Search Results (11,748)

This review explores the biofilm architecture and drug resistance of Enterococcus faecalis in clinical and environmental settings. The biofilm in E. faecalis is a heterogeneous, three-dimensional, mushroom-like or multilayered structure, characteristically forming diplococci or short chains interspersed with water channels for nutrient exchange and waste removal. Exopolysaccharides, proteins, lipids, and extracellular DNA create a protective matrix. Persister cells within the biofilm contribute to antibiotic resistance and survival. The heterogeneous architecture of the E. faecalis biofilm contains both dense clusters and loosely packed regions that vary in thickness, ranging from 10 to 100 µm, depending on the environmental conditions. The pathogenicity of the E. faecalis biofilm is mediated through complex interactions between genes and virulence factors such as DNA release, cytolysin, pili, secreted antigen A, and microbial surface components that recognize adhesive matrix molecules, often involving a key protein called enterococcal surface protein (Esp). Clinically, it is implicated in a range of nosocomial infections, including urinary tract infections, endocarditis, and surgical wound infections. The biofilm serves as a nidus for bacterial dissemination and as a reservoir for antimicrobial resistance. The effectiveness of first-line antibiotics (ampicillin, vancomycin, and aminoglycosides) is diminished due to reduced penetration, altered metabolism, increased tolerance, and intrinsic and acquired resistance. Alternative strategies for biofilm disruption, such as combination therapy (ampicillin with aminoglycosides), as well as newer approaches, including antimicrobial peptides, quorum-sensing inhibitors, and biofilm-disrupting agents (DNase or dispersin B), are also being explored to improve treatment outcomes. Environmentally, E. faecalis biofilms contribute to contamination in water systems, food production facilities, and healthcare environments. They persist in harsh conditions, facilitating the spread of multidrug-resistant strains and increasing the risk of transmission to humans and animals. Therefore, understanding the biofilm architecture and drug resistance is essential for developing effective strategies to mitigate their clinical and environmental impact. Full article

Background: With the increasing shift in drug design away from classical drug targets towards the modulation of protein-protein interactions, synthetic peptides are gaining increasing relevance. The synthesis and purification of peptides via solid-phase peptide synthesis (SPPS) strongly rely on trifluoroacetic acid (TFA) as a cleavage agent and ion-pairing reagent, respectively, resulting in peptides being obtained as TFA salts. Although TFA has excellent properties for peptide production, numerous studies highlight the negative impact of using peptides from TFA⁻ salts in biological assays. Methods: Investigated peptides were synthesized via SPPS and the TFA⁻ counterion was exchanged for Cl⁻ via freeze-drying in different concentrations of HCl. Detection and quantification of residual TFA⁻ were carried out via FT-IR, ¹⁹F-NMR, and HPLC using an evaporative light-scattering detector (ELSD). A liposomal fluorescence assay was used to test for the influence of the counterion on the peptides’ passive membrane permeability. Results: All TFA⁻ detection methods were successfully validated according to ICH guidelines. TFA⁻ removal with 10 mM HCl was determined to be the optimal condition. No impact on peptide purity was observed at all HCl concentrations. Influences on permeability coefficients depending on peptide sequence and salt form were found. Conclusions: This study presents a systematic investigation of the removal of TFA⁻ counterions from synthetic peptides and their replacement with Cl⁻ counterions. Detected counterion contents were used to understand the impact of sequence differences, especially positive charges, on the amount and potential localization of counterions. Our findings emphasize the importance of counterion quantification and specification in assays with synthetic peptides. Full article

Gastrointestinal (GI) cancers represent a significant global health burden, with high morbidity and mortality often linked to late-stage detection and metastatic disease. The progression of these malignancies is critically driven by angiogenesis, the formation of new blood vessels, and the surrounding dynamic tumor microenvironment (TME), a complex ecosystem comprising various cell types and non-cellular components. This comprehensive review, based on a systematic search of the PubMed database, synthesizes the existing literature to define the intertwined roles of angiogenesis and the TME in GI tumorigenesis. The TME’s influence creates conditions favorable for tumor growth, invasion, and metastasis, but sometimes induces resistance to current therapies. Available therapeutic strategies for inhibiting angiogenesis involve antibodies and oral tyrosine kinase inhibitors, while immune modulation within the tumor microenvironment is mainly achieved through checkpoint inhibitor antibodies and chemotherapy. Creative emerging strategies encompassing cellular therapies, bispecific antibodies, and new targets such as CD40, DLL4, and Ang2, amongst others, are focused on inhibiting proangiogenic pathways more profoundly, reversing resistance to prior drugs, and modulating the TME to enhance therapeutic efficacy. A deeper understanding of the complex interactions between components of the TME is crucial for addressing the unmet need for novel and effective therapeutic interventions against aggressive GI cancers. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

