Synergistic and Additive Effects of Menadione in Combination with Antibiotics on Multidrug-Resistant Staphylococcus aureus: Insights from Structure-Function Analysis of Naphthoquinones

Translating the machine; An assessment of clinician understanding of ophthalmological artificial intelligence outputs

INTRODUCTION

Advances in artificial intelligence offer the promise of automated analysis of optical coherence tomography (OCT) scans to detect ocular complications from anticancer drug therapy. To explore how such AI outputs are interpreted in clinical settings, we conducted a survey-based interview study with 27 clinicians -comprising 10 ophthalmic specialists, 10 ophthalmic practitioners, and 7 oncologists. Participants were first introduced to core AI concepts and realistic clinical scenarios, then asked to assess AI-generated OCT analyses using standardized Likert-scale questions, allowing us to gauge their understanding, trust, and readiness to integrate AI into practice.

METHODS

We developed a questionnaire through literature review and consultations with ophthalmologists, computer scientists, and AI researchers. A single investigator interviewed 27 clinicians across three specialties and transcribed their responses. Data were summarized as medians (ranges) and compared with Mann-Whitney U tests (α = 0.05).

RESULTS

We noted important differences in the impact of various explainability methods on trust, depending on the clinical or AI scenario nature and the staff expertise. Explanations of AI outputs increased trust in the AI algorithm when outputs simply reflected ground truth expert opinion. When clinical scenarios were complex with incorrect AI outcomes, a mixed response to explainability led to correctly reduced trust in experienced clinicians but mixed feedback amongst less experienced clinicians. All clinicians had a general consensus on lack of current knowledge in interacting with AI and desire more training.

CONCLUSIONS

Clinicians' trust in AI algorithms are affected by explainability methods and factors, including AI's performance, personal judgments and clinical experience. The development of clinical AI systems should consider the above and these responses ideally be factored into real-world assessments. Use of this study's findings could help improve the real world validity of medical AI systems by enhancing the human-computer interactions, with preferred explainability techniques tailored to specific situations.

SSL-DA: Semi-and Self-Supervised Learning with Dual Attention for Echocardiogram Segmentation

Echocardiogram analysis plays a crucial role in assessing and diagnosing cardiac function, providing essential data to support medical diagnoses of heart disease. A key task, accurately identifying and segmenting the left ventricle (LV) in echocardiograms, remains challenging and labor-intensive. Current automated cardiac segmentation methods often lack the necessary accuracy and reproducibility, while semi-automated or manual annotations are excessively time-consuming. To address these limitations, we propose a novel segmentation framework, semi-and self-supervised learning with dual attention (SSL-DA) for echocardiogram segmentation. We start with a temporal masking network for pre-training. This network captures valuable information, such as echocardiogram periodicity. It also provides optimized initialization parameters for LV segmentation. We then employ a semi-supervised network to automatically segment the left ventricle, enhancing the model's learning with channel and spatial attention mechanisms to capture global channel dependencies and spatial dependencies across annotations. We evaluated SSL-DA on the publicly available EchoNet-Dynamic dataset, achieving a Dice similarity coefficient of 93.34% (95% CI, 93.23-93.46%), outperforming most prior CNN-based models. To further assess the generalization ability of SSL-DA, we conducted ablation experiments on the CAMUS dataset. Experimental results confirm that SSL-DA can quickly and accurately segment the left ventricle in echocardiograms, showing its potential for robust clinical application.

Evaluation of insulin sensitivity temporal prediction by using quantile regression combined with neural network model

BACKGROUND

Stress-induced hyperglycemia, a pathologically high blood glucose level, is a frequent complication in intensive care units. Blood glucose (BG) level control is crucial but challenging due to patient variability. The Stochastic TARgeted (STAR) protocol is clinically used for blood glucose control, which uses the current and predicted future patient insulin sensitivity (SI) parameter to assess BG outcomes of alternative treatment options.

