Using novel micropore technology combined with artificial intelligence to differentiate Staphylococcus aureus and Staphylococcus epidermidis

Methods for identifying bacterial pathogens are broadly categorised into conventional culture-based microbiology, nucleic acid-based tests, and mass spectrometry. The conventional method requires several days to isolate and identify bacteria. Nucleic acid-based tests and mass spectrometry are relatively rapid and reliable, but they require trained technicians. Moreover, mass spectrometry requires expensive equipment. The development of a novel, inexpensive, and simple technique for identifying bacterial pathogens is needed. Through combining micropore technology and assembly machine learning, we developed a novel classifier whose receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.94, which rapidly differentiated between Staphylococcus aureus and Staphylococcus epidermidis in this proof-of-concept study. Morphologically similar bacteria belonging to an identical genus can be distinguished using our method, which requires no specific training, and may facilitate the diagnosis and treatment of patients with bacterial infections in remote areas and in developing countries.

Total variation denoising-based method of identifying the states of single molecules in break junction data

Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.

High-precision rapid testing of omicron SARS-CoV-2 variants in clinical samples using AI-nanopore

A digital platform that can rapidly and accurately diagnose pathogenic viral variants, including SARS-CoV-2, will minimize pandemics, public anxiety, and economic losses. We recently reported an artificial intelligence (AI)-nanopore platform that enables testing for Wuhan SARS-CoV-2 with high sensitivity and specificity within five minutes. However, which parts of the virus are recognized by the platform are unknown. Similarly, whether the platform can detect SARS-CoV-2 variants or the presence of the virus in clinical samples needs further study. Here, we demonstrated the platform can distinguish SARS-CoV-2 variants. Further, it identified mutated Wuhan SARS-CoV-2 expressing spike proteins of the delta and omicron variants, indicating it discriminates spike proteins. Finally, we used the platform to identify omicron variants with a sensitivity and specificity of 100% and 94%, respectively, in saliva specimens from COVID-19 patients. Thus, our results demonstrate the AI-nanopore platform is an effective diagnostic tool for SARS-CoV-2 variants.

Self-Assembled Monolayers of Gemini-Type Amphiphilic Hexabenzocoronenes on Gold: Contribution of Their Triethylene Glycol Side Chains to Self-Assembly Formation

We report on a two-dimensional self-assembled structure of a supramolecule with hydrophilic oligoethylene glycol (EG) units, which are capable of stronger electrostatic interactions than van der Waals (vdW) interactions between alkyl chains. For this purpose, hexabenzocoronene (HBC) with two hydrophobic dodecyl chains on one side of the HBC core and two hydrophilic triethylene glycol (TEG) chains on the other side of the HBC core (HBCGemini) and HBCGemini with a trinitrofluorenone (TNF) added to the end of one TEG chain (HBCTNFGemini) were employed. Scanning tunneling microscopy (STM) revealed the presence of multiple two-dimensional self-assembled structures in each of HBCGemini and HBCTNFGemini deposited on the gold substrate in vacuum. The role of polar functional groups in these observations is discussed based on semiempirical molecular orbital simulations. Two types of 2D organized structures of HBC-TEG were observed: one with rectangular and relatively dense unit cells and the other with nearly square and relatively sparse unit cells. In both organized structures, the phenyl group TEG units and alkyl chains were considered to be the main molecular interactions with each other. On the other hand, in HBCTNFGemini, three types of organized structures were observed, which could be explained by the mechanism of interdigitation of the TEG-containing side-chain moieties to form a dimeric core. The EG units are more flexible than the alkyl chains and thus can interact flexibly with the hydrophobic HBC core, and the glycol side chains facilitate the intermolecular interactions as well as the alkyl chains.

