Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Discovery of novel RNA viruses through analysis of fungi-associated next-generation sequencing data.

BACKGROUND: Like all other species, fungi are susceptible to infection by viruses. The diversity of fungal viruses has been rapidly expanding in recent years due to the availability of advanced sequencing technologies. However, compared to other virome studies, the research on fungi-associated viruses remains limited. RESULTS: In this study, we downloaded and analyzed over 200 public datasets from approximately 40 different Bioprojects to explore potential fungal-associated viral dark matter. A total of 12 novel viral sequences were identified, all of which are RNA viruses, with lengths ranging from 1,769 to 9,516 nucleotides. The amino acid sequence identity of all these viruses with any known virus is below 70%. Through phylogenetic analysis, these RNA viruses were classified into different orders or families, such as Mitoviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Mymonaviridae, Bunyavirales, and Partitiviridae. It is possible that these sequences represent new taxa at the level of family, genus, or species. Furthermore, a co-evolution analysis indicated that the evolutionary history of these viruses within their groups is largely driven by cross-species transmission events. CONCLUSIONS: These findings are of significant importance for understanding the diversity, evolution, and relationships between genome structure and function of fungal viruses. However, further investigation is needed to study their interactions.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms.

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

miR-126a-5p inhibits H1N1-induced inflammation and matrix protease secretion in lung fibroblasts by targeting ADAMTS-4.

Upregulation of ADAMTS-4 has been reported to have an important role in lung injury, and ADAMTS-4 expression is regulated by miR-126a-5p in abdominal aortic aneurysms. The aim of this study was to investigate whether miR-126a-5p/ADAMTS-4 plays a role in influenza-virus-induced lung injury. Lung fibroblasts were infected with H1N1 influenza virus to detect changes in miR-126a-5p and ADAMTS-4 expression, and cell viability was measured by CCK-8 assay. Inflammatory factors and matrix protease levels were examined using ELISA kits, and cell apoptosis was assessed by measuring the levels of apoptosis-related proteins. A dual luciferase assay was used to verify the regulatory relationship between miR-126a-5p and ADAMTS-4. H1N1 influenza virus reduced fibroblast viability, inhibited miR-126a-5p expression, and promoted ADAMTS-4 expression. Overexpression of miR-126a-5p attenuated the cellular inflammatory response, apoptosis, matrix protease secretion, and virus replication. Luciferase reporter assays revealed that miR-126a-5p inhibited ADAMTS-4 expression by targeting ADAMTS-4 mRNA. Further experiments showed that overexpression of ADAMTS-4 significantly reversed the inhibitory effects of miR-126a-5p on fibroblast inflammation, apoptosis, matrix protease secretion, and virus replication. Upregulation of miR-126a-5p inhibits H1N1-induced apoptosis, inflammatory factors, and matrix protease secretion, as well as virus replication in lung fibroblasts.

Exogenous Indole-3-Acetic Acid Suppresses Rice Infection of Magnaporthe oryzae by Affecting Plant Resistance and Fungal Growth.

Auxin is an important phytohormone that regulates diverse biologic processes, including plant growth and immunity. Indole-3-acetic acid (IAA), known as one of the main forms of auxin, is able to activate plant immunity. However, it is unknown whether IAA enhances plant resistance and/or suppresses the growth of the fungal pathogen Magnaporthe oryzae. Here, we found that IAA could induce expression levels of pathogenesis-related genes to enhance disease resistance and could control the development of blast disease through inhibiting M. oryzae infection. Exogenous IAA suppressed mycelial growth and delayed spore germination by inhibiting fungal endogenous IAA biosynthesis and impairing redox homeostasis, respectively. When applied to a field test, two IAA analogues, 1-naphthaleneacetic acid and 2,4-dichlorophenoxy acetic acid, can effectively control rice blast disease. Our study advances the understanding of IAA in controlling rice blast disease through suppressing pathogen growth and enhancing plant resistance.

