Five commonly used traditional Chinese medicine formulas in the treatment of ulcerative colitis: A network meta-analysis.

BACKGROUND: Currently, traditional Chinese medicine (TCM) formulas are commonly being used as adjunctive therapy for ulcerative colitis in China. Network meta-analysis, a quantitative and comprehensive analytical method, can systematically compare the effects of different adjunctive treatment options for ulcerative colitis, providing scientific evidence for clinical decision-making. AIM: To evaluate the clinical efficacy and safety of commonly used TCM for the treatment of ulcerative colitis (UC) in clinical practice through a network meta-analysis. METHODS: Clinical randomized controlled trials of these TCM formulas used for the adjuvant treatment of UC were searched from the establishment of the databases to July 1, 2022. Studies that met the inclusion criteria were screened and evaluated for literature quality and risk of bias according to the Cochrane 5.1 standard. The methodological quality of the studies was assessed using ReviewManager (RevMan) 5.4, and a funnel plot was constructed to test for publication bias. ADDIS 1.16 statistical software was used to perform statistical analysis of the treatment measures and derive the network relationship and ranking diagrams of the various intervention measures. RESULTS: A total of 64 randomized controlled trials involving 5456 patients with UC were included in this study. The adjuvant treatment of UC using five TCM formulations was able to improve the clinical outcome of the patients. Adjuvant treatment with Baitouweng decoction (BTWT) showed a significant effect [mean difference = 36.22, 95% confidence interval (CI): 7.63 to 65.76]. For the reduction of tumor necrosis factor in patients with UC, adjunctive therapy with BTWT (mean difference = -9.55, 95%CI: -17.89 to -1.41), Shenlingbaizhu powder [SLBZS; odds ratio (OR) = 0.19, 95%CI: 0.08 to 0.39], and Shaoyao decoction (OR = -23.02, 95%CI: -33.64 to -13.14) was effective. Shaoyao decoction was more effective than BTWT (OR = 0.12, 95%CI: 0.03 to 0.39), SLBZS (OR = 0.19, 95%CI: 0.08 to 0. 39), and Xi Lei powder (OR = 0.34, 95%CI: 0.13 to 0.81) in reducing tumor necrosis factor and the recurrence rate of UC. CONCLUSION: TCM combined with mesalazine is more effective than mesalazine alone in the treatment of UC.

Identification of a Predictive Model for Schizophrenia Based on SNPs in a Chinese Population.

Background: Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia. Methods: A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy. Results: 5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, p = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively. Conclusion: The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.

The Association Between Prolactin Levels and Cognitive Function in Female Patients With Severe Mental Disorders.

OBJECTIVE: Cognition impairments are considered as a fundamental characteristic of severe mental disorders (SMD). Recent studies suggest that hyperprolactinemia may exert a detrimental influence on cognitive performance in patients with SMD. The objective of this study was to investigate the correlation between serum prolactin levels and cognitive function in female individuals diagnosed with SMD. METHODS: We conducted a study on 294 patients with SMD and 195 healthy controls, aged between 14 to 55 years old. Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), while prolactin levels were measured in serum. Descriptive analysis and comparative analysis were performed to compare cognitive function and prolactin levels between groups, and linear regression models were used to explore the relationship between prolactin and cognitive function. RESULTS: Compared to the healthy control, individuals with SMD exhibited significantly higher levels of prolactin, while scoring lower on RBANS total and every index scores. Furthermore, a negative association between prolactin levels and cognitive function (RBANS total index score, attention, and delayed memory) was observed in SMD patients. Importantly, this inverse correlation between prolactin and cognition function (RBANS total index score, total scale score, and attention) persisted in patients who were not taking medications that could potentially influence serum prolactin levels. CONCLUSION: Our study reveals a significant correlation between elevated prolactin levels and cognitive impairment in female patients with SMD, underscoring the importance of monitoring prolactin levels in order to prevent cognitive deterioration among female SMD patients.

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

PURPOSE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration. METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs. RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting. CONCLUSION: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.

BACKGROUND: The detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks. METHODS: We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field. RESULTS: Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives. CONCLUSIONS: The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.

Estimates of late Early Cretaceous atmospheric CO2 from Mongolia based on stomatal and isotopic analysis of Pseudotorellia.

