Pregabalin Does Not Increase Risk of Heart Failure Exacerbation in Patients With Pre-existing Heart Failure.

BACKGROUND: Through actions of calcium channel trafficking inhibition and sodium/water retention, pregabalin may increase the risk of acute heart failure (AHF). OBJECTIVE: The objective of this study was to determine the prevalence of heart failure (HF) acute exacerbations, measured by a composite of emergency department (ED) visits, per-patient per-year (PPPY) hospitalizations, time-to first ED admission, and time-to hospitalizations in pre-existing HF patients taking pregabalin compared with those who were pregabalin-naive. METHODS: A retrospective cohort study of pregabalin users with HF were propensity score-matched to pregabalin-naïve patients with HF to evaluate the composite of ED admissions or PPPY hospitalizations, time-to first ED admission, and time-to hospitalizations during the 365 days post-index. Doubly robust generalized linear regression and Cox-proportional hazard regression modeling were undertaken for analysis of differences between groups. RESULTS: The matched cohort of 385 pregabalin users and 3460 pregabalin nonusers were principally middle-aged, equally gender distributed, and primary Caucasian. Most patients were on guideline-directed HF medical therapy. The estimated cumulative incidence of the primary outcome was a hazard ratio of 1.099 (95% CI: 0.789-1.530; P = 0.58). CONCLUSION AND RELEVANCE: This large, single-center, cohort study shows pregabalin is not associated with an increased risk of AHF events in patients with pre-existing HF.

Cancer incidence in immunocompromised patients: a single-center cohort study.

BACKGROUND: Diminished immune defense plays an important role in cancer development. Cancer risk in immunocompromised patients may differ. Identifying individuals with elevated cancer risk can inform strategies for routine cancer screening. This study aimed to understand and compare cancer incidence and risk in three patient groups: recipients of solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT); diagnosis of primary or secondary immunodeficiency disorder (PID/SID); and recipients of tumor necrosis factor inhibitor (TNF-i) therapy. METHODS: This retrospective cohort study used the University of Utah Health System database and Huntsman Cancer Institute tumor registry. Patients aged ≥18 years with SOT/HSCT, PID/SID or ≥ 3 months of TNF-i therapy were included. The date of transplant, diagnosis of PID/SID, or 1st TNF-i medication order date was defined as the index date. We calculated cumulative cancer incidence by Kaplan-Meier method. A Cox-proportional hazard regression model with a stepwise variable selection process was used to identify independent risk factors associated with the time to onset of a new primary cancer. RESULTS: In total, 13,887 patients were included which comprised of 2982 (21%) SOT/HSCT, 7542 (54%) PID/SID and 3363 (24%) patients receiving TNF-i. The mean (SD) age ranged from 46.8 (15) years - 50.4 (18.2) years. The proportion of white patients ranged from 72.3-84.8%. The estimated cumulative cancer incidence was 11.5% in the SOT/HSCT cohort, 14.3% in the PID/SID cohort, and 8.8% in the TNF-i cohort. The multivariable model adjusted for age, benign in-situ disease, Charlson Comorbidity Index, hypertension/cardiovascular disease/end stage renal disease, gender, race/ethnicity, and renal cyst as significant risk factors. The adjusted hazard ratios for cancer development in SOT/HSCT and PID/SID cohorts compared to the TNF-i cohort over the full follow-up period were 1.57 (95% CI: 1.16-2.13) and 2.14 (95% CI: 1.65-2.77), respectively. CONCLUSION: A significantly increased risk of cancer was observed in PID/SID patients and SOT/HSCT patients compared to TNF-i patients. Age ≥ 50 years, male gender, and clinical comorbidities were additional factors impacting cancer risk. PID/SID and SOT/HSCT patients may benefit from more intensive cancer screening.

RE-MIND2: comparative effectiveness of tafasitamab plus lenalidomide versus polatuzumab vedotin/bendamustine/rituximab (pola-BR), CAR-T therapies, and lenalidomide/rituximab (R2) based on real-world data in patients with relapsed/refractory diffuse large B-cell lymphoma.

