The impact of vitamin E, vitamin B6, and niacin intake on cataract incidence based on NHANES 2005-2008 data.

Objective: This investigation aims to elucidate the correlations between dietary intakes of vitamin E, B6, and niacin and the incidence of cataracts, utilizing the comprehensive NHANES 2005-2008 dataset to affirm the prophylactic roles of these nutrients against cataract formation. Methods: Using data from the NHANES 2005-2008 cycles, this analysis concentrated on 7,247 subjects after exclusion based on incomplete dietary or cataract data. The identification of cataracts was determined through participants' self-reported ophthalmic surgical history. Nutritional intake was gauged using the automated multiple pass method, and the data were analyzed using logistic and quantile regression analyses to investigate the relationship between vitamin consumption and cataract prevalence. Results: Our analysis identified significant inverse associations between the intake of vitamins E, B6, and niacin and the risk of cataract development. Specifically, higher intakes of vitamin B6 (OR = 0.85, 95% CI = 0.76-0.96, p = 0.0073) and niacin (OR = 0.98, 95% CI = 0.97-1.00, p = 0.0067) in the top quartile were significantly associated with a reduced likelihood of cataract occurrence. Vitamin E intake showed a consistent reduction in cataract risk across different intake levels (OR = 0.96, 95% CI = 0.94-0.99, p = 0.0087), demonstrating a nonlinear inverse correlation. Conclusion: The outcomes indicate that elevated consumption of vitamin B6 and niacin, in conjunction with regular vitamin E intake, may have the potential to delay or prevent cataract genesis. These results suggest a novel nutritional strategy for cataract prevention and management, advocating that focused nutrient supplementation could be instrumental in preserving eye health and reducing the risk of cataracts. Further research is recommended to validate these findings and establish optimal dosages for maximum benefit.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

Impact of Hemoglobin Levels on Composite Cardiac Arrest or Stroke Outcome in Patients With Respiratory Failure Due to COVID-19.

OBJECTIVES: Anemia has been associated with an increased risk of both cardiac arrest and stroke, frequent complications of COVID-19. The effect of hemoglobin level at ICU admission on a composite outcome of cardiac arrest or stroke in an international cohort of COVID-19 patients was investigated. DESIGN: Retrospective analysis of prospectively collected database. SETTING: A registry of COVID-19 patients admitted to ICUs at over 370 international sites was reviewed for patients diagnosed with cardiac arrest or stroke up to 30 days after ICU admission. Anemia was defined as: normal (hemoglobin ≥ 12.0 g/dL for women, ≥ 13.5 g/dL for men), mild (hemoglobin 10.0-11.9 g/dL for women, 10.0-13.4 g/dL for men), moderate (hemoglobin ≥ 8.0 and < 10.0 g/dL for women and men), and severe (hemoglobin < 8.0 g/dL for women and men). PATIENTS: Patients older than 18 years with acute COVID-19 infection in the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 6926 patients (median age = 59 yr, male = 65%), 760 patients (11.0%) experienced stroke (2.0%) and/or cardiac arrest (9.4%). Cardiac arrest or stroke was more common in patients with low hemoglobin, occurring in 12.8% of patients with normal hemoglobin, 13.3% of patients with mild anemia, and 16.7% of patients with moderate/severe anemia. Time to stroke or cardiac arrest by anemia status was analyzed using Cox proportional hazards regression with death as a competing risk. Covariates selected through clinical knowledge were age, sex, comorbidities (diabetes, hypertension, obesity, and cardiac or neurologic conditions), pandemic era, country income, mechanical ventilation, and extracorporeal membrane oxygenation. Moderate/severe anemia was associated with a higher risk of cardiac arrest or stroke (hazard ratio, 1.32; 95% CI, 1.05-1.67). CONCLUSIONS: In an international registry of ICU patients with COVID-19, moderate/severe anemia was associated with increased hazard of cardiac arrest or stroke.

