Self-reactive IgE exacerbates interferon responses associated with autoimmunity.

Canonically, immunoglobulin E (IgE) mediates allergic immune responses by triggering mast cells and basophils to release histamine and type 2 helper cytokines. Here we found that in human systemic lupus erythematosus (SLE), IgE antibodies specific for double-stranded DNA (dsDNA) activated plasmacytoid dendritic cells (pDCs), a type of cell of the immune system linked to viral defense, which led to the secretion of substantial amounts of interferon-α (IFN-α). The concentration of dsDNA-specific IgE found in patient serum correlated with disease severity and greatly potentiated pDC function by triggering phagocytosis via the high-affinity FcɛRI receptor for IgE, followed by Toll-like receptor 9 (TLR9)-mediated sensing of DNA in phagosomes. Our findings expand the known pathogenic mechanisms of IgE-mediated inflammation beyond those found in allergy and demonstrate that IgE can trigger interferon responses capable of exacerbating self-destructive autoimmune responses.

Security enhanced routing and spectrum allocation against crosstalk attacks for confidential lightpath in elastic optical networks.

Elastic optical network (EON) is a critical transmission infrastructure for emerging new applications due to its spectral efficiency and flexibility. Nowadays, numerous confidential lightpaths (CLPs) are carried over EON to support security-sensitive users. However, they are vulnerable to crosstalk attacks at the optical layer, typically aimed at eavesdropping on the carried data or even disrupting connections. Due to the transparent nature of the optical signals, such attacks are difficult to detect and could last for a long time, resulting in data leakage even spreading throughout the network. This paper presents a novel routing and spectrum allocation (RSA) algorithm to protect CLPs from crosstalk attacks. We investigate intra-channel and inter-channel crosstalk attacks and develop a metric to quantify crosstalk leakage risks (CLRs). We first formulate an ILP model to plan CLPs with a minimum CLR. To solve the same problem for large-scale networks, we also propose a heuristic algorithm, i.e., crosstalk-attack-aware RSA. Results indicate that the proposed algorithm is capable of reducing CLR by 23%.

All-solid highly sensitive fiber-tip magnetic field sensor based on a Fabry-Perot interferometer with a breakpoint structure.

An all-solid fiber-tip Fabry-Perot interferometer (FPI) coated with a nickel film is proposed and experimentally verified for magnetic field sensing with high sensitivity. It is fabricated by splicing a segment of a thin-wall capillary tube to a standard single-mode fiber (SMF), then inserting a tiny segment of fiber with a smaller diameter into the capillary tube, and creating an ultra-narrow air-gap at the SMF end to form an FPI. When the device is exposed to magnetic field, the capillary tube is strained due to the magnetostrictive effect of the nickel film coated on its outer surface. In addition, owing to the unique breakpoint sensitivity-enhancement structure of the air-gap FPI, the elongation of the capillary tube whose length is over 100 times longer than the air-gap width is entirely transferred to the cavity length change of the FPI, and the sensor is extremely sensitive to the magnetic field as proved by our experiments, achieving a high sensitivity of up to 2.236 nm/mT for a linear magnetic field range from 40 to 60 mT, as well as a low-temperature cross-sensitivity of 56 µT/°C. The all-solid stable structure, compact size (total length of ∼3.0 mm), and reflective working mode with high magnetic field sensitivity indicate that this sensor has good application prospects.

Engineering Detrimental Functional Groups in Conductive Additives Toward High-Performance All-Solid-State Batteries.

Conductive additives are of great importance for the adequate utilization of active materials in all-solid-state lithium batteries by establishing conductive networks in the composite cathode. However, it usually causes severe interfacial side reactions with solid electrolytes, especially sulfide electrolytes, leading to sluggish ion transportation and accelerated performance degradation. Herein, a simple hydrogen thermal reduction process is proposed on a commonly used conductive additive Super P, which effectively removes the surface oxygen functional groups and weakens the interfacial side reactions with sulfide. With a small amount of 1 wt % reduced Super P, ASSLBs demonstrates a competitive capacity of 180.2 mAh g-1, which is much higher than the 130.8 mAh g-1 of untreated Super P. Impressively, reduced Super P based ASSLBs also exhibit a higher capacity retention of 81.8 % than 64.6 % of untreated Super P. The cathode interfacial chemical evolutions reveal that reduced Super P could effectively alleviate the side reactions of sulfide. Reduced Super P shows better reversible capacity compared to reduced carbon nanofiber with almost no loss of capacity retention, due to its more complete conductive network. Our results highlight the importance of oxygen-containing functional groups for conductive additives, lightening the prospect of low-cost 0D conductive additives for practical ASSLBs.

Galectin from Trichinella spiralis alleviates DSS-induced colitis in mice by regulating the intestinal microbiota.

