Environmental acidification drives inter-organ energy mobilization to enhance reproductive performance in medaka (Oryzias latipes).

Anthropogenically environmental acidification impacts aquatic organisms, including teleosts, the largest group of vertebrates. Despite its significance, how teleosts allocate nutrient and energy among their organs to cope with acidic stress remains unclear. Our integrated analysis of physiological, metabolic, and gene expression data reveals that Japanese medaka (Oryzias latipes) mobilize energy resources among organs in response to acidic conditions. We found that the muscles lost carbohydrates and proteins and the liver accumulates all macronutrients in both sexes. Notably, female-specific energy mobilization between the liver and ovary were triggered by estrogen signaling, resulting in improved oocyte maturation and ovulation. Female produced more offspring under acidic stress. Furthermore, the offspring embryos exhibited smaller diameters and earlier hatching but demonstrated growth rates and acid tolerance. These metabolic changes suggest a trade-off in energy allocation by suppressing basal maintenance (33 % decrease in oxygen consumption) and growth (25 % decrease in muscle mass) but enhancing energy storage (159 % increase in liver mass in males and 127 % in females) and reproduction (165 % increase in ovary mass). This reallocation may improve medaka fitness and population sustainability in acidic environments. Further investigation into more species is needed to project the survival of aquatic animals in an acidified future.

Oscillatory DeltaC Expression in Neural Progenitors Primes the Prototype of Forebrain Development.

Notch signaling plays a pivotal role in regulating various developmental processes, particularly in controlling the timing of neuronal production within the developing neocortex. Central to this regulatory mechanism is the oscillatory pattern of Delta, which functions as a developmental clock modulator. Its deficiency profoundly impairs mammalian brain formation, highlighting its fundamental role in brain development. However, zebrafish carrying a mutation in the functional ortholog DeltaC (dlc) within their functional ortholog exhibit an intact forebrain structure, implying evolutionary variations in Notch signaling within the forebrain. In this study, we unveil the distinct yet analogous expression profiles of Delta and Her genes in the developing vertebrate forebrain. Specifically, for the first time, we detected the oscillatory expression of the Delta gene dlc in the developing zebrafish forebrain. Although this oscillatory pattern appeared irregular and was not pervasive among the progenitor population, attenuation of the dlc-involved Notch pathway using a γ-secretase inhibitor impaired neuronal differentiation in the developing zebrafish forebrain, revealing the indispensable role of the dlc-involved Notch pathway in regulating early zebrafish neurogenesis. Taken together, our results demonstrate the foundational prototype of dlc-involved Notch signaling in the developing zebrafish forebrains, upon which the intricate patterns of the mammalian neocortex may have been sculpted.

Sodium alginate hydrogels co-encapsulated with cell free fat extract-loaded core-shell nanofibers and menstrual blood stem cells derived exosomes for acceleration of articular cartilage regeneration.

This study presents a novel scaffold system comprising sodium alginate hydrogels (SAh) co-encapsulated with cell-free fat extract (CEFFE)-loaded core-shell nanofibers (NFs) and menstrual blood stem cell-derived exosomes (EXOs). The scaffold integrates the regenerative potential of EXOs and CFFFE, offering a multifaceted strategy for promoting articular cartilage repair. Coaxially electrospun core-shell NFs exhibited successful encapsulation of CEFFE and seamless integration into the SAh matrix. Structural modifications induced by the incorporation of CEFFE-NFs enhanced hydrogel porosity, mechanical strength, and degradation kinetics, facilitating cell adhesion, proliferation, and tissue ingrowth. The release kinetics of growth factors from the composite scaffold demonstrated sustained and controlled release profiles, essential for optimal tissue regeneration. In vitro studies revealed high cell viability, enhanced chondrocyte proliferation, and migration in the presence of EXOs/CEFFE-NFs@SAh composite scaffolds. Additionally, in vivo experiments demonstrated significant cartilage regeneration, with the composite scaffold outperforming controls in promoting hyaline cartilage formation and defect bridging. Overall, this study underscores the potential of EXOs and CEFFE-NFs integrated into SAh matrices for enhancing chondrocyte viability, proliferation, migration, and ultimately, articular cartilage regeneration. Future research directions may focus on elucidating underlying mechanisms and conducting long-term in vivo studies to validate clinical applicability and scalability.

