Stability-Enhanced Cisplatin Gold Nanoparticles As Therapeutic Anticancer Agents.

Using dendron chemistry, we developed stability enhanced, carboxylate surface modified (negatively charged dendron) AuNPs (Au-NCD). Since the carboxylate surface of Au-NCD is optimal for complexation with cisplatin (Pt) moieties, we further synthesized Pt loaded Au-NCD (Au-NCD/Pt) to serve as potential therapeutic anticancer agents. The size distribution, zeta potential and surface plasmon resonance of both Au-NCDs and Au-NCD/Pt were characterized via dynamic light scattering, scanning transmission electron microscopy and ultraviolet-visible spectrophotometry. Surface chemistry, Pt uptake, and Pt release were evaluated using inductively coupled plasma-mass spectrometry and X-ray photoelectron spectroscopy. Colloidal stability in physiological media over a wide pH range (1 to 13) and shelf-life stability (up to 6 months) were also assessed. Finally, the cytotoxicity of both Au-NCD and Au-NCD/Pt to Chinese hamster ovary cells (CHO K1; as a normal cell line) and to human lung epithelial cells (A549; as a cancer cell line) were evaluated. The results of these physicochemical and functional cytotoxicity studies with Au-NCD/Pt demonstrated that the particles exhibited superlative colloidal stability, cisplatin uptake and in vitro anticancer activity despite low amounts of Pt release from the conjugate.

[Predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragmatic thickening fraction for the timing of noninvasive-invasive mechanical ventilation transition in patients with acute exacerbation of chronic obstructive pulmonary disease].

OBJECTIVE: To evaluate the predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragm thickening fraction (RR/DTF) for noninvasive-invasive mechanical ventilation transition timing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), through ultrasound evaluation of diaphragm movement indicators. METHODS: Sixty-four patients diagnosed with AECOPD and undergoing non-invasive ventilation (NIV), who were admitted to the department of critical care medicine of the First Affiliated Hospital of Jinzhou Medical University from January 2022 to July 2023 were enrolled. They were divided into NIV successful group and NIV failure group based on the outcome of NIV within 24 hours. Clinical indicators such as RR/DTF, diaphragmatic excursion (DE), tidal volume (VT), respiratory rate (RR), pH value, partial pressure of carbon dioxide (PaCO2), and sputum excretion disorder were compared between the two groups after 2 hours of NIV. The factors influencing NIV failure were included in binary Logistic regression analysis, and an RR/DTF oriented risk prediction model was established. Receiver operator characteristic curve (ROC curve) analysis was used to assess the predictive value of this model for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients. RESULTS: Among 64 patients with AECOPD, with 43 in the NIV successful group and 21 in the NIV failure group. There were no statistically significant differences in baseline data such as age, gender, body mass index (BMI), oxygenation index (P/F), smoking history, and acute physiological and chronic health evaluation II (APACHE II) between the two groups of patients, indicating comparability. Compared to the NIV successful group, the NIV failure group showed a significantly increase in RR/DTF, RR, PaCO2, and sputum retention, while VT and DE were significantly decreased [RR/DTF (%): 1.00±0.18 vs. 0.89±0.22, RR (bpm): 21.64±3.13 vs. 19.62±2.98, PaCO2 (mmHg, 1 mmHg ≈ 0.133 kPa): 70.82±8.82 vs. 65.29±9.47, sputum retention: 57.1% vs. 30.2%, VT (mL): 308.09±14.89 vs. 324.48±23.82, DE (mm): 19.91±2.94 vs. 22.05±3.30, all P < 0.05]. Binary Logistic regression analysis showed that RR/DTF [odds ratio (OR) = 147.989, 95% confidence interval (95%CI) was 3.321-595.412, P = 0.010], RR (OR = 1.296, 95%CI was 1.006-1.670, P = 0.045), VT (OR = 0.966, 95%CI was 0.935-0.999, P = 0.044), PaCO2 (OR = 1.086, 95%CI was 1.006~1.173, P = 0.035), and sputum retention (OR = 4.533, 95%CI was 1.025-20.049, P = 0.046) were independent risk factors for predicting NIV failure in AECOPD patients. ROC curve analysis showed that the area under the curve (AUC) of 0.713 with a 95%CI of 0.587-0.839 (P = 0.005). The sensitivity was 72.73%, the specificity was 88.10%, the Youden index was 0.394, and the optimal cut-off value was 0.87. CONCLUSIONS: The RR/DTF risk prediction model has good predictive value for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients.

Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression.

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels.

LINC08148 promotes the caveola-mediated endocytosis of Zika virus through upregulating transcription of Src.

Long non-coding RNAs (lncRNAs) represent a new group of host factors involved in viral infection. Current study identified an intergenic lncRNA, LINC08148, as a proviral factor of Zika virus (ZIKV) and Dengue virus 2 (DENV2). Knockout (KO) or silencing of LINC08148 decreases the replication of ZIKV and DENV2. LINC08148 mainly acts at the endocytosis step of ZIKV but at a later stage of DENV2. RNA-seq analysis reveals that LINC08148 knockout downregulates the transcription levels of five endocytosis-related genes including AP2B1, CHMP4C, DNM1, FCHO1, and Src. Among them, loss of Src significantly decreases the uptake of ZIKV. Trans-complementation of Src in the LINC08148KO cells largely restores the caveola-mediated endocytosis of ZIKV, indicating that the proviral effect of LINC08148 is exerted through Src. Finally, LINC08148 upregulates the Src transcription through associating with its transcription factor SP1. This work establishes an essential role of LINC08148 in the ZIKV entry, underscoring a significance of lncRNAs in the viral infection. IMPORTANCE: Long non-coding RNAs (lncRNAs), like proteins, participate in viral infection. However, functions of most lncRNAs remain unknown. In this study, we performed a functional screen based on microarray data and identified a new proviral lncRNA, LINC08148. Then, we uncovered that LINC08148 is involved in the caveola-mediated endocytosis of ZIKV, rather than the classical clathrin-mediated endocytosis. Mechanistically, LINC08148 upregulates the transcription of Src, an initiator of caveola-mediated endocytosis, through binding to its transcription factor SP1. This study identifies a new lncRNA involved in the ZIKV infection, suggesting lncRNAs and cellular proteins are closely linked and cooperate to regulate viral infection.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and individualized information of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Cancer associated fibroblast secreted miR-432-5p targets CHAC1 to inhibit ferroptosis and promote acquired chemoresistance in prostate cancer.

Prostate cancer (PCa) ranks as the sixth most serious male malignant disease globally. While docetaxel (DTX) chemotherapy is the standard treatment for advanced PCa patients with distant metastasis, some individuals exhibit insensitivity or resistance to DTX. Cancer-associated fibroblasts (CAFs) play a pivotal role as stromal cells within the tumor microenvironment, influencing tumor development, progression, and drug resistance through exosomes. Ferroptosis, a novel form of programmed cell death, is characterized by intracellular iron accumulation that triggers lipid peroxidation, ultimately leading to cell demise. To delve into the potential mechanisms of chemotherapy resistance in prostate cancer, our research delved into the impact of CAF-derived exosomes on ferroptosis. Our findings revealed that CAF exosomes hindered the buildup of lipid reactive oxygen species (ROS) in prostate cancer cells induced by erastin, as well as mitigated erastin-induced mitochondrial damage, thereby impeding iron-induced cell death in prostate cancer cells. Furthermore, miR-432-5p was identified to diminish glutathione (GSH) consumption by targeting CHAC1, consequently inhibiting ferroptosis in prostate cancer cells. Our study found that miR-432-5p, originating from cancer-associated fibroblast (CAF) exosomes, suppresses ferroptosis by targeting CHAC1, thereby increasing resistance to docetaxel (DTX) in PCa. This research introduces a novel approach to address resistance to DTX.

Efficient Federated Learning for distributed NeuroImaging Data.

Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset.

CD146, a therapeutic target involved in cell plasticity.

Since its identification as a marker for advanced melanoma in the 1980s, CD146 has been found to have multiple functions in both physiological and pathological processes, including embryonic development, tissue repair and regeneration, tumor progression, fibrosis disease, and inflammations. Subsequent research has revealed that CD146 is involved in various signaling pathways as a receptor or co-receptor in these processes. This correlation between CD146 and multiple diseases has sparked interest in its potential applications in diagnosis, prognosis, and targeted therapy. To better comprehend the versatile roles of CD146, we have summarized its research history and synthesized findings from numerous reports, proposing that cell plasticity serves as the underlying mechanism through which CD146 contributes to development, regeneration, and various diseases. Targeting CD146 would consequently halt cell state shifting during the onset and progression of these related diseases. Therefore, the development of therapy targeting CD146 holds significant practical value.

