Search Results (103)

Background: Exosomes are nanoscale extracellular vesicles actively secreted by cells that play critical roles in disease biomarker discovery, drug delivery, and direct therapeutic applications. However, in vivo pharmacokinetic (PK) studies of exosomes remain limited, hindering their clinical translation. Due to their complex and heterogeneous composition, most existing PK methods rely on chemical or genetic labeling, which may alter their native behavior and complicate accurate analysis. Methods: To address this challenge, we developed a label-free liquid chromatography–tandem mass spectrometry (LC-MS/MS) method to investigate the PK of naive exosome-based therapeutic modalities. Exosomes were isolated from rat plasma using size exclusion chromatography (SEC) and quantified using multiple reaction monitoring (MRM) targeting specific exosomal peptides as surrogate analytes. Following intravenous administration of exosomes via the tail vein, plasma concentrations were determined by peptide peak areas, and PK parameters were calculated using a non-compartmental model. Results: The method was rigorously validated for specificity, linearity, sensitivity, and reproducibility. Its feasibility was further confirmed by successfully characterizing the PK profile of HEK 293F-derived exosomes in rats. Conclusions: This analytical strategy enables direct, label-free quantification of exosomes in plasma and provides a robust platform for advancing exosome-based drug development and translational research. Full article

Background: Niraparib is an oral poly (adenosine diphosphate-ribose) polymerase inhibitor with promising activity for patients with advanced breast cancer harboring germline BRCA1/2 mutations. Methods: LUZERN (NCT04240106) was a multicenter, open-label, Simon’s two-stage, phase II clinical trial evaluating the efficacy and safety of niraparib with aromatase inhibitors (AIs) for patients with HR-positive/HER2-negative advanced breast cancer with either a germline BRCA1/2 mutation (cohort A) or germline BRCA1/2 wild-type and homologous recombination deficiency (exploratory cohort B). Eligible patients received ≤1 line of chemotherapy and 1–2 prior lines of endocrine therapy for advanced disease with secondary resistance to the last AI-based regimen. Patients received niraparib (300 mg or 200 mg) plus an AI. The primary endpoint was the clinical benefit rate (CBR) in cohort A. Results: Between June 2020 and November 2022, 14 patients were enrolled in cohort A (n = 6 for stage I, n = 8 for stage II) and no patients were enrolled in cohort B. One patient was excluded from the efficacy analysis due to no prior AI treatment. Nearly all patients (92.9%) previously received a cyclin-dependent kinase 4/6 inhibitor, but no patients had received prior platinum-based chemotherapy. Median follow-up was 16.7 months (range: 13.2–18.2). The CBR was 46.2% (95% CI: 19.2–74.9), meeting the primary endpoint. Median progression-free survival was 5.5 months (95% CI: 1.9–8.5), and median overall survival was 18.1 months (95% CI: 9.7–NE). The safety profile was consistent with the known toxicity of both drugs. Conclusions: Niraparib combined with an AI has encouraging antitumor activity and a manageable safety profile in patients with AI-resistant HR-positive/HER2-negative advanced breast cancer with germline BRCA1/2 mutations. Full article

To overcome the limitations associated with chemically synthesized nanoparticles in cancer therapy, researchers have increasingly focused on developing nanoparticles with superior biocompatibility and prolonged tumor retention using biosynthetic methods. In this study, we first identified the presence of calcium peroxide nanoparticles (CaO₂ NPs) in the blood of individuals who had ingested calcium gluconate. Furthermore, the dropwise addition of calcium gluconate to human serum resulted in the spontaneous self-assembly of CaO₂ NPs. Next, following tail vein injection of fluorescently labeled CaO₂ NPs into subcutaneous tumor-bearing nude mice, we observed that the nanoparticles exhibited prolonged accumulation at the tumor sites compared to other organs through visible-light imaging. Immunofluorescence staining demonstrated that CaO₂ NPs co-localized with vesicular transport-associated proteins, such as PV-1 and Caveolin-1, as well as the albumin-binding-associated protein SPARC, suggesting that their transport from tumor blood vessels to the tumor site is mediated by Caveolin-1- and SPARC-dependent active transport pathways. Additionally, the analysis of various organs in normal mice injected with CaO₂ NPs at concentrations significantly higher than the experimental dose showed no apparent organ damage. Hemolysis assays indicated that hemolysis occurred only at calcium concentrations of 300 µg/mL, whereas the experimental concentration remained well below this threshold with no detectable hemolytic activity. In a subcutaneous tumor-bearing nude mouse model, treatment with docetaxel-loaded CaO₂ NPs showed a 68.5% reduction in tumor volume compared to free docetaxel (DTX) alone. These novel biosynthetic CaO₂ NPs demonstrated excellent biocompatibility, prolonged retention at the tumor site, safety, and drug-loading capability. Full article