Solid-State Single-Molecule Sensing with the Electronic Life-Detection Instrument for Enceladus/Europa (ELIE)

Growing evidence of the potential habitability of Ocean Worlds across our solar system is motivating the advancement of technologies capable of detecting life as we know it-sharing a common ancestry or physicochemical origin with life on Earth-or don't know it, representing a distinct emergence of life different than our one known example. Here, we propose the Electronic Life-detection Instrument for Enceladus/Europa (ELIE), a solid-state single-molecule instrument payload that aims to search for life based on the detection of amino acids and informational polymers (IPs) at the parts per billion to trillion level. As a first proof-of-principle in a laboratory environment, we demonstrate the single-molecule detection of the amino acid L-proline at a 10 μM concentration in a compact system. Based on ELIE's solid-state quantum electronic tunneling sensing mechanism, we further propose the quantum property of the HOMO-LUMO gap (energy difference between a molecule's highest energy-occupied molecular orbital and lowest energy-unoccupied molecular orbital) as a novel metric to assess amino acid complexity. Finally, we assess the potential of ELIE to discriminate between abiotically and biotically derived α-amino acid abundance distributions to reduce the false positive risk for life detection. Nanogap technology can also be applied to the detection of nucleobases and short sequences of IPs such as, but not limited to, RNA and DNA. Future missions may utilize ELIE to target preserved biosignatures on the surface of Mars, extant life in its deep subsurface, or life or its biosignatures in a plume, surface, or subsurface of ice moons such as Enceladus or Europa. One-Sentence Summary: A solid-state nanogap can determine the abundance distribution of amino acids, detect nucleic acids, and shows potential for detecting life as we know it and life as we don't know it.

Single-Molecule Identification of Nucleotides Using a Quantum Computer

Genomic information is essential for human health. Due to its large volume, genomic information can be potentially computed using quantum computers, which are rapidly developing. Genome analysis using quantum computers can accelerate the development of personalized medicine, innovative drugs, and novel diagnostics based on genomic information. However, genomic analysis, including nucleotide identification, has not yet been performed using quantum computers. Here, we demonstrate single-molecule identification of nucleotides using a quantum computer. We have designed a quantum gate that explains the single-molecule conductance of adenosine electronically bonded between electrodes. The quantum circuit consists of a reverse and an encoding quantum gate that can strongly distinguish adenosine among the four nucleotides. Our results are the first step toward the realization of genome analysis using quantum computers.

Direct biomolecule discrimination in mixed samples using nanogap-based single-molecule electrical measurement

In single-molecule measurements, metal nanogap electrodes directly measure the current of a single molecule. This technique has been actively investigated as a new detection method for a variety of samples. Machine learning has been applied to analyze signals derived from single molecules to improve the identification accuracy. However, conventional identification methods have drawbacks, such as the requirement of data to be measured for each target molecule and the electronic structure variation of the nanogap electrode. In this study, we report a technique for identifying molecules based on single-molecule measurement data measured only in mixed sample solutions. Compared with conventional methods that require training classifiers on measurement data from individual samples, our proposed method successfully predicts the mixing ratio from the measurement data in mixed solutions. This demonstrates the possibility of identifying single molecules using only data from mixed solutions, without prior training. This method is anticipated to be particularly useful for the analysis of biological samples in which chemical separation methods are not applicable, thereby increasing the potential for single-molecule measurements to be widely adopted as an analytical technique.

Aggregative movement of C4 mesophyll chloroplasts is promoted by low CO2 under high intensity blue light

C₄ plants supply concentrated CO₂ to bundle sheath (BS) cells, improving photosynthetic efficiency by suppressing photorespiration. Mesophyll chloroplasts in C₄ plants are redistributed toward the sides of the BS cells (aggregative movement) in response to environmental stresses under light. Although this chloroplast movement is common in C₄ plants, the significance and mechanisms underlying the aggregative movement remain unknown. Under environmental stresses, such as drought and salt, CO₂ uptake from the atmosphere is suppressed by closing stomata to prevent water loss. We hypothesized that CO₂ limitation may induce the chloroplast aggregative movement. In this study, the mesophyll chloroplast arrangement in a leaf of finger millet, an NAD-malic enzyme type C₄ plant, was examined under different CO₂ concentrations and light conditions. CO₂ limitation around the leaves promoted the aggregative movement, but the aggregative movement was not suppressed, even at the higher CO₂ concentration than in the atmosphere, under high intensity blue light. In addition, mesophyll chloroplasts did not change their arrangement under darkness or red light. From these results, it can be concluded that CO₂ limitation is not a direct inducer of the aggregative movement but would be a promoting factor of the movement under high intensity blue light.