The body mass index and the risk of ectopic pregnancy: a 5-year retrospective case-control study.

PURPOSE: Acknowledging the associated risk factors may have a positive impact on reducing the incidence of ectopic pregnancy (EP). In recent years, body mass index (BMI) has been mentioned in research. However, few studies are available and controversial on the relationship between EP and BMI. METHODS: We retrospectively studied the EP women as a case group and the deliveries as a control group in the central hospital of Wuhan during 2017 ~ 2021. χ2 test of variables associated with ectopic pregnancy was performed to find differences. Univariate and multivariate binary logistic regression analysis was conducted to analyze the association of the variables of age, parity, history of induced abortion, history of ectopic pregnancy, history of spontaneous abortion, history of appendectomy surgery and BMI (< 18.5 kg/m2, 18.5 ~ 24.9 kg/m2, 25 kg/m2 ~ 29.9 kg/m2, ≥ 30 kg /m2) with EP. RESULTS: They were 659 EP and 1460 deliveries. The variables of age, parity, history of induced abortion, history of ectopic pregnancy and BMI were different significantly(P < 0.05). Multivariate analysis showed that the variables of age > 35 years old [(OR (Odds Ratio), 5.415; 95%CI (Confidence Interval), 4.006 ~ 7.320, P < 0.001], history of ectopic pregnancy (OR, 3.944; 95%CI, 2.405 ~ 6.467; P < 0.001), history of induced abortion(OR, 3.365; 95%CI, 2.724 ~ 4.158, P < 0.001) and low BMI (< 18.5 kg/m2) (OR, 1.929; 95%CI, 1.416 ~ 2.628, P < 0.001])increased the risk of EP. CONCLUSION: The history of ectopic pregnancy, history of induced abortion and age > 35 years old were the risk factors with EP. In addition to these traditional factors, we found low BMI (< 18.5 kg/m2) with women may increase the risk to EP.

Quality of life, household income, and dietary habits are associated with the risk of sarcopenia among the Chinese elderly.

BACKGROUND: Health-related quality of life (HRQoL), which can be influenced by various aspects, especially socioeconomic status and lifestyle, has been identified as an important predictor of the prognosis of older adults. Dietary habit, a major part of lifestyle, can affect the nutritional status, which is closely correlated with the development of geriatric syndromes in the elderly. AIMS: The aim of the study was to examine the association of HRQoL, socioeconomic status, and lifestyle with the risk and severity of sarcopenia, a geriatric syndrome characterized by progressive loss of skeletal muscle mass, strength and function. METHODS: A cross-sectional retrospective study with 2877 participants aged ≥65 years was performed. HRQoL was assessed using EuroQoL Five Dimensions questionnaire. Socioeconomic status was assessed by the educational attainment, occupation, and household income. Lifestyle was assessed using 12 items closely related to Chinese living habits. The information of daily dietary habits including tea, alcohol, type of diet, and volume of drinking water were collected. The associations of HRQoL, socioeconomic status, and lifestyle with the risk of sarcopenia were examined by multivariate regression logistical analysis. The potential causal role of age, body mass index, and waist circumference in the effect of HRQoL on sarcopenia risk was analyzed by causal mediation analysis. RESULTS: High HRQoL [adjusted odds ratio (OR) =0.85, 95% confidence interval (CI) =0.69-0.95, P=0.034] and household income levels (adjusted OR =0.74, 95% CI =0.57-0.95, P=0.019) were inversely associated with the risk of sarcopenia. Meanwhile, more consumption of spicy food (adjusted OR =1.34, 95% CI =1.09-1.81, P =0.037) and occasionally drinking (adjusted OR =1.46, 95% CI =1.07-2.00, P =0.016, as compared to those never drinking) were associated with higher risk of sarcopenia, while skipping breakfast occasionally (adjusted OR =0.37, 95% CI =0.21-0.64, P <0.001, as compared to those eating breakfast every day) and less consumption of salt (adjusted OR =0.71, 95% CI =0.52-0.96, P =0.026, as compared to those consuming high amount of salt) were associated with lower risk of sarcopenia. Further causal mediation analysis aimed to explore how much age, body mass index, and waist circumference might explain the effect of HRQoL on the risk of sarcopenia showed that the estimated proportion that mediated the effect of HRQoL on the risk of sarcopenia by age was 28.0%. CONCLUSIONS: In summary, our findings demonstrate that low levels of HRQoL and household income, more intake of salt and spicy food, and occasional intake of alcohol are correlated with higher risk of sarcopenia, while skipping breakfast occasionally is associated with lower risk of sarcopenia in a Chinese population of older adults.

