Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Preparation of a nanocellulose/nanochitin coating on a poly(lactic acid) film for improved hydrolysis resistance.

Growing concerns regarding plastic waste have prompted various attempts to replace plastic packaging films with biodegradable alternatives such as poly(lactic acid) (PLA). However, their low hydrolysis resistance owing to the presence of aliphatic polyesters limits the shelf life of biodegradable polymers. Hydrolysis leads to the deterioration of mechanical performance, which is a key disadvantage of biodegradable plastics. In this study, a layer-by-layer (LBL) assembly method was used for the dip-coating of biorenewable, biodegradable nanocellulose/nanochitin on the PLA surface. Additional crosslinking and compression of the coated nanofibers, each containing carboxylic acid and amine groups, respectively, were induced through electromagnetic microwave irradiation to protect the PLA film by improving hydrolysis resistance. The coatings were examined by morphological observations and water contact angle measurements. The LBL coatings of differently charged nanofibers of 10.6 µm were reduced to 40 % after microwave treatment, and the thickness does not vary after the hydrolysis experiment. Microwave irradiation increased the water contact angle owing to amide linkage formation, thereby preventing the peeling off of coating layers. Improved hydrolysis resistance inhibited the reduction in molecular weight and tensile strength. These findings could be used to develop sustainable and biodegradable plastic packaging films with a prolonged shelf life.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. METHODS: In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. RESULTS: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. CONCLUSIONS: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Effects of 4-week downhill treadmill walking on the vertebral angle and postural muscle activity in participants with thoracic kyphosis and forward head posture: A comparative longitudinal study.

BACKGROUND: Maintaining correct posture and optimal spine function has become an important issue due to the increased use of computers and smartphones. OBJECTIVE: To investigate the effect of a 4-week downhill treadmill exercise (DTWE) program on participants with thoracic kyphosis and forward head posture (FHP). METHODS: Twenty-eight male participants were randomly assigned to the DTWE (n= 14) or standard treadmill walking exercise (STWE) (n= 14) group. They performed 30-minute exercise three times a week for 4 weeks. The vertebral angle was measured using a three-dimensional (3D) motion analysis system. Surface electromyography (EMG) was performed to record muscle activity in the thoracic erector spinae (TES), sternocleidomastoid muscle (SCM), and cervical erector spinae (CES). RESULTS: The DTWE group showed significant increases in the craniovertebral angle (CVA) and TES EMG activity and significant decreases in the thoracic kyphosis angle and SCM and CES EMG activity compared with those shown by the STWE group following the intervention (p< 0.05). However, lumbar lordosis or pelvic tilt angles did not differ significantly between the groups after the intervention (p> 0.05). CONCLUSIONS: DTWE can be effective in reducing thoracic kyphosis and FHP without causing compensatory movements of the lumbar spine and pelvis.

Performance evaluation of deep learning models for the classification and identification of dental implants.

STATEMENT OF PROBLEM: Dental implant systems can be identified using image classification deep learning. However, investigations on the accuracy of classifying and identifying implant design through an object detection model are lacking. PURPOSE: The purpose of this study was to evaluate the performance of an object detection deep learning model for classifying the implant designs of 103 types of implants. MATERIAL AND METHODS: From panoramic radiographs, 14 037 implant images were extracted. Implant designs were subdivided into 10 classes in the coronal, 13 in the middle, and 10 in the apical third. Classes with fewer than 50 images were excluded from the training dataset. Among the images, 80% were used as training data, and the remaining 20% as test data; the data were generated 3 times for 3-fold cross-validation (implant datasets 1, 2, and 3). Versions 5 and 7 of you only look once (YOLO) algorithm were used to train the model, and the mean average precision (mAP) was evaluated. Subsequently, data augmentation was performed using image processing and a real-enhanced super-resolution generative adversarial network, and the accuracy was re-evaluated using YOLOv7. RESULTS: The mAP of YOLOv7 in the 3 datasets was 0.931, 0.984, and 0.884, respectively, which were higher than the mAP of YOLOv5. After image processing in implant dataset-1, the mAP improved to 0.986 and, with the real-enhanced super-resolution generative adversarial network, to 0.988 and 0.986 at magnification ×2 and ×4, respectively. CONCLUSIONS: The object detection model for classifying implant designs found a high accuracy for 26 classes. The mAP of the model differed depending on the type of algorithm, image processing process, and detailed implant design.

