Atractylenolide II combined with Interferon-γ synergistically ameliorates colorectal cancer progression in vivo and in vitro by blocking the NF-kB p65/PD-L1 pathway.

Purpose: Atractylodes macrocephala Koidz is a widely used classical traditional Chinese herbal medicine, that has shown remarkable efficacy in cancers. Colorectal cancer (CRC) is the most common malignant tumor globally. Interferon (IFN)-γ, a prominent cytokine involved in anti-tumor immunity that has cytostatic, pro-apoptotic, and immune-stimulatory properties for the detection and removal of transformed cells. Atractylenolides-II (AT-II) belongs to the lactone compound that is derived from Atractylodes macrocephala Koidz with anti-cancer activity. However, whether AT-II combined with IFN-γ modulates CRC progression and the underlying mechanisms remain unclear. The present study aimed to elucidate the efficacy and pharmaceutical mechanism of action of AT-II combined with IFN-γ synergistically against CRC by regulating the NF-kB p65/PD-L1 signaling pathway. Methods: HT29 and HCT15 cells were treated with AT-II and IFN-γ alone or in combination and cell viability, migration, and invasion were then analyzed using Cell Counting Kit-8 (CCK-8) and Transwell assays, respectively. Furthermore, the underlying mechanism was investigated through western blot assay. The role of AT-II combined with IFN-γ on tumor growth and lung metastases was estimated in vivo. Finally, the population of lymphocytes in tumor tissues of lung metastatic C57BL/6 mice and the plasma cytokine levels were confirmed by flow cytometry and enzyme-linked immunosorbent assay (ELISA). Results: AT-II or the combination IFN-γ significantly inhibited the growth and migration abilities of CRC cells in vitro and in vivo. The biological mechanisms behind the beneficial effects of AT-II combined with IFN-γ were also measured and inhibition of p38 MAPK, FAK, Wnt/ß-catenin, Smad, and NF-kB p65/PD-L1 pathways was observed. Moreover, AT-II combined with IFN-γ significantly inhibited HCT15 xenograft tumor growth and lung metastases in C57BL/6 mice, which was accompanied by lymphocyte infiltration into the tumor tissues and inflammatory response inactivation. Conclusions: The results showed that the AT-II in combination with IFN-γ could be used as a potential strategy for tumor immunotherapy in CRC. More importantly, the mechanism by which AT-II suppressed CRC progressions was by inhibiting the NF-kB p65/PD-L1 signal pathway.

The association between renal pelvis urine density and the risk of severe infectious complications in patient with symptom-free hydronephrosis after shock wave lithotripsy: a multi-center prospective study.

Finding reliable and easy-to-obtain predictors of severe infectious complications after shock wave lithotripsy (SWL) is a major clinical need, particular in symptom-free hydronephrosis. Therefore, we aim to prospectively investigate the predictive value of Hounsfield units (HU) in renal pelvis urine for the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL. This multi-center prospective study was conducted from June 2020 to December 2023. The HU of renal pelvis urine was measured by non-enhanced computed tomography. The severe infectious complications included systemic inflammatory response syndrome, sepsis, and septic shock. Binary logistic regression models assessed the odds ratios (ORs) and 95% confidence intervals (CIs). Finally, 1,436 patients with ureteral stones were enrolled in this study. 8.9% (128/1,436) of patients experienced severe infectious complications after SWL treatment. After adjusting confounding variables, compared with the patients in the lowest renal pelvis urine density quartile, the OR (95% CI) for the highest quartile was 32.36 (13.32, 78.60). There was a positive linear association between the HU value of renal pelvis urine and the risk of severe infectious complications after SWL (P for trend < 0.001). Furthermore, this association was also observed stratified by age, gender, BMI, stone size, stone location and hydronephrosis grade (all P for interaction > 0.05). Additionally, the nonlinear association employed by restricted cubic splines is not statistically significant (nonlinear P = 0.256). The AUROC and 95%CI of renal pelvis urine density were 0.895 (0.862 to 0.927, P value < 0.001). The cut-off value was 12.0 HU with 78.59% sensitivity and 85.94% specificity. This multi-center prospective study demonstrated a positive linear association between HU in renal pelvis urine and the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL, regardless of age, gender, BMI, stone size, stone location, and hydronephrosis grade. These findings might be helpful in the SWL treatment decision-making process.

Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries.

The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences.

Continuous age- and sex-specific reference ranges of liver enzymes in Chinese children and application in pediatric non-alcoholic fatty liver disease.

BACKGROUND: Alanine aminotransferase (ALT) is widely used to screen patients with hepatic diseases. However, the current reference ranges (< 50 U/L) were developed by laboratories and have not been validated in populations with a large number of healthy individuals. METHODS: This study collected venous blood and anthropometric data from a total of 13,287 healthy children aged 3 months to 18 years who underwent routine physical examinations in the Department of Pediatric Healthcare. We applied the least mean square algorithm to establish age- and sex-related reference percentiles of serum levels of transaminases. For validation, we recruited 4276 children and adolescents with obesity/overweight who underwent evaluation and metabolic tests in the hospital. Using receiver operating characteristic curves, we determined age- and sex-specific upper limit percentiles of liver enzymes for fatty liver diseases. RESULTS: This study revealed a significant correlation between serum transaminase levels and age and sex (P < 0.01). These transaminase levels exhibited age- and sex-specific patterns. Among individuals in the non-alcoholic fatty liver disease (NAFLD) cohort, elevated ALT levels displayed a positive association with clinical markers of disease severity, including homeostatic model assessment of insulin resistance, waist-hip ratio, and serum uric acid levels (P < 0.01). According to the receiver operating characteristic curves, ALT levels at the 92.58th percentile for boys and the 92.07th percentile for girls yielded the highest accuracy and specificity. CONCLUSIONS: This study provides age- and sex-specific reference ranges for ALT, aspartate aminotransferase, and γ-glutamyltransferase in Chinese children and adolescents, making it the largest population study to date. Furthermore, the study establishes a precise upper limit for ALT levels, facilitating their use in NAFLD screening. Video Abstract.

M2 tumor-associated macrophage promoted DNA methylation in lung cancer metastasis via intensifying EZH2.

This study aimed to explore the interaction between the tumor-associated macrophage (TAM) and enhancer of zeste homolog 2 (EZH2) in tumor microenvironment of lung cancer are obscure. M2 type of TAM was induced by interleukin-4 (IL-4) and interleukin-13 (IL-13) in RAW264.7 cells. Subsequently, the co-culture system of the M2 RAW264.7 treating LLC-1 cells were constructed to evaluate the cell proliferation, migration and invasion abilities. On top of that, the M2 RAW264.7 was injected into the LLC-1 cells-bearing mice. Tumor growth and the number of metastatic nodes were observed. Moreover, DNA methylation, EZH2 expression, target genes of EZH2 and the M2 type TAM-related markers were detected in vivo and in vitro . Further experiments of EZH2 function in lung cancer were carried out by the addition of EZH2 inhibitor (GSK126) and si-EZH2. M2 type of TAM was induced with IL-4 and IL-13 with increased expression of CD206, CD68, CD163 and Arg1. Following co-culture with M2 type TAM, the proliferative, invasive, migrative abilities, tumor growth and metastasis, and the DNA methylation, EZH2 level were strengthened whereas the target genes of EZH2, including p21, CDKN2A, CDKN2B were reduced in LLC-1 cells and LLC-1 cell-bearing mice. Of note, GSK126 and si-EZH2 offset the M2 type TAM's effects, and inhibited the LLC-1 cell metastasis, DNA methylation and tumor growth. M2 type TAM promoted DNA methylation in LLC-1 cells and LLC-1 cell-bearing mice, which is related to the intensified EZH2.

Scutellarin Alleviates Diabetic Retinopathy via the Suppression of Nucleotide-Binding Oligomerization Domain (NOD)-Like Receptor Pyrin Domain Containing Protein 3 Inflammasome Activation.