We propose a pulsed modulated digital holographic microscopy (PM-DHM) technique for the dynamic measurement of flowing microparticles in microfluidic systems. By digitally tuning the pulse width and the repetition rate of a laser source within a single-frame exposure, this method enables the recording of multiple images of flowing microparticles at different time points within a single hologram, allowing the quantification of velocity and acceleration. We demonstrate the feasibility of PM-DHM by measuring the velocity, acceleration, and forces exerted on PMMA microspheres and red blood cells flowing in microfluidic chips. Compared to traditional frame-sampling-based imaging methods, this technique has a much higher time resolution (in a range of microseconds) that is limited only by the pulse duration. This method demonstrates significant potential for high-throughput label-free flow cytometry detection and offers promising applications in drug development and cell analysis. Full article

Natural products play a crucial role in drug discovery because of their structural diversity and biological activity. However, identifying their molecular targets remains a challenge. Traditional target identification approaches such as affinity-based protein profiling and activity-based protein profiling are limited by the need for chemical modification or reactive groups in natural products. The emergence of label-free techniques offers a powerful alternative for studying drug–target engagement in a physiological context. In particular, the cellular thermal shift assay (CETSA) exploits ligand-induced protein stabilization—a phenomenon where ligand binding enhances a protein’s thermal stability by reducing conformational flexibility—to assess drug binding without requiring chemical modifications. CETSA’s integration with advanced mass spectrometry and high-throughput platforms has dramatically expanded proteome coverage and sensitivity, enabling the simultaneous quantification of thousands of proteins and the identification of low-abundance targets in native cellular environments. This review highlights the application of key CETSA-based methods to target identification in natural products including Western blot-based CETSA, isothermal dose–response CETSA, mass spectrometry-based CETSA, and high-throughput CETSA. Case studies are presented that demonstrate their effectiveness in uncovering the mechanisms of action of different drugs. The current limitations of CETSA-based strategies are also explored, and future improvements to optimize their potential for drug discovery are discussed. Integrating CETSA with complementary approaches can enhance the target identification accuracy and efficiency for natural products and ultimately advance development of therapeutic applications. Full article

Synthetic cannabinoids (SCs), a class of widely abused new psychoactive substances, are characterized by their structural diversity and rapid evolution. Structure–affinity relationships are crucial for predicting pharmacological effects and potential toxicity. Traditional methods for affinity testing are often complex and less applicable to newly modified compounds. In contrast, Surface Plasmon Resonance (SPR) is a sensitive and label-free technology that detects molecular interactions by measuring refractive index changes on a metallic surface with the advantages of high sensitivity, low sample consumption, and high-throughput capability. In this study, we used SPR to determine the receptor affinity constants of 10 SCs, including some first-reported substances, and analyzed their structure–affinity relationships to validate the method’s reliability. The results showed that (1) indazole-based SCs exhibited stronger CB1 receptor affinity compared to their indole counterparts, (2) the head structure of p-fluorophenyl enhanced affinity relative to 5-fluoropentyl, (3) and the affinity rankings obtained from SPR experiments were consistent with those derived from traditional methods. These results collectively demonstrate the reliability and effectiveness of SPR in assessing CB1 receptor affinity and differentiating affinity differences among structurally similar analogs, with promising application prospects in drug research, particularly in the development and screening of therapeutic agents targeting cannabinoid receptors. Full article

