The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis.

BACKGROUND: Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). METHODS: The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). RESULTS: Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. CONCLUSIONS: To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.

Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm.

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g-1 (25 â„ƒ, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents.

Association between NDUFS1 from urinary extracellular vesicles and decreased differential renal function in children with ureteropelvic junction obstruction.

BACKGROUND: Ureteropelvic junction obstruction (UPJO) is the most common cause of pediatric congenital hydronephrosis, and continuous kidney function monitoring plays a role in guiding the treatment of UPJO. In this study, we aimed to explore the differentially expressed proteins (DEPs) in the urinary extracellular vesicles(uEVs) of children with UPJO and determine potential biomarkers of uEVs proteins that reflect kidney function changes. METHODS: Preoperative urine samples from 6 unilateral UPJO patients were collected and divided into two groups: differential renal function (DRF) ≥ 40% and DRF < 40%.We subsequently used data-independent acquisition (DIA) to identify and quantify uEVs proteins in urine, screened for DEPs between the two groups, and analyzed biofunctional enrichment information. The proteomic data were evaluated by Western blotting and enzyme-linked immunosorbent assay (ELISA) in a new UPJO testing cohort. RESULTS: After one-way ANOVA, a P adj value < 0.05 (P-value corrected by Benjamin-Hochberg) was taken, and the absolute value of the difference multiple was more than 1.5 as the screening basis for obtaining 334 DEPs. After analyzing the enrichment of the DEPs according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment combined with the protein-protein interaction (PPI) network results, we selected nicotinamide adenine dinucleotide-ubiquinone oxidoreductase core subunit S1 (NDUFS1) for further detection. The expression of NDUFS1 in uEVs was significantly lower in patients with DRF < 40% (1.182 ± 0.437 vs. 1.818 ± 0.489, P < 0.05), and the expression level of NDUFS1 was correlated with the DRF in the affected kidney (r = 0.78, P < 0.05). However, the NDUFS1 concentration in intravesical urine was not necessarily related to the change in DRF (r = 0.28, P = 0.24). CONCLUSIONS: Reduced expression of NDUFS1 in uEVs might indicate the decline of DRF in children with UPJO.

Gonadal Transcriptome Analysis Reveals that SOX17 and CYP26A1 are Involved in Sex Differentiation in the Chinese Soft-Shelled Turtle (Pelodiscus sinensis).

The Chinese soft-shelled turtle (Pelodiscus sinensis) is an important aquaculture animal in China and exhibits growth dimorphism. Single-male cultures are often selected for higher economic efficiency. However, the mechanism of sex differentiation in P. sinensis is not well-known. In this study, a comparative transcriptome analysis of male (ZZ)- and 17ß-oestradiol (E2)-induced pseudo-female (ZZ + E2)-stage embryonic gonads of P. sinensis was performed. A total of 420 differentially expressed genes (DEGs), which included 271 upregulated genes and 149 downregulated genes, were identified. These DEGs were mainly involved in several sex-related pathways, such as "ovarian steroidogenesis", "steroid hormone biosynthesis", "PPAR signalling pathway", and "metabolism of xenobiotics by cytochrome P450". In addition, 50 known and novel candidate genes involved in sex differentiation, such as the male-biased genes AMH, DMRT1, TBX1, and CYP26A1 and the female-biased genes CYP1A1, RASD1, and SOX17, were investigated and identified. For further verification, the full-length cDNAs of SOX17 and CYP26A1 were obtained. SOX17 contains a 1218-bp ORF and encodes 405 amino acids containing an HMG functional domain unique to the Sox superfamily. CYP26A1 contains a 1485-bp ORF and encodes 494 amino acids. Different expression levels of SOX17 and CYP26A1 could be detected in all the tested tissues of males and females. Notably, the expression of CYP26A1 was markedly greater in the gonads of male embryos (P < 0.05) than in those of female embryos, whereas the expression of SOX17 showed the opposite trend (P < 0.05). Taken together, the RNA-seq and qRT‒PCR results suggested potential roles for SOX17 and CYP26A1 in promoting female and male gonadal development, respectively, in P. sinensis. Our results provide new evidence for the mechanism of sex differentiation in P. sinensis.

Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial.

