Developing a Selection Framework for Zinc Ion-Based Biomaterial Design: Guided by the Biosafety Assessment of ZIF-8 and ZnO.

In recent years, nanomaterials have gained widespread use in the biomedical field, with ZIF-8 and ZnO emerging as promising candidates due to their remarkable performance in osteogenesis, angiogenesis, and antimicrobial therapy. However, before advancing these nanomaterials for clinical applications, it is imperative to evaluate their biocompatibility. In particular, comparing nanomaterials with similar biomedical functions is crucial for identifying the most suitable nanomaterials for further development and market entry. Our study aimed to compare the biocompatibility of nano-ZIF-8 and nano-ZnO under the same conditions. We found that nano-ZIF-8 exhibited lower toxicity both in vitro and in vivo compared to nano-ZnO. To gain insights into the underlying mechanisms responsible for this difference, we conducted further experiments to investigate lysosome damage, mitochondrial change, and the occurrence of ferroptosis. Additionally, we performed transcriptome sequencing to analyze the expression of relevant genes, thereby providing robust validation for our findings. In summary, our study highlighted the importance of evaluating nanomaterials with similar biomedical effects. Through this comparative study, we have not only shed light on the superior biocompatibility of nano-ZIF-8 over nano-ZnO, but also contributed valuable insights and methodological references for future material screening endeavors. Ultimately, our study served as a stepping stone toward the development of safer and more effective nanomaterials for various biomedical applications.

Natural pyrite-stimulative periodate activation: efficiency and mechanism study.

Natural pyrite (NP) is an alternative catalyst for wastewater purification via advanced oxidation processes (AOPs). However, the activation performance and mechanism of periodate (PI) by NP have not yet been revealed. Herein, this work examines the activation performance of NP towards PI and its application in the degradation of antibiotic wastewater. Interestingly, 95.69% of chlortetracycline (CTC) was degraded by NP within 20 min via PI activation. Besides, NP shows effective degradation of various pollutants such as rhodamine B (65.81%), sulfamethoxazole (89.04%), and sodium butylxanthate (99.77%) within 20 min. The active species quenching experiment suggested that the active species ∙ OH , IO 3 ∙ , 1O2 and the active complex of PI bonded with NP surface participated in CTC degradation. In addition, Fe(II) on NP surface is the main active site for PI activation, while Sn2- species accelerates the reduction of Fe(III) to Fe(II) and promotes sustained PI activation. This work provides new ideas for the application of NP in environmental pollution control.

Targeting the JAK2/STAT3 signaling pathway with natural plants and phytochemical ingredients: A novel therapeutic method for combatting cardiovascular diseases.

The aim of this article is to introduce the roles and mechanisms of the JAK2/STAT3 pathway in various cardiovascular diseases, such as myocardial fibrosis, cardiac hypertrophy, atherosclerosis, myocardial infarction, and myocardial ischemiareperfusion. In addition, the effects of phytochemical ingredients and different natural plants, mainly traditional Chinese medicines, on the regulation of different cardiovascular diseases via the JAK2/STAT3 pathway are discussed. Surprisingly, the JAK2 pathway has dual roles in different cardiovascular diseases. Future research should focus on the dual regulatory effects of different phytochemical ingredients and natural plants on JAK2 to pave the way for their use in clinical trials.

Development of a rapid-shaping and user-friendly membrane with long-lasting space maintenance for guided bone regeneration.

The success of guided bone regeneration (GBR) surgery depends largely on the use of GBR membranes to maintain space for bone regeneration and prevent soft tissue ingrowth. However, currently available commercial degradable GBR membranes are often limited by poor space maintenance ability and require additional suture or nail for fixation. To overcome these limitations, we developed a rapid-shaping, adhesive, and user-friendly GBR membrane (PLGA film-PGN) with long-lasting space maintenance by immersing an electrospun poly(lactide-co-glycolic acid) film in a photo-crosslinkable hydrogel composed of polyethylene glycol diacrylate, gelatin methacryloyl, and nanosilicate (PGN). The PGN hydrogel significantly improved the mechanical strength of the PLGA film-PGN and endowed it with plasticity and adhesive properties, making it more maneuverable. The maximum bending force that the PLGA film-PGN could withstand was over 55 times higher than that of the HEAL ALL film (a commonly used commercial GBR membrane). PLGA film-PGN also promoted the proliferation and osteogenic differentiation of rBMSCs. According to a critical-size rat calvarial defect model, PLGA film-PGN maintained the space within the defect area and significantly enhanced bone formation 4 weeks after the surgery. To conclude, the study provided a novel perspective on GBR membrane design and the multifunctional PLGA film-PGN membrane demonstrated great potential for bone defect reconstruction.