The causal impact of gut microbiota on circulating adipokine concentrations: a two-sample Mendelian randomization study.

PURPOSE: Evidence from previous experimental and observational research demonstrates that the gut microbiota is related to circulating adipokine concentrations. Nevertheless, the debate as to whether gut microbiome composition causally influences circulating adipokine concentrations remains unresolved. This study aimed to take an essential step in elucidating this issue. METHODS: We used two-sample Mendelian randomization (MR) to causally analyze genetic variation statistics for gut microbiota and four adipokines (including adiponectin, leptin, soluble leptin receptor [sOB-R], and plasminogen activator inhibitor-1 [PAI-1]) from large-scale genome-wide association studies (GWAS) datasets. A range of sensitivity analyses was also conducted to assess the stability and reliability of the results. RESULTS: The composite results of the MR and sensitivity analyses revealed 22 significant causal associations. In particular, there is a suggestive causality between the family Clostridiaceae1 (IVW: ß = 0.063, P = 0.034), the genus Butyrivibrio (IVW: ß = 0.029, P = 0.031), and the family Alcaligenaceae (IVW: ß=-0.070, P = 0.014) and adiponectin. Stronger causal effects with leptin were found for the genus Enterorhabdus (IVW: ß=-0.073, P = 0.038) and the genus Lachnospiraceae (NK4A136 group) (IVW: ß=-0.076, P = 0.01). Eight candidate bacterial groups were found to be associated with sOB-R, with the phylum Firmicutes (IVW: ß = 0.235, P = 0.03) and the order Clostridiales (IVW: ß = 0.267, P = 0.028) being of more interest. In addition, the genus Roseburia (IVW: ß = 0.953, P = 0.022) and the order Lactobacillales (IVW: ß=-0.806, P = 0.042) were suggestive of an association with PAI-1. CONCLUSION: This study reveals a causal relationship between the gut microbiota and circulating adipokines and may help to offer novel insights into the prevention of abnormal concentrations of circulating adipokines and obesity-related diseases.

Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha-1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha-1, MAE = 29.43 kg ha-1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

Neutrophil-derived nanovesicles deliver IL-37 to mitigate renal ischemia-reperfusion injury via endothelial cell targeting.

Renal ischemia-reperfusion injury (IRI) is one of the most important causes of acute kidney injury (AKI). Interleukin (IL)-37 has been suggested as a novel anti-inflammatory factor for the treatment of IRI, but its application is still limited by its low stability and delivery efficiency. In this study, we reported a novel engineered method to efficiently and easily prepare neutrophil membrane-derived vesicles (N-MVs), which could be utilized as a promising vehicle to deliver IL-37 and avoid the potential side effects of neutrophil-derived natural extracellular vesicles. N-MVs could enhance the stability of IL-37 and targetedly deliver IL-37 to damaged endothelial cells of IRI kidneys via P-selectin glycoprotein ligand-1 (PSGL-1). In vitro and in vivo evidence revealed that N-MVs encapsulated with IL-37 (N-MV@IL-37) could inhibit endothelial cell apoptosis, promote endothelial cell proliferation and angiogenesis, and decrease inflammatory factor production and leukocyte infiltration, thereby ameliorating renal IRI. Our study establishes a promising delivery vehicle for the treatment of renal IRI and other endothelial damage-related diseases.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Elucidation of antioxidant activities of intracellular and extracellular polysaccharides from Cordyceps militaris in vitro and their protective effects on ulcerative colitis in vivo.