PURPOSE: The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. EXPERIMENTAL DESIGN: A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. RESULTS: Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). CONCLUSION: In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.

Impact of perinatal factors on meconium aspiration syndrome in full-term newborns and the construction of a column chart prediction model: An observational study.

To explore the influence of perinatal-related factors on meconium aspiration syndrome (MAS) in full-term neonates and construct a nomogram prediction model for risk stratification of neonatal MAS and adoption of preventive measures. A total of 424 newborns and their mothers who were regularly examined at our hospital between January 2020 and December 2023 who had meconium-contaminated amniotic fluid during delivery were retrospectively selected as participants. Neonates were divided into MAS and non-MAS groups based on whether MAS occurred within 3 days after birth. Data from the 2 groups were analyzed, and factors influencing MAS were screened using multivariate logistic regression analysis. The R3.4.3 software was used to construct a nomogram prediction model for neonatal MAS risk. Receiver operating characteristic (ROC) curve analysis and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the performance of the model, and its clinical effectiveness was evaluated using a decision curve. Among the 424 neonates with meconium-stained amniotic fluid, 51 developed MAS within 3 days of birth (12.03%). Multivariate logistic regression analysis showed that a low amniotic fluid index before delivery (OR = 2.862, P = .019), advanced gestational age (OR = 0.526, P = .034), cesarean section (OR = 2.650, P = .013), severe amniotic fluid contamination (OR = 4.199, P = .002), low umbilical cord blood pH (OR = 2.938, P = .011), and low neonatal Apgar 1-min score (OR = 3.133, P = .006) were influencing factors of MAS in full-term neonates. Based on the above indicators, a nomogram prediction model for MAS risk of full-term newborns was constructed. The area under the ROC curve of the model was 0.931. The model was also tested for goodness-of-fit deviation (χ2 = 3.465, P = .903). Decision curve analysis found that the model was clinically effective in predicting the net benefit of MAS risk in neonates with meconium-stained amniotic fluid. The construction of a column chart prediction model for neonatal MAS risk based on prenatal amniotic fluid index, gestational age, delivery method, amniotic fluid contamination level, newborn umbilical blood pH value, and Apgar 1-min score has a certain application value.

Transcriptomic profiling and regulatory pathways of cardiac resident macrophages in aging.

Cardiovascular diseases are an array of age-related disorders, and accumulating evidence suggests a link between cardiac resident macrophages (CRMs) and the age-related disorders. However, how does CRMs alter with aging remains elusive. In the present study, aged mice (20 months old) have been employed to check for their cardiac structural and functional alterations, and the changes in the proportion of CRM subsets as well, followed by sorting of CRMs, including C-C Motif Chemokine Receptor 2 (CCR2)+ and CCR2- CRMs, which were subjected to Smart-Seq. Integrated analysis of the Smart-Seq data with three publicly available single-cell RNA-seq datasets revealed that inflammatory genes were drastic upregulated for both CCR2+ and CCR2- CRMs with aging, but genes germane to wound healing were downregulated for CCR2- CRMs, suggesting the differential functions of these two subsets. More importantly, inflammatory genes involved in damage sensing, complement cascades, and phagocytosis were largely upregulated in CCR2- CRMs, implying the imbalance of inflammatory response upon aging. Our work provides a comprehensive framework and transcriptional resource for assessing the impact of aging on CRMs with a potential for further understanding cardiac aging.

PPARδ agonist protects against osteoarthritis by activating AKT/mTOR signaling pathway-mediated autophagy.

Osteoarthritis (OA) is the most prevalent degenerative joint disease, and PPARs are involved in its pathogenesis; however, the specific mechanisms by which changes in PPARδ impact the OA pathogenesis yet to be discovered. The purpose of this study was to ascertain how PPARδ affects the onset and development of OA. In vitro, we found that PPARδ activation ameliorated apoptosis and extracellular matrix (ECM) degradation in OA chondrocytes stimulated by IL-1ß. In addition, PPARδ activation may modulate AKT/mTOR signaling to partially regulate chondrocyte autophagy and apoptosis. In vivo, injection of PPARδ agonist into the articular cavity improved ECM degradation, apoptosis and autophagy in rats OA models generated by destabilization medial meniscus (DMM), eventually delayed degeneration of articular cartilage. Thus, targeting PPARδ for OA treatment may be a possibility.

