Burden changes in notifiable infectious diseases in Taiwan during the COVID-19 pandemic.

Background: This study aimed to assess the impact of the COVID-19 pandemic on the disease burden of Taiwan's notifiable infectious diseases (NIDs). We compared disease burdens between the pandemic and pre-pandemic year of 2020 (with non-pharmaceutical interventions (NPIs)) and 2010 (without NPIs), respectively, to understand the overall pandemic impact on NIDs in Taiwan. Methods: Forty-three national NIDs were analyzed using the Statistics of Communicable Diseases and Surveillance Report by estimating the premature death and disability via different transmission categories, sex, and age groups. The study evaluated the impact of diseases by assessing the years lost due to death (YLLs), the duration of living with disability (YLDs), and the overall disability-adjusted life years (DALYs) by measuring both the severity of the illness and its duration. Results: Taiwan recorded 1,577 (2010) and 1,260 (2020) DALYs per million population and lost 43 NIDs, decreasing 317 DALYs per million population. Tuberculosis, HIV/AIDS and acute hepatitis B/D were the leading causes of DALYs, accounting for 89% (2010) and 77% (2020). Conclusion: Overall, this study provided the first insight of changes in disease burdens in NIDs between pre- and post-COVID-19 based on a nationwide viewpoint for further preventive measures and interventions to be focused on specific diseases by associated health administrations and policies.

Effects of font size, stroke, and background on the legibility of Chinese characters in virtual reality for the elderly.

To investigate the legibility of Chinese characters' font size, text background opacity, and font stroke for the elderly in virtual reality, we recruited old and young participants to conduct experiments with VR and used eye-tracking technology to record the data of task completion time and error rate. After analysis, we concluded that the minimum recognition font size for the elderly is 30 dmm, and the best font size is 60 dmm, which is 20 and 40 dmm for young people. The font style has a significant effect on old people (p = 0.000*). Besides, for font sizes smaller than 20 dmm and bigger than 50 dmm, text with strokes and over 50% semi-transparent backgrounds can improve legibility for the elderly. With a suitable font size, the influence of font style on the elderly is not significant. These conclusions can provide a reference for the elderly-oriented Chinese font design in VR.

To investigate the legibility of Chinese characters' font size, text background opacity, and font stroke for the elderly in virtual reality. We recruited old and young participants to conduct experiments with VR and concluded the minimum recognition font size and the best font size for them.

A Transformable Supramolecular Bispecific Cell Engager for Augmenting Natural Killer and T Cell-Based Cancer Immunotherapy.

Immune cells are pivotal in cancer immunotherapy, yet their therapeutic effectiveness is often hampered by limited tumor infiltration and inhibitory tumor microenvironments. An alkaline phosphatase (ALP)-responsive and transformable supramolecular bis-specific cell engager (Supra-BiCE) to harness natural killer (NK)/T cells for effective cancer immunotherapy is introduced here. The Supra-BiCE, consisting of both SA-P (a phosphorylated peptide targeting and blocking programmed cell death ligand 1 (PD-L1)) and SA-T (a phosphorylated peptide targeting and blocking T cell immunoglobulin and ITIM domain (TIGIT)) is constructed by a simple co-assembling strategy. Upon intravenous administration, Supra-BiCE self-assembles into nanoribbons and interacts with NK/T cells via TIGIT. Notably, these nanoribbons undergo transformation into long nanofibrils within ALP-overexpressing tumor regions, resulting in enhanced binding affinities of Supra-BiCE to both PD-L1 and TIGIT. Consequently, this leads to the accumulation and retention of NK/T cells within tumor regions. Furthermore, the combinatorial blockade of checkpoints by Supra-BiCE activates infiltrating NK/T cells. Moreover, the adjustable peptide ratio in Supra-BiCE enables customization for optimal therapeutic effects against distinct tumor types. Particularly, Supra-BiCE (T:P = 1:3) achieved 98.27% tumor suppression rate against colon carcinoma model. Overall, this study offers a promising tool for engaging NK and T cells for cancer immunotherapy.

Knocking down GALNT6 promotes pyroptosis of pancreatic ductal adenocarcinoma cells through NF-κB/NLRP3/GSDMD and GSDME signaling pathway.

