Engineering macrophage membrane-camouflaged nanoplatforms with enhanced macrophage function for mediating sonodynamic therapy of ovarian cancer.

Cancer immunotherapy has demonstrated remarkable efficacy in the treatment of cancer, and it has been successfully applied in the treatment of various solid tumors. However, the response rates to immunotherapy in patients with ovarian cancer remain modest because of the immunosuppressive tumor microenvironment (TME). Tumor-associated macrophages (TAMs) represent the predominant myeloid cell population within the TME, which adopt the protumorigenic M2 phenotype and are blinded by the "don't eat me" signals from tumor cells. These characteristics of TAMs result in insufficient phagocytic activation. In this study, we constructed a SIM@TR-NP-mediated combination therapy of sonodynamic and immunotherapy. SIM@TR-NPs were modified by engineered macrophage membranes with overexpressed sialic acid-binding Ig-like lectin 10 (Siglec-10), and were internally loaded with sonosensitizer 4,4',4'',4'''-(porphine-5,10,15,20-tetrayl)tetrakis(benzoic acid) and immune adjuvant resiquimod. SIM@TR-NPs can block "don't eat me" signals to enhance macrophage phagocytosis and trigger the polarization of TAMs toward the M1 phenotype, thereby improving the immunosuppressive TME. Simultaneously, upon ultrasound irradiation, SIM@TR-NP-mediated sonodynamic therapy (SDT) triggered immunogenic cell death in tumor cells, in combination with TAM-based immunotherapy, transforming the "immune cold tumor" into an "immune hot tumor". SIM@TR-NP-mediated sonodynamic immunotherapy exhibited potent antitumor efficacy in ovarian cancer and exhibited substantial potential for improving the immunosuppressive TME. This study presents an emerging therapeutic regimen for ovarian cancer that synergizes TAM-based antitumor immunotherapy and SDT.

Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients: A multicentre, randomised controlled trial.

BACKGROUND & AIMS: Malnutrition is prevalent among hospitalised patients, and increases the morbidity, mortality, and medical costs; yet nutritional assessments on admission are not routine. This study assessed the clinical and economic benefits of using an artificial intelligence (AI)-based rapid nutritional diagnostic system for routine nutritional screening of hospitalised patients. METHODS: A nationwide multicentre randomised controlled trial was conducted at 11 centres in 10 provinces. Hospitalised patients were randomised to either receive an assessment using an AI-based rapid nutritional diagnostic system as part of routine care (experimental group), or not (control group). The overall medical resource costs were calculated for each participant and a decision-tree was generated based on an intention-to-treat analysis to analyse the cost-effectiveness of various treatment modalities. Subgroup analyses were performed according to clinical characteristics and a probabilistic sensitivity analysis was performed to evaluate the influence of parameter variations on the incremental cost-effectiveness ratio (ICER). RESULTS: In total, 5763 patients participated in the study, 2830 in the experimental arm and 2933 in the control arm. The experimental arm had a significantly higher cure rate than the control arm (23.24% versus 20.18%; p = 0.005). The experimental arm incurred an incremental cost of 276.52 CNY, leading to an additional 3.06 cures, yielding an ICER of 90.37 CNY. Sensitivity analysis revealed that the decision-tree model was relatively stable. CONCLUSION: The integration of the AI-based rapid nutritional diagnostic system into routine inpatient care substantially enhanced the cure rate among hospitalised patients and was cost-effective. REGISTRATION: NCT04776070 (https://clinicaltrials.gov/study/NCT04776070).

Changes in enzyme activity, structure and growth strategies of the rhizosphere microbiome influenced by elevated temperature and CO2.