This study explores how aristolochic acid I (AAI) drives hepatocellular carcinoma (HCC). We first employ network toxicology and machine learning to map the key molecular target genes. Next, our research utilizes molecular docking to evaluate how AAI binds to these targets, and finally confirms the stability and dynamics of the resulting complexes through molecular dynamics simulations. We identified 193 overlapping target genes between AAI and HCC through databases such as PubChem, OMIM, and ChEMBL. Machine learning algorithms (SVM-RFE, random forest, and LASSO regression) were employed to screen 11 core genes. LASSO serves as a rapid dimension-reduction tool, SVM-RFE recursively eliminates the features with the smallest weights, and Random Forest achieves ensemble learning through decision trees. Protein–protein interaction networks were constructed using Cytoscape 3.9.1, and key genes were validated through GO and KEGG enrichment analyses, an immune infiltration analysis, a drug sensitivity analysis, and a survival analysis. Molecular-docking experiments showed that AAI binds to each of the core targets with a binding affinity stronger than −5 kcal mol⁻¹, and subsequent molecular dynamics simulations verified that these complexes remain stable over time. This study determined the potential molecular mechanisms underlying AAI-induced HCC and identified key genes (CYP1A2, ESR1, and AURKA) as potential therapeutic targets, providing valuable insights for developing targeted strategies to mitigate the health risks associated with AAI exposure. Full article

Boron-containing compounds (BCCs) have emerged as potential drugs. Their drug-like effects are mainly explained by their mechanisms of action in enzymes. Nowadays, some experimental data support the effects of specific BCCs on GPCRs, provided there are crystal structures that show them bound to G protein-coupled receptors (GPCRs). Some BCCs are recognized as potential ligands of GPCRs—the drug targets of many diseases. Objective: The aim of this study was to collecte up-to-date data on the interactions of BCCs with GPCRs. Methods: Data were collected from the National Center of Biotechnology Information, PubMed, Global Health, Embase, the Web of Science, and Google Scholar databases and reviewed. Results: Some experimental reports support the interactions of BCCs with several GPCRs, acting as their labels, agonists, or antagonists. These interactions can be inferred based on in silico and in vitro results if there are no available crystal structures for validating them. Conclusions: The actions of BCCs on GPCRs are no longer hypothetical, as the existing evidence supports BCCs’ interactions with and actions on GPCRs. Full article

Cardiovascular disease (CVD) is the leading cause of death worldwide, with pathophysiological mechanisms often involving platelet activation and chronic inflammation. While antiplatelet agents targeting adenosine diphosphate (ADP)-mediated pathways are well established in CVD management, less is known about drug interactions with the platelet-activating factor (PAF) pathway, a key mediator of inflammation. This study aimed to evaluate the effects of several commonly used cardiovascular and anti-inflammatory drug classes—including clopidogrel, non-steroidal anti-inflammatory drugs (NSAIDs), angiotensin II receptor blockers (ARBs), β-blockers, and analgesics—on platelet function via both the ADP and PAF pathways. Using human platelet-rich plasma (hPRP) from healthy donors, we assessed platelet aggregation in response to these two agonists in the absence and presence of graded concentrations of each of these drugs or of their usually prescribed combinations. The study identified differential drug effects on platelet aggregation, with some agents showing pathway-specific activity. Clopidogrel and NSAIDs demonstrated expected antiplatelet effects, while some (not all) antihypertensives exhibited additional anti-inflammatory potential. These findings highlight the relevance of evaluating pharmacological activity beyond traditional targets, particularly in relation to PAF-mediated inflammation and thrombosis. This dual-pathway analysis may contribute to a broader understanding of drug mechanisms and inform the development of more comprehensive therapeutic strategies for the prevention and treatment of cardiovascular, hypertension, and inflammation-driven diseases. Full article

The escalating global challenges of antimicrobial resistance (AMR) and cancer necessitate innovative therapeutic solutions from natural sources. This study investigated the multifaceted therapeutic potential of pigment-enriched plant extracts. We screened diverse plant extracts for antimicrobial and antibiofilm activity against multidrug-resistant bacteria and fungi. Hibiscus sabdariffa emerged as the most promising, demonstrating potent broad-spectrum antimicrobial and significant antibiofilm activity. Sub-inhibitory concentrations of H. sabdariffa robustly downregulated essential bacterial virulence genes and suppressed aflatoxin gene expression. Comprehensive chemical profiling via HPLC identified major anthocyanin glucosides, while GC-MS revealed diverse non-pigment bioactive compounds, including fatty acids and alcohols. Molecular docking suggested favorable interactions of key identified compounds (Cyanidin-3-O-glucoside and 1-Deoxy-d-arabitol) with E. coli outer membrane protein A (OmpA), indicating potential antiadhesive and antimicrobial mechanisms. Furthermore, H. sabdariffa exhibited selective cytotoxicity against MCF-7 breast cancer cells. These findings establish H. sabdariffa pigment-enriched extract as a highly promising, multi-functional source of novel therapeutics, highlighting its potential for simultaneously addressing drug resistance and cancer challenges through an integrated chemical, biological, and computational approach. Full article

