Search Results (481)

Stochastic differential equations (SDEs) with multiple conserved quantities are ubiquitous in scientific fields, modeling systems from molecular dynamics to celestial mechanics. While geometric numerical integrators that preserve single invariants are well-established, constructing efficient and high-order numerical schemes for SDEs with multiple conserved quantities remains a challenge. Existing approaches often suffer from high computational costs or lack desirable numerical properties like symmetry. This paper introduces two novel classes of projection-based numerical methods tailored for SDEs with multiple conserved quantities. The first method projects the increments of an underlying numerical scheme onto a discrete tangent space, ensuring all invariants are preserved by construction. The second method leverages a local coordinates approach, transforming the SDE onto the manifold defined by the invariants, solving it numerically, and then projecting back, guaranteeing the solution evolves on the correct manifold. We prove that both methods inherit the mean-square convergence order of their underlying schemes. Furthermore, we propose a simplified strategy that reduces computational expense by redefining the multiple invariants into a single one, offering a practical trade-off between exact preservation and efficiency. Numerical experiments confirm the theoretical findings and demonstrate the superior efficiency and structure-preserving capabilities of our methods. Full article

Background/Objectives: While morphological similarity and incomplete specimens pose a challenge to the precise identification of Coelogyne orchids, accurate species and genus assignment is essential for conservation and CITES enforcement. This study evaluated the efficacy of five DNA barcode regions—rbcL, matK, trnH-psbA, atpF-atpH, and ITS2—and their combinations for species- and genus-level discrimination within the genus Coelogyne, aiming to develop a rapid and simple diagnostic tool for use by customs officers and trade inspectors. This is the first comprehensive comparative analysis of these five barcode regions specifically within Coelogyne, a genus underrepresented in molecular identification studies, and the first to propose multi-locus combinations for potential practical use. This study identified DNA barcode regions with high resolution and reliability, providing a solid basis for practical identification kits. Such tools will enhance CITES enforcement by enabling rapid detection of Coelogyne species in trade, directly supporting their conservation and contributing to the reduction in illegal orchid trade. Methods: Using a CTAB protocol, genomic DNA was extracted from leaf samples belonging to 19 Coelogyne species. Sanger sequencing was performed after PCR amplification using published primer sets for every barcode region. Sequences were modified in BioEdit, and BLASTn (accessed 15 June 2025) was used to compare them to GenBank (NCBI Nucleotide). Amplification efficiency was calculated per locus. Species and genus identification success rates were determined by the congruence of top BLAST hits with morphologically pre-identified taxa. Multi-barcode combinations (matK + rbcL, ITS2 + matK, matK + trnH-psbA, rbcL + trnH-psbA, and matK + rbcL + trnH-psbA) were also assessed. Results: With rbcL, atpF-atpH, and ITS2 yielding ≤11%, the highest single-locus species identification rates were for trnH-psbA (21%) and matK (16%). Among single-locus barcodes, matK showed the highest performance, with 84% genus assignment. ITS2 reached 27%, but genus-level resolution remained limited for the rbcL, trnH-psbA and atpF-atpH barcodes. Multi-barcode approaches maintained species resolution: matK + rbcL + trnH-psbA, matK + rbcL, and matK + trnH-psbA correctly identified 16% of species and achieved 74–79% genus assignment. Conclusions: No single locus achieves robust species discrimination in Coelogyne, but trnH-psbA, matK and atpF-atpH provide the best single-marker performance. Using the matK locus alone, in combination with either trnH-psbA or rbcL, or all three together ensures consistent genus-level identification and significantly improves taxonomic resolution. This study introduces a novel multi-locus barcode strategy tailored to Coelogyne, offering a practical solution for identification and enforcement. While promising, this approach represents a potential application that requires further validation before routine implementation. Full article