The mechanism of extracellular CypB promotes glioblastoma adaptation to glutamine deprivation microenvironment.

Glioblastoma, previously known as glioblastoma multiform (GBM), is a type of glioma with a high degree of malignancy and rapid growth rate. It is highly dependent on glutamine (Gln) metabolism during proliferation and lags in neoangiogenesis, leading to extensive Gln depletion in the core region of GBM. Gln-derived glutamate is used to synthesize the antioxidant Glutathione (GSH). We demonstrated that GSH levels are also reduced in Gln deficiency, leading to increased reactive oxygen species (ROS) levels. The ROS production induces endoplasmic reticulum (ER) stress, and the proteins in the ER are secreted into the extracellular medium. We collected GBM cell supernatants cultured with or without Gln medium; the core and peripheral regions of human GBM tumor tissues. Proteomic analysis was used to screen out the target-secreted protein CypB. We demonstrated that the extracellular CypB expression is associated with Gln deprivation. Then, we verified that GBM can promote the glycolytic pathway by activating HIF-1α to upregulate the expression of GLUT1 and LDHA expressions. Meanwhile, the DRP1 was activated, increasing mitochondrial fission, thus inhibiting mitochondrial function. To explore the specific mechanism of its regulation, we constructed a si-CD147 knockout model and added human recombinant CypB protein to verify that extracellular CypB influenced the expression of downstream p-AKT through its cell membrane receptor CD147 binding. Moreover, we confirmed that p-AKT could upregulate HIF-1α and DRP1. Finally, we observed that extracellular CypB can bind to the CD147 receptor, activate p-AKT, and upregulate HIF-1α and DRP1 in order to promote glycolysis while inhibiting mitochondrial function to adapt to the Gln-deprived microenvironment.

The application of machine learning based on computed tomography images in the identification of renal tumors in children.

Background: The clinical manifestations of Wilms tumor and non-Wilms tumor in children are similar, and the only way to confirm the diagnosis is by postoperative pathology. Computed tomography (CT) is one of the main methods for preoperative diagnosis of the two, but it is also difficult to distinguish because it is easily affected by the subjective influence and the experience of the radiologists. Methods: The CT images of 82 children with renal tumors admitted to the Department of Pediatric Urology, Shandong Provincial Hospital from January 2011 to March 2022 were retrospectively analyzed. First, we drew the two-dimensional (2D) region of interest (ROI) of the largest cross-section on the corticomedullary phase (CMP) and nephrogenic phase (NP) images, and extracted seven types of 107 features in the ROI. Then, the texture features with similarity greater than 95% and repetition less than 90% were screened out, and the remaining texture features were further screened by analysis of variance (ANOVA) and recursive feature elimination (RFE). Finally, 15 texture feature were used to build the machine learning (ML) models. We used the synthetic minority oversampling technique (SMOTE) and 10-fold cross-validation to build ML models and verified them in the training, testing, and internal validation sets. The area under the receiver-operating characteristic curve (AUC) and calibration curve were used to evaluate the diagnostic performance. Results: We collected 77 CMP and 81 NP images, which were randomly divided into the training set and the testing set according to the ratio of 7:3. In the internal validation of CMP, the Mean-PCC-ANOVA-5-AE pipeline model achieved the highest AUC 0.792 [95% confidence interval (CI): 0.653-0.930], and its accuracy (ACC), sensitivity (SEN), and specificity (SPE) were 0.833, 0.539 and 0.927, respectively. Correspondingly, in NP, the Mean-PCC-ANOVA-2-LR pipeline model achieved the highest AUC 0.655 (95% CI: 0.485-0.82) in the internal validation. The ACC, SEN, and SPE were 0.696, 0.539, and 0.744, respectively. Conclusions: The ML models based on CT images have good diagnostic efficiency in differentiating Wilms tumors from non-Wilms tumors in children.

Effects of different treatment modalities on cardiovascular disease in ARR-positive hypertensive patients.

