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Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of Q EN , Q NIQE , Q SD , Q SSEQ and Q TMQI .

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.

Streptococcus agalactiae ( Strep. agalactiae ) is bovine mastitis pathogen and has thus became a matter of concern to dairy farms worldwide in terms of economic loss. The aims of this study were to (a) determine virulence genes, and (b) characterize the antimicrobial resistance (AMR) profiles and AMR genes and (c) figure out the relationship between AMR phenotypes and genotypes of Strep. agalactiae isolated from dairy cows in north China. A total of 20 virulence genes and 23 AMR genes of 140 isolates collected from 12 farms in six provinces were studied. The antimicrobial susceptibility of 10 veterinary commonly used antimicrobials were tested using the broth microdilution method. Results showed that all the isolates harbored the virulence genes lac IV, gapC , and dltA . The isolates that harbored the genes lacIII , fbsA , hylB , and cfb exhibited the high prevalence (99.29%), followed by isolates that harbored lac I (98.57%), bibA (97.86%), cylE (97.14%), lac II (92.14%), cspA (52.14%), pavA (25%), bca (2.14%), and scpB (0.71%). The fbsB , lmb , spbI , bac , and rib genes were not detected. The virulence patterns of B ( fbsA _ cfb _ cylE _ hylB _ bibA _ cspA _ gapC _ dltA _ lacIII/IV ) and C ( fbsA _ cfb _ bibA _ gapC _ dltA _ lacIV ) were dominant, accounting for 97.86% of the isolates. The following AMR genes were prevalent: pbp1A (97.14%), tet (M) (95.00%), lnu (A) (80.71%), erm (B) (75.00%), tet (O) (72.14%), blaZ (49.29%), tet (S) (29.29%), blaTEM (25.71%), erm (A) (17.14%), erm (C) (13.57%), tet (L) (10.71%), linB (2.86%), and erm (TR) (2.86%). The pbp2b , mecA1 , mecC , lnu (D), erm (F/G/Q), and mef (A) genes were not detected. Eighty percent of the isolates harbored AMR genes and were highly resistant to tetracycline, followed by macrolides (10.71%), lincosamides (9.29%) and β-lactams (4.29%). In conclusion, isolates only exhibited well correlation between tetracyclines resistance phenotype and genotype, and almost all isolates harbored intact combination of virulence genes.

Understanding distribution of bovine mastitis pathogen Klebsiella spp. can contribute to the treatment decision and the control within programs of bovine mastitis, we conducted a meta-analysis to investigate the epidemiology and antimicrobial resistance rates of Klebsiella spp. associated with bovine mastitis in China. Three databases, namely, PubMed, Google scholar, and China National Knowledge Infrastructure database, were utilized to obtain relevant publications. According to PRISMA reporting standards, a total of 38 publications were included in the research, among them, 7 papers included an AMR test. The pooled prevalence of Klebsiella spp. was 5.41% (95% CI: 3.87–7.50%). Subgroup analysis revealed that the prevalence was higher in South China (8.55%, 95% CI: 3.57–19.09%) than in North China (4.22%, 95% CI: 2.46–7.14%), in 2010–2020 (7.45%, 95% CI: 5.29–110.40%) than in 2000–2010 (3.14%, 95% CI: 1.90–15.14%), and in the clinical bovine mastitis cases (7.49%, 95% CI: 3.71–14.54%) than in the subclinical cases (4.03%, 95% CI: 1.55–10.08%). The pooled AMR rate revealed that Klebsiella spp. were most resistant to sulfonamides (45.07%, 95% CI: 27.72–63.71%), followed by tetracyclines (36.18%, 95% CI: 23.36–51.34%), aminoglycosides (27.47%, 95% CI: 17.16–40.92%), β-lactams (27.35%, 95% CI: 16.90–41.05%), amphenicol (26.82%, 95% CI: 14.17–44.87%), lincosamides (21.24%, 95% CI: 7.65–46.75%), macrolides (20.98%, 95% CI: 7.20–47.58%), polypeptides (15.51%, 95% CI: 6.46–32.78%), and quinolones (7.8%, 95% CI: 3.25–17.56%). The climate difference between South and North China and the natural pathogenicity of Klebsiella spp. may be the primary reasons for its distribution, and the prevalence of Klebsiella spp. indicated that the genus is an increasing hazard to the dairy industry. The prevalence of AMR in China is commonly higher than in the European countries and Canada, this is a very important concern for strategy programs to control bovine mastitis caused by Klebsiella spp. in China.

PurposeEpidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making.MethodsWe collected data from 320 patients with lung carcinoma, including sex, age, smoking history, serum tumour marker levels, maximum standardized uptake value, pathological results, computed tomography images, and EGFR mutation status. Artificial neural network (ANN) models based on multiple clinical characteristics were proposed to predict EGFR mutation status.ResultsA training set (n = 200) was used to develop predictive models of the EGFR mutation status (Model 1: area under the receiver operating characteristic curve [AUROC] = 0.910, 95% CI 0.861–0.945; Model 2: AUROC = 0.859, 95% CI 0.803–0.904; Model 3: AUROC = 0.711, 95% CI 0.643–0.773). A testing set (n = 50) and temporal validation data set (n = 70) were used to evaluate the generalisation performance of the established models (testing set: Model 1, AUROC = 0.845, 95% CI 0.715–0.932; Model 2, AUROC = 0.882, 95% CI 0.759–0.956; Model 3, AUROC = 0.817, 95% CI 0.682–0.912; temporal validation dataset: Model 1, AUROC = 0.909, 95% CI 0.816–0.964; Model 2, AUROC = 0.855, 95% CI 0.751–0.928; Model 3, AUROC = 0.831, 95% CI 0.723–0.910). The predictive abilities of the three ANN models were superior to that of a previous logistic regression model (P < 0.001, 0.027, and 0.050, respectively).ConclusionsANN models provide a non-invasive and readily available method for EGFR mutation status prediction.

The purpose of the present study was to evaluate the potential relationship of lymphocyte-to-monocyte ratio (LMR) with outcomes of septic patients at intensive care unit (ICU) admission. 3087 septic patients were included in the final cohort by using the Medical Information Mart for Intensive Care (MIMIC) database. We evaluated the association of different groups of LMR with 28-day survival and 1-year survival via Kaplan-Meier (K-M) analysis and Cox regression analysis. Subgroups analysis of LMR was performed to further explore the effect of LMR on survival. According to the optimal cut-off value, the cohort was divided into low-LMR and high-LMR groups. The 28-day and 1-year survival rates were 47.9% and 19.9%, respectively, in the low-LMR group, and 60.4% and 25.9%, respectively, in the high-LMR group. Univariate logistic regression and K-M analyses revealed that the 28-day and 1-year survival rates of the high-LMR group were higher than those of the low-LMR group (both < 0.001). A subgroup analysis of LMR identified a significant stepwise decrease in the risk of death at 28 days and 1 year from group 1 to group 4 (LMR increased gradually) after adjustment for multiple variables. We report for the first time that a lower LMR value is independently predictive of a poor prognosis in septic patients. Therefore, as an inexpensive and readily available indicator, LMR may facilitate stratification of prognosis in septic patients.