Cancer remains a persistent global health challenge, continuously driving the search for novel and effective therapeutic strategies. In the case of breast cancer, treatment decisions are primarily guided by factors such as the disease stage, histological grade, molecular receptor status, and the presence of genetic mutations. Understanding these parameters is crucial for tailoring interventions and improving clinical outcomes. To enhance prognostic and diagnostic accuracy, attention has increasingly turned to identifying molecular targets that play key roles in breast cancer development. Currently, standard treatments include surgery, chemotherapy, and radiotherapy. However, these approaches are often associated with significant side effects and a diminished quality of life. As a result, many breast cancer patients are turning to complementary therapies—including phytotherapy, nutritional interventions, and dietary supplements—to support conventional treatment, alleviate adverse effects, and improve overall well-being. Within the vast realm of medicinal flora, anticancer plants represent a compelling area of study, serving as a rich reservoir of bioactive compounds. These compounds have demonstrated significant promise in the ongoing battle against cancer. Often highlighted in traditional medicinal practices, these plants harbor a wide array of phytochemicals, such as alkaloids, flavonoids, polyphenols, and terpenoids. These phytochemicals manifest diverse biological activities, notably exhibiting pronounced anticancer properties. The exploration of these natural compounds has opened new avenues for developing innovative and targeted therapeutic strategies in cancer treatment. They achieve definitive chemotherapeutic and chemopreventive roles by integrating with specific molecular signals. Their multiple biological functions include antimutagenic, antiproliferative, antimetastatic, anti-angiogenesis, anti-inflammatory, antioxidant, and immunomodulatory properties, which collectively enable them to control cancer progression and intervene at various stages of cancer cell development. Moreover, these compounds are involved in regulating the cell cycle and microRNA, ultimately leading to cancer cell death by promoting apoptosis and autophagy, often mediated through ROS signaling. Thus, based on a large theoretical revision, we conclude that high-quality evidence is necessary in order to advise these products concerning their efficacy and safety. Also, clinical evidence should be supported by a comprehensive individual diagnosis and adequate research protocols in order to evaluate whether the benefits of these plant-produced interventions can outweigh their harms. Full article

Cancer therapy is rapidly evolving from a one-size-fits-all paradigm toward highly personalized approaches. Traditional chemotherapies and radiotherapies, while broadly applied, often yield suboptimal outcomes due to tumor heterogeneity and are limited by significant toxicities. In contrast, precision oncology tailors prevention, diagnosis, and treatment to the individual patient’s genetic and molecular profile. Key advancements underscore this shift: molecularly targeted drugs (e.g., trastuzumab for HER2-positive breast cancer, EGFR and ALK inhibitors for lung cancer) have improved efficacy and reduced toxicity compared to conventional therapy. Pharmacokinetic (PK) and pharmacodynamic (PD) considerations are central to personalizing treatment, explaining variability in drug exposure and response among patients and guiding dose optimization. Modern strategies like therapeutic drug monitoring and model-informed precision dosing seek to maintain drug levels in the therapeutic range, improving outcomes. Immunotherapies, including checkpoint inhibitors and CAR-T cells, have transformed oncology, though patient selection via biomarkers (such as PD-L1 expression or tumor mutational burden) is critical to identify likely responders. Innovative drug delivery systems, notably nanomedicine, address PK challenges by enhancing tumor-specific drug accumulation and enabling novel therapeutics. Furthermore, rational combination regimens (informed by PK/PD and tumor biology) are being designed to achieve synergistic efficacy and overcome resistance. Key barriers include the high cost of biomarker testing, insufficient laboratory infrastructure, and inconsistent reimbursement policies. Operational inefficiencies such as long turnaround times or lack of clinician awareness further limit the use of precision diagnostics. Regulatory processes also remain complex, particularly around the co-development of targeted drugs and companion diagnostics, and the evidentiary requirements for rare subgroups. Addressing these barriers will require harmonized policies, investment in infrastructure, and educational initiatives to ensure that the promise of personalized medicine becomes accessible to all patients. Ensuring that advances are implemented responsibly—guided by pharmacological insights, supported by real-world evidence, and evaluated within ethical and economic frameworks—will be critical to realizing the full potential of personalized cancer medicine. Full article

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article

Nanopores have emerged as powerful tools for single-molecule detection, enabling real-time analysis across diverse applications in genomics and molecular diagnostics. While natural pores laid the foundation for single-molecule detection, their limited diversity has driven advances in protein engineering and, more recently, de novo design to create customizable nanopore sensors. Computational approaches now allow for the design of nanopores with tailored geometries, enhanced stability, and specific molecular recognition functions. Together, these advances are ushering in a new era of programmable nanopore sensors with broad applications in diagnostics and molecular biotechnology. Full article

