Platform For Identification Research Articles

Enzyme-Free Identification of Monosaccharide Enantiomers on TiO2 Nanotube Array-Based Fabry-Pérot Interferometer.

Chirality is a vital property across various domains, especially for biological activity. Herein, an enzyme-free sensing platform for monosaccharide enantiomer identification was developed by utilizing the Fabry-Pérot interferometer feature of TiO2 nanotube arrays modified with enantioselective metal-organic framework and glucose oxidase-mimicking Au NPs. In this design, optical property is monitored by reflective interferometric Fourier transform spectroscopy (RIFTS), a highly sensitive technique for detecting changes in the average refractive index within nanotubular structures. Using glucose (Glu) enantiomers as the model targets, after the recognition of L-/d-Glu on mesoporous homochiral MIL-101 (Fe), Au NPs anchored in MIL-101(Fe) catalyze the oxidation of Glu molecules to produce hydrogen peroxide (H2O2). Benefiting from the confinement effect of frameworks, MIL-101(Fe), as an artificial enzyme with excellent peroxidase-like activity, catalyzes the conversion of 4-chloro-1-naphthol (4-CN) into insoluble precipitates. These gathered precipitates effectively change the average refractive index of the interferometric substrate. Based on the variation of effective optical thickness (ΔEOT) values, the enantioselective determination of l-Glu and d-Glu can be achieved. Moreover, the proposed RIFTS sensor also presents broad applicability for the identification of other monosaccharide enantiomers. As the enzyme-free homochiral interferometer is directly constructed on a Ti-metal sheet, the RIFTS platform offers a robust, sensitive, and low-cost device for monosaccharide enantiomer recognition.

Highly sensitive and rapid detection of Vibrio parahaemolyticus using a dual-recognition platform based on functionalized quantum dots and aptamer.

As one of the most harmful pathogenic bacteria in shrimp aquaculture, Vibrio parahaemolyticus often causes massive mortality in shrimp. Accurate and rapid detection of V. parahaemolyticus in shrimp farming is essential for avoiding huge economic losses caused by related diseases. In this study, we designed a dual-recognition platform for efficient identification and quantification of V. parahaemolyticus. First, the target bacterium was captured with magnetic beads functionalized by aptamers (Apt-MBs), and then, the broad-spectrum fluorescent probe FcMBL@CdSe-ZnS was used to detect the bacterium based on the interactions between fragment crystallizable mannose-binding lectin (FcMBL) and pathogenic bacteria. The proposed dual-recognition strategy centered around aptamers and FcMBL@CdSe-ZnS was applied to definite quantification of V. parahaemolyticus over a wide range of 10-108 CFU/mL with a limit of detection of 4 CFU/mL within 55 min. The feasibility was demonstrated by using the platform to detect V. parahaemolyticus from shrimp intestine, aquaculture water, and seawater.

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

One-step, rapid, nanoparticle-based biosensor platform for the simultaneous identification of hepatitis B virus and hepatitis C virus in clinical applications

