A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.

BACKGROUND: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals. METHODS: We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model's robustness. RESULTS: The H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9. CONCLUSION: The proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

"Blue-red-purple" multicolored lateral flow immunoassay for simultaneous detection of GM crops utilizing RPA and CRISPR/Cas12a.

Advanced multiplexed testing techniques should be designed and developed to ensure an accurate and reliable evaluation for unknown samples. In this study, an efficient platform coupled with the "Blue-Red-Purple" strategy based on recombinant polymerase amplification (RPA), CRISPR/Cas12a and lateral flow strip was established, which could realize the dual-target detection of CP4-EPSPS and Cry1Ab/Ac in genetically modified crops. The lateral flow immunoassay was developed using different colored microspheres to label the antibodies to realize the visualization of results and avoid cross-reactions. The proposed method exhibits high specificity, sensitivity and stability. The visual detection limits of standard plasmids and real samples reached 10 copies/µL and 0.5 %, respectively, which could be stored at 4 °C for 12 months with high detection ability. Moreover, the entire detection process could be completed within 50 min without any complex instruments or professional operators. These findings indicated that a sensitive, specific, rapid and accurate method was established for on-site detection of GM crops.

A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

MOTIVATION: Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. RESULTS: In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. AVAILABILITY AND IMPLEMENTATION: The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL.

Efficacy and comfort following the implantation of extended depth of focus, multifocal, and monofocal intraocular lenses in cataract patients.

BACKGROUND: Extended depth of focus (EDOF) and multifocal (Multi) intraocular lenses (IOL) can provide a fixed distance of near vision, which may result in some discomfort for patients who prefer different near distances. The aim of this study was to compare the vision, comfortable near distance (CND) and visual comfort in patients who underwent implantation of EDOF, Multi, and monofocal (Mono) IOLs. METHODS: A total of 100 eyes were implanted with Tecnis ZXR00, ZMB00 or ZCB00 IOLs. Uncorrected distance, intermediate, and near visual acuity (UDVA, UIVA, and UNVA, respectively), corrected distance visual acuity (CDVA), the fluctuations of CND, the ability to see at comfortable or standard near distance and visual comfort were evaluated at 3-month postoperative. RESULTS: At 3 months postoperative, the EDOF and Multi groups showed non-inferiority compared to the Mono group in the UDVA (P > 0.05) and CDVA (P > 0.05) but superiority in the UNVA (P < 0.001). The UIVA was better in the EDOF group, with comparable results for the Multi and Mono groups. There was no difference in preoperative and postoperative CND in the three groups. The CND visual acuity (CNDVA) was lower than the UNVA in the three groups, especially in the EDOF and Multi groups (P < 0.05). The CND effectively improved patients' near visual comfort and visual clarity, except for one patient in the Multi group who complained of severe fatigue and was unable to tolerate the experience at month 3. CONCLUSION: The EDOF and Multi IOLs achieved excellent visual quality and superior UNVA compared to the Mono IOL, but the CNDVA was significantly inferior to the UNVA. Patients' near visual experience can be effectively improved at their CND.

The E3 ligase ASB3 downregulates antiviral innate immunity by targeting MAVS for ubiquitin-proteasomal degradation.

E3 ubiquitin ligases are very important for regulating antiviral immunity during viral infection. Here, we discovered that Ankyrin repeat and SOCS box-containing protein 3 (ASB3), an E3 ligase, are upregulated in the presence of RNA viruses, particularly influenza A virus (IAV). Notably, overexpression of ASB3 inhibits type I IFN (IFN-I) responses induced by Sendai virus (SeV) and IAV, and ablation of ASB3 restores SeV and H9N2 infection-mediated transcription of IFN-ß and its downstream interferon-stimulated genes (ISGs). Interestingly, animals lacking ASB3 presented decreased susceptibility to H9N2 and H1N1 infections. Mechanistically, ASB3 interacts with MAVS and directly mediates K48-linked polyubiquitination and degradation of MAVS at K297, thereby inhibiting the phosphorylation of TBK1 and IRF3 and downregulating downstream antiviral signaling. These findings establish ASB3 as a critical negative regulator that controls the activation of antiviral signaling and describe a novel function of ASB3 that has not been previously reported.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia.

