Microenvironment matters: In vitro 3D bone marrow niches differentially modulate survival, phenotype and drug responses of acute myeloid leukemia (AML) cells.

Acute myeloid leukemia (AML) is a deadly form of leukemia with ineffective traditional treatment and frequent chemoresistance-associated relapse. Personalized drug screening holds promise in identifying optimal regimen, nevertheless, primary AML cells undergo spontaneous apoptosis during cultures, invalidating the drug screening results. Here, we reconstitute a 3D osteogenic niche (3DON) mimicking that in bone marrow to support primary AML cell survival and phenotype maintenance in cultures. Specifically, 3DON derived from osteogenically differentiated mesenchymal stem cells (MSC) from healthy and AML donors are co-cultured with primary AML cells. The AML cells under the AML_3DON niche showed enhanced viability, reduced apoptosis and maintained CD33+ CD34-phenotype, associating with elevated secretion of anti-apoptotic cytokines in the AML_3DON niche. Moreover, AML cells under the AML_3DON niche exhibited low sensitivity to two FDA-approved chemotherapeutic drugs, further suggesting the physiological resemblance of the AML_3DON niche. Most interestingly, AML cells co-cultured with the healthy_3DON niche are highly sensitive to the same sample drugs. This study demonstrates the differential responses of AML cells towards leukemic and healthy bone marrow niches, suggesting the impact of native cancer cell niche in drug screening, and the potential of re-engineering healthy bone marrow niche in AML patients as chemotherapeutic adjuvants overcoming chemoresistance, respectively.

Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool.

Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

Cystic fibrosis cell models for high-throughput analysis and drug screening.

Cystic fibrosis (CF) is a single-gene disorder that affects the lung, digestive system, and other organs. Mutations in the CF transmembrane conductance regulator (CFTR) gene are classified into several classes based on their pathogenic mechanism and clinical severity. The distinct and heterogeneous clinical behavior of each CF class and the respective CFTR mutations have made the development of a durable therapy for all CF patients extremely challenging. While the FDA-approved drug elexacaftor/tezacaftor/ivacaftor (Trikafta) benefits CF patients carrying at least one F508del mutation in CFTR, it's not effective for many CF patients carrying a variety of other CFTR mutations. To establish a better understanding of CF pathophysiology and aid the development of novel therapeutics for different classes of CF patients, we have created four CF-mutation-specific cell models that recapitulate respectively four distinct CF classes and disease phenotypes, as confirmed by sequencing, CFTR mRNA and protein quantification. The channel function of each cell model was first validated using a well-established FLIPR (Fluorescent Imaging Plate Reader) membrane potential assay and then assessed by the YFP-based functional assay. Integrated with a halide-sensitive fluorescent reporter, these CF cell models can be used for high-throughput drug screening, as demonstrated by a proof-of-concept study using Trikafta. These cell models have the potential to advance CFTR mutation-specific therapies thus addressing the unmet needs of CF patients with rare mutations.

Despite the improved clinical sensitivity of the Roche benzodiazepines II assay it cannot replace mass spectrometry in all patient populations.

Introduction: Benzodiazepines are frequently prescribed and misused therefore urine drug screening (UDS) is performed in many patient populations. Most current benzodiazepine immunoassays have poor sensitivity, particularly for detecting the metabolites of newer benzodiazepines such as lorazepam in urine. Objectives: We aimed to verify the clinical performance of the new qualitative Roche Benzodiazepines II (BNZ2) immunoassay, as well as compare its performance to the Roche Benzodiazepines Plus (BENZ) assay in two patient populations: UDS in the emergency department (ED) and compliance monitoring. Methods: An initial verification study was performed, selecting for samples containing clonazepam and lorazepam metabolites. Performance of the BNZ2 and BENZ assays was compared to liquid chromatography-tandem mass spectrometry (LC-MS/MS) as the reference method. Sensitivity, specificity, false positive rate (FPR) and false negative rate (FNR) were determined. Results: We verified the performance claims in the initial verification and demonstrated similar precision, with coefficient of variations (CVs) of 12.8% and 7.7% for negative and positive controls, respectively. Furthermore, we observed higher clinical sensitivity and lower FNR with the BNZ2 assay in both the ED and compliance monitoring populations due to improved cross-reactivity for lorazepam and clonazepam metabolites. Despite these improvements, the BNZ2 assay was unable to detect 27% of specimens positive by LC-MS/MS, including specimens from patients using benzodiazepines without prescription. Discussion: Due to its improved performance and rapid turnaround time, the BNZ2 assay should be implemented for UDS in the ED. However, the assay should not replace LC-MS/MS testing for compliance monitoring, as unsuspected benzodiazepine use may go undetected.