Accelerating the humification of mushroom waste by regulating nitrogen sources composition: Deciphering mechanism from bioavailability and molecular perspective.

Regulating nitrogen source composition is efficient approach to accelerate the spent mushroom substrate (SMS) composting process. However, currently, most traditional composting study only focuses on total C/N ratio of initial composting material. Rarely research concerns the effect of carbon or nitrogen components at different degradable level and their corresponding decomposed-substances on humification process. This study deciphers and compares the mechanism of mixed manure-N sources on SMS humification from bioavailability and molecular perspective. Two different biodegradable manure-N sources, cattle manure (CM) and Hainan chicken manure (CH), were added into the SMS composting with the different CM:CH ratio of 1:0, 3:1, 1:1, 1:3, and 0:1, respectively. The physicochemical properties and humic substances were determined to evaluate the compost quality. Coupling analysis of spectroscopy, fluorescence, and humic intermediate precursors were conducted to characterizing molecular formation process of humic acid (HA). The results indicated that regulating the carbon-nitrogen nutrient biodegradability of composting material by adding mixed nitrogen sources is an effective strategy to accelerate the SMS humification process. The C1H3 (CM:CH ratio of 1:3) and CH treatments obtained great physicochemical properties and the highest growth rate of HA (31.96% and 27.02%, respectively). The rapid reaction of polysaccharide, ketone, quinone, and amide in DOM (LCP1) might be the key for the fast humification in C1H3 and CH. The polyphenol, reducing sugar and amino acid originated from the labile-carbon-proportion I (LCP1) and recalcitrant-carbon-proportion (RCP), labile-carbon-proportion II (LCP2) and RCP, and labile-nitrogen-proportion I (LNP1), respectively, were the main driving intermediate precursors for the formation of HA. This study deciphers the SMS humification mechanism at molecular level and provides a strategy in accelerating-regulating the composting process. which will be beneficial for enhancing the disposing efficiency of SMS, producing high-quality organic fertilizer, and even popularizing to the similar types of organic waste in practical field.

Improved phyllosphere microbiome composition of tea plant with the application of small peptides in combination with rhamnolipid.

BACKGROUND: Small peptides play a crucial role in plant growth and adaptation to the environment. Exogenous small peptides are often applied together with surfactants as foliar fertilizers, but the impact of small peptides and surfactants on the tea phyllosphere microbiome remains unknown. RESULTS: In this study, we investigated the effects of small peptides and different surfactants on the tea phyllosphere microbiome using 16S and ITS sequencing. Our results showed that the use of small peptides reduced the bacterial diversity of the tea phyllosphere microbiome and increased the fungal diversity, while the use of surfactants influenced the diversity of bacteria and fungi. Furthermore, the addition of rhamnolipid to small peptides significantly improved the tea phyllosphere microbiome community structure, making beneficial microorganisms such as Pseudomonas, Chryseobacterium, Meyerozyma, and Vishniacozyma dominant populations. CONCLUSION: Our study suggests that the combined use of small peptides and surfactants can significantly modify the tea phyllosphere microbiome community structure, particularly for beneficial microorganisms closely related to tea plant health. Thus, this preliminary study offers initial insights that could guide the application of small peptides and surfactants in agricultural production, particularly with respect to their potential for modulating the phyllosphere microbiome community in tea plant management.

Enzymatic fermentation of rapeseed cake significantly improved the soil environment of tea rhizosphere.

