Efficacy of entecavir versus tenofovir in preventing hepatocellular carcinoma in patients with chronic hepatitis B with maintained virologic response.

BACKGROUND & AIMS: Several studies suggested that efficacy of tenofovir in reducing the risk of the development of hepatocellular carcinoma (HCC) might be better than that of entecavir. It remains unknown whether a change in therapy can further reduce the risk of HCC in patients receiving entecavir therapy and achieved goal of antiviral therapy, a maintained undetectable hepatitis B virus (HBV) DNA level in the serum. METHODS: A total of 1336 treatment-naïve chronic HBV mono-infected adult patients, who started entecavir or tenofovir treatment and achieved a maintained virologic response during follow-up were analysed. RESULTS: During a median 4.4 years of follow-up (range, 1.0-7.4 years) after achieving virologic response, 99 patients developed HCC. The 5-year cumulative HCC incidence rate was 7.3% and 6.3% for the entecavir and tenofovir groups, respectively, with similar risk of HCC between the two groups (adjusted HR, 0.82; 95% CI, 0.52-1.29; p = 0.3). The risk of HCC was similar in the propensity score-matched cohort (HR, 1.02; 95% CI, 0.68-1.52; p = 0.94) and inverse probability treatment weighting analysis (HR, 1.11; 95% CI, 0.74-1.66; p = 0.62). In the subgroup analysis, HCC risk was similar between the two drugs in both patients with and without cirrhosis. DISCUSSION: In patients showing maintained virologic response, no difference in the risk of HCC between entecavir and tenofovir was observed. This indicates entecavir might be as effective as tenofovir in the prevention of HCC among those patients and suggest that a change in therapy in anticipation of further reducing the risk of HCC might not be necessary for patients receiving entecavir and showing virologic response.

The Effects of a Mobile Wellness Intervention with Fitbit Use and Goal Setting for Workers.

Background and Introduction: There is strong evidence that worksite wellness programs can significantly improve the health profile of participating workers. To date, little is known about research on the effects of mobile wellness interventions in worksite settings. Furthermore, no studies have been conducted to evaluate mobile wellness interventions with activity trackers and tailoring strategies for physically inactive workers in manufacturing companies. This study aimed to examine the effects of a mobile wellness intervention with Fitbit and goal setting using brief counseling and text messaging among workers. Materials and Methods: A total of 79 (n = 79) workers from large manufacturing companies were allocated into an experimental group (n = 41) and a control group (n = 38). All participants were asked to wear an activity tracker (Fitbit Charger HR) during all waking hours for 5 weekdays. Participants in the experimental group received Fitbit, daily motivational text messaging, and biweekly counseling with a specifically designed workbook for 12 weeks, whereas Fitbit was only provided to the control group. Results: At the 12-week measurement, there were significant differences between the experimental group and control group on wellness (p < 0.001), physical activity behavior (p < 0.001), daily walking steps (p < 0.001), and physical activity self-efficacy (p < 0.001). Discussion and Conclusions: Although Fitbit facilitates an individual's activities by providing information about daily steps, the tracker itself, without additional goal-setting techniques, may be insufficient to encourage behavior change. These findings indicate that the mobile wellness intervention with Fitbit and goal setting using brief counseling and tailored text messaging is more effective for physically inactive workers.

Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement.

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain's response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.

Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot.

This study introduces a novel convolutional neural network (CNN) architecture, encompassing both single and multi-head designs, developed to identify a user's locomotion activity while using a wearable lower limb robot. Our research involved 500 healthy adult participants in an activities of daily living (ADL) space, conducted from 1 September to 30 November 2022. We collected prospective data to identify five locomotion activities (level ground walking, stair ascent/descent, and ramp ascent/descent) across three terrains: flat ground, staircase, and ramp. To evaluate the predictive capabilities of the proposed CNN architectures, we compared its performance with three other models: one CNN and two hybrid models (CNN-LSTM and LSTM-CNN). Experiments were conducted using multivariate signals of various types obtained from electromyograms (EMGs) and the wearable robot. Our results reveal that the deeper CNN architecture significantly surpasses the performance of the three competing models. The proposed model, leveraging encoder data such as hip angles and velocities, along with postural signals such as roll, pitch, and yaw from the wearable lower limb robot, achieved superior performance with an inference speed of 1.14 s. Specifically, the F-measure performance of the proposed model reached 96.17%, compared to 90.68% for DDLMI, 94.41% for DeepConvLSTM, and 95.57% for LSTM-CNN, respectively.

Propensity-Score-Matched Evaluation of Adverse Events Affecting Recovery after COVID-19 Vaccination: On Adenovirus and mRNA Vaccines.