PURPOSE: Diabetic retinopathy, a prevalent complication of diabetes, represents the leading cause of vision loss and blindness among middle-aged and elderly populations. Recent research has demonstrated the ameliorating effects of scutellarin on diabetes-associated complications such as diabetic retinopathy and type 2 diabetic cardiomyopathy. However, investigations into its protective impact and underlying mechanisms on diabetic retinopathy are scant. This study aims to explore the therapeutic potential of scutellarin in diabetic retinopathy treatment. METHODS: Diabetic retinopathy was induced in rats through intraperitoneal injections of streptozotocin (STZ, 60 mg/kg) administered daily for three consecutive days. Following this, diabetic retinopathy rats received daily intragastric administration of scutellarin (40 mg/kg) for 42 days. RESULTS: Our findings suggest that scutellarin alleviates histological damage in the retinal tissues of streptozotocin-challenged rats. Furthermore, scutellarin effectively enhances total retinal thickness and increases the number of ganglion cell layer (GCL) cells in the retinal tissues of streptozotocin-treated rats. Scutellarin also demonstrated anti-inflammatory and antioxidant effects in the retinal tissues of STZ-induced rats, as indicated by reduced levels of tumor necrosis factor-α, interleukin-1ß, and interleukin-6, and elevated levels of glutathione peroxidase, superoxide dismutase, and catalase. Additionally, scutellarin effectively inhibited the expression of NOD-like receptor pyrin domain containing protein 3 inflammasome-related markers in the retinal tissues of streptozotocin-administered rats. CONCLUSIONS: Collectively, our results indicate that scutellarin significantly reduces streptozotocin-induced retinal inflammation, an effect that may be partially attributed to the suppression of NLRP3 inflammasome activation.

Total Glucosides of Paeony Ameliorate Myocardial Injury in Chronic Heart Failure Rats by Suppressing PARP-1.

Total glucosides of paeony (TGP) have a potential protective effect on chronic heart failure (CHF) rats, but the mechanism remains unclear. PARP inhibition prevents the decrease in myocardial contractility. Therefore, we aim to investigate the effects and mechanisms of TGP on CHF and the role of PARP-1 in CHF. Left anterior descending ligation rats and adriamycin-treated H9C9 cells were used as CHF models, and captopril as a positive control for in vivo experiments. We found that TGP alleviated myocardial remodeling and improved cardiac morphology and function. TGP also reduced myocardial apoptosis and autophagy, decreased inflammatory factor release, and inhibited the PARP-1 and NF-κB proteins. Through cell transfection, we found that PAPR-1 knockdown inhibited NF-κB nuclear translocation. Additionally, TGP inhibited apoptosis, autophagy, and inflammation in CHF cells, while PARP-1 overexpression partially antagonized them. In conclusion, TGP has the potential to improve CHF and PARP-1 may be a potential target.

The potential effect of salvianolic acid B against rat ischemic brain injury in combination with mesenchymal stem cells.

BACKGROUND: Mesenchymal stem cells (MSCs) and Salvianolic acid B (SAB) are known to exert potent anti-inflammatory and anti-oxidative properties. But the effect of SAB and MSCs combination treatment on the cerebral ischemia/reperfusion injury (CI/RI) is not clear. METHODS: After the CI/RI animal model established, rats were administered with MSCs and SAB individually or combination treatment. To evaluate the therapeutic potential, behavioral tests, TTC staining, Hematoxylin-eosin (HE) staining, and immunofluorescence assays were performed to evaluate the neuroprotection and endogenous neurogenesis. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining and enzyme linked immunosorbent assay (ELISA) were performed to evaluate the anti-apoptosis and anti-inflammatory effect. Meanwhile, the expression of the TLR4/NF-ĸB/MYD88 signal pathway-related proteins was evaluated by Western blot. RESULTS: MSCs and SAB individually or combination treatment have protective effect in CI/RI rats. More importantly, the rats with the combination treatment showed a better behavioral recovery, neurogenesis and smaller infarct size compared with the rats administered with MSCs or SAB individually. Further research showed that the combination treatment decreased CI/RI induced inflammatory cytokines and oxidative stress, including inhibiting the production of IL-1ß, IL-6, TNF-α, decreasing the levels of malondialdehyde (MDA), and increased the activity of superoxide dismutase (SOD). In addition, the neuroprotection effect of SAB and MSCs combination was achieved through the regulation of TLR4/NF-κB/MyD88 signaling pathway related proteins, including inhibition the protein levels of TLR4, MYD88, p-NF-κB p65, TRAF6-and action of SIRT1 in brain tissues. CONCLUSION: The present study indicated that the MSCs and SAB combination treatment had better protective effect against rat ischemic brain injury. The combination of SAB and MSCs may provide a potent and promising strategy for the treatment of ischemic stroke and is worthy for further development.