Spin-Valley-Locked Electroluminescence for High-Performance Circularly Polarized Organic Light-Emitting Diodes.

Circularly polarized (CP) organic light-emitting diodes (OLEDs) have attracted attention in potential applications, including novel display and photonic technologies. However, conventional approaches cannot meet the requirements of device performance, such as high dissymmetry factor, high directionality, narrowband emission, simplified device structure, and low costs. Here, we demonstrate spin-valley-locked CP-OLEDs without chiral emitters but based on photonic spin-orbit coupling, where photons with opposite CP characteristics are emitted from different optical valleys. These spin-valley-locked OLEDs exhibit a narrowband emission of 16 nm, a high external quantum efficiency of 3.65%, a maximum luminance of near 98,000 cd/m2, and a gEL of up to 1.80, which are among the best performances of active single-crystal CP-OLEDs, achieved with a simple device structure. This strategy opens an avenue for practical applications toward three-dimensional displays and on-chip CP-OLEDs.

Synthesis and performance evaluation of polymer-ceramic composite microcapsules as reservoir protectant for natural gas hydrate drilling.

Natural gas hydrate is a promising unconventional natural gas source due to its high energy density and huge global reserves. During exploitation, the drilling fluid may invade the hydrate formation and induce hydrate decomposition, causing reservoir damage. Herein, a novel reservoir protectant made by bio-degradable temporary plugging material (BDTPM) was developed in the form of polymer-ceramic composite microcapsules. As an additive to the drilling fluid, the BDTPM can minimize drilling fluid intrusion by plugging the reservoir during drilling and afterwards maximize permeability recovery by degrading the material. The particle size distribution was in the range of 1-130 µm. The optimal mass ratio between modified ceramic particles, ethyl cellulose and epoxy resin was found to be 4:2:1. The plugging rate was 100% when ethyl cellulose and epoxy resin were mixed to coat the ceramic particles to form BDTPM, and the plugging performance was the best. At a temperature close to the typical hydrate reservoir environment (5 °C), 0.02 wt% low-temperature complex enzyme can degrade BDTPM, and the permeability recovery rate is 64.66%. The efficient reservoir protectant developed in this work could play an important role in the successful drilling of natural gas hydrate reservoirs.

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic examination of tissue slices, providing valuable insights into cellular structures and pathological features. On the other hand, genomic data provides information about tumor gene expression and functionality. The fusion of these two distinct data types is crucial for gaining a more comprehensive understanding of tumor characteristics and progression. In the past, many studies relied on single-modal approaches for tumor diagnosis. However, these approaches had limitations as they were unable to fully harness the information from multiple data sources. To address these limitations, researchers have turned to multi-modal methods that concurrently leverage both histopathological images and genomic data. These methods better capture the multifaceted nature of tumors and enhance diagnostic accuracy. Nonetheless, existing multi-modal methods have, to some extent, oversimplified the extraction processes for both modalities and the fusion process. In this study, we presented a dual-branch neural network, namely SG-Fusion. Specifically, for the histopathological modality, we utilize the Swin-Transformer structure to capture both local and global features and incorporate contrastive learning to encourage the model to discern commonalities and differences in the representation space. For the genomic modality, we developed a graph convolutional network based on gene functional and expression level similarities. Additionally, our model integrates a cross-attention module to enhance information interaction and employs divergence-based regularization to enhance the model's generalization performance. Validation conducted on glioma datasets from the Cancer Genome Atlas unequivocally demonstrates that our SG-Fusion model outperforms both single-modal methods and existing multi-modal approaches in both survival analysis and tumor grading.

Risk of coronal imbalance after posterior surgery for adolescent idiopathic scoliosis of type Lenke5C.