OBJECTIVE

Neural network (NN) models using quantile regression (QR) have enhanced SI prediction performance. However, remains a challenge in determining the optimal NN configuration to best predict SI. This study aims to find the NN configuration yielding the highest prediction accuracy to improve the STAR protocol and explores the behaviour of the QR method in predicting the percentiles of a non-Gaussian multi-mode distribution of a physiological parameter.

METHOD

Alternative NN architectures combined with QR were implemented and trained on a large dataset comprising 1,897 patients collected between 2011 and 2023 using five-fold cross-validation ensuring model robustness. Prediction performance was evaluated among NN configurations and compared using case-specific metrics across the global SI domain as well as within subdomains to analyse the models' local performance.

RESULTS

Outcomes indicate QR applied to simpler NN, consisting of one-hidden layer with four neurons, achieves a best prediction performance at a minimum network size. Using more complex NN did not improve the prediction performance significantly. However, at long prediction horizons, no compact network demonstrated improved outcomes. A more general methodological outcome of the study is that QR-based prediction does not need to be combined with complex NN to achieve the best prediction performance.

CONCLUSION

The QR-based method was found to be appropriate for the SI prediction problem in short-term predictions which may improve the STAR protocol's clinical outcomes. Overall, the study provides a generalisable, empirical approach to network configuration optimisation for similar problems.

A review of lightweight convolutional neural networks for ultrasound signal classification

Ultrasound signal processing plays an important role in medical image analysis. Embedded ultrasonography systems with low power consumption and high portability are suitable for disaster rescue, but due to the difficulty of ultrasonic signal recognition, operators need to have strong professional knowledge, and it is not easy to deploy ultrasonography systems in areas with relatively weak infrastructures. In recent years, with the continuous development in the field of deep learning and artificial intelligence, lightweight convolutional neural networks have brought new opportunities for ultrasound signal processing. This paper focuses on investigating lightweight convolutional neural networks applied to ultrasound signal classification. Combined with the characteristics of ultrasound signals, this paper provides a detailed review of lightweight algorithms from two perspectives: model compression and operational optimization. Among them, model compression deals with the overall framework to reduce network redundancy, and the latter aims at the lightweight design of the basic operational module "convolution" in the network. The experimental results of some classical models and algorithms on the ImageNet dataset are summarized. Through the comprehensive analysis, we present some problems and provide an outlook on the future development of lightweight techniques for ultrasound signal classification.

Shikonin Derivative Suppresses Colorectal Cancer Cells Growth via Reactive Oxygen Species-Mediated Mitochondrial Apoptosis and PI3K/AKT Pathway

Colorectal cancer (CRC) is one of the deadliest cancers globally, ranking as the third most prevalent and second most lethal malignancy worldwide. The standard treatment for CRC typically involves a combination of surgery, radiotherapy, and chemotherapy. Despite advancements in CRC treatment, the prognosis remains unsatisfactory, primarily due to unclear mechanisms underlying tumorigenesis and the aggression of CRC. The aberrant activation of the PI3K/AKT pathway is frequently implicated in the initiation, progression, and metastasis of CRC. Studies have demonstrated that shikonin (SK) exerts anti-cancer effects. In this study, we evaluated the anti-tumor activities of a series of semi-synthesized SK derivatives against CRC cells. Our findings revealed that the SK derivative (M12) significantly inhibited the proliferation and colony formation of CRC cells, reduced cell migration, and induced apoptosis. Mechanistically, M12 enhanced the production of reactive oxygen species and downregulated the mitochondrial membrane potential, ultimately leading to mitochondrial apoptosis. Furthermore, M12 exhibited anti-CRC effects by modulating the PI3K/AKT signaling pathway and significantly suppressed tumorigenicity without causing notable adverse effects in mice. Therefore, targeting the PI3K/AKT pathway could be a promising treatment for CRC. M12 appears to be a promising candidate for the effective and safe treatment of CRC.

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer

Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R 2 ) of 0.67 for PAMPA permeability prediction, and R 2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance.

