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Abstract Background A wide variety of diseases mimic inflammatory bowel disease (IBD). This study aimed to reduce the misdiagnosis among children with colonic ulcers. Methods Eighty-six pediatric patients with colonic ulcers detected by colonoscopy were enrolled in the retrospective study. Children were divided into different groups according to the final diagnosis. The clinical characteristics, laboratory examinations, endoscopic findings, and histopathological results were compared. Results IBD (n = 37) was just responsible for 43% of patients with colonic ulceration. Other diagnosis included autoimmune diseases (n = 9), infectious enteritis (n = 13), gastrointestinal allergy (n = 8), and other diseases (n = 19). Comparing IBD and non-IBD groups, children with IBD had a higher frequency of symptoms like weight loss/failure to thrive (P

This study examines the flood disaster management network within the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events. HIGHLIGHTS The study reveals a strategic shift in the flood disaster management network within the GBA, with decreased centrality of traditional departments like the Public Security Office and Department of Transportation, and increased engagement of the Science Bureau, highlighting a move toward specialized, technology-driven approaches.; The study identifies underutilized communication between key departments, such as the Central Committee, the Poverty Alleviation Office, and the Publicity Department, suggesting significant opportunities for improved integration and coordination in disaster management strategies.; The study recommends enhancing inter-departmental collaboration, integrating flood management with poverty alleviation, and increasing the Publicity Department's role for better communication, advocating for an adaptive, strategic network approach to improve disaster preparedness and responsiveness in the GBA.;

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and treatment. Herein, an optimized strategy for designing a Fe3O4-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering (SERS) and magnetic resonance imaging (MRI) is introduced. Excellent SERS activity of ultrasmall Fe3O4 nanoparticles (NPs) was discovered, and a 5 × 10−9 M limit of detection for crystal violet molecules was successfully obtained. The high-efficiency interfacial photon-induced charge transfer in Fe3O4 NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe, which was observed using ultraviolet–visible diffuse reflectance spectroscopy. Density functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe3O4 NPs significantly boosted the vibronic coupling resonances in the SERS system upon illumination. The subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Fe3O4-based bioprobe with high sensitivity and good specificity. Notably, Fe3O4-based bioprobes simultaneously exhibited T1-weighted MRI contrast enhancement with an active targeting capability for tumors in vivo. To the best of our knowledge, this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro, which has been previously realized only using semiconductor–metal complex materials. The non-metallic materials with SERS–MRI dual-modal imaging established in this report are a promising cancer diagnostic platform, which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

Abstract Domain walls affect significantly ferroelectric and magnetic properties of magnetoelectric multiferroics. The stereotype is that the ferroelectric polarization will reduce at the domain walls due to the incomplete shielding of depolarization field or the effects of gradient energy. By combining transmission electron microscopy and first-principles calculations, we demonstrate that the ferroelectric polarization of tail-to-tail 180° domain walls in ε-Fe2O3 is regulated by the bound charge density. A huge enhancement (43%) of ferroelectric polarization is observed in the type I domain wall with a low bound charge density, while the ferroelectric polarization is reduced to almost zero at the type II domain wall with a high bound charge density. The magnetic coupling across the type I and type II ferroelectric domain walls are antiferromagnetic and ferromagnetic, respectively. Revealing mechanisms for enhancing ferroelectric polarization and magnetic behaviors at ferroelectric domain walls may promote the fundamental research and potential applications of magnetoelectric multiferroics.

Abstract The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models’ performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

Osteoporosis is prevalent among the elderly and requires precise diagnostic approaches for effective treatment and prevention. This work introduces a machine learning-based method for the accurate grading of osteoporosis via the analysis of high-resolution computed tomography (HRCT) images of the spine. This approach allows for the precise extraction of the trabecular component of vertebral bodies, thereby enabling accurate measurement of bone density. Evaluated in 183 individuals, our method demonstrated enhanced stability in bone density prediction and reduced bias when juxtaposed with traditional random sampling techniques. Despite the inherent risk of overestimation, the machine learning model more accurately approximates the actual bone quality compared to conventional clinical methods, which typically involve the random extraction of 3–4 trabecular bone slices from the spine. These findings offer a novel approach to the clinical assessment of osteoporosis, underscoring the significant potential of integrating machine learning into existing medical image analysis workflows.

Objective To analyze the application of serological test results in the diagnosis and treatment of anti-M-induced hemolytic disease of the fetus and newborn(HDFN), and to explore HDFN prevention strategies. Methods The serological test results of 12 cases of HDFN caused by anti-M diagnosed in our laboratory from January 2017 to December 2023 were retrospectively analyzed, including blood group identification of mothers and children, serum total bilirubin/hemoglobin/antibody titer test, and three hemolysis tests in newborns. Clinical data of the children and mothers were collected, including pregnancy history, blood transfusion history, prenatal antibody testing, history of intrauterine blood transfusion and gestational week of delivery, and the prognosis of the children was followed up. Results All 12 cases of fetal neonatal hemolytic disease due to anti-M were RhD+ MN phenotype newborn born to RhD+ NN mother, with maternal- fetal incompatiblility in MN blood groups. In the ABO blood group system, ABO incompatibility between mother and child accounted for 41.7%(5/12).None of the mothers had a history of blood transfusion, and the median titer of the test at 4℃ was 32, and the median titer at 37℃ was 4. The mothers of 3 cases had a history of multiple intrauterine blood transfusions, with an incidence of 25%(3/12). One case had an abnormal first pregnancy, with an incidence of 8.3%(1/12), and seven cases had an abnormal pregnancy with a miscarriage, with an incidence of abnormal pregnancy and birth history of 58.3%(7/12). There were 6 cases of premature labor, with an incidence of 50%(6/12). The mothers in three cases underwent regular obstetric examination and the specificity of the antibodies was determined, accounting for 25%(3/12). Twelve children had free antibodies with a median titer of 6 at 4℃ and 2 at 37℃. Two children had anti-M antibodies that were not reactive at 37℃, with a negative rate of 16.7%(2/12). The positive rate of DAT and elution test was respectively 8.3%(1/12) and 16.7%(2/12) in the children. The median minimum hemoglobin value was 75 g/L, and all 12 children received blood transfusions. The median peak total bilirubin value was 157.5 μmol/L, and none of them reached the threshold for blood exchange. The rate of delayed anemia was 16.7%(2/12), the postnatal mortality rate was 8.3%(1/12), and 11 children was free of growth and neurodevelopmental delay in prognosis. Conclusion Anti-M can cause severe HDFN, which can also occur in primigravida. The intensity of antibody titer does not correlate with the severity of the disease, and it is prone to cause delayed anemia, which should be monitored regularly according to the serological characteristics of anti-M and clinical symptoms, and should be treated timely.