BACKGROUND: Rapeseed cake is an important agricultural waste. After enzymatic fermentation, rapeseed cake not only has specific microbial diversity but also contains a lot of fatty acids, organic acids, amino acids and their derivatives, which has potential value as a high-quality organic fertilizer. However, the effects of fermented rapeseed cake on tea rhizosphere microorganisms and soil metabolites have not been reported. In this study, we aimed to elucidate the effect of enzymatic rapeseed cake fertilizer on the soil of tea tree, and to reveal the correlation between rhizosphere soil microorganisms and nutrients/metabolites. RESULTS: The results showed that: (1) The application of enzymatic rapeseed cake increased the contents of soil organic matter (OM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP); increased the activities of soil urease (S-UE), soil catalase (S-CAT), soil acid phosphatase (S-ACP) and soil sucrase (S-SC); (2) The application of enzymatic rapeseed cake increased the relative abundance of beneficial rhizosphere microorganisms such as Chaetomium, Inocybe, Pseudoxanthomonas, Pseudomonas, Sphingomonas, and Stenotrophomonas; (3) The application of enzymatic rapeseed cake increased the contents of sugar, organic acid, and fatty acid in soil, and the key metabolic pathways were concentrated in sugar and fatty acid metabolisms; (4) The application of enzymatic rapeseed cake promoted the metabolism of sugar, organic acid, and fatty acid in soil by key rhizosphere microorganisms; enzymes and microorganisms jointly regulated the metabolic pathways of sugar and fatty acids in soil. CONCLUSIONS: Enzymatic rapeseed cake fertilizer improved the nutrient status and microbial structure of tea rhizosphere soil, which was beneficial for enhancing soil productivity in tea plantations. These findings provide new insights into the use of enzymatic rapeseed cake as an efficient organic fertilizer and expand its potential for application in tea plantations.

A deep learning model for rapid classification of tea coal disease.

BACKGROUND: The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification. RESULTS: Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging. CONCLUSIONS: This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

Low temperature response index for monitoring freezing injury of tea plant.

Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R2 = 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants.

Insight into humification of mushroom residues under addition of Rich-N sources: Comparing key molecular evolution processes using EEM-PARAFAC and 2D-FTIR-COS analysis.

Accelerating the humification of organic solid waste is one of the most important issues in composting. This present study aims to study and compare the humification process of different rich-N sources (chicken manure, cattle manure, and urea) addition during the composting of mushroom residues, from macro physicochemical properties to micro humic molecular structure evolution process. The physicochemical elements and humic components were determined for evaluating the compost quality and humification degree as composting proceed. The coupled analysis of excitation-emission matrix with parallel factor analysis (EEM-PARAFAC) and two-dimensional correlation with Fourier transform infrared spectrum (2D-FTIR-COS) were used to characterize the functional molecular structure evolution of dissolved organic matter during humification process. The results indicated that the rank order for humification level were the treatments of chicken manure (HM), urea (UM), cattle manure (CM), and single mushroom residue treatment (CK), with their humification index of 22.18%, 22.05%, 18.47%, and 16.52%, respectively. Humic substance, humic acid, and fulvic acid were obtained the highest in HM treatment with contents of 35.41 ± 0.86%, 23.32 ± 1.57%, and 10.97 ± 0.52%, respectively. The rich-N source addition enhanced the degradation of protein-like and polysaccharides-like substances in dissolved organic matter, thus accelerating the humification process of mushroom residues. The key structure evolution of dissolved organic matter in the HM treatment, in which the CO and CC stretching of quinone, amide, or ketone, and the C-O stretching of polysaccharides may be responsible for the faster formation of humus compared to the other nitrogen treatments. In this study, redundancy analysis indicated that the total nitrogen (TN) and nitrate nitrogen (NO3--N) may be the potential indicators for determining the humification level as composting proceed. The result provides significant insight into the humification mechanism of mushroom residue under different types of nitrogen sources at the molecular level, and will be reference for improving the composting technique in practical field.

Enzymes and microorganisms jointly promote the fermentation of rapeseed cake.