Analyzing and Forecasting Pediatric Fever Clinic Visits in High Frequency Using Ensemble Time-Series Methods After the COVID-19 Pandemic in Hangzhou, China: Retrospective Study.

BACKGROUND: The COVID-19 pandemic has significantly altered the global health and medical landscape. In response to the outbreak, Chinese hospitals have established 24-hour fever clinics to serve patients with COVID-19. The emergence of these clinics and the impact of successive epidemics have led to a surge in visits, placing pressure on hospital resource allocation and scheduling. Therefore, accurate prediction of outpatient visits is essential for informed decision-making in hospital management. OBJECTIVE: Hourly visits to fever clinics can be characterized as a long-sequence time series in high frequency, which also exhibits distinct patterns due to the particularity of pediatric treatment behavior in an epidemic context. This study aimed to build models to forecast fever clinic visit with outstanding prediction accuracy and robust generalization in forecast horizons. In addition, this study hopes to provide a research paradigm for time-series forecasting problems, which involves an exploratory analysis revealing data patterns before model development. METHODS: An exploratory analysis, including graphical analysis, autocorrelation analysis, and seasonal-trend decomposition, was conducted to reveal the seasonality and structural patterns of the retrospective fever clinic visit data. The data were found to exhibit multiseasonality and nonlinearity. On the basis of these results, an ensemble of time-series analysis methods, including individual models and their combinations, was validated on the data set. Root mean square error and mean absolute error were used as accuracy metrics, with the cross-validation of rolling forecasting origin conducted across different forecast horizons. RESULTS: Hybrid models generally outperformed individual models across most forecast horizons. A novel model combination, the hybrid neural network autoregressive (NNAR)-seasonal and trend decomposition using Loess forecasting (STLF), was identified as the optimal model for our forecasting task, with the best performance in all accuracy metrics (root mean square error=20.1, mean absolute error=14.3) for the 15-days-ahead forecasts and an overall advantage for forecast horizons that were 1 to 30 days ahead. CONCLUSIONS: Although forecast accuracy tends to decline with an increasing forecast horizon, the hybrid NNAR-STLF model is applicable for short-, medium-, and long-term forecasts owing to its ability to fit multiseasonality (captured by the STLF component) and nonlinearity (captured by the NNAR component). The model identified in this study is also applicable to hospitals in other regions with similar epidemic outpatient configurations or forecasting tasks whose data conform to long-sequence time series in high frequency exhibiting multiseasonal and nonlinear patterns. However, as external variables and disruptive events were not accounted for, the model performance declined slightly following changes in the COVID-19 containment policy in China. Future work may seek to improve accuracy by incorporating external variables that characterize moving events or other factors as well as by adding data from different organizations to enhance algorithm generalization.

The influence of media use degree on public depressive symptoms: mediating role of big five personality.

BACKGROUND: Mixed results have been found regarding the relationship between media use degree and depressive symptoms. The purpose of this study is to explore the relationship between media use degree, big five personality and depressive symptoms with a mediation model. METHOD: This was a cross-sectional study. With 9-item Patient Health Questionnaire (PHQ-9), 10-item Big Five Inventory (BFI-10) and self-designed media usage scale, 11,031 participants aged 12 and above in 120 cities in China were collected. Pearson correlation analysis and regression analysis were performed on the data. The Process plug-in was used to construct the mediation model and explore the relationship among media use degree, big five personality and depressive symptoms. The nonparametric percentile Bootstrap method was used to test the mediating effect of personality traits. RESULTS: The degree of media use was positively correlated with depressive symptoms (r = 0.20, P < 0.001), and big five personality played a mediating role between the degree of media use and depressive symptoms. Among five traits, extroversion (r=-0.12, P < 0.001), conscientiousness (r=-0.23, P < 0.001), openness (r=-0.03, P < 0.01) and agreeableness (r=-0.22, P < 0.001) were negatively correlated with depressive symptoms, and neuroticism (r = 0.25, P < 0.001) were positively correlated with depressive symptoms. In addition, extraversion (-0.004, -0.001), conscientiousness (-0.015, -0.008), agreeableness (-0.008, -0.001) and neuroticism (-0.015, -0.007) in big five personality played a mediating role between media use and depressive symptoms. CONCLUSION: The degree of media use positively predicted depressive symptoms, and excessive media use may bring risks to mental health. People with high neuroticism, low agreeableness, low conscientiousness and low extroversion are more likely to suffer from depressive symptoms.

