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Federated Learning (FL) enables clients to securely share gradients computed on their local data with the server, thereby eliminating the necessity to directly expose their sensitive local datasets. In traditional FL, the server might take advantage of its dominant position during the model aggregation process to infer sensitive information from the shared gradients of the clients. At the same time, malicious clients may submit forged and malicious gradients during model training. Such behavior not only compromises the integrity of the global model, but also diminishes the usability and reliability of trained models. To effectively address such privacy and security attack issues, this work proposes a Blockchain-based Privacy-preserving and Secure Federated Learning (BPS-FL) scheme, which employs the threshold homomorphic encryption to protect the local gradients of clients. To resist malicious gradient attacks, we design a Byzantine-robust aggregation protocol for BPS-FL to realize the cipher-text level secure model aggregation. Moreover, we use a blockchain as the underlying distributed architecture to record all learning processes, which ensures the immutability and traceability of the data. Our extensive security analysis and numerical evaluation demonstrate that BPS-FL satisfies the privacy requirements and can effectively defend against poisoning attacks.

Abstract Type II collagen (CII), a key component of cartilaginous tissues, offers an attractive option for fabricating biomimetic scaffolds that provide mechanical support and biochemical signals to facilitate cell adhesion, proliferation, and differentiation. Advances in fabrication technologies have enabled the creation of intricate and multicellular CII-based scaffolds that more accurately replicate the natural structure of tissues. Here, we review the application of CII in tissue engineering and highlight its crucial contributions to the regeneration of hyaline cartilage and intervertebral discs. Additionally, we discuss the versatile roles of CII in other biomedical applications, including disease modeling, bone regeneration, and the development of artificial immune organs.

Mangroves are vital coastal ecosystems that provide crucial links between land and sea. Tree height is a key indicator for assessing mangroves’ health status. Currently, there are still numerous challenges in estimating mangrove tree height. In this study, multiple deep learning and shallow machine learning regression models were developed to accurately estimate mangrove tree height using multi-dimensional Light Detection and Ranging (LiDAR) point clouds and their derivatives. We constructed a novel CNN_RepMLP model for mangrove tree height mapping. We also further verified the applicability of different types of regression models in estimating mangrove tree heights, and explored the influence of different LiDAR-derived features on the inversion accuracy for tree heights. The results indicated the following: (1) The CNN_RepMLP displayed satisfactory performance in the inversion of mangrove tree heights, and exhibited better robustness and generalization ability than the convolutional neural network (CNN) model. (2) Among the different LiDAR-derived feature combinations, combining height variables with intensity variables can not only mitigate the negative impact of height variables on inversion models, but also enhance the mangrove tree height inversion accuracy. (3) The ensemble learning framework with ExtraTrees as the meta-model can make better use of the differences and complementarities between different single base models, and exhibited better accuracy in estimating the height of mangrove trees compared with other ensemble learning models. (4) Multiple machine learning models based on multi-dimensional UAV-LiDAR point-cloud-derived features are suitable for the inversion of mangrove tree height. The CNN_RepMLP model outperformed the CNN and stacking ensemble learning models and had more detailed differentiation in terms of mangrove tree height. Its prediction results can more realistically reflect the spatial differentiation characteristics of mangrove tree height.

Mangroves are one of the most important marine ecosystems globally, their spatial distribution is crucial for promoting mangrove ecosystems conservation, restoration, and sustainable managements. This study proposed a novel Unet-Multi-Scale High-Resolution Vision Transformer (UHRViT) model for classifying mangrove species using unmanned aerial vehicle (UAV-RGB), UAV-LiDAR, and Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) images. The UHRViT utilized a multi-scale high-resolution visual Transformer as its backbone network and was designed to a multi-branch U-shaped network structure to extract features of different scales layer by layer, and to facilitate the interaction of high and low-level semantic information. We further verified the classification performance superiority of UHRViT model by comparing to HRViT and HRNetV2 algorithms. We also systematically investigated the effects of active–passive image combination ratios on mangrove communities mapping. The results revealed that: UAV-RGB images exhibited the better classification accuracy (mean F1-score＞95 %) for mangrove species than UAV-LiDAR and GF-3 SAR images; The classification performances and stability of UHRViT algorithm in the fifteen datasets outperformed the HRViT and HRNetV2 algorithms; Combining UAV-RGB with GF-3 SAR or UAV-LiDAR images respectively, both achieved better classifications than the single data source. Based on the UHRViT algorithm, the combination of UAV-RGB and UAV-LiDAR achieved the highest classification accuracy (Iou = 0.944, MIou = 50.2 %) for Avicennia corniculatum (AC). When the combination ratio of UAV-RGB with GF-3 SAR or UAV-LiDAR was 3:1, Avicennia marina and AC both obtained the optimal classification accuracy with average F1-scores of 98.19 % and 97.3 %, respectively. Our works revealed that the changes in the classification accuracies of mangrove communities under multi-sensor image combination ratios, and demonstrated that our model could effectively improve the classification accuracy of mangrove communities.