Diabetes mellitus (DM) is one of the leading causes of death and disability worldwide and its prevalence continues to rise. Chronic hyperglycemia exposes patients to severe complications. Among these, diabetic vascular lesions are the most destructive. Their primary driver is the synergistic interaction between hyperglycemia-induced oxidative stress and chronic inflammation. This review systematically elucidates how multiple pathological pathways—namely, metabolic dysregulation, mitochondrial dysfunction, endoplasmic reticulum stress, and epigenetic reprogramming—cooperate to drive oxidative stress and inflammatory cascades. Confronting this complex pathological network, natural products, unlike conventional single-target synthetic drugs, exert multi-target synergistic effects, simultaneously modulating several key pathogenic networks. This enables the restoration of redox homeostasis and the suppression of inflammatory responses, thereby improving vascular function and delaying both microvascular and macrovascular disease progression. However, the clinical translation of natural products still faces multiple challenges and requires comprehensive mechanistic studies and rigorous validation to fully realize their therapeutic potential. Full article

Motor vehicle crashes are a persistent cause of unintentional deaths in the United States. Scholarship on how manmade interventions and natural phenomena interact to effectuate such calamitous outcomes is longstanding. One manmade intervention of interest in the literature is daylight saving time (DST). Unfortunately, results on how the natural phenomena attributable to DST interact with driver behavior are inconsistent. To advance knowledge on DST-safety interactions, this study adopts a multilevel model approach to fatal motor vehicle crash outcomes in the contiguous United States. Results from a national analysis contextualize results from zonal analyses to unmask within- and between-time zone differences in DST-safety interactions. In the national analysis, motor vehicle crash fatalities decrease somewhat during DST (−0.10%). In the zonal analyses, motor vehicle crash fatalities decrease more so in the Central and Eastern time zones (−2.00% and −2.00%, respectively), but increase somewhat in the Pacific and Mountain time zones (+0.30%) during DST. The spatiotemporal context of the national analysis highlights specific policy implications from the zonal analyses to decrease the lethality of motor vehicle crashes. Specifically, interdictions to target alcohol and/or drug involvement in the northern latitudes of the Pacific and Mountain time zones during DST, the Central time zone at dawn or dusk before or after DST, and the northern latitudes in the Eastern time zone before or after DST are important. Generally, national DST-safety benefits mask zonal DST-safety costs in the Pacific and Mountain time zones. Full article

Background: The dual nature of cannabis, as both a promising therapeutic tool and a widely used recreational substance with potential risks, raises important societal controversies, including its unclear impacts regarding mental health. This narrative review provides a comprehensive overview of cannabis, addressing (i) its historical context; (ii) its chemical composition and pharmacokinetics; (iii) its pharmacological effects; (iv) its negative impacts on physiological and mental health; (v) its potential use as a drug for the treatment of neurological and psychiatric disorders; (vi) its relationship with the gut microbiome and how this interaction might influence mental functioning; (vii) the pathophysiology, prevalence, comorbidities, and treatment strategies of cannabis use disorder; and (viii) social perspectives on its legalization. Results: Cannabis presents a complex chemical profile and pharmacokinetics that show promise in treating numerous neurological, psychiatric, and psychological conditions. However, its use carries risks, which depend on factors such as compound concentration, dosage, consumption method, frequency of use, and individual vulnerability. Cannabis use disorder seems to be less severe than other substance use disorders, but it still constitutes a significant concern, as its manifestation is not uniform across all users. Conclusions: Cannabis demands a thorough understanding that goes beyond simplistic explanations and prejudices, standing as a plant of substantial clinical significance and highlighting the importance of personalized approaches to its use and increased awareness of how individuals respond to its effects. Full article

