Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments.

BACKGROUND: The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. RESULTS: Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. CONCLUSIONS: These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.

Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features.

OBJECTIVES: To assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs). METHODS: A total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model. RESULTS: Nine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively. CONCLUSIONS: The MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors. CLINICAL RELEVANCE STATEMENT: A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. KEY POINTS: Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.

Physicochemical properties and fractal characterizations of the functionalized porous clinoptilolites for controlling alginate delivery in growing cauliflower and leaf mustard.

Using natural clinoptilolite (NCP) as a carrier and alginate (Alg)-calcium as an active species, the porous silicon calcium alginate nanocomposite (Alg-Ca-NCP) was successfully fabricated via adsorption-covalence-hydrogen bond. Its structural features and physicochemical properties were detailed investigated by various characterizations. The results indicated that Alg-Ca-NCP presented the disordered lamellar structures with approximately uniform particles in size of 300-500 nm. Specially, their surface fractal evolutions between the irregular roughness and dense structures were demonstrated via the SAXS patterns. The results elucidated that the abundant micropores of NCP were beneficial for unrestricted diffusing of Alg-Ca, which was conducive to facilitate a higher loading and sustainable releasing. The Ca content of leaf mustard treated with Alg-Ca-NCP-0.5 was 484.5 mg/100g on the 21st day, higher than that by water (CK) and CaCl2 solution treatments, respectively. Meanwhile, the prepared Alg-Ca-NCPs presented the obvious anti-aging effects on peroxidase drought stress of mustard leaves. These demonstrations provided a simple and effective method to synthesize Alg-Ca-NCPs as delivery nanocomposites, which is useful to improve the weak absorption and low utilization of calcium alginate by plants.

A retrospective study comparing the efficacy of microwave ablation and stereotactic body radiotherapy in colorectal cancer lung metastases.

The purpose of the present study was to assess and compare the efficacy of microwave ablation (MWA) and stereotactic body radiotherapy (SBRT) in the treatment of lung metastases from patients with colorectal cancer (CRC) and to identify the preferable treatment modality based on patient and tumor characteristics. Records of 118 patients with CRC with a total of 307 lung metastases who underwent SBRT or MWA between January 2015 and December 2022 were retrospectively analyzed, including the essential clinicopathological information on patients (age, sex and underlying diseases), diagnosis and treatment information [primary tumor site, levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9], imaging data [diameter of lung metastasis, location of the metastasis (i.e., whether or not the tumor was adjacent to the vessel or bronchus) and internal features] and follow-up data (postoperative therapy, complications or adverse effects and survival outcomes). For statistical analysis of the local tumor progression (LTP), disease-free survival and overall survival (OS) rates, Cox regression analysis, along with the Kaplan-Meier method adjusted using inverse probability of treatment weighting (IPTW), were performed. The median follow-up duration in the present study was 31.5 months. Multivariable Cox regression analysis revealed that the CEA level, metastasis diameter and internal features were independent predictors of OS. In the IPTW-adjusted analysis, no significant difference in the 1-year OS rate was observed between the SBRT and MWA groups (92.9 vs. 93.9%; P=0.483); however, a notable discrepancy in the treatment modalities was noted, leading to significant differences in the 2- and 3-year OS rates (65.9 vs. 57.6%, P=0.001, and 44.7 vs. 36.4%, P<0.001, respectively). A significant interaction effect for the treatment modality was observed for LTP (P=0.021). In conclusion, the present study revealed that SBRT and MWA have similar therapeutic effects in terms of prolonging the survival of patients with CRC with lung metastases; however, regarding the local control of lung metastases, MWA is associated with a number of significant advantages.

Oral administration of RDP58 ameliorated DSS-induced colitis in intestinal microbiota dependent manner.