BackgroundLipoma, a benign tumor derived from mesenchymal tissue, significantly affects patients’ physical and psychological wellbeing. Increasing evidence points to a strong link between the gut microbiome (GM) and lipoma incidence. This study utilizes Mendelian Randomization (MR) to assess the potential causal relationships between the GM and lipoma development.MethodsWe conducted a two-sample MR analysis using genome-wide association study (GWAS) data from MiBioGen and FinnGen to explore the causal relationship between GM and lipoma. The GM dataset included 18,340 participants with 14,587 single nucleotide polymorphisms (SNPs), while the lipoma dataset comprised 412,181 participants with 21,306,349 SNPs. We employed 5 MR methods: Inverse Variance Weighted (IVW), Weighted Median, Simple Mode, MR-Egger, and Weighted Mode. Additional assessments included Cochran’s Q test for result heterogeneity, PRESSO analysis for horizontal pleiotropy, and sensitivity analyses through scatter plots, leave-one-out analyses, funnel plots, and forest plots.ResultsThe IVW method identified 18 gene predictors trans-genus associated with lipoma risk. Protective effects against benign lipoma (BL) were observed in the Eubacterium rectale group, Desulfovibrio, Ruminococcus1, Clostridium sensu stricto1, and Lachnospiraceae UCG001; conversely, Lachnospiraceae UCG008 was linked to increased BL risk. Desulfovibrio provided protection against TS-BL; however, the Family XIII AD3011 group, Eubacterium coprostanoligenes group, Lachnospiraceae NK4A136 group, and Parasutterella were associated with an increased TS-BL risk. The Clostridium innocuum group, Eubacterium rectale group, Anaerotruncus, Ruminiclostridium6, and Lachnospiraceae UCG001 offered protection against LS-BL, while Lachnospiraceae UCG008 was linked to an increased LS-BL risk. The Eubacterium brachy group, Odoribacter, Butyricimonas, Subdoligranulum, and Clostridium sensu stricto1 were protective against HFNS-BL; Ruminococcaceae UCG005 was associated with an increased HFNS-BL risk.ConclusionCompared to malignant tumors, research on lipomas has been relatively limited. This study, through MR analysis, provided new evidence of a causal relationship between specific GM and the development of lipomas. Certain gut bacterial species may act as protective or harmful factors in lipoma formation, offering new avenues for future treatment strategies. However, additional research is required to unravel the complexity of how GM influences the pathogenesis of lipomas.

Abstract Background Fibrosis is a significant pathological feature of chronic skeletal muscle injury, profoundly affecting muscle regeneration. Fibro-adipogenic progenitors (FAPs) have the ability to differentiate into myofibroblasts, acting as a primary source of extracellular matrix (ECM). the process by which FAPs differentiate into myofibroblasts during chronic skeletal muscle injury remains inadequately explored. Method mouse model with sciatic nerve denervated was constructed and miRNA expression profiles between the mouse model and uninjured mouse were analyzed. qRT/PCR and immunofluorescence elucidated the effect of miR-27b-3p on fibrosis in vivo and in vitro. Dual-luciferase reporter identified the target gene of miR-27b-3p, and finally knocked down or overexpressed the target gene and phosphorylation inhibition of Smad verified the influence of downstream molecules on the abundance of miR-27b-3p and fibrogenic differentiation of FAPs. Result FAPs derived from a mouse model with sciatic nerves denervated exhibited a progressively worsening fibrotic phenotype over time. Introducing agomiR-27b-3p effectively suppressed fibrosis both in vitro and in vivo. MiR-27b-3p targeted Transforming Growth Factor Beta Receptor 1 (TGF-βR1) and the abundance of miR-27b-3p was negatively regulated by TGF-βR1/Smad. Conclusion miR-27b-3p targeting the TGF-βR1/Smad pathway is a novel mechanism for regulating fibrogenic differentiation of FAPs. Increasing abundance of miR-27b-3p, suppressing expression of TGF-βR1 and inhibiting phosphorylation of smad3 presented potential strategies for treating fibrosis in chronic skeletal muscle injury.

The bidirectional relationship between osteochondral defects (OCD) and osteoarthritis (OA), with each condition exacerbating the other, makes OCD regeneration in the presence of OA challenging. Type II collagen (Col2) is important in OCD regeneration and the management of OA, but its potential applications in cartilage tissue engineering are significantly limited. This study investigated the regeneration capacity of Col2 scaffolds in critical-sized OCDs under surgically induced OA conditions and explored the underlying mechanisms that promoted OCD regeneration. Furthermore, the repair potential of Col2 scaffolds was validated in over critical-sized OCD models. After 90 days or 150 days since scaffold implantation, complete healing was observed histologically in critical-sized OCD, evidenced by the excellent integration with surrounding native tissues. The newly formed tissue biochemically resembled adjacent natural tissue and exhibited comparable biomechanical properties. The regenerated OA tissue demonstrated lower expression of genes associated with cartilage degradation than native OA tissue but comparable expression of genes related to osteochondral anabolism compared with normal tissue. Additionally, transcriptome and proteome analysis revealed the hindrance of TGF-β-Smad1/5/8 in regenerated OA tissue. In conclusion, the engrafting of Col2 scaffolds led to the successful regeneration of critical-sized OCDs under surgically induced OA conditions by inhibiting the TGF-β-Smad1/5/8 signaling pathway.

Summary: Bombinin-BO1 (BO1), a bombinin peptide derived from the skin secretion of Bombina orientalis, exhibits broad-spectrum antimicrobial activity. To date, the anticancer effect of BO1 remains unclear. This study confirmed cytotoxicity of BO1 on hepatocellular carcinoma cells by inducing S-phase cycle block and apoptosis. In addition, BO1 was found to be localized in cytoplasm through endocytosis. The combined results of pull down, mass spectrometry, and co-immunoprecipitation suggested that BO1 induced misfolding of CDK1 and degradation by competitively binding HSP90A with Cdc37. It was verified that overexpression of HSP90A in BO1-treated cells significantly inhibited degradation of CDK1. In vivo, BO1 inhibited tumor without being toxic to individuals. This study reveals the anti-tumor mechanism of BO1 in inducing cell-cycle arrest and apoptosis by interfering with HSP90A-Cdc37-CDK1 system. This is the first study to analyze the mechanism of BO1 regulation of tumor cells, providing theoretical basis for BO1 treatment of hepatocellular carcinoma.

BackgroundThis study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout.MethodsUsing the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium’s summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release.ResultSMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 × 10−13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 × 10−8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 × 10−6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 × 10−5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 × 10−5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue.ConclusionOur findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.