Machine learning and analytical methods for single-molecule conductance measurements

Single-molecule measurements of single-molecule conductance between metal nanogap electrodes have been actively investigated for molecular electronics, biomolecular analysis, and the search for novel physical properties at the nanoscale level. While it is a disadvantage that single-molecule conductance measurements exhibit easily fluctuating and unreliable conductance, they offer the advantage of rapid, repeated acquisition of experimental data through the repeated breaking and forming of junctions. Owing to these characteristics, recently developed informatics and machine learning approaches have been applied to single-molecule measurements. Machine learning-based analysis has enabled detailed analysis of individual traces in single-molecule measurements and improved its performance as a method of molecular detection and identification at the single-molecule level. The novel analytical methods have improved the ability to investigate for new chemical and physical properties. In this review, we focus on the analytical methods for single-molecule measurements and provide insights into the methods used for single-molecule data interrogation. We present experimental and traditional analytical methods for single-molecule measurements, provide examples of each type of machine learning method, and introduce the applicability of machine learning to single-molecule measurements.

Regulating Nonlinear Ion Transport through a Solid-State Pore by Partial Surface Coatings

Using functional nanofluidic devices to manipulate ion transport allows us to explore the nanoscale development of blue energy harvesters and iontronic building blocks. Herein, we report on a method to alter the nonlinear ionic current through a pore by partial dielectric coatings. A variety of dielectric materials are examined on both the inner and outer surfaces of the channel with four different patterns of coated or uncoated surfaces. Through controlling the specific part of the surface charge, the pore can behave like a resistor, diode, and bipolar junction transistor. We use numerical simulations to find out the reason for the asymmetric ion transport in the pore and illustrate the relationship between specifically charged surfaces and electroosmotic flow. These findings help understand the role of the corresponding surface composition in ion transport, which provides a direct approach to modify the electroosmotic-flow-driven ionic current rectification in the channel-based device via dielectric coatings.

Quantitative Microscopic Observation of Base-Ligand Interactions via Hydrogen Bonds by Single-Molecule Counting

Chemical properties have been based on statistical averages since the introduction of Avogadro's number. The lack of suitable methods for counting identified single molecules has posed challenges to counting statistics. The selectivity, affinity, and mode of hydrogen bonding between base and small molecules that make up DNA, which is vital for living organisms, have not yet been revealed at the single molecule level. Here, we show the quantitation of the above-mentioned parameters via single-molecule counting based on the combination of single-molecule electrical measurements and AI. The binding selectivity values of five ligands to four different base molecules were evaluated quantitatively by determining the ratio of the number of aggregates in a solution mixture of base molecules and a ligand. In addition, we show the ligand dependence of the mode and number of microscopic hydrogen bonds via single-molecule counting and quantum chemical calculations.

Interference of electrochemical ion diffusion in nanopore sensing

Stable and fast-responding ionic current is a prerequisite for reliable measurements of small objects with a nanopore. Here, we report on the interference of ion diffusion kinetics at liquid-electrode interfaces in nanopore sensing. Using platinum as electrodes, we observed a slow and large decrease in the ionic current through a nanopore in a salt solution suggestive of the considerable influence of the growing impedance at the liquid-metal interfaces via Cottrell diffusion. When detecting nanoparticles, the resistive pulses became weaker following the steady increase in the resistance at the partially polarizable electrodes. The interfacial impedance was also demonstrated to couple with the nanopore chip capacitance thereby degraded the temporal resolution of the ionic current measurements in a time-varying manner. These findings can be useful for choosing the suitable size and material of electrodes for the single-particle and -molecule analyses by ionic current.