Abstract Introduction Postherpetic neuralgia (PHN), a complication of herpes zoster, significantly impacts the quality of life of affected patients. Research indicates that early intervention for pain can reduce the occurrence or severity of PHN. This study aims to develop a predictive model and scoring table to identify patients at risk of developing PHN following acute herpetic neuralgia, facilitating informed clinical decision-making. Methods We conducted a retrospective review of 524 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University from December 2020 to December 2023 and classified them according to whether they had PHN, collecting a comprehensive set of 30 patient characteristics and disease-related indicators, 5 comorbidity indicators, 2 disease score values, and 10 serological indicators. Relevant features associated with PHN were identified using the least absolute shrinkage and selection operator (LASSO). Then, the patients were divided into a training set and a test set in a 4:1 ratio, with comparability tested using univariate analysis. Six models were established in the training set using machine learning methods: support vector machines, logistic regression, random forest, k-nearest neighbor, gradient boosting, and neural network. The performance of these models was evaluated in the test set, and a nomogram based on logistic regression was used to create a PHN prediction score table. Results Eight non-zero characteristic variables selected from the LASSO regression results were included in the model, including age [area under the curve (AUC) = 0.812, p

Cellular senescence has emerged as an important contributor to aging and age-related diseases. Non-ionizing radiation (NIR), including ultraviolet radiation and electromagnetic fields, has been increasingly recognized as a key inducer of premature senescence. In this review, we discuss the molecular mechanisms of NIR-induced cellular senescence and its effects on aging and age-related diseases. We also summarize the modulation strategies for NIR-induced cellular senescence. A better understanding of the complex relationship between non-ionizing radiation, cellular senescence and age-related pathology may lead to interventions to ameliorate radiation damage and delay aging. Further research is still needed to elucidate the precise mechanisms, dose-response effects, and to develop protective strategies against radiation-induced senescence.

Abstract Hydrogels capable of swift mechanical energy dissipation hold promise for a range of applications including impact protection, shock absorption, and enhanced damage resistance. Traditional energy absorption in such materials typically relies on viscoelastic mechanisms, involving sacrificial bond breakage, yet often suffers from prolonged recovery times. Here, we introduce a hydrogel designed for friction-based damping. This hydrogel features an internal structure that facilitates the motion of a chain walker within its network, effectively dissipating mechanical stress. The hydrogel network architecture allows for rapid restoration of its damping capacity, often within seconds, ensuring swift material recovery post-deformation. We further demonstrate that this hydrogel can significantly shield encapsulated cells from mechanical trauma under repetitive compression, owing to its proficient energy damping and rapid rebound characteristics. Therefore, this hydrogel has potential for dynamic load applications like artificial muscles and synthetic cartilage, expanding the use of hydrogel dampers in biomechanics and related areas.

Abstract Understanding how student peers influence learning outcomes is crucial for effective education management in complex social systems. The complexities of peer selection and evolving peer relationships, however, pose challenges for identifying peer effects using static observational data. Here we use both null-model and regression approaches to examine peer effects using longitudinal data from 5,272 undergraduates, where roommate assignments are plausibly random upon enrollment and roommate relationships persist until graduation. Specifically, we construct a roommate null model by randomly shuffling students among dorm rooms and introduce an assimilation metric to quantify similarities in roommate academic performance. We find significantly larger assimilation in actual data than in the roommate null model, suggesting roommate peer effects, whereby roommates have more similar performance than expected by chance alone. Moreover, assimilation exhibits an overall increasing trend over time, suggesting that peer effects become stronger the longer roommates live together. Our regression analysis further reveals the moderating role of peer heterogeneity. In particular, when roommates perform similarly, the positive relationship between a student’s future performance and their roommates’ average prior performance is more pronounced, and their ordinal rank in the dorm room has an independent effect. Our findings contribute to understanding the role of college roommates in influencing student academic performance.

BackgroundPostherpetic itch (PHI) is an easily overlooked complication of herpes zoster that greatly affects patients' quality of life. Studies have shown that early intervention can reduce the occurrence of itch. The aim of this study was to develop and validate a predictive model through a machine learning approach to identify patients at risk of developing PHI among patients with herpes zoster, making PHI prevention a viable clinical option.MethodWe conducted a retrospective review of 488 hospitalized patients with herpes zoster at The First Affiliated Hospital of Zhejiang Chinese Medical University and classified according to whether they had PHI. Fifty indicators of these participants were collected as potential input features for the model. Features associated with PHI were identified for inclusion in the model using the least absolute shrinkage selection operator (LASSO). Divide all the data into five pieces, and then use each piece as a verification set and the others as a training set for training and verification, this process is repeated 100 times. Five models, logistic regression, random forest (RF), k-nearest neighbor, gradient boosting decision tree and neural network, were built in the training set using machine learning methods, and the performance of these models was evaluated in the test set.ResultsSeven non-zero characteristic variables from the Lasso regression results were selected for inclusion in the model, including age, moderate pain, time to recovery from rash, diabetes, severe pain, rash on the head and face, and basophil ratio. The RF model performs better than other models. On the test set, the AUC of the RF model is 0.84 [(95% confidence interval (CI): 0.80–0.88], an accuracy of 0.78 (95% CI: 0.69–0.86), a precision of 0.61 (95% CI: 0.45–0.77), a recall of 0.73 (95% CI: 0.58–0.89), and a specificity of 0.79 (95% CI: 0.70–0.89).ConclusionsIn this study, five machine learning methods were used to build postherpetic itch risk prediction models by analyzing historical case data, and the optimal model was selected through comparative analysis, with the random forest model being the top performing model.