Microvirga sesbaniae sp. nov. and Microvirga yunnanensis sp. nov., Pink-Pigmented Bacteria Isolated from Root Nodules of Sesbania cannabina (Retz.) Poir.

Four pigment-producing rhizobial strains nodulating Sesbania cannabina (Retz.) Poir. formed a unique group in genus Microvirga in the phylogeny of a 16S rRNA gene and five housekeeping genes (gyrB, recA, dnaK, glnA, and atpD) in a genome analysis, phenotypic characteristics analysis, and chemotaxonomic analysis. These four strains shared as high as 99.3% similarity with Microvirga tunisiensis LmiM8T in the 16S rRNA gene sequence and, in an MLSA, were subdivided into two clusters, ANI (genome average nucleotide) and dDDH (digital DNA-DNA hybridization) which shared sequence similarities lower than the species thresholds with each other and with the reference strains for related Microvirga species. The polar lipids elucidated that phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and cardiolipin were the main components for strain SWF67558T and for strain HBU65207T, with the exception of PC. SWF67558T and HBU65207T strains had similar predominant cellular fatty acids, including C16:0, C18:0, summed feature 2, and summed feature8, but with different contents. In addition, all the four novel strains produced pink-pigment, and the main coloring material extract from strain SWF67558T was identified as zeaxanthin, which presented antioxidant ability and reduction power. With all the phylogenetic and phenotypic divergency, we proposed these pink-pigmented symbiotic bacteria as two novel species, named Microvirga sesbaniae sp. nov. and Microvirga yunnanensis sp. nov., with SWF67558T (=KCTC82331T=GDMCC1.2024T) and HBU65207T (=KCTC92125T=GDMCC1.2023T) as the type strains, respectively.

The H1/H5 domain contributes to OsTRBF2 phase separation and gene repression during rice development.

Transcription factors (TFs) tightly control plant development by regulating gene expression. The phase separation of TFs plays a vital role in gene regulation. Many plant TFs have the potential to form phase-separated protein condensates; however, little is known about which TFs are regulated by phase separation and how it affects their roles in plant development. Here, we report that the rice (Oryza sativa) single Myb TF TELOMERE REPEAT-BINDING FACTOR 2 (TRBF2) is highly expressed in fast-growing tissues at the seedling stage. TRBF2 is a transcriptional repressor that binds to the transcriptional start site of thousands of genes. Mutation of TRBF2 leads to pleiotropic developmental defects and misexpression of many genes. TRBF2 displays characteristics consistent with phase separation in vivo and forms phase-separated condensates in vitro. The H1/H5 domain of TRBF2 plays a crucial role in phase separation, chromatin targeting, and gene repression. Replacing the H1/H5 domain by a phase-separated intrinsically disordered region from Arabidopsis (Arabidopsis thaliana) AtSERRATE partially recovers the function of TRBF2 in gene repression in vitro and in transgenic plants. We also found that TRBF2 is required for trimethylation of histone H3 Lys27 (H3K27me3) deposition at specific genes and genome wide. Our findings reveal that phase separation of TRBF2 facilitates gene repression in rice development.

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.

Strain to shine: stretching-induced three-dimensional symmetries in nanoparticle-assembled photonic crystals.

Stretching elastic materials containing nanoparticle lattices is common in research and industrial settings, yet our knowledge of the deformation process remains limited. Understanding how such lattices reconfigure is critically important, as changes in microstructure lead to significant alterations in their performance. This understanding has been extremely difficult to achieve due to a lack of fundamental rules governing the rearrangements. Our study elucidates the physical processes and underlying mechanisms of three-dimensional lattice transformations in a polymeric photonic crystal from 0% to over 200% strain during uniaxial stretching. Corroborated by comprehensive experimental characterizations, we present analytical models that precisely predict both the three-dimensional lattice structures and the macroscale deformations throughout the stretching process. These models reveal how the nanoparticle lattice and matrix polymer jointly determine the resultant structures, which breaks the original structural symmetry and profoundly changes the dispersion of photonic bandgaps. Stretching induces shifting of the main pseudogap structure out from the 1st Brillouin zone and the merging of different symmetry points. Evolutions of multiple photonic bandgaps reveal potential optical singularities shifting with strain. This work sets a new benchmark for the reconfiguration of soft material structures and may lay the groundwork for the study of stretchable three-dimensional topological photonic crystals.