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Ag/AgCl electrodes enable reliable resistive pulse detections of nanoparticles

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Pt electrodes induce ionic current decay by time via the Cottrell diffusion

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Cottrell diffusion deteriorates the nanopore sensor temporal resolution

Dependence of Molecular Diode Behaviors on Aromaticity

A molecule-scale diode is an essential component for the concept of molecular electronics. Here we report on heterogeneous contact-mediated rectifying behavior in single-molecule junctions. We performed massive current versus voltage characteristics measurements of metal-molecule-metal structures under stretching by a mechanical break junction method. In-situ deformations of the molecular bridges were revealed to induce stochastic switching of the rectifying direction to varying rectification ratio derived from the induced asymmetry in the contact motifs at the molecule termini. Aromatic molecules were found to enable stronger rectifications via the more pronounced Fermi pinning effect to shift the molecular orbital levels by the applied voltage. Dissimilar anchoring groups also served to stabilize the single-molecule diode properties by bestowing a chemically defined difference in the electronic coupling strengths at the electrode-molecule links. The present findings provide a guide to design diodes with the smallest and simplest structures.

Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma

Single-cell RNA sequencing (scRNAseq) has been used to assess the intra-tumor heterogeneity and microenvironment of pancreatic ductal adenocarcinoma (PDAC). However, previous knowledge is not fully universalized. Here, we built a single cell atlas of PDAC from six datasets containing over 70 samples and >130,000 cells, and demonstrated its application to the reanalysis of the previous bulk transcriptomic cohorts and inferring cell–cell communications. The cell decomposition of bulk transcriptomics using scRNAseq data showed the cellular heterogeneity of PDAC; moreover, high levels of tumor cells and fibroblasts were indicative of poor-prognosis. Refined tumor subtypes signature indicated the tumor cell dynamics in intra-tumor and their specific regulatory network. We further identified functionally distinct tumor clusters that had close interaction with fibroblast subtypes via different signaling pathways dependent on subtypes. Our analysis provided a reference dataset for PDAC and showed its utility in research on the microenvironment of intra-tumor heterogeneity.

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Generation of reference single cell atlas for pancreatic adenocarcinoma

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Decomposition of bulk transcriptomics showed the heterogeneous microenvironment

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Refined tumor subtypes signature indicated the tumor cell dynamics in intra-tumor

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Two subtype of fibroblast support the growth of tumor cell with distinct pathways

OP0090 PRECLINICAL STUDIES OF A NOVEL CATHEPSIN C INHIBITOR IN MPO-ANCA-ASSOCIATED VASCULITIS MODEL

Background

MPO-ANCA-associated vasculitis (MPO-AAV) is a systemic small vessel vasculitis with the production of MPO-ANCA in the serum. This disease develops necrotizing and crescent glomerulonephritis (NCGN) and peritubular capillaritis-mediated interstitial damages in the kidneys, and pulmonary hemorrhage due to capillaritis in the lungs. Recent studies have revealed that neutrophil extracellular traps (NETs) induced by MPO-ANCA are critically involved in its pathogenesis,1 and neutrophil elastase (NE) plays an essential role in the formation of NETs.2 Cathepsin C (CatC) functions as a key enzyme in the activation process of several neutrophil serine proteases (NSPs) such as NE, proteinase 3 and cathepthin G by converting the inactive forms of the NSPs to the active forms by digesting dipeptides at the N-terminus of the enzymes.3

Objectives

Although glucocorticoids and immunosuppressive drugs used as the standard of cares can lead remission in MPO-AAV patients, there are remaining unmet medical needs such as severe side effects, resistance to the treatment and relapse. Therefore, development of new therapeutic strategies is awaited. The aim of this study is to demonstrate the efficacy of MOD06051, a novel CatC inhibitor, against MPO-AAV, using an MPO-AAV rat model established previously.4

Methods

In vitro studies: Cathpsins and NE inhibitory activity was measured using recombinant enzymes and fluorescent substrates. Cellular NE activity in the granulocytes differentiated from the primary human bone marrow-derived hematopietic stem cells under the presence or absence of MOD06051 was determined using fluorescent substrates.

In vivo studies: 4-week-old Wistar Kyoto (WKY) rats were immunized with human MPO according to Little’s protocol.4 The rats were divided into three groups (n=8 in each group), and vehicle (0.5% methylcellulose) or MOD06051 (0.3 or 3 mg/kg bid) was orally administered every day for 42 days. All rats were euthanized at the end of the study for serological and histological evaluations.

