Scientific Reports最新文献

The aim of this study was to advance post-traumatic stress disorder (PTSD) understanding in older adults (48-77 years) by determining if circulating cytokines (IL-1β, IL-2, IL-4, IL-6, IL-12p70, IL17A and TNFα), brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF-A) and neuroanatomical brain volumes (grey and white matter, hippocampus, and amygdala) significantly differed in those with versus without PTSD. While none of the tested cytokines showed a significant difference, serum BDNF and VEGF-A levels were found to be significantly higher in the PTSD cohort. The assay used for BDNF quantification was important, with differences in general BDNF detected, but not when pro- and mature BDNF were measured specifically. Additionally, BDNF genotyping revealed a significant difference in Val66Met genotype distribution by PTSD diagnosis, with Val66Met carriers generally having lower circulating levels of BDNF compared to their Val66Val counterparts, regardless of PTSD diagnosis. Neuroanatomically, an all-female subset was examined to find total grey and white matter volumes and left and right hippocampal volumes were significantly smaller in those with PTSD. Collectively, these results show that both novel (VEGF-A) and established targets (BDNF and neuroimaging) may serve as useful biomarkers for older adults with PTSD.

Diabetic nephropathy (DN) is a common complication of diabetes and a major cause of end-stage renal disease, with complex pathogenesis involving inflammation, oxidative stress, fibrosis, and ferroptosis. Ferroptosis is linked to DN progression, yet treatment options are limited, particularly for targeting ferroptosis. Dapagliflozin (DAPA), an SGLT2 inhibitor, shows renal protective effects in diabetes, but its role in renal fibrosis and ferroptosis in DN is unclear. This study investigated DAPA's effect on renal fibrosis in DN by inhibiting ferroptosis, using a streptozotocin-induced diabetic mouse model. Results indicated that DAPA improved renal function, reduced fibrosis, and suppressed ferroptosis markers in diabetic mice. In vitro, DAPA inhibited ferroptosis and fibrosis in HK-2 cells under high glucose conditions. Molecular docking and network pharmacology suggested DAPA's anti-fibrotic and anti-ferroptotic effects may involve the Nrf2 and TGF-β signaling pathways. DAPA also reduced serum creatinine and blood urea nitrogen in diabetic mice, improved glomerulosclerosis and interstitial fibrosis, decreased iron deposition, and enhanced antioxidant activity. Overall, DAPA's multi-target mechanisms significantly improve DN progression, suggesting its potential as a targeted therapy against ferroptosis. Future studies should further explore DAPA's applications.

The accurate calibration of semi-empirical fatigue models against experimental evidence is a critical step for achieving reliable predictions. Amongst many semi-empirical fatigue models, El Haddad's (EH) curve is widely exploited to characterise the fatigue endurance limit of defect-laden and cracked metallic alloys. A few deterministic computational models exist in this respect, however, they lack a robust probabilistic perspective and their implementation code is not publicly accessible. The authors of the present work have recently exploited Maximum a Posteriori (MAP) to robustly and probabilistically estimate EH's curves, even in case of data scarcity or incomplete datasets, combining experimental evidence and prior knowledge taken from literature. Whilst the implementation scheme was published, the associated code was not made available. Hereby, the authors present B-FADE, a flexible open-source Python package, aimed at releasing the implementation of the MAP approach with improvements, as well as several pre- and post-processing utilities to facilitate its deployment. The package is conferred with a sufficient level of abstraction, thus turning out to be easily extensible to future implementation of other relevant fatigue models.

This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categories using ultrasound images from the challenging GBC USG (GBCU) dataset. The proposed methodology enhances image quality and specifies gallbladder wall boundaries by employing sophisticated image processing techniques like median filtering and contrast-limited adaptive histogram equalization. Unlike traditional convolutional neural networks, which struggle with complex spatial patterns, the proposed transformer-based model, GBC Horizontal-Vertical Transformer (GBCHV), incorporates a GBCHV-Trans block with self-attention mechanisms. In order to make the model anatomy-aware, the square-shaped input patches of the transformer are transformed into horizontal and vertical strips to obtain distinctive spatial relationships within gallbladder tissues. The novelty of this model lies in its anatomy-aware mechanism, which employs horizontal-vertical strip transformations to depict spatial relationships and complex anatomical features of the gallbladder more accurately. The proposed model achieved an overall diagnostic accuracy of 96.21% by performing an ablation study. A performance comparison between the proposed model and seven transfer learning models is further conducted, where the proposed model consistently outperformed the transfer learning models, showcasing its superior accuracy and robustness. Moreover, the decision-making process of the proposed model is further explained visually through the utilization of Gradient-weighted Class Activation Mapping (Grad-CAM). With the integration of advanced deep learning and image processing techniques, the GBCHV-Trans model offers a promising solution for precise and early-stage classification of GBC, surpassing conventional methods with superior accuracy and diagnostic efficacy.

Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. This paper investigates the potential of generative AI within the realm of the World Wide Web, specifically focusing on image generation. Web developers already harness generative AI to help craft text and images, while Web browsers might use it in the future to locally generate images for tasks such as repairing broken webpages, conserving bandwidth, and enhancing privacy. To explore this research area, this paper developed WebDiffusion, a tool that allows to simulate a Web powered by stable diffusion, a popular text-to-image model, from both a client and server perspective. Such a tool is the first of its kind, paving the way towards a futuristic world wide web where web images can be created using generative AI. WebDiffusion further supports crowdsourcing of user opinions, which is used to evaluate the quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our findings suggest that generative AI is already capable of producing pertinent and high-quality Web images, even without requiring Web designers to manually input prompts, just by leveraging contextual information available within the webpages. However, direct in-browser image generation remains a challenge, as only highly powerful GPUs, such as the A40 and A100, can (partially) compete with classic image downloads. Nevertheless, this approach could be valuable for a subset of the images, for example, when fixing broken webpages or handling highly private content.

Smooth endoplasmic reticulum aggregates (SERa) are a type of dysmorphism in oocytes derived from controlled ovarian stimulation (COS). The effect of SERa on assisted reproductive techniques (ART) outcomes is debatable. Based on some evidence, SERa-positive (SERa+) oocytes cause complications including newborn demise, and compromise the outcome of the unaffected oocytes of the same cycle. While other reports demonstrated equal developmental competence between SERa + and SERa-negative (SERa-) oocytes/cycles. We conducted a prospective cross-sectional study on 315 women candidates for ART and compared the outcome among SERa+ (N = 73) and SERa- cycles (N = 217). Furthermore, for the first time, we investigated the prevalence of SERa + cycles in women with various infertility etiologies. Our results indicated that SERa + patients presented higher levels of Estradiol on the day of ovulation triggering (p = 0.02). Regarding the ART outcome, there were no differences in the number of retrieved oocytes, oocyte maturation and fertilization rates among the groups. However, the quality of the unaffected oocytes (p = 0.03), the rates of day-3 top-quality embryos (p = 0.01, and p = 0.03 for grades A and B, respectively), and clinical pregnancy (p = 0.05) in SERa + group were significantly reduced. Moreover, the prevalence of SERa + cycles gradually increased among endometriosis, POI/POR, PCOS, normal women, tubal factor, and idiopathic groups. Our study suggests that suboptimal situations such as elevated levels of Estradiol can increase the occurrence of SERa + oocytes. This suboptimal phenomenon can negatively influence the outcome of the cycle. Thus, optimization of COS, particularly in vulnerable groups such as women with idiopathic infertility may lower the SERa + cycle occurrence, improving the ART outcome.