Role of polymer template in crystal structure and photoactivity of Cu-TiO2 heterojunction nanostructures towards environmental remediation.

Exploring porous heterojunction nanomaterials as a photocatalyst for water depollution strategies towards environmental restoration is exceedingly difficult in the perspective of sustainable chemistry. Herein, we first report a porous Cu-TiO2 (TC40) heterojunction by using microphase separation of a novel penta-block copolymer (PLGA-PEO-PPO-PEO-PLGA) as a template through an evaporation induced self-assembly (EISA) method having nanorod-like particle shape. Furthermore, two types of photocatalyst were made with or without polymer template to clarify the function of that template precursor on the surface and morphology, as well as which variables are the most critical for a photocatalyst. TC40 heterojunction nanomaterial displayed higher BET surface area along with lower band gap value viz.2.98 eV compared to the other and these features make it a robust photocatalyst for wastewater treatment. In order to improve water quality, we have carried out experiments on the photodegradation of methyl orange (MO), highly toxic pollutants that cause health hazards and bioaccumulate in the environment. Our catalyst, TC40 exhibits the 100% photocatalytic efficiency towards MO dye degradation in 40 and 360 min at a rate constant of 0.104 ± 0.007 min-1 and 0.440 ± 0.03 h-1 under UV + Vis and visible light irradiation, respectively.

Improvement of Dynamic On-Resistance in GaN-Based Devices with a High-Quality In Situ SiN Passivation Layer.

In this paper, we compared the characteristics of normally-on/off AlGaN/GaN MISHEMTs passivated by an in situ/ex situ SiN layer. The devices passivated by the in situ SiN layer revealed enhanced DC characteristics, such as the drain current of 595 mA/mm (normally-on) and 175 mA/mm (normally-off) with the high on/off current ratio of ~107, respectively, compared with those of the devices passivated by the ex situ SiN layer. The MISHEMTs passivated by the in situ SiN layer also exhibited a much lower increase of dynamic on-resistance (RON) of 4.1% for the normally-on device and 12.8% for the normally-off device, respectively. Furthermore, the breakdown characteristics are greatly improved by employing the in situ SiN passivation layer, suggesting that the in situ SiN passivation layer can remarkably not only suppress the surface-trapping effects, but also decrease the off-state leakage current in the GaN-based power devices.

Identification of 130 Dental Implant Types Using Ensemble Deep Learning.

Purpose: To evaluate the accuracy and clinical usability of an identification model using ensemble deep learning for 130 dental implant types. Materials and Methods: A total of 28,112 panoramic radiographs were obtained from 30 domestic and foreign dental clinics. From these panoramic radiographs, 45,909 implant fixture images were extracted and labeled based on electronic medical records. Dental implants were classified into 130 types according to the manufacturer, the manufacturer's implant system, and the diameter and length of the implant fixture. Regions of interest were manually cropped, and data augmentation was performed. According to the minimum number of images collected per implant type, the datasets were classified into three sets: an overall total of 130 and two subsets that consisted of 79 and 58 types. EfficientNet and Res2Next algorithms were used for image classification in deep learning. After testing the performance of the two models, the ensemble learning technique was applied to improve accuracy. The top-1 accuracy, top-5 accuracy, precision, recall, and F1 scores were calculated according to algorithms and datasets. Results: For the 130 types, the top-1 accuracy, top-5 accuracy, precision, recall, and F1 scores were 75.27, 95.02, 78.84, 75.27, and 74.89, respectively. In all cases, the ensemble model performed better than EfficientNet and Res2Next. When using the ensemble model, the accuracy increased as the number of types decreased. Conclusion: The ensemble deep learning model for the identification of 130 types of dental implants showed higher accuracy than the existing algorithms. To further improve the performance and clinical usability of the model, images with higher quality and fine-tuned algorithms optimized for implant identification are required.