RE-MIND2 (NCT04697160) compared patient outcomes from the L-MIND (NCT02399085) trial of tafasitamab+lenalidomide with those of patients treated with other therapies for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are autologous stem cell transplant ineligible. We present outcomes data for three pre-specified treatments not assessed in the primary analysis. Data were retrospectively collected from sites in North America, Europe, and the Asia Pacific region. Patients were aged ≥18 years with histologically confirmed DLBCL and received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy). Patients enrolled in the observational and L-MIND cohorts were matched using propensity score-based 1:1 nearest-neighbor matching, balanced for six covariates. Tafasitamab+lenalidomide was compared with polatuzumab vedotin+bendamustine+rituximab (pola-BR), rituximab+lenalidomide (R2), and CD19-chimeric antigen receptor T-cell (CAR-T) therapies. The primary endpoint was overall survival (OS). Secondary endpoints included treatment response and progression-free survival. From 200 sites, 3,454 patients were enrolled in the observational cohort. Strictly matched patient pairs consisted of tafasitamab+lenalidomide versus pola-BR (n = 24 pairs), versus R2 (n = 33 pairs), and versus CAR-T therapies (n = 37 pairs). A significant OS benefit was observed with tafasitamab+lenalidomide versus pola-BR (HR: 0.441; p = 0.034) and R2 (HR: 0.435; p = 0.012). Comparable OS was observed in tafasitamab+lenalidomide and CAR-T cohorts (HR: 0.953, p = 0.892). Tafasitamab+lenalidomide appeared to improve survival outcomes versus pola-BR and R2, and comparable outcomes were observed versus CAR-T. Although based on limited patient numbers, these data may help to contextualize emerging therapies for R/R DLBCL. CLINICAL TRIAL REGISTRATION: NCT04697160 (January 6, 2021).

Co-transient expression of PSA-Fc and PAP-Fc fusion protein in plant as prostate cancer vaccine candidates and immune responses in mice.

KEY MESSAGE: PAP-FcK and PSA-FcK prostate cancer antigenic proteins transiently co-expressed in plant induce their specific humoral immune responses in mice. Prostate-specific antigen (PSA) and prostatic acid phosphatase (PAP) have been considered as immunotherapeutic antigens for prostate cancer. The use of a single antigenic agent is unlikely to be effective in eliciting immunotherapeutic responses due to the heterogeneous and multifocal nature of prostate cancer. Thus, multiple antigens have been combined to enhance their anti-cancer effects. In the current study, PSA and PAP were fused to the crystallizable region (Fc region) of immunoglobulin G1 and tagged with KDEL, the endoplasmic reticulum (ER) retention signal motif, to generate PSA-FcK and PAP-FcK, respectively, and were transiently co-expressed in Nicotiana benthamiana. Western blot analysis confirmed the co-expression of PSA-FcK and PAP-FcK (PSA-FcK + PAP-FcK) with a 1:3 ratios in the co-infiltrated plants. PSA-FcK, PAP-FcK, and PSA-FcK + PAP-FcK proteins were successfully purified from N. benthamiana by protein A affinity chromatography. ELISA showed that anti-PAP and anti-PSA antibodies successfully detected PAP-FcK and PSA-FcK, respectively, and both detected PSA-FcK + PAP-FcK. Surface plasmon resonance (SPR) analysis confirmed the binding affinity of the plant-derived Fc fusion proteins to FcγRI/CD64. Furthermore, we also confirmed that mice injected with PSA-FcK + PAP-FcK produced both PSA- and PAP-specific IgGs, demonstrating their immunogenicity. This study suggested that the transient plant expression system can be applied to produce the dual-antigen Fc fusion protein (PSA-FcK + PAP-FcK) for prostate cancer immunotherapy.

Self-Assembly of Hausmannite Mn3O4 Triangular Structures on Cocosin Protein Scaffolds for High Energy Density Symmetric Supercapacitor Application.

Recent advances in using biological scaffolds for nanoparticle synthesis have proven to be useful for preparing various nanostructures with uniform shape and size. Proteins are significant scaffolds for generating various nanostructures partly because of the presence of many functional groups to recognize different chemistries. In this endeavor, cocosin protein, an 11S allergen, is prepared from coconut fruit and employed as a potential scaffold for synthesizing Mn3O4 materials. The interaction between protein and manganese ions is studied in detail through isothermal calorimetric titration. At increased scaffold availability, the Mn3O4 material adopts the exact hexamer structure of the cocosin protein. The electrochemical supercapacitive properties of the cocosin-Mn3O4 material are found to have a high specific capacitance of 751.3 F g-1 at 1 A g-1 with cyclic stability (92% of capacitance retention after 5000 CV cycles) in a three-electrode configuration. The Mn3O4//Mn3O4 symmetric supercapacitor device delivers a specific capacitance of 203.8 F g-1 at 1 A g-1 and an outstanding energy and power density of 91.7 W h kg-1 and 899.5 W kg-1, respectively. These results show that cocosin-Mn3O4 could be considered a suitable electrode for energy storage applications. Moreover, the cocosin protein to be utilized as a novel scaffold in protein-nanomaterial chemistry could be useful for protein-assisted inorganic nanostructure synthesis in the future.