Cervical cancer affects 661,000 women worldwide; as a result, new treatment alternatives are still being sought, with steroid oximes being the most prominent. However, the molecular targets where steroid oximes exert their anticancer activity remain unknown. In this study, reports of the activity in cell lines were obtained, and the targets associated with cervical cancer were identified using bioinformatics tools, based on two- and three-dimensional structural similarity analysis. Subsequently, molecular targets were analyzed via molecular docking using Schrödinger software v.2022-4 to determine their effects compared with reference drugs. Interrelated proteins and isolated proteins were observed, suggesting both the multi-target and single-target activity of steroid oximes. The analysis revealed that 60% of the 42 identified proteins had previously been reported in the literature and were associated with cervical cancer in processes related to cell proliferation, invasion, migration, and apoptosis. Among them, SRC, IGF1R, and MDM2 showed feasibility for multi-target interaction, which is consistent with the lower IC₅₀ values reported for oximes in cervical cancer cell lines (HeLa and CaSki). This finding suggests that steroid oximes are multi-target molecules that can inhibit the proteins associated with cervical cancer, particularly through the IGF1R, MDM2, and SRC pathways related to cell proliferation and apoptosis, serving as a guideline for the future design of new steroidal oximes. Full article

This paper focuses on the numerical modeling of drug diffusion in biological tissues using fractional time-dependent parabolic equations with non-local boundary conditions. The model includes a Caputo fractional derivative to capture the non-local effects and memory inherent in biological processes, such as drug absorption and transport. The theoretical framework of the problem is based on the work of Alhazzani, et al.,which demonstrates the solution’s goodness, existence, and uniqueness. Building on this foundation, we present a robust numerical method designed to deal with the complexity of fractional derivatives and non-local interactions at the boundaries of biological tissues. Numerical simulations reveal how fractal order and non-local boundary conditions affect the drug concentration distribution over time, providing valuable insights into drug delivery dynamics in biological systems. The results underscore the potential of fractal models to accurately represent diffusion processes in heterogeneous and complex biological environments. Full article

Brain organoid technology has revolutionized in vitro modeling of human neurodevelopment and disease, providing unprecedented insights into cortical patterning, neural circuit assembly, and pathogenic mechanisms of neurological disorders. Critically, human brain organoids uniquely recapitulate human-specific developmental processes—such as the expansion of outer radial glia and neuromelanin—that are absent in rodent models, making them indispensable for studying human brain evolution and dysfunction. However, a major bottleneck persists: Extended culture periods (≥6 months) are empirically required to achieve late-stage maturation markers like synaptic refinement, functional network plasticity, and gliogenesis. Yet prolonged conventional 3D culture exacerbates metabolic stress, hypoxia-induced necrosis, and microenvironmental instability, leading to asynchronous tissue maturation—electrophysiologically active superficial layers juxtaposed with degenerating cores. This immaturity/heterogeneity severely limits their utility in modeling adult-onset disorders (e.g., Alzheimer’s disease) and high-fidelity drug screening, as organoids fail to recapitulate postnatal transcriptional signatures or neurovascular interactions without bioengineering interventions. We summarize emerging strategies to decouple maturation milestones from rigid temporal frameworks, emphasizing the synergistic integration of chronological optimization (e.g., vascularized co-cultures) and active bioengineering accelerators (e.g., electrical stimulation and microfluidics). By bridging biological timelines with scalable engineering, this review charts a roadmap to generate translationally relevant, functionally mature brain organoids. Full article

Polymicrobial biofilms involving fungal and bacterial species are increasingly recognized as contributors to persistent infections, particularly in the oral cavity. Candida albicans and Aggregatibacter actinomycetemcomitans are two commensals that can turn into opportunistic pathogens and are able to form robust biofilms. Objectives: This study aimed to assess the interaction dynamics between these two microorganisms and to evaluate their susceptibility to fluconazole and azithromycin in single- and mixed-species forms. Methods: Biofilm biomass was quantified using crystal violet assays, while biofilm cell viability was assessed through CFU enumeration (biofilm viability assay). To assess the resistance properties of single versus mixed-species coincubations, we applied the antimicrobial susceptibility test (AST) to each drug, and analysed spatial organization with confocal laser scanning microscopy, using PNA-FISH. Results: The results indicated that both species can coexist without significant mutual inhibition. However, a non-reciprocal synergism was also observed, whereby mixed-species biofilm conditions promoted the growth of A. actinomycetemcomitans, while C. albicans growth remained stable. As expected, antimicrobial tolerance was elevated in mixed cultures, likely due to enhanced extracellular matrix production and potential quorum-sensing interactions, contributing to increased resistance against azithromycin and fluconazole. Conclusions: This study provides novel insights into previously rarely explored interactions between C. albicans and A. actinomycetemcomitans. These findings underscore the importance of investigating interspecies interactions within polymicrobial biofilms, as understanding their mechanisms, such as quorum-sensing molecules and metabolic cooperation, can contribute to improved diagnostics and more effective targeted therapeutic strategies against polymicrobial infections. Full article