Data on the prognosis of clinically undiagnosed hypertensive patients who are aldosterone-to-renin ratio (ARR) positive are still scarce. Therefore, we investigated the clinical characteristics of clinically undiagnosed hypertensive patients who were ARR-positive and the influence of their different treatments on the occurrence and development of complications. A total of 285 hypertensive patients data with ARR ≥ 3.8 in the Second People's Hospital of Huai'an from January 2019 to December 2021 were collected, and 135 undiagnosed hypertensive patients were ultimately included in the analysis. According to their treatment strategy in various clinical departments, 135 patients were divided into the operation, spironolactone and control groups. Then, the clinical characteristics and the occurrence and development of complications in the three groups were compared. The results suggested that: (1) Only 34 (11.9%) of 285 hypertensive patients with ARR ≥ 3.8 were clearly diagnosed with Primary aldosteronism (PA) through functional tests, and the blood pressure (BP) compliance rate was only 50.30% during follow-up. (2) Based on exclusion criteria, 135 undiagnosed hypertensive patients were eventually included in the analysis. Patients in the surgery group had lower blood potassium levels and higher aldosterone levels than those in the other two groups, and their risk of new cerebrovascular complications was lower than that of the patients in the spironolactone group. (3) The risk of new cerebrovascular complications in the spironolactone group was 9.520 times higher than that of the control group, and this risk mainly occurred in patients with ARR values of 3.8-5.7. On the whole, surgery remains a good option for hypertensive patients with severe hyperaldosteronism and hypokalemia and those unable to undergo confirmatory tests; however, spironolactone therapy in patients with clinically undiagnosed hypertension, especially those with 3.8 ≤ ARR < 5.7, confered a higher risk of new cerebrovascular complications.

A comprehensive dataset for assessing the impact of ammonium salts and zeolite on anaerobic digestion performance, microbial dynamics, and metabolomic profiles.

This article presents comprehensive data derived from lab-scale batch anaerobic digesters that were subjected to inhibition by various sources of ammonia. To counter this inhibition, zeolite was introduced into selected digesters. The provided dataset offers a detailed depiction of degradation performance dynamics over time, as well as insights into both microbial and metabolic changes during the inhibition. In detail, 10 conditions were tested in triplicate. In a first series of 15 bioreactors ammonia was introduced to achieve a TAN concentration of 8 g/L, utilizing NH3 solution, NH4Cl salt, (NH4)2CO3 salt, or (NH4)2PO4 salt as inhibitors. A control condition without ammonia was also set up. A second series of 15 bioreactors was set up exactly as the first one, with the addition of zeolite at a concentration of 15 g/L. The data provided includes information on operational conditions, degradation performance measurements throughout the entire process (using biogas production and composition, dissolved organic and inorganic carbon, volatile fatty acids, pH, free and total ammonia nitrogen, apparent isotopic fractionation of biogas as indicators), microbial community analysis using 16S rRNA gene sequencing (50 samples analysed), and metabolomic analysis through liquid chromatography-mass spectrometry (LC-MS) (108 samples analysed). Sequencing data were generated by using IonTorrent PGM sequencer. The sequencing data have been deposited with links to project PRJEB52324, in ENA database from EBI (https://www.ebi.ac.uk/ena/browser/view/PRJEB52324). Sample accession numbers go from SAMEA14277573 to SAMEA14277621. The metabolomic data were generated using an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific, MA, US). The metabolomic data have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS7859 (https://www.ebi.ac.uk/metabolights/MTBLS7859). This data can be used as a source for comparisons with other studies focusing on the inhibition of anaerobic digestion by ammonia, particularly in the context of exploring microbial or metabolomic dynamics during inhibition. Additionally it provides a multi-omic dataset (metataxonomic and metabolomic) with detailed associated metadata describing anaerobic digesters. The dataset is directly is associated to the research article titled "Inhibition of anaerobic digestion by various ammonia sources resulted in subtle differences in metabolite dynamics." [1].

Near-infrared fluorescent indolizine-dicyanomethylene-4H-pyran hybrids for viscosity imaging in living cells.

The development of near-infrared organic fluorescent dyes with tunable emission profiles is highly required in the field of biological sensing and imaging. In this paper, we designed and synthesized two organic fluorescent dyes, DCM-1 and DCM-2, through the hybridization of indolizine and dicyanomethylene-4H-pyran skeleton. These two compounds show near-infrared fluorescence with emission maximum approximately at 640 and 680 nm, respectively. Notably, both DCM-1 and DCM-2 have specific responses to viscosity without being interfered by biological relevant species. Cell experiments demonstrate that DCM-1 and DCM-2 can detect dynamic changes in viscosity within living cells, suggesting their potential applications in chemical biology research.