IntroductionLong-term use of chemical fertilizers (CFs) can cause soil compaction and acidification. In recent years, bio-organic fertilizers (BOFs) have begun to replace CFs in some vegetables and cash crops, but the application of CFs or BOFs has resulted in crop quality and disease occurrence.MethodsThis study aimed to analyze the microbial mechanism of differences between CFs and BOFs in root disease, quality, and yield of tuber Chinese herbal medicine. We studied the effects of CFs, organic fertilizers, commercial BOFs, biocontrol bacteria BOFs, and biocontrol fungi BOFs on rhizosphere microbial community structure and function, root rot, quality, and yield of Codonopsis pilosula at different periods after application and analyzed the correlation.Results and discussionCompared to CFs, the emergence rate and yield in BOF treatments were increased by 21.12 and 33.65%, respectively, and the ash content, water content, and disease index in the BOF treatments were decreased by 17.87, 8.19, and 76.60%, respectively. The structural equation model showed that CFs promoted the quality and yield of C. pilosula by influencing soil environmental factors, while BOFs directly drove soil bacterial community to reduce disease index and improve the quality and yield of C. pilosula. There was a stronger interaction and stability of soil microbial networks after BOF treatments. Microlunatus, Rubrobacter, Luteitalea, Nakamurella, and Pedomicrobium were identified as effector bacteria, which were related to disease prevention and yield and quality increase of C. pilosula. Microbial functional analysis indicated that the signal transduction and amino acid metabolism of soil bacteria might play a major role in improving the quality and yield of C. pilosula in the early and middle growth stages. In conclusion, compared to CFs, BOFs obtained a lower disease index of root rot and a higher quality and yield of C. pilosula by changing the structure and function of the rhizosphere bacterial community.

Summary: In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

Pathogenic missense mutation of the FGF12 gene is responsible for a variable disease phenotypic spectrum. Disease-specific therapies require precise dissection of the relationship between different mutations and phenotypes. The lack of a proper animal model hinders the investigation of related diseases, such as early-onset epileptic encephalopathy. Here, an FGF12AV52H mouse model was generated using CRISPR/Cas9 technology, which altered the A isoform without affecting the B isoform. The FGF12AV52H mice exhibited seizure susceptibility, while no spontaneous seizures were observed. The increased excitability in dorsal hippocampal CA3 neurons was confirmed by patch-clamp recordings. Furthermore, immunostaining showed that the balance of excitatory/inhibitory neurons in the hippocampus of the FGF12AV52H mice was perturbed. The increases in inhibitory SOM+ neurons and excitatory CaMKII+ neurons were heterogeneous. Moreover, the locomotion, anxiety levels, risk assessment behavior, social behavior, and cognition of the FGF12AV52H mice were investigated by elevated plus maze, open field, three-chamber sociability, and novel object tests, respectively. Cognition deficit, impaired risk assessment, and social behavior with normal social indexes were observed, implying complex consequences of V52H FGF12A in mice. Together, these data suggest that the function of FGF12A in neurons can be immediate or long-term and involves modulation of ion channels and the differentiation and maturation of neurons. The FGF12AV52H mouse model increases the understanding of the function of FGF12A, and it is of great importance for revealing the complex network of the FGF12 gene in physiological and pathological processes.

Psoriasis is a chronic, relapsing, inflammatory skin disease and has been increasing year by year. It is linked to other serious illnesses, such as psoriatic arthritis, cardiometabolic syndrome, and depression, resulting in a notable decrease in the quality of life for patients. Existing therapies merely alleviate symptoms, rather than providing a cure. An in-depth under-standing of the pathogenesis of psoriasis is helpful to discover new therapeutic targets and develop effective novel therapeutic agents, so it has important clinical significance. This article reviews the new progress in the study of pathogenesis and natural products of psoriasis in recent years. These natural products were summarized, mainly classified as terpenoids, polyphenols and alkaloids. However, the translation of experimental results to the clinic takes a long way to go.

Abstract With the continuous development of the economy and society, along with the sustained population growth, the issue of water resources carrying capacity in China has attracted increasing attention. This paper constructs a model for evaluating the provincial water resources carrying capacity in China from four dimensions: water, economy, society, and ecology. Utilizing this model, we analyze the spatiotemporal variations in water resources carrying capacity among 31 provinces in China from 2005 to 2021. Additionally, we delve into the coupling coordination and influencing factors of water resources carrying capacity. The study reveals an overall increasing trend in China’s water resources carrying capacity index, with the ecological indicator exhibiting the most significant growth while the water resources sub-indicator lags behind. There are notable regional differences, with higher water resources carrying capacity observed in the eastern coastal areas and relatively lower capacity in the western regions. The ecological criterion becomes a core factor constraining water resources carrying capacity from 2005 to 2015, gradually giving way to the prominence of the social criterion since 2015. The coordination degree is relatively higher in the eastern regions, more scattered in the western regions, and relatively stable in the central regions. Based on the research findings, a series of recommendations are proposed, including strengthening environmental protection policies, optimizing water resources management mechanisms, improving water use efficiency, and promoting economic structural diversification. These suggestions aim to facilitate the sustainable development of water resources carrying capacity in China.

Abstract Topological domain structures have drawn great attention as they have potential applications in future electronic devices. As an important concept linking the quantum and classical magnetism, a magnetic Bloch point, predicted in 1960s but not observed directly so far, is a singular point around which magnetization vectors orient to nearly all directions. Here we show polar Bloch points in tensile-strained ultrathin ferroelectric PbTiO3 films, which are alternatively visualized by phase-field simulations and aberration-corrected scanning transmission electron microscopic imaging. The phase-field simulations indicate local steady-state negative capacitance around the Bloch points. The observation of polar Bloch points and their emergent properties consequently implies novel applications in future integrated circuits and low power electronic devices.