According to numerous reports, Trichinella spiralis (T. spiralis) and its antigens can reduce intestinal inflammation by modulating regulatory immunological responses in the host to maintain immune homeostasis. Galectin has been identified as a protein that is produced by T. spiralis, and its characterization revealed this protein has possible immune regulatory activity. However, whether recombinant T. spiralis galectin (rTs-gal) can cure dextran sulfate sodium (DSS)-induced colitis remains unknown. Here, the ability of rTs-gal to ameliorate experimental colitis in mice with inflammatory bowel disease (IBD) as well as the potential underlying mechanism were investigated. The disease activity index (DAI), colon shortening, inflammatory cell infiltration, and histological damage were used as indicators to monitor clinical symptoms of colitis. The results revealed that the administration of rTs-gal ameliorated these symptoms. According to Western blotting and ELISA results, rTs-gal may suppress the excessive inflammatory response-mediated induction of TLR4, MyD88, and NF-κB expression in the colon. Mice with colitis exhibit disruptions in the gut flora, including an increase in gram-negative bacteria, which in turn can result in increased lipopolysaccharide (LPS) production. However, injection of rTs-gal may inhibit changes in the gut microbiota, for example, by reducing the prevalence of Helicobacter and Bacteroides, which produce LPS. The findings of the present study revealed that rTs-gal may inhibit signalling pathways that involve enteric bacteria-derived LPS, TLR4, and NF-κB in mice with DSS-induced colitis and attenuate DSS-induced colitis in animals by modulating the gut microbiota. These findings shed additional light on the immunological processes underlying the beneficial effects of helminth-derived proteins in medicine.

Precise molecular engineering for the preparation of pyridinium photosensitizers with efficient ROS generation and photothermal conversion.

Organic photosensitizers (PSs) with aggregation-induced emission properties have great development potential in the integrated application of multi-mode diagnosis and treatment of photodynamic therapy (PDT) and photothermal therapy (PTT). However, preparing high-quality PSs with both optical and biological properties, high reactive oxygen species (ROS) and photothermal conversion ability are undoubtedly a great challenge. In this work, a series of pyridinium AIE PSs modified with benzophenone have been synthesized. A wide wavelength range of fluorescent materials was obtained by changing the conjugation and donor-acceptor strength. TPAPs5 has a significant advantage over similar compounds, and we have also identified the causes of high ROS generation and high photothermal conversion in terms of natural transition orbitals, excited state energy levels, ground-excited state configuration differences and recombination energy. Interestingly, migration of target sites was also found in biological imaging experiments, which also provided ideas for the design of double-targeted fluorescent probes. Therefore, the present work proposed an effective molecular design strategy for synergistic PDT and PTT therapy.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS: In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS: The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS: The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

A Water Environment-Based Simulated Method for Ultrasonic Testing of Slag Inclusion Weld Defects Based on Improved VMD.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.

Free Radical-Mediated Intramolecular Photocyclization of AIEgens Based on 2,3-Diphenylbenzo[b]thiophene S,S-Dioxide.

As an important category of photochemical reactions, photocyclization is regarded as an ideal entry point for building intelligent photoresponsive materials. Herein, a series of aggregation-induced emission luminogens (AIEgens) with sensitive photoresponsive behavior are developed based on 2,3-diphenylbenzo[b]thiophene S,S-dioxide (DP-BTO), and the impacts of substituents with different electronic structures are investigated. The comprehensive experimental and computational characterizations reveal that their photoresponsive activity is resulted from triplet diradical-mediated intramolecular photocyclization, followed by dehydrogenation to yield stable polycyclic photoproducts. This photocyclization process is active in solution but suppressed in the solid state, and thus can act as a supplementary nonradiative decay channel for the excited state to contribute to AIE effect. Moreover, the generated triplet diradical intermediates upon light irradiation can effectively inhibit the growth of S. aureus, indicative of their promising application as antibacterial agents. This work provides an in-depth mechanistic description about the photocyclization of DP-BTO derivatives and furnishes a perspective on the correlation of photochemical decay and photophysical property.

Reproduction of mode-locked pulses by spectrotemporal domain-informed deep learning.

The accurate reproduction of unique pulse states in a mode-locked fiber laser is an important scientific issue and has wide applications in the laser industry. We present what we believe to be a novel method for automatically and precisely reproducing targeted soliton states in a mode-locked fiber laser by spectrotemporal domain-informed deep learning. Targeted solitons are experimentally reproduced via a superior matching process with a spectrotemporal mean square error (MSE) of 3.99 × 10-5. The outstanding feature of our reproduction algorithm is that the pulse information in both the spectral and temporal domains is jointly adopted for reconstructing targeted soliton states from white noise, rather than establishing arbitrary mode-locked pulse states, as described in previous studies. Additionally, a single-layer perceptron model is proposed to retrieve the phase distribution of a mode-locked pulse, validating the physical completeness of our reproduction approach. Our approach advances ultrafast laser technology, enabling the precise control of pulse dynamics in applications such as optical communication and nonlinear optics.