Near-Infrared Absorbing BODIPY-Xanthene Hybrids for Multiplexed Photoacoustic Imaging.

We report a series of ethenylene-bridged D-π-A BODIPY-xanthene hybrid dyes with precisely regulated absorption bands ranging from the far-red to the near-infrared region (NIR, 700-1000 nm) through rational molecular design. These dyes have excellent photoacoustic properties, and their biocompatibility can be significantly improved by facilely introducing water-soluble groups. In vivo two-channel multiplexed photoacoustic imaging demonstrated their high-resolution imaging capabilities, making them promising candidates for future NIR bioimaging applications.

Exploring the Two-Way Link between Migraines and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study.

BACKGROUND: The objective of this study is to utilize Mendelian randomization to scrutinize the mutual causality between migraine and venous thromboembolism (VTE) thereby addressing the heterogeneity and inconsistency that were observed in prior observational studies concerning the potential interrelation of the two conditions. METHODS: Employing a bidirectional Mendelian randomization approach, the study explored the link between migraine and VTE, incorporating participants of European descent from a large-scale meta-analysis. An inverse-variance weighted (IVW) regression model, with random-effects, leveraging single nucleotide polymorphisms (SNPs) as instrumental variables was utilized to endorse the mutual causality between migraine and VTE. SNP heterogeneity was evaluated using Cochran's Q-test and to account for multiple testing, correction was implemented using the intercept of the MR-Egger method, and a leave-one-out analysis. RESULTS: The IVW model unveiled a statistically considerable causal link between migraine and the development of VTE (odds ratio [OR] = 96.155, 95% confidence interval [CI]: 4.342-2129.458, p = 0.004), implying that migraine poses a strong risk factor for VTE development. Conversely, both IVW and simple model outcomes indicated that VTE poses as a weaker risk factor for migraine (IVW OR = 1.002, 95% CI: 1.000-1.004, p = 0.016). The MR-Egger regression analysis denoted absence of evidence for genetic pleiotropy among the SNPs while the durability of our Mendelian randomization results was vouched by the leave-one-out sensitivity analysis. CONCLUSION: The findings of this Mendelian randomization assessment provide substantiation for a reciprocal causative association between migraine and VTE within the European population.

Effect of longitudinal changes of cachexia on the efficacy and toxicity of immune checkpoint inhibitors in esophageal squamous cell cancer (ESCC) patients.

PURPOSE: Immune checkpoint inhibitors (ICIs) have enhanced survival in advanced esophageal squamous cell cancer (ESCC) patients, but their efficacy varies. Cachexia, characterized by muscle loss and significant weight loss, might influence ICI response. This study examines the relationship between cachexia's longitudinal changes and ICI outcomes in ESCC patients. METHODS: ESCC patients undergoing at least two ICI cycles from 2017 to 2021 were studied. Cachexia's baseline and evolving patterns during ICI treatment were observed. Kaplan-Meier and Cox regression analyses were used to assess cachexia's effect on ICI efficacy. Chi-square tests were used to determine cachexia's link to immune-related adverse effects (irAEs). RESULTS: Two hundred seventy-eight ICI-treated patients had a median progression-free survival (PFS) of 5.78 months and overall survival (OS) of 8.3 months. Pretreatment cachexia led to worse outcomes: PFS 7.87 versus 5.3 months, time to progression (TTP) 10.9 versus 6.1 months, and OS 14.3 versus 9.2 months. Irreversible cachexia showed the poorest results. Cachexia's changes weren't associated with irAEs. CONCLUSION: Baseline and evolving cachexia significantly impact ICI efficacy in ESCC patients. Continuous cachexia monitoring during ICI therapy is crucial for optimal ESCC management.

[Analysis of constituents in different parts of Forsythia suspensa by UPLC-Q-TOF-MS and evaluation of their anti-inflammatory activity].

