Chronic inflammatory diseases, such as cancer, diabetes mellitus, stroke, ischemic heart diseases, neurodegenerative conditions, and COVID-19 have had a high number of deaths worldwide in recent years. The accurate detection of the biomarkers for chronic inflammatory diseases can significantly improve diagnosis, as well as therapy and clinical care in patients. Graphene derivative materials (GDMs), such as pristine graphene (G), graphene oxide (GO), and reduced graphene oxide (rGO), have shown tremendous benefits for biosensing and in the development of novel biosensor devices. GDMs exhibit excellent chemical, electrical and mechanical properties, good biocompatibility, and the facility of surface modification for biomolecular recognition, opening new opportunities for simple, accurate, and sensitive detection of biomarkers. This review shows the recent advances, properties, and potentialities of GDMs for developing robust biosensors. We show the main electrochemical and optical-sensing methods based on GDMs, as well as their design and manufacture in order to integrate them into robust, wearable, remote, and smart biosensors devices. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers with improved sensitivity, reaching limits of detection from the nano to atto range concentration.

The morphological analysis of patterns in dried droplets has allowed the generation of efficient techniques for the detection of molecules of medical interest. However, the effectiveness of this method to reveal the coexistence of macromolecules of the same species, but different conformational states, is still unknown. To address this problem, we present an experimental study on pattern formation in dried droplets of bovine serum albumin (BSA), in folded and unfolded conformational states, in saline solution (NaCl). Folded proteins produce a well-defined coffee ring and crystal patterns all over the dry droplet. Depending on the NaCl concentration, the crystals can be small, large, elongated, entangled, or dense. Optical microscopy reveals that the relative concentration of unfolded proteins determines the morphological characteristics of deposits. At a low relative concentration of unfolded proteins (above 2%), small amorphous aggregates emerge in the deposits, while at high concentrations (above 16%), the “eye-like pattern”, a large aggregate surrounded by a uniform coating, is produced. The radial intensity profile, the mean pixel intensity, and the entropy make it possible to characterize the patterns in dried droplets. We prove that it is possible to achieve 100% accuracy in identifying 4% of unfolded BSA contained in a protein solution.

The quality control of medicines guarantees the effectiveness of treatments for diseases. We explore the use of texture analysis of patterns in dried droplets as a tool to readily detect both impurities and changes in drug concentration. Four types of medicines associated with different routes of administration were analyzed: Methotrexate, Ciprofloxacin, Clonazepam, and Budesonide. We use NaCl and a hot substrate at 63 ${}^{\circ }$C to promote aggregate formation and to reduce droplet drying time. Depending on the medicine, optical microscopy reveals different complex aggregates such as circular to oval splatters, fern-like islands, crown shapes, crown needle-like and bump-like patterns as well as dendritic branched and star-like crystals. We use some physical features of the stains (as the stain diameter and superficial area) and gray level co-occurrence matrix (GLCM) to characterize patterns of dried droplets. Finally, we show that structural analysis of stains can achieve 95% accuracy in identifying medicines with 30% water dilution, while it achieves 99% accuracy in detecting drugs with 10% other substances.

Hyperlipidemia is the elevated concentration of lipids in the blood, and it increases the probability of arterial obstruction, infarctions, and other complications of the circulatory system. While there are indications that qualitative analysis of blood stains could potentially identify patients with this pathology, the efficacy of this method remains uncertain. In this paper, we report an experimental study that investigates the formation of patterns in dried blood droplets with varying concentrations of ultrapure water. Two blood samples, one healthy and one with moderate hyperlipidemia, were examined to determine the ideal water and blood mixtures for detecting high lipid concentrations. Numerous intricate patterns were observed throughout the central region and periphery of the dried droplet. These patterns encompass various forms, such as plaques, bump-like patterns, and a range of cracks including random, radial, and ortho-radial configurations. By calculating the entropy of the Gray Level Co-occurrence Matrix (GLCM) and analyzing ROC curves, we determined that solutions with 4% and 12% hematocrit (indicating a high percentage of ultrapure water) exhibit over 95% accuracy in differentiating high lipid concentrations. These findings provide a promising outlook for the development of diagnostic methods based on the study of diluted blood coatings.

Foliar fertilization, an effective agricultural practice, involves the application of nutrients directly through droplets on plant leaves. The mechanisms of mass transport and deposition that arise from the drying of a drop determine the distribution of mass on a surface. Understanding these processes is crucial for optimizing foliar fertilization, ensuring even nutrient distribution, and improving crop yields and quality. This study experimentally investigates deposit formation from the evaporation of fertilizer droplets in various configurations: sessile, vertical, and pendant. We explored the effects of initial droplet volume, vapor pressure, and sorbitol presence on the final deposit morphology. The results reveal distinctive morphological patterns. Sessile drops exhibit two types of deposits—central crystal accumulation with fibrous structures or entirely fibrous structures. In contrast, vertical drops display two zones—fibrous structures at the bottom and small aggregates at the top. On the other hand, pendant drops predominantly feature intertwined crystals with peripheral fibrous structures. We found that high vapor pressures (RH = 60%) inhibit deposit formation within 72 h. Furthermore, the study measures relative evaporation time, showing that sessile droplets exhibit the longest evaporation times, followed by vertical and pendant droplets. Texture analysis, based on GLCM entropy, reveals that deposits generated under low vapor pressure (RH = 20%) show no significant differences in their entropy values, regardless of the droplet configuration and its initial volume. However, at intermediate vapor pressure (RH = 40%), entropy values vary significantly with droplet volume and configuration, being higher in sessile drops and lower in vertical ones. Additionally, we investigated the impact of sorbitol on the coating of sessile fertilizer droplets. We find that configurational entropy decreases exponentially with sorbitol concentration, inducing a morphological transition from fibrous structures to dispersed small aggregates. These findings highlight the complexity of pattern formation in fertilizer deposits and their potential implications for optimizing surface coating processes.

This study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classification of milk types and detection of adulteration. Dried droplets of milk containing NaCl concentrations of $0\%$, $2\%$, and $4\%$ were analyzed, revealing distinct morphologies, including amorphous, cross-shaped, and dendritic crystals. These structures were quantitatively characterized using lacunarity to assess their discriminatory power. Two classification approaches were evaluated: one based on lacunarity analysis alone and another incorporating deep learning. Both methods yielded high classification accuracies, with lacunarity achieving $95.04\%\pm 6.66\%$, while deep learning reached $95.22\%\pm 4.47\%$. Notably, the highest performance was obtained with $2\%$ NaCl, where lacunarity reached $97.08\%\pm 2.27\%$ and deep learning $96.88\%\pm 2.8\%$, indicating improved precision and stability. While deep learning demonstrated more consistent performance across test cases, lacunarity alone captured highly discriminative structural features, making it a valuable complementary tool. The integration of NaCl and lacunarity analysis offers a robust and interpretable methodology for ensuring the quality and authenticity of dairy products, particularly in detecting adulteration, where morphological contrast is less evident.