Improved camptothecin encapsulation and efficacy by environmentally sensitive block copolymer/chitosan/fucoidan nanoparticles for targeting lung cancer cells.

In this study, thermo-sensitive poly(N-isopropyl acrylamide) (PNP) was polymerized with pH-sensitive poly(acrylic acid) (PAA) to prepare a PAA-b-PNP block copolymer. Above its cloud point, the block copolymer self-assembled into nanoparticles (NPs), encapsulating the anticancer drug camptothecin (CPT) in situ. Chitosan (CS) and fucoidan (Fu) further modified these NPs, forming Fu-CPT-NPs to enhance biocompatibility, drug encapsulation efficiency (EE), and loading content (LC), crucially facilitating P-selectin targeting of lung cancer cells through a pulmonary drug delivery system. The EE and LC reached 82 % and 3.5 %, respectively. According to transmission electron microscope observation, these Fu-CPT-NPs had uniform spherical shapes with an average diameter of ca. 250 nm. They could maintain their stability in a pH range of 5.0-6.8. In vitro experimental results revealed that the Fu-CPT-NPs exhibited good biocompatibility and had anticancer activity after encapsulating CPT. It could deliver CPT to cancer cells by targeting P-selectin, effectively increasing cell uptake and inducing cell apoptosis. Animal study results showed that the Fu-CPT-NPs inhibited lung tumor growth by increasing tumor cell apoptosis without causing significant tissue damage related to generating reactive oxygen species in lung cancer cells. This system can effectively improve drug-delivery efficiency and treatment effects and has great potential for treating lung cancer.

Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network.

The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time sequence, transmitting memory, learning, storing, and fast convergence for monitoring the health and diagnosing the rotor and collet failure of DATHs. A handpiece mimicking a dentist's hand load of 100 g was employed to repeatedly mill a glass porcelain block back and forth for cutting. An accelerometer was employed to capture vibration signals during free-running of unrestrained operation of the handpiece, aiming to discern the characteristic features of these vibrations. These data were then utilized to create a diagnostic health classification (DHC) for further developing a TCN, a 1D convolutional neural network (CNN), and long short-term memory (LSTM) prediction models. The three frameworks were used and compared for machine learning to establish DHC prediction models for the DATH. The experimental results indicate that, in terms of DHC predicted for the experimental dataset, the square categorical cross-entropy loss function error of the TCN framework was generally lower than that of the 1D CNN, which did not have a memory framework or the drawback of the vanishing gradient problem. In addition, the TCN framework outperformed the LSTM model, which required a longer history to provide sufficient diagnostic ability. Still, high accuracies were achieved both in the direction of feed-drive milling and in the gravity of the handpiece through vibration signals. In general, the failure classification prediction model could accurately predict the health and failure mode of the dental handpiece before the use of the DATH when an embedded sensor was available. Therefore, this model could prove to be a beneficial tool for predicting the deterioration patterns of real dental handpieces in their remaining useful life.

Production of bacterial cellulose (BC)/nisin composite with enhanced antibacterial and mechanical properties through co-cultivation of Komagataeibacter xylinum and Lactococcus lactis subsp. lactis.

By employing co-cultivation technique on Komagataeibacter xylinum and Lactococcus lactis subsp. lactis, bacterial cellulose (BC)/nisin films with improved antibacterial activity and mechanical properties were successfully produced. The findings demonstrated that increased nisin production is associated with an upregulation of gene expression. Furthermore, results from Scanning electronic microscopy (SEM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and Thermogravimetric analysis (TG) confirmed the integration of nisin within BC. While being biocompatible with human cells, the BC/nisin composites exhibited antimicrobial activity. Moreover, mechanical property analyses showed a noticeable improvement in Young's modulus, tensile strength, and elongation at break by 161, 271, and 195 %, respectively. Additionally, the nisin content in fermentation broth was improved by 170 % after co-culture, accompanied by an 8 % increase in pH as well as 10 % decrease in lactate concentration. Real-time reverse transcription PCR analysis revealed an upregulation of 11 nisin-related genes after co-cultivation, with the highest increase in nisA (5.76-fold). To our knowledge, this is the first study which demonstrates that an increase in secondary metabolites after co-culturing is modulated by gene expression. This research offers a cost-effective approach for BC composite production and presents a technique to enhance metabolite concentration through the regulation of relevant genes.