Micro-nano bubbles (MNBs), typically characterized by diameters ranging from tens of micrometers to hundreds of nanometers, have gained significant attention in recent years due to advancements in nanotechnology and related characterization methods. This technology has shown great promise in the field of petroleum engineering. Among the various applications, the integration of MNBs with gel technology plays a critical role in enhancing drilling safety. This paper aims to systematically review the current status, challenges, and optimization strategies for the application of MNBs in petroleum engineering, with a particular focus on their combined use with gel technology in oilfield applications. The paper first introduces the preparation methods and physicochemical properties of MNBs tailored for oilfield applications. It then systematically reviews the use of MNBs in the following three key areas of petroleum engineering: drilling, enhanced oil recovery (EOR), and oil–water separation. The paper also compares domestic and international technological approaches, highlighting the challenges associated with the large-scale application of MNBs in China. Notably, in the areas of drilling and enhanced oil recovery, the synergistic use of MNBs and gel technology has demonstrated significant potential. The gel–MNB combined technology demonstrates particular promise for China’s special reservoirs, as gel’s high molecular weight compensates for MNBs’ sedimentation defects, while their synergistic effects on interfacial tension reduction and drilling fluid stabilization provide an eco-efficient approach for extreme conditions. Additionally, focusing on the combined application of gel and MNB technology, along with adjustments in gel stability and MNB size, could offer a promising solution for the efficient and sustainable development of special reservoirs (such as those with high temperature, pressure, and salinity) in China. Full article

Only about 5% of colorectal cancers are hereditary, which is due to the low carrier rate of pathogenic gene mutations. The typical pattern of these cases is intergenerational aggregation within families and early onset. But public awareness of early diagnosis and intervention of HCRC is insufficient, resulting in most patients being diagnosed only after developing cancer, thereby missing the optimal window for treatment. This article reviews the latest developments in precision screening, treatment, evaluation and prevention strategies for HCRC, including innovative uses of artificial intelligence (AI) in molecular diagnostics, imaging technology advances, and potential application prospects. Regarding precision screening, tests of genomics, transcriptomics, microbiome, etc., combined with personalised risk stratification, can, respectively, effectively detect pathogenic mutations and “cancer-promoting” intestinal environments in the preclinical stage. AI combined with endoscopic and imaging tools has improved the accuracy of polyp detection and tumor profiling. Liquid biopsy and molecular marker detection provide new non-invasive monitoring solutions. In precision treatment, beyond traditional approaches like surgery and chemotherapy, immunotherapy with checkpoint inhibitors may be considered for HCRC patients with mismatch repair deficiency (dMMR). For patients harboring somatic mutations such as KRAS or BRAF V600E, targeted therapy can be guided by these specific mutations. Regarding precision assessment, AI incorporates microsatellite instability (MSI) detection and imaging diagnostic techniques, crucial for integrating genetic, environmental, and lifestyle data in follow-up. This helps assess the risk of recurrence and adjust the long-term medication regimens, as well as provide effective nutritional support and psychological counselling. In summary, the rapid development of precision medicine is driving the clinical management of HCRC into the era of tailored care, aiming to optimise patient outcomes. Full article

Quantum dots (QDs) have emerged as promising nanomaterials in cancer therapeutics owing to their tunable optical properties, versatile surface functionalization, and potential for simultaneous imaging and drug delivery. This review focuses on targeted quantum dots (TQDs), highlighting their role in overcoming the limitations of passive drug delivery strategies, such as poor specificity, high systemic toxicity, and limited therapeutic efficacy. We begin by outlining the fundamentals of QDs, including their types, heterostructures, and biomedical formulations. Recent advances in tailoring QD physicochemical properties to the cancer microenvironment are discussed, with emphasis on routes of administration and targeting strategies. The review critically examines different molecular targeting approaches—such as folate receptors, transferrin receptors, aptamers, antibodies, peptides, and hyaluronic acid—used to enhance therapeutic precision. Furthermore, we summarize progress in TQD-based combination therapies, including chemotherapy–photodynamic therapy, photothermal therapy, radiotherapy, and multimodal platforms that integrate therapy with imaging. Special attention is given to the role of QDs in theranostic, hydrogels, nanocomposites, and hybrid systems that enable controlled drug release and real-time monitoring. Despite significant advancements, challenges remain regarding biocompatibility, safety, and regulatory approval. Overall, this review provides an integrative perspective on the design, functionalization, and biomedical applications of TQDs, underscoring their potential to improve cancer treatment outcomes through enhanced specificity, reduced side effects, and multifunctional theranostic capabilities. Highlight of novelty: This review uniquely emphasizes the latest advances in targeted quantum dots (TQDs), particularly in surface functionalization, hybrid nanostructures, biodistribution, and multimodal theranostic applications, providing an updated perspective that extends beyond conventional QD-based cancer therapies. Full article