This study aims to characterize and identify the chemical constituents in 11 parts of Forsythia suspensa by using ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry(UPLC-Q-TOF-MS) combined with a self-established chemical constituent database, including leaves, flowers, fruits, green F. suspensa, old F. suspensa, and seeds. The quality attributes and differences of different parts of F. suspensa were evaluated by principal component analysis, partial least square discriminant analysis, and other stoichiometric methods. A total of 79 compounds were identified, including 13 phenylethanol glycosides, 10 lignans, 12 flavonoids, 10 organic acids, 14 terpenoids, and 20 other types of compounds. Among them, 34 compounds were the main variables of difference between the different parts of F. suspensa, and the content of each component was relatively higher in the leaves and green F. suspensa. The LPS-induced inflammation model of RAW264.7 cells was applied to study the anti-inflammatory activity of the extracts of the different parts of F. suspensa and the main constituents. The results show that the extracts of green F. suspensa, flower, twig, and stem exhibited anti-inflammatory activity, and the constituents such as forsythoside A, phyllyrin, phillygenin, and(+)-pinoresinol-ß-D-glucopyranoside could significantly inhibit anti-inflammatory activity released by NO. The chemical constituent in different parts of F. suspensa is analyzed comprehensively, and the anti-inflammatory activity is evaluated in this study, which provides a reference for the development and comprehensive utilization of F. suspensa resources.

Wet-Stable Lamellar Wood Sponge with High Elasticity and Fatigue Resistance Enabled by Chemical Cross-Linking.

The excessive consumption of fossil-based plastics and the associated environmental concerns motivate the increasing exploitation of sustainable biomass-based materials for advanced applications. Natural wood-derived lamellar wood sponges via a top-down approach have recently attracted significant attention; however, the insufficient compressive fatigue resistance and lack of structural stability in water limit their wide applications. Here, we report a facile chemical cross-linking strategy to tackle these challenges, by which the cellulose fibrils in the lamellas are covalently bridged to enhance their connectivity. The cross-linked wood sponges demonstrate high compressibility up to 70% strain and exceptional compressive fatigue resistance (∼5% plastic deformation after 10,000 cycles at 50% strain). The interfibrillar cross-linking inhibits the swelling of cellulose fibrils and preserves the arch-shaped lamellas of the sponge in water, endowing the wood sponge with excellent wet stability. Such highly elastic and wet-stable lamellar wood sponges offer a sustainable alternative to synthetic polymer-based sponges used in diverse applications.

Association of glycogen synthase kinase-3ß with cognitive impairment in type 2 diabetes patients: a six-year follow-up study.

Background: Our previous multicenter case-control study showed that aging, up-regulation of platelet glycogen synthase kinase-3ß (GSK-3ß), impaired olfactory function, and ApoE ϵ4 genotype were associated with cognitive decline in type 2 diabetes mellitus (T2DM) patients. However, the causal relationship between these biomarkers and the development of cognitive decline in T2DM patients remains unclear. Methods: To further investigate this potential relationship, we designed a 6-year follow-up study in 273 T2DM patients with normal cognitive in our previous study. Baseline characteristics of the study population were compared between T2DM patients with and without incident mild cognitive impairment (MCI). We utilized Cox proportional hazard regression models to assess the risk of cognitive impairment associated with various baseline biomarkers. Receiver operating characteristic curves (ROC) were performed to evaluate the diagnostic accuracy of these biomarkers in predicting cognitive impairment. Results: During a median follow-up time of 6 years (with a range of 4 to 9 years), 40 patients (16.13%) with T2DM developed MCI. Participants who developed incident MCI were more likely to be older, have a lower education level, have more diabetic complications, a higher percentage of ApoE ϵ4 allele and a higher level of platelet GSK-3ß activity (rGSK-3ß) at baseline (P<0.05). In the longitudinal follow-up, individuals with higher levels of rGSK-3ß were more likely to develop incident MCI, with an adjusted hazard ratio (HR) of 1.60 (95% confidence interval [CI] 1.05, 2.46), even after controlling for potential confounders. The AUC of the combination of age, rGSK-3ß and ApoEϵ4 allele predicted for incident MCI was 0.71. Conclusion: Platelet GSK-3ß activity could be a useful biomarker to predict cognitive decline, suggesting the feasibility of identifying vulnerable population and implementing early prevention for dementia.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.