Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Additive-free Absorbable Keratin Sponge With Procoagulant Activity for Noncompressible Hemostasis.

The development of a natural, additive-free, absorbable sponge with procoagulant activity for noncompressible hemostasis remains a challenging task. In this study, we extracted high molecular weight keratin (HK) from human hair and transformed it into a hemostatic sponge with a well-interconnected pore structure using a foaming technique, freeze-drying, and oxidation cross-linking. By controlling the cross-linking degree, the resulting sponge demonstrated excellent liquid absorption ability, shape recovery characteristics, and robust mechanical properties. The HK10 sponge exhibited rapid liquid absorption, expanding up to 600% within 5 s. Moreover, the HK sponge showed superior platelet activation and blood cell adhesion capabilities. In SD rat liver defect models, the sponges demonstrated excellent hemostatic performance by sealing the wound and expediting coagulation, reducing the hemostatic time from 825 to 297 s. Furthermore, HK sponges have excellent biosafety, positioning them as a promising absorbable sponge with the potential for the treatment of noncompressible hemostasis.

Near-Infrared-II Imaging Revealed Hypothermia Regulates Neuroinflammation Following Brain Injury by Increasing the Glymphatic Influx.

Advanced in vivo imaging techniques have facilitated the comprehensive visual exploration of animal biological processes, leading to groundbreaking discoveries such as the glymphatic system. However, current limitations of macroscopic imaging techniques impede the precise investigation of physiological parameters regulating this specialized lymphatic transport system. While NIR-II fluorescence imaging has demonstrated advantages in peripheral lymphatic imaging, there are few reports regarding its utilization in the glymphatic system. To address this, a noninvasive transcranial macroscopic NIR-II fluorescence imaging model is developed using a cyanine dye-protein coupled nanoprobe. NIR-II imaging with high temporal and spatial resolution reveals that hypothermia can increase the glymphatic influx by reducing the flow rate of cerebrospinal fluid. In addition, respiratory rate, respiratory amplitude, and heart rate all play a role in regulating the glymphatic influx. Thus, targeting the glymphatic influx may alter the trajectory of immune inflammation following brain injury, providing therapeutic prospects for treating brain injury with mild hypothermia.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation.

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

[Analysis of Plasma Metabolic Profile in Children with Transfusion-Dependent Thalassemia].

OBJECTIVE: To explore the plasma metabolomic characteristics of children with transfusion-dependent thalassemia (TDT), and reveal the changes of metabolic pattern in children with TDT. METHODS: 23 children with TDT who received regular blood transfusion in Ganzhou Women and Children's Health Care Hospital in 2021 were selected, and 11 healthy children who underwent physical examination during the same period were selected as the control group. The routine indexes between children with TDT and the control group were compared, and then the metabolic composition of plasma samples from children with TDT and the control group was detected by liquid chromatography-mass spectrometry. An OPLS-DA model was established to perform differential analysis on the detected metabolites, and the differential metabolic pathways between the two groups were analyzed based on the differential metabolites. RESULTS: The results of routine testing showed that the indexes of ferritin, bilirubin, total bile acid, glucose and triglycerides in children with TDT were significantly higher than those in healthy controls, while hemoglobin and total cholesterol were significantly lower (all P <0.05). However there was no significant difference in lactate dehydrogenase between the two groups (P >0.05). Compared with the control group, 190 differential metabolites (VIP>1) were identified in TDT children. Among them, 168 compounds such as arginine, proline and glycocholic acid were significantly increased, while the other 22 compounds such as myristic acid, eleostearic acid, palmitic acid and linoleic acid were significantly decreased. The metabolic pathway analysis showed that the metabolic impact of TDT on children mainly focused on the upregulation of amino acid metabolism and downregulation of lipid metabolism. CONCLUSION: The amino acid and lipid metabolism in children with TDT were significantly changed compared with the healthy control group. This finding is helpful to optimize the treatment choice for children with TDT, and provides a new idea for clinical treatment.