Synthesis and Evaluation of Fluorinated Piperazine-Hydroxyethylamine Analogues as Potential Antiplasmodial Candidates

In this manuscript, twenty-one novel fluorinated piperazine-hydroxyethylamine analogues were synthesized and tested against Plasmodium falciparum (Pf). Among tested compounds, two 13 g and 14 g exhibited promising inhibitory activity on Pf3D7 with IC50 values of 0.28 and 0.09 μM, respectively. Neither of the hits exhibited cytotoxicity on HepG2 cells up to 150 μM and Vero cells up to 20 μM. Compounds 13 g and 14 g were also evaluated against chloroquine-resistant PfDd2 and displayed IC50 values of 0.11 and 0.10 μM, respectively. Next, 13 g and 14 g were administered to the Plasmodium berghei mice model at 30 mg/kg intraperitoneally for four consecutive doses, which showed 25 % and 50 % reduction in the parasitemia load, respectively. The efficacy of hits 13 g and 14 g was improved along with mean survival time when administered in combination with artesunate. On liver-stage parasites, compounds 13 g and 14 g showed >80 % inhibition at 1 μM. Compound 14 g was also tested for toxicity in mice at 100 mg/kg dose, which revealed no abnormality in mice organs. Preliminary pharmacokinetic studies of compound 14 g exhibited absorption and maintained a presence in the body for more than six hours.

Menadione as Antibiotic Adjuvant Against P. aeruginosa: Mechanism of Action, Efficacy and Safety

Background/Objectives: Antibiotic resistance in chronic lung infections caused by Pseudomonas aeruginosa requires alternative approaches to improve antibiotic efficacy. One promising approach is the use of adjuvant compounds that complement antibiotic therapy. This study explores the potential of menadione as an adjuvant to azithromycin against planktonic cells and biofilms of P. aeruginosa, focusing on its mechanisms of action and cytotoxicity in pulmonary cell models. Methods: The effect of menadione in improving the antibacterial and antibiofilm potency of azithromycin was tested against P. aeruginosa. Mechanistic studies in P. aeruginosa and AZMr-E. coli DH5α were performed to probe reactive oxygen species (ROS) production and bacterial membrane disruption. Cytotoxicity of antibacterial concentrations of menadione was assessed by measuring ROS levels and membrane integrity in Calu-3 and A549 lung epithelial cells. Results: Adding 0.5 µg/mL menadione to azithromycin reduced the minimum inhibitory concentration (MIC) by four-fold and the minimum biofilm eradication concentration (MBEC) by two-fold against P. aeruginosa. Adjuvant mechanisms of menadione involved ROS production and disruption of bacterial membranes. Cytotoxicity tests revealed that antibacterial concentrations of menadione (≤64 µg/mL) did not affect ROS levels or membrane integrity in lung cell lines. Conclusions: Menadione enhanced the efficacy of azithromycin against P. aeruginosa while exhibiting a favorable safety profile in lung epithelial cells at antibacterial concentrations. These findings suggest that menadione is a promising antibiotic adjuvant. However, as relevant data on the toxicity of menadione is sparse, further toxicity studies are required to ensure its safe use in complementing antibiotic therapy.

Phytochemical Profiling and Anti-VanA Activity of Pulegone Extracted from Ziziphora tenuior Flower Against Vancomycin-Resistant Enterococci: An In Silico Approach

Ziziphora tenuior is a herb known for its potent pharmaceutical activities. However, the specific compounds of the flowers of this herb have not been fully studied yet. This study used GC-MS to conduct a chemical analysis of the methanol and dichloromethane extracts of Z. tenuior flowers. Additionally, it sought to assess the potential antibacterial activity of the extracts against vancomycin-resistant enterococci (VRE) bacteria by predicting the interactions between one of the most prevalent compounds in the extracts and the D-alanyl-D-lactate ligase (VanA) protein, which is responsible for enterococci resistant to vancomycin. The results revealed a total of 15 compounds in the methanolic extract and 12 compounds in the dichloromethane extract. Among these, 5-methyl-2-(1-methylethylidene)-cyclohexanone, also known as pulegone, constituting 52.6 % of the methanolic extract and 34.6 % of the dichloromethane extract, was the most abundant compound in the extracts. Furthermore, the in-silico analysis demonstrated that pulegone exhibited significant interactions with VanA, as indicated by docking energy values of -7 kcal/mol and the formation of one hydrogen bond. The study suggests that pulegone shows promise as an antibacterial agent against VRE by potentially interacting with VanA protein and serving as a key inhibitor in fighting vancomycin resistance.