Background: Human papillomavirus (HPV)-associated oral and oropharyngeal squamous cell carcinomas have risen dramatically in incidence over recent decades. Yet, unlike cervical neoplasia, there is no established screening paradigm for HPV-driven oropharyngeal dysplasia, as precursor lesions are often occult and are not easily accessible for examination. This drives an urgent need for non-invasive biomarkers to enable early detection, risk stratification, and timely intervention. Objective of this review is to highlight advances in liquid biopsy modalities, specifically saliva- and blood-based biomarkers—in the context of HPV-driven oral carcinogenesis—and to evaluate their utility in early cancer detection, prognostic, post-treatment surveillance, and recurrence monitoring. Methods: We performed a narrative review of PubMed-indexed studies (2015–2025) focusing on HPV-positive oral and oropharyngeal squamous cell carcinomas. and liquid biopsy analytes. Key sources were high-impact original studies and meta-analyses from 2020–2025 examining circulating tumor DNA (ctDNA), viral nucleic acids, circulating tumor cells (CTCs), extracellular vesicles (EVs), and related biomarkers in saliva and blood. Reported data on assay performance, biases, and validation were reviewed to highlight how oral cancer findings align with trends seen in other solid tumors. Results: In reviewing recent studies (2015–2025), we found consistent evidence that saliva best captures locoregional tumor signals while plasma circulating tumor HPV DNA (ctHPV DNA) reflects systemic disease, and that using both matrices improves detection over either alone. Dual-fluid testing will potentially enable earlier identification of molecular residual disease with clinically meaningful lead time before radiographic recurrence, supporting risk-adapted surveillance. Overall, literature favors standardized pre-analytics and combined saliva plus plasma workflows to enhance early detection and follow-up in HPV-positive oral and oropharyngeal squamous cell carcinomas. Conclusions: Liquid biopsy approaches offer promising tools for the early, non-invasive detection and real-time monitoring of HPV-associated oral cancers. Realizing their full clinical potential will require robust prospective validation and standardization of pre-analytical protocols. Integrating salivary and blood biomarkers into tailored surveillance programs may further support earlier intervention and improved patient outcomes, while potentially reducing reliance on unnecessary invasive procedures. Full article

Fabricating breast tumor models that mimic the natural breast tissue-like microenvironment (normal or cancerous) both physically and bio-metabolically, despite extended research, is still a challenge. A native-mimicking breast tumor model is the demand since complex biophysiological mechanisms in the native breast tissue hinder deciphering the root causes of cancer initiation and progression. Hydrogels, which mimic the natural extracellular matrix (ECM), are increasingly demanded for various biomedical applications, including tissue engineering and tumor modeling. Their biomimetic 3D network structures have demonstrated significant potential to enhance the breast tumor model, treatment, and recovery. Additionally, 3D tumor organoids cultivated within hydrogels maintain the physical and genetic traits of native tumors, offering valuable platforms for personalized medicine and therapy response evaluation. Hydrogels are broadly classified into static and dynamic hydrogels. Static hydrogels, however, are inert to external stimuli and do not actively participate in biological processes or provide scaffolding systems. Dynamic hydrogels, on the other hand, adapt and respond to the surrounding microenvironment or even create new microenvironments according to physiological cues. Dynamic hydrogels typically involve reversible molecular interactions—through covalent or non-covalent bonds—enabling the fabrication of hydrogels tailored to meet the mechanical and physiological properties of target tissues. Although both static and dynamic hydrogels can be advanced by incorporating active nanomaterials, their combinations with dynamic hydrogels provide enhanced functionalities compared to static hydrogels. Further, engineered hydrogels with adipogenic and angiogenic properties support tissue integration and regeneration. Hydrogels also serve as efficient delivery systems for chemotherapeutic and immunotherapeutic agents, enabling localized, sustained release at tumor sites. This approach enhances therapeutic efficacy while minimizing systemic side effects, supporting ongoing research into hydrogel-based breast cancer therapies and reconstructive solutions. This review summarizes the roles of dynamic hydrogels in breast tumor models. Furthermore, this paper discusses the advantages of integrating nanoparticles with dynamic hydrogels for drug delivery, cancer treatment, and other biomedical applications, alongside the challenges and future perspectives. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a leading cause of morbidity and mortality in Mexico, with more than 20,000 new cases annually and a rising proportion of drug-resistant strains. This work addresses the molecular epidemiology of TB in the Mexican context, emphasizing its role in understanding transmission, genetic diversity, and resistance mechanisms. To achieve this, we reviewed molecular typing approaches including spoligotyping, Mycobacterial Interspersed Repetitive Unit–Variable Number Tandem Repeat (MIRU-VNTR) analysis, and whole-genome sequencing (WGS), which have been applied to characterize circulating lineages and identify drug-resistance-associated mutations. The results indicate that the Euro-American lineage (L4) predominates across the country, although significant regional variation exists, with Haarlem, LAM, T, and X sub lineages dominating in different states, and occasional detection of Asian (L2) and Indo-Oceanic (L1) lineages. Key resistance mutations were identified in katG, rpoB, pncA, and gyrA, contributing to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, particularly in border and marginalized regions. These findings highlight how social factors, such as migration, urban overcrowding, and comorbidities including diabetes and HIV, influence transmission dynamics. We conclude that integrating molecular tools with epidemiological surveillance is crucial for strengthening public health strategies and guiding interventions tailored to Mexico’s heterogeneous TB burden. Full article