AIM: To assess the efficacy of artificial natural light in preventing incident myopia in primary school-age children. METHODS: This is a prospective, randomized control, intervention study. A total of 1840 students from 39 classes in 4 primary schools in Foshan participated in this study. The whole randomization method was adopted to include classes as a group according to 1:1 randomized control. Classrooms in the control group were illuminated by usual light, and classrooms in the intervention group were illuminated by artificial natural light. All students received uncorrected visual acuity and best-corrected visual acuity measurement, non-cycloplegic autorefraction, ocular biometric examination, slit lamp and strabismus examination. Three-year follow-up, the students underwent same procedures. Myopia was defined as spherical equivalent refraction ≤ -0.50 D and uncorrected visual acuity

Abstract Background Large bone defects pose a clinical treatment challenge; inhibiting transferrin receptor 2 (TfR2), which is involved in iron metabolism, can promote osteogenesis. Iron-based metal-organic frameworks (MOF-Fe) particles not only inhibit TfR2 but also serve as biomimetic catalysts to remove hydrogen peroxide in reactive oxygen species (ROS); excess ROS can disrupt the normal functions of osteoblasts, thereby hindering bone regeneration. This study explored the potential effects of MOF-Fe in increasing osteogenic activity and clearing ROS. Methods In vitro experiments were performed to investigate the osteogenic effects of MOF-Fe particles and assess their impact on cellular ROS levels. To further validate the role of MOF-Fe in promoting bone defect repair, we injected MOF-Fe suspensions into the femoral defects of SD rats and implanted MOF-Fe-containing hydrogel scaffolds in rabbit cranial defect models and observed their effects on bone healing. Results In vitro, the presence of MOF-Fe significantly increased the expression levels of osteogenesis-related genes and proteins compared to those in the control group. Additionally, compared to those in the untreated control group, the cells treated with MOF-Fe exhibited a significantly increased ability to remove hydrogen peroxide from ROS and generate oxygen and water within the physiological pH range. In vivo experiments further confirmed the positive effect of MOF-Fe in promoting bone defect repair. Conclusion This study supports the application of MOF-Fe as an agent for bone regeneration, particularly for mitigating ROS and activating the bone morphogenetic protein (BMP) pathway, demonstrating its potential value.

Abstract Kaempferol (KA), an natural antioxidant of traditional Chinese medicine (TCM), is extensively used as the primary treatment for inflammatory digestive diseases with impaired redox homeostasis. Severe acute pancreatitis (SAP) was exacerbated by mitochondrial dysfunction and abundant ROS, which highlights the role of antioxidants in targeting mitochondrial function. However, low bioavailability and high dosage of KA leading to unavoidable side effects limits clinical transformation. The mechanisms of KA with poor bioavailability largely unexplored, hindering development of the efficient strategies to maximizing the medicinal effects of KA. Here, we engineered a novel thioketals (TK)-modified based on DSPE-PEG2000 liposomal codelivery system for improving bioavailability and avoiding side effects (denotes as DSPE-TK-PEG2000-KA, DTM@KA NPs). We demonstrated that the liposome exerts profound impacts on damaging intracellular redox homeostasis by reducing GSH depletion and activating Nrf2, which synergizes with KA to reinforce the inhibition of inadequate fission, excessive mitochondrial fusion and impaired mitophagy resulting in inflammation and apoptosis; and then, the restored mitochondrial homeostasis strengthens ATP supply for PAC renovation and homeostasis. Interestingly, TK bond was proved as the main functional structure to improve the above efficacy of KA compared with the absence of TK bond. Most importantly, DTM@KA NPs obviously suppresses PAC death with negligible side effects in vitro and vivo. Mechanismly, DTM@KA NPs facilitated STAT6-regulated mitochondrial precursor proteins transport via interacting with TOM20 to further promote Drp1-dependent fission and Pink1/Parkin-regulated mitophagy with enhanced lysosomal degradation for removing damaged mitochondria in PAC and then reduce inflammation and apoptosis. Generally, DTM@KA NPs synergistically improved mitochondrial homeostasis, redox homeostasis, energy metabolism and inflammation response via regulating TOM20-STAT6-Drp1 signaling and promoting mitophagy in SAP. Consequently, such a TCM’s active ingredients-based nanomedicine strategy is be expected to be an innovative approach for SAP therapy. Graphical Abstract

Abstract The combination of immune checkpoint inhibitors and immunogenic cell death (ICD) inducers has become a promising strategy for the treatment of various cancers. However, its efficacy remains unmet because of the dense stroma and defective vasculatures in the tumor microenvironment (TME) that restricts the intratumoral infiltration of cytotoxic T lymphocytes (CTLs). Herein, cancer-associated fibroblasts (CAFs)-targeted nanoemulsions are tailored to combine the ICD induction and the TME reprogramming to sensitize checkpoint blockade immunotherapy. Melittin, as an ICD inducer and an antifibrotic agent, is efficiently encapsulated into the nanoemulsion accompanied by a nitric oxide donor to improve its bioavailability and tumor targeting. The nanoemulsions exhibited dual functionality by directly inducing direct cancer cell death and enhancing the tumoral immunogenicity, while also synergistically reprogramming the TME through reversing the activated CAFs, decreasing collagen deposition and restoring tumor vessels. Consequently, these nanemulsions successfully facilitated the CTLs infiltration and suppressing the recruitment of immunosuppressive cells. A combination of AE-MGNPs and anti-CTLA-4 antibody greatly elicited a striking level of antitumor T-cell response to suppress tumor growth in CAFs-rich colorectal tumor models. Our work emphasized the integration of the ICD induction with simultaneous modulation of the TME to enhance the sensitivity of patients to checkpoint blockade immunotherapy.