NIR-II nanoprobes for investigating the glymphatic system function under anesthesia and stroke injury.

The glymphatic system plays an important role in the transportation of cerebrospinal fluid (CSF) and the clearance of metabolite waste in brain. However, current imaging modalities for studying the glymphatic system are limited. Herein, we apply NIR-II nanoprobes with non-invasive and high-contrast advantages to comprehensively explore the function of glymphatic system in mice under anesthesia and cerebral ischemia-reperfusion injury conditions. Our results show that the supplement drug dexmedetomidine (Dex) enhances CSF influx in the brain, decreases its outflow to mandibular lymph nodes, and leads to significant differences in CSF accumulation pattern in the spine compared to isoflurane (ISO) alone, while both ISO and Dex do not affect the clearance of tracer-filled CSF into blood circulation. Notably, we confirm the compromised glymphatic function after cerebral ischemia-reperfusion injury, leading to impaired glymphatic influx and reduced glymphatic efflux. This technique has great potential to elucidate the underlying mechanisms between the glymphatic system and central nervous system diseases.

Magnetic propelled hydrogel microrobots for actively enhancing the efficiency of lycorine hydrochloride to suppress colorectal cancer.

Research and development in the field of micro/nano-robots have made significant progress in the past, especially in the field of clinical medicine, where further research may lead to many revolutionary achievements. Through the research and experiment of microrobots, a controllable drug delivery system will be realized, which will solve many problems in drug treatment. In this work, we design and study the ability of magnetic-driven hydrogel microrobots to carry Lycorine hydrochloride (LH) to inhibit colorectal cancer (CRC) cells. We have successfully designed a magnetic field driven, biocompatible drug carrying hydrogel microsphere robot with Fe3O4 particles inside, which can achieve magnetic field response, and confirmed that it can transport drug through fluorescence microscope. We have successfully demonstrated the motion mode of hydrogel microrobots driven by a rotating external magnetic field. This driving method allows the microrobots to move in a precise and controllable manner, providing tremendous potential for their use in various applications. Finally, we selected drug LH and loaded it into the hydrogel microrobot for a series of experiments. LH significantly inhibited CRC cells proliferation in a dose- and time-dependent manner. LH inhibited the proliferation, mobility of CRC cells and induced apoptosis. This delivery system can significantly improve the therapeutic effect of drugs on tumors.

Genetic Insights into the Association and Causality Between Blood Metabolites and Alzheimer's Disease.

Background: Alzheimer's disease (AD) is an increasing public health concern with the aging of the global population. Understanding the genetic correlation and potential causal relationships between blood metabolites and AD may provide important insights into the metabolic dysregulation underlying this neurodegenerative disorder. Objective: The aim of this study was to investigate the causal relationship between blood metabolites and AD using Mendelian randomization (MR) analysis. Methods: Association data were obtained from three large-scale genome-wide association studies of 486 blood metabolites (N = 7,824), AD (71,880 cases and 383,378 controls), early-onset AD (N = 303,760), and late-onset AD (N = 307,112). Causal associations between blood metabolites and AD were assessed using inverse variance weighting (IVW), MR-Egger, and weighted median methods. Bidirectional two-sample MR analysis was used to identify causal blood metabolites. MR-PRESSO, MR-Egger, and Cochran-Q were used to quantify instrumental variable heterogeneity and horizontal pleiotropy. Results: Using MR and sensitivity analysis, we identified 40 blood metabolites with potential causal associations with AD. After applying false discovery rate (FDR) correction, two metabolites, gamma-glutamylphenylalanine (OR = 1.15, 95% CI: 1.06-1.24, p = 3.88×10-4, q = 0.09) and X-11317 (OR = 1.16, 95% CI: 1.08-1.26, p = 1.14×10-4, q = 0.05), retained significant associations with AD. Reverse MR analysis indicated no significant causal effect of AD on blood metabolites. No significant instrumental variable heterogeneity or horizontal pleiotropy was found. Conclusions: This two-sample MR study provides compelling evidence for a potential causal relationship between blood metabolic dysregulation and susceptibility to AD. Further investigation of the biological relevance of the identified metabolites to AD and additional supporting evidence is warranted.