BackgroundWhile some studies have suggested a link between gut microbiota (GM) and abortion, the causal relationship remains unclear.MethodsTo explore the causal relationship between GM and abortion, including spontaneous abortion (SA) and habitual abortion (HA), we performed a two-sample Mendelian randomization (MR) analysis. We used summary statistics data from MiBioGen and FinnGen for genome-wide association studies (GWAS), with GM data as the exposure variable and abortion data as the outcome variable.ResultsIn the absence of heterogeneity and horizontal pleiotropy, the inverse-variance weighted (IVW) method identified five genetically predicted GM genera linked to the risk of abortions. Lactococcus was negatively correlated with the risk of SA, whereas the Eubacterium fissicatena group was positively correlated with the risk of SA. Genetic predictions of Coprococcus3 and Odoribacter were linked to a reduced risk of HA, while the Eubacterium ruminantium group was associated with an increased risk of HA.ConclusionOur study suggests a genetic causal relationship between specific GM and two types of abortions, improving our understanding of the pathological relationship between GM and abortion.

The unique gradient structure and complex composition of osteochondral tissue pose significant challenges in defect regeneration. Restoration of tissue heterogeneity while maintaining hyaline cartilage components has been a difficulty of an osteochondral tissue graft. A novel class of multi-crosslinked polysaccharide-based three-dimensional (3D) printing inks, including decellularized natural cartilage (dNC) and nano-hydroxyapatite, was designed to create a gradient scaffold with a robust interface-binding force. Herein, we report combining a dual-nozzle cross-printing technology and a gradient crosslinking method to create the scaffolds, demonstrating stable mechanical properties and heterogeneous bilayer structures. Biofunctional assessments revealed the remarkable regenerative effects of the scaffold, manifesting three orders of magnitude of mRNA upregulation during chondrogenesis and the formation of pure hyaline cartilage. Transcriptomics of the regeneration site in vivo and scaffold cell interaction tests in vitro showed that printed porous multilayer scaffolds could form the correct tissue structure for cell migration. More importantly, polysaccharides with dNC provided a hydrophilic microenvironment. The microenvironment is crucial in osteochondral regeneration because it could guide the regenerated cartilage to ensure the hyaline phenotype.

MXene, a family of two-dimensional (2D) transition metal carbides and nitrides have attracted extensive interests for many biochemical applications, including tumour elimination, biosensors, and magnetic resonance imaging (MRI). In this article, we firstly discovered that Ti3C2Tx MXene exhibited a highly efficient adsorption capability as hemoperfusion absorbent towards middle-molecular mass and protein bound uremic toxins in the end stage of renal disease (ESRD) treatment. Molecular scale investigations reveal that the high efficiency of MXene for the removal of uremic toxins could be attributed to synergistic effects of physical/chemical adsorption, electrostatic interaction surface of 2D MXene, and transformation of protein secondary structure. 2D MXene materials could be used as a new hemoperfusion sorbent with ultrahigh efficiency for removing uremic toxins during the treatment of kidney disease.

In response to the challenge of existing wireless communication anti-jamming methods in effectively handling unknown jamming, this paper proposes a multi-parameter control anti-jamming algorithm for wireless communication systems based on the Linear–Quadratic Regulator (LQR). First, the proposed algorithm models the wireless communication system as a linear switched system based on the modulation and coding scheme. Subsequently, a feedback controller design method based on the LQR is introduced. By utilizing the multiple Lyapunov function method combined with linear matrix inequalities, sufficient criteria for the asymptotic stability of the system under unknown jamming conditions are provided. Finally, theoretical analysis and simulation results indicate that the proposed algorithm can rapidly adjust modulation and coding schemes as well as transmission power in complex jamming environments, thereby maintaining bit error rate (BER) stability and enhancing the reliability of the communication system.

Background: Recent studies have established associations between the gut microbiota (GM) and thyroid diseases (TDs). However, their causal relationships remain elusive. Methods: To investigate this causality, we conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) data from MiBioGen and FinnGen, with GM as the exposure and six TDs as outcomes. Results: We identified 32 microbial taxa linked to the risk of six TDs. The Clostridium innocuum group, Ruminiclostridium5, and Lachnoclostridium exhibited protective effects against nontoxic diffuse goiter (NDG). Conversely, an increased risk of NDG was associated with Ruminococcaceae UCG002, Alistipes, Methanobrevibacter, Marvinbryantia, and Ruminococcaceae UCG014. Bifidobacterium and Sutterella were protective against nontoxic multinodular goiter (NMG), while the Ruminococcus gauvreauii group and Rikenellaceae RC9 gut group heightened NMG risk. Protective effects against nontoxic single thyroid nodule (NSTN) were observed with Defluviitaleaceae UCG011, Ruminococcus1, and Ruminococcaceae UCG010, whereas increased risk was linked to Alistipes, the Ruminococcus gauvreauii group, and Lachnospiraceae UCG010. Ruminiclostridium9, Victivallis, and Butyricimonas offered protection against thyrotoxicosis with Graves’ Disease (GD), while the Eubacterium rectale group, Desulfovibrio, Bifidobacterium, Collinsella, Oscillospira, and Catenibacterium were risk factors. For thyrotoxicosis with Plummer Disease (PD), protective taxa included Butyricimonas and Lachnospira, whereas Dorea, Eggerthella, Odoribacter, Lactobacillus, Intestinimonas, and Phascolarctobacterium increased risk. Lastly, Parasutterella was protective against thyrotoxicosis with toxic single thyroid nodule (TSTN), while increased risk was associated with Sutterella, Oscillibacter, and Clostridium sensu stricto1. Conclusions: Our findings support a causal relationship between specific GM and TDs at the genetic level, laying the foundation for future research into potential mechanisms and the identification of novel therapeutic targets.