A skeleton-based action recognition model is proposed for the spatiotemporal sampling graph convolutional network (ST-GCN) based on the self-calibration mechanism to address the problem of existing action recognition algorithms disregarding the dependence of spatiotemporal information context and lacking multilevel receptive fields for feature extraction. First, this paper introduces the working principles of ST-GCN and 3D-GCN, Transformer, and self-attention mechanism and analyzes whether 3D-GCN and Transformer cannot effectively model the global and local spatiotemporal contexts, respectively. Second, a spatiotemporal sampling graph convolutional network is proposed to effectively perform the spatiotemporal context modeling. This network divides the global action into multiple subactions by employing a series of continuous temporal multiframes as spatiotemporal sampling, establishes the local crosstemporal dependency by computing the correlation between a single node and all nodes in the sampling frequency frame with the nonlocal network, and establishes the global crosstemporal dependency by combining the nonlocal network and temporal convolution to compute the correlation between a single sampling subaction and global subactions. Subsequently, to effectively improve the multilevel receptive field for capturing more discriminating temporal features, a temporal self-calibrating convolutional network is proposed for convoluting in two different scales of space-time. Further, two abovementioned features can be combined: one is the space–time of the original scale, while the other is the potential space-time with a smaller scale using downsampling operation; here, the latter adaptively establishes the dependence between the remote space-time and channel and models the interchannel dependence by differentiating the characteristics of each channel. Meanwhile, the spatiotemporal sampling graph convolutional and temporal self-calibration networks are combined to construct the spatiotemporal-sampling graph convolutional network based on self-calibration mechanism, and end-to-end training is performed on this model using the multistream network. Finally, to confirm the effectiveness and superior performance of the model feature extraction, some experimental work is performed on the skeleton-based action recognition based on the NTU-RGB+D and NTU-RGB+D120 skeleton-based action datasets, and the findings reveal that the recognition accuracy under X-View and X-Sub of the NTU-RGB+D dataset reaches up to 95.2% and 88.8%, respectively, confirming the generalization ability of the model on the NTU-RGB+D120 dataset. This work displays that the model has excellent recognition accuracy and generalization ability and corroborates the effective spatiotemporal feature extraction ability and excellent performance of the action recognition model.

Abstract Langerhans cell histiocytosis (LCH) involving the gastrointestinal tract is a rare condition for which clinical experience is limited. We describe the cases of two patients who initially presented with chronic diarrhoea, hypoproteinaemia, and intermittent fever. These findings suggest that in cases of refractory diarrhoea accompanied by recurrent hypoalbuminaemia, especially with abdominal rash, LCH should be considered. Gastrointestinal endoscopy, biopsy, and imaging studies are essential for obtaining a definitive diagnosis. This approach might be helpful for the early recognition of gastrointestinal tract involvement in LCH.

Abstract Improvement of fabrication efficiency and part performance was the main challenge for the large-scale powder bed fusion (PBF) process. In this study, a dynamic monitoring and feedback system of powder bed temperature field using an infrared thermal imager has been established and integrated into a four-laser PBF equipment with a working area of 2000 mm × 2000 mm. The heat-affected zone (HAZ) temperature field has been controlled by adjusting the scanning speed dynamically. Simultaneously, the relationship among spot size, HAZ temperature, and part performance has been established. The fluctuation of the HAZ temperature in four-laser scanning areas was decreased from 30.85 ℃ to 17.41 ℃. Thus, the consistency of the sintering performance of the produced large component has been improved. Based on the controllable temperature field, a dynamically adjusting strategy for laser spot size was proposed, by which the fabrication efficiency was improved up to 65.38%. The current research results were of great significance to the further industrial applications of large-scale PBF equipment.

Cellular traction forces (CTFs) are generated by adherent cells and involved in regulating migration, morphology, and homeostasis. Accurate measurement of CTFs is crucial for understanding fundamental biological processes such as morphogenesis, angiogenesis, and wound healing. However, directly measuring CTFs, which typically range in the nanonewton scale, is challenging. Cellular traction force microscopy (TFM) has been developed to quantify CTFs, but detailed operational procedures and complex data analysis limit its applicability. In this study, hydrogels embedded with fluo-spheres serve as the substrate for TFM measurement under a detailed TFM protocol. Additionally, we designed a user-friendly program for easy parameter setting. An open-source program called Python Fourier transform traction cytometry (pyFTTC) is introduced for data analysis, utilizing particle image velocimetry (PIV) to calculate the traction force from a batch of images. Cross-correlation based PIV and L2-regularized FTTC are applied to all images for data analysis. This article provides a straightforward protocol for quantifying CTFs in standard laboratories, facilitating both cell biology studies and biomaterials development.

Screw-based material extrusion 3D printing technology offers the potential for manufacturing fibre-reinforced composites with component gradients by mixing materials within the printhead. However, mixing composites with high fibre content sufficiently in a limited range for rapid component switching presented a challenge. Herein, an in-situ variable component screw-based printhead was developed. Pins with different geometries were added to 8, 12, and 16 mm diameter extrusion screws, to enhance the mixing efficiency. A particle tracking model based on computational fluid dynamics simulations was used to optimise the geometry and distribution of pins by taking 0-40 wt% short carbon fibre-reinforced polyether-ether-ketone (SCF/PEEK) as an example. The incorporation of pins enhanced the mixing efficiency but aggravated the dead zone, which decreased the variable component response rate. An optimal design of pins that balanced mixing efficiency and response rate was developed, by which the variable component response rate was promoted by 369%, while the degree of mixing was improved by 50%. Benefitting from the enhanced mixing, the tensile strength of in-situ mixed 20 wt% SCF/PEEK composites was increased by 23%. The in-situ variable component screw-based extrusion printhead developed and optimised in this investigation provided a novel approach to fabricating fibre-reinforced composites with variable components.

Abstract Background Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. Methods Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. Results We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. Conclusions The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Significant advancements in our understanding and clinical treatment of cholangiocarcinoma (CCA) have been achieved over the past 5 years. Groundbreaking studies have illuminated the immune landscape and pathological characteristics of the tumor microenvironment in CCA. The development of immune- and metabolism-based classification systems has enabled a nuanced exploration of the tumor microenvironment and the origins of CCA, facilitating a detailed understanding of tumor progression modulation. Despite these insights, targeted therapies have not yet yielded satisfactory clinical results, highlighting the urgent need for innovative therapeutic strategies. This review delineates the complexity and heterogeneity of CCA, examines the current landscape of therapeutic strategies and clinical trials, and delves into the resistance mechanisms underlying targeted therapies. Finally, from a single-cell and spatial transcriptomic perspective, we address the challenge of therapy resistance, discussing emerging mechanisms and potential strategies to overcome this barrier and enhance treatment efficacy.

