Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

OBJECTIVES: Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer's axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS: Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model's performance against sonographer's diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. RESULTS: In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer's diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer's diagnostic ability, increasing accuracy from 71.9% to 79.2%. CONCLUSION: The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. CLINICAL RELEVANCE STATEMENT: Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer's axillary diagnosis. KEY POINTS: Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound. Our AI model outperformed sonographer's visual diagnosis on axillary ultrasound. Our deep learning radiomics model can improve sonographers' diagnosis and might assist in surgical decision-making.

Striatal functional alterations link to distinct symptomatology across mood states in bipolar disorder.

BACKGROUND: As a central hub in cognitive and emotional brain circuits, the striatum is considered likely to be integrally involved in the psychopathology of bipolar disorder (BD). However, it remains unclear how alterations in striatal function contribute to distinct symptomatology of BD during different mood states. METHODS: Behavioral assessment (i.e., emotional symptoms and cognitive performance) and neuroimaging data were collected from 125 participants comprising 31 (hypo)manic, 31 depressive and 31 euthymic patients with BD, and 32 healthy controls. We compared the functional connectivity (FC) of striatal subregions across BD mood states with healthy controls and then used a multivariate data-driven approach to explore dimensional associations between striatal connectivity and behavioral performance. Finally, we compared the FC and behavioral composite scores, which reflect the individual weighted representation of the associations, among different mood states. RESULTS: Patients in all mood states exhibited increased FC between the bilateral ventral rostral putamen (VRP) and ventrolateral thalamus. Bipolar (hypo)mania uniquely exhibited increased VRP connectivity and superior ventral striatum connectivity. One latent component was identified, whereby increased FCs of striatal subregions were associated with distinct psychopathological symptomatology (more manic symptoms, elevated positive mood, less depressive symptoms and worse cognitive performance). Bipolar (hypo)manic patients had the highest FC and behavioral composite scores while bipolar depressive patients had the lowest. CONCLUSIONS: Our data demonstrated both trait features of BD and state features specific to bipolar (hypo) mania. The findings underscored the fundamental role of the striatum in the pathophysiological processes underlying specific symptomatology across all mood states.

Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia.

Background: Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia. Objective: We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models. Methods: We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification. Results: We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76-0.92) in contrast to the single-modality models, with AUC improvements of 8.88 to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification model (accuracy 71.43%) compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that made a major contribution to disease classification. Conclusion: We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.

Circ_0020460 drives tumorigenesis in cervical cancer through miR-485-3p sponging.

Deregulation of circular RNAs (circRNAs) is widely recognized in cancer progression. Our study aims to investigate the role of circ_0020460 in the development of cervical cancer (CC) and its potential mechanism of action. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assays were used to detect the expression levels of circ_0020460, miR-485-3p and C-X-C motif chemokine ligand 1 (CXCL1). The roles of circ_0020460 on cell proliferation, cell migration, cell invasion, cell apoptosis, and angiogenesis were investigated using cell counting kit-8 (CCK-8) and Ethynyl deoxyuridine (Edu) assay, wound healing assay, transwell assay, flow cytometry assay, and tube formation assay, respectively. The putative relationship predicted by bioinformatics analysis was verified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Xenograft models were constructed to explore the role of circ_0020460 in vivo. The expression of circ_0020460 and CXCL1 expression were increased, while miR-485-3p expression was declined in CC tissues and cells. Circ_0020460 knockdown suppressed CC cell proliferation, cell migration, cell invasion, angiogenesis, and promoted cell apoptosis. Circ_0020460 functioned as a miR-485-3p sponge to inhibit miR-485-3p level, and the anti-cancer effects mediated by circ_0020460 knockdown were reversed by miR-485-3p inhibitor. MiR-485-3p bound to CXCL1 3' untranslated region (3'UTR) to degrade CXCL1 expression, and the anti-cancer effects of miR-485-3p restoration were impaired by CXCL1 overexpression. Circ_0020460 downregulation inhibited CC xenograft tumor growth. These results suggest that circ_0020460 promoted the malignant behavior of CC cells by modulating the miR-485-3p/CXCL1 axis.

Sinomenine protects against atherosclerosis in apolipoprotein E-knockout mice by inhibiting of inflammatory pathway.