Wearable Localized Surface Plasmon Resonance-Based Biosensor with Highly Sensitive and Direct Detection of Cortisol in Human Sweat.

Wearable biosensors have the potential for developing individualized health evaluation and detection systems owing to their ability to provide continuous real-time physiological data. Among various wearable biosensors, localized surface plasmon resonance (LSPR)-based wearable sensors can be versatile in various practical applications owing to their sensitive interactions with specific analytes. Understanding and analyzing endocrine responses to stress is particularly crucial for evaluating human performance, diagnosing stress-related diseases, and monitoring mental health, as stress takes a serious toll on physiological health and psychological well-being. Cortisol is an essential biomarker of stress because of the close relationship between cortisol concentration in the human body and stress level. In this study, a flexible LSPR biosensor was manufactured to detect cortisol levels in the human body by depositing gold nanoparticle (AuNP) layers on a 3-aminopropyltriethoxysilane (APTES)-functionalized poly (dimethylsiloxane) (PDMS) substrate. Subsequently, an aptamer was immobilized on the surface of the LSPR substrate, enabling highly sensitive and selective cortisol capture owing to its specific cortisol recognition. The biosensor exhibited excellent detection ability in cortisol solutions of various concentrations ranging from 0.1 to 1000 nM with a detection limit of 0.1 nM. The flexible LSPR biosensor also demonstrated good stability under various mechanical deformations. Furthermore, the cortisol levels of the flexible LSPR biosensor were also measured in the human epidermis before and after exercise as well as in the morning and afternoon. Our biosensors, which combine easily manufactured flexible sensors with sensitive cortisol-detecting molecules to measure human stress levels, could be versatile candidates for human-friendly products.

Magnetically Actuated Microscaffold with Controllable Magnetization and Morphology for Regeneration of Osteochondral Tissue.

Magnetic microscaffolds capable of targeted cell delivery have been developed for tissue regeneration. However, the microscaffolds developed so far with similar morphologies have limitations for applications to osteochondral disease, which requires simultaneous treatment of the cartilage and subchondral bone. This study proposes magnetically actuated microscaffolds tailored to the cartilage and subchondral bone for osteochondral tissue regeneration, named magnetically actuated microscaffolds for cartilage regeneration (MAM-CR) and for subchondral bone regeneration (MAM-SBR). The morphologies of the microscaffolds were controlled using a double emulsion and microfluidic flow. In addition, due to their different sizes, MAM-CR and MAM-SBR have different magnetizations because of the different amounts of magnetic nanoparticles attached to their surfaces. In terms of biocompatibility, both microscaffolds were shown to grow cells without toxicity as potential cell carriers. In magnetic actuation tests of the microscaffolds, the relatively larger MAM-SBR moved faster than the MAM-CR under the same magnetic field strength. In a feasibility test, the magnetic targeting of the microscaffolds in 3D knee cartilage phantoms showed that the MAM-SBR and MAM-CR were sequentially moved to the target sites. Thus, the proposed magnetically actuated microscaffolds provide noninvasive treatment for osteochondral tissue disease.

Green and Sustainable Hot Melt Adhesive (HMA) Based on Polyhydroxyalkanoate (PHA) and Silanized Cellulose Nanofibers (SCNFs).