Development of lung segmentation method in x-ray images of children based on TransResUNet.

Background: Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems. Objective: In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images. Methods: The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation. Results: Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822. Conclusions: This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.

Artesunate Alleviates Hyperoxia-Induced Lung Injury in Neonatal Mice by Inhibiting NLRP3 Inflammasome Activation.

Bronchopulmonary dysplasia (BPD) is a chronic respiratory disease in preterm infants that may cause persistent lung injury. Artesunate exhibits excellent anti-inflammatory in lung injury caused by various factors. This study aimed to investigate the effect of the artesunate on hyperoxia-induced lung injury in neonatal mice and its mechanism. A BPD model of hyperoxic lung injury in neonatal mice was established after hyperoxia (75% oxygen) exposure for 14 days, and part of the mice received intraperitoneal injections of the artesunate. H&E staining was used to observe the pathology of lung tissue, and the degree of oxidative stress in the lung tissue was determined by commercial kits. The levels of inflammatory cytokines in the serum and lung tissues of neonatal mice were detected by an enzyme-linked immunosorbent assay. Immunohistochemical experiments were performed to further evaluate the expression of IL-1ß. The real-time quantitative polymerase chain reaction was used to determine the mRNA level of the NLRP3 inflammasome. The western blot assay was used to measure the levels of NLRP3 inflammasome and NF-κB pathway-related proteins. Artesunate ameliorated weight loss and lung tissue injury in neonatal mice induced by hyperoxia. The level of malondialdehyde was decreased, while the activity of superoxide dismutase and the level of glutathione increased after artesunate treatment. Artesunate reduced the level of inflammation cytokines TNF-α, IL-6, and IL-1ß in the serum and lung. Moreover, artesunate inhibited the mRNA expression and protein levels of NLRP3, ASC, and caspase-1, as well as the phosphorylation of the NF-κB and IκBα. Our findings suggest that artesunate treatment can attenuate hyperoxia-induced lung injury in BPD neonatal mice by inhibiting the activation of NLRP3 inflammasome and the phosphorylation of the NF-κB pathway.

Cytocompatibility of Ti3C2Tx MXene with Red Blood Cells and Human Umbilical Vein Endothelial Cells and the Underlying Mechanisms.

Two-dimensional (2D) nanomaterials have been widely used in biomedical applications because of their biocompatibility. Considering the high risk of exposure of the circulatory system to Ti3C2Tx, we studied the cytocompatibility of Ti3C2Tx MXene with red blood cells (RBCs) and human umbilical vein endothelial cells (HUVECs) and showed that Ti3C2Tx had excellent compatibility with the two cell lines. Ti3C2Tx at a concentration as high as 200 µg/mL caused a negligible percent hemolysis of 0.8%. By contrast, at the same treatment concentration, graphene oxide (GO) caused a high percent hemolysis of 50.8%. Scanning electron microscopy revealed that RBC structures remained intact in the Ti3C2Tx treatment group, whereas those in the GO group completely deformed, sunk, and shrunk, which resulted in the release of cell contents. This difference can be largely ascribed to the distinct surficial properties of the two nanosheets. In specific, the fully covered surface-terminating -O and -OH groups leading to Ti3C2Tx had a very hydrophilic surface, thereby hindering its penetration into the highly hydrophobic interior of the cell membrane. However, the strong direct van der Waals attractions coordinated with hydrophobic interactions between the unoxidized regions of GO and the lipid hydrophobic tails can still damage the integrity of the cell membranes. In addition, the sharp and keen-edged corners of GO may also facilitate its relatively strong cell membrane damage effects than Ti3C2Tx. Thus, the excellent cell membrane compatibility of Ti3C2Tx nanosheets and their ultraweak capacity to provoke excessive ROS generation endowed them with much better compatibility with HUVECs than GO nanosheets. These results indicate that Ti3C2Tx has much better cytocompatibility than GO and provide a valuable reference for the future biomedical applications of Ti3C2Tx.