Effects of Low Protein Diet on Production Performance and Intestinal Microbial Composition in Pigs.

In order to study the effects of a low protein diet on the production performance and intestinal microbiota composition of Hexi pigs, twenty-seven Hexi pigs with an initial body weight of 60.50 ± 2.50 kg were randomly divided into three groups (control group (CG), group 1 (G1), and group 2 (G2)) and participated in a 60-day finishing trial. The CG was fed a normal protein level diet with a protein level of 16.0%, and G1 and G2 were fed a low protein level diet with protein levels of 14.0% and 12.0%, respectively. The results showed that the low protein level diet had no significant effect on the production performance of Hexi pigs, compared with the CG, the slaughter rate of G1 and G2 increased by 2.49% (p > 0.05) and 6.18% (p > 0.05), the shear force decreased by 2.43% (p > 0.05) and 15.57% (p > 0.05), the cooking loss decreased by 24.02% (p < 0.05) and 21.09% (p > 0.05), and the cooking percentage increased by 13.20% (p > 0.05) and 11.59% (p > 0.05). From 45 min to 24 h and 48 h after slaughter, each group of pH decreased by 1.02, 0.66, and 0.42. For muscle flesh color, the lightness (L) increased by 13.31% (p > 0.05) and 18.01% (p > 0.05) in G1 and G2 and the yellowness (b) increased by 7.72% (p > 0.05) and 13.06% (p > 0.05). A low protein level diet can improve the intestinal flora richness and diversity of growing and finishing pigs. In the jejunum, the ACE index (899.95), Simpson index (0.90), and Shannon (4.75) index were higher in G1 than in the other groups, but the Chao1 index (949.92) was higher in G2 than in the remaining two groups. Proteobacteria, Actinobacteria, Euryarchaeota, and Verrucomicrobia were significantly higher in G1 than in the CG. The relative abundances of Lactobacillus, Terrisporobacter, and Megasphaera in G1 was significantly higher than in the CG (p < 0.05). In the cecum, the ACE index (900.93), Chao1 index (879.10), Simpson index (0.94), and Shannon (5.70) index were higher in G1 than in the remaining groups. The Spirochaetes in G2 were significantly higher than in the other groups, but the Verrucomicrobia was significantly lower than in the other groups. The relative abundances of Lactobacillus were higher in G1 and G2 than in the CG (p > 0.05). The relative abundances of unidentified_Clostridiales and Terrisporobacter in G2 were significantly lower than in the CG (p < 0.05). The relative abundance of Turicibacter in G1 was significantly lower than in the CG (p < 0.05). The relative abundances of other bacterial genera in G1 and G2 were increased by 30.81% (p > 0.05) and 17.98% (p > 0.05).

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

First principles prediction of two-dimensional Janus STiXY2 (X = Si, Ge; Y = N, P, As) materials.

Since the successful experimental synthesis of MoSi2N4, the "MA2Z4 family" has attracted the interest of researchers from many fields due to its excellent physical and chemical properties. In this work, we propose a novel two-dimensional Janus STiXY2 (X = Si, Ge; Y = N, P, As) monolayer using first principles. Under biaxial strain and an applied electric field, we investigate the controllable electronic properties of Janus STiXY2 (X = Si, Ge; Y = N, P, As) structures. Our predictions demonstrate that the 2D STiXY2 materials are structurally and dynamically stable. Using the HSE functional, we show that these 2D STiXY2 materials are indirect semiconductors with band gaps of 0.99, 1.142, 0.834, 1.322, 0.735, and 0.215 eV, respectively. Additionally, we found that, except for the STiXAs2 (X = Si, Ge) monolayer, the influence of biaxial strain on electronic characteristics is significantly greater than that of the applied electric field. Finally, we calculated the carrier mobilities of these Janus structures and found that the STiGeP2 monolayer has the highest electron carrier mobility in the x-direction with 8175.66 cm2 s-1 V-1, while the STiGeAs2 monolayer has the highest electron carrier mobility in the y-direction, 2897.94 cm2 s-1 V-1. They are all larger than those of the experimentally synthesized MoS2 (∼200 cm2 s-1 V-1). The results may provide insights for the study of novel Janus monolayers with potential application in electronic devices.

