Search Results (792)

This study presents an integrated approach combining molecular, phytochemical, and biological analyses to characterize a newly discovered Zizania specimen from the northern Nile Delta, Egypt. Genetic fingerprinting using RAPD and ISSR markers revealed 85% band-sharing similarity with Zizania texana (Z. texana), though distinct morphological and genetic traits suggested potential intraspecific variation. Phytochemical profiling identified high concentrations of bioactive compounds, including quercetin (42.1 µg/mL), β-caryophyllene (11.21%), and gallic acid (23.4 µg/mL), which are pertinent and correlated with robust biological activities. The ethanolic leaf extract exhibited significant antioxidant capacity (IC₅₀ = 38.6 µg/mL in DPPH assay), potent antimicrobial effects against Candida albicans (C. albicans) (IC₅₀ = 4.9 ± 0.6 µg/mL), and dose-dependent cytotoxicity against cancer cell lines. MCF-7 has the lowest IC₅₀ (28.3 ± 1.5 µg/mL), indicating the highest potency among the tested cell lines. In contrast, HepG2 demonstrates moderate sensitivity (IC₅₀ = 31.4 ± 1.8 µg/mL), while A549 shows the highest IC₅₀ value (36.9 ± 2.0 µg/mL), indicating greater resistance. These findings underscore the taxonomic novelty of the specimen and its potential as a source of natural antioxidants, antimicrobials, and anticancer agents. The study highlights the importance of interdisciplinary approaches in resolving taxonomic uncertainties and unlocking the medicinal value of understudied aquatic plants. Full article

The unique molecular fingerprint spectral characteristics in the terahertz (THz) band provide distinct advantages for non-destructive and rapid biomolecular detection. However, conventional THz metasurface biosensors still face significant challenges in achieving highly sensitive and precise detection. This study proposes a sensing platform based on quasi-bound states in the continuum (Quasi-BIC), which enhances molecular fingerprint recognition through resonance amplification. We designed a symmetric graphene double-split square ring metasurface structure. By modulating the Fermi level of graphene, this system generated continuously tunable Quasi-BIC resonance peaks across a broad THz spectral range, achieving precise spectral overlap with the characteristic absorption lines of lactose (1.19 THz and 1.37 THz) and tyrosine (0.958 THz). The results demonstrated a remarkable 763-fold enhancement in absorption peak intensity through envelope analysis for analytes with 0.1 μm thickness, compared to conventional bare substrate detection. This terahertz BIC metasurface sensor demonstrates high detection sensitivity, holding significant application value in fields such as biomedical diagnosis, food safety, and pharmaceutical testing. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder with a pressing need for novel therapeutics. However, current medications only offer symptomatic relief, without tackling the underlying pathology. To explore the bioactive potential of marine-derived fungi, this study focused on Aspergillus terreus C23-3, a strain isolated from the coral Pavona cactus in Xuwen County, China, which showed a richer metabolite fingerprint among the three deposited A. terreus strains. AntiSMASH analysis based on complete genome sequencing predicted 68 biosynthetic gene clusters (BGCs) with 7 BGCs synthesizing compounds reported to have anti-AD potential, including benzodiazepines, benzaldehydes, butenolides, and lovastatin. Liquid chromatography coupled with mass spectrometry (LC-MS)-based combinational metabolomic annotation verified most of the compounds predicted by BGCs with the acetylcholinesterase (AChE) inhibitor territrem B characterized from its fermentation extract. Subsequently, molecular docking showed that these compounds, especially aspulvione B1, possessed strong interactions with AD-related targets including AChE, cyclin-dependent kinase 5-p25 complex (CDK5/p25), glycogen synthase kinase-3β (GSK-3β), and monoamine oxidase-B (MAO-B). In conclusion, the genomic–metabolomic analyses and molecular docking indicated that C23-3 is a high-value source strain for anti-AD natural compounds. Full article