Abstract PtII based organometallic photosensitizers (PSs) have emerged as novel potent photodynamic inactivation (PDI) reagents through their enhanced intersystem crossing (ISC) processes. Currently, few PtII PSs have been investigated as antibacterial materials, with relatively poor performances reported and with structure‐activity relationships not well described. Herein, a pair of configurational isomers are reported of Bis‐BODIPY (4,4‐difluoro‐boradizaindacene) embedded PtII PSs. The cis‐isomer (cis‐BBP) displayed enhanced 1O2 generation and better bacterial membrane anchoring capability as compared to the trans‐isomer (trans‐BBP). The effective PDI concentrations (efficiency > 99.9%) for cis‐BBP in Acinetobacter baumannii (multi‐drug resistant (MDR)) and Staphylococcus aureus are 400 nM (12 J cm−2) and 100 nM (18 J cm−2), respectively; corresponding concentrations and light doses for trans‐BBP in the two bacteria are 2.50 µM (30 J cm−2) and 1.50 µM (18 J cm−2), respectively. The 50% and 90% minimum inhibitory concentration (MIC50 and MIC90) ratio of trans‐BBP to cis‐BBP is 22.22 and 24.02 in A. baumannii (MDR); 21.29 and 22.36 in methicillin resistant S. aureus (MRSA), respectively. Furthermore, cis‐BBP displays superior in vivo antibacterial performance, with acceptable dark and photoinduced cytotoxicity. These results demonstrate cis‐BBP is a robust light‐assisted antibacterial reagent at sub‐micromolecular concentrations. More importantly, configuration of PtII PSs should be an important issue to be considered in further PDI reagents design.

Abstract Locus coeruleus (LC) dysfunction is involved in the pathophysiology of depression; however, the neural circuits and specific molecular mechanisms responsible for this dysfunction remain unclear. Here, it is shown that activation of tyrosine hydroxylase (TH) neurons in the LC alleviates depression‐like behaviors in susceptible mice. The dorsolateral septum (dLS) is the most physiologically relevant output from the LC under stress. Stimulation of the LCTH‐dLSSST innervation with optogenetic and chemogenetic tools bidirectionally can regulate depression‐like behaviors in both male and female mice. Mechanistically, it is found that brain‐derived neurotrophic factor (BDNF), but not norepinephrine, is required for the circuit to produce antidepressant‐like effects. Genetic overexpression of BDNF in the circuit or supplementation with BDNF protein in the dLS is sufficient to produce antidepressant‐like effects. Furthermore, viral knockdown of BDNF in this circuit abolishes the antidepressant‐like effect of ketamine, but not fluoxetine. Collectively, these findings underscore the notable antidepressant‐like role of the LCTH‐dLSSST pathway in depression via BDNF‐TrkB signaling.

Highlights A nanofiber composite reinforced organohydrogel with multifunctionality is prepared. The composite organohydrogel possesses multiple interfacial bondings and multi-level strengthening and toughening mechanism is proposed. The composite organohydrogel exhibits long-term strain sensing stability and can be used for high performance electromagnetic interference shielding.

Peripheral bacterial infections without impaired blood–brain barrier integrity have been attributed to the pathogenesis of Parkinson's disease (PD). Peripheral infection promotes innate immune training in microglia and exacerbates neuroinflammation. However, how changes in the peripheral environment mediate microglial training and exacerbation of infection-related PD is unknown. In this study, we demonstrate that GSDMD activation was enhanced in the spleen but not in the CNS of mice primed with low-dose LPS. GSDMD in peripheral myeloid cells promoted microglial immune training, thus exacerbating neuroinflammation and neurodegeneration during PD in an IL-1R-dependent manner. Furthermore, pharmacological inhibition of GSDMD alleviated the symptoms of PD in experimental PD models. Collectively, these findings demonstrate that GSDMD-induced pyroptosis in myeloid cells initiates neuroinflammation by regulating microglial training during infection-related PD. Based on these findings, GSDMD may serve as a therapeutic target for patients with PD.

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future.

Wetland vegetation is the basis for wetland ecosystems to regulate climate change, carbon sequestration and maintain biodiversity. Therefore, high-precision mapping and dynamic monitoring of wetland vegetation are essential for the effective management, restoration and sustainable development of wetland ecosystems. This study addressed to explore classification performance of backscattering coefficients and polarimetric decomposition parameters of multi-frequency SAR images, including single-polarimetric X-band TerraSAR (TS), full-polarimetric C-band Radarsat-2 (RS) and L-band ALOS PALSAR-2 (PS) for marsh vegetation mapping in Honghe National Nature Reserve, Northeast China. We proposed two transfer-learning strategies, and examined the feasibility of transfer-learning vegetation classifications between optical and SAR sensors, different frequencies SAR (X-, C- and L-band) and its derivative images, respectively. This paper further compared transfer-learning classification performance of marsh vegetation from polarimetric decomposition parameter images to backscattering coefficient images under the same SAR sensors. The results indicated that: (1) The three SAR images performed good classification ability in identifying marsh vegetation with the overall accuracies (OA) ranging from 0.74 to 0.88. For the same frequency SAR images, polarimetric decomposition parameters outperformed backscattering coefficients with an OA improvement ranging from 0.24 % to 2.41 %; (2) The longer wavelength SAR images produced better classification results, and L-band PS images realized the highest classification accuracy (OA = 0.871); (3) Full-polarimetric SAR images obtained the better transfer-learning classifications (OA > 0.8), and the transfer-learning classification ability of the same frequency SAR images outperformed the different frequencies; (4) The transfer learning classification results between SAR sensors outperformed that of between optical and SAR images. The results of this study provide a scientific basis for wetland change monitoring conservation and sustainable development.