In Vitro and In Vivo Biological Evaluation of Novel 1,4-Naphthoquinone Derivatives as Potential Anticancer Agents

A novel library of naphthoquinone derivatives (3-5 aa) was synthesized and evaluated for their anticancer properties. Specifically, compounds 5 i, 5 l, 5 o, 5 q, 5 r, 5 s, 5 t, and 5 v demonstrated superior cytotoxic activity against the cancer cell lines that were studied. All the studied compounds exhibited a higher selectivity index (SI) and a favourable safety profile than the standard drug doxorubicin. Notably, compound 5 v displayed a greater cytotoxic effect on MCF-7 cells (IC50=1.2 μM, and 0.9 μM at 24 h and 48 h, respectively) compared to the standard drug doxorubicin (IC50=2.4 μM, and 2.1 μM at 24 h and 48 h, respectively). To further investigate the mechanism of cytotoxic effect, additional anticancer studies were conducted with 5 v in MCF-7 cells. The studies are including morphological changes, AO/EB (acridine orange/ethidium bromide) double staining, apoptosis analysis, cell colony assay, SDS-PAGE and Western blotting, cell cycle analysis, and detecting reactive oxygen species (ROS) assay. The findings showed that 5 v triggered cytotoxic effects in MCF-7 cells through the initiation of cell cycle arrest at the G1/S phase and necrosis. In vivo ecotoxicity studies indicated that 5 v had lower toxicity towards zebrafish larvae (LC50=50.15 μM) and had an insignificant impact on cardiac functions. In vivo xenotransplantation of MCF-7 cells in zebrafish larvae demonstrated a significant reduction in tumour volume in the xenograft. Approximately 95 % of the zebrafish larvae with 5 v xenografts survived after 10 days of the treatment. Finally, a computational modelling study was conducted on four protein receptors, namely ER, EFGR, BRCA1, and VEFGR2. The findings highlight the importance of the aminonaphthoquinone moiety, amide linkage, and propyl thio moiety in enhancing the anticancer properties. 5 v exhibited superior drug-likeness features and docking scores (-9.1, -7.1, -8.9, and -10.9 kcal/mol) compared to doxorubicin (-7.2, -6.1, -6.9, and -7.3 kcal/mol) against ER, EFGR, BRCA1, and VEGFR2 receptors, respectively. Therefore, the notable antitumor effects of naphthoquinone derivatives (3-5 aa) suggest that these molecular frameworks may play a role in the development of promising anticancer agents for cancer treatment.

AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.

Exploring naphthoquinone and anthraquinone derivatives as antibiotic adjuvants against Staphylococcus aureus biofilms: Synergistic effects of menadione

Given the ability of Staphylococcus aureus to form biofilms and produce persister cells, making infections difficult to treat with antibiotics alone, there is a pressing need for an effective antibiotic adjuvant to address this public health threat. In this study, a series of quinone derivatives were evaluated for their antimicrobial and antibiofilm activities against methicillin-susceptible and methicillin-resistant S. aureus reference strains. Following analyses using broth microdilution, growth curve analysis, checkerboard assay, time-kill experiments, and confocal laser scanning microscopy, menadione was identified as a hit compound. Menadione exhibited a notable antibacterial profile (minimum inhibitory concentration, MIC = 4-16 μg/ml; minimum bactericidal concentration, MBC = 256 μg/ml) against planktonic S. aureus and its biofilms (minimum biofilm inhibitory concentration, MBIC50 = 0.0625-0.25 μg/ml). When combined with oxacillin, erythromycin, and vancomycin, menadione exhibited a synergistic or additive effect against planktonic cells and biofilms of two S. aureus reference strains and six clinical isolates, highlighting its potential as a suitable adjuvant for further development against S. aureus biofilm-associated infections.