The comprehensive analysis of microbial communities reveals the unique microbial identity of different olive varieties, paving the way for new strategies in their development and commercial exploitation. In this context, the present study aimed to explore the microbial diversity and functional characteristics of Tsounati variety olives from the Monemvasia region of Peloponnese, Greece, that were naturally fermented for three months. The bacterial and fungal microbiota of both olives and brines were fingerprinted throughout the fermentation through classical microbiological analysis combined with molecular techniques. Among the 148 isolated bacteria, 85 were lactic acid bacteria (LAB), and 63 belonged to the Enterobacteriaceae family, while the 178 fungal isolates comprised 136 yeasts and 42 non-yeast or yeast-like fungi. Metataxonomic analysis confirmed the dominance of the bacterial genera Lactiplantibacillus, Leuconostoc, along with the Enterobacteriaceae family, and it revealed the presence of Coleofasciculaceae cyanobacteria mostly in olives. The dominant fungal genera were yeasts, namely Saccharomyces, Nakazawaea, and Cyberlindnera. Using the Folin–Ciocalteu assay, the average total polyphenol content of Tsounati fermented olive samples was 761.80 ± 128.87 mg gallic acid equivalents kg⁻¹ after 90 days of fermentation. The concentrations of the triterpenic, maslinic, and oleanolic acids, as determined by HPLC, remained stable throughout fermentation, with average values of 4764 and 1807 mg kg⁻¹, respectively. Finally, sensory analysis revealed the rich aromatic character of Tsounati variety, highlighting its potential to be used for Greek-style table olive production. Full article

Cold environments challenge the structural and functional integrity of membrane proteins, requiring specialized adaptations to maintain activity under low thermal energy. Here, we investigate the molecular basis of cold tolerance in the peptide transporter PepT1 from the Antarctic icefish (Chionodraco hamatus, ChPepT1) using molecular dynamics simulations, binding free energy calculations (MM/GBSA), and dynamic network analysis. We compare ChPepT1 to its human ortholog (hPepT1), a non-cold-adapted variant, to reveal key features enabling psychrophilic function. Our simulations show that ChPepT1 displays enhanced global flexibility, particularly in domains adjacent to the substrate-binding site and the C-terminal domain (CTD). While hPepT1 loses substrate binding affinity as temperature increases, ChPepT1 maintains stable peptide interactions across a broad thermal range. This thermodynamic buffering results from temperature-sensitive rearrangement of hydrogen bond networks and more dynamic lipid interactions. Importantly, we identify a temperature-responsive segment (TRS, residues 660–670) within the proximal CTD that undergoes an α-helix to coil transition, modulating long-range coupling with transmembrane helices. Dynamic cross-correlation analyses further suggest that ChPepT1, unlike hPepT1, reorganizes its interdomain communication in response to temperature shifts. Our findings suggest that cold tolerance in ChPepT1 arises from a combination of structural flexibility, resilient substrate binding, and temperature-sensitive interdomain dynamics. These results provide new mechanistic insight into thermal adaptation in membrane transporters and offer a framework for engineering proteins with enhanced functionality in extreme environments. Full article

Brachial plexus root avulsion [BPRA] and concomitant spinal cord injury [SCI] represent devastating injuries that come with limited hope for recovery owing to the adult spinal cord’s loss of intrinsic ability to spontaneously regenerate. BPRA/SCI is an enormous public health issue the world over, and its catastrophic impact goes beyond the patient, the family, businesses, and national health budgets, draining billions of dollars annually. The rising population and economic growth have seen the incidence of SCI surging. Genomic, transcriptomic, and proteomic studies have yielded loads of information on the various molecular events that precede, regulate, and support both regenerative and degenerative pathways post-SCI. Metabolomics, on the other hand, comes in as the search for a cure and the objective monitoring of SCI severity and prognosis remains on the horizon. Despite the large number of review articles on metabolomics and its application fields such as in cancer and diabetes research, there is no comprehensive review on metabolite profiling to study disease mechanisms, biomarkers, or neuroprotection in SCI. First, we present a short review on BPRA/SCI. Second, we discuss potential benefits of metabolomics as applied in BPRA/SCI cases. Next, a look at the analytical techniques that are used in metabolomics. Next, we present an overview of the studies that have used metabolomics to reveal SCI metabolic fingerprints and point out areas of further investigation. Finally, we discuss future research directions. Full article