Atherosclerosis, a multifaceted and persistent inflammatory condition, significantly contributes to the progression of cardiocerebrovascular disorders, such as myocardial infarctions and cerebrovascular accidents. It involves the accumulation of cholesterol, fatty deposits, calcium and cellular debris in the walls of arteries, leading to the formation of plaques. Our aim is to investigate the potential of sinomenine to counteract atherosclerosis in mice lacking Apolipoprotein E (ApoE-/-) Mice. We employed the high-fat diet-induced method to induce atherosclerosis in ApoE-/- mice, and the mice were treated with sinomenine (5, 10, and 15 mg/kg) and simvastatin (0.5 mg/kg) for 12 weeks. Body weight, water intake, and food intake were assessed. Lipid parameters, oxidative stress, inflammatory cytokines, and mRNA levels were estimated. Sinomenine treatment remarkably (P < 0.001) suppressed body weight, along with food and water intake. Sinomenine altered the levels of total cholesterol (TC), high-density lipoprotein (HDL), triglyceride (TG), low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL), which were modulated in the atherosclerosis group. Sinomenine treatment also altered the levels of oxidative stress parameters such as glutathione peroxidase (GPx), catalase (CAT), malonaldehyde (MDA), superoxide dismutase (SOD) and glutathione (GSH). In addition, it modulated cardiac parameters like C-reactive protein (CRP), endothelin-1 (ET-1), thromboxane B2 (TXB2), nitric oxide (NO), cardiac troponin I (cTnI), lactate dehydrogenase (LDH), and creatinine kinase isoenzymes (CK-MB). Inflammatory cytokines interleukin (IL)-1α, IL-1ß, TNF-α, IL-6, and IL-10 were also affected. Sinomenine further suppressed the mRNA expression of IL-6, IL-17, IL-10, tumor necrosis factor-α (TNF-α), Il-1ß, monocyte chemoattractant protein-1 (MCP-1), MCP-2, MCP-3, transforming Growth Factor-1ß (TGF-1ß), vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1). The results suggest that sinomenine remarkably suppressed the development of atherosclerosis in the early stage.

Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter.

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process. Subsequently, the optimal tracking control issue for the original unmanned helicopter system is transformed into an optimization stabilization problem. By means of the ADP and neural network techniques, the feedforward controller and optimal feedback controller are skillfully designed. Compared with the conventional backstepping approach, the designed anti-disturbance optimal controller can make the unmanned helicopter accomplish the tracking task with less energy. Finally, simulation comparisons demonstrate the validity of the developed control scheme.

Predictive nomograms of repeat intrahepatic recurrence and overall survival after radiofrequency ablation of recurrent colorectal liver metastases.

OBJECTIVES: This study was conducted to develop nomograms for predicting repeat intrahepatic recurrence (rIHR) and overall survival (OS), after radiofrequency ablation (RFA), treatment in patients with recurrent colorectal liver metastases (CLMs) after hepatectomy based on clinicopathologic features. METHODS: A total of 160 consecutive patients with recurrent CLMs after hepatectomy who were treated with ultrasound-guided percutaneous RFA from 2012 to 2022 were retrospectively included. Patients were randomly divided into a training cohort and a validation cohort, with a ratio of 8:2. Potential prognostic factors associated with rIHR and OS, after RFA, were identified by using the competing-risks and Cox proportional hazard models, respectively, and were used to construct the nomogram. The nomogram was evaluated by Harrell's C-index and a calibration curve. RESULTS: The 1-, 2-, and 3-year rIHR rates after RFA were 58.8%, 70.2%, and 74.2%, respectively. The 1-, 3- and 5-year OS rates were 96.3%, 60.4%, and 38.5%, respectively. In the multivariate analysis, mutant RAS, interval from hepatectomy to intrahepatic recurrence ≤ 12 months, CEA level >5 ng/ml, and ablation margin <5 mm were the independent predictive factors for rIHR. Mutant RAS, largest CLM at hepatectomy >3 cm, CEA level >5 ng/ml, and extrahepatic disease were independent predictors of poor OS. Two nomograms for rIHR and OS were constructed using the respective significant variables. In both cohorts, the nomogram demonstrated good discrimination and calibration. CONCLUSIONS: The established nomograms can predict individual risk of rIHR and OS after RFA for recurrent CLMs and contribute to improving individualized management.

A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder.

Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.

A novel nomogram for prediction of intrahepatic recurrence-free survival in patients with HCC followed by radiofrequency ablation.

OBJECTIVE: Although radiofrequency ablation (RFA) has been considered as the favourable treatment option for hepatocellular carcinoma (HCC), there still exist some challenges for new recurrence after RFA. The present study aims to determine the factors affecting recurrence and develop an effective model to predict intrahepatic recurrence-free survival (RFS). METHODS: Patients with HCC followed by RFA between 2000 and 2021 were included in this study. Multivariable Cox regression analysis was used to determine the independent prognostic factors and establish the nomogram predicting intrahepatic RFS after RFA. The predictive performance of the nomogram was assessed according to the C-index, calibration plots, and Kaplan-Meier curves stratified by the tertiles. RESULTS: A total of 801 sessions in 660 patients (including 1155 lesions) were enrolled into this study. Intrahepatic new recurrence was observed in all patients during the follow-up, and the mean intrahepatic RFS was 21.9 months in the present cohort. According to multivariate COX regression analysis, five independent prognostic factors affecting intrahepatic RFS were determined, including age, Child-Pugh class, tumour distribution, number of tumours, and a-fetoprotein (AFP). Based on all independent prognostic factors, the nomogram model was developed and evaluated, which achieved favourable discrimination and calibration. CONCLUSION: This study established five independent prognostic factors and constructed a nomogram model to predict intrahepatic RFS for HCC patients followed by RFA. It could better help clinicians select RFA candidates, as well as offering the important information about whether patients need receive comprehensive treatment to prevent new recurrence after RFA. ADVANCES IN KNOWLEDGE: (1) In this study, 5 preoperative clinic-pathological variables were determined as the independent prognostic factors affecting RFS after RFA in the current largest sample size. (2) Based on these independent prognostic factors, a prognostic nomogram predicting RFS after RFA was established, which may be used to select patients who benefit from RFA and could help both surgeons and patients provide useful information for choosing the personalized treatment.

Integrated analysis of differentially expressed genes and miRNA expression profiles in dilated cardiomyopathy.

Background: Although dilated cardiomyopathy (DCM) is a prevalent form of cardiomyopathy, the molecular mechanisms underlying its pathogenesis and progression remain poorly understood. It is possible to identify and validate DCM-associated genes, pathways, and miRNAs using bioinformatics analysis coupled with clinical validation methods. Methods: Our analysis was performed using 3 mRNA datasets and 1 miRNA database. We employed several approaches, including gene ontology (GO) analysis, KEGG pathway enrichment analysis, protein-protein interaction networks analysis, and analysis of hub genes to identify critical genes and pathways linked to DCM. We constructed a regulatory network for DCM that involves interactions between miRNAs and mRNAs. We also validated the differently expressed miRNAs in clinical samples (87 DCM ，83 Normal) using qRT-PCR.The miRNAs' clinical value was evaluated by receiver operating characteristic curves (ROCs). Results: 78 differentially expressed genes (DEGs) and 170 differentially expressed miRNAs (DEMs) were associated with DCM. The top five GO annotations were collagen-containing extracellular matrix, cell substrate adhesion, negative regulation of cell differentiation, and inflammatory response. The most enriched KEGG pathways were the Neurotrophin signaling pathway, Thyroid hormone signaling pathway, Wnt signaling pathway, and Axon guidance. In the PPI network, we identified 10 hub genes, and in the miRNA-mRNA regulatory network, we identified 8 hub genes and 15 miRNAs. In the clinical validation, we found 13 miRNAs with an AUC value greater than 0.9. Conclusion: Our research offers novel insights into the underlying mechanisms of DCM and has implications for identifying potential targets for diagnosis and treatment of this condition.

Automated highway pavement crack recognition under complex environment.

The pavement is vulnerable to damage from natural disasters, accidents and other human factors, resulting in the formation of cracks. Periodic pavement monitoring can facilitate prompt detection and repair the pavement diseases, thereby minimizing casualties and property losses. Due to the presence of numerous interferences, recognizing highway pavement cracks in complex environments poses a significant challenge. Nevertheless, several computer vision approaches have demonstrated notable success in tackling this issue. We have employed a novel approach for crack recognition utilizing the ResNet34 model with a convolutional block attention module (CBAM), which not only saves parameters and computing power but also ensures seamless integration of the module as a plug-in. Initially, ResNet18, ResNet34, and ResNet50 models were trained by employing transfer learning techniques, with the ResNet34 network being selected as a fundamental model. Subsequently, CBAM was integrated into ResBlock and further training was conducted. Finally, we calculated the precision, average recall on the test set, and the recall of each class. The results demonstrate that by integrating CBAM into the ResNet34 network, the model exhibited improved test accuracy and average recall compared to its previous state. Moreover, our proposed model outperformed all other models in terms of performance. The recall rates for transverse crack, longitudinal crack, map crack, repairing, and pavement marking were 88.8%, 86.8%, 88.5%, 98.3%, and 99.9%, respectively. Our model achieves the highest precision of 92.9% and the highest average recall of 92.5%. However, the effectiveness in detecting mesh cracks was found to be unsatisfactory, despite their significant prevalence. In summary, the proposed model exhibits great potential for crack identification and serves as a crucial foundation for highway maintenance.

