Scientific Reports最新文献

Organoids, 3D tissue cultures that mimic real organs, offer valuable models for research. Traditional culture methods rely on manual feeding and orbital shakers, making them labor-intensive and inconsistent. Microfluidic systems have shown their potential to improve reproducibility by controlling media exchange and culture conditions, yet most still require standard incubators, which limit continuous monitoring due to space and humidity constraints. To address this, we developed a modular platform that integrates automated feeding, real-time imaging, and environmental control, eliminating the need for a conventional incubator. A key feature is a vertically oriented PDMS/glass chip that supports precise media delivery and monitoring while preserving incubation conditions, making it ideal for morphological studies. We demonstrated the platform's ability to maintain metabolic stability and media distribution over time using cerebral organoids. This platform improves organoid research by combining microfluidics, automation, and imaging, enhancing disease modeling, drug testing, and regenerative medicine applications.

This paper addresses a two-time-scale separation-based redesign technique for achieving robust consensus and transient performance recovery in second-order multi-agent systems (MASs) subject to both unmodeled dynamics and external disturbances. The proposed protocol is developed for both undirected and directed information-exchange topologies. A key novelty of this work lies in its ability to not only guarantee consensus but also explicitly recover the nominal transient performance of the agents-a capability that is typically lost in the presence of uncertainties. Lyapunov stability analysis ensures that all MASs signals remain bounded, while the consensus tracking errors asymptotically converge to zero. Simulation results demonstrate the effectiveness and robustness of the proposed approach.

Periodontitis is a highly prevalent chronic inflammatory disease that causes progressive destruction of the tooth-supporting apparatus and is linked to multiple systemic disorders. Despite its high prevalence, early biomarkers capable of predicting individual susceptibility remain elusive. The multifactorial nature of the disease and the lack of a validated, specific indicators hinder reliable early diagnosis before irreversible tissue damage occurs. CD5⁺ B cells, known for their autoreactive potential and role in bone resorption, have been previously found elevated in advanced periodontitis. Due to their association with tissue destruction, these cells may hold predictive value even at earlier stages. However, their systemic relevance in early periodontal inflammation is scarcely explored. This cross-sectional study examined differential subsets of circulating CD5⁺ B cells as potential systemic biomarkers of early periodontal disease. Sixty patients were grouped as healthy, gingivitis and moderate chronic periodontitis. Multiple regression analysis revealed that besides age (p = 0.009), the double negative (CD27^-IgD^-) subset of CD5⁺ memory B cells was related to periodontitis, while total CD5⁺ B cell levels remained unchanged. To characterize the oral inflammatory milieu, salivary cytokines and the composition of the subgingival microbiome were analyzed. Salivary IL-8 levels and IL-17A detection rates were significantly elevated in periodontitis. Microbiome profiling further identified an exploratory correlation between Megasphaera and salivary IL-8 in gingivitis, which may represent an early-stage signal but requires validation in larger follow-up cohorts.

α-thalassemia, a hereditary disorder, common in malaria-endemic regions, provides selective advantage by conferring partial protection against severe Plasmodium falciparum malaria. This study investigated the distribution of α-thalassemia genotypes and Plasmodium falciparum carriage among 1401 asymptomatic individuals aged 1-60 years, across Ghana's coastal, forest, and Sahel savanna ecological zones. DNA was extracted from archived dried blood spots and genotyped for α-thalassemia using multiplex PCR, while malaria was detected through RDT, microscopy, and PCR. Participants from the forest zone had the highest malaria prevalence by PCR (36.5%) compared to those in the Sahel savannah zone (27.6%), and coastal zone (23.5%), p < 0.0001. In the coastal zone, 54.3% (482/887) of participants had the wild type genotype (αα/αα), 41.8% (371/887) were heterozygous carriers (-α/αα), and 3.8% (34/887) were homozygous recessive for α-thalassemia (-α/-α). In the forest zone participants had 66.0% (134/203) wild type, 30.5% (62/203) heterozygous, and 3.5% (7/203) homozygous individuals, while participants in Sahel savannah recorded 62.7% (195/311) wild type, 31.5% (98/311) heterozygous, and 5.8% (18/311) homozygous recessive genotypes. There were higher odds of having asexual parasites (Odds Ratio = 1.23) and an increased odds (Odds Ratio = 1.46) of gametocyte carriage in the homozygous recessive group compared to the wild type, p = 0.447 but there were no statistically significant association observed between α-thalassemia genotype and the presence of asexual P. falciparum stages.