This study aimed to observe adverse events following immunisation (AEFIs) that affected recovery within two weeks after COVID-19 vaccination and investigate their risks in propensity-score-matched populations. Data were collected from 447,346 reports from the VAERS between 1 January 2021 and 31 July 2021. Propensity-score-matched populations were constructed by adjusting for demographic characteristics and 11 underlying diseases in eligible subjects who received 1 of 3 COVID-19 vaccines: 19,462 Ad26.COV2.S, 120,580 mRNA-1273, and 100,752 BNT162b2. We observed that 88 suspected AEFIs (22 in Ad26.COV2.S, 62 in mRNA-1273, and 54 in BNT162b2) were associated with an increased risk of delayed recovery within 2 weeks after COVID-19 vaccinations. Nervous system, musculoskeletal and connective tissue, gastrointestinal, skin, and subcutaneous tissue disorders were the most common AEFIs after COVID-19 vaccination. Interestingly, four local and systemic reactions affected recovery in different vaccine recipients during our study period: asthenic conditions and febrile disorders in Ad26.COV2.S and mRNA-1273; general signs and symptoms in mRNA-1273 and BNT162b2; injection site reactions in Ad26.COV2.S and BNT162b2. Although it is necessary to confirm a causal relationship with COVID-19 vaccinations, some symptoms, including paralysis, allergic disorders, breathing abnormalities, and visual impairment, may hinder the recovery of these recipients.

Machine Learning to Identify Psychomotor Behaviors of Delirium for Patients in Long-Term Care Facility.

This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients. A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 clinical variables and the survey items from the short confusion assessment method (S-CAM), and developed a mobile-based application. Multiple machine learning algorithms, including four rule-mining algorithms (C4.5, CBA, MCAR, and LEM2) and four other statistical learning algorithms (LR, ANNs, SVMs with three kernel functions, and random forest), were validated by paired Wilcoxon signed-rank tests on both macro-averaged F1 and weighted average F1-measures during the 10-times stratified 2-fold cross-validation. The LEM2 algorithm achieved the best prediction performance (macro-averaged F1-measure of 49.35%; weighted average F1-measure of 96.55%), correctly identifying adult patients at delirium risk. In the pairwise comparison between predictive powers observed from independent models, the LEM2 model showed a medium or large effect size between 0.4925 and 0.8766 when compared with LR, ANN, SVM with RBF, and MCAR models. We have confirmed that acute consciousness in S-CAM assessment is closely associated with different predictors for screening three psychomotor behaviors of delirium: 1) education level, dementia type or its level, sleep disorder, dehydration, and infection in mixed-type delirium; 2) gender, education level, dementia type, dehydration, bedsores, and foley catheter in hyperactive delirium; and 3) pain, sleep disorder, and haloperidol use in hypoactive delirium.

Preparation of polymeric micelles consisting of poly(propylene glycol) and poly(caprolactone).

The ring-opening polymerization of epsilon-caprolactone (CL) was carried out with polypropylene glycol (PPG) as an initiator in the presence of the monomer activator HCl. Et2O to synthesize poly(epsilon-caprolactone)-poly(propyleneglycol)-poly(epsilon-caprolactone) (PCL-PPG-PCL) triblock copolymers with change of length PPG and PCL. The micelle formation of PCL-PPG-PCL triblock copolymers in an aqueous phase was confirmed by NMR, dynamic light scattering and fluorescence techniques. The critical micelle concentration (CMC) of the PCL-PPG-PCL triblock copolymers, determined from fluorescence measurements, was in range of 1.4 x 10(-3)-4.6 x 10(-3) mg/ml with dependence on block lengths of PPG and PCL. The partition equilibrium constant, K(v), which is an indicator of the hydrophobicity of the micelles of the PCL-PPG-PCL triblock copolymers in aqueous media, was also changed with dependence on length PPG and PCL. We confirmed that the PCL-PPG-PCL triblock copolymers formed micelles and hence may be potential hydrophobic drug carriers.

Time Course of Odor Categorization Processing.

The brain's mechanisms for categorizing different odors have long been a research focus. Previous studies suggest that odor categorization may involve multiple neurological processes within the brain with temporal and spatial neuronal activation. However, there is limited evidence regarding temporally mediated mechanisms in humans, especially millisecond odor processing. Such mechanisms may be important because different brain areas may play different roles at a particular activation time during sensory processing. Here, we focused on how the brain categorizes odors at specific time intervals. Using multivariate electroencephalography (EEG) analysis, we found that similarly perceived odors induced similar EEG signals during 50-100, 150-200, and 350-400 ms at the theta frequency. We also found significant activation at 100-150 and 350-400 ms at the gamma frequency. At these two frequencies, significant activation was observed in some olfactory-associated areas, including the orbitofrontal cortex. Our findings provide essential evidence that specific periods may be related to odor quality processing during central olfactory processing.