Chemical modifications are the standard for small interfering RNAs (siRNAs) in therapeutic applications, but predicting their off-target effects remains a significant challenge. Current approaches often rely on sequence-based encodings, which fail to fully capture the structural and protein–RNA interaction details critical for off-target prediction. In this study, we developed a framework to generate reproducible structure-based chemical features, incorporating both molecular fingerprints and computationally derived siRNA–hAgo2 complex structures. Using an RNA-Seq off-target study, we generated over 30,000 siRNA–gene data points and systematically compared nine distinct types of feature representation strategies. Among the datasets, the highest predictive performance was achieved by Dataset 3, which used extended connectivity fingerprints (ECFPs) to encode siRNA and mRNA features. An energy-minimized dataset (7R), representing siRNA–hAgo2 structural alignments, was the second-best performer, underscoring the value of incorporating reproducible structural information into feature engineering. Our findings demonstrate that combining detailed structural representations with sequence-based features enables the generation of robust, reproducible chemical features for machine learning models, offering a promising path forward for off-target prediction and siRNA therapeutic design that can be seamlessly extended to include any modification, such as clinically relevant 2′-F or 2′-OMe. Full article

Natural colorants, with their sustainable origins, offer a promising alternative for various applications. Advanced studies have unveiled the remarkable properties, resilience, and durability of these ancient dyes, which our ancestors developed through sustainable material processing. This serves as a testament to the potential of sustainable solutions in our field. As part of our research, we prepared three medieval temperas using gum arabic, parchment glue, and casein glue. These tempera were explicitly designed to protect the purples obtained from Chrozophora tinctoria extracts. A comprehensive multi-analytical approach guides our research on natural colorants. Central to this approach is the use of molecular fluorescence by microspectrofluorimetry, a key tool in our study. By analyzing the emission and excitation spectra in the visible range, we can identify specific formulations. This method is further supported by fingerprinting techniques, including Fourier Transform Infrared Spectroscopy (FTIR) and High-Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD). These are further complemented by Fiber Optics Reflectance Spectroscopy (FORS) and colorimetry. Building on our understanding of orcein purples, we have extended our research to purples derived from Chrozophora tinctoria extracts. Our findings reveal the unique properties of Chrozophora tinctoria, which can be accurately distinguished from orcein purples, highlighting the distinctiveness of each. Full article

Background/Objectives: Severe malaria, mainly caused by Plasmodium falciparum, remains a significant therapeutic challenge due to increasing drug resistance and adverse effects. Flavonoids, known for their wide range of bioactivities, offer a promising route for antimalarial drug discovery. The aim of this study was to elucidate key structural features associated with antimalarial activity in flavonoids and to develop accurate, interpretable predictive models. Methods: Curated databases of flavonoid structures and their activity against P. falciparum strains and enzymes were constructed. Molecular fingerprinting and decision tree analyses were used to identify key pharmacophoric groups. Subsequently, molecular descriptors were generated and reduced to build multiple classification and regression models. Results: These models demonstrated high predictive accuracy, with test set accuracies ranging from 92.85% to 100%, and R² values from 0.64 to 0.97. Virtual screening identified novel flavonoid candidates with potential inhibitory activity. These were further evaluated using molecular docking and molecular dynamics simulations to assess binding affinity and stability with Plasmodium proteins (FabG, FabZ, and FabI). The predicted active ligands exhibited stable pharmacophore interactions with key protein residues, providing insights into binding mechanisms. Conclusions: This study provides highly predictive models for antimalarial flavonoids and enhances the understanding of structure–activity relationships, offering a strong foundation for further experimental validation. Full article