Complementary strategies to be used in conjunction with animal models for multiple sclerosis drug discovery: adapting preclinical validation of drug candidates to the need of remyelinating strategies.

INTRODUCTION: The quest for novel MS therapies focuses on promoting remyelination and neuroprotection, necessitating innovative drug design paradigms and robust preclinical validation methods to ensure efficient clinical translation. The complexity of new drugs action mechanisms is strengthening the need for solid biological validation attempting to address all possible pitfalls and biases precluding access to efficient and safe drugs. AREAS COVERED: In this review, the authors describe the different in vitro and in vivo models that should be used to create an integrated approach for preclinical validation of novel drugs, including the evaluation of the action mechanism. This encompasses 2D, 3D in vitro models and animal models presented in such a way to define the appropriate use in a global process of drug screening and hit validation. EXPERT OPINION: None of the current available tests allow the concomitant evaluation of anti-inflammatory, immune regulators or remyelinating agents with sufficient reliability. Consequently, the collaborative efforts of academia, industry, and regulatory agencies are essential for establishing standardized protocols, validating novel methodologies, and translating preclinical findings into clinically meaningful outcomes.

Improving iPSC Differentiation Using a Nanodot Platform.

Differentiation of induced pluripotent stem cells (iPSCs) is an extremely complex process that has proven difficult to study. In this research, we utilized nanotopography to elucidate details regarding iPSC differentiation by developing a nanodot platform consisting of nanodot arrays of increasing diameter. Subjecting iPSCs cultured on the nanodot platform to a cardiomyocyte (CM) differentiation protocol revealed several significant gene expression profiles that were associated with poor differentiation. The observed expression trends were used to select existing small-molecule drugs capable of modulating differentiation efficiency. BRD K98 was repurposed to inhibit CM differentiation, while iPSCs treated with NSC-663284, carmofur, and KPT-330 all exhibited significant increases in not only CM marker expression but also spontaneous beating, suggesting improved CM differentiation. In addition, quantitative polymerase chain reaction was performed to determine the gene regulation responsible for modulating differentiation efficiency. Multiple genes involved in extracellular matrix remodeling were correlated with a CM differentiation efficiency, while genes involved in the cell cycle exhibited contrasting expression trends that warrant further studies. The results suggest that expression profiles determined via short time-series expression miner analysis of nanodot-cultured iPSC differentiation can not only reveal drugs capable of enhancing differentiation efficiency but also highlight crucial sets of genes related to processes such as extracellular matrix remodeling and the cell cycle that can be targeted for further investigation. Our findings confirm that the nanodot platform can be used to reveal complex mechanisms behind iPSC differentiation and could be an indispensable tool for optimizing iPSC technology for clinical applications.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach.

BACKGROUND: Diabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant-derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes-associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications. METHODOLOGY: The research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein-ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse-grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability. RESULTS: Karanjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug-likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood-brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha-amylase) at -9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand-protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M. CONCLUSION: Karanjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.

Using the refractive index of latent fingerprints for the quantification and characterisation of sample deposition.

Latent fingerprints (LFPs) are predominantly used for personal identification, but in recent years research has shown their potential for drug screening. Despite this there is no standardised collection method to allow accurate drug test interpretation. We sought to help address this by characterising different variables related to sweat deposition in LFPs as the knowledge is limited. A series of experiments were conducted firstly to validate a novel tool called the Ridgeway (Intelligent Fingerprint Ltd. UK) to quantify the amount of sweat deposited from a LFP using the refractive index (RI). A significant positive correlation was observed between the Ridgeway score (Rs) and LFP mass [r = 0.868, p < 0.01]. The Rs was used as means to investigate optimal sampling to characterise sample deposition for drug screening purposes. It was found with a consistent disposition pressure (300 - 400 g) and surface (glass slide) no significant difference was observed between the left and right index finger [left: p = 0.938; right: p = 0.838]. Significantly higher Rs [p<0.01] were obtained when 10 cumulative LFPs were deposited compared to a single LFP, suggesting a larger sweat quantity. We also wanted to investigate optimal eccrine sweat sampling to confirm drug ingestion over drug contamination of the fingerprint. We found that wearing gloves did not significantly improve mean difference in Rs when compared to no gloves [p = 0.239]. To produce eccrine only LFPs, external contamination (e.g. sebaceous sweat) needs to be removed. Soap with lint free tissue was significantly better for this compared to antibacterial hand gel [p<0.01]. Our findings showed that the Ridgeway tool effectively quantified LFPs at the point of deposition using a refractive index and enabled us to establish conditions for consistent LFP sampling.