Antioxidant properties of inorganic nanoparticles in aqueous media are attracting growing interest due to their high surface reactivity. Materials such as cerium oxide, iron oxide, silver, and gold exhibit distinct radical-scavenging behaviors at the nanoscale, but reliable quantification remains challenging. Conventional assays developed for molecular antioxidants cannot be directly applied because probes such as 2,2-diphenyl-1-picrylhydrazyl (DPPH) require methanol–water mixtures and are unstable in aqueous nanoparticle suspensions, while other assays are affected by nanoparticle-induced absorption or fluorescence changes. Here we demonstrate strategies to correct these interferences by independently measuring nanoparticle optical properties after oxidation and customizing assay conditions to account for the dilute, per-particle concentrations of nanomaterials. Using a high-throughput 96-well format, four adapted assays revealed that silver, ceria, and iron oxide nanoparticles possess substantially higher antioxidant capacities than Trolox, while gold showed negligible activity. This optimized approach enables reproducible comparison of nanoparticle antioxidants and provides a platform for tailoring nanostructures with enhanced radical-scavenging properties. Full article

This study presents an enantiomeric-ratio-driven strategy for constructing mechanically robust supramolecular gels using cyclohexane bisurea derivatives. By employing non-equimolar enantiomeric mixtures, we achieved an ultralow critical gelation concentration (CGC < 2 mg/mL) in toluene, representing a reduction of more than fivefold compared to homochiral single-enantiomer systems. Rheological measurements revealed substantially enhanced mechanical properties in the non-equimolar gels, with yield stress and storage modulus values up to 17 and 20 times higher, respectively, than those of single-enantiomer gels. Morphological analyses (SEM and POM) indicated that pure enantiomers form isolated crystalline fibers with limited connectivity, whereas racemic mixtures yield disordered amorphous aggregates. In contrast, non-equimolar mixtures self-assemble into hierarchical “sea urchin-like” architectures, wherein crystalline fibers radiate from central cores to form densely interconnected networks. This unique structural motif underpins both the ultralow CGC and superior mechanical performance. Complementary FT-IR, XRD, and DSC analyses demonstrated that chiral imbalance modulates hydrogen-bonding interactions and structural order, while molecular dynamics (MD) simulations provided insight into the divergent self-assembly pathways among homochiral, racemic, and non-equimolar systems. This work provides a stereochemically guided approach for designing high-performance supramolecular gels with tailored hierarchical structures and enhanced functionality. Full article

Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug from multiple candidates. We present SEATS (Systematic Efficacy Assignment with Treatment Seats), which adapts conventional models like Random Forest and XGBoost for multiclass drug assignment by allocating probabilistic “treatment seats” to drugs based on efficacy. This approach helps models learn clinically relevant distinctions. Additionally, we assess an interpretable Optimal Decision Tree (ODT) model designed specifically for drug assignment. Trained on the BeatAML2 cohort and validated on the GDSC AML cell line dataset, integrating SEATS with Random Forest and XGBoost improved prediction accuracy and consistency. The ODT model offered competitive performance with clear, interpretable decision paths and minimal feature requirements, facilitating clinical use. SEATS reorients standard models towards personalized drug selection. Combined with the ODT framework it provides effective, interpretable strategies for precision oncology, underscoring the potential of tailored machine learning solutions in supporting real-world treatment decisions. Full article