Pressure Drop Performance of Porous Composites Based on Cotton Cellulose Nanofiber and Aramid Nanofiber for Cigarette Filter Rod.

Porous composites have been widely used in the adsorption and catalysis field due to their special structure, abundant sites, and light weight. In this work, an environmentally friendly porous composite was successfully prepared via a facile freeze-drying method, in which cotton cellulose nanofiber (CCNF) was adopted as the main framework to construct the connected flue structure, and aramid nanofiber (ANF) was used as a reinforcer to enhance its thermal property. As-prepared porous materials retained a regulated inter-connected hole structure and controllable porosity after ice template evolution and possessed improved resistance to thermal collapse with the introduction of a small amount of aramid nanofiber, as evaluated and verified by FTIR, SEM, and TGA measurements. With the increased addition of cotton cellulose nanofiber and aramid nanofiber, the porous composites exhibited decreased porosity and increased pressure drop performance. For the CCNF/ANF-5 sample, the pressure drop was 1867 Pa with a porosity of 7.46 cm3/g, which best met the required pressure drop value of 1870 Pa. As-prepared porous composite with adjustable interior structure and enhanced thermal property could be a promising candidate in the tobacco field.

Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet.

Lung segmentation using computed tomography (CT) images is important for diagnosing various lung diseases. Currently, no lung segmentation method has been developed for assessing the CT images of preschool children, which may differ from those of adults due to (1) presence of artifacts caused by the shaking of children, (2) loss of a localized lung area due to a failure to hold their breath, and (3) a smaller CT chest area, compared with adults. To solve these unique problems, this study developed an automatic lung segmentation method by combining traditional imaging methods with ResUnet using the CT images of 60 children, aged 0-6 years. First, the CT images were cropped and zoomed through ecological operations to concentrate the segmentation task on the chest area. Then, a ResUnet model was used to improve the loss for lung segmentation, and case-based connected domain operations were performed to filter the segmentation results and improve segmentation accuracy. The proposed method demonstrated promising segmentation results on a test set of 12 cases, with average accuracy, Dice, precision, and recall of 0.9479, 0.9678, 0.9711, and 0.9715, respectively, which achieved the best performance relative to the other six models. This study shows that the proposed method can achieve good segmentation results in CT of preschool children, laying a good foundation for the diagnosis of children's lung diseases.

Baicalin Inhibits Inflammation in Rats with Chronic Obstructive Pulmonary Disease by the TLR2/MYD88/NF-κBp65 Signaling Pathway.

Objective: Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease with a relatively high morbidity and death rate. This study aimed to investigate the inhibitory effect of baicalin (BA) on inflammation in COPD rats and its possible mechanism. Methods: The experimental COPD of SD rats were induced by LPS, smoking, and cold stimulation, and they were randomly divided into the control group, COPD group, COPD + LB group, COPD + MB group, and COPD + HB group. The test of pulmonary function and the HE staining were carried out in COPD rats. The levels of TNF-α, IL-1ß, IL-6, IL-10, and IL-8, as well as GSH, SOD, and MDA in serum, were detected by ELISA. The levels of TLR2, MYD88, TNF-α, and IL-1ß mRNA in BALF were detected by qPCR. The expression of TLR2/MYD88/NF-κBp65 pathway-related proteins was also detected by the Western blot and immunohistochemistry assays. Results: Compared to the COPD model group, BA treatment significantly improved the pulmonary function and pathologic changes, reduced the levels of TNF-α, IL-1ß, IL-6, IL-10, IL-8, and MDA, and increased the levels of IL-10, SOD, and GSH in COPD rats. In addition, BA could also decrease the protein levels of MYD88, p-NF-κBp65/NF-κBp65, TLR2, and TLR4 but increase the protein level of p-IκBa/IκB in lung tissue of COPD rats. Conclusion: BA ameliorated inflammatory response and oxidative stress in COPD rats by regulating the TLR2/MYD88/NF-κBp65 signaling pathway.