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.

AIM: To quantify the performance of artificial intelligence (AI) in detecting glaucoma with spectral-domain optical coherence tomography (SD-OCT) images. METHODS: Electronic databases including PubMed, Embase, Scopus, ScienceDirect, ProQuest and Cochrane Library were searched before May 31, 2023 which adopted AI for glaucoma detection with SD-OCT images. All pieces of the literature were screened and extracted by two investigators. Meta-analysis, Meta-regression, subgroup, and publication of bias were conducted by Stata16.0. The risk of bias assessment was performed in Revman5.4 using the QUADAS-2 tool. RESULTS: Twenty studies and 51 models were selected for systematic review and Meta-analysis. The pooled sensitivity and specificity were 0.91 (95%CI: 0.86-0.94, I2=94.67%), 0.90 (95%CI: 0.87-0.92, I2=89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93-11.15, I2=89.31%) and 0.11 (95%CI: 0.07-0.16, I2=95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15-148.15, I2=100%) and 0.95 (95%CI: 0.93-0.97). There was no threshold effect (Spearman correlation coefficient=0.22, P>0.05). CONCLUSION: There is a high accuracy for the detection of glaucoma with AI with SD-OCT images. The application of AI-based algorithms allows together with "doctor+artificial intelligence" to improve the diagnosis of glaucoma.

Interleukin-21 receptor signaling promotes metabolic dysfunction-associated steatohepatitis-driven hepatocellular carcinoma by inducing immunosuppressive IgA+ B cells.

BACKGROUND: Dysregulation of immune surveillance is tightly linked to the development of metabolic dysfunction-associated steatohepatitis (MASH)-driven hepatocellular carcinoma (HCC); however, its underlying mechanisms remain unclear. Herein, we aimed to determine the role of interleukin-21 receptor (IL-21R) in MASH-driven HCC. METHODS: The clinical significance of IL-21R was assessed in human HCC specimens using immunohistochemistry staining. Furthermore, the expression of IL-21R in mice was assessed in the STAM model. Thereafter, two different MASH-driven HCC mouse models were applied between IL-21R-deficient mice and wild type controls to explore the role of IL-21R in MASH-driven HCC. To further elucidate the potential mechanisms by which IL-21R affected MASH-driven HCC, whole transcriptome sequencing, flow cytometry and adoptive lymphocyte transfer were performed. Finally, flow cytometry, enzyme-linked immunosorbent assay, immunofluorescent staining, chromatin immunoprecipitation assay and western blotting were conducted to explore the mechanism by which IL-21R induced IgA+ B cells. RESULTS: HCC patients with high IL-21R expression exhibited poor relapse-free survival, advanced TNM stage and severe steatosis. Additionally, IL-21R was demonstrated to be upregulated in mouse liver tumors. Particularly, ablation of IL-21R impeded MASH-driven hepatocarcinogenesis with dramatically reduction of lipid accumulation. Moreover, cytotoxic CD8+ T lymphocyte activation was enhanced in the absence of IL-21R due to the reduction of immunosuppressive IgA+ B cells. Mechanistically, the IL-21R-STAT1-c-Jun/c-Fos regulatory axis was activated in MASH-driven HCC and thus promoted the transcription of Igha, resulting in the induction of IgA+ B cells. CONCLUSIONS: IL-21R plays a cancer-promoting role by inducing IgA+ B cells in MASH-driven hepatocarcinogenesis. Targeting IL-21R signaling represents a potential therapeutic strategy for cancer therapy.

Recent advances in organic waste pyrolysis and gasification in a CO2 environment to value-added products.