ObjectivesViral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) infections remain a major global public health challenge, particularly in low- and middle-income countries. It is crucial to utilize a pointof-care (POC) testing platform that is sensitive, specific, rapid, and user-friendly for screening and diagnosis of the two infections. Here, a novel molecular diagnostic assay, integrating multiplex loop-mediated isothermal amplification with a gold nanoparticle-based lateral flow biosensor (mLAMP-AuNPs-LFB) was developed and applied for one-step, visual, rapid, sensitive, and specific identification of HBV and HCV.MethodsThe AuNPs-based LFB was devised and constructed for the simultaneous detection of HBV and HCV. The HBV-LAMP and HCV-LAMP primers were designed against the S and 5′-untranslated region (5′-UTR) genes from the major HBV genotypes (B, C, D, B/C recombinant, and C/D recombinant) and HCV subtypes (1b, 2a, 3a, 3b, and 6a) in China, respectively. Our assay conditions, both multiplex-LAMP amplification temperature and time were optimized. The sensitivity and specificity of our assay were tested, and the feasibility of our assay was verified through clinical samples.ResultsThe AuNPs-based LFB used here was successfully manufactured according to our devise manual. The two unique independent primer pairs were successfully designed based on the S and 5′-UTR genes, respectively. The optimal mLAMP-AuNPs-LFB detection process, involving rapid nucleic acid isolation (10 min), mLAMP (63 °C for 35 min), and visual AuNPs-LFB interpretation (less than 2 min), could be completed within 50 min. The HBV&HCV-mLAMP-AuNPs-LFB assay can detect the target genes (HBV-S and HCV-5′-UTR) with as low as 20 copies of plasmid template per test, and the specificity was 100% for the experimental pathogens.ConclusionsThe preliminary results manifested that our mLAMP-AuNPs-LFB assay is a valuable tool and has tremendous potential as a POC testing approach for HBV and HCV identification, especially in undeveloped regions.

CAR-Machine: CAR-T Cell Target Identification Platform for Pediatric Acute Leukemia and Implementation to Identify Novel Targets for Pediatric AML

CAR-Machine: CAR-T Cell Target Identification Platform for Pediatric Acute Leukemia and Implementation to Identify Novel Targets for Pediatric AML

Spaceborne particle identification platform and its application based on convolutional neural network

The identification of particle types in space radiation particle detection is a problem of great concern in scientific research and engineering applications. At present, the particle types are mainly identified by the difference in energy deposition of particles in the sensor, the difference in waveform in the sensor, the flight time and energy of particles, and the difference in the deflection path of particles in the electric field, such as traditional telescope detectors, particle pulse waveform analysis, flight time TOF system and electrostatic analyzer. However, the current on-orbit particle identification logic is relatively simple and the identification accuracy is limited. Convolutional neural networks have powerful target classification capabilities and are good at capturing and extracting target feature details, which can improve the accuracy of particle energy measurement and identification. Based on the environment commonly used in space environment detection payloads, this paper proposes a method for building an on-orbit convolutional neural network particle identification platform to achieve particle type identification. The platform first constructs a multi-dimensional input data set, completes the model training and weight derivation with the help of the software platform, and completes the waveform inference and data set expansion through the hardware platform. The established identification platform is used to train and test the neutron and gamma waveform data obtained in actual tests, and the accuracy of the identification of the software and hardware platforms is analyzed, completing the verification of the platform. The establishment and application of this platform provides a new idea and method for particle measurement and identification in future space environment detection, and has strong engineering practice significance.

ERNA-IDO: A One-stop Platform for Identification, Interactome Discovery, and Functional Annotation of Enhancer RNAs.

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

A-273 Rapid, Portable Invasive Fungal Infection Diagnostic and Candida Antimicrobial Susceptibility Testing