PREMISE: The Aptian-Albian (121.4-100.5 Ma) was a greenhouse period with global temperatures estimated as 10-15°C warmer than pre-industrial conditions, so it is surprising that the most reliable CO2 estimates from this time are <1400 ppm. This low CO2 during a warm period implies a very high Earth-system sensitivity in the range of 6 to 9°C per CO2 doubling between the Aptian-Albian and today. METHODS: We applied a well-vetted paleo-CO2 proxy based on leaf gas-exchange principles (Franks model) to two Pseudotorellia species from three stratigraphically similar samples at the Tevshiin Govi lignite mine in central Mongolia (~119.7-100.5 Ma). RESULTS: Our median estimated CO2 concentration from the three respective samples was 2132, 2405, and 2770 ppm. The primary reason for the high estimated CO2 but with relatively large uncertainties is the very low stomatal density in both species, where small variations propagate to large changes in estimated CO2. Indeed, we found that at least 15 leaves are required before the aggregate estimated CO2 approaches that of the full data set. CONCLUSIONS: Our three CO2 estimates all exceeded 2000 ppm, translating to an Earth-system sensitivity (~3-5°C/CO2 doubling) that is more in keeping with the current understanding of the long-term climate system. Because of our large sample size, the directly measured inputs did not contribute much to the overall uncertainty in estimated CO2; instead, the inferred inputs were responsible for most of the overall uncertainty and thus should be scrutinized for their value choices.

Effect of biochar and iron ore tailing waste amendments on cadmium bioavailability in a soil and peanut seedling system.

Biochar and iron ore tailing waste have been widely separately applied for remediation of various contaminants, but the remediation effect of their combination on cadmium (Cd) pollution is unclear. In this study, the peanut biochar (BC), thermally activated iron ore tailing waste (TS), and the products of the co-pyrolysis of peanut shell and iron ore tailing waste (TSBC) were prepared for stabilizing Cd and reducing its bio-accessibility in soil and peanut seedling system. Present amendments enhanced soil pH, cation exchange capacity, electrical conductivity, and organic carbon content. The application of BC, TS, and TSBC led to decreases in acid-extractable Cd proportion of 2.2-8.81%, 2.43-7.20%, and 7.84-11.57%, respectively, and increases in the residual Cd proportion of 3.48-8.33%, 3.27-11.50%, and 9.02-13.45%, respectively. There were no significant differences in Cd accumulation in peanut roots due to three amendments treatments, especially at low Cd concentrations (i.e., Cd concentration of 0, 1, and 2 mg·kg-1), and with a relatively small reduction (2.16-9.05%) in root Cd accumulation under the high Cd treatments of 5 and 10 mg·kg-1. The Cd concentrations in seedling roots were significantly positively related to the acid-extractable Cd fraction, with a Pearson correlation coefficient of r = 0.999. The maximum toxicity mitigating effects were found in TSBC treatment, with increases in the ranges of 9.80-17.58% for fresh weight, 5.59-14.99% for dry weight, 5.16-10.17% for plant height, 5.96-10.34% for root length, 5.43-21.67% for chlorophyll a content, 17.17-71.28% for chlorophyll b content, and 13.11-39.60% for carotenoid content in peanut seedlings. Therefore, TSBC is a promising amendment for minimizing Cd contamination in peanut crops and utilizing industrial solid waste materials efficiently.

Evaluation of the factors influencing blood transfusion during minimally invasive direct coronary artery bypass surgery.

INTRODUCTION: To analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence. METHODS: A retrospective analysis was performed for patients undergoing MIDCAB operations that no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations. RESULTS: Of the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy is 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin (Hb) less than 120 g/L, prothrombin time (PT) greater than 13.75, body mass index (BMI) less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention (PCI), weight lower than 67 kg were the six major risk factors of allogeneic transfusion. CONCLUSION: The XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.

Methylisothiazolinone pollution inhibited root stem cells and regeneration through auxin transport modification in Arabidopsis thaliana.