Results

In vitro studies: MOD06051 inhibited the enzymatic activity of human recombinant CatC with an IC50 value of 1.5 nM, and no other cathepsins nor NE inhibition was observed at 10 μM. The NE activity in primary human granulocytes was suppressed by MOD06051 with an IC50 value of 18 nM.

In vivo studies: MPO-ANCA was induced in all groups at the same level. The percentage of affected glomeruli including those with NCGN, NET-forming neutrophils in the peripheral blood and glomeruli, and glomerular neutrophil counts were significantly suppressed by MOD06051 treatment in a dose-dependent manner. Furthermore, hematuria score, urinary NGAL (Neutrophil Gelatinase-Associated Lipocalin), tubular erythrocyte cast counts, and pulmonary hemorrhage foci were significantly decreased in the 3 mg/kg of MOD06051 treated group with the similar trends in 0.3 mg/kg group.

Conclusion

MOD06051 showed sepcific inhibition of CatC activity. This compound suppressed the serine proteases activation in primary human neutrophils and NET formation in the MPO-AAV model rats, resulting in amelioration of MPO-ANCA-induced tissue destruction, including NCGN and tubular interstitial damages in the kidneys, and disorder of alveolar septal capillaries in the lungs. MOD06051 appears to be a promising agent for treatment of MPO-AAV patients.

References

[1]Nakazawa D, et al. Nat Rev Rheumatol 15: 91-101, 2019.

[2]Papayannopoulos V, et al. J Cell Biol 191: 677-691, 2010.

[3]Korkmaz B, et al. Pharmacol Ther 190: 202-236, 2018.

[4]Little MA, et al. Am J Pathol 174: 1212-1220, 2009.

Disclosure of Interests

Akihiro Ishizu Grant/research support from: Modulus Discovery, Inc., Mai Taniguchi: None declared, Suishin Arai: None declared, Yuka Nishibata Grant/research support from: Modulus Discovery, Inc., Sakiko Masuda Grant/research support from: Modulus Discovery, Inc., Utano Tomaru: None declared, Takafumi Shimizu Shareholder of: Modulus Discovery, Inc., Employee of: Modulus Discovery, Inc., William Sinko Shareholder of: Modulus Discovery, Inc., Employee of: Modulus Discovery, Inc., Tadashi Nagakura Shareholder of: Modulus Discovery, Inc., Employee of: Modulus Discovery, Inc., Yoh Terada Shareholder of: Modulus Discovery, Inc., Employee of: Modulus Discovery, Inc.

Direct observation of DNA alterations induced by a DNA disruptor

DNA alterations, such as base modifications and mutations, are closely related to the activity of transcription factors and the corresponding cell functions; therefore, detection of DNA alterations is important for understanding their relationships. Particularly, DNA alterations caused by exposure to exogenous molecules, such as nucleic acid analogues for cancer therapy and the corresponding changes in cell functions, are of interest in medicine for drug development and diagnosis purposes. However, detection of comprehensive direct evidence for the relationship of DNA modifications/mutations in genes, their effect on transcription factors, and the corresponding cell functions have been limited. In this study, we utilized a single-molecule electrical detection method for the direct observation of DNA alterations on transcription factor binding motifs upon exposure to a nucleic acid analogue, trifluridine (FTD), and evaluated the effects of the DNA alteration on transcriptional activity in cancer cell line cells. We found ~ 10% FTD incorporation at the transcription factor p53 binding regions in cancer cells exposed to FTD for 5 months. Additionally, through single-molecule analysis of p53-enriched DNA, we found that the FTD incorporation at the p53 DNA binding regions led to less binding, likely due to weaken the binding of p53. This work suggests that single-molecule detection of DNA sequence alterations is a useful methodology for understanding DNA sequence alterations.