Background Patients undergoing radical gastrectomy demonstrate considerable variability in their prognoses, underscoring the urgent need for reliable biomarkers to inform personalized therapeutic strategies. This study seeks to develop and validate a novel prognostic model by combining preoperative immune, inflammatory, and nutritional biomarkers with computed tomography (CT) imaging features, thereby facilitating the prediction of outcomes in patients with gastric cancer who have undergone radical resection and aiding in the formulation of personalized clinical treatment strategies. Methods This retrospective study analyzed consecutive patients with a preoperative diagnosis of gastric cancer who underwent radical gastrectomy at two participating centers between January 2015 and December 2016. Based on predefined inclusion and exclusion criteria, eligible patients were randomly allocated to either a training cohort, which was used for model development and internal estimation of parameters, or a validation cohort, which served for independent testing of the model’s predictive performance. We assessed a range of preoperative hematological parameters and CT imaging features. Factors associated with overall survival (OS) were identified using least absolute shrinkage and selection operator (LASSO) regression analysis, and a prognostic model was subsequently constructed. Results A total of 393 patients were enrolled in the study and randomly allocated to the training and validation cohorts in a 7:3 ratio. The final prognostic model incorporated eight hematological indicators: white blood cell (WBC) count, hemoglobin (HB), total protein (TP), creatinine (Cr), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and systemic immune-inflammatory index (SII), in addition to CT staging characteristics. Time-dependent receiver operating characteristic (ROC) analysis of the risk scores yielded areas under the curve (AUCs) of 0.715, 0.740, and 0.736 for the training set, and 0.597, 0.631, and 0.657 for the validation set at 1, 3, and 5 years, respectively. High-risk patients had a substantially worse overall survival rate than low-risk patients, according to Kaplan-Meier analysis. Conclusion The immune, inflammatory, and nutritional-CT (IIN-CT) model, which integrates preoperative immune, inflammatory, and nutritional biomarkers with CT imaging features, significantly improves the accuracy of preoperative prognostic predictions in gastric cancer patients.

Neural tube defects (NTDs) are common and severe birth defects. Folate supplementation can prevent NTDs, but the underlying molecular mechanisms are unclear. Aberrant wnt/β-catenin pathway activation leads to defective anteroposterior patterning, resulting in NTDs, but little is known about whether epigenetic factors contribute to this process. Here, we performed ChIP and Cut&Tag to explore H3K4me3 in folate-deficient cells and NTDs mouse models. Our findings show folate deficiency increased H3K4me3 levels at wnt target genes promoters, enhancing their transcription. This effect was mediated by reduced expression of histone demethylase KDM5A, leading to the maintenance of H3K4me3 marks and activation of wnt/β-catenin signalling. Similarly, wnt/β-catenin pathway was activated in KDM5A-KO cells, differentiation of neuronal progenitors cells from mouse ESCs under folate deficiency and folate-deficient NTD mice. Intriguingly, KDM5A depletion in zebrafish embryos resulted in defective neurodevelopment and increased wnt signalling. Furthermore, the transcription factor PAX2 downregulated KDM5A under folate-deficient conditions. Clinically, increased H3K4me3 levels and wnt target genes expression were observed in low-folate NTDs brain samples. All these findings suggest KDM5A-dependent epigenetic regulation of wnt signaling is crucial in low folate NTDs, implicating a potential therapeutic target.

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems.

Rabbit coccidiosis is caused by infection with one or, more commonly, several Eimeria species that parasitize the hepatobiliary ducts or intestinal epithelium of rabbits. Currently, there are eleven internationally recognized species of rabbit coccidia, with the complete mitochondrial (mt) genomes of six species commonly infecting rabbits having been sequenced and annotated. Eimeria kongi was initially discovered in 2011 and prompted a preliminary study on this species. Through traditional morphological analysis, E. kongi was identified as a novel species of rabbit coccidia. To further validate this classification, we sequenced and annotated its mitochondrial genome. The complete mt genome of E. kongi spans 6258 bp and comprises three cytochrome genes (cytb, cox1, cox3), fourteen gene fragments for the large subunit (LSU) rRNA, and nine gene fragments for the small subunit (SSU) rRNA, lacking transfer RNA (tRNA) genes. Moreover, phylogenetic analysis of the mitochondrial genome sequence of E. kongi revealed its clustering with six other species of rabbit coccidia into a monophyletic group. Additionally, E. irresidua and E. flavescens were grouped within the lineage lacking oocyst residuum, consistent with their morphological characteristics. Consistent with multiple molecular phylogenies, in this investigation, E. kongi was further confirmed as a new species of rabbit coccidia. Our research findings are of great significance for the classification of coccidia and for coccidiosis prevention and control in rabbits.