Surface plasmon resonance (SPR) sensing technology has been widely utilized in fields such as biomedicine, food safety, and drug screening for real-time, rapid, and label-free detection of biomolecular interactions. However, conventional SPR sensing methods find it difficult to provide the necessary sensitivity and stability when detection applications go toward ultra-low concentrations and tiny molecular weight analytes. Here, we present a high-sensitivity Goos–Hänchen shift sensing based on SPR and beam displacement amplification technology (BDAT). The incorporation of BDAT significantly amplifies the magnitude of GH shift with remarkable stability, enhancing the sensing sensitivity by an order of magnitude. The sensor achieves a sensitivity of 3.62 × 10⁴ μm/RIU and a minimum detection limit of 3.10 × 10⁻⁵ RIU. Furthermore, both theoretical and experimental results demonstrate that GH shift sensing offers superior performance compared with traditional intensity-based SPR, particularly for low-concentration solutions. The BDAT approach amplifies GH shifts by at least 12 times, significantly improving sensitivity. With the use of SPR and BDAT, we are able to generate a large GH shift, which makes it easier to detect low concentrations and offers a wide range of possible uses in clinical diagnostics and biomedicine. Full article

Conducting detailed cellular analysis of complex biological samples poses challenges in cell sorting and recovery for downstream analysis. Label-free microfluidics provide a promising solution for these complex applications. In this work, we investigate particle manipulation on two label-free microdevice designs using cDEP to enrich E. coli from whole human blood to mimic infection workflows. E. coli is still a growing source of bacteremia, sepsis, and other infections in modern countries, affecting millions of patients globally. The two microfluidic designs were evaluated for throughput, scaling, precision targeting, and high-viability recovery. While CytoChip D had the potential for higher throughput, given its continuous method of DEP-based sorting to accommodate larger clinical samples like a 10 mL blood draw, it could not effectively recover the bacteria. CytoChip B achieved a high-purity recovery of over 98% of bacteria from whole human blood, even in concentrations on the order of <100 CFU/mL, demonstrating the feasibility of processing and recovering ultra-low concentrations of bacteria for downstream analysis, culture, and drug testing. Future work will aim to scale CytoChip B for larger volume throughput while still achieving high bacteria recovery. Full article

Hyaluronic acid (HA)-based delivery systems for doxorubicin (DOX) have been developed to selectively target cancer cells and enhance their therapeutic effects while reducing systemic side effects. However, conventional methods for preparing HA-based drug delivery systems are often limited by multistep synthetic processes, time-consuming purification, and the use of crosslinkers or surfactants, which can cause undesired toxicities. To resolve these issues, we developed a facile one-pot method to prepare self-assembled sodium hyaluronate/doxorubicin (HA/DOX) nanoaggregates by mixing HA and DOX. The self-assembled HA/DOX nanoaggregates were formed via cation–π interactions between the aromatic moiety of DOX and Na⁺ ions in HA as well as electrostatic interactions between HA and DOX. The optimized HA/DOX nanoaggregates with a [DOX]/[HA] molar ratio of 5 had an average particle size of approximately 250 nm and a sphere-like shape. In vitro studies revealed that HA/DOX nanoaggregates effectively targeted CD44-overexpressing cancer cells, selectively delivering DOX into the cell nuclei more efficiently than free DOX and resulting in enhanced cytotoxic effects. Annexin V and transferase dUTP nick-end labeling assays confirmed that HA/DOX nanoaggregates induced apoptosis via DNA fragmentation more effectively than free DOX. Full article

Background: The wide variability in clinical responses to anti-tumor immunotherapy drives the search for personalized strategies. One of the promising approaches is drug screening using patient-derived models composed of tumor and immune cells. In this regard, the selection of an appropriate in vitro model and the choice of cellular response assay are critical for reliable predictions. Fluorescence lifetime imaging microscopy (FLIM) is a powerful, non-destructive tool that enables direct monitoring of cellular metabolism on a label-free basis with a potential to resolve metabolic rearrangements in immune cells associated with their reactivity. Objective: The aim of the study was to develop a patient-derived glioma explant model enriched by autologous peripheral lymphocytes and explore FLIM of the redox-cofactor NAD(P)H in living lymphocytes to measure the responses of the model to immune checkpoint inhibitors. Methods: The light microscopy, FLIM of NAD(P)H and flow cytometry were used. Results: The results demonstrate that the responsive models displayed a significant increase in the free NAD(P)H fraction α₁ after treatment, associated with a shift towards glycolysis due to lymphocyte activation. The non-responsive models exhibited no alterations or a decrease in the NAD(P)H α₁ after treatment. The FLIM data correlated well with the standard assays of immunotherapy drug response in vitro, including morphological changes, the T-cells activation marker CD69, and the tumor cell proliferation index Ki67. Conclusions: The proposed platform that includes tumor explants co-cultured with lymphocytes and the NAD(P)H FLIM assay represents a promising solution for the patient-specific immunotherapeutic drug screening. Full article

Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery. Full article

Background/Objectives: SARS-CoV-2 is one of the viruses that caused worldwide health issues. This effect is mainly due to the wide range of disease prognoses it can cause. The aim of this study is to determine protein profiles that can be used as potential biomarkers for patients’ stratification, as well as potential targets for drug development. Methods: Eighty peripheral blood samples were collected from heathy as well as SARS-CoV-2 patients admitted at a major tertiary care center in Riyadh, Saudi Arabia. A label-free quantitative mass spectrometry-based proteomic analysis was conducted on the extracted sera. Protein–protein interactions and functional annotations of identified proteins were performed using the STRING. Results: In total, two-hundred-eighty-eight proteins were dysregulated among all four categories. Dysregulated proteins were mainly involved in the network map of SARS-CoV-2, immune responses, complement activation, and lipid transport. Compared to healthy subjects, the most common upregulated protein in all three categories were CRP, LGALS3BP, SAA2, as well as others involved in SARS-CoV-2 pathways such as ZAP70 and IGLL1. Notably, we found fifteen proteins that significantly discriminate between healthy/recovered subjects and moderate/under medication patients, among which are the SERPINA7, HSPD1 and TTC41P proteins. These proteins were also significantly downregulated in under medication versus moderate patients. Conclusions: Our results emphasize the possible association of specific proteins with the SARS-CoV-2 pathogenesis and their potential use as disease biomarkers and drug targets. Our study also gave insights about specific proteins that are likely increased upon infection but are likely restored post recovery. Full article

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents. Full article

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields. Full article

Background/Objectives: High-grade gliomas are the most common primary malignant brain tumors in adults. These neoplasms remain predominantly incurable due to the genetic diversity within each tumor, leading to varied responses to specific drug therapies. With the advent of new targeted and immune therapies, which have demonstrated promising outcomes in clinical trials, there is a growing need for image-based techniques to enable early prediction of treatment response. This study aimed to evaluate the potential of radiomics and artificial intelligence implementation in predicting progression-free survival (PFS) in patients with highest-grade glioma (CNS WHO 4) undergoing a standard treatment plan. Methods: In this retrospective study, prediction models were developed in a cohort of 51 patients with pathologically confirmed highest-grade glioma (CNS WHO 4) from the authors’ institution and the repository of the Cancer Imaging Archive (TCIA). Only patients with confirmed recurrence after complete tumor resection with adjuvant radiotherapy and chemotherapy with temozolomide were included. For each patient, 109 radiomic features of the tumor were obtained from a preoperative magnetic resonance imaging (MRI) examination. Four clinical features were added manually—sex, weight, age at the time of diagnosis, and the lobe of the brain where the tumor was located. The data label was the time to recurrence, which was determined based on follow-up MRI scans. Artificial intelligence algorithms were built to predict PFS in the training set (n = 75%) and then validate it in the test set (n = 25%). The performance of each model in both the training and test datasets was assessed using mean absolute percentage error (MAPE). Results: In the test set, the random forest model showed the highest predictive performance with 1-MAPE = 92.27% and a C-index of 0.9544. The decision tree, gradient booster, and artificial neural network models showed slightly lower effectiveness with 1-MAPE of 88.31%, 80.21%, and 91.29%, respectively. Conclusions: Four of the six models built gave satisfactory results. These results show that artificial intelligence models combined with radiomic features could be useful for predicting the progression-free survival of high-grade glioma patients. This could be beneficial for risk stratification of patients, enhancing the potential for personalized treatment plans and improving overall survival. Further investigation is necessary with an expanded sample size and external multicenter validation. Full article