Enhancing UAV Visual Landing Recognition with YOLO's Object Detection by Onboard Edge Computing.

A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding the emergent landing platform and for future reconnaissance area missions. This paper proposes an approach to enhance the visual capabilities of this system by using the You Only Look Once (YOLO)-based object detection (OD) with Tensor RTTM acceleration technique, an automated visual tracking gimbal camera control system, and multithread programing for image transmission to the ground station. With lightweight edge computing (EC), the mean average precision (mAP) was satisfied and we achieved a higher frame per second (FPS) rate via YOLO accelerated with TensorRT for an onboard UAV. The OD compares four YOLO models to recognize objects of interest for landing spots at the home university first. Then, the trained dataset with YOLOv4-tiny was successfully applied to another field with a distance of more than 100 km. The system's capability to accurately recognize a different landing point in new and unknown environments is demonstrated successfully. The proposed approach substantially reduces the data transmission and processing time to ground stations with automated visual tracking gimbal control, and results in rapid OD and the feasibility of using NVIDIA JetsonTM Xavier NX by deploying YOLOs with more than 35 FPS for the UAV. The enhanced visual landing and future reconnaissance mission capabilities of real-time UAVs were demonstrated.

Dopamine-dependent functions of hyaluronic acid/dopamine/silk fibroin hydrogels that highly enhance N-acetyl-L-cysteine (NAC) delivered from nasal cavity to brain tissue through a near-infrared photothermal effect on the NAC-loaded hydrogels.

Hyaluronic acid/silk fibroin (HA/SF or HS) hydrogels with remarkable mechanical characteristics have been reported as tissue engineering biomaterials. Herein, the addition of dopamine/polydopamine (DA/PDA) to HS hydrogels to develop multifunctional HA/PDA/SF (or HDS) hydrogels for the delivery of drugs such as N-acetyl-L-cysteine (NAC) from nasal to brain tissue is examined. Herein, DA-dependent functions of HDS hydrogels with highly adhesive forces, photothermal response (PTR) effects generated by near infrared (NIR) irradiation, and anti-oxidative effects were demonstrated. An in-vitro study shows that the HDS/NAC hydrogels could open tight junctions in the RPMI 2650 cell line, a model cell of the nasal mucosa, as demonstrated by the decreased values of transepithelial electrical resistance (TEER) and more discrete ZO-1 staining than those for the control group. This effect was markedly enhanced by NIR irradiation of the HDS/NAC-NIR hydrogels. Compared to the results obtained using NAC solution, an in-vivo imaging study (IVIS) in rats showed an approximately nine-fold increase in the quantity of NAC delivered from the nasal cavity to the brain tissue in the span of 2 h through the PTR effect generated by the NIR irradiation of the nasal tissue and administration of the HDS/NAC hydrogels. Herein, dopamine-dependent multifunctional HDS hydrogels were studied, and the nasal administration of HDS/NAC-NIR hydrogels with PTR effects generated by NIR irradiation was found to have significantly enhanced NAC delivery to brain tissues.

Identification of tumor immunophenotypes associated with immunotherapy response in bladder cancer.

OBJECTIVES: This study aims to reveal immunophenotypes associated with immunotherapy response in bladder cancer, identify the signature genes of immune subtypes, and provide new molecular targets for improving immunotherapy response. METHODS: Bladder cancer immunophenotypes were characterized in the bulk RNA sequencing dataset GSE32894 and Imvigor210, and gene expression signatures were established to identify the immunophenotypes. Expression of gene signatures were validated in single-cell RNA sequencing dataset GSE145140 and human proteins expression data source. Investigation of Immunotherapy Response was performed in IMvigor210 dataset. Prognosis of tumor immunophenotypes was further analyzed. RESULTS: Inflamed and immune-excluded immunophenotypes were characterized based on the tumor immune cell scores. Risk score models that were established rely on RNA sequencing profiles and overall survival of bladder cancer cohorts. The inflamed tumors had lower risk scores, and the low-risk tumors were more likely to respond to atezolizumab, receiving complete response/partial response (CR/PR). Patients who responded to atezolizumab had higher SRRM4 and lower NPHS1 and TMEM72 expression than the non-responders. SRRM4 expression was a protective factor for bladder cancer prognosis, while the NPHS1 and TMEM72 showed the opposite pattern. CONCLUSION: This study provided a novel classification method for tumor immunophenotypes. Bladder cancer immunophenotypes can predict the response to immune checkpoint blockade. The immunophenotypes can be identified by the expression of signature genes.