Chiral photonic topological states in Penrose quasicrystals.

Electromagnetic topological edge states typically are created in photonic systems with crystalline symmetry and these states emerge because of the topological feature of bulk Bloch bands in momentum space according to the bulk-edge correspondence principle. In this work, we demonstrate the existence of chiral topological electromagnetic edge states in Penrose-tiled photonic quasicrystals made of magneto-optical materials, without relying on the concept of bulk Bloch bands in momentum space. Despite the absence of bulk Bloch bands, which naturally defiles the conventional definition of topological invariants in momentum space characterizing these states, such as the Chern number, we show that some bandgaps in these photonic quasicrystals still could host unidirectional topological electromagnetic edge states immune to backscattering in both cylinders-in-air and holes-in-slab configurations. Employing a real-space topological invariant based on the Bott index, our calculations reveal that the bandgaps hosting these chiral topological edge states possess a nontrivial Bott index of ±1, depending on the direction of the external magnetic field. Our work opens the door to the study of topological states in photonic quasicrystals.

Bacterial cellulose production by a strain of Komagataeibacter rhaeticus isolated from residual loquat.

In this study, loquat extract was selected as a promising substrate for bacterial cellulose (BC) production. A new BC-producing bacterial strain was isolated from residual loquat and identified as Komagataeibacter rhaeticus. BC production with different carbon sources and with loquat extract was investigated. Among all tested carbon sources, glucose was demonstrated to be the best substrate for BC production by K. rhaeticus, with up to 7.89 g/L dry BC obtained under the optimal initial pH (5.5) and temperature (28 °C) with 10 days of fermentation. The total sugar and individual sugars were investigated in different loquat extracts, in which fructose, glucose, and sucrose were the three main sugars. When loquat extract was prepared with a solid‒liquid (S-L) ratio of 2:1, the concentrations of glucose, fructose, and sucrose were 7.91 g/L, 9.31 g/L, and 2.84 g/L, respectively. The BC production obtained from loquat extract was higher than that of other carbon sources except glucose, and 6.69 g/L dry BC was obtained from loquat extract with an S-L ratio of 2:1. After BC production, all sugars substantially decreased, with the utilization of glucose, fructose, and sucrose reaching 93.9%, 87.9%, and 100%, respectively. These results suggested that the different sugars in loquat extract were all carbon sources participating in BC production by K. rhaeticus. Structural and physicochemical properties were investigated by SEM, TGA, XRD, and FT-IR spectroscopy. The results showed that the structural, chemical group, and water holding capacity of BC obtained from loquat extract were similar to those of BC obtained from glucose, but the crystallinity and thermal stability of BC were higher than those of BC from mannose and lactose but lower than those of BC from glucose and fructose. KEY POINTS: • A new BC-producing strain was isolated and identified as Komagataeibacter rhaeticus. • Loquat extract is an alternative substrate for BC production. • The BC obtained from loquat extract owns advanced physicochemical properties.

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration.

BACKGROUND: Age-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis. METHODS: This study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database. RESULTS: Eleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1). CONCLUSIONS: Four possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

Longitudinal quantification of choriocapillaris flow deficits in persistent placoid maculopathy: a case report.

BACKGROUND: Persistent placoid maculopathy (PPM) is a rare idiopathic chorioretinopathy characterized by choriocapillaris (CC) hypoperfusion. In a case of PPM, we quantified CC flow deficits (FDs) over time and observed an increase in CC perfusion as the visual acuity and outer photoreceptor anatomy improved. CASE PRESENTATION: A 58-year-old man was diagnosed with PPM in both eyes based on the patient's clinical presentation and imaging. He presented with sudden-onset central scotomas in both eyes for about two months. On referral, the best corrected visual acuity (BCVA) was 20/20 in the right eye and 20/100 in the left eye. Plaque-like yellowish macular lesions were observed bilaterally and autofluorescence imaging showed bilateral hyperautofluorescent lesions. Fluorescein angiography (FA) revealed early-phase hyper-fluorescent staining that intensified in the late phases, while indocyanine green angiography (ICGA) displayed persistent hypofluorescence in both eyes. Foveal centered swept source optical coherence tomography (SS-OCT) B-scans showed bilateral focal deposits on the level of retinal pigment epithelium (RPE) and disruption of outer photoreceptor bands. The CC FDs were quantified on SS-OCT angiography (SS-OCTA) images using a previously published algorithm that was validated. The CC FD% was 12.52% in the right eye and 14.64% in the left eye within a 5 mm circle centered on the fovea. After 5 months of steroid treatment, BCVA remained 20/20 in the right eye and improved to 20/25 in the left eye. On OCT imaging, the outer photoreceptor bands fully recovered in both eyes, while some focal deposits remained along the RPE in the left eye. The CC perfusion in both eyes improved, with CC FD% decreasing from 12.52% to 9.16% in the right eye and from 14.64% to 9.34% in the left eye. CONCLUSIONS: Significant impairment of macular CC perfusion was detected after the onset of PPM. Improvement in central macular CC perfusion corresponded with improvements in BCVA and outer retinal anatomy. Our findings suggest that imaging and quantification of CC FDs could serve as a valuable imaging strategy for diagnosing PPM and for following disease progression.