Evaluation of the Time Difference Method in Identifying Hepatocellular Carcinoma in Current CEUS LR-M Category Nodules.

OBJECTIVE: The aim of the work described here was to explore a potential method for improving the diagnostic detection of hepatocellular carcinoma (HCC) based on the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) Version 2017. METHODS: We retrospectively evaluated 585 liver nodules in 427 patients at risk for HCC from December 2020 to March 2023. The nodules were categorized as LR-1 to LR-M based on CEUS LI-RADS Version 2017 and were randomly subclassified into a developmental cohort (DC) and a validation cohort (VC) at 3:1. In the DC, the cutoff value of the time difference (∆T) for differentiating HCC from other malignancies by LR-M was calculated and used to reclassify nodules in the VC. The diagnostic effect on HCC detection before and after reclassification was further assessed. RESULTS: According to the current CEUS LI-RADS, 140 of 426 (32.9%) confirmed HCC nodules were misclassified as LR-M. In the DC (439 nodules), the receiver operating characteristic (ROC) curve revealed that the cutoff value of ∆T (wash-out onset time minus contrast arrival time) recommended for HCC diagnosis was greater than 21 s. In the VC (146 nodules), 34 HCCs were correctly categorized as LR-5 according to the cutoff value, and after reclassification, LR-5 had higher accuracy (67.1% vs. 89.0%, p < 0.001) and sensitivity (56.0% vs. 87.2%, p < 0.001) for HCC diagnosis with high specificity (100% vs. 94.6%, p = 0.500). CONCLUSION: Using the time difference method could identify HCC nodules misdiagnosed as LR-M and improve the diagnostic performance of current CEUS LI-RADS.

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.

Machine learning-based prediction model for myocardial ischemia under high altitude exposure: a cohort study.

High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.

EUS-guided bridging technique for treatment of biliary-enteric anastomotic stenosis after pancreaticoduodenectomy.

Biliary-enteric anastomotic stenosis is one of the main long-term complications after pancreaticoduodenectomy, with an incidence of 2%-8%. Although the relevant reports and studies are relatively few, the consequences such as biliary obstruction and refractory cholangitis seriously affect the quality of life of patients. In this case, the patient is not willing to receive conventional surgery again. This paper provides a bridge technique of EUS-guided Biliary Drainage (EUS-BD) to treat biliary-enteric anastomotic stenosis and solve the problem of obstructive jaundice in the patient.

Rational design of a polypropylene composite foam with open-cell structure via graphite conductive network for sound absorption.

An exciting result is reported in this study where a polypropylene (PP) foam with a high open-cell content was achieved by constructing a thermally conductive network for the first time. PP and nano-graphite particles were used as substrate and filler, respectively, to prepare the PP-graphite (PP-G) composite foam by twin-screw blending, hot pressing, and supercritical CO2 foaming. The nano-graphite particles can effectively adjust the microstructure of the PP-G foam and achieve a high porosity. When the amount of nano-graphite is 10.0 wt%, the PP-G foam exhibits optimal sound absorption performance, compression resistance, heat insulation, and hydrophobic properties. In the human-sensitive frequency range of 1000-6000 Hz, the corresponding average SAC is above 0.9, and the internal tortuosity is 5.27. After 50 cycles of compression, the compressive stress is 980 kPa and the SAC loss is only 7.8%. This study also innovatively proposed a new strategy to achieve the simple and rapid preparation of open-cell PP foams by increasing the thermal conductivity of the foaming substrate.

Transduodenal accessory papillary Rendezvous technique assisted ERCP in the treatment of severe pancreatic duct stenosis caused by chronic pancreatitis in children.

Chronic pancreatitis in children is an irreversible inflammatory disease, which can cause intractable abdominal pain and abnormal internal and external secretion function of the pancreas, seriously affecting the growth and development of children and the quality of life. ERCP has become the first choice because of its good effect and less trauma. However, the severe stenosis of pancreatic duct caused by chronic pancreatitis may make ERCP more difficult. Here we used the rendezvous technique to assist ERCP to complete the treatment of severe pancreatic duct stenosis and abdominal pain.

Fimepinostat Impairs NF-κB and PI3K/AKT Signaling and Enhances Gemcitabine Efficacy in H3.3K27M-Diffuse Intrinsic Pontine Glioma.

Diffuse intrinsic pontine glioma (DIPG) is the most aggressive pediatric brain tumor, and the oncohistone H3.3K27M mutation is associated with significantly worse clinical outcomes. Despite extensive research efforts, effective approaches for treating DIPG are lacking. Through drug screening, we identified the combination of gemcitabine and fimepinostat as a potent therapeutic intervention for H3.3K27M DIPG. H3.3K27M facilitated gemcitabine-induced apoptosis in DIPG, and gemcitabine stabilized and activated p53, including increasing chromatin accessibility for p53 at apoptosis-related loci. Gemcitabine simultaneously induced a prosurvival program in DIPG through activation of RELB-mediated NF-κB signaling. Specifically, gemcitabine induced the transcription of long terminal repeat elements, activated cGAS-STING signaling, and stimulated noncanonical NF-κB signaling. A drug screen in gemcitabine-treated DIPG cells revealed that fimepinostat, a dual inhibitor of HDAC and PI3K, effectively suppressed the gemcitabine-induced NF-κB signaling in addition to blocking PI3K/AKT activation. Combination therapy comprising gemcitabine and fimepinostat elicited synergistic antitumor effects in vitro and in orthotopic H3.3K27M DIPG xenograft models. Collectively, p53 activation using gemcitabine and suppression of RELB-mediated NF-κB activation and PI3K/AKT signaling using fimepinostat is a potential therapeutic strategy for treating H3.3K27M DIPG. SIGNIFICANCE: Gemcitabine activates p53 and induces apoptosis to elicit antitumor effects in H3.3K27M DIPG, which can be enhanced by blocking NF-κB and PI3K/AKT signaling with fimepinostat, providing a synergistic combination therapy for DIPG.

Trichoderma asperellum boosts nitrogen accumulation and photosynthetic capacity of wolfberry (Lycium chinense) under saline soil stress.

Trichoderma can promote plant growth under saline stress, but the mechanisms remain to be revealed. In this study, we investigate photosynthetic gas exchange, photosystem II (PSII) performance, nitrogen absorption and accumulation in a medicinal plant wolfberry (Lycium chinense) in saline soil supplemented with Trichoderma biofertilizer (TF). Larger nitrogen and biomass accumulation were found in plants supplemented with TF than with organic fertilizer (OF), suggesting that Trichoderma asperellum promoted plant growth and nitrogen accumulation under saline stress. T. asperellum strengthened root nitrogen (N) absorption according to greater increased root NH4+ and NO3- influxes under supplement with TF than OF, while nitrogen assimilative enzymes such as nitrate reductase, nitrite reductase and glutamine synthetase activities in roots and leaves were also stimulated. Thus, the elevated N accumulation derived from the induction of T. asperellum on nitrogen absorption and assimilation. Greater increased photosynthetic rate (Pn) and photosynthetic N-use efficiency under supplement with TF than OF illustrated that T. asperellum enhanced photosynthetic capacity and N utilization under saline stress. Although increased leaf stomatal conductance contributed to carbon (C) isotope fractionation under TF supplement, leaf 13C abundance was significantly increased by supplement with TF rather than OF, indicating that T. asperellum raised CO2 assimilation to a greater extent, reducing C isotope preference. Trichoderma asperellum optimized electron transport at PSII donor and acceptor sides under saline stress because of lower K and J steps in chlorophyll fluorescence transients under supplement with TF than OF. The amount of PSII active reaction centers was also increased by T. asperellum. Thus, PSII performance was upgraded, consistent with greater heightened delayed chlorophyll fluorescence transients and I1 peak under supplement with TF than OF. In summary, TF acted to increase N nutrient acquisition and photosynthetic C fixation resulting in enhanced wolfberry growth under saline soil stress.