A lightweight intelligent laryngeal cancer detection system for rural areas.

OBJECTIVE: Early diagnosis of laryngeal cancer (LC) is crucial, particularly in rural areas. Despite existing studies on deep learning models for LC identification, challenges remain in selecting suitable models for rural areas with shortages of laryngologists and limited computer resources. We present the intelligent laryngeal cancer detection system (ILCDS), a deep learning-based solution tailored for effective LC screening in resource-constrained rural areas. METHODS: We compiled a dataset comprised of 2023 laryngoscopic images and applied data augmentation techniques for dataset expansion. Subsequently, we utilized eight deep learning models-AlexNet, VGG, ResNet, DenseNet, MobileNet, ShuffleNet, Vision Transformer, and Swin Transformer-for LC identification. A comprehensive evaluation of their performances and efficiencies was conducted, and the most suitable model was selected to assemble the ILCDS. RESULTS: Regarding performance, all models attained an average accuracy exceeding 90 % on the test set. Particularly noteworthy are VGG, DenseNet, and MobileNet, which exceeded an accuracy of 95 %, with scores of 95.32 %, 95.75 %, and 95.99 %, respectively. Regarding efficiency, MobileNet excels owing to its compact size and fast inference speed, making it an ideal model for integration into ILCDS. CONCLUSION: The ILCDS demonstrated promising accuracy in LC detection while maintaining modest computational resource requirements, indicating its potential to enhance LC screening accuracy and alleviate the workload on otolaryngologists in rural areas.

The association of alcohol consumption with the risk of sarcopenia: a dose-response meta-analysis.

Background: Sarcopenia is defined as a progressive loss of skeletal muscle mass plus a decline in muscle strength and/or reduced physical performance with advancing age. The results of current studies on the relationship between drinking and sarcopenia remain controversial.Objectives: The aim of this meta-analysis was to evaluate the association of alcohol consumption with the risk of sarcopenia.Methods: Systematic searches were conducted without language restrictions from the beginning of each database to September 20, 2023 on PubMed, Embase, Cochrane Library, Web of Science, Wanfang Data, Chinese BioMedical Literature, and China national knowledge infrastructure databases. Meta-analysis was conducted to pool the study-specific odds ratios (ORs) with 95% confidence interval (CI).Results: Sixty-two studies with 454,643 participants were enrolled. The meta-analysis of proportions revealed that alcohol consumption was not associated with the presence of sarcopenia, with a pooled OR of 0.964 (95% CI = 0.912-1.019). Further subgroup analysis indicated that alcohol consumption was correlated with lower risk of sarcopenia in men (OR = 0.763; 95% CI = 0.622-0.938; P = .010). The nonlinear dose-response analysis suggested a J-shaped association between alcohol consumption and the risk of sarcopenia, with a nadir at the amounts of alcohol consumption of 6.6 grams/day (OR = 0.765; 95% CI = 0.608-0.957; P < .05).Conclusions: The results of this meta-analysis indicate that alcohol consumption is not a risk factor for the development of sarcopenia. Any suggestion of a putative protective effect of alcohol should be treated with caution, particularly in light of the overall lack of relationship reported in the present comprehensive meta-analysis.