Methylisothiazolinone (MIT) is a widely used preservative and biocide to prevent product degradation, yet its potential impact on plant growth remains poorly understood. In this study, we investigated MIT's toxic effects on Arabidopsis thaliana root growth. Exposure to MIT significantly inhibited Arabidopsis root growth, associated with reduced root meristem size and root meristem cell numbers. We explored the polar auxin transport pathway and stem cell regulation as key factors in root meristem function. Our findings demonstrated that MIT suppressed the expression of the auxin efflux carrier PIN1 and major root stem cell regulators (PLT1, PLT2, SHR, and SCR). Additionally, MIT hindered root regeneration by downregulating the quiescent center (QC) marker WOX5. Transcriptome analysis revealed MIT-induced alterations in gene expression related to oxidative stress, with physiological experiments confirming elevated reactive oxygen species (ROS) levels and increased cell death in root tips at concentrations exceeding 50 µM. In summary, this study provides critical insights into MIT's toxicity on plant root development and regeneration, primarily linked to modifications in polar auxin transport and downregulation of genes associated with root stem cell regulation.

Efficient pyramid channel attention network for pathological myopia recognition with pretraining-and-finetuning.

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristics of pathology distribution in PM are global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime.

Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment.

Prolonged sitting can easily result in pressure injury (PI) for certain people who have had strokes or spinal cord injuries. There are not many methods available for tracking contact surface pressure and shear force to evaluate the PI risk. Here, we propose a smart cushion that uses two-dimensional force sensors (2D-FSs) to measure the pressure and shear force in the buttocks. A machine learning algorithm is then used to compute the shear stresses in the gluteal muscles, which helps to determine the PI risk. The 2D-FS consists of a ferroelectret coaxial sensor (FCS) unit placed atop a ferroelectret film sensor (FFS) unit, allowing it to detect both vertical and horizontal forces simultaneously. To characterize and calibrate, two experimental approaches are applied: one involves simultaneously applying two perpendicular forces, and one involves applying a single force. To separate the two forces, the 2D-FS is decoupled using a deep neural network technique. Multiple FCSs are embedded to form a smart cushion, and a genetic algorithm-optimized backpropagation neural network is proposed and trained to predict the shear strain in the buttocks to prevent PI. By tracking the danger of PI, the smart cushion based on 2D-FSs may be further connected with home-based intelligent care platforms to increase patient equality for spinal cord injury patients and lower the expense of nursing or rehabilitation care.

Lung Cancer Biomarkers Associated with Increased Peripheral Arterial Stiffness in Middle-aged Chinese Adults.

AIMS: Previous evidence suggests that serum lung cancer biomarkers are associated with inflammatory conditions; however, their relationship with peripheral arterial stiffness remains unclear. Therefore, the present study investigated the relationship between serum lung cancer biomarkers and peripheral arterial stiffness in middle-aged Chinese adults. METHODS: In total, 3878 middle-aged Chinese adults were enrolled in this study. Increased peripheral arterial stiffness was assessed using the brachial-ankle pulse wave velocity and ankle-brachial index. Univariate and multivariate logistic regression analyses were used to determine the independent effects of serum lung cancer biomarkers on the risk of increased peripheral arterial stiffness. A receiver operating characteristic curve analysis was used to assess the diagnostic ability of serum lung cancer biomarkers in distinguishing increased peripheral arterial stiffness. RESULTS: Serum levels of carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin-19 fragment 21-1, and pro-gastrin-releasing peptide were higher in subjects with increased peripheral arterial stiffness than in those without (P＜0.05). After adjusting for other risk factors, serum CEA and NSE levels were found to be independently associated with increased peripheral arterial stiffness. The corresponding adjusted odds ratios (ORs) for increased peripheral arterial stiffness in CEA level quartiles were 1.00, 1.57, 2.15, and 6.13. The ORs for increased peripheral arterial stiffness in the quartiles of NSE levels were 1.00, 4.92, 6.65, and 8.01. CONCLUSIONS: Increased serum CEA and NSE levels are closely linked to increased peripheral arterial stiffness, and high serum CEA and NSE levels are potential risk markers for peripheral arterial stiffness in middle-aged Chinese adults.

Synthetic Microbial Community Promotes Bacterial Communities Leading to Soil Multifunctionality in Desertified Land.