The persistent combustion of fossil fuels has resulted in a widespread greenhouse effect attributable to the continual elevation of carbon dioxide (CO2) levels in the atmosphere. Recent research indicates that utilizing CO2 as a pyrolysis gasification medium diminishes CO2 emissions and concurrently augments the value of the resultant pyrolysis gasification products. This paper reviews recent advancements in the pyrolysis gasification of organic solid wastes under a CO2 atmosphere. Meanwhile, the mechanisms of CO2 influence in the pyrolysis and gasification processes were also discussed. In comparison to noble gases, CO2 exhibits reactivity with char at≥710 °C, resulting in additional mass loss of the sample. In addition, CO2 was able to increase the specific surface area and stability of biochar and reduce biooil toxicity by lowering the content of cyclic compounds in the biooil, while CO2 was able to react with GPRs with some volatile products (e.g., light hydrocarbons) to increase biogas yield. Finally, CO2 also prevents catalyst deactivation by reducing secondary coke formation. We also recommend directing future attention toward utilizing unpurified CO2 in pyrolysis and gasification. This review aims to expand the utilization of CO2 and advocate for applying pyrolysis gasification products.

Case report: Synchronous tumors of the female reproductive tract in systemic lupus erythematosus: report of two cases and review of the literature.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple systems. Patients with SLE are prone to a variety of malignancies, especially neoplasms of the female reproductive tract. Synchronous tumors, considered to involve multiple sites, are rare in the female reproductive tract. There are hardly any reports of SLE with synchronous reproductive tract tumors. Case presentation: We report the occurrence of two to three reproductive tract tumors in two women with SLE. A 52-year-old woman was diagnosed with vulvar cancer and cervical cancer. Another woman, aged 67, was diagnosed with concurrent vulvar cancer, vaginal cancer, and cervical cancer and also presented with a suspected lung cancer. Conclusion: The presence of synchronous tumors of the reproductive tract in patients with SLE is uncommon and can be easily disregarded. It is crucial to highlight that SLE patients with multiple primary malignancies exhibit notable late-stage presentation at the time of diagnosis, inadequate disease-free survival, poor overall survival, rapid progression rates, and mortality. Consequently, greater awareness must be raised regarding synchronous reproductive tract tumors in patients with SLE. Regular comprehensive cancer screening and management should be implemented for individuals diagnosed with SLE.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Mechanisms Underlying the Overlooked Chiral Fungicide-Driven Enantioselective Proliferation of Antibiotic Resistance in Earthworm Intestinal Microbiome.

From a "One Health" perspective, the global threat of antibiotic resistance genes (ARGs) is associated with modern agriculture practices including agrochemicals application. Chiral fungicides account for a considerable proportion of wildly used agrochemicals; however, whether and how their enantiomers lead to differential proliferation of antibiotic resistance in agricultural environments remain overlooked. Focused on the soil-earthworm ecosystem, we for the first time deciphered the mechanisms underlying the enantioselective proliferation of antibiotic resistance driven by the enantiomers of a typical chiral fungicide mandipropamid (i.e., R-MDP and S-MDP) utilizing a multiomic approach. Time-series metagenomic analysis revealed that R-MDP led to a significant enhancement of ARGs with potential mobility (particularly the plasmid-borne ARGs) in the earthworm intestinal microbiome. We further demonstrated that R-MDP induced a concentration-dependent facilitation of plasmid-mediated ARG transfer among microbes. In addition, transcriptomic analysis with verification identified the key aspects involved, where R-MDP enhanced cell membrane permeability, transfer ability, biofilm formation and quorum sensing, rebalanced energy production, and decreased cell mobility versus S-MDP. Overall, the findings provide novel insights into the enantioselective disruption of microbiome and resistome in earthworm gut by chiral fungicides and offer significant contributions to the comprehensive risk assessment of chiral agrochemicals in agroecosystems.

Zea mays cultivation, biochar, and arbuscular mycorrhizal fungal inoculation influenced lead immobilization.