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids’ contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

Background and aimsRoot-knot nematodes (RKN; Meloidogyne spp.) are among the highly prevalent and significantly detrimental pathogens that cause severe economic and yield losses in crops. Currently, control of RKN primarily relies on the application of chemical nematicides but it has environmental and public health concerns, which open new doors for alternative methods in the form of biological control.MethodsIn this study, we investigated the nematicidal and attractive activities of an endophytic strain WF01 against Meloidogyne incognita in concentration-dependent experiments. The active nematicidal metabolite was extracted in the WF01 crude extract through the Sephadex column, and its structure was identified by nuclear magnetic resonance and mass spectrometry data.ResultsThe strain WF01 was identified as Aspergillus tubingensis based on morphological and molecular characteristics. The nematicidal and attractive metabolite of A. tubingensis WF01 was identified as oxalic acid (OA), which showed solid nematicidal activity against M. incognita, having LC50 of 27.48 μg ml–1. The Nsy-1 of AWC and Odr-7 of AWA were the primary neuron genes for Caenorhabditis elegans to detect OA. Under greenhouse, WF01 broth and 200 μg ml–1 OA could effectively suppress the disease caused by M. incognita on tomatoes respectively with control efficiency (CE) of 62.5% and 70.83%, and promote plant growth. In the field, WF01-WP and 8% OA-WP formulations showed moderate CEs of 51.25%–61.47% against RKN in tomato and tobacco. The combined application of WF01 and OA resulted in excellent CEs of 66.83% and 69.34% toward RKN in tomato and tobacco, respectively. Furthermore, the application of WF01 broth or OA significantly suppressed the infection of J2s in tomatoes by upregulating the expression levels of the genes (PAL, C4H, HCT, and F5H) related to lignin synthesis, and strengthened root lignification.ConclusionAltogether, our results demonstrated that A. tubingensis WF01 exhibited multiple weapons to control RKN mediated by producing OA to lure and kill RKN in a concentration-dependent manner and strengthen root lignification. This fungus could serve as an environmental bio-nematicide for managing the diseases caused by RKN.

Summary: Background: Bivalent SARS-CoV-2 vaccines were developed to counter increasing susceptibility to emerging SARS-CoV-2 variants. We evaluated the durability of immunity and protection following first bivalent vaccination among nursing home residents. Methods: We evaluated anti-spike and neutralization titers from blood in 653 community nursing home residents before and after each monovalent booster, and a bivalent vaccine. Concurrent clinical outcomes were evaluated using electronic health record data from a separate cohort of 3783 residents of Veterans Affairs (VA) nursing homes who had received at least the primary series monovalent vaccination. Using target trial emulation, we compared VA residents who did and did not receive the bivalent vaccine to measure vaccine effectiveness against infection, hospitalization, and death. Findings: In the community cohort, Omicron BA.5 neutralization activity rose after each monovalent and bivalent booster vaccination regardless of prior infection history. Titers declined over time but six months post-bivalent vaccination, BA.5 neutralization persisted at detectable levels in 75% of infection-naive and 98% of prior-infected individuals. In the VA nursing home cohort, bivalent vaccine added effectiveness to monovalent booster vaccination by 18.5% for infection (95% confidence interval (CI) −5.6, 34.0%), and 29.2% for hospitalization or death (95% CI −14.2, 56.2%) over five months. Interpretation: The level of protection declined after bivalent vaccination over a 6 month period and may open a window of added vulnerability before the next updated vaccine becomes available, suggesting a subset of nursing home residents may benefit from an additional vaccination booster. Funding: CDC, NIH, VHA.

This work aims to evaluate the effects of the operation of Qinshan nuclear Power Plant (QNPP) on tritium (3H) and carbon-14 (14C) levels in seafood and assess the health risks caused by seafood consumption. Five kinds of seafood, including marine fish, prawn, razor clam, crabs, and seaweed, were collected from QNPP and the sea around Hangzhou Bay. The activity concentrations of tissue free water tritium (TFWT), organically bound tritium (OBT) and 14C were determined, respectively, and the annual intake and annual effective dose (AED) were calculated. The results showed that the TFWT, OBT, and 14C activity concentrations of the seafood in the surrounding area of QNPP ranged from 2.00 to 74.75 Bq/L,

There are a large amount of audio signals stored in third-party storage centers. To improve the privacy and security of audio signals, a digital audio encryption and forensics watermarking scheme is proposed. We defined the signal energy ratio feature of audio signals and designed the embedding method by quantifying the feature, aiming to improve the security of watermarking system. We firstly encrypted the original audio by using scrambling and multiplication, to get the encrypted data. Secondly, we divided the encrypted data into frames and compressed each frame by sampling to get the compressed data. After that, we embedded the compressed data and frame number into the encrypted data to get the watermarked signal being uploaded to third-party storage centers. Authorized users download encrypted data and verify the authenticity of the data. For the intact data, they decrypt it directly to get the audio signal. If the downloaded data has been attacked, they locate the attacked frame and extract the compressed data to reconstruct the attacked signal approximately. Furthermore, we decrypted the reconstructed signal to obtain the expression meaning of the original audio. The experimental results demonstrate the effectiveness of the proposed scheme.

Abstract Hydrogels have been designed to react to many different stimuli which find broad applications in tissue engineering and soft robotics. However, polymer networks bearing mechano-responsiveness, especially those displaying on-demand self-stiffening and self-softening behavior, are rarely reported. Here, we design a mechano-controlled biocatalytic system at the molecular level that is incorporated into hydrogels to regulate their mechanical properties at the material scale. The biocatalytic system consists of the protease thrombin and its inhibitor, hirudin, which are genetically engineered and covalently coupled to the hydrogel networks. The catalytic activity of thrombin is reversibly switched on by stretching of the hydrogels, which disrupts the noncovalent inhibitory interaction between both entities. Under cyclic tensile-loading, hydrogels exhibit self-stiffening or self-softening properties when substrates are present that can self-assemble to form new networks after being activated by thrombin or when cleavable peptide crosslinkers are constitutional components of the original network, respectively. Additionally, we demonstrate the programming of bilayer hydrogels to exhibit tailored shape-morphing behavior under mechanical stimulation. Our developed system provides proof of concept for mechanically controlled reversible biocatalytic processes, showcasing their potential for regulating hydrogels and proposing a biomacromolecular strategy for mechano-regulated soft functional materials.