[Effects of α1-antitrypsin on motor function in mice with immature brain white matter injury]. / α1-æŠ—èƒ°è›‹ç™½é ¶å¯¹æœªæˆç†Ÿè„‘ç™½è´¨æŸä¼¤å°é¼ è¿åŠ¨åŠŸèƒ½çš„å½±å“.

OBJECTIVES: To investigate the effects of α1-antitrypsin (AAT) on motor function in adult mice with immature brain white matter injury. METHODS: Five-day-old C57BL/6J mice were randomly assigned to the sham surgery group (n=27), hypoxia-ischemia (HI) + saline group (n=27), and HI+AAT group (n=27). The HI white matter injury mouse model was established using HI methods. The HI+AAT group received intraperitoneal injections of AAT (50 mg/kg) 24 hours before HI, immediately after HI, and 72 hours after HI; the HI+saline group received intraperitoneal injections of the same volume of saline at the corresponding time points. Brain T2-weighted magnetic resonance imaging scans were performed at 7 and 55 days after modeling. At 2 months of age, adult mice were evaluated for static, dynamic, and coordination parameters using the Catwalk gait analysis system. RESULTS: Compared to the sham surgery group, mice with HI injury showed high signal intensity on brain T2-weighted magnetic resonance imaging at 7 days after modeling, indicating significant white matter injury. The white matter injury persisted at 55 days after modeling. In comparison to the sham surgery group, the HI+saline group exhibited decreased paw print area, maximum contact area, average pressure, maximum pressure, paw print width, average velocity, body velocity, stride length, swing speed, percentage of gait pattern AA, and percentage of inter-limb coordination (left hind paw → left front paw) (P<0.05). The HI+saline group showed increased inter-paw distance, percentage of gait pattern AB, and percentage of phase lag (left front paw → left hind paw) compared to the sham surgery group (P<0.05). In comparison to the HI+saline group, the HI+AAT group showed increased average velocity, body velocity, stride length, and swing speed (right front paw) (P<0.05). CONCLUSIONS: The mice with immature brain white matter injury may exhibit significant motor dysfunction in adulthood, while the use of AAT can improve some aspects of their motor function.

An efficient detection model based on improved YOLOv5s for abnormal surface features of fish.

Detecting abnormal surface features is an important method for identifying abnormal fish. However, existing methods face challenges in excessive subjectivity, limited accuracy, and poor real-time performance. To solve these challenges, a real-time and accurate detection model of abnormal surface features of in-water fish is proposed, based on improved YOLOv5s. The specific enhancements include: 1) We optimize the complete intersection over union and non-maximum suppression through the normalized Gaussian Wasserstein distance metric to improve the model's ability to detect tiny targets. 2) We design the DenseOne module to enhance the reusability of abnormal surface features, and introduce MobileViTv2 to improve detection speed, which are integrated into the feature extraction network. 3) According to the ACmix principle, we fuse the omni-dimensional dynamic convolution and convolutional block attention module to solve the challenge of extracting deep features within complex backgrounds. We carried out comparative experiments on 160 validation sets of in-water abnormal fish, achieving precision, recall, mAP50, mAP50:95 and frames per second (FPS) of 99.5, 99.1, 99.1, 73.9% and 88 FPS, respectively. The results of our model surpass the baseline by 1.4, 1.2, 3.2, 8.2% and 1 FPS. Moreover, the improved model outperforms other state-of-the-art models regarding comprehensive evaluation indexes.

Analysis and experimental validation of IL-17 pathway and key genes as central roles associated with inflammation in hepatic ischemia-reperfusion injury.