Dynamic Lane Reversal Strategy in Intelligent Transportation Systems in Smart Cities.

Route guidance strategies are an important part of advanced traveler information systems, which are a subsystem of intelligent transportation systems (ITSs). In previous research, many scholars have proposed a variety of route guidance strategies to guide vehicles in order to relieve traffic congestion, but few scholars have considered a strategy to control transportation infrastructure. In this paper, to cope with tidal traffic, we propose a dynamic lane reversal strategy (DLRS) based on the density of congestion clusters over the total road region. When the density reaches 0.37, the reversible lane converts to the opposite direction. When the density falls off to below 0.22, the reversible lane returns back to the conventional direction. The simulation results show that the DLRS has better adaptability for coping with the fluctuation in tidal traffic.

Spatially Modulated Fiber Speckle for High-Sensitivity Refractive Index Sensing.

A fiber speckle sensor (FSS) based on a tapered multimode fiber (TMMF) has been developed to measure liquid analyte refractive index (RI) in this work. By the lateral and axial offset of input light into TMMF, several high-order modes are excited in TMMF, and the speckle pattern is spatially modulated, which affects an asymmetrical speckle pattern with a random intensity distribution at the output of TMMF. When the TMMF is immersed in the liquid analyte with RI variation, it influences the guided modes, as well as the mode interference, in TMMF. A digital image correlations method with zero-mean normalized cross-correlation coefficient is explored to digitize the speckle image differences, analyzing the RI variation. It is found that the lateral- and axial-offsets-induced speckle sensor can enhance the RI sensitivity from 6.41 to 19.52 RIU-1 compared to the one without offset. The developed TMMF speckle sensor shows an RI resolution of 5.84 × 10-5 over a linear response range of 1.3164 to 1.3588 at 1550 nm. The experimental results indicate the FSS provides a simple, efficient, and economic approach to RI sensing, which exhibits an enormous potential in the image-based ocean-sensing application.

[Artificial intelligence in wearable electrocardiogram monitoring].

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Effective Therapy of Drug-Resistant Bacterial Infection by Killing Planktonic Bacteria and Destructing Biofilms with Cationic Photosensitizer Based on Phosphindole Oxide.

Developing effective therapies to fight against biofilm-associated infection is extremely urgent. The complex environment of biofilm forces the bacteria to evade the elimination of antibiotics, resulting in recalcitrant chronic infections. To address this issue, a cationic antibacterial agent based on phosphindole oxide (ß-PM-PIO) is designed and prepared. The unique molecular structure endows ß-PM-PIO with aggregation-induced emission feature and efficient singlet oxygen generation ability. ß-PM-PIO shows excellent visual diagnostic function to planktonic bacteria and biofilm. In addition, owing to the synergistic effect of phototoxicity and dark toxicity, ß-PM-PIO can achieve superb antibacterial and antibiofilm performance against Gram-positive bacteria with less potential of developing drug resistance. Notably, ß-PM-PIO also holds excellent anti-infection capacity against drug-resistant bacteria in vivo with negligible side effects. This work offers a promising platform to develop advanced antibacterial agents against multidrug-resistant bacterial infection.

Individually tunable array reflector for amplitude and phase modulation.

Based on graphene's phase modulation property and vanadium dioxide's amplitude modulation property, we developed an array reflector for terahertz frequencies that is individually adjustable. Starting with a theoretical analysis, we look into the effects of voltage on the Fermi level of graphene and temperature on the conductivity of vanadium dioxide, analyze the beam focusing characteristics, and finally link the controllable quantities with the reflected beam characteristics to independently regulate each cell in the array. The simulation findings demonstrate that the suggested array structure can precisely manage the focus point's position, intensity, and scattering degree and that, with phase compensation, it can control the wide-angle incident light. The array structure offers a novel concept for adjustable devices and focusing lenses, which has excellent potential for study and application.