A Multifunctional Herb-Derived Glycopeptide Hydrogel for Chronic Wound Healing

Chronic wounds constitute an increasingly prevalent global healthcare issue, characterized by recurring bacterial infections, pronounced oxidative stress, compromised functionality of immune cells, unrelenting inflammatory reactions, and deficits in angiogenesis. In response to these multifaceted challenges, the study introduced a stimulus-responsive glycopeptide hydrogel constructed by oxidized Bletilla striata polysaccharide (OBSP), gallic acid-grafted ε-Polylysine (PLY-GA), and paeoniflorin-loaded micelles (MIC@Pae), called OBPG&MP. The hydrogel emulates the structure of glycoprotein fibers of the extracellular matrix (ECM), exhibiting exceptional injectability, self-healing, and biocompatibility. It adapts responsively to the inflammatory microenvironment of chronic wounds, sequentially releasing therapeutic agents to eradicate bacterial infection, neutralize reactive oxygen species (ROS), modulate macrophage polarization, suppress inflammation, and encourage vascular regeneration and ECM remodeling, playing a critical role across the inflammatory, proliferative, and remodeling phases of wound healing. Both in vitro and in vivo studies confirmed the efficacy of OBPG&MP hydrogel in regulating the wound microenvironment and enhancing the regeneration and remodeling of chronic wound skin tissue. This research supports the vast potential for herb-derived multifunctional hydrogels in tissue engineering and regenerative medicine.

Physico-Chemical Properties of Copper-Doped Hydroxyapatite Coatings Obtained by Vacuum Deposition Technique

The hydroxyapatite and copper-doped hydroxyapatite coatings (Ca10-xCux(PO4)6(OH)2; xCu = 0, 0.03; HAp and 3CuHAp) were obtained by the vacuum deposition technique. Then, both coatings were analyzed by the X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and water contact angle techniques. Information regarding the in vitro antibacterial activity and biological evaluation were obtained. The XRD studies confirmed that the obtained thin films consist of a single phase associated with hydroxyapatite (HAp). The obtained 2D and 3D SEM images did not show cracks or other types of surface defects. The FTIR studies' results proved the presence of vibrational bands characteristic of the hydroxyapatite structure in the studied coating. Moreover, information regarding the HAp and 3CuHAp surface wettability was obtained by water contact angle measurements. The biocompatibility of the HAp and 3CuHAp coatings was evaluated using the HeLa and MG63 cell lines. The cytotoxicity evaluation of the coatings was performed by assessing the cell viability through the MTT assay after incubation with the HAp and 3CuHAp coatings for 24, 48, and 72 h. The results proved that the 3CuHAp coatings exhibited good biocompatible activity for all the tested intervals. The ability of Pseudomonas aeruginosa 27853 ATCC (P. aeruginosa) cells to adhere to and develop on the surface of the HAp and 3CuHAp coatings was investigated using AFM studies. The AFM studies revealed that the 3CuHAp coatings inhibited the formation of P. aeruginosa biofilms. The AFM data indicated that P. aeruginosa's attachment and development on the 3CuHAp coatings were significantly inhibited within the first 24 h. Both the 2D and 3D topographies showed a rapid decrease in attached bacterial cells over time, with a significant reduction observed after 72 h of exposure. Our studies suggest that 3CuHAp coatings could be suitable candidates for biomedical uses such as the development of new antimicrobial agents.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Beyond the Coagulation Cascade: Vitamin K and Its Multifaceted Impact on Human and Domesticated Animal Health

Vitamin K (VK) is an essential micronutrient impacting many systems in the body. This lipid-soluble vitamin is found in various plant and animal products and is absorbed via the lymphatic system. This biomolecule's importance to human health includes but is not limited to its promotion of brain, cardiovascular, bone, and immune functions. These biological properties are also necessary for maintaining domesticated animal health. The synergistic impact of both VK and vitamin D (VD) maximizes these health benefits, specifically for the circulatory and skeletal systems. This manuscript reviews VK's properties, molecular structures, nutrikinetics, mechanisms of action, daily requirements, safety in supplemental form, biomarkers used for its detection, and impacts on various organs. The purpose of synthesizing this information is to evaluate the potential uses of VK for the treatment or prevention of diseases.