The impacts of global warming and increased CO2 levels on soil processes and crop growth are concerning. Soil enzymes in the rhizosphere, produced mainly by microbes, play a vital role in nutrients mobilization for plants. Nevertheless, a comprehensive understanding of how microbial communities in the rhizosphere respond to increased temperatures and CO2 levels, particularly in relation to nutrient acquisition, is still lacking. Addressing this problem, we grew soybeans under elevated temperature (ET, +2 °C) and CO2 levels (eCO2, +300 ppm), both individually and in combination (eCO2 + eT), in rhizobox mesocosms. Enzyme activity and microbial communities in soybean rhizospheres were investigated using soil zymography. eCO2 increased enzyme activity by 2.5 % to 8.7 %, while eT expanded the hotspot area from 1.8 % to 3.3 %. The combined factors amplified both the hotspot area by 5.3 % to 10.1 % and enzyme activity by 35.4 % to 67.3 %. Compared to ambient conditions, rhizosphere communities under eCO2 were predominantly comprised of r-strategist keystone taxa, including Acidobacteria, Proteobacteria, and Ascomycota. On the contrary, eT induced a shift in the microbial community towards K-selected taxa, characterized by an increased relative abundance of Basidiomycota and Actinobacteria. Furthermore, the combination of eCO2 and eT led to an increase in the relative abundance of key bacterial species (Acidobacteria, Proteobacteria, and Actinobacteria) as well as fungi (Ascomycota and Basidiomycota). These findings indicate the potential significance of enzyme hotspots in modulating responses to climate change. Changes in enzyme activity and hotspot area could indicate the alteration in microbial growth strategies. The treatments exhibited distinct changes in the composition of microbial communities, in network organization, and in the proportion of species designated as r or K-strategists. Overall, these findings highlight the combined effects of global change factors on bacterial and fungal communities, providing insights into their growth strategies and nutrient mobilization under climate change scenarios.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Paper-Based Fluorescent Sensor for Rapid Multi-Channel Detection of Tetracycline Based on Graphene Quantum Dots Coated with Molecularly Imprinted Polymer.

In this paper, we developed a paper-based fluorescent sensor using functional composite materials composed of graphene quantum dots (GQDs) coated with molecularly imprinted polymers (MIPs) for the selective detection of tetracycline (TC) in water. GQDs, as eco-friendly fluorophores, were chemically grafted onto the surface of paper fibers. MIPs, serving as the recognition element, were then wrapped around the GQDs via precipitation polymerization using 3-aminopropyltriethoxysilane (APTES) as the functional monomer. Optimal parameters such as quantum dot concentration, grafting time, and elution time were examined to assess the sensor's detection performance. The results revealed that the sensor exhibited a linear response to TC concentrations in the range of 1 to 40 µmol/L, with a limit of detection (LOD) of 0.87 µmol/L. When applied to spiked detection in actual water samples, recoveries ranged from 103.3% to 109.4%. Overall, this paper-based fluorescent sensor (MIPs@GQDs@PAD) shows great potential for portable, multi-channel, and rapid detection of TC in water samples in the future.

An overview of the progress made in research into the Mpox virus.

Mpox is a zoonotic illness caused by the Mpox virus (MPXV), a member of the Orthopoxvirus family. Although a few cases have been reported outside Africa, it was originally regarded as an endemic disease limited to African countries. However, the Mpox outbreak of 2022 was remarkable in that the infection spread to more than 123 countries worldwide, causing thousands of infections and deaths. The ongoing Mpox outbreak has been declared as a public health emergency of international concern by the World Health Organization. For a better management and control of the epidemic, this review summarizes the research advances and important scientific findings on MPXV by reviewing the current literature on epidemiology, clinical characteristics, diagnostic methods, prevention and treatment measures, and animal models of MPXV. This review provides useful information to raise awareness about the transmission, symptoms, and protective measures of MPXV, serving as a theoretical guide for relevant institutions to control MPXV.

Progressive Learning With Cross-Window Consistency for Semi-Supervised Semantic Segmentation.

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes with consistent performance gain demonstrate the superiority of our framework. Our code is released at https://jack-bo1220.github.io/project/CWC.html.

Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study.