Rapeseed cake is a by-product of rapeseed oil separation. The nutritional components of rapeseed cake mainly include a variety of carbohydrates, proteins, and minerals. In order to improve the conversion rate of rapeseed cake, we studied the physicochemical properties, the structure of microbial communities, and the composition of metabolites in rapeseed cake after enzymatic fermentation. The results showed that the addition of enzymatic preparation increased microbial diversity. The relative abundance of Bacillus, Lysinibacillus, Empedobacter, Debaryomyces, Hyphopichia, and Komagataella in enzymatic fermentation was significantly higher than that in natural fermentation. Unlike natural fermentation, microbial diversity during enzymatic fermentation is specific, which improves the efficiency of fermentation. Otherwise, enzymatic fermentation promotes the conversion of macromolecular substances in rapeseed cake, which increases small metabolites, such as fatty acids, organic acids, amino acids and their derivatives. The metabolite enrichment pathway is mostly concentrated in sugar metabolism and fatty acid metabolism. In conclusion, after adding enzymatic preparation, enzymes and microorganisms jointly promote the transformation of macromolecules during the fermentation of rapeseed cake, which laid a good foundation for further utilization of rapeseed cake.

Ligand Engineering Triggered Efficiency Tunable Emission in Zero-Dimensional Manganese Hybrids for White Light-Emitting Diodes.

Zero-dimensional (0D) hybrid manganese halides have emerged as promising platforms for the white light-emitting diodes (w-LEDs) owing to their excellent optical properties. Necessary for researching on the structure-activity relationship of photoluminescence (PL), the novel manganese bromides (C13H14N)2MnBr4 and (C13H26N)2MnBr4 are reported by screening two ligands with similar atomic arrangements but various steric configurations. It is found that (C13H14N)2MnBr4 with planar configuration tends to promote a stronger electron-phonon coupling, crystal filed effect and concentration-quenching effect than (C13H26N)2MnBr4 with chair configuration, resulting in the broadband emission (FWHM = 63 nm) to peak at 539 nm with a large Stokes shift (70 nm) and a relatively low photoluminescence quantum yield (PLQY) (46.23%), which makes for the potential application (LED-1, Ra = 82.1) in solid-state lighting. In contrast, (C13H26N)2MnBr4 exhibits a narrowband emission (FWHM = 44 nm) which peaked at 515 nm with a small Stokes shift (47 nm) and a high PLQY of 64.60%, and the as-fabricated white LED-2 reaches a wide colour gamut of 107.8% National Television Standards Committee (NTSC), thus highlighting the immeasurable application prospects in solid-state display. This work clarifies the significance of the spatial configuration of organic cations in hybrids perovskites and enriches the design ideas for function-oriented low-dimensional emitters.

Localized, time-dependent responses of rat cranial bone to repeated mild traumatic brain injuries.

While it is well-established that bone responds dynamically to mechanical loading, the effects of mild traumatic brain injury (mTBI) on cranial bone composition are unclear. We hypothesized that repeated mTBI (rmTBI) would change the microstructure of cranial bones, without gross skull fractures. To address this, young adult female Piebald Viral Glaxo rats received sham, 1×, 2× or 3× closed-head mTBIs delivered at 24 h intervals, using a weight-drop device custom-built for reproducible impact. Skull bones were collected at 2 or 10 weeks after the final injury/sham procedure, imaged by micro computed tomography and analyzed at predetermined regions of interest. In the interparietal bone, proximal to the injury site, modest increases in bone thickness were observed at 2 weeks, particularly following 2× and 3× mTBI. By 10 weeks, 2× mTBI induced a robust increase in the volume and thickness of the interparietal bone, alongside a corresponding decrease in the volume of marrow cavities in the diploë region. In contrast, neither parietal nor frontal skull samples were affected by rmTBI. Our findings demonstrate time- and location-dependent effects of rmTBI on cranial bone structure, highlighting a need to consider microstructural alterations to cranial bone when assessing the consequences of rmTBI.

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet.

Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.

Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data.