BACKGROUND: Although the pathogenesis of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), has not been fully elucidated, accumulating researches suggest that intestinal microbiota imbalance contributes to the development of IBD in patients and animal models. RDP58, a peptide-based computer-assisted rational design, has been demonstrated to be effective in protecting against a wide range of autoimmune and inflammatory diseases. However, the underlying mechanism by which RDP58 protects against IBD mediated by intestinal microbiota has yet to be elucidated. METHODS: The colitis model was induced by continuously administering 2.5 % (wt/vol) dextran sodium sulfate (DSS) solution for 7 days. The manifestations of colon inflammation were assessed via daily weight changes, colon length, tumor necrosis factor-alpha (TNF-α) level, disease activity index (DAI) score, pathology score, and intestinal barrier permeability. Intestinal microbiota analysis was carried out by 16S-rRNA sequencing. Colonic short chain fatty acids (SCFAs) and regulatory T cells (Tregs) were also detected. To further confirm the protective effect of RDP58 on intestinal microbiota, broad-spectrum antibiotic cocktail (ABX) treatment and fecal microbial transplantation (FMT) experiment were performed. RESULTS: Oral administration of RDP58 ameliorated DSS-induced mice colitis by altering the diversity and composition of intestinal microbiota. Notably, RDP58 significantly upregulated SCFAs-producing microbiota, thereby promoting the generation of Tregs. ABX and FMT were performed to verify the above mechanism. CONCLUSIONS: RDP58 ameliorated DSS-induced colitis through altering intestinal microbiota and enhancing SCFAs and Tregs production in intestinal microbiota dependent manner, potentially provide a novel therapy for IBD.

Dry powder inhalation containing muco-inert ciprofloxacin and colistin co-loaded liposomes for pulmonary P. Aeruginosa biofilm eradication.

Pseudomonas aeruginosa (PA), a predominant pathogen in lung infections, poses significant challenges due to its biofilm formation, which is the primary cause of chronic and recalcitrant pulmonary infections. Bacteria within these biofilms exhibit heightened resistance to antibiotics compared to their planktonic counterparts, and their secreted toxins exacerbate lung infections. Diverging from traditional antibacterial therapy for biofilm eradication, this study introduces a novel dry powder inhalation containing muco-inert ciprofloxacin and colistin co-encapsulated liposomes (Cipro-Col-Lips) prepared using ultrasonic spray freeze drying (USFD) technique. This USFD dry powder is designed to efficiently deliver muco-inert Cipro-Col-Lips to the lungs. Once deposited, the liposomes rapidly diffuse into the airway mucus, reaching the biofilm sites. The muco-inert Cipro-Col-Lips neutralize the biofilm-secreted toxins and simultaneously trigger the release of their therapeutic payload, exerting a synergistic antibiofilm effect. Our results demonstrated that the optimal USFD liposomal dry powder formulation exhibited satisfactory in vitro aerosol performance in terms of fine particle fraction (FPF) of 44.44 ± 0.78 %, mass median aerodynamic diameter (MMAD) of 4.27 ± 0.21 µm, and emitted dose (ED) of 99.31 ± 3.31 %. The muco-inert Cipro-Col-Lips effectively penetrate the airway mucus and accumulate at the biofilm site, neutralizing toxins and safeguarding lung cells. The triggered release of ciprofloxacin and colistin works synergistically to reduce the biofilm's antibiotic resistance, impede the development of antibiotic resistance, and eliminate 99.99 % of biofilm-embedded bacteria, including persister bacteria. Using a PA-beads induced biofilm-associated lung infection mouse model, the in vivo efficacy of this liposomal dry powder aerosol was tested, and the results demonstrated that this liposomal dry powder aerosol achieved a 99.7 % reduction in bacterial colonization, and significantly mitigated inflammation and pulmonary fibrosis. The USFD dry powder inhalation containing muco-inert Cipro-Col-Lips emerges as a promising therapeutic strategy for treating PA biofilm-associated lung infections.

PLEKHM2 deficiency induces impaired mitochondrial clearance and elevated ROS levels in human iPSC-derived cardiomyocytes.