PURPOSE: To investigate the incidence, risk factors, and clinical impact of persistent coronal imbalance (PCI) in Lenke5C adolescent idiopathic scoliosis (AIS) undergoing posterior spinal fusion (PSF). METHODS: We analyzed the medical records of 112 Lenke5C AIS patients. They were grouped into PCI (+) group and PCI (-) group according to the occurrence of PCI at 2 years after surgery. Coronal and sagittal parameters were measured and compared between both groups preoperatively, immediately postoperatively, and 2 years postoperatively. Scoliosis Research Society-22 (SRS-22) score was used to evaluate clinical outcomes. RESULTS: Of the 112 patients, 12 had coronal imbalance persisting 2 years after surgery. Logistic regression analysis indicated risk factors including older age [odds ratio (OR) = 1.841, 95% confidence interval (CI) = 1.147-2.132, p = .001], lower preoperative flexibility main thoracic (MT) curve (OR = 1.308, 95% CI = 1.041-2.015, p = .016), greater preoperative apical vertebral translation (AVT) of the thoracolumbar/lumbar (TL/L) curve (AVT-TL/L) (OR = 2.291, 95% CI = 1.120-4.719, p = .001), larger preoperative lowest instrumented vertebra tilt (LIV lilt) (OR = 2.141, 95% CI = 1.491-3.651, p = .011), and postoperative immediate coronal imbalance (OR = 5.512, 95% CI = 4.531-6.891, p = .001). The satisfaction and total score of the SRS-22 scale in the PCI (+) group were lower than those in the PCI (-) group at 2 years after surgery (p <.05). CONCLUSIONS: We found a 10.7% incidence of PCI in patients with Lenke5C AIS undergoing PSF. PCI adversely affects clinical outcomes. Risk factors of PCI included older age, reduced preoperative MT curve flexibility, increased preoperative AVT in the TL/L curve, greater preoperative LIV tilt, and immediate postoperative coronal imbalance.

Organic polaritonic light-emitting diodes with high luminance and color purity toward laser displays.

Achieving high-luminescence organic light-emitting devices (OLEDs) with narrowband emission and high color purity is important in various optoelectronic fields. Laser displays exhibit outstanding advantages in next-generation display technologies owing to their ultimate visual experience, but this remains a great challenge. Here, we develop a novel OLED based organic single crystals. By strongly coupling the organic exciton state to an optical microcavity, we obtain polariton electroluminescent (EL) emission from the polariton OLEDs (OPLEDs) with high luminance, narrow-band emission, high color purity, high polarization as well as excellent optically pumped polariton laser. Further, we evaluate the potential for electrically pumped polariton laser through theoretical analysis and provide possible solutions. This work provides a powerful strategy with a material-device combination that paves the way for electrically driven organic single-crystal-based polariton luminescent devices and possibly lasers.

Corrigendum: Characterizing the neutrophilic inflammation in chronic rhinosinusitis with nasal polyps.

[This corrects the article DOI: 10.3389/fcell.2021.793073.].

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.

Molecular Orientation-Dependent Photonic Polarization Engineering in Organic Single-Crystal-Filled Microcavities.

Designing the polarization degree of freedom of light is crucial in many fields and has widespread application in, for example, all-optical circuits. In this work, we find that in an organic microcavity filled with anisotropic single crystals the cavity modes can be modulated to be elliptically polarized, i.e., partially circularly polarized and partially linearly polarized. The circular polarization component originates from the Rashba-Dresselhaus spin splitting, while the linear polarization component is due to the dislocation of linearly polarized modes. The dislocation of the linear polarizations is ascribed to the orientation of individual molecules and the molecular packing arrangement; hence, the linear polarizations can be controlled by properly structuring the molecular distributions. Our results pave the way for enriching and engineering the polarization properties of individual optical cavity modes in organic microstructures, which may favor the development of polarized lasers with various polarizations.

Comparing Artificial Intelligence-Based Versus Conventional Endotracheal Tube Monitoring Systems in Clinical Practice.