Objectives: Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI). The primary objective is to assess the model's accuracy in detecting and classifying lesions, comparing its performance in WLI and NBI. Methods: We applied DL to segment and classify NBI and WLI of vocal cord leukoplakia, and used pathological diagnosis as the gold standard. Results: The DL model autonomously detected lesions with an average intersection-over-union (IoU) >70%. In classification tasks, the model differentiated between lesions in the surgical group with a sensitivity of 93% and a specificity of 94% for WLI, and a sensitivity of 99% and a specificity of 97% for NBI. In addition, the model achieved a mean average precision of 81% in WLI and 92% in NBI, with an IoU threshold >0.5. Conclusions: The model proposed by us is helpful in assisting in accurate diagnosis of vocal cord leukoplakia from NBI and WLI.

Drug repositioning based on residual attention network and free multiscale adversarial training.

BACKGROUND: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. RESULTS: This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. CONCLUSIONS: The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Scalable, Controlled Bimodal Pore-Structured Polymer Coating for Efficient Passive Daytime Radiative Cooling.

Outdoor thermal irritation poses a serious threat to public health, with the frequent occurrence of increasingly intense heat waves. With the global goal of carbon peaking and carbon neutrality, there is an urgent need for a strategy that is efficient and can provide localized outdoor cooling without an intensive energy input. This paper demonstrated a rapidly formable polyurethane-based coating with controlled bimodal spherical micropores. Nano-Al2O3 particles (300 nm) embedded in the polymer were used for targeted enhancement of reflectance at 0.38-0.5 wavelengths. The enhanced film reflected 93% solar irradiance and selectively transmitted 95% thermal radiation (8-13 µm), enabling rapid cooling and the creation of a comfortable thermal microclimate to avoid overheating of 6-11 °C during daytime conditions. The ultrawide material compatibility and excellent adaptive mechanical strength of polyurethane-based coatings are expected to benefit the sustainable development of society in a wide range of fields, from health to economics.

Pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries under topical anesthesia.

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery. Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery. However, patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery. The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts. Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system. Immunological, neuropsychological, and pharmacological factors work together in the enhancement of intraoperative pain. Accumulating published literatures have focused on the pain enhancement during the second-eye phacoemulsification surgeries. In this review, we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb. 1, 2024. We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries. This review aimed to provide novel insights into strategies for improving patients' intraoperative experience in second-eye cataract surgeries.

Ultrafast Dual-Shock Chemistry Synthesis of Ordered/Disordered Hybrid Carbon Anodes: High-Rate Performance of Li-Ion Batteries.

Graphite exhibits crystal anisotropy, which impedes the mass transfer of ion intercalation and extraction processes in Li-ion batteries. Herein, a dual-shock chemical strategy has been developed to synthesize the carbon anode. This approach comprised two key phases: (1) a thermal shock utilizing ultrahigh temperature (3228 K) can thermodynamically facilitate graphitization; (2) a mechanical shock (21.64 MPa) disrupting the π-π interactions in the aromatic chains of carbon can result in hybrid-structured carbon composed of crystalline and amorphous carbon. The optimized carbon (DSC-200-0.3) demonstrates a capacity of 208.61 mAh/g at a 10C rate, with a significant enhancement comparing with 15 mAh/g of the original graphite. Impressively, it maintains 81.06% capacity even after 3000 charge-discharge cycles. Dynamic process analysis reveals that this superior rate performance is attributed to a larger interlayer spacing facilitating ion transport comparing with the original graphite, disordered amorphous carbon for additional lithium storage sites, and crystallized carbon for enhanced charge transfer. The dual-shock chemical approach offers a cost-effective and efficient method to rapidly produce hybrid-structured carbon anodes, enabling 10C fast charging capabilities in lithium-ion batteries.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Neural network enabled fringe projection through scattering media.

