Turkish journal of biology = Turk biyoloji dergisi最新文献

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.

Background/objective: Despite advances in treatment, achieving effective and durable responses with chemotherapy remains a significant challenge in lung cancer management. This study investigates the effects of montelukast sodium (MLS) and desloratadine (DES), alone and in combination with cisplatin (CIS), on cell viability, apoptosis, cell cycle distribution, and antioxidant gene expression in A549 and DMS114 lung cancer cell lines.

Materials and methods: Cells were treated with CIS, MLS, DES, and their combinations for 24-72 h. Cell viability was assessed via MTS assay; apoptosis and cell cycle progression were analyzed by flow cytometry. The expression of antioxidant-related genes (GPX4, GSR, GCLC) was quantified using qRT-PCR.

Results: MLS and DES reduced cell viability individually in both cell lines in a dose- and time-dependent manner. The combination of CIS and MLS showed near-synergistic effects in A549 cells. The combination significantly enhanced apoptosis, particularly in DMS114 cells. In contrast, CIS combined with DES showed antagonistic interactions in both lines, with no significant increase in apoptosis compared to CIS alone. MLS combined with CIS also enhanced G0/G1 phase arrest, while the combination of DES and CIS had no additive effect on the cell cycle. DES alone or with CIS significantly upregulated GPX4 and GCLC, suggesting activation of antioxidant defense mechanisms. Meanwhile, MLS alone or combined with CIS led to a decrease in GCLC expression, indicating a possible impairment of redox homeostasis.

Conclusion: MLS enhances CIS-induced cytotoxicity and apoptosis in lung cancer cells and modulates redox gene expression, potentially improving therapeutic efficacy. In contrast, DES may attenuate CIS activity through antioxidant gene upregulation. These findings support the potential of MLS as an effective adjuvant in CIS-based lung cancer treatment. However, the antagonistic effect observed with DES highlights the importance of careful evaluation of candidates for drug repurposing.

Background/aim: Cardiovascular diseases (CVDs) are a leading cause of global mortality, prompting the need for advanced predictive tools. While machine learning (ML) offers a powerful solution, there are significant challenges to clinical translation. This systematic review synthesizes the current state of ML in heart disease prediction, evaluating algorithmic performance, data utilization, and key translational challenges.

Materials and methods: Following PRISMA guidelines, a systematic search of literature published up to 2025 was conducted. From an initial pool of over 2500 records, a rigorous screening process yielded 65 studies for in-depth qualitative synthesis.

Results: Analysis showed that ensemble learning models dominate prediction tasks on structured data, achieving high accuracy on benchmarks. Deep learning (DL) is increasingly applied to unstructured data like electrocardiogram signals and cardiac imaging. Despite high performance reported in models, a significant translational gap exists. This is driven by a pervasive lack of external validation, an overreliance on limited public datasets, and the black-box nature of complex models that reduces clinical trust. The adoption of explainable artificial intelligence is a key trend aimed at mitigating these challenges.

Conclusion: While ML shows significant potential, its utility remains largely confined to academic settings. The future of the field depends on a fundamental research shift, rather than on incremental accuracy gains. Progress requires a concerted focus on robust external validation, the development of large-scale representative datasets, and the creation of interpretable systems that can be effectively integrated into clinical workflows to improve patient outcomes.

Background: Pyrazole-based chalcone hybrids are notable in medicinal chemistry for their potential biological activity, although their tumor-specific cytotoxicity and mechanisms remain unknown in OSCC cells. This first study of pyrazole-chalcone hybrids in OSCC cells explores the tumor-selective cytotoxic effects and underlying cell death mechanisms triggered by a series of 10 newly synthesized pyrazole-based compounds (MS1MS10) in OSCC cell lines.

Material and methods: The cytotoxic effects of the compounds were assessed using the MTT assay on four human OSCC cell lines and three types of normal human oral cells. The tumor-selectivity index (TS) and potency-selectivity expression (PSE) were calculated, and active compounds were subjected to cell cycle analysis. For QSAR modeling, 3096 descriptors comprising physicochemical, structural, and quantum chemical features were created using the most energetically advantageous conformations found through CORINA optimization.