Polyhydroxyalkanoate (PHA), with a long chain length and high poly(4-hydroxybutyric acid) (P4HB) ratio, can be used as a base polymer for eco-friendly and biodegradable adhesives owing to its high elasticity, elongation at break, flexibility, and processability; however, its molecular structures must be adjusted for adhesive applications. In this study, surface-modified cellulose nanofibers (CNFs) were used as a hydrophobic additive for the PHA-based adhesive. For the surface modification of CNFs, double silanization using tetraethyl orthosilicate (TEOS) and methyltrimethoxysilane (MTMS) was performed, and the thermal and structural properties were evaluated. The hydrophobicity of the TEOS- and MTMS-treated CNFs (TMCNFs) was confirmed by FT-IR and water contact angle analysis, with hydrophobic CNFs well dispersed in the PHA. The PHA-CNFs composite was prepared with TMCNFs, and its morphological analysis verified the good dispersion of TMCNFs in the PHA. The tensile strength of the composite was enhanced when 10% TMCNFs were added; however, the viscosity decreased as the TMCNFs acted as a thixotropic agent. Adding TMCNFs to PHA enhanced the flowability and infiltration ability of the PHA-TMCNFs-based adhesive, and an increase in the loss tangent (Tan δ) and adjustment of viscosity without reducing the adhesive strength was also observed. These changes in properties can improve the flowability and dispersibility of the PHA-TMCNFs adhesive on a rough adhesive surface at low stress. Thus, it is expected that double-silanized CNFs effectively improve their interfacial adhesion in PHA and the adhesive properties of the PHA-CNFs composites, which can be utilized for more suitable adhesive applications.

Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22-37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18-88), and the Information eXtraction from Images (IXI, n = 567, age range 19-86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75-3.12, 7.08-10.50, and 8.04-9.86 years, as well as Pearson's correlation coefficients of 0.11-0.42, 0.64-0.85, and 0.63-0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.

Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction.

MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the long scan time of MRI, the parallel MRI method for reconstructing a high-fidelity MR image from under-sampled multi-coil k-space data is widely used. In this study, we propose a method to reconstruct a high-fidelity MR image from under-sampled multi-coil k-space data using deep-learning. The proposed multi-domain Neumann network with sensitivity maps (MDNNSM) is based on the Neumann network and uses a forward model including coil sensitivity maps for parallel MRI reconstruction. The MDNNSM consists of three main structures: the CNN-based sensitivity reconstruction block estimates coil sensitivity maps from multi-coil under-sampled k-space data; the recursive MR image reconstruction block reconstructs the MR image; and the skip connection accumulates each output and produces the final result. Experiments using the fastMRI T1-weighted brain image dataset were conducted at acceleration factors of 2, 4, and 8. Qualitative and quantitative experimental results show that the proposed MDNNSM method reconstructs MR images more accurately than other methods, including the generalized autocalibrating partially parallel acquisitions (GRAPPA) method and the original Neumann network.

Improved Clinical and Radiologic Outcomes Seen after Superior Capsule Reconstruction using Long Head Biceps Tendon Autograft.

PURPOSE: The objective of this study was to investigate the clinical and radiologic outcomes after superior capsule reconstruction (SCR) with biceps tendon (BT) for irreparable rotator cuff tears. METHODS: The retrospective study period was May 2015 through February 2018. The average follow-up was 32 months (24-48 months) after surgery. Study inclusion criteria included an arthroscopic SCR performed using only our technique and minimum 2-year clinical follow-up by office visit and survey. Exclusion criteria included irreparable subscapularis tear and those patients lost to follow-up. This method enabled SCR by using the extraarticular portion and the intraarticular portion and making it 2 to 3 bundles by moving back and forth in the intraarticular area. Physical examination and functional scoring procedures were performed before surgery and at 3, 6, 12, and 24 months after surgery. Radiography and magnetic resonance imaging (MRI) were performed before surgery, after surgery (only radiography), and at 6 and 24 months after surgery. RESULTS: Fifty-three shoulders involving 45 consecutive patients underwent BT technique for irreparable massive rotator cuff tears. The visual analog scale (VAS), American Shoulder and Elbow Surgeons (ASES), and constant score (CS) showed statistically significant improvement (VAS, 4.1-1.0; ASES, 60.9-82.7; and CS, 64.9-80.0; P < .0001). The shoulder active range of motion improved significantly by 23 for forward elevation (125.3-148.4; P < .0001) and by 12 for external rotation (38.0-50.9, P < .0001). The acromiohumeral distance (AHD) was significantly increased by 2.7 mm (4.4 ± 1.4 mm -> 7.1 ± 1.3 mm). No graft tear was detected in 39 patients (86.7%) during follow-up (24-48 months). CONCLUSIONS: SCR via our technique improved clinical and radiologic outcomes. Thirty-five (77.7%) patients achieved 17-point improvement (the minimally clinically important difference) in the last follow-up of ASES score. Clinical scores and AHD had significantly increased, and good healed rate (86.7%) was observed in MRI. LEVEL OF EVIDENCE: Level IV, retrospective case series.

