Artificial intelligence performance in testing microfluidics for point-of-care.

Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

Targeting hexokinase 2 for oral cancer therapy: structure-based design and validation of lead compounds.

The pursuit of small molecule inhibitors targeting hexokinase 2 (HK2) has significantly captivated the field of cancer drug discovery. Nevertheless, the creation of selective inhibitors aimed at specific isoforms of hexokinase (HK) remains a formidable challenge. Here, we present a multiple-pharmacophore modeling approach for designing ligands against HK2 with a marked anti-proliferative effect on FaDu and Cal27 oral cancer cell lines. Molecular dynamics (MD) simulations showed that the prototype ligand exhibited a higher affinity towards HK2. Complementing this, we put forth a sustainable synthetic pathway: an environmentally conscious, single-step process facilitated through a direct amidation of the ester with an amine under transition-metal-free conditions with an excellent yield in ambient temperature, followed by a column chromatography avoided separation technique of the identified lead bioactive compound (H2) that exhibited cell cycle arrest and apoptosis. We observed that the inhibition of HK2 led to the loss of mitochondrial membrane potential and increased mitophagy as a potential mechanism of anticancer action. The lead H2 also reduced the growth of spheroids. Collectively, these results indicated the proof-of-concept for the prototypical lead towards HK2 inhibition with anti-cancer potential.

Polyethylene Glycol-Mediated Directional Conjugation of Biological Molecules for Enhanced Immunoassays at the Point-of-Care.

Rapid and reliable point-of-care (POC) diagnostic tests can have a significant impact on global health. One of the most common approaches for developing POC systems is the use of target-specific biomolecules. However, the conjugation of biomolecules can result in decreased activity, which may compromise the analytical performance and accuracy of the developed systems. To overcome this challenge, we present a polymer-based cross-linking protocol for controlled and directed conjugation of biological molecules. Our protocol utilizes a bifunctional thiol-polyethylene glycol (PEG)-hydrazide polymer to enable site-directed conjugation of IgG antibodies to the surface of screen-printed metal electrodes. The metal surface of the electrodes is first modified with thiolated PEG molecules, leaving the hydrazide groups available to react with the aldehyde group in the Fc fragments of the oxidized IgG antibodies. Using anti-Klebsiella pneumoniae carbapenemase-2 (KPC-2) antibody as a model antibody used for antimicrobial resistance (AMR) testing, our results demonstrate a ~10-fold increase in antibody coupling compared with the standard N-hydroxysuccinimide (NHS)-based conjugation chemistry and effective capture (>94%) of the target KPC-2 enzyme antigen on the surface of modified electrodes. This straightforward and easy-to-perform strategy of site-directed antibody conjugation can be engineered for coupling other protein- and non-protein-based biological molecules commonly used in POC testing and development, thus enhancing the potential for improved diagnostic accuracy and performance.

Solid-state emitting twisted π-conjugate as AIE-active DSE-gen: in vitro anticancer properties against FaDu and 4T1 with biocompatibility and bioimaging.

Dual-state emissive fluorogens (DSE-gens) are currently defining their importance as a transpiring tool in biological and biomedical applications. This work focuses on designing and synthesizing indole-anthracene-based solid-state emitting twisted π-conjugates using a metal-free protocol to achieve AIE-active DSE-gens, expanding their scope in biological applications. Special effort has been made to introduce proficient and photo/thermostable DSE-gens that inhibit cancer but not normal cells. Here, the lead DSE-gen initially detects cancer and normal cells by bioimaging; however, it could also confirm and distinguish cancer cells from normal cells by its abated fluorescence signal after killing cancer cells. In contrast, the fluorescence signals for a normal cell remain unscathed. Surprisingly, these molecules displayed decent anticancer properties against FaDu and 4T1 but not MCF-7 cell lines. From a series of newly designed indole-based molecules, we report one single 2,3,4-trimethoxybenzene-linked DSE-gen (the lead), exhibiting high ROS generation, less haemolysis, and less cytotoxicity than doxorubicin (DOX) for normal cells, crucial parameters for a biocompatible in vitro anticancer probe. Thus, we present a potentially applicable anticancer drug, offering a bioactive material with bioimaging efficacy and a way to detect dead cancer cells selectively. The primary mechanism behind the identified outcomes is deciphered with the support of experimental (steady-state and time-resolved fluorescence, biological assays, cellular uptake) and molecular docking studies.

Combining structure-based and 3D QSAR pharmacophore models to discover diverse ligands against EGFR in oral cancer.

Background: Epidermal growth factor receptor-tyrosine kinase (EGFR-TK) is a well-known hallmark of oral and oropharyngeal cancers, as its overexpression leads to poor prognosis and malignancy. The activating EGFR mutations (particularly T790M and L858R double mutant) are a major challenge causing drug resistance, especially in the treatment of oral cancers. Methodology: This paper is an effort to exploit both structure-based and ligand-based pharmacophore modeling to discover EGFR-TK inhibitors, which show inhibition of proliferation of erlotinib-resistant FaDu and Cal27 oral cancer cells. Interestingly, the hit compound H2 also showed an effect on the downstream glucose and lactate metabolism pathways. Conclusion: The results indicate the potential of H2 to be developed as an EGFR-based metabolic inhibitor for oral cancer treatment.