The systems involved use mechanical and electronic scanning and pulse-echo ranging, as well as focusing with acoustic lenses and digital processing (12). The more detailed descriptions in this chapter will be restricted to systems employed for microscopy, diagnostic medicine and non-destructive evaluation.KeywordsAcoustical ImagingSeismic ExplorationAcoustic MicroscopeAcoustic IntensityScanning Acoustic MicroscopeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.