Hepatic ischemia-reperfusion injury (HIRI) elicits an immune-inflammatory response that may result in hepatocyte necrosis and apoptosis, ultimately culminating in postoperative hepatic dysfunction and hepatic failure. The precise mechanisms governing the pathophysiology of HIRI remain incompletely understood, necessitating further investigation into key molecules and pathways implicated in disease progression to guide drug discovery and potential therapeutic interventions. Gene microarray data was downloaded from the GEO expression profile database. Integrated bioinformatic analyses were performed to identify HIRI signature genes, which were subsequently validated for expression levels and diagnostic efficacy. Finally, the gene expression was verified in an experimental HIRI model and the effect of anti-IL17A antibody intervention in three time points (including pre-ischemic, post-ischemic, and at 1 h of reperfusion) on HIRI and the expression of these genes was investigated. Bioinformatic analyses of the screened characterized genes revealed that inflammation, immune response, and cell death modulation were significantly associated with HIRI pathophysiology. CCL2, BTG2, GADD45A, FOS, CXCL10, TNFRSF12A, and IL-17 pathway were identified as key components involved in the HIRI. Serum and liver IL-17A expression were significantly upregulated during the initial phase of HIRI. Pretreatment with anti-IL-17A antibody effectively alleviated the damage of liver tissue, suppressed inflammatory factors, and serum transaminase levels, and downregulated the mRNA expression of CCL2, GADD45A, FOS, CXCL10, and TNFRSF12A. Injection of anti-IL17A antibody after ischemia and at 1 h of reperfusion failed to demonstrate anti-inflammatory and attenuating HIRI benefits relative to earlier intervention. Our study reveals that the IL-17 pathway and related genes may be involved in the proinflammatory mechanism of HIRI, which may provide a new perspective and theoretical basis for the prevention and treatment of HIRI.

Detection of lard adulteration in 3 kinds of vegetable oils by liquid chromatography-mass spectrometry with porous graphite carbon column.

Liquid chromatography‒mass spectrometry employing porous graphite carbon columns and an n-octane-isopropanol mobile phase was utilized for the separation of triacylglycerols (TAGs) in various edible oils, aiming to identify lard adulteration in soybean, corn, and sunflower seed oils. Experiments were conducted using a Hypercarb column (2.1 mm × 100 mm, 5 µm) and an n-octane-isopropanol (70:30, V/V) mobile phase at a flow rate of 0.25 mL· min-1 and a column temperature of 60 °C. Detection was achieved through atmospheric pressure chemical ionization-mass spectrometry. Analysis of diverse edible oil samples revealed that oils of the same type shared similar TAG compositions, while different types exhibited distinct TAG profiles. Distinct variations in triglyceride composition were observed across different edible oils. Based on liquid chromatography‒mass spectrometry analysis, the characteristic component 1-stearic acid-2-palmitic acid-3-oleic acid glyceride (SPO), which may also include PSO, was identified in lard through principal component analysis and orthogonal partial least squares discriminant analysis. This component served as a marker for detecting as low as 0.1% lard adulteration in soybean, corn, and sunflower seed oils. The technique offers a precise and effective approach for the identification of lard adulteration in these edible oils.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis.

Background: Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP. Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized. Results: This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively. Conclusions: Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Umbrella review of risk factors for inflammatory bowel disease: a study protocol.

INTRODUCTION: Inflammatory bowel disease (IBD) is a chronic idiopathic inflammatory disorder that arises from complex interactions between genetics, environment and gut microbiota. It encompasses Crohn's disease, ulcerative colitis and IBD-unclassified. The protracted course of IBD imposes a significant burden on patients' quality of life, economic productivity, social functioning, as well as treatment, hospitalisation and surgery. This study aims to conduct an umbrella review of meta-analyses to systematically evaluate the methodology's quality, potential biases and validity of all epidemiological evidence focused on risk factors for IBD while providing an overview of the evidence concerning IBD risk factors. METHODS AND ANALYSIS: We will systematically search, extract and analyse data from reported systematic reviews and meta-analyses that specifically focus on the risk factors of IBD, following the guidelines outlined in Preferred Reporting Items for Overviews of Reviews. Our search will encompass PubMed, Embase, Web of Science and the Cochrane Database of Systematic Reviews from the initial period up until April 2023 (last update), targeting systematic reviews and meta-analyses based on non-interventional studies. Inclusion criteria allow for systematic reviews and meta-analyses evaluating IBD risk factors across all countries and settings, regardless of ethnicity or sex. The identified risk factors will be categorised according to the health ecological model into innate personal traits, behavioural lifestyles, interpersonal networks, socioeconomic status and macroenvironments. To assess methodological quality for each meta-analysis included in our study, two authors will employ a measurement tool to assess the methodological quality of systematic reviews (AMSTAR)-2, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) criteria along with evidence classification criteria. ETHICS AND DISSEMINATION: Ethical approval is not required for this umbrella review. We will seek to submit the results for publication in a peer-reviewed journal or present it at conferences. PROSPERO REGISTRATION NUMBER: CRD42023417175.