A clinically compatible in vitro drug-screening platform identifies therapeutic vulnerabilities in primary cultures of brain metastases.

PURPOSE: Brain metastases represent the most common intracranial tumors in adults and are associated with a poor prognosis. We used a personalized in vitro drug screening approach to characterize individual therapeutic vulnerabilities in brain metastases. METHODS: Short-term cultures of cancer cells isolated from brain metastasis patients were molecularly characterized using next-generation sequencing and functionally evaluated using high-throughput in vitro drug screening to characterize pharmacological treatment sensitivities. RESULTS: Next-generation sequencing identified matched genetic alterations in brain metastasis tissue samples and corresponding short-term cultures, suggesting that short-term cultures of brain metastases are suitable models for recapitulating the genetic profile of brain metastases that may determine their sensitivity to anti-cancer drugs. Employing a high-throughput in vitro drug screening platform, we successfully screened the cultures of five brain metastases for response to 267 anticancer compounds and related drug response to genetic data. Among others, we found that targeted treatment with JAK3, HER2, or FGFR3 inhibitors showed anti-cancer effects in individual brain metastasis cultures. CONCLUSION: Our preclinical study provides a proof-of-concept for combining molecular profiling with in vitro drug screening for predictive evaluation of therapeutic vulnerabilities in brain metastasis patients. This approach could advance the use of patient-derived cancer cells in clinical practice and might eventually facilitate decision-making for personalized drug treatment.

A class of chemical compounds enhances clustering of muscle nicotinic acetylcholine receptor in cultured myogenic cells.

Neuromuscular signal transmission is affected in various diseases including myasthenia gravis, congenital myasthenic syndromes, and sarcopenia. We used an ATF2-luciferase system to monitor the phosphorylation of MuSK in HEK293 cells introduced with MUSK and LRP4 cDNAs to find novel chemical compounds that enhanced agrin-mediated acetylcholine receptor (AChR) clustering. Four compounds with similar chemical structures carrying benzene rings and heterocyclic rings increased the luciferase activities 8- to 30-folds, and two of them showed continuously graded dose dependence. The effects were higher than that of disulfiram, a clinically available aldehyde dehydrogenase inhibitor, which we identified to be the most competent preapproved drug to enhance ATF2-luciferase activity in the same assay system. In C2C12 myotubes, all the compounds increased the area, intensity, length, and number of AChR clusters. Three of the four compounds increased the phosphorylation of MuSK, but not of Dok7, JNK. ERK, or p38. Monitoring cell toxicity using the neurite elongation of NSC34 neuronal cells as a surrogate marker showed that all the compounds had no effects on the neurite elongation up to 1 µM. Extensive docking simulation and binding structure prediction of the four compounds with all available human proteins using AutoDock Vina and DiffDock showed that the four compounds were unlikely to directly bind to MuSK or Dok7, and the exact target remained unknown. The identified compounds are expected to serve as a seed to develop a novel therapeutic agent to treat defective NMJ signal transmission.

Pneumatic extrusion bioprinting-based high throughput fabrication of a melanoma 3D cell culture model for anti-cancer drug screening.

The high incidence of malignant melanoma highlights the need for in vitro models that accurately represent the tumour microenvironment, enabling developments in melanoma therapy and drug screening. Despite several advancements in 3D cell culture models, appropriate melanoma models for evaluating drug efficacy are still in high demand. The 3D pneumatic extrusion-based bioprinting technology offers numerous benefits, including the ability to achieve high-throughput capabilities. However, there is a lack of research that combines pneumatic extrusion-based bioprinting with analytical assays to enable efficient drug screening in 3D melanoma models. To address this gap, this study developed a simple and highly reproducible approach to fabricate a 3D A375 melanoma cell culture model using the pneumatic extrusion-based bioprinting technology. To optimise this method, the bioprinting parameters for producing 3D cell cultures in a 96-well plate were adjusted to improve reproducibility while maintaining the desired droplet size and a cell viability of 92.13± 6.02%. The cross-linking method was optimised by evaluating cell viability and proliferation of the 3D bioprinted cells in three different concentrations of calcium chloride. The lower concentration of 50 mM resulted in higher cell viability and increased cell proliferation after 9 days of incubation. The A375 cells exhibited a steadier proliferation rate in the 3D bioprinted cell cultures, and tended to aggregate into spheroids, whereas the 2D cell cultures generally formed monolayered cell sheets. In addition, we evaluated the drug responses of four different anti-cancer drugs on the A375 cells in both the 2D and 3D cell cultures. The 3D cell cultures exhibited higher levels of drug resistance in all four tested anti-cancer drugs. This method presents a simple and cost-effective method of producing and analysing 3D cell culture models that do not add additional complexity to current assays and shows considerable potential for advancing 3D cell culture models' drug efficacy evaluations.