Plant cultivation can influence the immobilization of heavy metals in soil. However, the roles of soil amendments and microorganisms in crop-based phytoremediation require further exploration. In this study, we evaluated the impact of Zea mays L. cultivation, biochar application, and arbuscular mycorrhizal fungi (AMF) inoculation on soil lead (Pb) immobilization. Our results indicated that biochar addition resulted in a significant, 42.00%, reduction in AMF colonization. Plant cultivation, AMF inoculation, and biochar addition all contributed to enhanced Pb immobilization, as evidenced by decreased levels of diethylenetriaminepentaacetic acid- and CaCl2-extractable Pb in the soil. Furthermore, soil subjected to plant cultivation with AMF and biochar displayed reduced concentrations of bioavailable Pb. Biochar addition altered the distribution of Pb fractions in the soil, transforming the acid-soluble form into the relatively inert reducible and oxidizable forms. Additionally, biochar, AMF, and their combined use promoted maize growth parameters, including height, stem diameter, shoot and root biomass, and phosphorus uptake, while simultaneously reducing the shoot Pb concentration. These findings suggest a synergistic effect in Pb phytostabilization. In summary, despite the adverse impact of biochar on mycorrhizal growth, cultivating maize with the concurrent use of biochar and AMF emerges as a recommended and effective strategy for Pb phytoremediation.IMPORTANCEHeavy metal contamination in soil is a pressing environmental issue, and phytoremediation has emerged as a sustainable approach for mitigating this problem. This study sheds light on the potential of maize cultivation, biochar application, and arbuscular mycorrhizal fungi (AMF) inoculation to enhance the immobilization of Pb in contaminated soil. The findings demonstrate that the combined use of biochar and AMF during maize cultivation can significantly improve Pb immobilization and simultaneously enhance maize growth, offering a promising strategy for sustainable and effective Pb phytoremediation practices. This research contributes valuable insights into the field of phytoremediation and its potential to address heavy metal pollution in agricultural soils.

Enhanced absorption of SO2 from phosphogypsum decomposition by phosphate slurry for phosphoric acid production.

Phosphogypsum (PG) is a major industrial by-product of wet process phosphoric acid production, and untreated PG stockpiled on land will cause severe environmental pollution. Thermal treatment of PG is currently the mainstream treatment method PG can be thermally decomposed to produce CaO, and the decomposition process produces large amounts of SO2. In this paper, phosphate slurry was used to absorb SO2 generated during the PG decomposition to produce phosphoric acid. The effects of operating conditions such as pressure, inlet SO2 concentration, and additive content on the desulfurization efficiency, as well as phosphoric acid yield, were investigated. Under the optimal experimental parameters, the desulfurization efficiency was 100% in the first 3 h, and decreased to 67.42% after 5 h, the maximum phosphate concentration in the solution was 1445.92 mg/L. The Density functional theory (DFT) calculations showed that SO2 and O2 adsorbed on the surface of P2O5 underwent to generate SO3, which can react with H2O to produce H2SO4. Moreover, it was found that Fe3+ could enhance the catalytic oxidation process of SO2 and O2 by decreasing the reaction energy barrier. This study should be helpful for the recycling of phosphorus resources.

Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.

RNA-Seq and miRNA-Seq data from Epstein-Barr virus-infected tree shrews reveal a ceRNA network contributing to immune microenvironment regulation.