Tertiary RNA Folding-Targeted Drug Screening Strategy Using a Protein Nanopore.

Bacterial riboswitch RNAs are attractive targets for novel antibiotics against antibiotic-resistant superbacteria. Their binding to cognate metabolites is essential for the regulation of bacterial gene expression. Despite the importance of RNAs as therapeutic targets, the development of RNA-targeted, small-molecule drugs is limited by current biophysical methods. Here, we monitored the specific interaction between the adenine-sensing riboswitch aptamer domain (ARS) and adenine at the single-molecule level using α-hemolysin (αHL) nanopores. During adenine-induced tertiary folding, adenine-bound ARS intermediates exhibited characteristic nanopore events, including a two-level ionic current blockade and a ∼ 5.6-fold longer dwell time than that of free RNA. In a proof-of-concept experiment, tertiary RNA folding-targeted drug screening was performed using a protein nanopore, which resulted in the discovery of three new ARS-targeting hit compounds from a natural compound library. Taken together, these results reveal that αHL nanopores are a valuable platform for ultrasensitive, label-free, and single-molecule-based drug screening against therapeutic RNA targets.

Structure-based modification of pyrazolone derivatives to inhibit mTORC1 by targeting the leucyl-tRNA synthetase-RagD interaction.

The enzyme leucyl-tRNA synthetase (LRS) and the amino acid leucine regulate the mechanistic target of rapamycin (mTOR) signaling pathway. Leucine-dependent mTORC1 activation depends on GTPase activating protein events mediated by LRS. In a prior study, compound BC-LI-0186 was discovered and shown to interfere with the mTORC1 signaling pathway by inhibiting the LRS-RagD interaction. However, BC-LI-0186 exhibited poor solubility and was metabolized by human liver microsomes. In this study, in silico physicochemical properties and metabolite analysis of BC-LI-0186 are used to investigate the addition of functional groups to improve solubility and microsomal stability. In vitro experiments demonstrated that 7b and 8a had improved chemical properties while still maintaining inhibitory activity against mTORC1. The results suggest a new strategy for the discovery of novel drug candidates and the treatment of diverse mTORC1-related diseases.

A preclinical trial of perventricular pulmonary valve implantation: Pericardial versus aortic porcine valves mounted on self-expandable stent.

Perventricular pulmonary valve implantation (PPVI) of a xenograft valve can be a less invasive technique that avoids cardiopulmonary bypass in patients who require pulmonary valve replacement. We compared the hemodynamics, durability, and histologic changes between two different xenogenic valves (pericardial vs. aortic valve porcine xenografts) implanted into the pulmonary valve position using a PPVI technique and evaluated the safety and efficacy of PPVI as a preclinical study. In 18 sheep, pericardial (group porcine pericardial [PP], n = 9) or aortic valve (group porcine aortic valve [PAV], n = 9) xenogenic porcine valves manufactured as a stented valve were implanted using a PPVI technique. The porcine tissues were decellularized, alpha-galactosidase treated, fixed with glutaraldehyde after space-filler treatment, and detoxified to improve durability. Hemodynamic and immunohistochemical studies were performed after the implantation; radiologic and histologic studies were performed after a terminal procedure. All stented valves were positioned properly after the implantation, and echocardiography and cardiac catheterization demonstrated good hemodynamic state and function of the valves. All the anti-α-Gal IgM and IgG titers were below 0.3 optical density. Computed tomography of extracted valves demonstrated no significant differences in the degree of calcification between the two groups (P = .927). Microscopic findings revealed a minimal amount of calcification and no significant infiltration of macrophage or T-cell in both groups, regardless of the implantation duration. The PPVI is a feasible technique. Both stented valves made of PP and PAV showed no significant differences in hemodynamic profile, midterm durability, and degree of degenerative dystrophic calcification.

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.

To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today's complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

Surface modification of solid-state nanopore by plasma-polymerized chemical vapor deposition of poly(ethylene glycol) for stable device operation.