Pleckstrin homology domain-containing family M member 2 (PLEKHM2) is an essential adaptor for lysosomal trafficking and its homozygous truncation have been reported to cause early onset dilated cardiomyopathy (DCM). However, the molecular mechanism of PLEKHM2 deficiency in DCM pathogenesis and progression is poorly understood. Here, we generated an in vitro model of PLEKHM2 knockout (KO) induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to elucidate the potential pathogenic mechanism of PLEKHM2-deficient cardiomyopathy. PLEKHM2-KO hiPSC-CMs developed disease phenotypes with reduced contractility and impaired calcium handling. Subsequent RNA sequencing (RNA-seq) analysis revealed altered expression of genes involved in mitochondrial function, autophagy and apoptosis in PLEKHM2-KO hiPSC-CMs. Further molecular experiments confirmed PLEKHM2 deficiency impaired autophagy and resulted in accumulation of damaged mitochondria, which triggered increased reactive oxygen species (ROS) levels and decreased mitochondrial membrane potential (Δψm). Importantly, the elevated ROS levels caused oxidative stress-induced damage to nearby healthy mitochondria, resulting in extensive Δψm destabilization, and ultimately leading to impaired mitochondrial function and myocardial contractility. Moreover, ROS inhibition attenuated oxidative stress-induced mitochondrial damage, thereby partially rescued PLEKHM2 deficiency-induced disease phenotypes. Remarkably, PLEKHM2-WT overexpression restored autophagic flux and rescued mitochondrial function and myocardial contractility in PLEKHM2-KO hiPSC-CMs. Taken together, these results suggested that impaired mitochondrial clearance and increased ROS levels play important roles in PLEKHM2-deficient cardiomyopathy, and PLEKHM2-WT overexpression can improve mitochondrial function and rescue PLEKHM2-deficient cardiomyopathy.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Risk assessment of pneumothorax in colorectal lung metastases treated by percutaneous thermal ablation: a multicenter retrospective cohort study.

PURPOSE: To evaluate the risk of pneumothorax in the percutaneous image-guided thermal ablation (IGTA) treatment of colorectal lung metastases (CRLM). METHODS: Data regarding patients with CRLM treated with IGTA from five medical institutions in China from 2016 to 2023 were reviewed retrospectively. Pneumothorax and non-pneumothorax were compared using the Student's t -test, χ 2 test and Fisher's exact test. Univariate logistic regression analysis was conducted to identify potential risk factors, followed by multivariate logistic regression analysis to evaluate the predictors of pneumothorax. Interactions between variables were examined and used for model construction. Receiver operating characteristic curves and nomograms were generated to assess the performance of the model. RESULTS: A total of 254 patients with 376 CRLM underwent 299 ablation sessions. The incidence of pneumothorax was 45.5%. The adjusted multivariate logistic regression model, incorporating interaction terms, revealed that tumour number [odds ratio (OR)=8.34 (95% CI: 1.37-50.64)], puncture depth [OR=0.53 (95% CI: 0.31-0.91)], pre-procedure radiotherapy [OR=3.66 (95% CI: 1.17-11.40)], peribronchial tumour [OR=2.32 (95% CI: 1.04-5.15)], and emphysema [OR=56.83 (95% CI: 8.42-383.57)] were significant predictive factors of pneumothorax (all P <0.05). The generated nomogram model demonstrated a significant prediction performance, with an area under the receiver operating characteristic curve of 0.800 (95% CI: 0.751-0.850). CONCLUSIONS: Pre-procedure radiotherapy, tumour number, peribronchial tumour, and emphysema were identified as risk factors for pneumothorax in the treatment of CRLM using percutaneous IGTA. Puncture depth was found to be a protective factor against pneumothorax.

Nanotechnology-Based Drug Delivery Systems to Control Bacterial-Biofilm-Associated Lung Infections.