Patterning of hierarchically aligned single-walled carbon nanotube Langmuir-Blodgett films by microcontact printing.

We present a patterning method for hierarchically aligned assembly of single-walled carbon nanotubes (SWNTs) using a combination of the Langmuir-Blodgett (LB) technique and soft lithography. The LB technique allows one to control the alignment and the surface density of SWNTs by adjusting surface pressure of the film at the air-water interface. The aligned SWNT Langmuir films are successfully transferred onto the polydimethylsiloxane (PDMS) or silicon substrate with unidirectional alignment, and SWNT patterns with various shapes are fabricated on silicon and flexible poly(ethylene terephthalate) (PET) substrates by contacting and peeling off the PDMS stamp from the substrates via microcontact printing or lift-up methods. The SWNT patterning technique using the combination of soft lithography and the LB method can be applied in various fields, such as flexible high-speed transistors, high-efficiency solar cells, and transparent electrodes.

Ultra fast UV-photo detector based on single-walled carbon nanotube/PEDOT-PSS composites.

Single-walled carbon nanotube (SWNT)/Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS), composites (SWNT/PEDOT-PSS) have been prepared using SWNTs surface modified with a natural gum, 'gum arabic' by simple mixing process. Thin films of SWNTs, PEDOT-PSS and the composites were prepared by vacuum filtration technique and were exposed to ultraviolet (UV) radiations for photoconductivity measurements. The surface resistivity of pristine SWNTs film increased from initial value of 50 omega to 92 omega and that of the polymer film decreased from 6.7 Komega to 3.1 Komega while the resistivity of the composite film decreased from 267 omega to 232 omega upon UV illumination. When the lamp was switched off, the initial resistivities of PEDOT: PSS and SWNTs films were recovered very slowly. Interestingly, on the other hand the composite films demonstrated a very fast relaxation within a few minutes. An on-off cycle ruled out the possibility of local heating effect and revealed that the switching property was originated from the fast transport of charge and heat in the composite films. This property of composite film might open up optoelectronic applications involving photoconductivity, such as photo sensors, organic light emitting diodes (OLED) and organic solar cells. Here in, we demonstrate the application of the SWNT/PEDOT-PSS composite film based device as a UV sensor.

Fabrication of gold nanoflowers by template-assisted electrodeposition.

Gold (Au) nanoflowers have been fabricated using anodic aluminum oxide (AAO) templates assisted electrochemical deposition on the Au film, in which the templates were coated by poly (dimethyl siloxane) (PDMS). PDMS is a viscous, soft material which helps the AAO template to stick to the Au film and assists in the formation of flower-like nanostructures. First, Au nanoplates grew in one-dimensional (1D) pores of the AAO template and uniformly distributed on the Au film; afterwards, the nanoplates continued to grow three-dimensionally because the contracted PDMS provided more space and further formed flower-like shape. The Au nanostructures were characterized by field emission scanning electron microscopy (FE-SEM) as well as energy-dispersive spectroscopy (EDS). Enhanced fluorescence was observably detected along the change of the surface morphology and the nanoflowers exhibited a higher intensity than other Au nanostructures.

Preparation of hierarchically aligned carbon nanotube films using the Langmuir-Blodgett technique.

Hierarchically-aligned single-walled carbon nanotube (SWNT) films over large areas were fabricated by using Langmuir-Blodgett (LB) technique. Thiophenyl-modified SWNTs spreading solution in chloroform was prepared through amidation reaction of oxidized SWNTs. The resulting SWNTs were found to form stable colloidal suspensions in organic solvents, such as chloroform, which is a suitable solvent for the LB application. The compression of the thiophenyl-modified SWNTs spread onto the water surface of an LB trough leading to a uniform SWNT Langmuir monolayer, where SWNTs were aligned parallel to the trough barrier. Optical anisotropy of SWNTs LB films on quartz substrate was confirmed by polarized UV-Vis/NIR spectroscopic measurement. Moreover, the electrical conductivity of the resulting SWNT films, which were parallel to the tube axis, was found to be approximately 15 times higher than those that were perpendicular to the axis, reflecting anisotropic electrical properties due to the uniaxial alignment of individual SWNT bundles.

Fabrication of carbon nanotube AFM probes using the Langmuir-Blodgett technique.