Soil desertification is an important challenge in global soil management, and effectively and stably restoring soil function is an urgent problem. Using synthetic microbial communities (SynComs) is a burgeoning microbial strategy aimed at enhancing soil nutrients through functional synergies among diverse microorganisms; nevertheless, their effectiveness in restoring desertified soils remains unknown. In this study, we conducted a two-year field experiment using a SynCom constructed by in situ probiotic bacteria and set up control, chemical fertilizer, and combined SynCom-chemical fertilizer (combined fertilizer) treatments to investigate the linkage between microbial communities and soil multifunctionality in the soil surface layer (0-10 cm). Both the bacterial and fungal communities differed the most under the combined fertilizer treatment compared to the control. The bacterial communities differed more under treatments of the SynCom than the chemical fertilizer, while the fungal communities differed more under the chemical fertilizer treatment than the SynCom treatment. Regarding soil function, the SynCom strengthened the correlation between enzyme activities and both bacterial communities and functional properties. pH and available potassium were the main influencing factors under the chemical fertilizer and combined fertilizer treatments. The beta-diversity of the bacterial communities was significantly correlated with soil multifunctionality. Random forest analyses showed that the SynCom significantly enhanced the bacterial communities, driving soil multifunctionality, and that some potential microbial taxa drove multiple nutrient cycles simultaneously. In summary, the SynCom effectively increased the abundance of most carbon, nitrogen, and phosphorus functional genes as well as soil enzyme activities. The bacterial community composition contributed significantly to soil multifunctionality. Hence, the development of novel microbial agents holds significant potential for improving soil functionality and managing desertification.

A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning.

BACKGROUND: The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare. OBJECTIVE: Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques. METHODS: The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance. RESULTS: A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance. CONCLUSION: The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.

Feeding live yeast (Saccharomyces cerevisiae) improved performance of mid-lactation dairy cows by altering ruminal bacterial communities and functions of serum antioxidation and immune responses.

BACKGROUND: The utilization of live yeast (Saccharomyces cerevisiae, YE) in dairy cows is gaining traction in dairy production as a potential strategy to improve feed efficiency and milk yield. However, the effects of YE on dairy cow performance remain inconsistent across studies, leaving the underlying mechanisms unclear. Hence, the primary aim of this study was to investigate the impact of YE supplementation on lactation performance, ruminal microbiota composition and fermentation patterns, as well as serum antioxidant capacity and immune functions in dairy cows. RESULTS: Supplementation with YE (20 g/d/head) resulted in enhancements in dairy cow's dry matter intake (DMI) (P = 0.016), as well as increased yields of milk (P = 0.002) and its components, including solids (P = 0.003), fat (P = 0.014), protein (P = 0.002), and lactose (P = 0.001) yields. The addition of YE led to significant increases in the concentrations of ammonia nitrogen (NH3-N) (P = 0.023), acetate (P = 0.005), propionate (P = 0.025), valerate (P = 0.003), and total volatile fatty acids (VFAs) (P < 0.001) in rumen fermentation parameters. The analysis of 16s rRNA gene sequencing data revealed that the administration of YE resulted in a rise in the relative abundances of three primary genera including Ruminococcus_2 (P = 0.010), Rikenellaceae_RC9_gut_group (P = 0.009), and Ruminococcaceae_NK4A214_group (P = 0.054) at the genus level. Furthermore, this increase was accompanied with an enriched pathway related to amino acid metabolism. Additionally, enhanced serum antioxidative (P < 0.05) and immune functionalities (P < 0.05) were also observed in the YE group. CONCLUSIONS: In addition to improving milk performance, YE supplementation also induced changes in ruminal bacterial community composition and fermentation, while enhancing serum antioxidative and immunological responses during the mid-lactation stage. These findings suggest that YE may exert beneficial effects on both rumen and blood metabolism in mid-lactation dairy cows.

Performance test and analysis of tetracycline degradation using a Micro-Nano Bubble system.

Antibiotics and organic residues from Tetracycline (TC) and other pharmaceuticals administered to aquatic living organism have negative impacts on aquatic environment by killing-off non-target living organisms and developing antibiotic-resistant bacteria. In this study, Micro-Nano Bubble (MNB) system was used to remove TC residues. MNB system demonstrated good level of degradation efficiency, as resulted in experiment where in time of 100 min, the TC degraded at rate of 82.66% from its initial concentration of TC when the initial concentration was 1 mg/L. When the initial concentration was increased to 10 mg/L, MNB system degraded TC at 64.35% of their initial, this means MNB system demonstrated good level of efficiency for TC removal and indicated that it is more efficient in TC degradation under the conditions of low initial TC concentration and high availability of dissolved oxygen (DO). In the system as the temperature increased there was a significant decrease in DO saturation which was related to the TC complex structure that contain multiple function groups such as amino groups, hydroxyl and carboxyl which possess high strong affinity with oxygen that leads to their adsorption onto bubble surface. This study provides significant insights into the application of MNB system for the removal of organic residues within aquatic ecosystem and underscores the need for further exploration of MNB technology for environmental remediation.