Airway mucus dysfunction and impaired immunological defenses are hallmarks of several lung diseases, including asthma, cystic fibrosis, and chronic obstructive pulmonary diseases, and are mostly causative factors in bacterial-biofilm-associated respiratory tract infections. Bacteria residing within the biofilm architecture pose a complex challenge in clinical settings due to their increased tolerance to currently available antibiotics and host immune responses, resulting in chronic infections with high recalcitrance and high rates of morbidity and mortality. To address these unmet clinical needs, potential anti-biofilm therapeutic strategies are being developed to effectively control bacterial biofilm. This review focuses on recent advances in the development and application of nanoparticulate drug delivery systems for the treatment of biofilm-associated respiratory tract infections, especially addressing the respiratory barriers of concern for biofilm accessibility and the various types of nanoparticles used to combat biofilms. Understanding the obstacles facing pulmonary drug delivery to bacterial biofilms and nanoparticle-based approaches to combatting biofilm may encourage researchers to explore promising treatment modalities for bacterial-biofilm-associated chronic lung infections.

[Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes].

BACKGROUND: Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration. METHODS: The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups. RESULTS: A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy. CONCLUSIONS: The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.

Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

BACKGROUND: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS: We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS: Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

A convenient scoring system to distinguish intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis based on magnetic resonance imaging features.

OBJECTIVES: To develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). METHODS: A total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM. RESULTS: Six independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score. CONCLUSIONS: The established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features. CLINICAL RELEVANCE STATEMENT: A reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features. KEY POINTS: • Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). • A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.

Mannose binding lectin-associated serine protease-1 is a novel contributor to myocardial ischemia/reperfusion injury.

BACKGROUND: The lectin pathway has been demonstrated to play a critical role in the pathological process of myocardial ischemia/reperfusion injury (IRI). Mannose-binding lectin (MBL)-associated serine protease-1 (MASP-1), especially different from other components of the lectin pathway, mediates proinflammatory and procoagulant reactions independent of complement cascades. However, the role of MASP-1 in myocardial IRI remains unknown so far. METHODS: Myocardial IRI was established with 45 min ischemia and 24 h reperfusion in mice. C1 inhibitor, as the natural inhibitor of MASP-1, was administrated at 20 IU/Kg via tail vein 5 min before surgical operation. Cardiac function and myocardial infarct size were assessed. Myocardial histology and fibrosis were evaluated by H&E and Masson staining, respectively. Deposition of MASP-1, expression of PAR-1/4 and neutrophil extracellular traps (NET) were investigated on myocardium tissue by IHC staining. Cell apoptosis was detected by TUNEL assay. Levels of myocardial enzymes and proinflammatory cytokines were determined by ELISA. RESULTS: Inhibition of MASP-1 with C1 INH improved cardiac function and alleviated myocardium tissue injury (infarct size, enzymes, histology and fibrosis) after myocardial IRI. Deposition of MASP-1 and expression PAR-1, as well as NET formation in myocardial tissue were suppressed by MASP-1 inhibitor, while PAR-4 was elevated. Levels of apoptosis, HMGB-1 and IL-6 were lower after blocking MASP-1. Yet, IL-8 and TNF-α remained unchanged. CONCLUSIONS: MASP-1, as a new contributor, played a critical role in myocardial IRI. Inhibition of MASP-1 protected myocardial tissue from IRI probably via regulation of PARs/NET pathway. This may provide a novel target strategy against myocardial IRI.

Amphipathic Solvent-Assisted Synthetic Strategy for Random Lamellae of the Clinoptilolites with Flower-like Morphology and Thinner Nanosheet for Adsorption and Separation of CO2 and CH4.

The random lamellae of the synthetic CP were synthesized with a hydrothermal approach using o-Phenylenediamine (OPD) as a modifier. The decreases in the order degree of the CP synthesized in the presence of the OPD resulted from the loss of long-range order in a certain direction. Subsequently, the ultrasonic treatment and washing were conducive to further facilitate the disordered arrangements of its lamellae. The possible promotion mechanism regarding the nucleation and growth behaviors of the sol-gel particles was proposed. The fractal evolutions of the aluminosilicate species with crystallization time implied that the aluminosilicate species became gradually smooth to rough during the crystallization procedures since the amorphous structures transformed into flower-like morphologies. Their gas adsorption and separation performances indicated that the adsorption capacity of CO2 at 273 K reached up to 2.14 mmol·g-1 at 1 bar, and the selective factor (CO2/CH4) up to 3.4, much higher than that of the CPs synthesized without additive OPD. The breakthrough experiments displayed a longer breakthrough time and enhancement of CO2 uptake, showing better performance for CO2/CH4 separation. The cycling test further highlighted their efficiency for CO2/CH4 separation.