In this study, we extracted the polysaccharides from C. militaris fruiting bodies (CFIPs), mycelial intracellular polysaccharides (CMIPs), and fermentation broth extracellular polysaccharides (CFEPs) to investigate their physicochemical properties, antioxidant capacities, and effects on oxazolone-induced zebrafish ulcerative colitis (UC). Our results revealed differences in monosaccharide composition and surface structure among CFIPs, CMIPs, and CFEPs. The molar ratios of glucose to mannose in CFIPs, glucose to xylose in CMIPs, and xylose to glucose in CFEPs were 7.57: 1.6, 7.26: 1.81, and 5.44: 2.98 respectively. Moreover, CFEPs exhibited significantly (p < 0.05) higher chemical antioxidant capacity compared to CMIPs and CFIPs. Surprisingly, CFEP treatment didn't show a significant effect in protecting against H2O2-induced oxidative damage in RAW 264.7 cells. After 3 d of treatment, the levels of ROS, MDA, and MPO in the CFIPs group exhibited a significant (p < 0.05) reduction by 37.82 %, 68.15 %, and 22.77 % respectively. Additionally, the ACP and AKP increased by 60.33 % and 96.99 %. Additionally, C. militaris polysaccharides (CMPs) were found to effectively improve UC by activating the MyD88/NF-κB signaling pathway in vivo. These findings confirm the distinct physicochemical properties of these three types of CMP and their potential for development into antioxidant-rich anti-inflammatory health foods.

The effects of micro- and nanoplastics on the central nervous system: A new threat to humanity?

Given the widespread production and use of plastics, poor biodegradability, and inadequate recycling, micro/nanoplastics (MNPs) have caused widespread environmental pollution. As a result, humans inevitably ingest MNPs through various pathways. However, there is still no consensus on whether exposure to MNPs has adverse effects on humans. This article aims to provide a comprehensive overview of the knowledge of MNPs and the potential mechanisms of their impact on the central nervous system. Numerous in vivo and in vitro studies have shown that exposure to MNPs may pass through the blood-brain barrier (BBB) and lead to neurotoxicity through impairments in oxidative and inflammatory balance, neurotransmitter alternation, nerve conduction-related key enzymes, and impact through the gut-brain axis. It is worth noting that MNPs may act as carriers and have more severe effects on the body when co-exposed with other substances. MNPs of smaller sizes cause more severe harm. Despite the scarcity of reports directly relevant to humans, this review brings together a growing body of evidence showing that exposure to MNPs disturbs neurons and has even been found to alter the memory and behavior of organisms. This effect may lead to further potential negative influence on the central nervous system and contribute to the development of other diseases such as central nervous system inflammation and Parkinson 's-like neurodegenerative disorders. There is a need further to investigate the threat of MNPs to human health.

Causal effect between gut microbiota and gastroesophageal reflux disease: a bidirectional two-sample Mendelian randomization study.

Previous observational studies have found that the gut microbiota is closely related to the pathogenesis of gastroesophageal reflux disease (GERD), while their causal relationship is unclear. A two-sample multivariate Mendelian randomization analysis was implemented to estimate the causal effect of gut microbiota on GERD. The gut microbiota aggregated statistics were derived from a meta-analysis of the largest available genome-wide association studies (GWAS) conducted by the MiBioGen alliance ( n  = 13 266). GERD aggregated statistics were derived from published GWAS (129 080 cases and 473 524 controls). A bidirectional two-sample Mendelian randomization study was conducted to explore the causal relationship between gut microbiota and GERD using the inverse variance weighted (IVW), Mendelian randomization Egger, single model, weighted median, and weighted model. To verify the stability of the main results of Mendelian randomization analysis, we performed sensitivity analysis. Based on the results of IVW, we found that Anaerostipes was causally associated with an increased risk of GERD [odds ratio (OR): 1.09, P  = 0.018]. Eight gut microbiota taxa ( Actinobacteria, Bifidobacteriales, Bifidobacteriaceae, Clostridiales vadin BB60 group, Rikenellaceae, Lachnospiraceae UCG004, Methanobrevibacter , and unknown genus id.1000000073 ) are predicted to act causally in suppressing the risk of GERD ( P  < 0.05). In addition, reverse Mendelian randomization analyses revealed that the abundance of 15 gut microbiota taxon was found to be affected by GERD. No significant estimation of heterogeneity or pleiotropy is detected. Our study presents a complicated causal relationship between gut microbiota and GERD that offers guidance on the selection of appropriate probiotics as clinical interventions for GERD.

Pterostilbene nanoemulsion promotes Nrf2 signaling pathway to downregulate oxidative stress for treating Alzheimer's disease.

