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We aimed to conduct a state-of-the-art review of the current literature and offer further insights into the methodological aspects concerning induced sputum. The increasing popularity of sputum induction as a non-invasive and cost-effective method for obtaining lower airway secretions from patients who cannot produce sputum naturally has led to extensive research and applications in respiratory conditions like asthma and COPD. This technique allows for analysis of the cellular and biochemical components of the sputum to take place, providing insights into airway inflammation, immune cells, and help in predicting treatment response. Furthermore, induced sputum enables various analyses, including microRNA and gene expression studies and immunophenotyping. The procedure is generally safe and well tolerated, even in patients with airflow limitations; however, monitoring lung function is essential, especially in those with airway hyperresponsiveness. Optimal saline solution concentration and inhalation duration have been investigated, recommending a 15–20 min induction with hypertonic saline. Expectoration involves coughing at the end of each inhalation time. Careful handling during sputum processing is necessary for obtaining accurate results in cell cytology, immunocytochemistry, and in situ hybridization. Overall, induced sputum offers significant advantages as a preferred alternative for large-scale and repeated airway sampling, despite some technical demands and limitations.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by periods of exacerbation and remission, making disease monitoring and management challenging. Endoscopy, the gold standard for assessing disease activity and severity, involves invasive procedures and is associated with patient discomfort and risks. Using machine learning (ML) to combine fecal calprotectin with other clinical or biological tests can significantly enhance the non-invasive prediction of endoscopic disease activity (EDA) in UC. Aim: To prove that by fusing fecal calprotectin with other clinical data into an ML model, the performance of the non-invasive prediction of EDA can be significantly improved. Methods: We conducted a prospective, observational, single-center study encompassing 103 patients diagnosed with UC. We employed multilayer perceptron models as the core ML algorithm for predicting EDA. For the constructed models, we utilized the varImp function from the caret library in R to assess the significance of each variable in predicting the outcome. Results: Calprotectin as a sole predictor obtained an accuracy of 70% and an area under the curve (AUC) of 0.68. Combining calprotectin with the list of selected predictors that were fed to the MLP models improved accuracy and the AUC. The accuracy of the algorithm on the test set was 85%. Similarly, the AUC increased to 0.93. This is the first study to propose the use of calprotectin as a predictor in an ML model to estimate UC endoscopic disease activity. Conclusion: The deployment of this ML model can furnish doctors and patients with valuable evaluation of endoscopic disease activity which can be highly beneficial for individuals with UC who need long-term treatment.

The study of intestinal permeability is gaining growing interest due to its relevance in the onset and progression of several gastrointestinal and non-gastrointestinal diseases. Though the involvement of impaired intestinal permeability in the pathophysiology of such diseases is recognized, there is currently a need to identify non-invasive biomarkers or tools that are able to accurately detect alterations in intestinal barrier integrity. On the one hand, promising results have been reported for novel in vivo methods based on paracellular probes, i.e., methods that can directly assess paracellular permeability and, on the other hand, on fecal and circulating biomarkers able to indirectly assess epithelial barrier integrity and functionality. In this review, we aimed to summarize the current knowledge on the intestinal barrier and epithelial transport pathways and to provide an overview of the methods already available or currently under investigation for the measurement of intestinal permeability.

Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Alcohol-associated hepatitis (AH) is the most severe form of alcohol-related liver disease. The natural course of alcohol-related liver disease is influenced by heavy alcohol consumption and abstinence periods. Differentiating between AH and decompensated cirrhosis (DC) could be extremely challenging in clinical practice due to clinical and bioclinical similarities. The severity of AH is made on bioclinical grounds, the severe form necessitating corticotherapy treatment. Liver biopsy is still the standard of care for establishing the diagnosis in atypical presentations. The pathogenesis of AH is an interplay between gene expression, cytokine dysregulation, the immune system and the gut microbiota. Non-invasive tests are increasingly and widely used for the purpose of early diagnosis and reliable prognostication. The non-invasive tests are emerging in concordance with disease pathogenesis. In this review, we describe the non-invasive tools that can distinguish AH from DC. We outline the available cut-offs and their performance in diagnosis and prognosis, as well as in assessing the treatment response to corticotherapy. Promising circulating biomarkers like keratin 18, microRNAs and sphingolipids will be in the review.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by periods of exacerbation and remission, making disease monitoring and management challenging. Endoscopy, the gold standard for assessing disease activity and severity, involves invasive procedures and is associated with patient discomfort and risks. Using machine learning (ML) to combine fecal calprotectin with other clinical or biological tests can significantly enhance the non-invasive prediction of endoscopic disease activity (EDA) in UC. Aim: To prove that by fusing fecal calprotectin with other clinical data into an ML model, the performance of the non-invasive prediction of EDA can be significantly improved. Methods: We conducted a prospective, observational, single-center study encompassing 103 patients diagnosed with UC. We employed multilayer perceptron models as the core ML algorithm for predicting EDA. For the constructed models, we utilized the varImp function from the caret library in R to assess the significance of each variable in predicting the outcome. Results: Calprotectin as a sole predictor obtained an accuracy of 70% and an area under the curve (AUC) of 0.68. Combining calprotectin with the list of selected predictors that were fed to the MLP models improved accuracy and the AUC. The accuracy of the algorithm on the test set was 85%. Similarly, the AUC increased to 0.93. This is the first study to propose the use of calprotectin as a predictor in an ML model to estimate UC endoscopic disease activity. Conclusion : The deployment of this ML model can furnish doctors and patients with valuable evaluation of endoscopic disease activity which can be highly beneficial for individuals with UC who need long-term treatment., Competing Interests: The authors declare that they have no conflicts of interest.