IMPORTANCE: Intra-arterial therapies(IATs) are promising options for unresectable hepatocellular carcinoma(HCC). Stratifying the prognostic risk before administering IAT is important for clinical decision-making and for designing future clinical trials. OBJECTIVE: To develop and validate a machine learning(ML)-based decision support model(MLDSM) for recommending IAT modalities for unresectable HCC. DESIGN, SETTING, AND PARTICIPANTS: Between October 2014 and October 2022, a total of 2,959 patients with HCC who underwent initial IATs were enroled retrospectively from 13 tertiary hospitals. These patients were divided into the training cohort (n = 1700), validation cohort (n = 428), and test cohort (n = 200). MAIN OUTCOMES AND MEASURES: Thirty-two clinical variables were input, and five supervised ML algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM) and Random Forest (RF), were compared using the areas under the receiver operating characteristic curve (AUC) with the DeLong test. RESULTS: A total of 1856 patients were assigned to the IAT alone Group(I-A), and 1103 patients were assigned to the IAT combination Group(I-C). The 12-month death rates were 31.9% (352/1103) in the I-A group and 50.4% (936/1856) in the I-C group. For the test cohort, in the I-C group, the CatBoost model achieved the best discrimination when 30 variables were input, with an AUC of 0.776 (95% confidence intervals [CI], 0.833-0.868). In the I-A group, the LGBM model achieved the best discrimination when 24 variables were input, with an AUC of 0.776 (95% CI, 0.833-0.868). According to the decision trees, BCLC grade, local therapy, and diameter as top three variables were used to guide clinical decisions between IAT modalities. CONCLUSIONS AND RELEVANCE: The MLDSM can accurately stratify prognostic risk for HCC patients who received IATs, thus helping physicians to make decisions about IAT and providing guidance for surveillance strategies in clinical practice.

Epidermal growth factor-loaded, dehydrated physical microgel-formed adhesive hydrogel enables integrated care of wet wounds.

Integrated wound care, a sequential process of promoting wound hemostasis, sealing, and healing, is of great clinical significance. However, the wet environment of wounds poses formidable challenges for integrated care. Herein, we developed an epidermal growth factor (EGF)-loaded, dehydrated physical microgel (DPM)-formed adhesive hydrogel for the integrated care of wet wounds. The DPMs were designed using the rational combination of hygroscopicity and reversible crosslinking of physical hydrogels. Unlike regular bioadhesives, which consider interfacial water as a barrier to adhesion, DPMs utilize water to form desirable adhesive structures. The hygroscopicity allowed the DPMs to absorb interfacial water and subsequently, the interfacial adhesion was realized by the interactions between tissue and DPMs. The reversible crosslinks further enabled DPMs to integrate into hydrogels (DPM-Gels), thus achieving wet adhesion. Importantly, the water-absorbing gelation mode of DPMs enabled facile loading of biologically active EGF to promote wound healing. We demonstrated that the DPM-Gels possessed wet tissue adhesive performance, with about 40 times the wet adhesive strength of fibrin glue and about 4 times the burst pressure of human blood pressure. Upon application at the injury site, the EGF-loaded DPM-Gels sequentially promoted efficient wound hemostasis, stable sealing, and quick healing, achieving integrated care of wet wounds.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. MATERIAL AND METHODS: The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. RESULTS: The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949). CONCLUSION: The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Variability in soybean yield responses to elevated atmospheric CO2: Insights from non-structural carbohydrate remobilisation during seed filling.

The increasing atmospheric CO2 concentration (e[CO2]) has mixed effects on soybean most varieties' yield. This study elucidated the effect of e[CO2] on soybean yield and the underlying mechanisms related to photosynthetic capacity, non-structural carbohydrate (NSC) accumulation, and remobilisation. Four soybean cultivars were cultivated in open-top chambers at two CO2 levels. Photosynthesis rates were determined from R2 to R6. Plants were sampled at R5 and R8 to determine carbohydrate concentrations. There were significant variations in yield responses among the soybean cultivars under e[CO2], from no change in DS1 to a 22% increase in SN14. DS1 and SN14 had the smallest and largest increase, respectively, in daily carbon assimilation capacity. Under e[CO2], DS1, MF5, and XHJ had an increase in Ci, at which point the transition from Rubisco-limited to ribulose-1,5-bisphosphate regeneration-limited photosynthesis occurred, in contrast with SN14. Thus, the cultivars might have distinct mechanisms that enhance photosynthesis under e[CO2] conditions. A positive correlation was between daily carbon assimilation response to e[CO2] and soybean yield, emphasising the importance of enhanced photosynthate accumulation before the R5 stage in determining yield response to e[CO2]. E[CO2] significantly influenced NSC accumulation in vegetative organs at R5, with variation among cultivars. There was enhanced NSC remobilisation during seed filling, indicating cultivar-specific responses to the remobilisation of sucrose and soluble sugars, excluding sucrose and starch. A positive correlation was between leaf and stem NSC remobilisation and yield response to e[CO2], emphasising the role of genetic differences in carbohydrate remobilisation mechanisms in determining soybean yield variation under elevated CO2 levels.