Lymphomatoid granulomatosis with the central nervous system involvement as the main manifestation: a case report.

BACKGROUND: Lymphomatoid granulomatosis (LyG) is a rare extralymphatic lymphoproliferative disease characterized by lymphocytic invasion into vascular walls and damage to blood vessels. The lungs are affected in 90% of LyG cases, followed by the skin, central nervous system (CNS), kidneys and liver. CASE PRESENTATION: Here we report a case of a young woman with LyG, with CNS involvement as the initial clinical manifestation. Computer tomography (CT) scans showed multiple nodular, patchy and flocculent high-density shadows in both lungs without mediastinal lymph node enlargement. Magnetic resonance imaging (MRI) scans showed multiple abnormal signal intensities in the right cerebellar hemisphere, frontal, parietal and temporal lobes, and dorsal brainstem, which became patchy and annular after enhancement. The post-operative pathological analysis of lesion samples confirmed the diagnosis of grade II LyG. CONCLUSIONS: LyG should be concerned in young adults showing multiple radiological brain and lung lesions. Resection and postoperative medication of steroid hormones and IFN-α may be effective in the treatment of LyG.

Two-dimensional Pd3(AsSe4)2 as a photocatalyst for the solar-driven oxygen evolution reaction: a first-principles study.

The relationship between the structure and properties of materials is the core of material research. Bulk Pd3(PS4)2 materials have been successfully synthesized in the field of three-dimensional materials. After that, various studies on two-dimensional layered materials were conducted. Inspired by these successes, this work used density functional theory based on first principles to explore similar two-dimensional Pd3(AsX4)2, where X is S, Se, or Te belonging to the same group. Our findings demonstrate that the Pd3(AsS4)2 and Pd3(AsSe4)2 monolayers, with HSE06 band gaps of 2.37 and 1.36 eV, respectively, are indirect semiconductors. Additionally, their carrier mobilities [523.23 cm2 s-1 V-1 and 440.6 cm2 s-1 V-1] are also proved to be superior to MoS2 [∼200 cm2 s-1 V-1]. The optical calculations indicate that the Pd3(AsSe4)2 monolayer yields suitable valence band edge positions for the visible-light-driven water splitting reactions. More interestingly, at a low applied voltage of 0.14 V, Pd3(AsSe4)2 exhibits outstanding oxygen evolution reaction performance. In this study, the possible mechanism for the ability of Pd3(AsSe4)2 monolayer to promote photocatalysis and oxygen evolution was explained, which may pave the way for the practical design of further solar-driven high-quality water splitting photocatalysis.

Electronic Properties and Electrocatalytic Water Splitting Activity for Precious-Metal-Adsorbed Silicene with Nonmetal Doping.

Since nonmetal (NM)-doped two-dimensional (2D) materials can effectively modulate their physical properties and chemical activities, they have received a lot of attention from researchers. Therefore, the stability, electronic properties, and electrocatalytic water splitting activity of precious-metal (PM)-adsorbed silicene doped with two NM atoms are investigated based on density functional theory (DFT) in this paper. The results show that NM doping can effectively improve the stability of PM-adsorbed silicene and exhibit rich electronic properties. Meanwhile, by comparing the free energies of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) intermediates of 15 more stable NM-doped systems, it can be concluded that the electrocatalytic water splitting activity of the NM-doped systems is more influenced by the temperature. Moreover, the Si-S2-Ir-doped system exhibits good HER performance when the temperature is 300 K, while the Si-N2-Pt-doped system shows excellent OER activity. Our theoretical study shows that NM doping can effectively promote the stability and electrocatalytic water splitting of PM-adsorbed silicene, which can help in the application of silicene in electrocatalytic water splitting.