Background: Pancreatic ductal adenocarcinoma (PDAC), the most prevalent type of pancreatic cancer, is a highly lethal malignancy with poor prognosis. Polypeptide N-acetylgalactosaminyltransferase-6 (GALNT6) is frequently overexpressed in PDAC. However, the role of GALNT6 in the PDAC remains unclear. Methods: The expression of GALNT6 in pancreatic cancer and normal tissues were analyzed by bioinformatic analyses and immunohistochemistry. CCK8 and colony formation were used to detect cell proliferation. Flow cytometry was applied to detect cell cycle.The pyroptosis was detected by scanning electron microscopy. The mRNA expression was detected by qRT-PCR. The protein expression and localization were detected by western blot and immunofluorescence assay. ELISA was used to detect the levels of inflammatory factors. Results: The expression of GALNT6 was associated with advanced tumor stage, and had an area under curve (AUC) value of 0.919 in pancreatic cancer based on the cancer genome atlas (TCGA) dataset. Knockdown of GALNT6 inhibited cell proliferation, migration, invasion and cell cycle arrest of PDAC cells. Meanwhile, knockdown of GALNT6 increased the expression levels of interleukin-1ß (IL-1ß), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and interleukin-18 (IL-18), the release of inflammasome and an increasing of Gasdermin D (GSDMD), N-terminal of GSDMD (GSDMD-N), Gasdermin E (GSDME) and N-terminal of GSDME (GSDME-N) in PDAC cells. GALNT6 suppressed the expression of NOD-like receptor thermal protein domain associated protein 3 (NLRP3) and GSDMD by glycosylation of NF-κB and inhibiting the nucleus localization of NF-κB. Additionally, GALNT6 promotes the degradation of GSDME by O-glycosylation. Conclusion: We found that GALNT6 is highly expressed in pancreatic cancer and plays a carcinogenic role. The results suggested that GALNT6 regulates the pyroptosis of PDAC cells through NF-κB/NLRP3/GSDMD and GSDME signaling. Our study might provides novel insights into the roles of GALNT6 in PDAC progression.

A review on ergonomics evaluations of virtual reality.

BACKGROUND: Virtual reality (VR) is a combination of technologies that allow the user to interact with a computer-simulated environment with the experience of immersion, interactivity, and imagination. However, ergonomic problems related to virtual reality have adverse effects on the health and experience of users, which restrict the application of virtual reality technology. OBJECTIVE: The paper aims to provide an overview of the ergonomics evaluation of VR for further development of software and hardware of VR. METHODS: This paper describes and discusses the ergonomics issues involved in the software and hardware of VR from three aspects: visual, physiological, and cognitive. The paper also summarizes the research methods and evaluation metrics. RESULTS: Many attempts have been made to study ergonomics issues of VR, mainly including pressure, muscle fatigue, thermal comfort, visual fatigue, and motion sickness. Ergonomics studies are very valuable for research related to virtual reality. There is a summary table that lists the main evaluation metrics and methods. CONCLUSIONS: According to current research, this review gives three recommendations for further research on VR, which will be helpful for further human-centered research and design work within the VR industry.

Enzyme-instructed self-assembly enabled fluorescence light-up for alkaline phosphatase detection.

Alkaline phosphatase (ALP) exists in both normal and pathological tissues. Spatiotemporal variations in ALP levels can reveal its potential physiological functions and changes that occur during pathological conditions. However, it is still challenging to exploit fluorescent probes that can measure ALP activity under good spatial and temporal resolutions. Herein, enzyme-instructed self-assembly (EISA) was used to construct a high-performing analytical tool (MN-pY) to probe ALP activity. MN-pY alone (free state) showed negligible fluorescence but presented an almost 13-fold increase in fluorescence intensity in the presence of ALP (assembly state). Mechanism study indicated the increase in fluorescence intensity was due to hydrogelation and formation of supramolecular fibrils, mainly consisting of dephosphorylated MN-Y. The dephosphorylation and further fibrillation of MN-pY could induce the formation of a "hydrophobic pocket", leading to a further increase in fluorescence intensity. Moreover, MN-pY could selectively illuminate HeLa cells with a higher ALP expression but not LO2 cells with lower ALP levels, promising a potential application in cancer diagnosis.

Enzyme-Instructed Self-Assembly Enabled Monomer-Excimer Transition to Construct Higher Ordered Luminescent Supramolecular Assembly for Activity-based Bioimaging.

It is challenging to construct high-performing excimer-based luminescent analytic tools at low molecular concentrations. We report that enzyme-instructed self-assembly (EISA) enables the monomer-excimer transition of a coumarin dye (Cou) at low molecular concentrations, and the resulting higher ordered luminescent supramolecular assemblies (i.e., nanofibers) efficiently record the spatiotemporal details of alkaline phosphatase (ALP) activity in vitro and in vivo. Cou was conjugated to short self-assembly peptides with a hydrophilic ALP-responsive group. By ALP triggering, EISA actuated a nanoparticles-nanofibers transition at low peptide concentrations followed by monomer-excimer transition of Cou. Analysis of structure-property relationships revealed that the self-assembly motif was a prerequisite for peptides to induce the monomer-excimer transition of Cou. Luminescent supramolecular nanofibers of pYD (LSN-pYD) illuminated the intercellular bridge of cancer cells and distinguished cancer cells (tissues) from normal cells (tissues) efficiently and rapidly, promising potential use for the early diagnosis of cancer. This work extends the functions of EISA and provides a new application of supramolecular chemistry.

A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG.

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about -2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction.

Tobacco mosaic virus, TMV for short, is widely distributed in the global tobacco industry and has a significant impact on tobacco production. It can reduce the amount of tobacco grown by 50-70%. In this research of study, we aimed to identify tobacco mosaic virus proteins and healthy tobacco leaf proteins by using machine learning approaches. The experiment's results showed that the support vector machine algorithm achieved high accuracy in different feature extraction methods. And 188-dimensions feature extraction method improved the classification accuracy. In that the support vector machine algorithm and 188-dimensions feature extraction method were finally selected as the final experimental methods. In the 10-fold cross-validation processes, the SVM combined with 188-dimensions achieved 93.5% accuracy on the training set and 92.7% accuracy on the independent validation set. Besides, the evaluation index of the results of experiments indicate that the method developed by us is valid and robust.