An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough.

Background: Due to the phenotypic similarities among different pediatric respiratory diseases with chronic cough, primary doctors often misdiagnose and the misuse of examinations is prevalent. In the pre-diagnosis stage, the patients' chief complaints and other information in the electronic medical record (EMR) provide a powerful reference for respiratory experts to make preliminary disease judgment and examination plan. In this paper, we proposed an intelligent prediagnosis system to predict disease diagnosis and recommend examinations based on EMR text. Methods: We examined the clinical notes of 178,293 children with chronic cough symptoms from retrospective EMR data. The dataset is split into 7:3 for training and testing. From the testing set, we also extract 5% of samples for validation. We proposed a medical-semantic-aware convolution neural network (MSCNN) framework that can accomplish two downstream tasks from the same medical language model through transfer learning. First, a medical language model based on the word2vec algorithm was built to generate embeddings for the text data. Then, text convolutional neural network (TextCNN) was used to build models for disease prediction and examination recommendation. Results: We implemented 5 algorithms for disease prediction. In the disease prediction task, our algorithm outperformed the baseline methods on all metrics, with a top-1 accuracy (AC) of 0.68 and a top-3 AC of 0.923 on the testing set. By adding data enhancement, the top-3 AC reached 0.926. In the examination recommendation task, the overall AC on the testing set was 0.93 and the macro average (MA) F1-score was 0.88. The average area under the curve (AUC) on the training set was 0.97 while on the testing set it was 0.86. Conclusions: We constructed an intelligent prediagnosis system with an MSCNN framework that can predict diseases and make examination recommendations based on EMR data. Our approach achieved good results on a retrospective clinical dataset and thus has great potential for the application of automated diagnosis assist in clinical practice during pre-diagnosis stage, which will provide help for primary level doctors or doctors in basic-level hospitals. Due to the generality of the proposed framework, it can be straight forwardly extended to prediagnosis for other diseases.

Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images.

Background and Aims: Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of "concentric circles" signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods: A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children's Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect "concentric circle" signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results: The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the "concentric circle" detection model, the detection rate, recall, specificity, and F1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by "concentric circle" signs, and the accuracy, recall, specificity, and F1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion: The model established in this paper can realize the automatic detection of "concentric circle" signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of "concentric circles" in ultrasound images for the judgment of children with intussusception.

Alliance chain-based simulation on a new clinical research data pricing model.

Background: Multicenter clinical research faces many challenges, including how to quantitatively evaluate the data contribution of each research center. However, few data pricing model meets the requirements to the scenario. Thus, a suitable mechanism to measure the data value for clinical research is required. Methods: Extensive documents were acquired and analyzed, including a rare disease list from the National Health Commission, data structures of the electronic medical records (EMR) system, diagnosis-related groups (DRGs) regulations from the Health Commission of Zhejiang Province, and the Clinical Service Price List of Zhejiang Province. Nine senior experts were invited as consultants from hospital and enterprises with professional field of clinical research, data governance, and health economics. After brainstorming and expert evaluation, seven data attributes were identified as the main factors affecting the value of medical data. Different weights were assigned for each attribute based on its influence on data value. Each attribute was quantized to an index based on proposed algorithms. The data value models for chronic diseases and other diseases were distinguished given the different sensitivity of data timeliness. A simulation system using blockchain and federated learning techniques was constructed to verify the data pricing model in the scenario of clinical research. Results: A comprehensive clinical data pricing model is proposed and the simulation of three research centers with 50 million real clinical data entries was conducted to verify its effectiveness. It demonstrates that the proposed model can compute medical data value quantitatively. Conclusions: Quantitative evaluation of the value of medical data for multicenter clinical research based on the proposed data pricing model works well in simulation. This model will be improved by real-world applications in the near future.

Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence.

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis.