Clinico-pathological study of esophageal mucoepidermoid carcinoma: a 10-year survival from a single center.

BACKGROUND: Mucoepidermoid Carcinoma of the Esophagus (MECE) is a relatively rare tumor type, with most of the current data derived from case reports or small sample studies. This retrospective study reports on the 10-year survival data and detailed clinicopathological characteristics of 48 patients with esophageal MEC. METHODS: Data were collected from 48 patients who underwent curative surgery for esophageal MEC at the Fourth Hospital of Hebei Medical University between January 1, 2004, and December 31, 2020. These were compared with contemporaneous cases of Esophageal Squamous Cell Carcinoma (ESCC) and Esophageal Adenocarcinoma (EAC). Using the Kaplan-Meier method and multivariate Cox regression analysis, we investigated the clinicopathological factors affecting the survival of patients with MEC. RESULTS: The incidence of MECE was predominantly higher in males, with a male-to-female ratio of approximately 7:1. The mid-thoracic segment emerged as the most common site of occurrence. A mere 6.3% of cases were correctly diagnosed preoperatively. The lymph node metastasis rate stood at 35.4%. The overall 1-year, 3-year, 5-year, and 10-year survival rates for all patients were 85.4%, 52.1%, 37.0%, and 31.0%, respectively. Post 1:1 propensity score matching, no significant statistical difference was observed in the Overall Survival (OS) between MEC patients and those with Esophageal Squamous Cell Carcinoma (ESCC) and Esophageal Adenocarcinoma (EAC) (P = 0.119, P = 0.669). Univariate analysis indicated that T staging and N staging were the primary factors influencing the prognosis of esophageal MEC. CONCLUSIONS: MECE occurs more frequently in males than females, with the mid-thoracic segment being the most common site of occurrence. The rate of accurate preoperative endoscopic diagnosis is low. The characteristic of having a short lesion length yet exhibiting significant extramural invasion may be a crucial clinicopathological feature of MECE. The OS of patients with MEC does not appear to significantly differ from those with esophageal squamous carcinoma and adenocarcinoma.

Early-life exposure to tobacco, genetic susceptibility, and accelerated biological aging in adulthood.

Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high-polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

Preparation, Isolation and Antioxidant Function of Peptides from a New Resource of Rumexpatientia L. ×Rumextianshanicus A. Los.

Rumexpatientia L. ×Rumextianshanicus A. Los (RRL), known as "protein grass" in China, was recognized as a new food ingredient in 2021. However, the cultivation and product development of RRL are still at an early stage, and no peptide research has been reported. In this study, two novel antioxidant peptides, LKPPF and LPFRP, were purified and identified from RRL and applied to H2O2-induced HepG2 cells to investigate their antioxidant properties. It was shown that 121 peptides were identified by ultrafiltration, gel filtration chromatography, and LC-MS/MS, while computer simulation and molecular docking indicated that LKPPF and LPFRP may have strong antioxidant properties. Both peptides were not cytotoxic to HepG2 cells at low concentrations and promoted cell growth, which effectively reduced the production of intracellular ROS and MDA, and increased cell viability and the enzymatic activities of SOD, GSH-Px, and CAT. Therefore, LKPPF and LPFRP, two peptides, possess strong antioxidant activity, which provides a theoretical basis for their potential as food additives or functional food supplements, but still need to be further investigated through animal models as well as cellular pathways.

Acoustic Wave-Induced FeRh Magnetic Phase Transition and Its Application in Antiferromagnetic Pattern Writing and Erasing.

We explore the FeRh magnetic phase transition (MPT) and magnetic phase domain (MPD) with the introduction of surface acoustic waves (SAWs). The effects of the SAW pulses with different pulse widths and powers on resistance-temperature loops are investigated, revealing that the SAW can reduce the thermal hysteresis. Meanwhile, the SAW-induced comb-like antiferromagnetic (AFM) phase domains are observed. By changing the pulse width and SAW frequency, we further realize a writing-erasing process of the different comb-like AFM phase domains in the mixed-phase regime of the cooling transition branch. Resistance measurements also display the repeated SAW writing-erasing and the nonvolatile characteristic clearly. MPT paths are measured to demonstrate that short SAW pulses induce isothermal MPT and write magnetic phase patterns via the dynamic strain, whereas long SAW pulses erase patterns via the acoustothermal effect. The Preisach model is introduced to model the FeRh MPT under the SAW pulses, and the calculated results correspond well with our experiments, which reveals the SAW-induced energy modulation promotes FeRh MPT. COMSOL simulations of the SAW strain field also support our results. Our study not only can be used to reduce the thermal hysteresis but also extends the application of the SAW as a tool to write and erase AFM patterns for spintronics and magnonics.

Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy.

Background: Due to the widespread use of imaging techniques, the detection rate of early-stage lung cancer has increased. Video-assisted thoracoscopic surgery (VATS) sublobectomy has emerged as a prominent alternative to lobectomy, offering advantages like reduced resection range, better preservation of lung function, and enhanced postoperative quality of life. However, sublobectomy is more intricate than lobectomy, necessitating a higher level of surgical proficiency and anatomical understanding. Methods: Three electronic databases were searched to capture relevant studies from January 2016 to March 2023, which related to the application of three-dimensional(3D) technology in VATS sublobectomy. Results: Currently, clinical departments such as orthopedics, hepatobiliary surgery, and urology have started using 3D technology. This technology is expected to be widely used in thoracic surgery in future. Now 3D technology assists in preoperative planning, intraoperative navigation and doctor-patient communication. Conclusion: 3D technologies, instrumental in locating pulmonary nodules and identifying variations in target lung segmental vessels and bronchi, play pivotal roles in VATS sublobectomy, especially in preoperative planning, intraoperative navigation, and doctor-patient communication. The limitations of 3D technology in clinical application are analyzed, and the future direction of existing 3D technology development is prospected.

Near-Infrared Dual Greenhouse Gas Sensor Based on Hollow-Core Photonic Crystal Fiber for Gas-Cell In-Situ Applications.

A greenhouse gas sensor has been developed to simultaneously detect multiple gas species within a hollow-core photonic bandgap fiber (HC-PBF) structure entirely composed of fibers. To enhance sensitivity, the gas cell consists of HC-PBF enclosed between two single-mode fibers fused with a reflective end surface to double the absorption length. The incorporation of side holes for gas diffusion allows for analysis of the relationship between gas diffusion speed, number of drilled side holes, and energy loss. As the number of drilled holes increases, the response time decreases to less than 3 min at the expense of energy loss. Gas experiments demonstrated detection limits of 0.1 ppm for methane and 2 ppm for carbon dioxide, with an average time of 50 s. In-situ testing conducted in rice fields validates the effectiveness of the developed gas detection system using HC-PBF cells, establishing all-fiber sensors with high sensitivity and rapid response.

Shikonin attenuates cerebral ischemia/reperfusion injury via inhibiting NOD2/RIP2/NF-κB-mediated microglia polarization and neuroinflammation.

OBJECTIVES: Microglia-mediated neuroinflammation plays a crucial role in the pathophysiological process of multiple neurological disorders such as ischemic stroke, which still lacks effective therapeutic agents. Shikonin possesses anti-inflammatory and neuroprotective properties. However, its underlying mechanism remains elusive. This study aimed to investigate whether Shikonin confers protection against cerebral ischemia/reperfusion (I/R) injury by modulating microglial polarization and elucidate the associated mechanisms. METHODS: This study employed an oxygen-glucose deprivation and reoxygenation (OGD/R) BV2 microglial cellular model and a middle cerebral artery occlusion/reperfusion (MCAO/R) animal model to investigate the protection and underlying mechanism of Shikonin against ischemic stroke. RESULTS: The results demonstrated that Shikonin treatment significantly reduced brain infarction volume and improved neurological function in MCAO/R rats. Simultaneously, Shikonin treatment significantly reduced microglial proinflammatory phenotype and levels of proinflammatory markers (inducible-NO synthase (iNOS), tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1ß), and IL-6), increased microglial anti-inflammatory phenotype and levels of anti-inflammatory markers (Arginase-1 (Arg1), transforming growth factor-beta (TGF-ß), and IL-10), reversed the expression of Nucleotide-binding oligomerization domain 2 (NOD2) and phosphorylation receptor interacting protein 2 (p-RIP2), and suppressed nuclear factor kappa-B (NF-κB) signaling activation in the ischemic penumbra regions. These effects of Shikonin were further corroborated in OGD/R-treated BV2 cells. Furthermore, overexpression of NOD2 markedly attenuated the neuroprotective effects of Shikonin treatment in MCAO/R rats. NOD2 overexpression also attenuated the regulatory effects of Shikonin on neuroinflammation, microglial polarization, and NF-κB signaling activation. CONCLUSION: This study illustrates that Shikonin mitigates inflammation mediated by microglial proinflammatory polarization by inhibiting the NOD2/RIP2/NF-κB signaling pathway, thereby exerting a protective role. The findings uncover a potential molecular mechanism for Shikonin in treating ischemic stroke.

Ginsenoside Rd enhances blood-brain barrier integrity after cerebral ischemia/reperfusion by alleviating endothelial cells ferroptosis via activation of NRG1/ErbB4-mediated PI3K/Akt/mTOR signaling pathway.

The incidence of ischemic stroke is increasing year by year and showing a younger trend. Impaired blood-brain barrier (BBB) is one of the pathological manifestations caused by cerebral ischemia, leading to poor prognosis of patients. Accumulating evidence indicates that ferroptosis is involved in cerebral ischemia/reperfusion injury (CIRI). We have previously demonstrated that Ginsenoside Rd (G-Rd) protects against CIRI-induced neuronal injury. However, whether G-Rd can attenuate CIRI-induced disruption of the BBB remains unclear. In this study, we found that G-Rd could upregulate the levels of ZO-1, occludin, and claudin-5 in ipsilateral cerebral microvessels and bEnd.3 cells, reduce endothelial cells (ECs) loss and Evans blue (EB) leakage, and ultimately improve BBB integrity after CIRI. Interestingly, the expressions of ACSL4 and COX2 were upregulated, the expressions of GPX4 and xCT were downregulated, the levels of GSH was decreased, and the levels of MDA and Fe2+ were increased in ischemic tissues and bEnd.3 cells after CIRI, suggesting that ECs ferroptosis occurred after CIRI. However, G-Rd can alleviate CIRI-induced BBB disruption by inhibiting ECs ferroptosis. Mechanistically, G-Rd prevented tight junction loss and BBB leakage by upregulating NRG1, activating its tyrosine kinase ErbB4 receptor, and then activating downstream PI3K/Akt/mTOR signaling, thereby inhibiting CIRI-induced ferroptosis in ECs. Taken together, these data provides data support for G-Rd as a promising therapeutic drug for cerebral ischemia.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model.

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations.

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Cell of Origin of Pancreatic cancer: Novel Findings and Current Understanding.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) stands as one of the most lethal diseases globally, boasting a grim 5-year survival prognosis. The origin cell and the molecular signaling pathways that drive PDAC progression are not entirely understood. This review comprehensively outlines the categorization of PDAC and its precursor lesions, expounds on the creation and utility of genetically engineered mouse models used in PDAC research, compiles a roster of commonly used markers for pancreatic progenitors, duct cells, and acinar cells, and briefly addresses the mechanisms involved in the progression of PDAC. We acknowledge the value of precise markers and suitable tracing tools to discern the cell of origin, as it can facilitate the creation of more effective models for PDAC exploration. These conclusions shed light on our existing understanding of foundational genetically engineered mouse models and focus on the origin and development of PDAC.

SeMet alleviates AFB1-induced oxidative stress and apoptosis in rabbit kidney by regulating Nrf2//Keap1/NQO1 and PI3K/AKT signaling pathways.

The purpose of this study was to explore the protective effect of SeMet on renal injury induced by AFB1 in rabbits and its molecular mechanism. Forty rabbits of 35 days old were randomly divided into control group, AFB1 group (0.3 mg AFB1/kg b.w), 0.2 mg/kg Se + AFB1 group (0.3 mg AFB1/kg b.w + 0.2 mg SeMet/kg feed) and 0.4 mg/kg Se + AFB1 group (0.3 mg AFB1/kg b.w + 0.4 mg SeMet/kg feed). The SeMet treatment group was fed different doses of SeMet diets every day for 21 days. On the 17-21 day, the AFB1 treatment group, the 0.2 mg/kg Se + AFB1 group and the 0.4 mg/kg Se + AFB1 group were administered 0.3 mg AFB1 /kg b.w by gavage (dissolved in 0.5 ml olive oil) respectively. The results showed that AFB1 poisoning resulted in the changes of renal structure, the increase of renal coefficient and serum biochemical indexes, the ascent of ROS and MDA levels, the descent of antioxidant enzyme activity, and the significant down-regulation of Nrf2, HO-1 and NQO1. Besides, AFB1 poisoning increased the number of renal apoptotic cells, rised the levels of PTEN, Bax, Caspase-3 and Caspase-9, and decreased the levels of PI3K, AKT, p-AKT and Bcl-2. In summary, SeMet was added to alleviate the oxidative stress injury and apoptosis of kidney induced by AFB1, and the effect of 0.2 mg/kg Se + AFB1 is better than 0.4 mg/kg Se + AFB1.

Long-Term Exposure to Low Concentrations of Ambient Benzene and Mortality in a National English Cohort.

Background: Benzene affects human health through environmental exposure in addition to occupational contact. However, few studies have examined the associations between long-term exposure to low concentrations of ambient benzene and mortality risks in nonoccupational settings.Methods: This prospective cohort study consists of 393,042 participants without stroke, myocardial infarction, or cancer at baseline from the UK Biobank. Annual average concentrations of benzene for each year during follow-up were measured using air dispersion models. The main outcomes were all-cause mortality and mortality from specific causes. Cox proportional-hazards models with time-varying exposure measurements were used to estimate the hazard ratios and 95% confidence intervals (CIs) for mortality risks. Restricted cubic spline models were used to estimate exposure-response relationships.Measurements and Main Results: With each interquartile range increase in the average annual concentration of benzene, the adjusted hazard ratios of mortality risk from all causes, cardiovascular disease, cancer, and respiratory disease were 1.26 (95% CI, 1.24-1.27), 1.24 (95% CI, 1.21-1.28), 1.27 (95% CI, 1.25-1.29), and 1.25 (95% CI, 1.20-1.30), respectively. The monotonically increasing exposure-response curves showed no threshold and plateau within the observed concentration range. Furthermore, the effect of benzene exposure on mortality persisted across different subgroups and was somewhat stronger in younger and White people (P for interaction < 0.05).Conclusions: Long-term exposure to low concentrations of ambient benzene significantly increases mortality risk in the general population. Ambient benzene represents a potential threat to public health, and further investigations are needed to support timely pollution regulation and health protection.

Weighted gene co-expression network analysis for hub genes in colorectal cancer.

BACKGROUND: This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA). METHODS: Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established. RESULTS: After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC. CONCLUSIONS: CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.

OBJECTIVE: To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS: A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS: The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION: The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer.

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Antimicrobial peptide-producing dermal preadipocytes defend against Candida albicans skin infection via the FGFR-MEK-ERK pathway.

Dermal fibroblasts (dFBs) defend against deep bacterial skin infections by differentiating into preadipocytes (pAds) that produce the antimicrobial peptide cathelicidin; this differentiation is known as the dermal reactive adipogenesis response. However, the role of dFBs in fungal infection remains unknown. Here, we found that cathelicidin-producing pAds were present in high numbers in skin lesions from patients with cutaneous Candida granulomas. Second, we showed that dermal Candida albicans (C. albicans) infection in mice robustly triggered the dermal reactive adipogenesis response and induced cathelicidin expression, and inhibition of adipogenesis with pharmacological inhibitors of peroxisome proliferator-activated receptor γ (PPARγ) impaired skin resistance to C. albicans. In vitro, C. albicans products induced cathelicidin expression in pAds, and differentiating pAds markedly suppressed the growth of C. albicans by producing cathelicidin. Finally, we showed that C. albicans induced an antimicrobial response in pAds through the FGFR-MEK-ERK pathway. Together, our data reveal a previously unknown role of dFBs in the defense against skin infection caused by C. albicans.

Targeted treatment of atherosclerosis with protein-polysaccharide nanoemulsion co-loaded with photosensitiser and upconversion nanoparticles.

Macrophages are the most abundant cell group in atherosclerosis (AS) lesions and play a vital role in all stages of AS progression. Recent research has shown that reactive oxygen species (ROS) generation from photodynamic therapy (PDT) induces macrophage autophagy to improve abnormal lipid metabolism and inflammatory environment. Especially in macrophage-derived foam cells, which has become a potential strategy for the treatment of AS. In this study, we prepared the conjugate (DB) of dextran (DEX) and bovine serum albumin (BSA). The DB was used as the emulsifier to prepare nanoemulsion loaded with upconversion nanoparticles (UCNPs) and chlorin e6 (Ce6) (UCNPs-Ce6@DB). The DEX modified on the surface of the nanoemulsion can recognise and bind to the scavenger receptor class A (SR-A) highly expressed on macrophages and promote the uptake of macrophage-derived foam cells in AS plates through SR-A-mediated endocytosis. In addition, UCNPs-Ce6@DB-mediated PDT enhanced ROS generation and induced autophagy in macrophage-derived foam cells, enhanced the expression of ABCA1, a protein closely related to cholesterol efflux, and inhibited the secretion of pro-inflammatory cytokines. Ultimately, UCNPs-Ce6@DB was shown to inhibit plaque formation in mouse models of AS. In conclusion, UCNPs-Ce6@DB offers a promising treatment for AS.