The projection of fringes plays an essential role in many applications, such as fringe projection profilometry and structured illumination microscopy. However, these capabilities are significantly constrained in environments affected by optical scattering. Although recent developments in wavefront shaping have effectively generated high-fidelity focal points and relatively simple structured images amidst scattering, the ability to project fringes that cover half of the projection area has not yet been achieved. To address this limitation, this study presents a fringe projector enabled by a neural network, capable of projecting fringes with variable periodicities and orientation angles through scattering media. We tested this projector on two types of scattering media: ground glass diffusers and multimode fibers. For these scattering media, the average Pearson's correlation coefficients between the projected fringes and their designed configurations are 86.9% and 79.7%, respectively. These results demonstrate the effectiveness of the proposed neural network enabled fringe projector. This advancement is expected to broaden the scope of fringe-based imaging techniques, making it feasible to employ them in conditions previously hindered by scattering effects.

Investigating the Impact of Pineapple-Whey Protein Fermentation Products on Cefixime-Induced Intestinal Flora Dysbiosis in Mice Using 16S Sequencing and Untargeted Metabolomics Techniques.

In our previous study, a new fermented food (PWF) created by utilizing pineapple by-products and whey proteins as a matrix via co-fermentation with lactic acid bacteria and yeast was developed, and, in the current study, we examined the impact of a pineapple-whey protein fermentation product on a cefixime-induced dysbiosis model in mice using 16S sequencing and untargeted metabolomics techniques. The results indicated that the pineapple-whey protein fermentation product played a positive role in restoring the intestinal flora. In this study, cefixime reduced the overall abundance of intestinal flora and decreased the relative abundance of probiotics in the gut, while also inhibiting amino acid metabolism. The addition of PWF normalized the intestinal flora to a steady state, significantly increasing the populations of Weissella, Lactococcus, Faecalibaculum, and Bacteroides acidophilus, while decreasing the numbers of Akkermansia and Escherichia-Shigella. Additionally, PWF modulated microbial metabolites, such as L-glutamate and L-threonine, and upregulated amino-acid-related metabolic pathways, including those involving glycine, serine, and threonine. In conclusion, PWF can alleviate intestinal flora dysbiosis and metabolic disturbances induced by antibiotic interventions. It is suggested that PWF could be a potential dietary strategy for patients with antibiotic-associated diarrhea.

Approaching Ultimate Synthesis Reaction Rate of Ni-Rich Layered Cathodes for Lithium-Ion Batteries.

Nickel-rich layered oxide LiNixCoyMnzO2 (NCM, x + y + z = 1) is the most promising cathode material for high-energy lithium-ion batteries. However, conventional synthesis methods are limited by the slow heating rate, sluggish reaction dynamics, high energy consumption, and long reaction time. To overcome these challenges, we first employed a high-temperature shock (HTS) strategy for fast synthesis of the NCM, and the approaching ultimate reaction rate of solid phase transition is deeply investigated for the first time. In the HTS process, ultrafast average reaction rate of phase transition from Ni0.6Co0.2Mn0.2(OH)2 to Li- containing oxides is 66.7 (% s-1), that is, taking only 1.5 s. An ultrahigh heating rate leads to fast reaction kinetics, which induces the rapid phase transition of NCM cathodes. The HTS-synthesized nickel-rich layered oxides perform good cycling performances (94% for NCM523, 94% for NCM622, and 80% for NCM811 after 200 cycles at 4.3 V). These findings might also assist to pave the way for preparing effectively Ni-rich layered oxides for lithium-ion batteries.

Paediatric off-label use of drugs in Gansu, China: a multicentre cross-sectional study.

OBJECTIVE: To examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors. DESIGN: The prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis. SETTING: A multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020. RESULTS: We retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians. CONCLUSION: Off-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm.