Results: According to the obtained results, the compounds MS4 (PSE = 1443.6, TS = 71.2), MS7 (PSE > 15,304.5, TS > 247.4), and MS8 (PSE > 7141.4, TS > 169.0) showed the highest TS and PSE values, comparable to those of doxorubicin and 5-FU. The cytotoxic compounds MS7 and MS8, as well as the cytostatic compound MS4, significantly (p < 0.05) increased the cell population in the S and G2/M phases while decreasing the population in the G1 phase. Notably, no significant accumulation was detected in the sub-G1 phase, indicating the absence of DNA fragmentation-associated apoptosis. QSAR analysis suggests the importance of 3D structure and lipophilicity in TS expression, while ADMET analysis further revealed the drug-likeness properties of the active compounds. The obtained information is expected to contribute significantly to the literature on the design and development of new compounds.

Conclusion: This study demonstrates the potent tumor-specific cytotoxic and cytostatic effects of pyrazole-based chalcone hybrids on OSCC cell lines, offering valuable insights for targeted anticancer drug development.

Background/aim: Green carbon dots (GCDs) are a rapidly developing class of nanomaterials that are revolutionizing various scientific disciplines due to their unique optical properties, low toxicity, and sustainable synthesis. This review offers a comprehensive roadmap for the field, emphasizing the synergy between GCDs and artificial intelligence (AI).

Materials and methods: We begin by detailing the sustainable synthesis of GCDs, highlighting green chemistry principles and the transformative role of AI in optimizing their production. Subsequently, we explore the critical characterization of GCDs, including their structural, optical, and biocompatibility assessment. The core of this study explores the diverse biomedical applications of GCDs, including their integration into intelligent drug delivery systems enhanced by AI, utility in advanced diagnostics and biosensing, and contribution to state-of-the-art bioimaging techniques by deep learning (DL).

Results: Analysis of the literature confirms that AI-driven optimization is crucial for enhancing the scalability and reproducibility of GCD production. Furthermore, the integration of DL models significantly boosts the analytical precision and real-time capabilities of these platforms, validating the profound convergence of the fields.

Conclusion: This review provides a holistic roadmap, concluding that the AI- GCD synergy is indispensable for developing the next generation of smart nanomedicines. Future efforts must prioritize addressing scalability, standardization, and regulatory pathways to accelerate successful clinical translation.

Background/aim: The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment.

Materials and methods: The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges-such as environmental variability, data imbalance, and computational constraints-along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed.

Results: This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems.

Conclusion: Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.

Background: Developing novel chemotherapeutics with high anticancer efficacy and low toxicity remains a critical challenge in oncology. Natural products have shown promise due to their multitargeted activity and favorable safety profiles. The present study investigates the combined anticancer effects of royal jelly (RJ) and ellagic acid (EA), two potent antioxidants of animal and plant origin.

Materials and methods: Royal jelly (RJ) and ellagic acid (EA) were applied to HT29 (ATCC HTB-38, Human colorectal adenocarcinoma), HCT116 (ATCC CCL-247, Human colorectal carcinoma) and BEAS-2B (ATCC CRL-3588, human bronchial epithelium) cell lines, and their antiproliferative effects were evaluated using a xCELLigence Real-Time Cell Analyzer (RTCA MP). The effect of the combination of RJ and EA on the glycolytic pathway was determined using a Seahorse XFe24 Analyzer, and the apoptotic process was evaluated by DNA laddering and the expression of the Bcl-2 and Bax genes in the apoptotic pathway through real-time quantitative PCR (RT-qPCR). The transcriptome profiling of the combination of RJ and EA on colorectal cancer cells was performed by Total RNA Sequencing analysis.