Performance of low shrinkage Bis-EFMA based bulk-fill dental resin composites.

OBJECTIVES: The purpose of this study was evaluating the performance of new Bis-EFMA based bulk-fill composites with common methacrylate based composites and commercial dental composites. METHODS: The Bis-EFMA monomer was synthesized and the novel Bis-EFMA based bulk-fill composites were prepared. The resin composite samples were co-cultured with human gingival epithelial cells and human dental pulp stem cells to test the biocompatibility. The edge adaptation was observed under a combination of stereoscope and scanning electron microscope. The internal hardness was measured using a Vickers microhardness tester after one-time filling of cavities prepared in extracted teeth. After friction and wear test on the surface of the resin composites, the surface morphology and volume wear of each group were measured by the optical profilometer. The color stability was measured by a colorimeter. RESULTS: Direct contact with human gingival epithelial cells and human dental pulp stem cells did not cause significant changes in their growth density and morphology, indicating good biocompatibility of Bis-EFMA group (p > 0.05). The continuous margin proportion of the Bis-EFMA group was as good as commercial bulk-fill composites (p > 0.05). The sectional microhardness results showed that the Bis-EFMA group had the highest microhardness. After the friction and wear test, the volume wear of the Bis-EFMA group was minimal, indicating its good wear resistance and mechanical strength. Color changes in all resin groups after 28 days of immersion were within the clinically acceptable range. SIGNIFICANCE: The addition of Bis-EFMA demonstrated excellent biocompatibility, edge adaptation and color stability comparable to commonly used clinical bulk-fill composites, along with preferable mechanical strength, friction and wear resistance. Bis-EFMA based bulk-fill composites have the potential to be employed as a bulk filling material in commercial dental composite applications.

Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework.

BACKGROUND: The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. METHODS: These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. RESULTS: Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. CONCLUSIONS: This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.

A new spider species of Belisana Thorell, 1898 (Araneae, Pholcidae) from Guizhou Province, south-western China.

Background: China exhibits remarkable diversity of the spider genus Belisana Thorell, 1898, with 62 species recorded to date. However, the largest number of Belisana species was found in Yunnan Province (23 ssp.), while only seven species were found in Guizhou Province. New information: In this paper, Belisanawangchengi sp. nov. as a new species is described from Guizhou Province, China.

Enhancing anti-angiogenic immunotherapy for melanoma through injectable metal-organic framework hydrogel co-delivery of combretastatin A4 and poly(I:C).

The interplay between vascularization and macrophage-induced immune suppression plays a crucial role in melanoma treatment. In this study, we propose a novel combination approach to combat melanoma by simultaneously inhibiting tumor vascularization and enhancing macrophage-mediated anti-tumor responses. We investigate the potential of combining combretastatin A4 (CA4), a vascular-disrupting agent, with poly(I:C) (PIC), an immunostimulatory adjuvant. This combination approach effectively suppresses melanoma cell proliferation, disrupts vascularization, and promotes macrophage polarization towards the M1 phenotype for melanoma suppression. To facilitate efficient co-delivery of CA4 and PIC for enhanced anti-angiogenic immunotherapy, we develop an injectable metal-organic framework hydrogel using Zeolitic Imidazolate Framework-8 (ZIF-8) and hyaluronic acid (HA) (ZIF-8/HA). Our findings demonstrate that ZIF-8 enables efficient loading of CA4 and enhances the stability of PIC against RNAase degradation in vitro. Furthermore, the developed co-delivery hydrogel system, PIC/CA4@ZIF-8/HA, exhibits improved rheological properties, good injectability and prolonged drug retention. Importantly, in vivo experiments demonstrate that the PIC/CA4@ZIF-8/HA formulation significantly reduces the dosage and administration frequency while achieving a more pronounced therapeutic effect. It effectively inhibits melanoma growth by suppressing angiogenesis, destroying blood vessels, promoting M1 macrophage infiltration, and demonstrating excellent biocompatibility. In conclusion, our study advances anti-angiogenic immunotherapy for melanoma through the potent combination of PIC/CA4, particularly when administered using the PIC/CA4@ZIF-8/HA formulation. These findings provide a new perspective on clinical anti-angiogenic immunotherapy for melanoma, emphasizing the importance of targeting tumor vascularization and macrophage-mediated immune suppression simultaneously.