2026-02-24
ELHAM TARAHOMI et al.
Background/aim: Neuregulin 3 (NRG3) is an epidermal growth factor-like ligand that is involved in neuronal circuit formation in mammals. However, the expression profile of its zebrafish ortholog, nrg3b, remains poorly defined. The transcriptomic analyses of zebrafish sco-spondin mutants, which display alterations in their neurodevelopmental pathways, have indicated potential dysregulation of Nrg3b. Guided by these insights, this study aimed to characterize the temporal and spatial expression patterns of nrg3b in vivo in embryonic and adult zebrafish. Materials and methods: Publicly available RNA-seq data from heterozygous and maternal-zygotic zebrafish sco-spondin mutants at 5, 15, and 30 days postfertilization (dpf) were analyzed to evaluate differential expression, coexpression network positioning, and functional enrichment. Temporal expression of nrg3b was validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR) in embryos at 1, 3, and 5 dpf. Spatial nrg3b localization at 5 dpf was validated by whole-mount in situ hybridization (WISH), alongside neuronal subtype specificity using GABAergic and glutamatergic markers. Adult brain expression was further evaluated via ISH on telencephalon sections. Results: Developmental stage was the main driver of early transcriptomic variation, with minor genotype-specific differences detected at 5 dpf. Network analysis positioned nrg3b as a hub gene within a neuronal function associated module. RT-qPCR showed a significant increase in nrg3b expression on days 3 and 5 compared to day 1. WISH revealed strong nrg3b expression in anterior brain regions and the spinal cord at 5 dpf, with greater overlap observed with the glutamatergic marker compared to the GABAergic one. In adults, ISH suggested the expression of nrg3b in telencephalic nuclei, particularly in dorsomedial and ventral regions. Conclusion: nrg3b exhibits stage-dependent upregulation and preferential enrichment in excitatory neuronal regions during development, with sustained expression in adult telencephalon. These findings suggest a potential role for Nrg3b in synaptic organization and provide a foundation for future studies on the Nrg3b–ErbB4 signaling axis in zebrafish neurodevelopment.
2026-02-24
DUYGU ALTIPARMAK et al.
Background/aim: High-grade serous ovarian cancer (HGSOC) remains one of the most aggressive forms of ovarian malignancy and frequently shows resistance to conventional therapies. This study aimed to synthesize a novel series of purine analogues, 6-[(4-substituted benzyl amine)/(4-substituted aniline)]-9-cyclopentyl purines, and evaluate their anticancer efficacy against HGSOC cell lines. Materials and methods: We assessed the biological effects of the synthesized purine analogues on the OVCAR3, OVSAHO, and KURAMOCHI HGSOC cell lines using the sulforhodamine B assay. To investigate the mechanism of action, we conducted flow cytometry and western blot analyses, focusing on DNA replication and apoptosis. Results: Among the tested compounds, compound 8 showed significant cytotoxic activity with IC50 values in the low micromolar range. Preliminary data from flow cytometry and western blot analyses indicated that compound 8 may inhibit DNA replication and induce apoptosis, as reflected by changes in cell viability and cell-cycle progression. Conclusion: Compound 8 may disrupt key proliferative mechanisms in cancer cells by interfering with DNA synthesis and activating programmed cell death pathways. These findings suggest that compound 8 is a promising lead candidate for further development in ovarian cancer therapeutics.
2026-02-24
KADER ŞAHİN et al.
Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug design. In this work, it was aimed to investigate the SAR of a series of pyrazole-based compounds using this advanced integrative computational strategy. Materials and methods: The dataset consisted of 54 pyrazole derivatives, of which 50 compounds were used for model construction and 4 compounds were reserved as a test set for validation. Although the test set was limited in size, the selected compounds were structurally representative of the training set, sharing the same core scaffold while covering different substitution patterns and biological activity values. The 4D-QSAR approach included multiple conformations of each compound and utilized matrix-based representations of geometric and electronic properties to capture dynamic molecular behavior. A pharmacophore model was generated using EMRE software based on the spatial and electronic features of used compounds. EMRE is an in-house software developed by our research group. It has been employed in several previously published 4D-QSAR studies for electron-conformational matrix of contiguity construction, pharmacophore modeling, descriptor matrix generation, and activity prediction. EMRE operates on standard geometric and electronic descriptors derived from quantum-chemical calculations, ensuring methodological transparency and reproducibility despite its proprietary implementation. Comparable performance trends obtained with EMRE-based 4D-QSAR models have been reported in previous studies, supporting the validity of the software for pharmacophore-driven QSAR analysis (Şahin et al., 2011; Sahin and Saripinar, 2020; Sahin et al., 2021). Using this framework, a total of 204 molecular descriptors were computed using Spartan 07. To reduce redundancy and prevent overfitting, descriptor selection was optimized through a genetic algorithm (GA)-based procedure (Fernandez et al., 2011), and only statistically significant descriptors with low intercorrelation were retained for model construction. Subsequently, multiple ML algorithms, including artificial neural network, decision tree, and hybrid models, were evaluated to enhance prediction accuracy. Results: Among all the tested models, the gradient boosting machine and random forest (GBM+RF) hybrid algorithm yielded the highest predictive performance, with an R² value of 0.99978. To assess the robustness of the ML models, the training and validation procedures were repeated using different random seed initializations. The resulting performance metrics showed only minor variations across runs, indicating that the predictive performance of the GBM, RF, and GBM+RF hybrid models was not sensitive to random seed selection. The overall dataset comprised 54 pyrazole derivatives, with 50 molecules used for model construction and 4 reserved for validation. Although the high R² value indicates strong internal consistency, it should be interpreted with caution due to the relatively small sample sizes for the construction of the model and the test subset. Overall, the integration of 4D-QSAR and ML approaches demonstrated strong predictive capability and effectively captured the key geometric and electronic features associated with biological activity. Conclusion: The electron conformational-GA computational strategy provides a robust framework for the rational design and virtual screening of pyrazole derivatives, leveraging multi-conformer modeling and a diverse set of molecular descriptors to identify potentially active compounds. The study was limited by the small size of the training and test set and the absence of experimental validation, which may constrain the generalizability of the findings. Future work could address these limitations by applying the model to larger and more diverse ligand dataset, performing virtual screening of compound libraries to discover novel hits, and validating promising candidates through in vitro assays. Overall, these findings support the potential of this scaffold for drug discovery, while further experimental studies are warranted to confirm the predicted activities and refine the predictive power of the model. Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug design. In this work, it was aimed to investigate the SAR of a series of pyrazole-based compounds using this advanced integrative computational strategy. Materials and methods: The dataset consisted of 54 pyrazole derivatives, of which 50 compounds were used for model construction and 4 compounds were reserved as a test set for validation. Although the test set was limited in size, the selected compounds were structurally representative of the training set, sharing the same core scaffold while covering different substitution patterns and biological activity values. The 4D-QSAR approach included multiple conformations of each compound and utilized matrix-based representations of geometric and electronic properties to capture dynamic molecular behavior. A pharmacophore model was generated using EMRE software based on the spatial and electronic features of used compounds. EMRE is an in-house software developed by our research group. It has been employed in several previously published 4D-QSAR studies for electron-conformational matrix of contiguity construction, pharmacophore modeling, descriptor matrix generation, and activity prediction. EMRE operates on standard geometric and electronic descriptors derived from quantum-chemical calculations, ensuring methodological transparency and reproducibility despite its proprietary implementation. Comparable performance trends obtained with EMRE-based 4D-QSAR models have been reported in previous studies, supporting the validity of the software for pharmacophore-driven QSAR analysis (Şahin et al., 2011; Sahin and Saripinar, 2020; Sahin et al., 2021). Using this framework, a total of 204 molecular descriptors were computed using Spartan 07. To reduce redundancy and prevent overfitting, descriptor selection was optimized through a genetic algorithm (GA)-based procedure (Fernandez et al., 2011), and only statistically significant descriptors with low intercorrelation were retained for model construction. Subsequently, multiple ML algorithms, including