Carbon nanotube (CNT)-tipped atomic force microscopy (AFM) probes have shown a significant potential for obtaining high-resolution imaging of nanostructure and biological materials. In this paper, we report a simple method to fabricate single-walled carbon nanotube (SWNT) nanoprobes for AFM using the Langmuir-Blodgett (LB) technique. Thiophenyl-modified SWNTs (SWNT-SHs) through amidation of SWNTs in chloroform allowed to be spread and form a stable Langmuir monolayer at the water/air interface. A simple two-step transfer process was used: (1) dipping conventional AFM probes into the Langmuir monolayer and (2) lifting the probes from the water surface. This results in the attachment of SWNTs onto the tips of AFM nanoprobes. We found that the SWNTs assembled on the nanoprobes were well-oriented and robust enough to maintain their shape and direction even after successive scans. AFM measurements of a nano-porous alumina substrate and deoxyribonucleic acid using SWNT-modified nanoprobes revealed that the curvature diameter of the nanoprobes was less than 3 nm and a fine resolution was obtained than that from conventional AFM probes. We also demonstrate that the LB method is a scalable process capable of simultaneously fabricating a large number of SWNT-modified nanoprobes.

Sensitivity enhancement by Au nanoparticles in surface plasmon resonance chemical sensors.

We investigated the sensitivity enhancement in chemical sensors by coupling Au nanoparticles that have the specific size and surface density on sensor chips as those found in label-free detection systems. The Au particles with 10, 30, 60 nm diameter were conjugated by amine groups of cystamine modified chips. The surface density of Au particles was controlled by the reaction time and the concentration of the solution containing the particles. In order to investigate the sensitivity enhancement, we compared the resonance angle shifts with or without Au particles in a methanol aqueous solution. We know that the sensitivity depends on the size and surface density of particles. The sensitivity increased by 57% with the adsorption of 30 nm diameter particles and low surface density due to the coupling effect of localized surface plasmon derived by the size and density of specific Au nanoparticles and surface plasmon waves.

Hydrogen fermentation of food waste by alkali-shock pretreatment: microbial community analysis and limitation of continuous operation.

In the study, at first, batch tests were performed to investigate the effect of alkali-shock on H2 production from food waste (FW). After alkali-pretreatment of FW at pH 9.0-13.0, the FW was cultivated under mesophilic condition at pH 6.0 for 30 h without external inoculum addition. The amount of H2 production from FW pretreated at pH 11.0 and 12.0 was higher than that achieved in other pretreatment pH. The main metabolite was butyrate, and Clostridium were dominant at pH 11.0 and 12.0. Meanwhile, lactate was the main metabolite with Enterococcus and Streptococcus being the dominant genus at other pretreatment pH. When the batch process was switched to a continuous mode, H2 production was significantly dropped due to the increased activity of H2-consumers. The reliability of alkali-pretreatment at pH 11.0 was proven by repeating the scale-up batch process, recording 1.57±0.11 mol H2/mol hexose(added) (17±2LH2/kg FW) and 4.39±0.32LH2/L/d.

Random networks of single-walled carbon nanotubes promote mesenchymal stem cell's proliferation and differentiation.

Studies on the interaction of cells with single-walled carbon nanotubes (SWCNTs) have been receiving increasing attention owing to their potential for various cellular applications. In this report, we investigated the interactions between biological cells and nanostructured SWCNTs films and focused on how morphological structures of SWCNT films affected cellular behavior such as cell proliferation and differentiation. One directionally aligned SWCNT Langmuir-Blodgett (LB) film and random network SWCNT film were fabricated by LB and vacuum filteration methods, respectively. We demonstrate that our SWCNT LB and network film based scaffolds do not show any cytotoxicity, while on the other hand, these scaffolds promote differentiation property of rat mesenchymal stem cells (rMSCs) when compared with that on conventional tissue culture polystyrene substrates. Especially, the SWCNT network film with average thickness and roughness values of 95 ± 5 and 9.81 nm, respectively, demonstrated faster growth rate and higher cell thickness for rMSCs. These results suggest that systematic manipulation of the thickness, roughness, and directional alignment of SWCNT films would provide the convenient strategy for controlling the growth and maintenance of the differentiation property of stem cells. The SWCNT film could be an alternative culture substrate for various stem cells, which often require close control of the growth and differentiation properties.

Effect of the accuracy of pH control on hydrogen fermentation.

pH, known as the most important parameter in H2 fermentation, cannot be precisely controlled in a scaled-up fermenter as in a lab fermenter. In the preset work, to assess the effect of pH control accuracy on H2 fermentation, the pH was controlled at 6.0±0.1, 6.0±0.3, 6.0±0.5, 6.0±0.7, and 6.0±0.9 during batch fermentation of food waste. Up to deviation of ±0.3, a high H2 yield of 1.67-1.73 mol H2/mol hexose(added) was attained with producing butyrate as a major metabolite (>70% of total organic acids produced). A huge drop of H2 production, however, was observed at deviation >±0.5 with lowered substrate utilization and increased production of lactate. Next generation sequencing results showed that Clostridium was found to be the dominant genus (76.4% of total number of sequences) at deviation of ±0.1, whereas the dominant genus was changed to lactic acid bacteria such as Streptococcus and Lactobacillus with increase of deviation value.