Drug Evaluation of Parkinson's Disease Patient-Derived Midbrain Organoids Using Mesoporous Au Nanodot-Patterned 3D Concave Electrode.

Brain organoids are being recognized as valuable tools for drug evaluation in neurodegenerative diseases due to their similarity to the human brain's structure and function. However, a critical challenge is the lack of selective and sensitive electrochemical sensing platforms to detect the response of brain organoids, particularly changes in the neurotransmitter concentration upon drug treatment. This study introduces a 3D concave electrode patterned with a mesoporous Au nanodot for the detection of electrochemical signals of dopamine in response to drugs in brain organoids for the first time. The mesoporous Au nanodot-patterned film was fabricated using laser interference lithography and electrochemical deposition. Then, the film was attached to a polymer-based 3D concave mold to obtain a 3D concave electrode. Midbrain organoids generated from Parkinson's disease (PD) patient-derived iPSCs with gene mutations (named as PD midbrain organoid) or normal midbrain organoids were positioned on the developed 3D concave electrode. The 3D concave electrode showed a 1.4 times higher electrochemical signal of dopamine compared to the bare gold electrode. And the dopamine secreted from normal midbrain organoids or PD midbrain organoids on the 3D concave electrode could be detected electrochemically. After the treatment of PD midbrain organoids with levodopa, the drug for PD, the increase in dopamine level was detected due to the activation of dopaminergic neurons by the drug. The results suggest the potential of the proposed 3D concave electrode combined with brain organoids as a useful tool for assessing drug efficacy. This sensing system can be applied to a variety of organoids for a comprehensive drug evaluation.

Assessing knowledge gaps and educational needs in urine drug test interpretation among health care professionals.

OBJECTIVES: Urine drug testing (UDT) is a critical tool used in medical, forensic, and occupational settings, but interpreting results can be challenging. We performed a study to assess the ability of health care professionals to interpret UDT results accurately. METHODS: In total, 911 clinical and laboratory professionals in the United States and Canada responded to a survey with questions gauging expertise in UDT interpretation. Responses were analyzed to identify knowledge gaps. RESULTS: Toxicologists and laboratory PhD scientists performed well, with means of 4.82 and 4.63 questions answered correctly (out of 6 possible), respectively. Physicians specializing in pathology, emergency medicine, primary care, and internal medicine, however, displayed concerning knowledge gaps, as did laboratorians with nondoctoral degrees. Experience and training correlated with interpretation accuracy. Identification of simulated compliance as well as understanding opioid exposure, metabolism, and immunoassay cross-reactivity were among the most clinically significant knowledge gaps. More than 30% of survey respondents indicated that they would seek UDT information from the internet or peers rather than clinical or laboratory experts. CONCLUSIONS: The study highlighted the need for targeted education and better collaboration between clinical and laboratory experts and other health care professionals to ensure that when physicians order UDT, they can accurately interpret results and reduce harm.

Network Medicine: A Potential Approach for Virtual Drug Screening.

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Advancing Cardiovascular Drug Screening Using Human Pluripotent Stem Cell-Derived Cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have emerged as a promising tool for studying cardiac physiology and drug responses. However, their use is largely limited by an immature phenotype and lack of high-throughput analytical methodology. In this study, we developed a high-throughput testing platform utilizing hPSC-CMs to assess the cardiotoxicity and effectiveness of drugs. Following an optimized differentiation and maturation protocol, hPSC-CMs exhibited mature CM morphology, phenotype, and functionality, making them suitable for drug testing applications. We monitored intracellular calcium dynamics using calcium imaging techniques to measure spontaneous calcium oscillations in hPSC-CMs in the presence or absence of test compounds. For the cardiotoxicity test, hPSC-CMs were treated with various compounds, and calcium flux was measured to evaluate their effects on calcium dynamics. We found that cardiotoxic drugs withdrawn due to adverse drug reactions, including encainide, mibefradil, and cetirizine, exhibited toxicity in hPSC-CMs but not in HEK293-hERG cells. Additionally, in the effectiveness test, hPSC-CMs were exposed to ATX-II, a sodium current inducer for mimicking long QT syndrome type 3, followed by exposure to test compounds. The observed changes in calcium dynamics following drug exposure demonstrated the utility of hPSC-CMs as a versatile model system for assessing both cardiotoxicity and drug efficacy. Overall, our findings highlight the potential of hPSC-CMs in advancing drug discovery and development, which offer a physiologically relevant platform for the preclinical screening of novel therapeutics.