artificial neural network, decision tree, and hybrid models, were evaluated to enhance prediction accuracy. Results: Among all the tested models, the gradient boosting machine and random forest (GBM+RF) hybrid algorithm yielded the highest predictive performance, with an R² value of 0.99978. To assess the robustness of the ML models, the training and validation procedures were repeated using different random seed initializations. The resulting performance metrics showed only minor variations across runs, indicating that the predictive performance of the GBM, RF, and GBM+RF hybrid models was not sensitive to random seed selection. The overall dataset comprised 54 pyrazole derivatives, with 50 molecules used for model construction and 4 reserved for validation. Although the high R² value indicates strong internal consistency, it should be interpreted with caution due to the relatively small sample sizes for the construction of the model and the test subset. Overall, the integration of 4D-QSAR and ML approaches demonstrated strong predictive capability and effectively captured the key geometric and electronic features associated with biological activity. Conclusion: The electron conformational-GA computational strategy provides a robust framework for the rational design and virtual screening of pyrazole derivatives, leveraging multi-conformer modeling and a diverse set of molecular descriptors to identify potentially active compounds. The study was limited by the small size of the training and test set and the absence of experimental validation, which may constrain the generalizability of the findings. Future work could address these limitations by applying the model to larger and more diverse ligand dataset, performing virtual screening of compound libraries to discover novel hits, and validating promising candidates through in vitro assays. Overall, these findings support the potential of this scaffold for drug discovery, while further experimental studies are warranted to confirm the predicted activities and refine the predictive power of the model. Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug design. In this work, it was aimed to investigate the SAR of a series of pyrazole-based compounds using this advanced integrative computational strategy. Materials and methods: The dataset consisted of 54 pyrazole derivatives, of which 50 compounds were used for model construction and 4 compounds were reserved as a test set for validation. Although the test set was limited in size, the selected compounds were structurally representative of the training set, sharing the same core scaffold while covering different substitution patterns and biological activity values. The 4D-QSAR approach included multiple conformations of each compound and utilized matrix-based representations of geometric and electronic properties to capture dynamic molecular behavior. A pharmacophore model was generated using EMRE software based on the spatial and electronic features of used compounds. EMRE is an in-house software developed by our research group. It has been employed in several previously published 4D-QSAR studies for electron-conformational matrix of contiguity construction, pharmacophore modeling, descriptor matrix generation, and activity prediction. EMRE operates on standard geometric and electronic descriptors derived from quantum-chemical calculations, ensuring methodological transparency and reproducibility despite its proprietary implementation. Comparable performance trends obtained with EMRE-based 4D-QSAR models have been reported in previous studies, supporting the validity of the software for pharmacophore-driven QSAR analysis (Şahin et al., 2011; Sahin and Saripinar, 2020; Sahin et al., 2021). Using this framework, a total of 204 molecular descriptors were computed using Spartan 07. To reduce redundancy and prevent overfitting, descriptor selection was optimized through a genetic algorithm (GA)-based procedure (Fernandez et al., 2011), and only statistically significant descriptors with low intercorrelation were retained for model construction. Subsequently, multiple ML algorithms, including artificial neural network, decision tree, and hybrid models, were evaluated to enhance prediction accuracy. Results: Among all the tested models, the gradient boosting machine and random forest (GBM+RF) hybrid algorithm yielded the highest predictive performance, with an R² value of 0.99978. To assess the robustness of the ML models, the training and validation procedures were repeated using different random seed initializations. The resulting performance metrics showed only minor variations across runs, indicating that the predictive performance of the GBM, RF, and GBM+RF hybrid models was not sensitive to random seed selection. The overall dataset comprised 54 pyrazole derivatives, with 50 molecules used for model construction and 4 reserved for validation. Although the high R² value indicates strong internal consistency, it should be interpreted with caution due to the relatively small sample sizes for the construction of the model and the test subset. Overall, the integration of 4D-QSAR and ML approaches demonstrated strong predictive capability and effectively captured the key geometric and electronic features associated with biological activity. Conclusion: The electron conformational-GA computational strategy provides a robust framework for the rational design and virtual screening of pyrazole derivatives, leveraging multi-conformer modeling and a diverse set of molecular descriptors to identify potentially active compounds. The study was limited by the small size of the training and test set and the absence of experimental validation, which may constrain the generalizability of the findings. Future work could address these limitations by applying the model to larger and more diverse ligand dataset, performing virtual screening of compound libraries to discover novel hits, and validating promising candidates through in vitro assays. Overall, these findings support the potential of this scaffold for drug discovery, while further experimental studies are warranted to confirm the predicted activities and refine the predictive power of the model.