Tissue-adhesive, stretchable and compressible physical double-crosslinked microgel-integrated hydrogels for dynamic wound care.

Integrated wound care through sequentially promoting hemostasis, sealing, and healing holds great promise in clinical practice. However, it remains challenging for regular bioadhesives to achieve integrated care of dynamic wounds due to the difficulties in adapting to dynamic mechanical and wet wound environments. Herein, we reported a type of dehydrated, physical double crosslinked microgels (DPDMs) which were capable of in situ forming highly stretchable, compressible and tissue-adhesive hydrogels for integrated care of dynamic wounds. The DPDMs were designed by the rational integration of the reversible crosslinks and double crosslinks into micronized gels. The reversible physical crosslinks enabled the DPDMs to integrate together, and the double crosslinked characteristics further strengthen the formed macroscopical networks (DPDM-Gels). We demonstrated that the DPDM-Gels simultaneously possess outstanding tensile (∼940 kJ/m3) and compressive (∼270 kJ/m3) toughness, commercial bioadhesives-comparable tissue-adhesive strength, together with stable performance under hundreds of deformations. In vivo results further revealed that the DPDM-Gels could effectively stop bleeding in various bleeding models, even in an actual dynamic environment, and enable the integrated care of dynamic skin wounds. On the basis of the remarkable mechanical and appropriate adhesive properties, together with impressive integrated care capacities, the DPDM-Gels may provide a new approach for the smart care of dynamic wounds. STATEMENT OF SIGNIFICANCE: Integrated care of dynamic wounds holds great significance in clinical practice. However, the dynamic and wet wound environments pose great challenges for existing hydrogels to achieve it. This work developed robust adhesive hydrogels for integrated care of dynamic wounds by designing dehydrated, physical double crosslinked microgels (DPDMs). The reversible and double crosslinks enabled DPDMs to integrate into macroscopic hydrogels with high mechanical properties, appropriate adhesive strength and stable performance under hundreds of external deformations. Upon application at the injury site, DPDM-Gels efficiently stopped bleeding, even in an actual dynamic environment and showed effectiveness in integrated care of dynamic wounds. With the fascinating properties, DPDMs may become an effective tool for smart wound care.

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of this study was to develop a machine learning model to predict SAP after sICH. METHODS: We retrospectively reviewed 748 patients diagnosed with sICH and collected data from 4 dimensions-demographic features, clinical features, medical history, and laboratory tests. Five machine learning algorithms-logistic regression, gradient boosting decision tree, random forest, extreme gradient boosting, and category boosting-were used to build and validate the predictive model. We also applied recursive feature elimination with cross-validation to obtain the best feature combination for each model. Predictive performance was evaluated by area under the receiver operating characteristic curve. RESULTS: SAP was diagnosed in 237 patients. The model developed by category boosting yielded the most satisfactory outcomes overall with area under the receiver operating characteristic curves in the training set and test set of 0.8307 and 0.8178, respectively. CONCLUSIONS: The incidence of SAP after sICH in our center was 31.68%. Machine learning could potentially provide assistance in the prediction of SAP after sICH.

Learning to Holistically Detect Bridges From Large-Size VHR Remote Sensing Imagery.