The identification of circulating microRNAs (miRNAs) in peripheral blood and other body fluids has led to considerable research interest in investigating their potential clinical application as non-invasive biomarkers of cancer, including lung cancer, the deadliest malignancy worldwide. Several studies have found that alterations in the levels of miRNAs in circulation are able to discriminate lung cancer patients from healthy individuals (diagnosis) and are associated with patient outcome (prognosis) and treatment response (prediction). Increasing evidence indicates that circulating miRNAs may function as mediators of cell-to-cell communication, affecting biological processes associated with tumor initiation and progression. This review is focused on the most recent studies that provide evidence of the potential value of circulating miRNAs in blood and other body fluids as non-invasive biomarkers of lung cancer in terms of diagnosis, prognosis, and response to treatment. The status of their potential clinical application in lung cancer is also discussed, and relevant clinical trials were sought and are described. Because of the relevance of their biological characteristics and potential value as biomarkers, this review provides an overview of the canonical biogenesis, release mechanisms, and biological role of miRNAs in lung cancer.

Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.

The study of intestinal permeability is gaining growing interest due to its relevance in the onset and progression of several gastrointestinal and non-gastrointestinal diseases. Though the involvement of impaired intestinal permeability in the pathophysiology of such diseases is recognized, there is currently a need to identify non-invasive biomarkers or tools that are able to accurately detect alterations in intestinal barrier integrity. On the one hand, promising results have been reported for novel in vivo methods based on paracellular probes, i.e., methods that can directly assess paracellular permeability and, on the other hand, on fecal and circulating biomarkers able to indirectly assess epithelial barrier integrity and functionality. In this review, we aimed to summarize the current knowledge on the intestinal barrier and epithelial transport pathways and to provide an overview of the methods already available or currently under investigation for the measurement of intestinal permeability.

A suitable, non-invasive biomarker for assessing endoscopic disease activity (EDA) in ulcerative colitis (UC) has yet to be identified. Our study aimed to develop a cost-effective and non-invasive machine learning (ML) method that utilizes the cost-free Inflammatory Bowel Disease Questionnaire (IBDQ) score and low-cost biological predictors to estimate EDA. Four random forest (RF) and four multilayer perceptron (MLP) classifiers were proposed. The results show that the inclusion of IBDQ in the list of predictors that were fed to the models improved accuracy and the AUC for both the RF and the MLP algorithms. Moreover, the RF technique performed noticeably better than the MLP method on unseen data (the independent patient cohort). This is the first study to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA. The deployment of this ML model can furnish doctors and patients with valuable insights into EDA, a highly beneficial resource for individuals with UC who need long-term treatment.

Background: Insulin resistance plays a relevant role in the onset of non-alcoholic fatty liver disease (NAFLD) and its progression to non-alcoholic steatohepatitis (NASH) and fibrosis. Irisin is an exercise-induced myokine involved in the regulation of energy homeostasis and glucose metabolism. Additionally, pre-clinical models have shown a potential role of irisin in the pathogenesis of NAFLD. The aim of this study is to explore the association between irisin, histological features and biomarkers of liver fibrogenesis in non-diabetic, non-obese, biopsy-proven NAFLD individuals., Methods: Forty-one patients with histological evidence of NAFLD were included. Circulating irisin and direct markers of fibrogenesis N-terminal type III collagen propeptide (PRO-C3) and type VI collagen cleavage product (PRO-C6) were measured by ELISA., Results: Median age of the cohort was 45 years (41-51) and 80.4% were male. Significant fibrosis (stage ≥ 2) was present in 36.6% of cases. Circulating irisin, PRO-C3 and PRO-C6 levels were significantly higher in subjects with fibrosis stage ≥ 2 when compared to those with fibrosis stage < 2 (5.96 ng/mL (95% CI = 4.42-9.19) vs. 2.42 ng/mL (95% CI = 1.73-5.95), p = 0.033; 9.5 ng/mL (95% CI = 7.7-13.6) vs. 6.2 ng/mL (95% CI = 4.9-8.9), p = 0.016; 6.6 ng/mL (95% CI = 5.6-7.9) vs. 5.1 ng/mL (95% CI = 4.2-5.4), p = 0.013, respectively). Irisin levels were similarly distributed between the features of NASH. Circulating irisin positively correlated with both PRO-C3 and PRO-C6 levels (r = 0.47, p = 0.008 and r = 0.46, p = 0.002)., Conclusions: Increased circulating irisin levels may identify a more aggressive phenotype of liver disease with increased fibrogenesis and more severe liver damage.