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

Lightweight and robust cellulose/MXene/polyurethane composite aerogels as personal protective wearable devices for electromagnetic interference shielding.

The increasing electromagnetic pollution is urgently needed as an electromagnetic interference shielding protection device for wearable devices. Two-dimensional transition metal carbides and nitrides (MXene), due to their interesting layered structure and high electrical conductivity, are ideal candidates for constructing efficient conductive networks in electromagnetic interference shielding materials. In this work, lightweight and robust cellulose/MXene/polyurethane composite aerogels were prepared by mixing cellulose nanofiber (CNF) suspensions with MXene, followed by freeze-drying and coating with polyurethane. In this process, CNF effectively assembled MXene nanosheets into a conductive network by enhancing the interactions between MXene nanosheets. The prepared aerogel exhibited the shielding effectiveness of 48.59 dB in the X-band and an electrical conductivity of 0.34 S·cm-1. Meanwhile, the composite aerogel also possessed excellent thermal insulation, infrared stealth, mechanical and hydrophobic properties, and can be used as a wearable protective device to protect the human body from injuries in different scenarios while providing electromagnetic interference shielding protection.

Coupling Antisite Defect and Lattice Tensile Stimulates Facile Isotropic Li-Ion Diffusion.

Despite widely used as a commercial cathode, the anisotropic 1D channel hopping of lithium ions along the [010] direction in LiFePO4 prevents its application in fast charging conditions. Herein, an ultrafast nonequilibrium high-temperature shock technology is employed to controllably introduce the Li-Fe antisite defects and tensile strain into the lattice of LiFePO4. This design makes the study of the effect of the strain field on the performance further extended from the theoretical calculation to the experimental perspective. The existence of Li-Fe antisite defects makes it feasible for Li+ to move from the 4a site of the edge-sharing octahedra across the ab plane to 4c site of corner-sharing octahedra, producing a new diffusion channel different from [010]. Meanwhile, the presence of a tensile strain field reduces the energy barrier of the new 2D diffusion path. In the combination of electrochemical experiments and first-principles calculations, the unique multiscale coupling structure of Li-Fe antisite defects and lattice strain promotes isotropic 2D interchannel Li+ hopping, leading to excellent fast charging performance and cycling stability (high-capacity retention of 84.4% after 2000 cycles at 10 C). The new mechanism of Li+ diffusion kinetics accelerated by multiscale coupling can guide the design of high-rate electrodes.

A Dual-mode platform for the rapid detection of Escherichia coli O157:H7 based on CRISPR/Cas12a and RPA.

Escherichia coli O157:H7 (E. coli O157:H7) is a foodborne pathogenic microorganism that is commonly found in the environment and poses a significant threat to human health, public safety, and economic stability worldwide. Thus, early detection is essential for E. coli O157:H7 control. In recent years, a series of E. coli O157:H7 detection methods have been developed, but the sensitivity and portability of the methods still need improvement. Therefore, in this study, a rapid and efficient testing platform based on the CRISPR/Cas12a cleavage reaction was constructed. Through the integration of recombinant polymerase amplification and lateral flow chromatography, we established a dual-interpretation-mode detection platform based on CRISPR/Cas12a-derived fluorescence and lateral flow chromatography for the detection of E. coli O157:H7. For the fluorescence detection method, the limits of detection (LODs) of genomic DNA and E. coli O157:H7 were 1.8 fg/µL and 2.4 CFU/mL, respectively, within 40 min. Conversely, for the lateral flow detection method, LODs of 1.8 fg/µL and 2.4 × 102 CFU/mL were achieved for genomic DNA and E. coli O157:H7, respectively, within 45 min. This detection strategy offered higher sensitivity and lower equipment requirements than industry standards. In conclusion, the established platform showed excellent specificity and strong universality. Modifying the target gene and its primers can broaden the platform's applicability to detect various other foodborne pathogens.