The present work reports the synthesis and structural design of three novel Zn(II) complexes [Zn(L¹)(CH₃COO)(H₂O)] (1), [Zn(L²)₂] (2), and [Zn(L³)₂] (3) with carbazate ligands, 2-acetylpyridine-methylcarbazate (HL¹), 2-acetylpyridine-ethylcarbazate (HL²), and 2-acetylpyridine-benzylcarbazate (HL³). All compounds were characterized by spectroscopic methods, and the crystal structures of the complexes were elucidated by single-crystal X-ray. Based on the analysis, distorted square pyramid geometry is suggested for complex (1) and an octahedral geometry is suggested for complexes (2) and (3) with the ligands exhibiting an NNO-donor system. The 3D Hirshfeld surface and the 2D fingerprint plot were used to study the non-covalent interactions in the crystal structures. The in vitro antibacterial investigation of the free ligands and their complexes was performed against different strains of periodontopathogen bacteria. The Zn(II) complexes showed more potent antibacterial activity than the free ligand. Molecular docking studies showed the metal complexes as promising candidates for further therapeutic exploration, particularly in targeting the ATP-binding cassette transporter with peptidase domain of the cariogenic bacteria S. mutans (PDB code 5XE9) and the prolyl tripeptidyl aminopeptidase from P. gingivalis anaerobic bacteria (PDB code 2EEP) inhibition. Full article

Polymer brushes (PBs) are transformative surface-modifying nanostructures, yet their synthesis via controlled methods like copper-mediated surface-initiated atom-transfer radical polymerization (Cu⁰-SI-ATRP) faces reproducibility challenges due to a lack of understanding of parameter interdependencies. This study systematically evaluates the Cu⁰-SI-ATRP process for polyacrylamide brushes (PAM-PBs), aiming to clarify key parameters that influence the synthesis process. This evaluation followed a step-by-step characterization that tracked molecular changes through infrared spectroscopy (IR) and surface development by contact angle (CA) through two different mixing methods: ultrasonic mixing and process simplification (Method A) and following literature-based parameters (Method B). Both methods, consisting of surface activation, 3-aminopropyltriethoxysilane (APTES) deposition, bromoisobutyryl bromide (BiBB) anchoring, and polymerization, were analyzed by varying parameters like concentration, temperature, and time. Results showed ultrasonication during surface activation enhanced siloxane (1139→1115 cm⁻¹) and amine (1531 cm⁻¹) group availability while reducing APTES concentration to 1 Vol% without drying sufficed for BiBB anchoring. BiBB exhibited insensitivity to concentration but benefited from premixing, evidenced by sharp C–Br (~1170 cm⁻¹) and methyl (3000–2800 cm⁻¹) bands. Additionally, it was observed that PAM-PBs improved with Method A, which had reduced variance in polymer fingerprint regions compared to Method B. Adding to the above, CA measurements gave complementary step-by-step information along the modifications of the surface, revealing distinct wettability behaviors between bulk PAM and synthesized PAM-PBs (from 51° to 37°). As such, this work identifies key parameter influence (e.g., mixing, BiBB concentration), simplifies steps (drying omission, lower APTES concentration), and demonstrates a step-by-step, systematic parameter decoupling that reduces variability. In essence, this detailed parameter analysis addresses the PAM-PBs synthesis process with better reproducibility than the previously reported synthesis method and achieves the identification of characteristic behaviors across the step-by-step process without the imperative need for higher-cost characterizations. Full article

