Optical biosensing of markers of mucosal inflammation

Iron oxide nanoparticles for treatment and diagnosis of chronic inflammatory diseases: A systematic review

Chronic inflammatory conditions are among the most prevalent diseases worldwide. Several debilitating diseases such as atherosclerosis, inflammatory bowel disease, rheumatoid arthritis, and Alzheimer's are linked to chronic inflammation. These conditions often develop into complex and fatal conditions, making early detection and treatment of chronic inflammation crucial. Current diagnostic methods show high variability and do not account for disease heterogeneity and disease-specific proinflammatory markers, often delaying the disease detection until later stages. Furthermore, existing treatment strategies, including high-dose anti-inflammatory and immunosuppressive drugs, have significant side effects and an increased risk of infections. In recent years, superparamagnetic iron oxide nanoparticles (SPIONs) have shown tremendous biomedical potential. SPIONs can function as imaging modalities for magnetic resonance imaging, and as therapeutic agents due to their magnetic hyperthermia capability. Furthermore, the surface functionalization of SPIONs allows the detection of specific disease biomarkers and targeted drug delivery. This systematic review explores the utility of SPIONs against chronic inflammatory disorders, focusing on their dual role as diagnostic and therapeutic agents. We extracted studies indexed in the Web of Science database from the last 10 years (2013-2023), and applied systematic inclusion criteria. This resulted in a final selection of 38 articles, which were analyzed for nanoparticle characteristics, targeted diseases, in vivo and in vitro models used, and the efficacy of the therapeutic or diagnostic modalities. The results revealed that ultrasmall SPIONs are excellent for imaging arterial and neuronal inflammation. Furthermore, novel therapies using SPIONs loaded with chemotherapeutic drugs show promise in the treatment of inflammatory diseases. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Unrevealing the connection between real sample analysis and analytical method. The case of cytokines

Cytokines are compounds that belong to a special class of signaling biomolecules that are responsible for several functions in the human body, being involved in cell growth, inflammatory, and neoplastic processes. Thus, they represent valuable biomarkers for diagnosing and drug therapy monitoring certain medical conditions. Because cytokines are secreted in the human body, they can be detected in both conventional samples, such as blood or urine, but also in samples less used in medical practice such as sweat or saliva. As the importance of cytokines was identified, various analytical methods for their determination in biological fluids were reported. The gold standard in cytokine detection is considered the enzyme-linked immunosorbent assay method and the most recent ones have been considered and compared in this study. It is known that the conventional methods are accompanied by a few disadvantages that new methods of analysis, especially electrochemical sensors, are trying to overcome. Electrochemical sensors proved to be suited for the elaboration of integrated, portable, and wearable sensing devices, which could also facilitate cytokines determination in medical practice.

Adenoviral-vectored universal influenza vaccines administered intranasally reduce lung inflammatory responses upon viral challenge 15 months post-vaccination

IMPORTANCE

Vaccines targeting highly conserved proteins can protect broadly against diverse viral strains. When a vaccine is administered to the respiratory tract, protection against disease is especially powerful. However, it is important to establish that this approach is safe. When vaccinated animals later encounter viruses, does reactivation of powerful local immunity, including T cell responses, damage the lungs? This study investigates the safety of mucosal vaccination of the respiratory tract. Non-replicating adenoviral vaccine vectors expressing conserved influenza virus proteins were given intranasally. This vaccine-induced protection persists for at least 15 months. Vaccination did not exacerbate inflammatory responses or tissue damage upon influenza virus infection. Instead, vaccination with nucleoprotein reduced cytokine responses and histopathology, while neutrophil and T cell responses resolved earlier. The results are promising for safe vaccination at the site of infection and thus have implications for the control of influenza and other respiratory viruses.

Incubating Green Synthesized Iron Oxide Nanorods for Proteomics-Derived Motif Exploration: A Fusion to Deep Learning Oncogenesis

The nanotechnological arena has revolutionized the diagnostic efficacies by investigating the protein corona. This displays provoking proficiencies in determining biomarkers and diagnostic fingerprints for early detection and advanced therapeutics. The green synthesized iron oxide nanoparticles were prepared via Withania coagulans and were well characterized using UV-visible spectroscopy, X-ray diffraction analysis, Fourier transform infrared spectroscopy, and nano-LC mass spectrophotometry. Iron oxides were rod-shaped with an average size of 17.32 nm and have crystalline properties. The as-synthesized nanotool mediated firm nano biointeraction with the proteins in treatment with nine different cancers. The resultant of the proteome series was filtered oddly that highlighted the variant proteins within the differentially expressed proteins on behalf of nano-bioinformatics. Further magnification focused on S13_N, RS15, RAB, and 14_3_3 domains and few abundant motifs that aid scanning biomarkers. The entire set of variant proteins contracting to common proteins elucidates the underlining mechanical proteins that are marginally assessed using the robotic nanotechnology. Additionally, the iron rods indirectly possess a prognostic effect in manipulating expression of proteins through a smarter route. Thereby, such biologically designed nanotools provide a dual approach for medical studies.

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

OBJECTIVE

Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible.

METHODS AND PROCEDURES

In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses.

RESULTS

The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection.

CONCLUSION

Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure.

CLINICAL IMPACT

With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.