Pterostilbene, a stilbene compound, demonstrates neuroprotective effects through its antioxidant and anti-inflammatory properties. However, pterostilbene exhibits low bioavailability. We developed a pterostilbene nanoemulsion with better release stability and particle size. Behavioral tests, including the Y maze, new object recognition, and water maze, revealed that the pterostilbene nanoemulsion demonstrated a more significant effect on improving learning and memory function than pterostilbene. Immunofluorescence analysis revealed that pterostilbene nanoemulsion was more potent in safeguarding hippocampal neurons and inhibiting apoptosis and oxidative stress than pterostilbene. Further results from the Western blot and quantitative reverse transcription polymerase chain reaction indicated that the enhanced efficacy of pterostilbene nanoemulsion may be attributed to its stronger promotion of the nuclear factor erythroid 2-related factor 2 signaling pathway. Hence, enhanced drug delivery efficiency decreased dosage requirements and increased the bioavailability of pterostilbene, thereby potentially providing a safe, effective, and convenient treatment option for patients with Alzheimer's disease.

Combination Therapy of Bone Marrow Mesenchymal Stem Cell Transplantation and Electroacupuncture for the Repair of Intrauterine Adhesions in Rats: Mechanisms and Functional Recovery.

Transplantation of bone marrow mesenchymal stem cells (BMSCs) has demonstrated promising clinical utility in the treatment of endometrial injury and the restoration of fertility. However, since the efficacy of BMSCs after transplantation is not stable, it is very important to find effective ways to enhance the utilisation of BMSCs. Electroacupuncture (EA) has some positive effects on the chemotaxis of stem cells and diseases related to uterine injury. In this study, we established the intrauterine adhesion (IUA) model of the Sprague-Dawley rat using lipopolysaccharide infection and mechanical scratching. Phosphate-buffered saline, BMSCs alone, and BMSCs combined with EA were randomly administered to the rats. Fluorescent cell labelling showed the migration of transplanted BMSCs. H&E staining, Masson staining, Western blot, immunohistochemistry, ELISA, and qRT-PCR were utilised to detect changes in endometrial morphology and expressions of endometrial receptivity-related factors, endometrial pro-inflammatory factors, and fibrosis factors. Finally, we conducted a fertility test to measure the recovery of uterine function. The results showed that EA promoted transplanted BMSCs to migrate into the injured uterus by activating the SDF-1/CXCR4 axis. Endometrial morphology showed the most significant improvement in the BMSC + EA group. The expressions of endometrial pro-inflammatory factors and fibrosis indexes in the BMSC + EA group were lower than those in the model and BMSC groups. Further studies revealed that the expression of endometrial receptivity-related factors and the number of embryos implanted on day 8 of gestation increased in the BMSC + EA group compared with the model group and the BMSC group.

Polysaccharides from an edible mushroom, Hericium erinaceus, alleviate ulcerative colitis in mice by inhibiting the NLRP3 inflammasomes and reestablish intestinal homeostasis.

This study aimed to assess the effects of polysaccharides extracted from Hericium erinaceus fruiting bodies (HEFPs) on the inflammatory response to oxidative stress in a mouse model of ulcerative colitis (UC) induced by ingestion of dextran sodium sulfate. The results indicated reduced oxidative damage in the HEFPs groups, as evidenced by significantly decreased malondialdehyde levels and significantly increased levels of the antioxidant enzymes superoxide dismutase and catalase in colon homogenates, compared with those in the Model Control (MC) group. Additionally, compared with the levels in the MC group, the levels of the pro-inflammatory factors IL-6, IL-1ß, and TNF-α in the positive-control (PC) and HEFPs groups were significantly lower, and that of the anti-inflammatory factor IL-10 was significantly higher. qRT-PCR analyses revealed that the colon expression patterns of IL-6, IL-1ß, TNF-α, and IL-18 were consistent with the serum levels. Western-blotting results indicated significantly lower levels of NLRP3, ASC, and caspase 1 P20 in the HEFPs and PC groups than in the MC group. These findings suggest that HEFPs alleviate UC by suppressing the NLRP3 inflammasome/Caspase-1 pathway. Lachnospiraceae, Clostridiales, Parabacteroides, Oscillibacter, and Clostridium XlVa genera were more abundant in the gut microbiota of the HEFPs group than that of the MC group.

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.

Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification.

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.