Background and Objective: Dysregulated tyrosine kinase signaling is a central driver of tumorigenesis, metastasis, and therapeutic resistance. While tyrosine kinase inhibitors (TKIs) have revolutionized targeted cancer treatment, identifying compounds with optimal bioactivity remains a critical bottleneck. This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. Methods: A curated dataset of 28,314 small molecules from the ChEMBL database targeting 11 tyrosine kinases was analyzed. Using Morgan fingerprints and physicochemical descriptors (e.g., molecular weight, LogP, hydrogen bonding), ten supervised models, including dANN, SVM, CatBoost, and CNN, were trained and optimized through a randomized hyperparameter search. Model performance was evaluated using F1-score, ROC–AUC, precision–recall curves, and log loss. Results: SVM achieved the highest F1-score (87.9%) and accuracy (85.1%), while dANNs yielded the lowest log loss (0.25096), indicating superior probabilistic reliability. CatBoost excelled in ROC–AUC and precision–recall metrics. The integration of Morgan fingerprints significantly improved bioactivity prediction across all models by enhancing structural feature recognition. Conclusions: This work highlights the transformative role of machine learning—particularly dANNs and SVM—in rational drug discovery. By enabling accurate bioactivity prediction, our model pipeline can effectively reduce experimental burden, optimize compound selection, and support personalized cancer treatment design. The proposed framework advances kinase inhibitor screening pipelines and provides a scalable foundation for translational applications in precision oncology. By enabling early identification of bioactive compounds with favorable pharmacological profiles, the results of this study may support more efficient candidate selection for clinical drug development, particularly in regards to cancer therapy and kinase-associated disorders. Full article

This study aimed to isolate and genetically characterize Arcobacter species from broiler chickens sampled at three slaughterhouses in Grenada, West Indies. A total of 126 samples—including cloacal swabs, intestinal contents, and meat—from 42 birds were cultured using a chromogenic agar medium. Arcobacter spp. were detected in 21.4% (9/42) of the birds. Among the sample types, meat exhibited the highest prevalence at 14.3% (6/42), followed by fecal samples at 7.1% (3/42) and cloacal swabs at 2.4% (1/42). Genus- and species-specific polymerase chain reaction assays on 33 isolates identified five Arcobacter species: A. butzleri, A. cryaerophilus, and A. skirrowii (each 18.2%), as well as A. cibarius and A. thereius (each 6.1%). Genetic diversity was further assessed via Enterobacterial Repetitive Intergenic Consensus–polymerase chain reaction, which revealed 13 distinct genotypic fingerprints forming six clusters, with a high discriminatory power (D = 0.96). This study represents the first documented isolation and molecular characterization of five Arcobacter species from broiler chickens in Grenada across multiple sample types. These findings underscore the zoonotic implications of isolating Arcobacter spp., particularly in contaminated poultry meat destined for human consumption. The presence of Arcobacter spp. in poultry carcasses poses a significant public health concern. To mitigate this public health risk, recommendations include surveillance for the presence of this pathogen in Hazard Analysis and Critical Control Points plans or other tools used to identify pathogens compromising food safety and public health. Full article

Hepatocellular carcinoma (HCC) imposes a significant burden on global public health. Exposure to aflatoxins, potent mycotoxins produced by Aspergillus fungi contaminating staple foods, and chronic hepatitis B virus (HBV) infection are major etiological factors, especially where they co-exist. This review examines the critical role of the p53 tumor suppressor pathway as a primary target and convergence point for the carcinogenic actions of aflatoxins and HBV. Aflatoxin B₁ (AFB₁), a Group 1 carcinogen, exerts significant genotoxicity, characteristically inducing a specific hotspot mutation (R249S) in the TP53 gene via DNA adduct formation, thereby compromising p53’s critical tumor suppressor functions. This R249S mutation is considered a molecular fingerprint of aflatoxin exposure. Concurrently, the HBV X protein (HBx) functionally inactivates wild-type p53 through direct binding and by promoting its degradation. The synergistic disruption of the p53 pathway, driven by AFB₁-induced mutation and amplified by HBV-mediated functional inhibition, significantly enhances the risk of HCC development. This review addresses how aflatoxin exposure alters key aspects of p53 and how this damage interacts with HBV-mediated p53 suppression, providing crucial insights into hepatocarcinogenesis. The knowledge synthesized here underscores the importance of mitigating aflatoxin exposure alongside HBV control for effective HCC prevention and treatment strategies. Full article