2025-12-30
M.BURCU KÜLAHCI et al.
Background/aim: This study aimed to investigate the phenotypic resistance and distribution of efflux pump-associated antimicrobial resistance genes in Staphylococcus spp. isolated from dairy and meat samples. Antimicrobial resistance in foodborne bacteria increases with antibiotic exposure and biocides, particularly through efflux mechanisms. Thus, monitoring potential genetic reservoirs in the food chain is very important. Materials and methods: A total of 132 dairy and meat samples were collected for the study, and Staphylococcus spp. were isolated using Mannitol salt phenol red agar. Antimicrobial susceptibility was evaluated using the Clinical and Laboratory Standards Institute’s microdilution method. Twenty-six resistant isolates were identified by 16S rDNA sequencing. The effect of reserpine on MIC values was evaluated using microdilution tests to assess the role of efflux pumps in antibiotic resistance and biocide tolerance. Antibiotic resistance and efflux pump genes were detected using real-time PCR with specific primers. Results: Of the 77 isolates evaluated, 26 (33.8%) were resistant to at least one antibiotic. Resistance to tetracycline (69.2%) and cefuroxime (38.5%) were the most common. The administration of reserpine reduced minimum inhibitory concentration (MIC) values across all cefuroxime-resistant isolates and in a subset of tetracycline- and nitrofurantoin-resistant strains, suggesting the potential involvement of efflux pumps. It also lowered MICs for triclosan (46.7%) and povidone-iodine (32%). The most frequently detected efflux pump genes were smr (88.5%), efrA (84.6%), efrB (80.8%), mdeA (84.6%), and norE (80.8%). qacA/B was not detected in any isolate. Conclusion: Genes encoding efflux pump proteins were commonly found in Staphylococcus spp. isolated from dairy and meat samples. Reserpine inhibition tests confirmed the phenotypic effects of these genes. These results suggest efflux-mediated resistance can significantly impact antibiotic tolerance and biocides in foodborne isolates. Continued surveillance and control strategies are essential to limit the spread of these resistance genes in the food chain.
2025-12-30
XIAOJIN LIU et al.
Background/aim: Oxidative stress is a major contributor to melanocyte dysfunction and hair graying by impairing key signaling pathways. Eucommia ulmoides bark extract (EUE), rich in antioxidant phytochemicals, has shown potential in combating oxidative damage. This study investigated the protective and promelanogenic effects of EUE under hydrogen peroxide (H₂O₂)-induced oxidative stress, with a focus on the Wnt/β-catenin signaling pathway. Materials and methods: An oxidative stress model was established using B16 cells and a C57BL/6 mouse hair follicle model. Results: EUE significantly improved melanocyte survival and reduced intracellular reactive oxygen species (ROS). Mechanistically, EUE activated the Wnt/β-catenin pathway, leading to upregulation of the microphthalmia-associated transcription factor (MITF) and its downstream melanogenic enzymes (TYR, TRP-1, TRP-2), thereby enhancing tyrosinase activity and restoring melanin synthesis. In vivo, topical application of EUE protected hair follicles from H₂O₂-induced depigmentation and promoted follicular pigmentation. Conclusion: Our results demonstrate that EUE mitigates oxidative stress and promotes melanogenesis primarily by activating the Wnt/β-catenin-MITF signaling axis. These findings provide strong mechanistic evidence supporting EUE as a potential therapeutic strategy for oxidative stress-related hair graying.