Abstract Background Major depressive disorder (MDD) is a prevalent and devastating psychiatric illness. Unfortunately, the current therapeutic practice, generally depending on the serotonergic system for drug treatment is unsatisfactory and shows intractable side effects. Multiple evidence suggests that dopamine (DA) and dopaminergic signals associated with neuroinflammation are highly involved in the pathophysiology of depression as well as in the mechanism of antidepressant drugs, which is still in the early stage of study and well worthy of investigation. Methods We established two chronic stress models, including chronic unpredictable mild stress (CUMS), and chronic social defeat stress (CSDS), to complementarily recapitulate depression-like behaviors. Then, hippocampal tissues were used to detect inflammation-related molecules and signaling pathways. Pathological changes in depressive mouse hippocampal astrocytes were examined by RNA sequencing. After confirming the dopamine receptor 2 (Drd2)/β-arrestin2 signaling changes in the depressive mice brain, we then established the depressive mouse model using the β-arrestin2 knockout mice or administrating the β-arrestin2-biased Drd2 agonist to investigate the roles. Label-free mass spectrometry was used to identify the β-arrestin2-binding proteins as the underlying mechanisms. We modeled neuroinflammation with interleukin-6 (IL-6) and corticosterone treatment and characterized astrocytes using multiple methods including cell viability assay, flow cytometry, and confocal immunofluorescence. Results Drd2-biased β-arrestin2 pathway is significantly changed in the progression of depression, and genetic deletion of β-arrestin2 aggravates neuroinflammation and depressive-like phenotypes. Mechanistically, astrocytic β-arrestin2 retains STAT3 in the cytoplasm by structural combination with STAT3, therefore, inhibiting the JAK–STAT3 pathway-mediated inflammatory activation. Furtherly, pharmacological activation of Drd2/β-arrestin2 pathway by UNC9995 abolishes the inflammation-induced loss of astrocytes and ameliorates depressive-like behaviors in mouse model for depression. Conclusions Drd2/β-arrestin2 pathway is a potential therapeutic target for depression and β-arrestin2-biased Drd2 agonist UNC9995 is identified as a potential anti-depressant strategy for preventing astrocytic dysfunctions and relieving neuropathological manifestations in mouse model for depression, which provides insights for the therapy of depression.

Abstract Overexpression of classically activated macrophages (M1) subtypes and assessed reactive oxygen species (ROS) levels are often observed in patients with ulcerative colitis. At present, the treatment system of these two problems has yet to be established. Here, the chemotherapy drug curcumin (CCM) is decorated with Prussian blue analogs in a straightforward and cost‐saving manner. Modified CCM can be released in inflammatory tissue (acidic environment), eventually causing M1 macrophages to transform into M2 macrophages and inhibiting pro‐inflammatory factors. Co(III) and Fe(II) have abundant valence variations, and the lower REDOX potential in CCM‐CoFe PBA enables ROS clearance through multi‐nanomase activity. In addition, CCM‐CoFe PBA effectively alleviated the symptoms of UC mice induced by DSS and inhibited the progression of the disease. Therefore, the present material may be used as a new therapeutic agent for UC.

Abstract Transcription Factor 4 (TCF4) has been associated with autism, schizophrenia, and other neuropsychiatric disorders. However, how pathological TCF4 mutations affect the human neural tissue is poorly understood. Here, we derive neural progenitor cells, neurons, and brain organoids from skin fibroblasts obtained from children with Pitt-Hopkins Syndrome carrying clinically relevant mutations in TCF4. We show that neural progenitors bearing these mutations have reduced proliferation and impaired capacity to differentiate into neurons. We identify a mechanism through which TCF4 loss-of-function leads to decreased Wnt signaling and then to diminished expression of SOX genes, culminating in reduced progenitor proliferation in vitro. Moreover, we show reduced cortical neuron content and impaired electrical activity in the patient-derived organoids, phenotypes that were rescued after correction of TCF4 expression or by pharmacological modulation of Wnt signaling. This work delineates pathological mechanisms in neural cells harboring TCF4 mutations and provides a potential target for therapeutic strategies for genetic disorders associated with this gene.

Abstract Fine‐grained visual classification is challenging due to the inherently subtle intra‐class object variations. To solve this issue, a novel framework named channel attention and progressive multi‐granularity training network, is proposed. It first exploits meaningful feature maps through the channel attention module and captures multi‐granularity features by the progressive multi‐granularity training module. For each feature map, the channel attention module is proposed to explore channel‐wise correlation. This allows the model to re‐weight the channels of the feature map according to the impact of their semantic information on performance. Furthermore, the progressive multi‐granularity training module is introduced to fuse features cross multi‐granularity. And the fused features pay more attention to the subtle differences between images. The model can be trained efficiently in an end‐to‐end manner without bounding box or part annotations. Finally, comprehensive experiments are conducted to show that the method achieves state‐of‐the‐art performances on the CUB‐200‐2011, Stanford Cars, and FGVC‐Aircraft datasets. Ablation studies demonstrate the effectiveness of each part in our module.

Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal using a single radar altimeter and retracking technique due to the diversity and complexity of coastal waveforms. To solve this issue, we proposed an approach of estimating water level of the coastal zone in Beibu Gulf, China, by combination of waveform classifications and multiple sub-waveform retrackers. This paper stacked Random Forest (RF), XGBoost and CatBoost algorithms for building an ensemble learning (SEL) model to classify coastal waveforms, and further evaluated the performance of three retracking strategies in refining waveforms using Cryosat-2, SARAL, Sentinel-3 altimeters. We compared the estimation accuracy of the coastal water levels between the single altimeter and synergistic multi-altimeter, and combined Breaks for Additive Season and Trend (BFAST), Mann-Kendall mutation test (MK) with Long Short-Term Memory (LSTM) algorithms to track the historical change process of coastal water levels, and predict its future development trend. This paper found that: (1) The SEL algorithm achieved high-precision classification of different coastal waveforms with an average accuracy of 0.959, which outperformed three single machine learning algorithms. (2) Combination of Threshold Retracker and ALES+ Retracker (TR_ALES+) achieved the better retracking quality with an improvement of correlation coefficient (R, 0.089~0.475) and root mean square error (RMSE, 0.008∼ 0.029 m) when comparing to the Threshold Retracker & Primary Peak COG Retracker and Threshold Retracker & Primary Peak Threshold Retracker. (3) The coastal water levels of Cryosat-2, SARAL, Sentinel-3 and multi-altimeter were in good agreement (R>0.66, RMSE