Endotracheal tube dislodgement is a common patient safety incident in clinical settings. Current clinical practices, primarily relying on bedside visual inspections and equipment checks, often fail to detect endotracheal tube displacement or dislodgement promptly. This study involved the development of a deep learning, artificial intelligence (AI)-based system for monitoring tube displacement. We also propose a randomized crossover experiment to evaluate the effectiveness of this AI-based monitoring system compared to conventional methods. The assessment will focus on immediacy in detecting and handling of tube anomalies, the completeness and accuracy of shift transitions, and the degree of innovation diffusion. The findings from this research are expected to offer valuable insights into the development and integration of AI in enhancing care provision and facilitating innovation diffusion in medical and nursing research.

Smart Response Biomaterials for Pain Management.

The intricate nature of pain classification and mechanism constantly affects the recovery of diseases and the well-being of patients. Key medical challenges persist in devising effective pain management strategies. Therefore, a comprehensive review of relevant methods and research advancements in pain management is conducted. This overview covers the main categorization of pain and its developmental mechanism, followed by a review of pertinent research and techniques for managing pain. These techniques include commonly prescribed medications, invasive procedures, and noninvasive physical therapy methods used in rehabilitation medicine. Additionally, for the first time, a systematic summary of the utilization of responsive biomaterials in pain management is provided, encompassing their response to physical stimuli such as ultrasound, magnetic fields, electric fields, light, and temperature, as well as changes in the physiological environment like reactive oxygen species (ROS) and pH. Even though the application of responsive biomaterials in pain management remains limited and at a fundamental level, recent years have seen the examination and debate of relevant research findings. These profound discussions aim to provide trends and directions for future research in pain management.

Straw removal reduces Cd availability and rice Cd accumulation in Cd-contaminated paddy soil: Cd fraction, soil microorganism structure and porewater DOC and Cd.

The impacts of straw removal on rice Cd absorption, behaviour of Cd and microbial community in rhizosphere soil were investigated in paddy fields over two consecutive seasons. The results of the experiments in two fields revealed that straw removal promoted the transformation of soil Cd from acid-extractable and oxidisable fraction to residual fraction and reduced soil DTPA-Cd content with the reduction in DOC and Cd ions in soil porewater, thereby decreasing Cd content in rice. Specifically, the Cd content in brown rice was below 0.2 mg·kg-1 when all rice straw and roots were removed in the slightly Cd-contaminated soils. The α-diversity of soil microbial communities was less influenced by continuous straw removal, ß-diversity was altered and the relative abundances of Anaeromyxobacter, Methylocystis and Mycobacterium microbes were increased. Redundancy analysis and network analysis exhibited that soil pH predominantly influenced the microbial community. Path analysis revealed that the Cd content in brown rice could be directly influenced by the soil Total-Cd and DTPA-Cd, as well as soil pH and OM. Straw removal, including roots removal, is an economical and effective technique to reduce Cd accumulation in rice plants.

Compartmentalized mitochondrial ferroptosis converges with optineurin-mediated mitophagy to impact airway epithelial cell phenotypes and asthma outcomes.

A stable mitochondrial pool is crucial for healthy cell function and survival. Altered redox biology can adversely affect mitochondria through induction of a variety of cell death and survival pathways, yet the understanding of mitochondria and their dysfunction in primary human cells and in specific disease states, including asthma, is modest. Ferroptosis is traditionally considered an iron dependent, hydroperoxy-phospholipid executed process, which induces cytosolic and mitochondrial damage to drive programmed cell death. However, in this report we identify a lipoxygenase orchestrated, compartmentally-targeted ferroptosis-associated peroxidation process which occurs in a subpopulation of dysfunctional mitochondria, without promoting cell death. Rather, this mitochondrial peroxidation process tightly couples with PTEN-induced kinase (PINK)-1(PINK1)-Parkin-Optineurin mediated mitophagy in an effort to preserve the pool of functional mitochondria and prevent cell death. These combined peroxidation processes lead to altered epithelial cell phenotypes and loss of ciliated cells which associate with worsened asthma severity. Ferroptosis-targeted interventions of this process could preserve healthy mitochondria, reverse cell phenotypic changes and improve disease outcomes.

EGFR/TKIs induce excessive apoptosis of bladder carcinoma cells by arresting cell cycle and promoting mitochondrial peroxidation damage.