Results: RJ with EA, when used in combination, significantly reduced the extracellular acidification rate (ECAR), effectively inhibiting aerobic glycolysis, especially in HCT116, and induced apoptosis in HCT116 and HT29 cells by increasing the Bax/Bcl-2 ratio compared to cases treated with EA or RJ alone (p < 0.05). GSEA analyses revealed that the treatment of both cell lines increased the expression of apoptosis and p53 pathway-related genes while suppressing the genes associated with the E2F target, G2M checkpoint, oxidative phosphorylation, and MYC target mechanism, indicating a directly proportional relationship with the antiproliferative effect on cancer cells and increased apoptosis.

Conclusion: RJ with EA used in combination demonstrates potent anticancer effects in colorectal cancer by suppressing glycolysis and activating apoptosis, with apparent therapeutic potential as a novel cancer treatment strategy.

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.

Background/aim: Wastewater from industrial, agricultural, and residential sources poses significant environmental and public health risks due to the presence of dyes, heavy metals, and organic pollutants. Conventional treatment methods are often inadequate for the complete removal of these pollutants. Therefore, the development of sustainable, environmentally friendly, and highly efficient treatment techniques has become increasingly important. The aim of this review is to evaluate the application of biological and green nanomaterials in wastewater treatment and to compare their effectiveness against different types of pollutants (dyes, heavy metals, and organics).

Materials and methods: This review provides detailed information on the removal of various pollutants from wastewater using green and biological nanomaterials, particularly based on articles published in recent years. The review examines the structures, synthesis methods, and application areas of biopolymers, metals, metal oxides, carbon-based, and polymer-structured nanomaterials synthesized using plant extracts and microorganism-supported systems. In addition, the integration of these nanomaterials with mechanisms such as adsorption, photocatalysis, bioseparation, and membrane filtration is discussed.

Results: Green and biological nanomaterials demonstrate high performance in the removal of various pollutants owing to their low toxicity, large surface area, and diverse functional groups. The synthesis of these nanomaterials using biological agents both reduces environmental impact and enhances their purification capacity. However, further research and innovation are required regarding scale-up, long-term stability, reusability, and cost-effectiveness.

Conclusion: Biological and green nanomaterials represent promising alternatives for sustainable wastewater treatment. This review summarizes the current status of these materials and provides guidance for future research. Multidisciplinary approaches and expanded pilot-scale studies are essential to accelerate the transition toward industrial applications.

Background/aim: The SH-SY5Y neuroblastoma cell line is a popular in vitro model for neurodegenerative disease research, especially Parkinsons disease (PD) research, but its use is complicated by limitations like the persistence of neuroblastoma-like features, unstable differentiation, mitochondrial dysfunction, and cellular stress. To address these limitations, this study tested a blended, nine-day differentiation protocol that sequentially applied all-trans retinoic acid (RA), brain-derived neurotrophic factor (BDNF), and dibutyryl cyclic adenosine monophosphate (dbcAMP). By evaluating key neuronal, dopaminergic, and PD-related markers, the research aims to determine if these differentiated SH-SY5Y cells are a suitable model for studying PD.

Materials and methods: A blended differentiation protocol using RA, BDNF, and dbcAMP was applied to SH-SY5Y cells. Morphological changes were evaluated by immunofluorescence microscopy. Furthermore, mostly dopaminergic neuronal markers associated with PD were used for characterization purposes. Nanoliquid chromatography coupled with tandem mass spectrometry proteome analysis was performed to identify changes in protein expression related to differentiation.

Results: Differentiation led to neuron-like morphology with extended neurites. Gene expression analyses revealed upregulation of several neuronal markers, such as Nestin and MAP2, indicating progression from progenitor to neuron-like states. Furthermore, some dopaminergic markers, such as TH and Nurr1, showed elevated expression with asynchronous expression patterns, suggesting heterogeneity in the differentiation process. Proteomic analysis indicated significant changes in cell differentiation and neurogenesis. Transient expression of key neuronal markers was observed. The cells required continuous external stimuli.

Conclusion: While SH-SY5Y cells exhibited dopaminergic characteristics following the blended differentiation protocol, the transient expression of key neuronal markers and the need for continuous external stimuli raised concerns about the stability and functional maturity of these differentiated cells as an in vitro PD model. These findings suggest that SH-SY5Y cells might not fully capture the properties of mature neurons.