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048 × 2,048 to 16,384 × 16,384 pixels, and collectively feature 59,737 bridges. These bridges span diverse backgrounds, and each of them has been manually annotated, using both an oriented bounding box (OBB) and a horizontal bounding box (HBB). Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. The SDFF architecture performs inter-layer feature fusion (IFF) to incorporate multi-scale context in the dynamic image pyramid (DIP) of the large-size image, and the SSRW strategy is employed to ensure an equitable balance in the regression weight of bridges with various aspect ratios. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset. The dataset and source code will be released at https://luo-z13.github.io/GLH-Bridge-page/.

Se Alleviated Pb-Caused Neurotoxicity in Chickens: SPS2-GPx1-GSH-IL-2/IL-17-NO Pathway, Selenoprotein Suppression, Oxidative Stress, and Inflammatory Injury.

Lead (Pb), a heavy metal environmental pollutant, poses a threat to the health of humans and birds. Inflammation is one of the most common pathological phenomena in the case of illness and poisoning. However, the underlying mechanisms of inflammation remain unclear. The cerebellum and the thalamus are important parts of the nervous system. To date, there have been no reports of Pb inducing inflammation in animal cerebellums or thalami. Selenium (Se) can relieve Pb poisoning. Therefore, we aimed to explore the mechanism by which Se alleviates Pb toxicity to the cerebellums and thalami of chickens by establishing a chicken Pb or/and Se treatment model. Our results demonstrated that exposure to Pb caused inflammatory damage in cerebellums and thalami, evidenced by the characteristics of inflammation, the decrease in anti-inflammatory factors (interleukin (IL)-2 and interferon-γ (INF-γ)), and the increase in pro-inflammatory factors (IL-4, IL-6, IL-12ß, IL-17, and nitric oxide (NO)). Moreover, we found that the IL-2/IL-17-NO pathway took part in Pb-caused inflammatory injury. The above findings were reversed by the supplementation of dietary Se, meaning that Se relieved inflammatory damage caused by Pb via the IL-2/IL-17-NO pathway. In addition, an up-regulated oxidative index malondialdehyde (MDA) and two down-regulated antioxidant indices (glutathione (GSH) and total antioxidant capacity (TAC)) were recorded after the chickens received Pb stimulation, indicating that excess Pb caused an oxidant/antioxidant imbalance and oxidative stress, and the oxidative stress mediated inflammatory damage via the GSH-IL-2 axis. Interestingly, exposure to Pb inhibited four glutathione peroxidase (GPx) family members (GPx1, GPx2, GPx3, and GPx4), three deiodinase (Dio) family members (Dio1, Dio2, and Dio3), and fifteen other selenoproteins (selenophosphate synthetase 2 (SPS2), selenoprotein (Sel)H, SelI, SelK, SelM, SelO, SelP1, SelPb, SelS, SelT, SelU, and selenoprotein (Sep)n1, Sepw1, Sepx1, and Sep15), suggesting that Pb reduced antioxidant capacity and resulted in oxidative stress involving the SPS2-GPx1-GSH pathway. Se supplementation, as expected, reversed the changes mentioned above, indicating that Se supplementation improved antioxidant capacity and mitigated oxidative stress in chickens. For the first time, we discovered that the SPS2-GPx1-GSH-IL-2/IL-17-NO pathway is involved in the complex inflammatory damage mechanism caused by Pb in chickens. In conclusion, this study demonstrated that Se relieved Pb-induced oxidative stress and inflammatory damage via the SPS2-GPx1-GSH-IL-2/IL-17-NO pathway in the chicken nervous system. This study offers novel insights into environmental pollutant-caused animal poisoning and provides a novel theoretical basis for the detoxification effect of Se against oxidative stress and inflammation caused by toxic pollutants.

Long Non-Coding RNA-Cardiac-Inducing RNA 6 Mediates Repair of Infarcted Hearts by Inducing Mesenchymal Stem Cell Differentiation into Cardiogenic Cells through Cyclin-Dependent Kinase 1.