Abstract Background Protein–protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. Results We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. Conclusion The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .

In this study, the effects of variations in the height (h) and bottom radius (r) of black silicon microstructures on their absorptance and photoelectric response efficiency were analyzed. By using the relation cot⁡θ2=hr to combine the parameters, it was found that changes in morphology affected the absorptance of black silicon microstructures, with h being directly proportional to the absorptance, while r was inversely proportional. A positive correlation was observed between cot⁡θ2 and absorptance. However, the correlation between cot⁡θ2 and photoelectric response efficiency was not significant. Through Raman spectroscopy analysis of the samples, it was concluded that as the laser ablation energy density increased, more lattice defects were introduced, weakening the charge carrier transport efficiency. This study further elucidated the mechanism by which microstructural changes impacted the absorptance and energy density of black silicon, providing valuable insights for optimizing its energy density.

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.

Green foxtail is a troublesome weed in crop fields across China. A nova target HPPD inhibitor is widely used to control weeds in agricultural production. Mesotrione, an HPPD inhibitor, cannot control green foxtail effectively under the recommended field dose, indicating that green foxtail is tolerant to mesotrione. Interestingly, a green foxtail biotype that exhibits a greater tolerance to mesotrione (GR50 value 463.2 g ai ha−1) than that of the wild biotype (GR50 value 271.9 g ai ha−1) was found in Jilin Province, China. The HPPD genes isolated from the two biotypes genome were aligned, while no difference was found in the amino acid of the HPPD compared with that of the wild biotype. Through the qPCR experiment, the HPPD gene copy number variation and overexpression were also not found. Cytochrome P450 inhibitors (malathion and PBO), pretreatment, could effectively reverse the tolerance. Compared with the MT biotype, the in vivo activity of P450s was higher after the mesotrione treatment in the HT biotype. Therefore, P450s might be involved in the mechanism of tolerance.

BackgroundThe relationship between plaque psoriasis and both MASLD and lean MASLD has not been sufficiently explored in the current literature.MethodThis retrospective and observational study was carried out from January 2021 to January 2023 at The First Affiliated Hospital of Zhejiang Chinese Medical University. Patients diagnosed with plaque psoriasis and a control group consisting of individuals undergoing routine physical examinations were enrolled. The incidence of MASLD and lean MASLD among these groups was compared. Additionally, patients with plaque psoriasis were divided into those with MASLD, those with lean MASLD, and a control group with only psoriasis for a serological comparative analysis.ResultsThe incidence of MASLD in the observation group and the control group was 43.67% (69/158) and 22.15% (35/158), respectively (p

Current research efforts on neurodegenerative diseases are focused on identifying novel and reliable biomarkers for early diagnosis and insight into disease progression. Salivary analysis is gaining increasing interest as a promising source of biomarkers and matrices for measuring neurodegenerative diseases. Saliva collection offers multiple advantages over the currently detected biofluids as it is easily accessible, non-invasive, and repeatable, allowing early diagnosis and timely treatment of the diseases. Here, we review the existing findings on salivary biomarkers and address the potential value in diagnosing neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease and Amyotrophic lateral sclerosis. Based on the available research, β-amyloid, tau protein, α-synuclein, DJ-1, Huntington protein in saliva profiles display reliability and validity as the biomarkers of neurodegenerative diseases.

This study delves into the efficiency and structure of the social organization network involved in flood disaster management in China’s Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Leveraging the global knowledge graph, we analyzed social media data and news reports to identify the pivotal roles and interactions of various social organization types in flood disaster governance. The empirical findings underscore the central role of national public social organizations, evidenced by their high frequency of news coverage and mentions. Meanwhile, International Social Organizations and Local Public Social Organizations exhibit growing influence and participation. As China shifts toward a comprehensive disaster reduction strategy, the study observes increased diversity and decentralization among these organizations, with non-governmental organizations and volunteer groups playing vital roles in resource mobilization, information dissemination, and disaster coordination. Network analysis unveils evolving interaction patterns and co-occurrence frequencies among social organizations, reflecting the evolving roles of different organization types in disaster relief. This research aims to provide actionable insights to enhance the effectiveness of social organizations in disaster management, contributing to the establishment of a more resilient and efficient disaster management framework in the GBA.

Fine particulate matter (PM2.5) exposure is strongly associated with vascular endothelial senescence, a process implicated in cardiovascular diseases. While there is existing knowledge on the impact of Lycium barbarum polysaccharide (LBP) on vascular endothelial damage, the protective mechanism of LBP against PM2.5-induced vascular endothelial senescence remains unclear. In this study, we investigated the impact of PM2.5 exposure on vascular endothelial senescence and explored the intervention effects of LBP in human umbilical vein endothelial cells (HUVECs). We found that PM2.5 exposure dose-dependently reduced cell viability and proliferation in HUVECs while increasing the production of reactive oxygen species (ROS), malondialdehyde (MDA), and hydrogen peroxide (H2O2). Additionally, PM2.5 exposure inhibited the activity of superoxide dismutase (SOD). Notably, PM2.5 exposure induced autophagy impairments and cellular senescence. However, LBP mitigated PM2.5-induced cell damage. Further studies demonstrated that correcting autophagy impairment in HUVECs reduced the expression of the senescence markers P16 and P21 induced by PM2.5. This suggests the regulatory role of autophagy in cellular senescence and the potential of LBP in improving HUVECs senescence. These findings provide novel insights into the mechanisms underlying PM2.5-induced cardiovascular toxicity and highlight the potential of LBP as a therapeutic agent for improving vascular endothelial health.