Imaging Assessment of Visceral Pleural Surface Invasion by Lung Cancer: Comparison of CT and Contrast-Enhanced Radial T1-Weighted Gradient Echo 3-Tesla MRI.

OBJECTIVE: To compare the diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3-tesla (3T) magnetic resonance imaging (MRI) and computed tomography (CT) for the detection of visceral pleural surface invasion (VPSI). Visceral pleural invasion by non-small-cell lung cancer (NSCLC) can be classified into two types: PL1 (without VPSI), invasion of the elastic layer of the visceral pleura without reaching the visceral pleural surface, and PL2 (with VPSI), full invasion of the visceral pleura. MATERIALS AND METHODS: Thirty-three patients with pathologically confirmed VPSI by NSCLC were retrospectively reviewed. Multidetector CT and contrast-enhanced 3T MRI with a free-breathing radial three-dimensional fat-suppressed volumetric interpolated breath-hold examination (VIBE) pulse sequence were compared in terms of the length of contact, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. Supplemental evaluation of the tumor-pleura interface (smooth versus irregular) could only be performed with MRI (not discernible on CT). RESULTS: At the tumor-pleura interface, radial VIBE MRI revealed a smooth margin in 20 of 21 patients without VPSI and an irregular margin in 10 of 12 patients with VPSI, yielding an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score for VPSI detection of 91%, 83%, 95%, 91%, 91%, and 87%, respectively. The McNemar test and receiver operating characteristics curve analysis revealed no significant differences between the diagnostic accuracies of CT and MRI for evaluating the contact length, angle of mass margin, or arch distance-to-maximum tumor diameter ratio as predictors of VPSI. CONCLUSION: The diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3T MRI and CT were equal in terms of the contact length, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. The advantage of MRI is its clear depiction of the tumor-pleura interface margin, facilitating VPSI detection.

Far-infrared rays enhance mitochondrial biogenesis and GLUT3 expression under low glucose conditions in rat skeletal muscle cells.

Far-infrared rays (FIR) are known to have various effects on atoms and molecular structures within cells owing to their radiation and vibration frequencies. The present study examined the effects of FIR on gene expression related to glucose transport through microarray analysis in rat skeletal muscle cells, as well as on mitochondrial biogenesis, at high and low glucose conditions. FIR were emitted from a bio-active material coated fabric (BMCF). L6 cells were treated with 30% BMCF for 24 h in medium containing 25 or 5.5 mM glucose, and changes in the expression of glucose transporter genes were determined. The expression of GLUT3 (Slc2a3) increased 2.0-fold (p < 0.05) under 5.5 mM glucose and 30% BMCF. In addition, mitochondrial oxygen consumption and membrane potential (ΔΨm) increased 1.5- and 3.4-fold (p < 0.05 and p < 0.001), respectively, but no significant change in expression of Pgc-1a, a regulator of mitochondrial biogenesis, was observed in 24 h. To analyze the relationship between GLUT3 expression and mitochondrial biogenesis under FIR, GLUT3 was down-modulated by siRNA for 72 h. As a result, the ΔΨm of the GLUT3 siRNA-treated cells increased 3.0-fold (p < 0.001), whereas that of the control group increased 4.6-fold (p < 0.001). Moreover, Pgc-1a expression increased upon 30% BMCF treatment for 72 h; an effect that was more pronounced in the presence of GLUT3. These results suggest that FIR may hold therapeutic potential for improving glucose metabolism and mitochondrial function in metabolic diseases associated with insufficient glucose supply, such as type 2 diabetes.

Recent Trends in Morphology-Controlled Synthesis and Application of Mesoporous Silica Nanoparticles.