Nuclear delivery of dual anticancer drug-based nanomedicine constructed by cisplatinum-induced peptide self-assembly.

Nuclear delivery of anticancer drugs, particularly dual complementary anticancer drugs, can significantly improve chemotherapy efficacy. However, successful examples are rare. We reported a novel dual anticancer drug-based nanomedicine with nuclear accumulation properties. The nanomedicine was formed by chelation between a drug peptide amphiphile Rh-GFFYERGD (Rh represents Rhein, 1,8-dihydroxy-3-carboxy anthraquinonea) and cisplatinum (Pt). A single molecule of the drug peptide amphiphile could chelate up to 8 equiv. of cisplatinum in the resulting nanofibers. The nanofibers with a 1 : 4 ratio of Rh-GFFYERGD to cisplatinum demonstrated remarkable cellular uptake, and more significantly, superior nuclear accumulation properties. Additionally, the nanofibers could also bind to the DNA molecule more efficiently than those formed by the drug peptide amphiphile. Thus the nanofibers exhibited excellent anticancer properties both in vitro and in vivo. We envision a significant therapeutic potential of the dual anticancer drug-based nanomedicine with cisplatinum in cancer.

Selective pericellular hydrogelation by the overexpression of an enzyme and a membrane receptor.

The selective formation of nanomedicines around cancer cells is very important for cancer therapy because it increases the inhibitory capacity and decreases the systemic toxicity. However, successful examples are rare. Taking advantage of the overexpression of both the enzyme alkaline phosphatase (ALP) and the cell membrane receptor (CCK2R), we demonstrated in this study the selective formation of supramolecular nanofibers and hydrogels in the pericellular space of two cancer cell lines (HeLa and HepG2 cells). Both cell lines showed high expression levels of extracellular ALP and membrane-bound CCK2R. ALP efficiently converted Comp. 1 to a self-assembling molecule (Comp. 2). Comp. 2 interacted with CCK2R, thereby facilitating the self-assembly and formation of hydrogels around the cancer cells. The selective pericellular hydrogelations efficiently inhibited cancer cells. Pericellular hydrogelation around cancer cells is a promising strategy to control the formation of nanomedicines spatiotemporally in cellular microenvironments for cancer therapy and diagnostics.

Tandem Molecular Self-Assembly Selectively Inhibits Lung Cancer Cells by Inducing Endoplasmic Reticulum Stress.

The selective formation of nanomaterials in cancer cells and tumors holds great promise for cancer diagnostics and therapy. Until now, most strategies rely on a single trigger to control the formation of nanomaterials in situ. The combination of two or more triggers may provide for more sophisticated means of manipulation. In this study, we rationally designed a molecule (Comp. 1) capable of responding to two enzymes, alkaline phosphatase (ALP), and reductase. Since the A549 lung cancer cell line showed elevated levels of extracellular ALP and intracellular reductase, we demonstrated that Comp. 1 responded in a stepwise fashion to those two enzymes and displayed a tandem molecular self-assembly behavior. The selective formation of nanofibers in the mitochondria of the lung cancer cells led to the disruption of the mitochondrial membrane, resulting in an increased level of reactive oxygen species (ROS) and the release of cytochrome C (Cyt C). ROS can react with proteins, resulting in endoplasmic reticulum (ER) stress and the unfolded protein response (UPR). This severe ER stress led to disruption of the ER, formation of vacuoles, and ultimately, apoptosis of the A549 cells. Therefore, Comp. 1 could selectively inhibit lung cancer cells in vitro and A549 xenograft tumors in vivo. Our study provides a novel strategy for the selective formation of nanomaterials in lung cancer cells, which is powerful and promising for the diagnosis and treatment of lung cancer.

sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter.

How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields.

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control.

To recognize the user's motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming.

Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we propose a method in which 11 basic one-stroke sketching shapes are identified from the sEMG signals generated by the forearm and upper arm muscles from 4 subjects. Time domain features such as integrated electromyography, root mean square and mean absolute value were extracted with analysis windows of two length conditions for pattern recognition. After reducing data dimensionality using principal component analysis, the shapes were classified using Gene Expression Programming (GEP). The performance of the GEP classifier was compared to the Back Propagation neural network (BPNN) and the Elman neural network (ENN). Feature extraction with the short analysis window (250 ms with a 250 ms increment) improved the recognition rate by around 6.4% averagely compared with the long analysis window (2500 ms with a 2500 ms increment). The average recognition rate for the eleven basic one-stroke sketching patterns achieved by the GEP classifier was 96.26% in the training set and 95.62% in the test set, which was superior to the performance of the BPNN and ENN classifiers. The results show that the GEP classifier is able to perform well with either length of the analysis window. Thus, the proposed GEP model show promise for recognizing sketching based on sEMG signals.