An in vivo model is necessary for toxicology. This review analyzed the uses of zebrafish (Danio rerio) in toxicology based on bibliometrics. Totally 56,816 publications about zebrafish from 2002 to 2023 were found in Web of Science Core Collection, with Toxicology as the top 6 among all disciplines. Accordingly, the bibliometric map reveals that “toxicity” has become a hot keyword. It further reveals that the most common exposure types include acute, chronic, and combined exposure. The toxicological effects include behavioral, intestinal, cardiovascular, hepatic, endocrine toxicity, neurotoxicity, immunotoxicity, genotoxicity, and reproductive and transgenerational toxicity. The mechanisms include oxidative stress, inflammation, autophagy, and dysbiosis of gut microbiota. The toxicants commonly evaluated by using zebrafish model include nanomaterials, arsenic, metals, bisphenol, and dioxin. Overall, zebrafish provide a unique and well-accepted model to investigate the toxicological effects and mechanisms. We also discussed the possible ways to address some of the limitations of zebrafish model, such as the combination of human organoids to avoid species differences.

Aiming at the problems of insufficient feature expression ability and weak generalization ability of the traditional Res2Net model in the field of voice print recognition, this paper proposes a feature extraction module known as the SE-DR-Res2Block, which combinedly uses dense connection and residual connection. The combination of low-semantic features with spatial information characteristics allows focusing more on detailed information and high-semantic information that concentrates on global information as well as abstract features. This can compensate for the loss of some detailed information caused by abstraction. First, the feature of each layer in the dense connection structure is derived from the feature output of all previous layers to realize feature reuse. Second, the structure and working principle of the ECAPA-TDNN network using traditional Res2Block is introduced. To achieve more efficient feature extraction, the dense connection is used to further realize full feature mining. Based on SE-block, a more efficient feature extraction module, SE-DR-Res2Net, is proposed by combining the residual join and dense links. As compared to the traditional SE-Block structures, the convolutional layers are used here instead of fully connected layers. Because they not only reduce the number of parameters needed for training but also allow weight sharing, thereby reducing overfitting. Therefore, effective extraction of feature information from different layers is essential for obtaining multiscale expression as well as maximizing the reuse of features. During the collection of more scale-specific feature information, a large number of dense structures can lead to a dramatic increase in parameters and computational complexity. By using partial residual structures instead of dense structures, we can effectively prevent the dramatic increase in parameter quantity while maintaining the performance to a certain extent. Finally, to verify the effectiveness of the module, SE-Res2block, Full-SE-Res2block, SE-DR-Res2block, and Full-SE-DR-Res2block are adopted based on the different network models. Voxceleb1 and SITW (speakers in the wild) datasets were used for Voxceleb1 and SITW, respectively. The performance comparison of Res2Net-50 models with different modules on the Voxceleb1 dataset shows that SE-DR-Res2Net-50 achieves the best equal error rate of 3.51%, which also validates the adaptability of this module on different networks. The usage of different modules on different networks, as well as experiments and analyses conducted on different datasets, were compared. The experimental results showed that the optimal equal error rates of the ECAPA-TDNN network model using SE-DR-Res2block had reached 2.24% and 3.65%, respectively. This verifies the feature expression ability of this module, and the corresponding results based on different test data sets also confirm its excellent generalization ability.

Abstract Background Treatment options beyond the first-line setting for recurrent or metastatic nasopharyngeal carcinoma (RM-NPC) are limited. The role of the multitarget tyrosine kinase inhibitor anlotinib in RM-NPC is unclear. Methods In this prospective, single-arm, phase 2 trial, patients with histologically confirmed RM-NPC and failure of at least two lines of prior systemic treatments were eligible. Anlotinib was given at 12 mg once daily on days 1–14 every 3 weeks until disease progression or intolerable toxicities. The primary end point was disease control rate, defined as the percentage of patients achieving complete response, partial response, or stable disease by RECIST criteria. Results From April 2019 to March 2021, 39 patients were enrolled and received a median of 4 cycles (range, 0.5–20) of anlotinib treatment. Partial response and stable disease were observed in 8 and 20 patients, respectively. The disease control rate was 71.8%, and objective response rate was 20.5%. With a median follow-up of 17.2 months, the median progression-free survival was 5.7 months. The 12-month overall survival was 58.3%, and the median overall survival was not reached. The most frequent grade 3/4 treatment-related adverse events were hand-foot syndrome (23.7%), oral mucositis (21.0%), hypertension (7.9%), and triglyceride elevation (7.9%). Hemorrhage, all grade 1 or 2, occurred in 34.2% of the patients. Conclusions Anlotinib monotherapy exhibited promising anti-tumor activities and disease control for heavily pretreated RM-NPC patients with a tolerable toxicity profile. Trial registration ClinicalTrials.gov: NCT03906058.

Abstract Background Chinese/Mandarin language instruction to undergraduate students from non-Chinese speaking countries has become a topic of increasing interest, driven by China’s influential role in global political and economic dynamics. With Mandarin being the language spoken by approximately 70% of Chinese speakers, it is not only taught within China but also in institutions around the world. While teaching any second or foreign language presents unique challenges, the specific challenges faced in Mandarin language education have not received comprehensive scrutiny, prompting the need for further investigation. Objectives The primary objective of this study was to explore the challenges associated with teaching and learning Mandarin from the perspectives of non-native Mandarin language teachers. By adopting a qualitative (phenomenological) research approach, we aimed to uncover the multifaceted challenges that confront both educators and learners in the context of Mandarin language instruction. Methodology The research methodology employed for this study involved the use of semi-structured interviews conducted with 15 Mandarin language teachers, who were chosen through theoretical sampling. These interviews were transcribed into text files and subsequently subjected to qualitative data analysis, which was facilitated by the use of MAQDA software. This approach allowed us to identify and categorize the various challenges and problems encountered in the teaching and learning of Mandarin. Findings The research findings revealed a range of challenges and problems experienced in Mandarin language education. These challenges were grouped into two primary thematic categories: psycho-linguistic and educational challenges. The linguistic challenges are deeply rooted in the distinctive features of the Mandarin language, presenting obstacles that necessitate innovative teaching strategies and materials. Educational challenges extend beyond native speaker proficiency, emphasizing the requirement for a comprehensive pedagogical skill set among instructors. Implications : The implications of this research extend to various stakeholders in the field of Chinese/Mandarin language education, including policymakers, educators, curriculum designers, and learners. Understanding the linguistic, educational, and psychological challenges can inform the development of more effective teaching methods, curriculum design, and motivation-enhancing strategies, ultimately promoting a more successful and engaging Mandarin language learning experience for non-native speakers.