Epstein-Barr virus (EBV) infection in humans is ubiquitous and associated with various diseases. Remodeling of the immune microenvironment is the primary cause of EBV infection and pathogenesis; however, the underlying mechanism has not been fully elucidated. In this study, we used whole-transcriptome RNA-Seq to detect mRNAs, long non-coding RNAs (lncRNA), and microRNA (miRNA) profiles in the control group, 3 days, and 28 days after EBV infection, based on the tree shrew model that we reported previously. First, we estimated the proportion of 22 cell types in each sample using CIBERSORT software and identified 18 high-confidence DElncRNAs related to immune microenvironment regulation after EBV infection. Functional enrichment analysis of these differentially expressed lncRNAs primarily focused on the autophagy, endocytosis, and ferroptosis signalling pathways. Moreover, EBV infection affects miRNA expression patterns, and many miRNAs are silenced. Finally, three competing endogenous RNA regulatory networks were built using lncRNAs that significantly correlated with immune cell types, miRNAs that responded to EBV infection, and potentially targeted the mRNA of the miRNAs. Among them, MRPL42-AS-5 might act as an hsa-miR-296-5p "sponge" and compete with target mRNAs, thus increasing mRNA expression level, which could induce immune cell infiltration through the cellular senescence signalling pathway against EBV infection. Overall, we conducted a complete transcriptomic analysis of EBV infection in vivo for the first time and provided a novel perspective for further investigation of EBV-host interactions.

Detecting EGFR gene amplification using a fluorescence in situ hybridization platform based on digital microfluidics.

Signal transduction mediated by epidermal growth factor receptor (EGFR) gene affects the proliferation, invasion, metastasis, and angiogenesis of tumor cells. In particular, non-small cell lung cancer (NSCLC) patients with increased in copy number of EGFR gene are often sensitive to tyrosine kinase inhibitors. Despite being the standard for detecting EGFR amplification in the clinic, fluorescence in situ hybridization (FISH) traditionally involves repetitive and complex benchtop procedures that are not only time consuming but also require well-trained personnel. To address these limitations, we develop a digital microfluidics-based FISH platform (DMF-FISH) that automatically implements FISH operations. This system mainly consists of a DMF chip for reagent operation, a heating array for temperature control and a signal processing system. With the capability of automatic droplet handling and efficient temperature control, DMF-FISH performs cell digestion, gradient elution, hybridization and DAPI staining without manual intervention. In addition to operational feasibility, DMF-FISH yields comparable performance with the benchtop FISH protocol but reducing the consumption of DNA probe by 87 % when tested with cell lines and clinical samples. These results highlight unique advantages of the fully automated DMF-FISH system and thus suggest its great potential for clinical diagnosis and personalized therapy of NSCLC.

The efficacy of rituximab plus belimumab or telitacicept in refractory lupus nephritis.

OBJECTIVE: Lupus nephritis is a severe and common complication of systemic lupus erythematosus (SLE). The pathogenesis of lupus nephritis is characterized by B-cell activation and autoantibody formation. Rituximab and belimumab, as well as telitacicept, target B cells through different mechanisms, potentially exerting a synergistic effect in the treatment of lupus nephritis. This study aims to investigate the efficacy and safety of treatment with rituximab followed by belimumab or telitacicept in the management of refractory lupus nephritis. METHODS: We conducted a single-center, open-label, retrospective study, including 25 patients with refractory lupus nephritis. All patients received combination therapy with rituximab in individualized dosages to achieve peripheral B-cell depletion, and then followed by belimumab or telitacicept. The follow-up period was at least 12 months, and the primary end point was renal remission rate at the last follow-up. RESULTS: During a median follow-up of 19 (13, 29) months, 20 of 25 (80%) patients achieved objective remission (OR), including 19 (76%) patients achieved complete renal response (CRR). After rituximab (712 ± 416mg in average), 18 patients received belimumab and seven patients received telitacicept. In the rituximab plus telitacicept group, all patients achieved CRR; while in the rituximab plus belimumab group, 12 (66.7%) patients achieved CRR and 13 (72.2%) patients achieved OR. The mean SLEDAI-2K score decreased from 15 ± 6 to 6 ± 6, representing an average reduction of 60%. At the last follow-up, 18/25 (72%) had prednisone ≤ 5 mg/d or even discontinued prednisone use. Adverse effects were mainly immunoglobulin deficiency, respiratory tract infection, urinary tract infections, and rash. No death occurred. CONCLUSIONS: Rituximab followed by belimumab or telitacicept may be effective in inducing remission in refractory lupus nephritis, with tolerable adverse effects.