In recent years, bladder carcinoma (BC) has shown an increasing incidence, with poor patient outcomes. In clinical practice, BC is still mainly treated by surgery combined with chemoradiotherapy. However, as chemotherapy resistance of tumor cells becomes more and more obvious, it is urgent to find more effective BC treatment regimes. With the increasing application and growing attention paid to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in various neoplastic diseases, EGFR-TKIs have been considered as a new treatment direction in the future. In this study, the research team used AG1478, an EGFR-TKI, to intervene with the BC cell line T24. It was found that the cell activity was statistically decreased, the apoptosis was enhanced, and the cells were dominantly arrested in the G0/G1 phase, confirming the future therapeutic potential of EGFR-TKIs in BC. Besides, the research team further observed that AG1478 also promoted pyroptosis in T24 cells, and its mechanism is related to the induction of mitochondrial oxidative stress damage. The findings lay a more reliable foundation for the future application of EGFR-TKIs in BC.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

E3 Ubiquitin Ligase NEDD4L Negatively Regulates Skin Tumorigenesis by Inhibiting IL-6/GP130 Signaling Pathway.

IL-6 signaling plays a crucial role in the survival and metastasis of skin cancer. NEDD4L acts as a suppressor of IL-6 signaling by targeting GP130 degradation. However, the effects of the NEDD4L-regulated IL-6/GP130 signaling pathway on skin cancer remain unclear. In this study, protein expression levels of NEDD4L and GP130 were measured in tumor tissues from patients with cutaneous squamous cell carcinoma. Skin tumors were induced in wild-type and Nedd4l-knockout mice, and activation of the IL-6/GP130/signal transducer and activator of transcription 3 signaling pathway was detected. The results indicated a negative correlation between the protein expression levels of NEDD4L and GP130 in cutaneous squamous cell carcinoma tissues from patients. Nedd4l deficiency significantly promoted 7,12-dimethylbenz[a]anthracene/12-O-tetradecanoylphorbol-13-acetate-induced skin tumorigenesis and benign-to-malignant conversion by activating the IL-6/GP130/signal transducer and activator of transcription 3 signaling pathway, which was abrogated by supplementation with the GP130 inhibitor SC144. Furthermore, our findings suggested that NEDD4L can interact with GP130 and promote its ubiquitination in skin tumors. In conclusion, our results indicate that NEDD4L could act as a tumor suppressor in skin cancer, and inhibition of GP130 could be a potential therapeutic method for treating this disease.

Knockdown of KBTBD7 attenuates septic lung injury by inhibiting ferroptosis and improving mitochondrial dysfunction.

Lung injury in sepsis is caused by an excessive inflammatory response caused by the entry of pathogenic microorganisms into the body. It is also accompanied by the production of large amounts of ROS. Ferroptosis and mitochondrial dysfunction have also been shown to be related to sepsis. Finding suitable sepsis therapeutic targets is crucial for sepsis research. BTB domain-containing protein 7 (KBTBD7) is involved in regulating inflammatory responses, but its role and mechanism in the treatment of septic lung injury are still unclear. In this study, we evaluated the role and related mechanisms of KBTBD7 in septic lung injury. In in vitro studies, we established an in vitro model by inducing human alveolar epithelial cells with lipopolysaccharide (LPS) and found that KBTBD7 was highly expressed in the in vitro model. KBTBD7 knockdown could reduce the inflammatory response by inhibiting the secretion of pro-inflammatory factors and inhibit the production of ROS, ferroptosis and mitochondrial dysfunction. Mechanistic studies show that KBTBD7 interacts with FOXA1, promotes FOXA1 expression, and indirectly inhibits SLC7A11 transcription. In vivo studies have shown that knocking down KBTBD7 improves lung tissue damage in septic lung injury mice, inhibits inflammatory factors, ROS production and ferroptosis. Taken together, knockdown of KBTBD7 shows an alleviating effect on septic lung injury in vitro and in vivo, providing a potential therapeutic target for the treatment of septic lung injury.

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.