Abstract Background Traumatic wounds are associated with a higher risk of invasive fungal infections (IFI), which lead to a significant increase in amputation or death. Actionable treatment decisions are hampered because true infections can be difficult to differentiate from external wound contaminates, and tissue biopsy and quantification of fungal load is required to determine if treatment is necessary, which currently takes several days. GeneCapture is developing the CAPTURE 60-minute, portable infection identification platform to detect infectious levels of common fungi including the genera Aspergillus, Fusarium, and Scedosporium, the family Herpotrichiellaceae, and the order Mucorales. The company is also pursuing rapid identification and antimicrobial susceptibility testing for Candida species from multiple infection types. Methods This study seeks to determine the Limit of Detection (LoD), sensitivity and specificity of the CAPTURE ID assay for fungal pathogens. Since tissue biopsy samples must be used to assess the presence of IFI in wounds, GeneCapture has developed a manual process for the crude homogenization of muscle tissue to allow this matrix to be used as the starting sample for the ID assay. Mock samples were tested for inclusivity and exclusivity on a series of IFI ID panels against multiple strains of the included fungi, against fungi not yet specifically detected on the panel, and against bacteria that commonly infect wounds. While the LoD for yeasts may be determined in CFU/mL, there is no similar, easy-to-define metric for molds. GeneCapture has developed a quantitative PCR method to detect the single copy beta-tubulin gene to roughly correlate fungal dry mass and nuclei number to assay signal for molds cultured in Potato Dextrose Broth. Determining beta-tubulin copy numbers for extracts from mock samples then allowed detection of the progression of IFI over the course of infection. Lastly, antimicrobial susceptibility testing (AST) was performed on two species of Candida to validate utilizing GeneCapture’s rapid phenotypic AST method for fungal infections. Results To date, the CAPTURE ID panel has been tested against 27 included or excluded strains of fungi or bacteria with 97% sensitivity and 100% specificity. The current LoD for mold organisms varies from approximately 1,000 nuclei for Aspergillus species, to 100,000 nuclei for Fusarium species. Additional work to understand this range is underway. Lastly, we have correctly determined the susceptibility of Candida species using our novel approach within 4-6 hours. Conclusions GeneCapture’s ID and AST technology offer a unique solution to address the increasing prevalence of fungal infections. Our platform can identify the most common fungal components in a wound within 60 minutes of receiving a tissue biopsy sample. Candida infections occur across multiple matrices and rapidly identifying the species of Candida and then determining its phenotypic susceptibility profile will have a large impact on treating these difficult pathogens.

Development of an evaluation system for rational drug use in patients with chronic kidney disease using the Delphi method.

Chronic kidney disease (CKD) stages 3-4 present a significant clinical challenge due to the absence of a systematic approach to managing associated medication-related problems (MRPs). This lack of a structured framework hinders the timely identification and effective intervention for these complications, potentially compromising patient safety and prognosis. This study aims to leverage the Delphi method to establish an evaluation index for a rational drug use evaluation system dedicated to CKD patients in stages 3-4. This system will function as a platform for the continuous identification and management of MRPs, ultimately contributing to improved medication safety and patient outcomes. This research uses the modified Delphi technique to develop an evaluation system for rational drug use in patients with chronic kidney disease. The initial questionnaire was developed by literature review for patients with chronic kidney disease. Twenty-six senior experts formed a panel in order to evaluate items across two Delphi rounds. Consensus was defined as at least 95% agreement (first round) and 85% agreement (second round), agreeing with an average score of at least 4.5 (first round) and 4.0 (second round). Items that fulfill the stipulated criteria are eligible for inclusion in the consensus list. All experts participated in both rounds (100% response rate). Consensus was achieved on three patient-related items in the first round of 34 items. Based on expert feedback, 18 revised items were included in the second round after refining, restructuring, and removing some elements. Following two rounds of consultation, 20 items achieved consensus, encompassing aspects such as drug selection, dosage assessment, treatment duration, prescription and dispensing practices, patient-related factors, and other relevant considerations. This study has successfully identified 20 key evaluation indicators for a rational drug use evaluation system specifically designed for CKD patients in stages 3 and 4. This system will serve as a tool for continuous MRP identification and timely intervention, ultimately enhancing medication safety and patient prognosis.

Cell Membrane-Camouflaged Biomimetic Magnetic Fluorescent Nanoparticles with Enhanced Stability for High-Performance Lead Drug Discovery.