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback-Leibler Divergence with a Gaussian Mixture Model.

Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback-Leibler divergence's measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device's vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments.

In Situ Encapsulation of Camptothecin by Self-Assembly of Poly(acrylic acid)-b-Poly(N-Isopropylacrylamide) and Chitosan for Controlled Drug Delivery.

Camptothecin (CPT) has been shown to exhibit anticancer activity against several cancers. Nevertheless, CPT is very hydrophobic with poor stability, and thus its medical application is limited. Therefore, various drug carriers have been exploited for effectively delivering CPT to the targeted cancer site. In this study, a dual pH/thermo-responsive block copolymer of poly(acrylic acid-b-N-isopropylacrylamide) (PAA-b-PNP) was synthesized and applied to encapsulate CPT. At temperatures above its cloud point, the block copolymer self-assembled to form nanoparticles (NPs) and in situ encapsulate CPT, owing to their hydrophobic interaction as evidenced by fluorescence spectrometry. Chitosan (CS) was further applied on the surface through the formation of a polyelectrolyte complex with PAA for improving biocompatibility. The average particle size and zeta potential of the developed PAA-b-PNP/CPT/CS NPs in a buffer solution were 168 nm and -30.6 mV, respectively. These NPs were still stable at least for 1 month. The PAA-b-PNP/CS NPs exhibited good biocompatibility toward NIH 3T3 cells. Moreover, they could protect the CPT at pH 2.0 with a very slow-release rate. At pH 6.0, these NPs could be internalized by Caco-2 cells, followed by intracellular release of the CPT. They became highly swollen at pH 7.4, and the released CPT was able to diffuse into the cells at higher intensity. Among several cancer cell lines, the highest cytotoxicity was observed for H460 cells. As a result, these environmentally-responsive NPs have the potential to be applied in oral administration.

Manipulating XXY Planar Platform Positioning Accuracy by Computer Vision Based on Reinforcement Learning.

With the rise of Industry 4.0 and artificial intelligence, the demand for industrial automation and precise control has increased. Machine learning can reduce the cost of machine parameter tuning and improve high-precision positioning motion. In this study, a visual image recognition system was used to observe the displacement of an XXY planar platform. Ball-screw clearance, backlash, nonlinear frictional force, and other factors affect the accuracy and reproducibility of positioning. Therefore, the actual positioning error was determined by inputting images captured by a charge-coupled device camera into a reinforcement Q-learning algorithm. Time-differential learning and accumulated rewards were used to perform Q-value iteration to enable optimal platform positioning. A deep Q-network model was constructed and trained through reinforcement learning for effectively estimating the XXY platform's positioning error and predicting the command compensation according to the error history. The constructed model was validated through simulations. The adopted methodology can be extended to other control applications based on the interaction between feedback measurement and artificial intelligence.

A Morphing Point-to-Point Displacement Control Based on Long Short-Term Memory for a Coplanar XXY Stage.

In this study, visual recognition with a charge-coupled device (CCD) image feedback control system was used to record the movement of a coplanar XXY stage. The position of the stage is fedback through the image positioning method, and the positioning compensation of the stage is performed by the image compensation control parameter. The image resolution was constrained and resulted in an average positioning error of the optimized control parameter of 6.712 µm, with the root mean square error being 2.802 µm, and the settling time being approximately 7 s. The merit of a long short-term memory (LSTM) deep learning model is that it can identify long-term dependencies and sequential state data to determine the next control signal. As for improving the positioning performance, LSTM was used to develop a training model for stage motion with an additional dial indicator with an accuracy of 1 µm being used to record the XXY position information. After removing the assisting dial indicator, a new LSTM-based XXY feedback control system was subsequently constructed to reduce the positioning error. In other words, the morphing control signals are dependent not only on time, but also on the iterations of the LSTM learning process. Point-to-point commanded forward, backward and repeated back-and-forth repetitive motions were conducted. Experimental results revealed that the average positioning error achieved after using the LSTM model was 2.085 µm, with the root mean square error being 2.681 µm, and a settling time of 2.02 s. With the assistance of LSTM, the stage exhibited a higher control accuracy and less settling time than did the CCD imaging system according to three positioning indices.