Single-Molecule Classification of Aspartic Acid and Leucine by Molecular Recognition through Hydrogen Bonding and Time-Series Analysis

Amino acid detection/identification methods are important for understanding biological systems. In this study, we developed the single-molecule measurement for investigated quantum tunneling enhancement by chemical modification and machine learning based time series analysis for develop accurate amino acid discrimination. We performed single-molecule measurement of L-aspartic Acid (Asp) and L-leucine (Leu) with mercaptoacetic acid (MAA) chemical modified nano-gap. The measured current was investigated by machine learning based time series analysis method for accurate amino acid discrimination. Compared to measurements using bare nano-gap, it is found that MAA modification improves the difference in the conductance-time profiles between Asp and Leu through the hydrogen bonding facilitated tunneling phenomena. It is also found that this method enables determination of relative concentration. even in the mixture of Asp and Leu. It improves selective analysis for amino acids, and therefore would be applicable in medicine, diagnosis, and single-molecule peptide sequencing.

Angelica keiskei (Ashitaba) has potential as an antithrombotic health food

Angelica keiskei (Ashitaba) is a large perennial herb that is native to the Pacific coast of Japan. It has recently become popular as a healthy food in Asian countries because it might have various physiological benefits including antithrombotic properties. Most studies of the bioactive constituents from Ashitaba have focused on the activities of the major chalcones, xanthoangelol and 4-hydroxyderricin. However, other chalcones, flavanones and coumarins have also been isolated from Ashitaba, precisely characterized, and investigated in vivo. Platelets play a key role in haemostasis and wound healing processes. Dysregulated platelet activity is associated with the progression of platelet aggregation and decreased venous blood flow, which results in thrombotic diseases. A minor chalcone, xanthoangelol E, inhibits TXB2 synthesis in rabbit platelets, which seems to be the source of the belief that Ashitaba has antithrombotic properties. However, recent data showed that xanthoangelol and 4-hydroxyderricin inhibited the aggregation of rabbit platelets. Platelet aggregation stimulated by collagen was also inhibited in whole blood incubated with Xanthoangelol or 4-hydroxyderricin. Plasminogen activator inhibitor-1 is the primary physiological inhibitor of tissue type plasminogen activator, a key protease of the fibrinolytic system. An increase in plasma of this inhibitor is associated with thrombotic conditions. Ashitaba yellow exudate inhibited the elevation of plasma plasminogen activator inhibitor-1 in mice induced by obesity or chronic low-grade inflammation. These studies showed the yellow exudate from stem cuttings and chalcones isolated from Ashitaba roots and leaves might have antithrombotic activity. This article reviews the possible antithrombotic properties of Ashitaba.

Salt Gradient Control of Translocation Dynamics in a Solid-State Nanopore

Tuning capture rates and translocation time of analytes in solid-state nanopores are one of the major challenges for their use in detecting and analyzing individual nanoscale objects via ionic current measurements. Here, we report on the use of salt gradient for the fine control of capture-to-translocation dynamics in 300 nm sized SiN_x nanopores. We demonstrated a decrease up to a factor of 3 in the electrophoretic speed of nanoparticles at the pore exit along with an over 3-fold increase in particle detection efficiency by subjecting a 5-fold ion concentration difference across the dielectric membrane. The improvement in the sensor performance was elucidated to be a result of the salt-gradient-mediated electric field and electroosmotic flow asymmetry at nanochannel orifices. The present findings can be used to enhance nanopore sensing capability for detecting biomolecules such as amyloids and proteins.

Hybridization of Bogoliubov Quasiparticles between Adjacent CuO_{2} Layers in the Triple-Layer Cuprate Bi_{2}Sr_{2}Ca_{2}Cu_{3}O_{10+δ} Studied by Angle-Resolved Photoemission Spectroscopy

Hybridization of Bogoliubov quasiparticles (BQPs) between the CuO_{2} layers in the triple-layer cuprate high-temperature superconductor Bi_{2}Sr_{2}Cu_{2}Cu_{3}O_{10+δ} is studied by angle-resolved photoemission spectroscopy (ARPES). In the superconducting state, an anticrossing gap opens between the outer- and inner-BQP bands, which we attribute primarily to interlayer single-particle hopping with possible contributions from interlayer Cooper pairing. We find that the d-wave superconducting gap of both BQP bands smoothly develops with momentum without an abrupt jump in contrast to a previous ARPES study. Hybridization between the BQPs also gradually increases in going from the off nodal to the antinodal region, which is explained by the momentum dependence of the interlayer single-particle hopping. As possible mechanisms for the enhancement of the superconducting transition temperature, the hybridization between the BQPs as well as the combination of phonon modes of the triple CuO_{2} layers and spin fluctuations represented by a four-well model are discussed.