The outstanding journey towards the investigation of mesoporous materials commences with the discovery of high surface area porous silica materials, named MCM-41 (Mobil Composition of Matter-41) according to the inventors' name Mobile scientists in the United States. Based on a self-assembled supramolecular templating mechanism, the synthesis of mesoporous silica has extended to wide varieties of silica categories along with versatile applications of all these types in many fields. These silica families have some extraordinary structural features, like highly tunable nanoscale sized pore diameter, good Brunauer-Emmett-Teller (BET) surface areas, good flexibility to accommodate different organic and inorganic functional groups, metals etc., onto their surface. As a consequence, thousands of scientists and researchers throughout the world have reported numerous silica materials in the form of published articles, communication, reviews, etc. Beside this, attention is also given to the morphology-oriented synthesis of silica nanoparticles and their significant effects on the emerging fields of study like catalysis, energy applications, sensing, environmental, and biomedical research. This review highlights a consolidated overview of those morphology-based mesoporous silica particles, emphasizing their syntheses and potential role in many promising fields of research.

rt269I Type of Hepatitis B Virus (HBV) Leads to HBV e Antigen Negative Infections and Liver Disease Progression via Mitochondrial Stress Mediated Type I Interferon Production in Chronic Patients With Genotype C Infections.

Hepatitis B virus infection is a serious global health problem and causes life-threatening liver disease. In particular, genotype C shows high prevalence and severe liver disease compared with other genotypes. However, the underlying mechanisms regarding virological traits still remain unclear. This study investigated the clinical factors and capacity to modulate Type I interferon (IFN-I) between two HBV polymerase polymorphisms rt269L and rt269I in genotype C. This report compared clinical factors between rt269L and rt269I in 220 Korean chronic patients with genotype C infections. The prevalence of preC mutations between rt269L and rt269I was compared using this study's cohort and the GenBank database. For in vitro and in vivo experiments, transient transfection using HBV genome plasmid and HBV virion infection using HepG2-hNTCP-C4 and HepaRG systems and hydrodynamic injection of HBV genome into mice tails were conducted, respectively. This report's clinical data indicated that rt269I vs. rt269L was more significantly related to HBV e antigen (HBeAg) negative serostatus, lower levels of HBV DNA and HBsAg, and disease progression. Our epidemiological study showed HBeAg negative infections of rt269I infections were attributed to a higher frequency of preC mutations at 1896 (G to A). Our in vitro and in vivo studies also found that rt269I could lead to mitochondrial stress mediated STING dependent IFN-I production, resulting in decreasing HBV replication via the induction of heme-oxygenase-1. In addition, we also found that rt269I could lead to enhanced iNOS mediated NO production in an IFN-I dependent manner. These data demonstrated that rt269I can contribute to HBeAg negative infections and liver disease progression in chronic patients with genotype C infections via mitochondrial stress mediated IFN-I production.

High Sensitive pH Sensor Based on AlInN/GaN Heterostructure Transistor.

The AlInN/GaN high-electron-mobility-transistor (HEMT) indicates better performances compared with the traditional AlGaN/GaN HEMTs. The present work investigated the pH sensor functionality of an analogous HEMT AlInN/GaN device with an open gate. It was shown that the Al0.83In0.17N/GaN device demonstrates excellent pH sense functionality in aqueous solutions, exhibiting higher sensitivity (−30.83 μA/pH for AlInN/GaN and −4.6 μA/pH for AlGaN/GaN) and a faster response time, lower degradation and good stability with respect to the AlGaN/GaN device, which is attributed to higher two-dimensional electron gas (2DEG) density and a thinner barrier layer in Al0.83In0.17N/GaN owning to lattice matching. On the other hand, the open gate geometry was found to affect the pH sensitivity obviously. Properly increasing the width and shortening the length of the open gate area could enhance the sensitivity. However, when the open gate width is too larger or too small, the pH sensitivity would be suppressed conversely. Designing an optimal ratio of the width to the length is important for achieving high sensitivity. This work suggests that the AlInN/GaN-based 2DEG carrier modulated devices would be good candidates for high-performance pH sensors and other related applications.