Diving deep: zebrafish models in motor neuron degeneration research.

In the dynamic landscape of biomedical science, the pursuit of effective treatments for motor neuron disorders like hereditary spastic paraplegia (HSP), amyotrophic lateral sclerosis (ALS), and spinal muscular atrophy (SMA) remains a key priority. Central to this endeavor is the development of robust animal models, with the zebrafish emerging as a prime candidate. Exhibiting embryonic transparency, a swift life cycle, and significant genetic and neuroanatomical congruencies with humans, zebrafish offer substantial potential for research. Despite the difference in locomotion-zebrafish undulate while humans use limbs, the zebrafish presents relevant phenotypic parallels to human motor control disorders, providing valuable insights into neurodegenerative diseases. This review explores the zebrafish's inherent traits and how they facilitate profound insights into the complex behavioral and cellular phenotypes associated with these disorders. Furthermore, we examine recent advancements in high-throughput drug screening using the zebrafish model, a promising avenue for identifying therapeutically potent compounds.

Dual-driven AND molecular logic gates for label-free and sensitive ratiometric fluorescence sensing and inhibitors screening.

Due to the complexity of regulatory networks of disease-related biomarkers, developing simple, sensitive, and accurate methods has remained challenging for precise diagnosis. Herein, an "AND" logic gates DNA molecular machine (LGDM) was constructed, which was powered by the catalytic hairpin assembly (CHA). It was coupled with dual-emission CdTe quantum dots (QDs)-based cation exchange reaction (CER) for label-free, sensitive, and ratiometric fluorescence detection of APE1 and miRNA biomarkers. Benefiting from synergistic signal amplification strategies and a ratiometric fluorometric output mode, this LGDM enables accurate logic computing with robust and significant output signals from weak inputs. It offers improved sensitivity and selectivity even in cell extracts. Using dual-emission spectra CdTe QDs, with a ratiometric signal output mode, ensured good stability and effectively prevented false-positive signals from intrinsic biological interferences compared to the approach relying on a single signal output mode, which enabled the LGDM to achieve rapid, efficient, and accurate natural drug screening against APE1 inhibitors in vitro and cells. The developed method provides impetus to streamline research related to miRNA and APE1, offering significant promise for widespread application in drug development and clinical analysis.

Gastric Cancer Assembloids Derived from Patient-Derived Xenografts: A Preclinical Model for Therapeutic Drug Screening.

The construction of reliable preclinical models is crucial for understanding the molecular mechanisms involved in gastric cancer and for advancing precision medicine. Currently, existing in vitro tumor models often do not accurately replicate the human gastric cancer environment and are unsuitable for high-throughput therapeutic drug screening. In this study, droplet microfluidic technology is employed to create novel gastric cancer assembloids by encapsulating patient-derived xenograft gastric cancer cells and patient stromal cells in Gelatin methacryloyl (GelMA)-Gelatin-Matrigel microgels. The usage of GelMA-Gelatin-Matrigel composite hydrogel effectively alleviated cell aggregation and sedimentation during the assembly process, allowing for the handling of large volumes of cell-laden hydrogel and the uniform generation of assembloids in a high-throughput manner. Notably, the patient-derived xenograft assembloids exhibited high consistency with primary tumors at both transcriptomic and histological levels, and can be efficiently scaled up for preclinical drug screening efforts. Furthermore, the drug screening results clearly demonstrated that the in vitro assembloid model closely mirrored in vivo drug responses. Thus, these findings suggest that gastric cancer assembloids, which effectively replicate the in vivo tumor microenvironment, show promise for enabling more precise high-throughput drug screening and predicting the clinical outcomes of various drugs.

Using the Zebrafish Larval Model of Infection to Investigate Antibiotic Efficacy and Combination Treatments Against Staphylococcus aureus.

There is an increasing need for new treatment regimens to combat antibiotic-resistant strains of bacteria. Staphylococcus aureus is a clinically important, opportunist pathogen that has developed resistance to a range of antibiotics. The zebrafish larval model of systemic disease has been increasingly utilized to elucidate S. aureus virulence mechanisms and host-pathogen interactions. Here, we outline how this model can be used to investigate the effects of different antibiotics alone and in combination against S. aureus.