Manganese(III)-promoted highly stereoselective phosphorylation of acyclic tertiary enamides to synthesize E-selective ß-phosphoryl enamides.

A concise manganese(III)-promoted stereoselective ß-phosphorylation of acyclic tertiary enamides and diverse H-phosphine oxides was achieved. This reaction proceeds with absolute E-selectivity in contrast to Z-selectivity obtained in other previous works and affords various E-selective ß-phosphorylated tertiary enamides in high efficiency. To the best of our knowledge, this is the first case of E-selective ß-phosphorylation of tertiary enamides through C-H functionalization. In addition, the method features broad substrate scope, good functional group compatibility and efficient scale-up.

Different Lewis Acid Promotor-Steered Highly Regioselective Phosphorylation of Tertiary Enamides.

Different Lewis acid promotor-steered highly regioselective phosphorylation of tertiary enamides with diverse H-phosphonates or H-phosphine oxides was developed. Under the catalysis of iron salt, the phosphonyl group was introduced into the α-position of tertiary enamides, affording various α-phosphorylated amides in high efficiency. On the other hand, the ß-phosphorylated tertiary enamides were efficiently obtained as the products in the presence of manganese(III) acetylacetonate.

Bioorthogonal DOPA-NGF activated tissue engineering microunits for recovery from traumatic brain injury by microenvironment regulation.

Stem cell treatment is vital for recovery from traumatic brain injury (TBI). However, severe TBI usually leads to excessive inflammation and neuroinhibitory factors in the injured brain, resulting in poor neural cell survival and uncontrolled formation of glial scars. In this study, a bioorthogonal microenvironment was constructed on biodegradable poly(lactide-co-glycolide) (PLGA) microcarriers through immobilization of mussel-inspired bioorthogonal 3,4-dihydroxyphenylalanine-containing recombinant nerve growth factor (DOPA-NGF) and human umbilical cord mesenchymal stem cells (hUMSCs) for minimally invasive therapy of TBI. Cell culture and RNA-seq analysis revealed enhanced extracellular matrix (ECM) secretion and viability of hUMSCs on PLGA microcarriers compared to 2D culture. Immobilized DOPA-NGF further promoted adhesion, proliferation, and gene expression in RSC96 neurotrophic cells and hUMSCs. Specifically, the neurotrophin receptor of NT-3 (NTRK3) in hUMSCs was activated by DOPA-NGF, leading to MYC transcription and paracrine enhancement to build an adjustable biomimetic microenvironment. After transplantation of microunits in animal models, the motor and learning-memory ability of TBI mice were improved through rollbacks of overactivated inflammatory reaction regulation, neuronal death, and glial scar formation after injury. This was attributed to the paracrine enhancement of hUMSCs activated by the DOPA-NGF. Our study provides a neural regenerative microenvironment-based therapeutic strategy to advance the effects of transplanted hUMSCs in cell-based regenerative medicine for TBI therapy. STATEMENT OF SIGNIFICANCE: Extensive studies have demonstrated the importance of the microenvironment for posttraumatic brain injury recovery. However, an efficient method that can mimic the neural regenerative microenvironment to strengthen stem cell therapy and brain injury recovery is still absent. In this study, the minimally invasive transplantation of DOPA-NGF immobilized biodegradable microcarriers with mesenchymal stem cells was found to be an effective method for regeneration of injured brain. Moreover, transcriptome analysis revealed that neurotrophin receptor of NT-3 (NTRK3) was activated by DOPA-NGF for MYC transcription and paracrine enhancement to build a kind of adjustable biomimetic microenvironment for brain injury therapy. This study provides a neural regenerative microenvironment-based therapeutic strategy to advance the transplanted hUMSCs in cell-based regenerative medicine for neural recovery.