In Situ Release of Ulvan from Crosslinked Ulvan/Chitosan Complex Films and Their Evaluation as Wound Dressings.

When a wound forms due to any injuries, it should be covered with a functional wound dressing for accelerating wound healing and reducing infection. In this study, crosslinked ulvan/chitosan complex films were prepared with or without the addition of glycerol and chlorophyll, and their wound healing properties were evaluated for potential application in wound dressing. The results showed that the tensile strength and elongation at break of the prepared ulvan/chitosan complex films were 2.23-2.48 MPa and 83.8-108.5%, respectively. Moreover, their water vapor transmission rates (WVTRs) were in the range of 1791-2029 g/m2-day, providing suitable environment for wound healing. Particularly, these complex films could release ulvan in situ in a short time, and the film with chlorophyll added had the highest release rate, reaching 62.8% after 20 min of releasing. In vitro studies showed that they were biocompatible toward NIH 3T3 and HaCaT cells, and promoted the migration of NIH 3T3 cells. These complex films could protect HaCaT cells from oxidative damage and reduce the production of reactive oxygen species (ROS); the addition of chlorophyll also effectively reduced the inflammatory response induced by LPS as found in the reduction in both NO and IL-6. Animal models showed that the complex films added with glycerol and chlorophyll could promote wound healing in the early stage, while accelerating the regeneration of dermal glands and collagen production. Briefly, these ulvan/chitosan complex films had good physiochemical properties and biological activity, and could accelerate wound healing both in vitro and in vivo.

Automated Machine Learning System for Defect Detection on Cylindrical Metal Surfaces.

Metal workpieces are indispensable in the manufacturing industry. Surface defects affect the appearance and efficiency of a workpiece and reduce the safety of manufactured products. Therefore, products must be inspected for surface defects, such as scratches, dirt, and chips. The traditional manual inspection method is time-consuming and labor-intensive, and human error is unavoidable when thousands of products require inspection. Therefore, an automated optical inspection method is often adopted. Traditional automated optical inspection algorithms are insufficient in the detection of defects on metal surfaces, but a convolutional neural network (CNN) may aid in the inspection. However, considerable time is required to select the optimal hyperparameters for a CNN through training and testing. First, we compared the ability of three CNNs, namely VGG-16, ResNet-50, and MobileNet v1, to detect defects on metal surfaces. These models were hypothetically implemented for transfer learning (TL). However, in deploying TL, the phenomenon of apparent convergence in prediction accuracy, followed by divergence in validation accuracy, may create a problem when the image pattern is not known in advance. Second, our developed automated machine-learning (AutoML) model was trained through a random search with the core layers of the network architecture of the three TL models. We developed a retraining criterion for scenarios in which the model exhibited poor training results such that a new neural network architecture and new hyperparameters could be selected for retraining when the defect accuracy criterion in the first TL was not met. Third, we used AutoKeras to execute AutoML and identify a model suitable for a metal-surface-defect dataset. The performance of TL, AutoKeras, and our designed AutoML model was compared. The results of this study were obtained using a small number of metal defect samples. Based on TL, the detection accuracy of VGG-16, ResNet-50, and MobileNet v1 was 91%, 59.00%, and 50%, respectively. Moreover, the AutoKeras model exhibited the highest accuracy of 99.83%. The accuracy of the self-designed AutoML model reached 95.50% when using a core layer module, obtained by combining the modules of VGG-16, ResNet-50, and MobileNet v1. The designed AutoML model effectively and accurately recognized defective and low-quality samples despite low training costs. The defect accuracy of the developed model was close to that of the existing AutoKeras model and thus can contribute to the development of new diagnostic technologies for smart manufacturing.

Preparation and characterization of fast dissolving ulvan microneedles for transdermal drug delivery system.

Dissolving microneedles made from natural polymers recently have gained much attention as an efficient transdermal drug delivery system (TDDS). For the first time, ulvan, a sulfated polysaccharide extracted from Ulva lactuca, was applied to fabricate dissolving microneedles through a two-step casting method. The ulvan microneedles (UMNs) made from 4% ulvan solution were in a pyramidal shape with an average height of 655 µm and an aspect ratio of 2.63. The in vitro skin insertion study showed the UMNs could totally penetrate into the porcine skin to the dermis layer and rapidly dissolved as the needle height was reduced by 90.3% after post-insertion of only 2 min. The rapid dissolution of UMNs in situ thus could release the loaded model drugs of rhodamine 6G (R6G) and bovine serum albumin-fluorescein isothiocyanate conjugate (FITC-BSA) in the skin tissue. The in vitro drug release profiles through porcine skin revealed the UMNs markedly enhanced the cumulative release of FITC-BSA. In addition, the UMNs had good biocompatibility towards normal cells of HaCaT and NIH3T3. Briefly, this study demonstrates the rapidly dissolving UMNs could effectively carry the drug into skin and thus can be developed as a potential TDDS in the pharmaceutical and cosmeceutical fields.