With the rapid development of sustainable materials and additive manufacturing technologies, glass fiber-reinforced recycled polypropylene (GF/RPP) has shown enormous potential for application in fused deposition modeling (FDM). However, the mechanical performance of GF/RPP parts is significantly affected by the FDM process parameters, and multi-objective parameter optimization remains a critical challenge. To address this, a novel multi-attribute decision-making (MADM) framework based on the interval-valued T-spherical fuzzy weighted power Heronian mean operator and Combined Compromise Solution (IVTSFWPHM-CoCoSo) is proposed to optimize FDM process parameters. The framework employs the IVTSFWPHM operator to handle experimental uncertainty, capture interactions among multiple mechanical properties, and reduce the influence of extreme values. The improved entropy weight and criteria importance through intercriteria correlation (IEW-CRITIC) method are used to determine weights. Finally, CoCoSo is applied to reliably rank the parameter combinations. The results show that a printing temperature of 240 °C, a layer thickness of 0.3 mm, and an infill density of 60% achieve the best overall mechanical properties across different raster angles, improving performance by approximately 10.7%. The effectiveness of the proposed method is further validated through comparison with existing methods and scanning electron microscopy analysis. This study provides a practical reference for complex decision-making in the FDM printing of recycled composites.

DC-DC converters serve as essential components in advanced power electronic systems, facilitating precise and efficient energy conversion across various applications. Conventional boost configurations, while efficient, inherently introduce a right-half-plane zero in the control-to-output dynamics, resulting in non-minimum-phase behavior. This characteristic limits bandwidth and degrades transient response. To address these challenges, a novel converter topology is introduced that mitigates non-minimum-phase effects and improves overall efficiency. The proposed configuration employs a coupled inductor to transfer a portion of the input energy directly to the output. In contrast, an active switched inductor network reduces voltage and current stresses on switching devices, thereby enabling soft switching. Both active switches operate under soft-switching conditions, where all diodes are turned off softly, which reduces switching losses and enhances operational efficiency. Comprehensive steady-state analysis determines voltage gain, semiconductor stress, and power losses. Small-signal modeling establishes the control-to-output voltage transfer function. Theoretical results are validated through experimental measurements from a laboratory-scale prototype and corroborated by simulation results, confirming the effectiveness and reliability of the proposed converter.

The ability of early hominins to adapt to diverse landscapes and ecological niches was a key factor in their range expansion. However, the behavioral and cognitive prerequisites underlying this adaptability remain poorly understood. Assuming that resource procurement played a pivotal role in hominin dispersal, we investigate the mobility-subsistence system of early Oldowan groups who occupied the Lower Omo landscape of southern Ethiopia some 2.3 million years ago (Ma). With its extensive archaeological record and scattered lithic resources, this context provides a landscape-scale record of early hominin behavior. Multiple lines of evidence derived from an integrated environmental, spatial and technological analysis indicate a regionally-structured mobility strategy, involving the provisioning of quartz pebbles and the transport of partially transformed products from distant sources to the biotic-rich but stone-poor margins of the paleo-Omo River, where highly mobile foraging activities were concentrated along the meandering channel. This regional strategy of early hominin displacement reflects a "Shungura exception" in the Early Pleistocene record. It coincides with the emergence of stone tool-mediated activities in the Lower Omo Valley, marking a tipping point in the capacity of early hominins to develop environment-specific adaptive strategies‒a crucial asset for their expansion into new ecological niches.

Sjögren's Syndrome (SS) is a long-term autoimmune disorder marked by damage to exocrine glands and irregularities in the immune system. Currently, there is a significant lack of effective diagnostic biomarkers and targeted therapeutic options for this disorder. This research aimed to identify molecular biomarkers that could be used for SS diagnosis and to explore therapeutic candidates that could address current clinical needs. Multiple public datasets (GSE84844, GSE40611, GSE7451, GSE208260) were retrieved from the Gene Expression Omnibus (GEO) database, including samples of salivary glands, saliva, salivary ducts, and blood from both SS patients and healthy individuals. The GEO2R tool was used to identify Differentially Expressed Genes (DEGs). Five machine learning algorithms-Decision Tree, XGBoost, Random Forest, Lasso Regression, and Gradient Boosting Machines (GBM)-were applied to screen key diagnostic biomarkers. RT-PCR in PBMCs and IHC in salivary gland tissues were used to validate the expression levels of the candidate genes. Furthermore, CIBERSORT was employed to analyze immune cell infiltration in the blood of SS patients. Network pharmacology and molecular docking using CB-Dock2 were performed to explore paeoniflorin's therapeutic potential against SS. Eight common DEGs were identified, among which EPSTI1, IFI44, and IFIT1 were core biomarkers. These genes were significantly upregulated in SS patients (P < 0.05) and exhibited high diagnostic efficacy (AUC: 0.89, 0.90, 0.88, respectively). Blood samples from SS patients showed increased proportions of memory B cells and activated dendritic cells, as well as decreased CD4 naive T cells and γδ T cells (P < 0.05)-changes that correlated with the expression of the three core genes. Paeoniflorin shared 110 common targets with SS (enriched in immune and inflammatory pathways) and bound stably to the three biomarkers (Vina scores: -7.6, -8.7, -7.7). EPSTI1, IFI44, and IFIT1 are promising candidate diagnostic biomarkers for SS, with potential links to immune dysregulation. Paeoniflorin may exert immunomodulatory effects by targeting these genes, but this remains a hypothesis requiring experimental validation. This study provides preliminary insights into SS biomarkers and therapeutic candidates, but prospective clinical validation, functional experiments, and in vivo studies are essential before translation into practice.