Promoting tissue repair using deferoxamine nanoparticles loaded biomimetic gelatin/HA composite hydrogel.

To effectively address underlying issues and enhance the healing process of hard-to-treat soft tissue defects, innovative therapeutic approaches are required. One promising strategy involves the incorporation of bioactive substances into biodegradable scaffolds to facilitate synergistic tissue regeneration, particularly in vascular regeneration. In this study, we introduce a composite hydrogel design that mimics the extracellular matrix by covalently combining gelatin and hyaluronic acid (HA), with the encapsulation of deferoxamine nanoparticles (DFO NPs) for potential tissue regeneration applications. Crosslinked hydrogels were fabricated by controlling the ratio of HA in the gelatin-based hydrogels, resulting in improved mechanical properties, enhanced degradation ability, and optimised porosity, compared with hydrogel formed by gelatin alone. The DFO NPs, synthesized using a double emulsion method with poly (D,L-lactide-co-glycolide acid), exhibited a sustained release of DFO over 12 d. Encapsulating the DFO NPs in the hydrogel enabled controlled release over 15 d. The DFO NPs, composite hydrogel, and the DFO NPs loaded hydrogel exhibited excellent cytocompatibility and promoted cell proliferationin vitro. Subcutaneous implantation of the composite hydrogel and the DFO NPs loaded hydrogel demonstrated biodegradability, tissue integration, and no obvious adverse effects, evidenced by histological analysis. Furthermore, the DFO NPs loaded composite hydrogel exhibited accelerated wound closure and promoted neovascularisation and granular formation when tested in an excisional skin wound model in mice. These findings highlight the potential of our composite hydrogel system for promoting the faster healing of diabetes-induced skin wounds and oral lesions through its ability to modulate tissue regeneration processes.

Osteopontin-driven partial epithelial-mesenchymal transition governs the development of middle ear cholesteatoma.

Cholesteatoma is a common disease of the middle ear. Currently, surgical removal is the only treatment option and patients face a high risk of relapse. The molecular basis of cholesteatoma remains largely unknown. Here, we show that Osteopontin (OPN), a predominantly secreted protein, plays a crucial role in the development of middle ear cholesteatoma. Global transcriptome analysis revealed the loss of epithelial features and an enhanced immune response in human cholesteatoma tissues. Quantitative RT-PCR and immunohistochemical staining of middle ear cholesteatoma validated the reduced expression of epithelial markers, as well as the elevated expression of mesenchymal markers including Vimentin and Fibronectin, but not N-Cadherin, α-smooth muscle actin (α-SMA) or ferroptosis suppressor protein 1 (FSP1), indicating a partial epithelial-mesenchymal transition (EMT) state. Besides, the expression of OPN was significantly elevated in human cholesteatoma tissues. Treatment with OPN promoted cell proliferation, survival and migration and led to a partial EMT in immortalized human keratinocyte cells. Importantly, blockade of OPN signaling could remarkably improve the cholesteatoma-like symptoms in SD rats. Our mechanistic study demonstrated that the AKT-zinc finger E-box binding homeobox 2 (ZEB2) axis mediated the effects of OPN. Overall, these findings suggest that targeting the OPN signaling represents a promising strategy for the treatment of middle ear cholesteatoma.