2025-12-30
ZARIFEH ADAMPOUR et al.
Background/aim: The cross-kingdom biofilm structure formed by Candida tropicalis and Streptococcus mutans may increase caries formation. The aim of this study was to evaluate the in vitro effect of the exogenous tyrosol on single- and dual-species biofilms as well as planktonic cultures formed by C. tropicalis and S. mutans. Materials and methods: The antimicrobial efficacy of tyrosol was evaluated through broth microdilution, colony-forming unit (CFU) enumeration, and XTT reduction tests to assess cell viability and metabolic activity. Transmission electron microscopy (TEM) was used to examine ultrastructural changes in planktonic cells. Biofilm dynamics were visualized via scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). The in vitro cytotoxicity of tyrosol was evaluated using NIH/3T3 fibroblast cells. Results: XTT results showed that the biofilm-reducing effect of amphotericin B (AMB) on single C. tropicalis biofilm at the minimum inhibitory concentration (MIC) and 2× MIC was significantly higher than that of control (47% and 48%, respectively) (p 0.05). However, AMP resistance increased in dual culture. CFU enumeration, TEM, SEM, and CLSM data supported these findings. The effect of tyrosol on NIH/3T3 fibroblast cells was suppressive at low concentrations (1–4 mg/mL) and enhancing at high concentrations (4.5–20 mg/mL). Conclusion: This study investigated the antimicrobial and antibiofilm properties of tyrosol against C. tropicalis and S. mutans, individually and in combination. The results showed that tyrosol inhibited growth and biofilm formation, particularly in dual-species biofilms. Although S. mutans had greater resistance, overall microbial viability was reduced. Despite some observed increase in AMP resistance, tyrosol was selectively cytotoxic, indicating its promise as a natural therapeutic agent pending further research.
2025-12-30
HONGFU LIU et al.
This corrigendum is to address an issue regarding the manuscript’s previous publication. The authors noticed that the representative flow cytometry image for the AAA group in Figure 3B, as well as the representative Histone H3 and β-actin Western blot bands in Figure 4A, were incorrectly used in the original published version of this paper and indicated that the relevant corrections do not affect the results or the conclusions of the study.To rectify this oversight and ensure the accuracy of the published work, the correct figures are included for your reference. Link to the original article: https://doi.org/10.55730/1300-0152.2754
2025-12-30
VOLKAN ERGİN et al.
Background/aim: Fibroblast Growth Factor Receptor (FGFR) gene fusions are recognized as pivotal oncogenic drivers, contributing to cancer initiation and progression across diverse malignancies. These fusions often represent significant therapeutic targets, particularly in challenging malignancies like cholangiocarcinoma. This study aimed to characterize the novel FGFR2::SHTN1 fusion, identify it as a de novo chimeric protein, and elucidate its precise oncogenic mechanism. Materials and methods: FGFR2::SHTN1 fusions were identified via cancer genomics databases and modeled using AlphaFold and HADDOCK. SHTN1 variants were expressed in Neuro-2a cells for coimmunoprecipitation, purification, and native polyacrylamide gel electrophoresis to assess oligomerization. Structural modeling included membrane embedding with Chemistry at HARvard Macromolecular Mechanics–Graphical User Interface (CHARMM–GUI). Results: We found that FGFR2::SHTN1 is an in-frame fusion formed by the joining of upstream FGFR2 exons 1–17 with downstream SHTN1 exons 7–17 in human, resulting in a chimeric protein retaining the intact FGFR2 tyrosine kinase domain. Our analyses revealed that Shootin1 inherently forms oligomers through its coiled–coil domains, which, within the fusion, mediate ligand-independent dimerization and constitutive activation of FGFR2. Conclusion: Our findings establish FGFR2::SHTN1 as a potent oncogenic driver in various cancers, particularly in cholangiocarcinoma, highlighting a unique mechanism of constitutive activation mediated by Shootin1’s CCD-II domain. This study represents the first molecular characterization of the FGFR2::SHTN1 fusion, advances understanding of FGFR2 fusion biology, and identifies a particular target for future diagnostic and therapeutic strategies in relevant malignancies.
2025-12-30
SEVİL KÖSE et al.
Background/aim: Glioblastoma multiforme (GBM) is one of the most aggressive and fatal malignancies of the central nervous system. Despite advancements in treatment strategies, effective therapies for GBM remain insufficient, necessitating further improvements. Notably, miR-22 has been found to be significantly downregulated in both glioblastoma tissues and cell lines. In this study, we aim to evaluate miR-22 expression levels in GBM (U87) and CD133-positive (CD133+) GBM stem cells (GSCs) and to investigate its effects on proliferation, colony formation, migration, invasion, and wound-healing in U87 and CD133+ U87 cells in vitro.Materials and methods: We isolated CD133+ U87 cells using magnetic-activated cell sorting and determined the percentage of CD133+ cells by flow cytometry. qRT-PCR detected miR-22 expression. We transfected miR-22 miRNA into U87, CD133+, and CD133⁻ U87 cells using a lipid-based transfection reagent. Cell viability was assessed spectrophotometrically on days 1, 3, 5, and 7 using the CCK-8 viability assay. Transwell assays were used to analyze migration and invasion. Wound healing was assessed using a scratch assay. Results: MiR-22 expression was lower in CD133+ U87 cells than in U87 cells. MiR-22 overexpression suppressed proliferation in U87, CD133+, and CD133⁻ U87 cells. MiR-22 overexpression also inhibited migration and invasion in both CD133+ and CD133⁻ U87 cells and impaired wound-healing capacity in both U87 and CD133⁻ U87 cells.Conclusion: These results suggest that miR-22 acts as a tumor suppressor in GBM and CD133+ GSCs. Therefore, miR-22 represents a potential therapeutic target for cancer stem cell-based glioblastoma treatment.
2025-10-30
DERYA DOĞANAY et al.
Background/aim: This study evaluates the challenges associated with overcoming antimicrobial resistance and innovative approaches to combat multidrug-resistant (MDR) bacterial infections. Materials and methods: Novel codoped carbon quantum dots (CCQDs) were synthesized using citric acid as the carbon source and L-cysteine as the nitrogen codoping atom. The formulation in which citric acid was retained was designated as CCQDs-1, whereas the purified version, from which citric acid was removed, was termed CCQDs-2. The antibacterial properties of CCQDs-1 and CCQDs-2 were compared using the agar well diffusion method. This study comprehensively characterizes these nanomaterials and evaluates their antibacterial potential, both alone and in combination with antibiotics, against a spectrum of gram-positive (G+) and gram-negative (G‒) bacterial strains. Results: The study demonstrates the significant antibacterial efficacy of CCQDs, with notable variations observed between citric acid-containing and citric acid-neutralized formulations. The QDs exhibited remarkable characteristics, including a quantum yield of 90.3%–90.6%, intense fluorescence, and distinctive interactions with various antibiotics. In addition to their intrinsic antibacterial activity, the QDs also exhibited synergistic effects when combined with certain antibiotics. A synergistic effect was particularly observed when CCQDs-2 were combined with antibiotics such as gentamicin, levofloxacin, and clindamycin, suggesting potential mechanisms such as membrane permeability disruption and efflux pump saturation. Conclusion: These findings underscore the promising potential of carbon-based QDs as innovative, biocompatible solutions to address the critical global challenge of antimicrobial resistance.