This study aims to investigate the induction effect of LncRNA-CIR6 on MSC differentiation into cardiogenic cells in vitro and in vivo. In addition to pretreatment with Ro-3306 (a CDK1 inhibitor), LncRNA-CIR6 was transfected into BMSCs and hUCMSCs using jetPRIME. LncRNA-CIR6 was further transfected into the hearts of C57BL/6 mice via 100 µL of AAV9-cTnT-LncRNA-CIR6-ZsGreen intravenous injection. After three weeks of transfection followed by AMI surgery, hUCMSCs (5 × 105/100 µL) were injected intravenously one week later. Cardiac function was evaluated using VEVO 2100 and electric mapping nine days after cell injection. Immunofluorescence, Evans blue-TTC, Masson staining, FACS, and Western blotting were employed to determine relevant indicators. LncRNA-CIR6 induced a significant percentage of differentiation in BMSCs (83.00 ± 0.58)% and hUCMSCs (95.43 ± 2.13)% into cardiogenic cells, as determined by the expression of cTnT using immunofluorescence and FACS. High cTNT expression was observed in MSCs after transfection with LncRNA-CIR6 by Western blotting. Compared with the MI group, cardiac contraction and conduction function in MI hearts treated with LncRNA-CIR6 or combined with MSCs injection groups were significantly increased, and the areas of MI and fibrosis were significantly lower. The transcriptional expression region of LncRNA-CIR6 was on Chr17 from 80209290 to 80209536. The functional region of LncRNA-CIR6 was located at nucleotides 0-50/190-255 in the sequence. CDK1, a protein found to be related to the proliferation and differentiation of cardiomyocytes, was located in the functional region of the LncRNA-CIR6 secondary structure (from 0 to 17). Ro-3306 impeded the differentiation of MSCs into cardiogenic cells, while MSCs transfected with LncRNA-CIR6 showed a high expression of CDK1. LncRNA-CIR6 mediates the repair of infarcted hearts by inducing MSC differentiation into cardiogenic cells through CDK1.

Nanoparticles transfected with plasmid-encoded lncRNA-OIP5-AS1 inhibit renal ischemia-reperfusion injury in mice via the miR-410-3p/Nrf2 axis.

Nanostructures composed of liposomes and polydopamine (PDA) have demonstrated efficacy as carriers for delivering plasmids, effectively alleviating renal cell carcinoma. However, their role in acute kidney injury (AKI) remains unclear. This study aimed to investigate the effects of the plasmid-encoded lncRNA-OIP5-AS1@PDA nanoparticles (POP-NPs) on renal ischemia/reperfusion (RI/R) injury and explore the underlying mechanisms. RI/R or OGD/R models were established in mice and HK-2 cells, respectively. In vivo, vector or POP-NPs were administered (10 nmol, IV) 48 h after RI/R treatment. In the RI/R mouse model, the OIP5-AS1 and Nrf2/HO-1 expressions were down-regulated, while miR-410-3p expression was upregulated. POP-NPs treatment effectively reversed RI/R-induced renal tissue injury, restoring altered levels of blood urea nitrogen, creatinine, malondialdehyde, inflammatory factors (IL-8, IL-6, TNF-α), ROS, apoptosis, miR-410-3p, as well as the suppressed expression of SOD and Nrf2/HO-1 in the model mice. Similar results were obtained in cell models treated with POP-NPs. Additionally, miR-410-3p mimics could reverse the effects of POP-NPs on cellular models, partially counteracted by Nrf2 agonists. The binding relationship between OIP5-AS1 and miR-410-3p, alongside miR-410-3p and Nrf2, has been substantiated by dual-luciferase reporter and RNA pull-down assays. The study revealed that POP-NPs can attenuate RI/R-induced injury through miR-410-3p/Nrf2 axis. These findings lay the groundwork for future targeted therapeutic approaches utilizing nanoparticles for RI/R-induced AKI.

The Z-Scheme MIL-88B(Fe)/BiOBr Heterojunction Promotes Fe(III)/Fe(II) Cycling and Photocatalytic-Fenton-Like Synergistically Enhances the Degradation of Ciprofloxacin.