Dissociation of water on atomic oxygen-covered Rh nanoclusters supported on graphene/Ru(0001).

We studied the dissociation of water (H2O*, with * denoting adspecies) on atomic oxygen (O*)-covered Rh nanoclusters (RhO* ) supported on a graphene film grown on a Ru(0001) surface [G/Ru(0001)] under ultrahigh-vacuum conditions and with varied surface-probe techniques and calculations based on density-functional theory. The graphene had a single rotational domain; its lattice expanded by about 5.7% to match the Ru substrate structurally better. The Rh clusters were grown by depositing Rh vapors onto G/Ru(0001); they had an fcc phase and grew in (111) orientation. Water adsorbed on the Rh clusters was dissociated exclusively in the presence of O*, like that on a Rh(111) single-crystal surface. Contrary to the case on Rh(111)O* , excess O* (even at a saturation level) on small RhO* clusters (diameter of 30-34 Å) continued to promote, instead of inhibiting, the dissociation of water; the produced hydroxyl (OH*) increased generally with the concentration of O* on the clusters. The difference results from more reactive O* on the RhO* clusters. O* on RhO* clusters activated the dissociation via both the formation of hydrogen bonds with H2O* and abstraction of H directly from H2O*, whereas O* on Rh(111)O* assisted the dissociation largely via the formation of hydrogen bonds, which was readily obstructed with an increased O* coverage. As the disproportionation (2 OH* → H2O* + O*) is endothermic on the RhO* clusters but exothermic on Rh(111)O* , OH* produced on RhO* clusters showed a thermal stability superior to that on the Rh(111)O* surface-thermally stable up to 400 K.

Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process.

The complexity of the internal components of dental air turbine handpieces has been increasing over time. To make operations reliable and ensure patients' safety, this study established long short-term memory (LSTM) prediction models with the functions of learning, storing, and transmitting memory for monitoring the health and degradation of dental air turbine handpieces. A handpiece was used to cut a glass porcelain block back and forth. An accelerometer was used to obtain vibration signals during the free running of the handpiece to identify the characteristic frequency of these vibrations in the frequency domain. This information was used to establish a health index (HI) for developing prediction models. The many-to-one and many-to-many LSTM frameworks were used for machine learning to establish prediction models for the HI and degradation trajectory. The results indicate that, in terms of HI predicted for the testing dataset, the mean square error of the many-to-one LSTM framework was lower than that that of a logistic regression model, which did not have a memory framework. Nevertheless, high accuracies were achieved with both of the two aforementioned approaches. In general, the degradation trajectory prediction model could accurately predict the degradation trend of the dental handpiece; thus, this model can be a useful tool for predicting the degradation trajectory of real dental handpieces in the future.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

Primary bone anaplastic lymphoma kinase positive anaplastic large-cell lymphoma: A case report and review of the literature.

BACKGROUND: Primary bone lymphoma (PBL) is an uncommon extranodal disease that represents approximately 1%-3% of lymphomas. Anaplastic lymphoma kinase (ALK) positive anaplastic large-cell lymphoma (ALCL) is an extremely rare type of PBL. The aim of this report is describe the symptoms, diagnosis, and treatment of primary bone ALK-positive ALCL. CASE SUMMARY: A 66-year-old man presented to our hospital with neck and shoulder pain and intermittent fever that lasted for 1 mo. After extensive evaluation, positron emission tomography-computed tomography (CT) examination showed multiple osteolytic bone lesions without other sites lesions. CT-guided biopsy of the T10 vertebral body was performed, and the pathology results showed that neoplastic cells were positive for ALK-1, CD30, and CD3. A diagnosis of primary bone ALK positive ALCL was ultimately made. The patient was in partial response after four cycle soft cyclophosphamide, doxorubicin, vincristine, and prednisone chemotherapy, and we planned to repeat the biopsy and radiological examination after completion of the fifth cycle of therapy. CONCLUSION: Primary bone ALK positive ALCL is a rare disease and physicians should keep in mind that ALCL can present with isolated osseous involvement without nodal involvement, and lymphoma should be considered in the differential diagnosis of primary bone lesions.