Computational healthcare: Present and future perspectives (Review)

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.

Association between adherence to home-based walking exercise with a pedometer and one-year adverse outcomes among lower extremity peripheral artery disease patients with endovascular treatment

Background

Home-based exercise after endovascular treatment (EVT) for lower extremity peripheral artery disease (LE-PAD) patients with intermittent claudication is suggested as an alternative therapy for supervised exercise; however, an association of adherence to home-based exercise with clinical adverse events has not been fully investigated.

Purpose

We aimed to investigate the association of adherence to home-based exercise with 1-year major adverse events (MAE), patency, and leg symptoms after EVT in a contemporary Japanese registry.

Methods

A total of 500 patients with LE-PAD within the Long Term Outcome of Endovascular Therapy for PAD with Intermittent Claudication Observational Prospective Multicenter (ASHIMORI-IC) registry (UMINCTR, UMINehab724.203718753) who underwent EVT between January 2016 and March 2019 were included in the analysis. After EVT, all patients were instructed to do home-based walking exercise with a pedometer. The study population was divided and compared between 2 groups according to adherence to home-based exercise: well-adherence and poor-adherence. The adherence of home-based exercise was as defined by step count derived from a pedometer on sites. The primary outcome was MAE defined as composite of all-cause death, myocardial infarction, stroke, target vessel revascularization, and major amputation of target lower limb for one year. The main secondary outcome was 1-year primary patency of the treated lesion, and the improvement of leg symptom (6-minute walk distance [6MWD] and claudication distance). The study followed the Consensus definitions from peripheral academic research consortium criteria.

Results

Overall, the mean age was 72.8 years, and 78% were men. At 1 year, MAE occurred in 45 patients (9.0%), and the primary patency rate was 85.3% (94.2% of EVT for aortoiliac and 71.9% of EVT for femoropopliteal). A significant difference in the incidence of MAE was observed between the well-adherence group and the poor-adherence group (10 of 233 patients [4.3%] vs. 35 of 267 patients [13.1%]; P<0.001). After multivariate Cox regression analysis, patients in the well-adherence group showed the lower hazard ratio for 1-year MAE (0.30; 95% confidence interval, 0.15–0.58; P<0.001) compared to those in the poor-adherence group. In the well-adherence group, compared with the poor-adherence group, higher primary patency rate (88.9% vs 81.5%; p=0.015), longer claudication onset distance (370 m [IQR 240–453 m] vs 240m [IQR 126–324 m]; P<0.001), and longer 6MWD (422 m [IQR 359–483 m] vs 325 m [IQR 213–400 m]; P<0.001) were observed even after adjusting for each baseline value.

Conclusion

Our study demonstrates the importance of adherence to home-based walking exercise after EVT in LE-PAD patients.

Funding Acknowledgement

Type of funding sources: None.

Inertial focusing and zeta potential measurements of single-nanoparticles using octet-nanochannels

Capture-to-translocation dynamics control is an important issue for single-particle and -molecule analyses by resistive pulse waveforms. Here, we report on regulated motions for accurate zeta-potential assessments of single nanoscale objects passing through an octet-nanochannel. We observed ionic spike signals consisting of eight consecutive sub-pulses signifying the ion blockage at the eight sensing zones in series upon electrophoretic translocation of individual nanoparticles. We find an exponential decrease to saturation of the channel-to-channel translocation duration as a nanobead moves forward, reflecting the more restricted radial motion degrees of freedom via inertial effects at the downstream side of the octet channel. This finding enabled a protocol for single-nanoparticle zeta potential estimation impervious to the uncertainty stemming from the stochastic nature of the translocation dynamics. The multi-channel approach presented in this study may be used as a useful tool for analyzing particles and molecules of variable sizes.