Tea height, leaf area index, canopy water content, leaf chlorophyll, and nitrogen concentrations are important phenotypic parameters to reflect the status of tea growth and guide the management of tea plantation. UAV multi-source remote sensing is an emerging technology, which can obtain more abundant multi-source information and enhance dynamic monitoring ability of crops. To monitor the phenotypic parameters of tea canopy more efficiently, we first deploy UAVs equipped with multispectral, thermal infrared, RGB, LiDAR, and tilt photography sensors to acquire phenotypic remote sensing data of tea canopy, and then, we utilize four machine learning algorithms to model the single-source and multi-source data, respectively. The results show that, on the one hand, using multi-source data sets to evaluate H, LAI, W, and LCC can greatly improve the accuracy and robustness of the model. LiDAR + TC data sets are suggested for assessing H, and the SVM model delivers the best estimation (Rp2 = 0.82 and RMSEP = 0.078). LiDAR + TC + MS data sets are suggested for LAI assessment, and the SVM model delivers the best estimation (Rp2 = 0.90 and RMSEP = 0.40). RGB + TM data sets are recommended for evaluating W, and the SVM model delivers the best estimation (Rp2 = 0.62 and RMSEP = 1.80). The MS +RGB data set is suggested for studying LCC, and the RF model offers the best estimation (Rp2 = 0.87 and RMSEP = 1.80). On the other hand, using single-source data sets to evaluate LNC can greatly improve the accuracy and robustness of the model. MS data set is suggested for assessing LNC, and the RF model delivers the best estimation (Rp2 = 0.65 and RMSEP = 0.85). The work revealed an effective technique for obtaining high-throughput tea crown phenotypic information and the best model for the joint analysis of diverse phenotypes, and it has significant importance as a guiding principle for the future use of artificial intelligence in the management of tea plantations.

Future of Structured Lipids: Enzymatic Synthesis and Their New Applications in Food Systems.

Structured lipids (SLs) refer to a new type of functional lipid obtained by modifying natural triacylglycerol (TAG) through the restructuring of fatty acids, thereby altering the composition, structure, and distribution of fatty acids attached to the glycerol backbones. Due to the unique functional characteristics of SLs (easy to absorb, low in calories, reduced serum TAG, etc.), there is increasing interest in the research and application of SLs. SLs were initially prepared using chemical methods. With the wide application of enzymes in industries and the advantages of enzymatic synthesis (mild reaction conditions, high catalytic efficiency, environmental friendliness, etc.), synthesis of SLs using lipase has aroused great interest. This review summarizes the reaction system of SL production and introduces the enzymatic synthesis and application of some of the latest SLs discussed/developed in recent years, including medium- to long-chain triacylglycerol (MLCT), diacylglycerol (DAG), EPA- and DHA-enriched TAG, human milk fat substitutes, and esterified propoxylated glycerol (EPG). Lastly, several new ways of applying SLs (powdered oil, DAG plastic fat, inert gas spray oil, and emulsion) in the future food industry are also highlighted.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging.

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Detection of an anti-angina therapeutic module in the effective population treated by a multi-target drug Danhong injection: a randomized trial.

It's a challenge for detecting the therapeutic targets of a polypharmacological drug from variations in the responsed networks in the differentiated populations with complex diseases, as stable coronary heart disease. Here, in an adaptive, 31-center, randomized, double-blind trial involving 920 patients with moderate symptomatic stable angina treated by 14-day Danhong injection(DHI), a kind of polypharmacological drug with high quality control, or placebo (0.9% saline), with 76-day following-up, we firstly confirmed that DHI could increase the proportion of patients with clinically significant changes on angina-frequency assessed by Seattle Angina Questionnaire (ΔSAQ-AF ≥ 20) (12.78% at Day 30, 95% confidence interval [CI] 5.86-19.71%, P = 0.0003, 13.82% at Day 60, 95% CI 6.82-20.82%, P = 0.0001 and 8.95% at Day 90, 95% CI 2.06-15.85%, P = 0.01). We also found that there were no significant differences in new-onset major vascular events (P = 0.8502) and serious adverse events (P = 0.9105) between DHI and placebo. After performing the RNA sequencing in 62 selected patients, we developed a systemic modular approach to identify differentially expressed modules (DEMs) of DHI with the Zsummary value less than 0 compared with the control group, calculated by weighted gene co-expression network analysis (WGCNA), and sketched out the basic framework on a modular map with 25 functional modules targeted by DHI. Furthermore, the effective therapeutic module (ETM), defined as the highest correlation value with the phenotype alteration (ΔSAQ-AF, the change in SAQ-AF at Day 30 from baseline) calculated by WGCNA, was identified in the population with the best effect (ΔSAQ-AF ≥ 40), which is related to anticoagulation and regulation of cholesterol metabolism. We assessed the modular flexibility of this ETM using the global topological D value based on Euclidean distance, which is correlated with phenotype alteration (r2: 0.8204, P = 0.019) by linear regression. Our study identified the anti-angina therapeutic module in the effective population treated by the multi-target drug. Modular methods facilitate the discovery of network pharmacological mechanisms and the advancement of precision medicine. (ClinicalTrials.gov identifier: NCT01681316).