Biomimetic nanoengineering empowers nanoparticles with enhanced biointerfacial capabilities by directly utilizing cell membranes (CMs) of natural origin. This top-down technique provides a powerful tool for the screening of potentially active compounds in complex matrices. Herein, cartilaginous end plate (CEP) cell membrane biomimetic Nile red (NR)-loaded zeolitic imidazolate frameworks-8 (ZIF-8) modified magnetic graphene oxide (CEP/MGO-ZIF-8-NR) nanocomposites with enhanced stability were accurately prepared by chemical bonding and used as a drug discovery platform for the specific identification and effective extraction of drug leads with anti-intervertebral disc degeneration (IDD) in Yaobitong capsules (YBTCs). The constructed CEP/MGO-ZIF-8-NR exhibited excellent magnetic properties, fluorescence properties, and stability. In addition, drug binding experiments showed that the CEP/MGO-ZIF-8-NR nanocomposites possessed higher adsorption capacity, faster adsorption rate, and superior selectivity compared with uncoated MGO-ZIF-8-NR. Ultimately, four potential bioactive molecules, including ginsenoside Ro, ginsenoside Rg1, astringin, and chikusetsusaponin V methyl ester, were successfully screened and identified in vitro from YBTC. The results of the CCK-8 assay and BrdU ELISA kit showed that the screened compounds promoted CEP cell proliferation in a concentration-dependent manner. Cellular distribution experiments revealed that CEP/MGO-ZIF-8-NR could rapidly escape from lysosomes and into the cytoplasm. And the pharmacological activity of these compounds was further confirmed by real-time cytomorphological imaging of CEP cells by confocal laser scanning microscopy (CLSM). Overall, this surface engineering strategy endows bioaffinity sample pretreatment materials with tremendous versatility, improves drug screening efficiency, and broadens the horizons and methodologies for drug lead discovery.

Recombinase-aid amplification combined with lateral flow detection assay for sex identification of the great white pelican (Pelecanus onocrotalus)

Sex identification in avian species is essential for biodiversity conservation and ecological studies. However, the sex of nearly half of the birds could not be identified based on their external appearance. It is difficult to visually identify sex to monitor the ecology and conservation of wild populations. In this study, we designed primer pairs for large white pelican using recombinase-based isothermal amplification combined with a lateral flow dipstick (RAA-LFD) assay for chromo-helicase-DNA binding protein (CHD) genes mapped to W chromosomes and an ultra-conserved element (UCE) located on chromosome 6, respectively. Our result showed that the raaW4-RAA-LFD can detect up to 0.1 ng of genomic DNA (gDNA) templates of female pelicans in 30 min at 39 ℃ and accurately distinguish female from male without any cross reactivity. RaaUCE2-RAA-LFD can amplify both male and female pelicans with a detection limit of 25 pg. To further evaluate the assay, 15 white pelicans of unknown sex were tested using the RAA-LFD assay and conventional polymerase chain reaction (PCR). The results of the raaW4-RAA-LFD assay were consistent with those of the conventional PCR. The developed RAA-LFD assay is equipped with field-deployable instruments and offers a field platform for rapid and reliable sex identification in pelicans.

Enhancing taxonomy-based extraction: Leveraging information from online community platforms for digital skills demand identification in job ads

The rapid technological changes have revolutionised how we function, including how we search for work and what skills we need to be equipped with to perform the tasks at the workplace. As employers more often recruit using online job advertisements, their content becomes a natural source of information for analytical purposes on the skills demanded in the labour market, especially for analysing emerging skills like digital. There are still some challenges with the extraction of information from online content. However, the extraction improvements go hand in hand with new technological developments like natural language processing techniques. This article presents the experimental method of updating the classification of digital skills to keep it up to date for information extraction applied to online job advertisements. The evaluation proved this method successfully identified terms related to programming skills but failed to identify terms associated with artificial intelligence sufficiently. The latter is related to the fact that the AI field is among the fastest developing areas of technology advancement, and new terms (e.g. Chatgpt) always appear.

MALDI-TOF mass spectrometry from nucleic acid: development and evaluation of a novel platform for identification of mycobacteria and detection of genetic markers of resistance.