The wound healing process is usually susceptible to different bacterial infections due to the complex physiological environment, which significantly impairs wound healing. The topical application of antibiotics is not desirable for wound healing because the excessive use of antibiotics might cause bacteria to develop resistance and even the production of super bacteria, posing significant harm to human well-being. Wound dressings based on adhesive, biocompatible, and multi-functional hydrogels with natural antibacterial agents have been widely recognized as effective wound treatments. Hydrogels, which are three-dimensional (3D) polymer networks cross-linked through physical interactions or covalent bonds, are promising for topical antibacterial applications because of their excellent adhesion, antibacterial properties, and biocompatibility. To further improve the healing performance of hydrogels, various modification methods have been developed with superior biocompatibility, antibacterial activity, mechanical properties, and wound repair capabilities. This review summarizes hundreds of typical studies on various ingredients, preparation methods, antibacterial mechanisms, and internal antibacterial factors to understand adhesive hydrogels with natural antibacterial agents for wound dressings. Additionally, we provide prospects for adhesive and antibacterial hydrogels in biomedical applications and clinical research.

Early epilepsy is a prominent feature in patients with CDKL5-deficiency disorder (CDD). The underlying mechanism for excessive excitability in CDD is largely unknown. The brain organoid model has been recently developed to resemble many critical features of early human brain development. Here, we used a brain organoid model to investigate the cellular electrophysiological basis for hyper-excitability in CDD patients. Our study employed cortical organoids derived from two CDD patients harboring the same CDKL5 mutation (R59X) and two controls from their healthy parents. Whole-cell patch-clamp recordings revealed higher action potential (AP) firing rate and lower rheobase in both CDD organoids, indicating increased intrinsic neuronal excitability. We further found dysfunction of voltage-gated ion channels in CDD neurons that leads to hyperexcitability, including higher Na+ and K+ current densities and a negative shift in Na+ channel activation. In contrast to neuronal properties, we found that glutamatergic neurotransmission and the electrophysiological properties of glial cells were not altered in CDD organoids. In support of our CDD findings, we further discovered similar electrophysiologic properties in cortical organoids derived from a Rett syndrome (RTT) patient, including alterations in AP firings and Na+ and K+ channel function suggesting a convergent mechanism. Together, our study suggests a critical role of intrinsic neuronal hyperexcitability and ion channel dysfunction, seen in early brain development in both CDD and RTT disorders. This investigation provides potential novel drug targets for developing treatments of early epilepsy in such disorders.

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands.

Mangroves are highly productive wetland ecosystems, located at the interlocking area of tropical and subtropical coastal zones. Accurately mapping the distribution, quality and quantity of species are crucial for mangrove management, protection, and restoration. This study proposed a mangrove species mapping approach by combining recursive feature elimination (RFE) with deep learning (DL) algorithms, and further assess the effectiveness of feature selection for DL (DeeplabV3+ and PSPNet) algorithm to improve classification accuracy under the high dimensional UAV image datasets. We constructed an ensemble learning models (SEL) by stacking five base models (Random Forest, XGBoost, LightGBM, CatBoost, and AdaBoost), and evaluate the classification ability of mangrove species between SEL and RFE-DL algorithms. Comparison of the classifications of mangrove species was to evaluate the accuracy differences between SEL and base models. Results indicated that: (1) RFE algorithm could improve the classification accuracy of DL algorithms. RFE-DL models using the optimal features achieved 94.8% of overall accuracy (OA), which was 0.2%-8.5% higher than only using the original multispectral bands; (2) SEL algorithm produced better classification performance than RFE-DL with a higher 1.6%-12.7% of overall accuracy. Mcnemar's test showed the classifications of mangrove species were significant differences between the three algorithms; (3) the SEL algorithm had a strong and stable ability for classifying mangrove species. The OA of six classification scenarios was from 75.5% to 92.2%, and the highest OA using SEL algorithm was 0.8%-4.2% higher than base models; (4) XGBoost algorithm had the highest importance, while AdaBoost had the lowest importance in the SEL-based classifications.

Dopamine agonists increase brain glutathione levels. Here the authors identify noncanonical signalling by Nrf2 as a potential mechanism for the neuroprotective effects of dopamine D2 receptor activation.

Vulcanization is an essential process to obtain high-performance rubber products. Diphenylguanidine (DPG) is often used as the secondary accelerator in the vulcanization process of natural rubber (NR) latex. However, DPG would make NR latex emulsion exhibit gelation, resulting in the negative vulcanization efficiency. In addition, exposure to DPG might lead to some physiological diseases during the production process of DPG doped NR latex. Hydroxypropyl-β-cyclodextrin (HP-β-CD) with the hydrophobic interior and hydrophilic exterior has the advantages of good water solubility, high bioavailability, reliable stability, and low toxicity. In this study, the inclusion complex of diphenylguanidine-hydroxypropyl-β-cyclodextrin (DPG-HP-β-CD) is prepared by ball milling with a host-guest molar ratio of 1 : 1, which has also been applied to the foaming process of NR latex. The mechanical properties of DPG-HP-β-CD inclusion complex/natural rubber latex foam (DPG-HP-β-CD/NRLF) have been significantly improved, including the tensile strength, elongation at break, hardness, compression set, resilience, and antiaging performance. Further, the usage of DPG has been reduced, leading to the reduction of toxicity and environmental hazards.

State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate.

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.