Constructing Randomly Lamellar HKUST-1@Clinoptilolite through Polyethylene Glycol-Assisted Hydrothermal Method and Coordinated Complexation for Enhanced Adsorptive Separation for CO2 and CH4.

Clinoptilolite (CP) was successfully synthesized via a hydrothermal route in the presence of polyethylene glycol (PEG), and it was then delaminated by washing using Zn2+ containing acid. HKUST-1, as one kind of the Cu-based MOFs, showed a high CO2 adsorption capacity owing to its large pore volume and specific surface area. In the present work, we selected one of the most efficient ways for preparing the HKUST-1@CP compounds via coordination between exchanged Cu2+ and ligand (trimesic acid). Their structural and textural properties were characterized by XRD, SAXS, N2 sorption isotherms, SEM, and TG-DSC profiles. Particularly, the effect of the additive PEG (average molecular weight of 600) on the induction (nucleation) periods and growth behaviors were detailed and investigated in the hydrothermal crystallization procedures of synthetic CPs. The corresponding activation energies of induction (En) and growth (Eg) periods during crystallization intervals were calculated. Meanwhile, the pore size of the inter-particles of HKUST-1@CP was 14.16 nm, and the BET specific area and pore volume were 55.2 m2/g and 0.20 cm3/g, respectively. Their CO2 and CH4 adsorption capacities and selectivity were preliminarily explored, showing 0.93 mmol/g for HKUST-1@CP at 298 K with the highest selective factor of 5.87 for CO2/CH4, and the dynamic separation performance was evaluated in column breakthrough experiments. These results suggested an efficient way of preparing zeolites and MOFs composites that is conducive to being a promising adsorbent for applications in gas separation.

Dispersion Performances of Naphthalimides Doped in Dual Temperature- and pH-Sensitive Poly (N-Isopropylacrylamide-co-acrylic Acid) Shell Assembled with Vinyl-Modified Mesoporous SiO2 Core for Fluorescence Cell Imaging.

Developing effective intelligent nanocarriers is highly desirable for fluorescence imaging and therapeutic applications but remains challenging. Using a vinyl-grafted BMMs (bimodal mesoporous SiO2 materials) as a core and PAN ((2-aminoethyl)-6-(dimethylamino)-1H-benzo[de]isoquinoline-1,3(2H)-dione))-dispersed dual pH/thermal-sensitive poly(N-isopropylacrylamide-co-acrylic acid) as a shell, PAN@BMMs with strong fluorescence and good dispersibility were prepared. Their mesoporous features and physicochemical properties were extensively characterized via XRD patterns, N2 adsorption-desorption analysis, SEM/TEM images, TGA profiles, and FT-IR spectra. In particular, their mass fractal dimension (dm) features based on SAXS patterns combined with fluorescence spectra were successfully obtained to evaluate the uniformity of the fluorescence dispersions, showing that the dm values increased from 2.49 to 2.70 with an increase of the AN-additive amount from 0.05 to 1%, along with the red shifting of their fluorescent emission wavelength from 471 to 488 nm. The composite (PAN@BMMs-I-0.1) presented a densification trend and a slight decrease in peak (490 nm) intensity during the shrinking process. Its fluorescent decay profiles confirmed two fluorescence lifetimes of 3.59 and 10.62 ns. The low cytotoxicity obtained via in vitro cell survival assay and the efficient green imaging performed via HeLa cell internalization suggested that the smart PAN@BMM composites are potential carriers for in vivo imaging and therapy.