This study aimed to investigate the role of a panel of salivary cytokines as biomarkers for early detection oral squamous cell carcinoma (OSCC), comparing their levels among healthy individuals, patients with oral leukoplakia (OL), and malignant lesions. Cytokine profiling analysis performed in a minimally invasive sample was correlated with clinicopathological variables in our patient cohorts. Unstimulated saliva was obtained from subjects with OSCC at early ( n = 33) and advanced ( n = 33) disease, OL with homogeneous ( n = 33) and proliferative verrucous ( n = 33) clinical presentations, and healthy controls ( n = 25). Salivary IL-1α, IL-6, IL-8, IP-10, MCP-1, TNF-α, HCC-1, and PF-4 levels were analyzed by a sensitive bead-based multiplex immunoassay. Mean levels of IL-6, IL-8, TNF-α, HCC-1, MCP-1, and PF-4 differed significantly between OSCC, OL, and control saliva ( p < 0.05). We found notably higher IL-6 and TNF-α in advanced compared to early OSCC stages. The area under the curve (AUC) for OSCC vs. control was greater than 0.8 for IL-6, IL-8, TNF-α, and HCC-1, and greater than 0.7 for PF-4. The presence of neck metastases (NM) was associated with increased IL-6 and TNF-α levels. Our findings suggest that salivary IL-6, IL-8, TNF-α, HCC-1, and PF-4 may discriminate between OSCC, OL, and healthy controls. IL-6 and TNF-α may indicate OSCC progression, being distinctive in the presence of NM.

Bariatric surgery has emerged as an effective treatment option in morbidly obese patients with non-alcoholic fatty liver disease (NAFLD). However, worsening or new onset of non-alcoholic steatohepatitis (NASH) and fibrosis have been observed. Caspase-cleaved keratin 18 (ccK18) has been established as a marker of hepatocyte apoptosis, a key event in NASH development. Thus, ccK18 measurements might be feasible to monitor bariatric surgery patients. Clinical data and laboratory parameters were collected from 39 patients undergoing laparoscopic Roux-en-Y gastric bypass at six timepoints, prior to surgery until one year after the procedure. ccK18 levels were measured and a high-throughput analysis of serum adipokines and cytokines was carried out. Half of the cohort's patients (20/39) presented with ccK18 levels indicative of progressed liver disease. 21% had a NAFLD-fibrosis score greater than 0.676, suggesting significant fibrosis. One year after surgery, a mean weight loss of 36.87% was achieved. Six and twelve months after surgery, ccK18 fragments were significantly reduced compared to preoperative levels ( p < 0.001). Yet nine patients did not show a decline in ccK18 levels ≥ 10% within one year postoperatively, which was considered a response to treatment. While no significant differences in laboratory parameters or ccK18 could be observed, they presented with a greater expression of leptin and fibrinogen before surgery. Consecutive ccK18 measurements monitored the resolution of NAFLD and identified non-responders to bariatric surgery with ongoing liver injury. Further studies are needed to elicit the pathological mechanisms in non-responders and study the potential of adipokines as prognostic markers.

The identification of circulating microRNAs (miRNAs) in peripheral blood and other body fluids has led to considerable research interest in investigating their potential clinical application as non-invasive biomarkers of cancer, including lung cancer, the deadliest malignancy worldwide. Several studies have found that alterations in the levels of miRNAs in circulation are able to discriminate lung cancer patients from healthy individuals (diagnosis) and are associated with patient outcome (prognosis) and treatment response (prediction). Increasing evidence indicates that circulating miRNAs may function as mediators of cell-to-cell communication, affecting biological processes associated with tumor initiation and progression. This review is focused on the most recent studies that provide evidence of the potential value of circulating miRNAs in blood and other body fluids as non-invasive biomarkers of lung cancer in terms of diagnosis, prognosis, and response to treatment. The status of their potential clinical application in lung cancer is also discussed, and relevant clinical trials were sought and are described. Because of the relevance of their biological characteristics and potential value as biomarkers, this review provides an overview of the canonical biogenesis, release mechanisms, and biological role of miRNAs in lung cancer.