Ulcerative colitis (UC) is an increasingly prevalent inflammatory condition affecting the intestinal mucosa, while nonspecific orbital inflammation (NSOI) is a common non-neoplastic orbital disorder. Exploring the molecular interplay between UC and NSOI may help physicians make earlier diagnoses and enhance treatment approaches. We analyzed gene expression datasets (GSE58331, GSE105149, GSE206285, and GSE179285) for UC and NSOI from the GEO database. Using WGCNA and differential expression analysis, we identified genes commonly altered in both diseases. GO enrichment, PPI networks, and transcription factor prediction were performed using Cytoscape plugins (cytoHubba and iRegulon). Machine learning techniques were employed to assess transcription factor activity and evaluate potential therapeutic targets among the hub genes. We conducted an association analysis using the TwoSampleMR package in R to explore potential causal relationships between NSOI and UC. A total of 85 intersecting genes between NSOI and UC were identified, and enrichment analyses revealed their roles in immune and inflammatory processes. Key biomarkers, including CXCL10, CXCR4, CXCL9, CD27, SELL, MMP9, CD79A, CD3E, GZMK, and CCL19, were highlighted, linking them to processes such as leukocyte migration, viral response, and monocyte differentiation. STAT1 was identified as a shared transcription factor influencing both diseases. Machine learning algorithms identified eight potential genes for diagnostic and therapeutic use, with CXCL10 emerging as a key player in the pathogenesis of NSOI and UC. CXCL10 likely regulates CXCR4, LCK, CCR7, and other genes involved in pathways such as cytokine-cytokine receptor interactions, HIV-1 infection, and Epstein-Barr virus infection. This study offers insights into the co-pathogenic mechanisms of UC and NSOI, providing a foundation for further mechanistic research and therapeutic development.

Systemic sclerosis (SSc) is a connective tissue disease characterized by progressive fibrosis of the skin and visceral organs, to date, skin fibrosis remains a clinical therapeutic challenge. Adipose-derived mesenchymal stem cells (AMSCs) have been considered extremely promising for the treatment of SSc, and the biological effects of MSCs are partly attributed to the secretion of exosomes (exos). Our aim was to determine whether exosomes derived from AMSCs have parental biological effects to AMSCs in the therapy of SSc skin fibrosis. In vitro cellular experiments, AMSCs and SSc skin fibroblasts were cocultured in direct contact and transwell indirect contact at a ratio of 1:5 and 1:10, respectively, then exosomes were extracted from the cell culture supernatant of AMSCs and identified, and the exosomes were cocultured with fibroblasts to investigate the effects of AMSCs and exosomes on fibroblast collagen synthesis. Repeated subcutaneous injections of bleomycin (BLM) to construct a model of SSc skin fibrosis in vivo experiments, then AMSCs and exosomes were injected subcutaneously to investigate their effects on skin fibrosis in the BLM mice. The results revealed that exosomes had similar biological functions to AMSCs, by inhibiting the TGF-β1/Smad3 axis, which alleviated collagen synthesis in skin fibroblasts from SSc patients and skin fibrosis in BLM models. In conclusion, AMSCs-derived exosomes may be "rising star candidates" for the treatment of SSc skin fibrosis.

Green synthesis of nanoparticles has emerged as a significant strategy to develop effective and eco-friendly insecticide agents to combat insecticide resistance and preserve environmental integrity and biodiversity. This study was thus designed to fabricate novel green synthesized NiFe₂O₄ nanoparticles (NiFe NPs) and investigate their potential insecticidal effects for the first time using Blaps polychresta beetle as an agricultural coleopteran pest model. Therefore, we prepared NiFe NPs following the hydrothermal synthesis procedure in the presence of lemon juice. The physiochemical characteristics of NiFe NPs were investigated employing SEM, TEM, FT-IR, XRD, TGA, VSM, and UV-Vis analysis. The lowest and most effective dose of NiFe NPs against male beetles was ascertained at a concentration of 0.03 mg/g body weight, reporting 67% mortality after 48 h. To study the insecticidal impact of NiFe NPs, EDX analysis demonstrated the bioaccumulation of NiFe NPs in testicular tissues of beetles, leading to pathophysiological consequences. Precisely, the oxidative stress incited by NiFe NPs led to disturbance of the antioxidant defense system, which was defined by augmentation of lipid peroxidation and suppression of antioxidant enzymes. Furthermore, the comet assay exhibited remarkable DNA impairment, while flow cytometry analysis showed substantial cellular necrosis and apoptosis in NiFe NPs-treated beetles compared to control insects. In correlation with these findings, several aberrations in the histological and ultrastructure attributes of testicular tissues were perceived, including impaired follicular and cyst walls, deteriorated parietal cells, necrosis, and vacuolations. These results implied that NiFe NPs triggered oxidative injury in the testes, resulting in male reproductive system dysfunction. Altogether, our findings accentuate the potential application of NiFe NPs as nanopesticides, paving the way for the sustainable and cost-effective management of insect pests in agriculture.