Prenatal opioids exposure can lead to both neonatal abstinence syndrome in newborns and neurological deficits later in life. Although opioids have been well studied in general, the cellular and molecular mechanisms by which opioids affect human fetal brain development has not been well understood. In this work, we have taken advantage of a human 3D-brain cortical organoid (hCO) that facilitated enormously the investigation of early human brain development. Using imaging, immunofluorescence, multi-electrode array (MEA) and patch clamp recording techniques, we have investigated the effect of methadone, a frequently used opioid during pregnancy, on early neural development, including neuronal growth, neural network activity and synaptic transmission in hCOs. Our results demonstrated that methadone dose-dependently halted the growth of hCOs and induced organoid disintegration after a prolonged exposure. In addition, methadone dose-dependently suppressed the firing of spontaneous action potentials in hCOs and this suppression could be reversed upon methadone withdrawal in hCOs treated with lower dosages. Further investigation using patch clamp whole cell configuration revealed that, at clinically relevant concentrations, methadone decreased the frequency and amplitude of excitatory postsynaptic currents in neurons, indicating a critical role of methadone in weakening synaptic transmission in neural networks in hCOs. In addition, methadone significantly attenuated the voltage-dependent Na+ current in hCOs. We conclude that methadone interrupts neural growth and function in early brain development.

Accumulating evidence has suggested that prenatal exposure to methadone causes multiple adverse effects on human brain development. Methadone not only suppresses fetal neurobehavior and alters neural maturation, but also leads to long-term neurological impairment. Due to logistical and ethical issues of accessing human fetal tissue, the effect of methadone on brain development and its underlying mechanisms have not been investigated adequately and are therefore not fully understood. Here, we use human cortical organoids which resemble fetal brain development to examine the effect of methadone on neuronal function and maturation during early development. During development, cortical organoids that are exposed to clinically relevant concentrations of methadone exhibited suppressed maturation of neuronal function. For example, organoids developed from 12th week till 24th week have an about 7-fold increase in AP firing frequency, but only half and a third of this increase was found in organoids exposed to 1 and 10 μM methadone, respectively. We further demonstrated substantial increases in INa (4.5-fold) and IKD (10.8-fold), and continued shifts of Na+ channel activation and inactivation during normal organoid development. Methadone-induced suppression of neuronal function was attributed to the attenuated increase in the densities of INa and IKD and the reduced shift of Na+ channel gating properties. Since normal neuronal electrophysiology and ion channel function are critical for regulating brain development, we believe that the effect of prolonged methadone exposure contributes to the delayed maturation, development fetal brain and potentially for longer term neurologic deficits.

The interactions at the graft–tissue interfaces are critical for the results of engraftments post-implantation. To improve the success rate of the implantations, as well as the quality of the patients' life, understanding the possible reactions between artificial materials and the host tissues is helpful in designing new generations of material-based grafts aiming at inducing specific responses from surrounding tissues for their own reparation and regeneration. To help researchers understand the complicated interactions that occur after implantations and to promote the development of better-designed grafts with improved biocompatibility and patient responses, in this review, the topics will be discussed from the basic reactions that occur chronologically at the graft–tissue interfaces after implantations to the existing and potential applications of the mechanisms of such reactions in designing of grafts. It offers a chance to bring up-to-date advances in the field and new strategies of controlling the graft–tissue interfaces.

The Maxwell–Wagner model and bipolar charge transport model both aim at describing the charge transport behaviours in dielectrics. The Maxwell–Wagner model performs well in calculating the polarisation current and electric field distribution, but it is too macroscopic to describe the microscopic behaviours of space charges. The bipolar charge transport model can well simulate the space charge accumulation, but it cannot quantitatively relate the microscopic transport behaviours with the macroscopic conductivities of dielectrics. Considering the shortages of the above two models, an improved charge transport model, named carrier dynamic equilibrium model, was proposed in this study to simulate the polarisation process of the low-density polyethylene/ethylene propylene diene monomer bi-layer dielectric by introducing a source term of carrier dynamic equilibrium. Effects of carrier mobility and non-equilibrium carrier lifetime on the simulation results were explored, and a comparison among the results of measurement and different models was also made. Compared with the Maxwell–Wagner model and bipolar charge transport model, the improved model has the best coincidence with measurements, which can provide an accurate reference for the design of high-voltage direct current insulation systems.

Surface charge accumulation on epoxy insulators is one of the most serious problems threatening the operation safety of the direct current gas-insulated transmission line (GIL), and can be efficiently inhibited by the surface modification technology. This paper investigated the mechanisms of fluorination modulated surface charge behaviors of epoxy resin through quantum chemical calculation (QCC) analysis of the molecular structure. The results show that after fluorination, the surface charge dissipation process of the epoxy sample is accelerated by the introduced shallow trap sites, which is further clarified by the carrier mobility model. The electron distribution probability of the highest occupied molecular orbitals (HOMO) under positive charging and the lowest unoccupied molecular orbitals (LUMO) under negative charging shows distinctive patterns. It is illustrated that electrons are likely to aggregate locally around benzenes for the positively charged molecular structure, while electrons tend to distribute all along the epoxy chain under negatively charging. The calculated results verify that fluorination can modulate surface charge behaviors of epoxy resin through redesigning its molecular structure, trap distribution and charging patterns.

Atmospheric nitrogen (N) deposition can increase the susceptibility of vascular plants to other stresses, but the physiological basis of such a response remains poorly understood. This study was designed to clarify the physiological mechanisms and to evaluate bioindicators of N deposition impact on vascular plants. We evaluate multiple physiological responses to ~4 years of simulated additional N deposition (30–90 kg N ha−1 year−1) on three age-classes (1a, 3a, and 5a) of Moso bamboo. A saturating response to the additional N deposition was found both in foliar N concentration and in Pn. However, 3- and 5-year-old bamboo seemed to be less tolerant to extremely high N deposition than 1-year-old bamboo since they were saturated at a lower N addition. Furthermore, C/N/P stoichiometric ratios were very sensitive to N deposition in all three-age classes of bamboo, but the responses to N deposition in the various age-classes were diverse. We also found that the highest additional N deposition suppressed stomatal conductance and transpiration rate, suggesting an induced water stress. The stress induced by the high N load was also seen in photochemistry, where it reduced potential and actual photosynthetic use of light energy, diminished photo-protection capacity, and increased risk of the photo-damage. High additional N deposition contributed to a decrease in the foliar soluble protein contents and to an increase in the peroxidase activity (POD). Our study suggested, for the first time, that although the photosynthetic rate was enhanced by the increased N deposition in Moso bamboo, long-term high N load causes negative effects, such as damage to photosystem II. In Moso bamboo photochemical parameters are more sensitive to N deposition than photosynthetic rate or foliar N concentration. Furthermore, plant age should be taken into account when assessing plants' susceptibility to changes in global change drivers, such as N deposition. These findings facilitate the revealing of the risks potentially caused to vascular plants by increased N deposition before any visible symptoms of injury are seen.