The Z-scheme MIL-88B/BiOBr (referred to as MxBy, whereas x and y are the mass of MIL-88B(Fe) and BiOBr) heterojunction photocatalysts are successfully prepared by a facile ball milling method. By adding low concentration H2O2 under visible light irradiation, the Z-scheme heterojunction and photocatalytic-Fenton-like reaction synergistically enhance the degradation and mineralization of ciprofloxacin (CIP). Among them, M50B150 showed efficient photodegradation efficiency and excellent cycling stability, with 94.6% removal of CIP (10 mg L-1) by M50B150 (0.2 g L-1) under 90 min of visible light. In the MxBy heterojunctions, the rapid transfer of photo-generated electrons not only directly decomposed H2O2 to generate ·OH, but also improved the cycle of Fe3+/Fe2+ pairs, which facilitated the reaction with H2O2 to generate ·OH and ·O2 - radicals. In addition, the effects of photocatalyst dosages, pH of CIP solution, and coexisting substances on CIP removal are systematically investigated. It is found that the photocatalytic- Fenton-like reaction can be carried out at a pH close to neutral conditions. Finally, the charge transfer mechanism of the Z-scheme is verified by electron spin resonance (ESR) signals. The ecotoxicity of CIP degradation products is estimated by the T.E.S.T tool, indicating that the constructed photocatalysis-Fenton-like system is a green wastewater treatment technology.

Double-Solvent-Induced Derivatization of Bi-MOF to Vacancy-Rich Bi4O5Br2: Toward Efficient Photocatalytic Degradation of Ciprofloxacin in Water and HCHO Gas.

MOF-derived photocatalytic materials have potential in degrading ciprofloxacin (CIP) in water and HCHO gas pollutants. Novel derivatization means and defect regulation are effective techniques for improving the performance of MOF-derived photocatalysis. Vacancy-rich Bi4O5Br2 (MBO-x) were derived in one step from Bi-MOF (CAU-17) by a modified double-solvent method. MBO-50 produced more oxygen vacancies due to the combined effect of the CAU-17 precursor and double solvents. The photocatalytic performance of MBO was evaluated by degrading CIP and HCHO. Thanks to the favorable morphology and vacancy structure, MBO-50 demonstrated the best photocatalytic efficiency, with 97.0% removal of CIP (20 mg L-1) and 90.1% removal of HCHO (6.5 ppm) at 60 min of light irradiation. The EIS Nyquist measurement, transient photocurrent response, photoluminescence spectra, and the calculation of energy band information indicated that the vacancy sites can effectively capture photoexcited electrons during the charge transfer process, thus limiting the recombination of electrons and holes, improving the energy band structure, and making it easier to produce superoxide anion radical (·O2-) and to degrade CIP and HCHO. The improvement of photocatalytic performance of MBO-50 in HCHO degradation due to the bromine vacancy generation and filling mechanism was discussed in detail. This work provides a promising new idea for the modulation of MOF-derived photocatalytic materials.

Ion imprinted polymers integrated into a multi-functional microfluidic paper-based analytical device for trace cadmium detection in water.

A novel multi-functional microfluidic paper-based analytical device (µPAD) integrated with ion imprinted polymers (IIPs) was proposed for specific, portable and low-cost detection of cadmium (Cd(II)) in water. The IIP was grafted on paper and integrated into the µPAD for separation of Cd(II) through multi-layer design. The paper-based screen printed carbon electrode (pSPCE) modified with reduced graphene oxide was fabricated and combined with the µPAD for electrochemical sensing of the separated Cd(II). Reduced graphene oxide (rGO) was prepared via electroreduction on the working electrode surface of the pSPCE (rGO/pSPCE), which provided a sensitization effect with an improved signal for Cd(II) detection. The µPAD developed with the integrated IIP and combined with rGO/pSPCE is able to detect Cd(II) with a linear range from 1 ng ml-1 to 100 ng ml-1 and a detection limit of 0.05 ng ml-1. The accuracy of this µPAD was evaluated with spiked real water samples and compared with that of the inductively coupled plasma mass spectrometry (ICP-MS) method, from which the recovery values ranged from 96.5% to 114.2% with RSDs <10% between the two methods. This µPAD demonstrated its advantages of low-cost, portability, and suitability for highly sensitive detection of Cd(II), making it a valuable tool for on-site analysis.