Hepatocellular carcinoma progression in hepatitis B virus-related cirrhosis patients receiving nucleoside (acid) analogs therapy: A retrospective cross-sectional study.

BACKGROUND: Antiviral therapy cannot completely block the progression of hepatitis B to hepatocellular carcinoma (HCC). Furthermore, there are few predictors of early HCC progression and limited strategies to prevent progression in patients with HBV-related cirrhosis who receive nucleos(t)ide analog (NA) therapy. AIM: The study aim was to clarify risk factors and the diagnostic value of alpha-fetoprotein (AFP) for HCC progression in NA-treated hepatitis B virus (HBV)-related cirrhosis patients. METHODS: In this retrospective cross-sectional study, we analyzed the clinical data of 266 patients with HBV-related cirrhosis who received NA treatment between February 2014 and April 2020 at Zhejiang Provincial People's Hospital. The patients were divided into two groups, 145 who did not progress to HCC (No-HCC group), and 121 who progressed to HCC during NA treatment (HCC group). The logistic regression analysis was used to analyze the risk factors of HCC progression. The diagnostic value of AFP for HCC was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Univariate analysis showed that age ≥ 60 years (P = 0.001), hepatitis B and alcoholic etiology (P = 0.007), smoking history (P < 0.001), family history of HBV-related HCC (P = 0.002), lamivudine resistance (P = 0.011), HBV DNA negative (P = 0.023), aspartate aminotransferase > 80 U/L (P = 0.002), gamma-glutamyl transpeptidase > 120 U/L (P = 0.001), alkaline phosphatase > 250 U/L (P = 0.001), fasting blood glucose (FBG) ≥ 6.16 (mmol/L) (P = 0.001) and Child-Pugh class C (P = 0.005) were correlated with HCC progression. In multivariate analysis, age ≥ 60 years [hazard ratio (HR) = 3.089, 95% confidence interval (CI): 1.437-6.631, P = 0.004], smoking history (HR = 4.001, 95%CI: 1.836-8.716, P < 0.01), family history of HBV-related HCC (HR = 6.763, 95%CI: 1.253-36.499, P < 0.05), lamivudine resistance (HR = 2.949, 95%CI: 1.207-7.208, P = 0.018), HBV DNA negative (HR = 0.026, 95%CI: 0.007-0.139, P < 0.01), FBG ≥ 6.16 mmol/L (HR = 7.219, 95%CI: 3.716-14.024, P < 0.01) were independent risk factors of HCC progression. ROC of AFP for diagnosis of HCC was 0.746 (95%CI: 0.674-0.818). A cutoff value of AFP of 9.00 ug/L had a sensitivity of 0.609, and specificity of 0.818 for diagnosing HCC. CONCLUSION: Age ≥ 60 years, smoking history, family history of HCC, lamivudine resistance, HBV DNA negative, FBG ≥ 6.16 mmol/L were risk factors of HCC progression. Serum AFP had limited diagnostic value for HCC.

Curcumin-laden dual-targeting fucoidan/chitosan nanocarriers for inhibiting brain inflammation via intranasal delivery.

Curcumin can reduce the production of brain inflammatory mediators and symptoms of brain diseases. However, a large amount of free curcumin needs to be administered to achieve an effective level in the brain because of its poor water-solubility. Fucoidan and chitosan were reported to respectively target P-selectin and acidic microenvironment expressed by pathologically inflammatory cells/tissues. Herein, the self-assembly of chitosan and fucoidan which could encapsulate curcumin was developed to form the multi-stimuli-responsive nanocarriers, and their pathological pH- and P-selectin-responsive aspects were characterized. Through intranasal delivery to the brain, these curcumin-containing chitosan/fucoidan nanocarriers with dual pH-/P-selectin-targeting properties to the brain lesions improved drug delivery, distribution, and accumulation in the inflammatory brain lesions as evidenced by an augmented inhibitory effect against brain inflammation. This promising multifunctional nanocarrier with a novel drug-delivery route should allow potential clinical biomedical uses by neurosurgeon in the future.