Anti-Semaphorin 4D Rescues Motor, Cognitive, and Respiratory Phenotypes in a Rett Syndrome Mouse Model.

Rett syndrome is a neurodevelopmental disorder caused by mutations of the methyl-CpG binding protein 2 gene. Abnormal physiological functions of glial cells contribute to pathogenesis of Rett syndrome. Semaphorin 4D (SEMA4D) regulates processes central to neuroinflammation and neurodegeneration including cytoskeletal structures required for process extension, communication, and migration of glial cells. Blocking SEMA4D-induced gliosis may preserve normal glial and neuronal function and rescue neurological dysfunction in Rett syndrome. We evaluated the pre-clinical therapeutic efficacy of an anti-SEMA4D monoclonal antibody in the Rett syndrome Mecp2T158A transgenic mouse model and investigated the contribution of glial cells as a proposed mechanism of action in treated mice and in primary glial cultures isolated from Mecp2T158A/y mutant mice. SEMA4D is upregulated in neurons while glial fibrillary acidic protein and ionized calcium binding adaptor molecule 1-positive cells are upregulated in Mecp2T158A/y mice. Anti-SEMA4D treatment ameliorates Rett syndrome-specific symptoms and improves behavioural functions in both pre-symptomatic and symptomatic cohorts of hemizygous Mecp2T158A/y male mice. Anti-SEMA4D also reduces astrocyte and microglia activation in vivo. In vitro experiments demonstrate an abnormal cytoskeletal structure in mutant astrocytes in the presence of SEMA4D, while anti-SEMA4D antibody treatment blocks SEMA4D-Plexin B1 signaling and mitigates these abnormalities. These results suggest that anti-SEMA4D immunotherapy may be an effective treatment option to alleviate symptoms and improve cognitive and motor function in Rett syndrome.

Simultaneous flow cytometric characterization of multiple cell types and metabolic states in the rat brain after repeated mild traumatic brain injury.

BACKGROUND: Cellular responses at the sub-acute phase of mild traumatic brain injury (mTBI), and their contribution to ongoing damage, are unclear, complex and require simultaneous assessment of multiple cells to elucidate. NEW METHOD: An 11-colour flow cytometry method for analysing brain cells was evaluated in a weight-drop rat model of repeated mTBI. Animals received sham, one, two or three mTBI delivered at 24 h intervals (n = 6/group). Cerebrum homogenates were prepared 11 days after first mTBI, in two cohorts of n = 3/group to enable same-day staining of fresh tissue. Percentages of neurons, astrocytes, microglia, mature oligodendrocytes and NeuN + CC1+ cells, neutrophils, macrophages and non-myeloid leukocytes, and their immunoreactivity for cell damage indicators (inducible nitric oxide synthase; iNOS, proliferating cell nuclear antigen; PCNA, 8-Oxo-2'-deoxyguanosine; 8OHDG and 4-hydroxynonenal; HNE), were assessed. RESULTS: Median fluorescence intensity (MFI) of iNOS in activated microglia increased following two, but not one or three, mTBI (p = 0.04). However, there were differences between processing cohorts in terms of percentages and MFI of some PCNA+, iNOS+, 8OHDG + and HNE + cell populations. COMPARISON WITH EXISTING METHODS: Previous applications of flow cytometry for rat brain analysis were typically limited to three or four markers. This method uses 11 markers to identify nine cell populations and evaluate their immunoreactivity to four metabolic indicators of cell damage. CONCLUSIONS: Flow cytometry can be useful for discerning injury-related changes in multiple rat brain cells. However, markers sensitive to subtle changes in experimental conditions must be identified in pilot experiments and subsequently analysed in the same tissue-processing cohort.