Introducing the antibacterial and photocatalytic degradation potentials of biosynthesized chitosan, chitosan-ZnO, and chitosan-ZnO/PVP nanoparticles

The development of nanomaterials has been speedily established in recent years, yet nanoparticles synthesized by traditional methods suffer unacceptable toxicity and the sustainability of the procedure for synthesizing such nanoparticles is inadequate. Consequently, green biosynthesis, which employs biopolymers, is gaining attraction as an environmentally sound alternative to less sustainable approaches. Chitosan-encapsulated nanoparticles exhibit exceptional antibacterial properties, offering a wide range of uses. Chitosan, obtained from shrimp shells, aided in the environmentally friendly synthesis of high-purity zinc oxide nanoparticles (ZnO NPs) with desirable features such as the extraction yield (41%), the deacetylation (88%), and the crystallinity index (74.54%). The particle size of ZnO NPs was 12 nm, while that of chitosan-ZnO NPs was 21 nm, and the bandgap energies of these nanomaterials were 3.98 and 3.48, respectively. The strong antibacterial action was demonstrated by ZnO NPs, chitosan-ZnO NPs, and chitosan-ZnO/PVP, particularly against Gram-positive bacteria, making them appropriate for therapeutic use. The photocatalytic degradation abilities were also assessed for all nanoparticles. At a concentration of 6 × 10-5 M, chitosan removed 90.5% of the methylene blue (MB) dye, ZnO NPs removed 97.4%, chitosan-coated ZnO NPs removed 99.6%, while chitosan-ZnO/PVP removed 100%. In the case of toluidine blue (TB), at a concentration of 4 × 10-3 M, the respective efficiencies were 96.8%, 96.8%, 99.5%, and 100%, respectively. Evaluation of radical scavenger activity revealed increased scavenging of ABTS and DPPH radicals by chitosan-ZnO/PVP compared to individual zinc oxide or chitosan-ZnO, where the IC50 results were 0.059, 0.092, 0.079 mg/mL, respectively, in the ABTS test, and 0.095, 0.083, 0.061, and 0.064 mg/mL in the DPPH test, respectively. Moreover, in silico toxicity studies were conducted to predict the organ-specific toxicity through ProTox II software. The obtained results suggest the probable safety and the absence of organ-specific toxicity with all the tested samples.

Microbiome Sex-Related Diversity in Non-Muscle-Invasive Urothelial Bladder Cancer

Sex-specific discrepancies in bladder cancer (BCa) are reported, and new studies imply that microbiome may partially explain the diversity. We aim to provide characterization of the bladder microbiome in both sexes diagnosed with non-muscle-invasive BCa with specific insight into cancer grade. In our study, 16S rRNA next-generation sequencing was performed on midstream urine, bladder tumor sample, and healthy-appearing bladder mucosa. Bacterial DNA was isolated using QIAamp Viral RNA Mini Kit. Metagenomic analysis was performed using hypervariable fragments of the 16S rRNA gene on Ion Torrent Personal Genome Machine platform. Of 41 sample triplets, 2153 taxa were discovered: 1739 in tumor samples, 1801 in healthy-appearing bladder mucosa and 1370 in midstream urine. Women were found to have smaller taxa richness in Chao1 index than men (p = 0.03). In comparison to low-grade tumors, patients with high-grade lesions had lower bacterial diversity and richness in urine. Significant differences between sexes in relative abundance of communities at family level were only observed in high-grade tumors.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.