The endoplasmic reticulum (ER) responds to changes in intracellular homeostasis through activation of the unfolded protein response (UPR). Yet, it is not known how UPR-signaling coordinates adaptation versus cell death. Previous studies suggested that signaling through PERK/ATF4 is required for cell death. We show that high levels of ER stress (i.e., ischemia-like conditions) induce transcription of the ubiquitin ligases Siah1/2 through the UPR transducers PERK/ATF4 and IRE1/sXBP1. In turn, Siah1/2 attenuates proline hydroxylation of ATF4, resulting in its stabilization, thereby augmenting ER stress output. Conversely, ATF4 activation is reduced upon Siah1/2 KD in cultured cells, which attenuates ER stress-induced cell death. Notably, Siah1a(+/-)::Siah2(-/-) mice subjected to neuronal ischemia exhibited smaller infarct volume and were protected from ischemia-induced death, compared with the wild type (WT) mice. In all, Siah1/2 constitutes an obligatory fine-tuning mechanism that predisposes cells to death under severe ER stress conditions.

To assess smooth muscle enfoldment and internal sphincter construction (SMESC) for improvement of continence after intersphincteric resection (ISR) for rectal cancer.Twenty-four Bama miniature pigs were randomly divided into a conventional ISR group and experimental SMESC group, with 12 pigs in each group. The proximal sigmoid colon was anastomosed directly to the anus in the ISR group. In the SMESC group, internal sphincter construction was performed. At 12 weeks before and after surgery, rectal resting pressure and anal canal length were assessed. Three-dimensional ultrasound was used to determine the thickness of the internal sphincter. After the animals were sacrificed, the rectum and anus were resected and pathological examinations were performed to evaluate the differences in sphincter thickness and muscle fibers.All 24 animals in the SMESC group and the ISR group survived the surgery. Twelve weeks post-surgery, the rectal resting pressure, length of the anal high-pressure zone and the postoperative internal sphincter thickness for the ISR group were significantly lower than for the SMESC group. There was a thickened area (about 2 cm) above the anastomotic stoma among animals from the SMESC group; in addition, the smooth muscles were significantly enlarged and enfolded when compared to the ISR group.This animal model study shows that the SMESC procedure achieved acceptable reconstruction of the internal anal neo-sphincter (IAN/S), without increasing surgical risk. However, the findings in this experimental animal model must be confirmed by clinical trials to determine the safety and efficacy of this procedure in clinical practice.

Rotator cuff tear repair poses significant challenges due to the complex gradient interface structure. In the face of disease-related disruptions in the tendon-bone interface (TBI), the strategy of constructing a biomimetic scaffold is a promising avenue. A novel 3D-printed rotator cuff scaffold loaded adipose stem cells (ADSCs), bone morphogenetic protein-2 (BMP-2), and collagen type I (COL I). The efficiency of the slow-release BMP-2 design depended on the dopamine-hyaluronic acid (HAD) and BMP-2 reaction. The cumulative release of BMP-2 was 44.97 5.45% at 4 weeks. The 3D-printed bilayer scaffold, incorporating COL I and BMP-2, effectively promoted the differentiation of ADSCs into osteogenic, tenogenic, and chondrogenic lineages in vitro. The combination of 3D-printed bioactive scaffolds and ADSCs demonstrated a superior repair effect on rotator cuff injuries in vivo. Therefore, these findings indicates that the 3D-printed biomimetic scaffold loaded with ADSCs and BMP-2 holds potential as a promising graft for TBI healing. [ABSTRACT FROM AUTHOR]

Introduction: Microorganisms exhibit intricate interconnections with tea plants; however, despite the well-established role of microorganisms in crop growth and development, research on microbes within the tea plant remains insufficient, particularly regarding endophytic microorganisms. Methods: In this study, we collected samples of leaves and rhizosphere soils from 'Zhuyeqi', 'Baojing Huangjincha#1', 'Baiye#1', and 'Jinxuan' varieties planted. Results: Our analyses revealed significant variations in tea polyphenol contents among tea varieties, particularly with the 'Zhuyeqi' variety exhibiting higher levels of tea polyphenols (>20% contents). Microbiome studies have revealed that endophytic microbial community in tea plants exhibited higher host specificity compared to rhizospheric microbial community. Analyses of across-ecological niches of the microbial community associated with tea plants revealed that soil bacteria serve as a significant reservoir for endophytic bacteria in tea plants, Bacillus may play a crucial role in shaping the bacterial community acrossecological niche within the tea plants with higher tea polyphenol levels. In the aforementioned analyses, the microbial community of 'Zhuyeqi' exhibited a higher degree of host specificity for leaf endophytic microorganisms, the topological structure of the co-occurrence network is also more intricate, harboring a greater number of potential core microorganisms within its nodes. A closer examination was conducted on the microbial community of 'Zhuyeqi', further analyses of its endophytic bacteria indicated that its endophytic microbial community harbored a greater abundance of biomarkers, particularly among bacteria, and the enriched Methylobacterium and Sphingomonas in 'Zhuyeqi' may play distinct roles in disease resistance and drought resilience in tea plants. Conclusion: In summary, this study has shed light on the intricate relationships of tea plant varieties with their associated microbial communities, unveiling the importance of microorganisms and tea varieties with higher tea polyphenols, and offering valuable insights to the study of microorganisms and tea plants. [ABSTRACT FROM AUTHOR]