Biopolymer adsorption onto a membrane is a significant issue in the reliability of solid-state nanopore devices, since it degrades the device performance or promotes device failure. In this work, a poly(ethylene glycol) (PEG) layer was coated on a silicon nitride (SiNx) membrane by plasma-polymerized vapor deposition to inhibit biopolymer adsorption. From optical observations, the deposited PEG layer demonstrated increased hydrophilicity and anti-adsorption property compared to the SiNx surface. Electrical properties of the PEG/SiNx nanopore were characterized, showing Ohmic behavior and a 6.3 times higher flicker noise power due to the flexible conformation of PEG in water. Antifouling performance of each surface was analyzed by measuring the average time from voltage bias to the first adsorption during DNA translocation experiments, where the modified surface enabled two times prolonged device operation. The time to adsorption was dependent on the applied voltage, implying adsorption probability was dominated by the electrophoretic DNA approach to the nanopore. DNA translocation behaviors on each surface were identified from translocation signals, as the PEG layer promoted unfolded and fast movement of DNA through the nanopore. This work successfully analyzed the effect of the PEG layer on DNA adsorption and translocation in solid-state nanopore experiments.

Electrical properties of graphene/In2O3 bilayer with remarkable uniformity as transparent conducting electrode.

A graphene/In2O3 bilayer (termed as GI-bilayer) is proposed as a transparent conducting electrode with remarkably improved areal-uniformity. To fabricate this new structure, an In2O3 layer with a thickness of less than 50 nm was grown by atomic layer deposition and then a graphene layer was grown by chemical vapor deposition and subsequently transferred onto the as-grown In2O3 layer. Electrical and optical properties of the GI-bilayer were systematically studied to verify effects of the underlying In2O3 layer. Hall measurements and following analysis showed a conductance enhancement of the GI-bilayer owing to p-type doping of graphene. Specifically, Raman analysis and ultraviolet photoelectron spectroscopy were performed to prove p-type doping of the graphene in the GI-bilayer. In addition, the GI-bilayer exhibited the significantly improved uniformity of the sheet resistance compared to that of a conventional monolayer of graphene. There was a duality on the role of the In2O3 underlayer in the GI-bilayer. It acted as a dopant layer to the graphene and lowered the sheet resistance from 863 to 510 Ω/sq as well as compensated microscale defects on graphene. More importantly, the In2O3 underlayer resulted in the extremely reduced standard deviation of sheet resistance from 150 to 7.5 Ω/sq over the area of 49 cm2.

The role of clinical pharmacy anticoagulation services in direct oral anticoagulant monitoring.

The role of dedicated anticoagulation management services (AMS) for patients receiving direct oral anticoagulant (DOAC) therapy is unclear. The objective of our study was to describe DOAC management in patients who were and were not managed by an AMS. We conducted a retrospective cohort study among patients with atrial fibrillation at the University of Utah Health (UUH) who received DOAC therapy between January 2013 and June 2016. Patients in the AMS group were managed by a pharmacist-led AMS whereas those in the non-AMS group were managed by other providers. The number and type of provider encounters and interventions related to DOAC therapy and a composite endpoint of thromboembolism, bleeding, and all-cause mortality were recorded. Overall, 90 and 370 patients were managed in the AMS and non-AMS groups, respectively. AMS group patients had greater chronic disease burden as measured by the Charlson comorbidity index. AMS group patients had more frequent DOAC-related encounters than non-AMS group patients but both groups had similar DOAC therapy intervention rates. Over half of patients in the AMS group received potentially duplicative interventions from their regular clinicians. The composite endpoint occurred in 18.9% and 13.5% of AMS and non-AMS group patients, respectively (p = 0.29). Patients managed by AMS providers were more complex and had more frequent encounters regarding their DOAC therapy than those managed by non-AMS providers. However, there was evidence of duplicative DOAC therapy management efforts. No difference between AMS and non-AMS groups in the composite clinical endpoint was detected.

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression.

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems.

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

Expression and In Vitro Function of Anti-Breast Cancer Llama-Based Single Domain Antibody VHH Expressed in Tobacco Plants.

Overexpression of human epidermal growth factor receptor type 2 (HER2) is considered as a prognostic factor of breast cancer, which is positively associated with recurrence when cancer metastasizes to the lymph nodes. Here, we expressed the single variable domain on a heavy chain (VHH) form of anti-HER2 camelid single domain antibody in tobacco plants and compared its in vitro anticancer activities with the anti-HER2 full size antibody. The gene expression cassette containing anti-HER2 camelid single domain antibody VHH fused to human IgG Fc region with KDEL endoplasmic reticulum (ER) (VHH-FcK) was transferred into the tobacco plant via the Agrobacterium-mediated transformation. The transformants were screened with polymerase chain reaction and Western blot analyses. Enzyme-linked immunosorbent assay (ELISA) confirmed the binding of the purified anti-HER2 VHH-FcK to the HER2-positive breast cancer cell line, SK-BR-3. Migration assay results confirmed anticancer activity of the plant-derived anticancer camelid single chain antibody. Taken together, we confirmed the possibility of using anti-HER2 VHH-FcK as a therapeutic anticancer agent, which can be expressed and assembled and purified from a plant expression system as an alternative antibody production system.