Complete identification methods are critical for evaluating nontuberculous mycobacteria (NTM). Here, we describe a novel diagnostic method for identification of eight NTM, Mycobacterium tuberculosis complex, and three drug resistance markers using PCR/matrix-assisted, laser-desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) from cultured organisms. With this technology, a multiplex end-point PCR is performed for targets of interest. Detection probes that are extended in the presence of a target are added. The extended probes have greater molecular weight and can be detected by MALDI-TOF MS. An AFB Primary Panel was designed to differentiate Mycobacterium avium; Mycobacterium intracellulare subsp. chimaera; Mycobacterium avium complex (other); Mycobacterium abscessus subsp. abscessus, bolletii, and massiliense; Mycobacterium kansasii, and M. tuberculosis complex. This design should cover 90% (3,483/3,691) of mycobacteria seen onsite. A development set of unblinded isolates (n = 217) was used to develop PCR primers, detection probes, and probe barcodes. It demonstrated 99.1% (215/217) agreement with reference methods. An evaluation set using blinded isolates (n = 320) showed an overall sensitivity of 94.3% (range by target: 90.0-100%). Overall specificity from negative media, non-target mycobacteria, and bacteria was 99.1% (108/109; range by target: 94.4-100%). Three drug resistance markers erm (41), rrl, and rrs demonstrated 100%, 91%, and 100% sensitivity, respectively, and >99% specificity. Limit of detection per target ranged from 2.2 × 103 to 9.9 × 106 CFU/mL. The AFB Primary Panel allows for mycobacterial speciation, subspeciation, and resistance mutation detection, which is essential for diagnosis, appropriate therapy, identifying outbreaks, and managing treatment-refractory disease. It can perform with high-throughput and high specificity and sensitivity from isolates.IMPORTANCEEven closely related mycobacteria can have unique treatment patterns, but differentiating these organisms is a challenge. Here, we tested an innovative platform that combines two commonly used technologies and creates something new: matrix-assisted, laser-desorption ionization time-of flight mass spectrometry was performed on PCR amplicons instead of on proteins. This created a robust system with the advantages of PCR (high discriminatory power, high throughput, detection of resistance) with the advantages of mass spectrometry (more targets, lower operational cost) in order to identify closely related mycobacterial organisms.

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple pesticides. Herein, we first synthesize a Eu(III)-functionalized HOF-on-HOF composite (Eu@PFC-1@MA-TPA) and then utilize principal component analysis (PCA) and a machine learning (ML) algorithm to achieve simultaneous identification of the pesticides 2,6-dichloro-4-nitroaniline (DCN) and thiabendazole (TBZ) and their mixtures. Eu@PFC-1@MA-TPA displays high quantitative identification ability, which can distinguish single DCN and TBZ as low as 1 μM and their mixtures at 5 μM through PCA. In addition, the hydrogel film Eu@PFC-1@MA-TPA/AG is fabricated to monitor DCN and TBZ in drinking water, tap water, river water, and apple juice with high sensitivity. Furthermore, based on the obvious fluorescence color variance of pesticides, Eu@PFC-1@MA-TPA/AG achieves visual and in situ imaging detection of single DCN and TBZ and their mixtures. More importantly, we construct an intelligent artificial vision platform integrating Eu@PFC-1@MA-TPA/AG with a DenseNet algorithm, which can identify the concentrations and types of DCN and TBZ and their mixtures within 1 s with over 98% accuracy. This work develops a precise and rapid analysis method for simultaneous identification of multiple pesticides through combining a visualized fluorescence sensor and an ML algorithm.

Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Utilizing Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intraendosomal environments of these cell types give rise to significant differences in many optical features, such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of >95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications, such as cell phenotyping or mapping intraendosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (>87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

Spectral Fingerprinting Coupled Machine Learning: An Optimized Tool for Cellular Phenotype Identification

Spectral fingerprinting has emerged as a powerful tool for unraveling intricate interactions between the intracellular environment and nanomaterials. This study leverages spectral fingerprinting to deepen our comprehension of the interplay between macrophages and nanotubes, utilizing their Near Infrared fluorescence to discern cellular characteristics. This study employs Raman microscopy to showcase significant differences in defect ratio and intracellular processing of nanotubes, among macrophage phenotypes. The near infrared features of DNA-SWCNTs serve as distinctive markers for identifying different macrophage phenotypes. Here we use a support vector machine learning model to accurately identify M1 and M2 macrophages, achieving an impressive accuracy of 95%. The implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time in-vivo cell differentiation monitoring.This study underlines the pivotal role of nanotubes in delineating the diverse micro-environments of distinct macrophage phenotypes for clinical research. It emphasizes the potential of short DNA sequences and machine learning in characterizing and classifying cell phenotypes, offering valuable insights into the intersection of nanotechnology and biomedical research.This innovative approach holds great promise in advancing our understanding of dynamic cellular processes, disease states, disease progression, and response to stimuli in-vivo. The platform’s sensitivity to minute changes in the Near Infrared spectrum positions it as a valuable tool for researchers and clinicians. It facilitates the real time monitoring of cellular behavior within living systems, offering insights that extend to both fundamental research and clinical applications. The implications of this research pave the way for future developments in nanotechnology and biomedicine, bridging the gap between spectral analysis and cellular dynamics for enhanced diagnostic and therapeutic interventions. Figure 1

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

SYSTCM: A systemic web platform for objective identification of pharmacological effects based on interplay of “traditional Chinese Medicine-components-targets”

Mechanism analysis is essential for the use and promotion of Traditional Chinese Medicine (TCM). Traditional methods of network analysis relying on expert experience lack an explanatory framework, prompting the application of deep learning and machine learning for objective identification of TCM pharmacological effects. A dataset was used to construct an interacted network graph between 424 molecular descriptors and 465 pharmacological targets to represent the relationship between components and pharmacological effects. Subsequently, the optimal identification model of pharmacological effects (IPE) was established through convolution neural networks of GoogLeNet structure. The AUC values are greater than 0.8, MCC values are greater than 0.7, and ACC values are greater than 0.85 across various test datasets. Subsequently, 18 recognition models of TCM efficacy (RTE) were created using support vector machines (SVM). Integration of pharmacological effects and efficacies led to the development of the systemic web platform for identification of pharmacological effects (SYSTCM). The platform, comprising 70,961 terms, including 636 Traditional Chinese Medicines (TCMs), 8190 components, 40 pharmacological effects, and 18 efficacies. Through the SYSTCM platform, (1) Total 100 components were predicted from TCMs with anti-inflammatory pharmacological effects. (2) The pharmacological effects of complete constituents were predicted from Coptidis Rhizoma (Huang Lian). (3) The principal components, pharmacological effects, and efficacies were elucidated from Salviae Miltiorrhizae radix et rhizome (Dan Shen). SYSTCM addresses subjectivity in pharmacological effect determination, offering a potential avenue for advancing TCM drug development and clinical applications. Access SYSTCM at http://systcm.cn.

Helical au nanostructure for SERS detection of hazardous molecular and chiral enantiomers

In recent years, incidents of pesticide pollution and abuse of feed additives have occurred frequently, which pose a great threat to human health. Raman spectroscopy has become an important method in the field of food safety due to its rapidity, simplicity and sensitivity. It is important to obtain complex structure to promote surface-enhanced Raman scattering (SERS) effect. In this study, gold helical nanoparticles with rich surface structure were synthesized using cysteine as induce agent. Notably, the complex helical structure and tip led to an excellent electromagnetic enhancement property. The helical structure showed ultra-sensitive detection of hazardous molecular, such as thiram and ractopamine. Interestingly, the D/L-Au structure had significant chiral optical activity and could be used as an unlabeled SERS platform for enantiomer identification. This study provided an effective strategy for the detection of pesticides and feed additives, which could be applied in other aspects of food safety in the future.