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors.

Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features-i.e., spatio-temporal features, energy-based features, shape based angular and geometric features-and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man-machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model.

Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky-Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the "leave-one-out" cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man-machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

Longitudinal changes in glycated haemoglobin following treatment intensification after inadequate response to two oral antidiabetic agents in patients with type 2 diabetes.

AIMS: To identify change in glycated haemoglobin (HbA1c) for 1 year after treatment intensification in patients with HbA1c >53 mmol/mol (7.0%) while on two classes of oral antidiabetic drugs (OADs). MATERIAL AND METHODS: A retrospective cohort study was conducted using a regional health plan claims database for the period January 1, 2010 to March 31, 2017. Patients with type 2 diabetes (T2DM) whose treatment was intensified with insulin, a glucagon-like peptide-1 receptor agonist or a third OAD within 365 days of having HbA1c ≥53 mmol/mol (7.0%) on two OADs were included. The HbA1c trajectory for 1 year after intensification was estimated using a mixed-effects regression model. RESULTS: The analysis included 1226 patients with a mean ± SD HbA1c at treatment intensification of 74.2 ± 18.7 mmol/mol (8.93 ± 1.7%). HbA1c was higher in the insulin group (74.2 mmol/mol) than in the non-insulin group (70.6 mmol/mol), as was the HbA1c decrease (P < 0.01) over the 1-year follow-up, particularly in patients with baseline HbA1c >9%. After intensification, insulin- and non-insulin-treated patients achieved an average change by month in HbA1c of -4.7 mmol/mol and -2.6 mmol/mol points, respectively. The analysis predicted HbA1c to be the lowest at 6 to 10 months post intensification, depending on intensification treatment and HbA1c at intensification; however, on average, HbA1c remained above 64.0 mmol/mol (8.0%). CONCLUSION: In patients with T2DM, intensification following an HbA1c value ≥53 mmol/mol (7.0%) while on two OADs was associated with a significant improvement in glycaemic control. Patients intensified with insulin had a higher baseline HbA1c but greater HbA1c reduction than those intensified with a non-insulin agent. However, HbA1c remained above 64 mmol/mol (8.0%) overall. Additional opportunity exists to further intensify therapy to improve glycaemic control.

Detection of metal corrosion characteristics in chlorine solution using solid state nanopore.

Nanopore structures were originally proposed for detection of biomolecule translocation through nanometer-scale pores that perforate membranes by transient changes in ionic current. In this study, these changes are utilized to detect corrosion of different metals in aqueous chlorine media. The corrosion behaviors of Cu, Al, Ti, and Cr were analyzed by monitoring the changes in ion current resulting from ion concentration variations in solutions due to corrosion of the metals. We observed that the Cu layer passivated by CuO x was severely corroded when a drastic change of ion current was induced, from 10 to 30 nS to the level of 104 nS, as soon as the bias voltage was applied. In the case of Al passivated by thin AlO x , the conductance increased from 10-30 to 166 ± 52 nS and became saturated. Highly localized pitting corrosion was observed on the periphery of the nanopore, where the electrical field was most concentrated. Finally, we observed that Ti and Cr passivated by oxide showed long-term stability without corrosion in 1 M KCl in the pH range of 4-11.

DNA translocation through a nanopore in an ultrathin self-assembled peptide membrane.

Here, we explore the possibility of using peptide-based materials as a membrane in solid-state nanopore devices in an effort to develop a sequence-specific, programmable biological membrane platform. We use a recently developed tyrosine-mediated self-assembly peptide sheet. At the air/water interface, the 5mer peptide YFCFY self-assembles into a uniform and robust two-dimensional (2D) structure, and the peptide sheet is easily transferred to a low-noise glass substrate. The thickness of the peptide membrane can be adjusted to approximately 5 nm (or even to 2 nm) by an etching process, and the diameters of the peptide nanopores can be precisely controlled using a focused electron beam with an attuned spot size. The ionic current noise of the peptide nanopore is comparable to those of typical silicon nitride nanopores or multilayer 2D materials. Using this membrane, we successfully observe translocation of 1000 bp double-stranded DNA with a sufficient signal-to-noise ratio of ∼30 and an elongated translocation speed of ∼1 bp µs-1. Our results suggest that the self-assembled peptide film can be used as a sensitive nanopore membrane and employed as a platform for applying biological functionalities to solid-state substrates.