Intramolecular Charge Transfer of Gold Nanoclusters for pH Indicating.

The investigation of the intramolecular charge transfer (ICT) process of gold nanoclusters (AuNCs) is critical to understand the unique features of the nanomaterials, which also benefits their further applications. Herein, 6-methyl-2-thiouracil (CH3-2-TU) and polyvinylpyrrolidone (PVP)-stabilized AuNCs are prepared, and the ICT behaviors are carefully studied. Protonation or deprotonation of the ligands around AuNCs could be used to regulate the ICT state, influencing the electron distribution and band gap. Shifted fluorescence emission phenomena are thus observed, which respond to external pH stimuli. In addition, the AuNCs are developed as color-switchable indicators for the highly sensitive detection of biogenic amines. As a proof of concept, the performance of this strategy in the evaluation of food spoilage by probing pH conditions is validated with satisfactory results. The discoveries in this work offer a convenient route to regulate the optical properties of AuNCs and the design of pH-based sensing applications.

Holistic bursting cells store long-term memory in auditory cortex.

The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex.

Quantification of Multiplex miRNAs by Mass Spectrometry with Duplex-Specific Nuclease-Mediated Amplification.

Early quantification of multiplex biomarkers such as microRNAs (miRNAs) is critical during disease pathologic development and therapy. To tackle challenges of low abundance and multiplexing, we herein report a mass-encoded biosensing approach with duplex-specific nuclease (DSN) mediated signal amplification. Magnetic Fe3O4 cores are coated with small gold nanoparticles (AuNPs), which are applied to achieve facile DNA immobilization subsequent separation. This biosensor integrates multiple mass reporters corresponding to different targets (five miRNAs as examples). Due to the excellent resolution of mass spectrometry, these targets can be successfully distinguished in a single spectrum. Wide detection ranges from 10 fM to 1 nM are achieved, and the limits of detection are estimated to be 10 fM. High selectivity is promised due to the enzyme activity of DSN, and practical application in human serum samples performs satisfactorily. The number of targets to be tested can be further expanded by designing different specific mass tags in theory. Therefore, the proposed method can be utilized as an important and valuable tool to quantify multiplex miRNAs for disease screening as well as biomedical investigations.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections.

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Ultrasensitive miRNA biosensor amplified by ladder hybridization chain reaction on triangular prism structured DNA.

Accurate and sensitive analysis of biomarkers is a promising way to provide comprehensive physio-pathological information that is significant for early diagnosis of certain diseases. miRNA is a type of noncoding small RNAs which are involved in the regulation of a number of cellular processes. It has been regarded as an important tumor biomarker. Herein, we have constructed a three-dimensional DNA layer on electrode interface and achieved ladder hybridization chain reaction strategy for the enrichment of electrochemical signals. In addition, duplex-specific nuclease catalyzed amplification is previously performed on magnetic nanocomposites, which further improves the sensitivity for the detection of target miRNA initiator. This approach shows great molecular recognition efficiency as well as cascade signal amplification. The analytical performances are superior. In addition, the identification of cancer cell types according to target biomarker information is achieved and the testing results in clinical serum samples further demonstrate its great potential utility for diagnosis.

Theoretical foundation for designing multilayer Halbach array magnets for benchtop NMR and MRI.

Multilayer Halbach array magnets support portable NMR and MRI, but optimizing their design to maximize performance and minimize the use of expensive magnet materials is challenging. This is partly because our theoretical understanding of such arrays is incomplete and computationally intensive. Here we provide a theoretical description of the magnetic field distribution and we demonstrate that inhomogeneity is greatest along the z axis in multilayer Halbach array magnets. This allows the configuration of the multilayer Halbach array magnets to be optimized in a way that takes into account homogeneity, magnet volume, and magnetic flux density. At the same time, our description simplifies the design of multilayer array magnets, while accommodating the possibility of different outer radii, lengths for each layer array, or the presence of separation between the rings. We validated the theoretical description in simulations of a three-layer Halbach array magnet, then with a prototype three-layer 1-T Halbach array magnet. After adjusting the position of magnet blocks in the neighboring rings, we achieved homogeneity of 220 ppm for a standard 5 mm NMR tube while the inner diameter of the magnet is 20 mm. Our work provides a theoretical foundation for designing multilayer Halbach array magnets to maximize homogeneity and minimize the use of magnet materials.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning.

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

Altered insular functional connectivity correlates to impaired vigilant attention after sleep deprivation: A resting-state functional magnetic resonance imaging study.

Objectives: This study used resting-state functional magnetic resonance imaging (rs-fMRI) scans to assess the dominant effects of 36 h total sleep deprivation (TSD) on vigilant attention and changes in the resting-state network. Materials and methods: Twenty-two healthy college students were enrolled in this study. Participants underwent two rs-fMRI scans, once in rested wakefulness (RW) and once after 36 h of TSD. We used psychomotor vigilance tasks (PVT) to measure vigilant attention. The region-of-interest to region-of-interest correlation was employed to analyze the relationship within the salience network (SN) and between other networks after 36 h of TSD. Furthermore, Pearson's correlation analysis investigated the relationship between altered insular functional connectivity and PVT performance. Results: After 36 h of TSD, participants showed significantly decreased vigilant attention. Additionally, TSD induced decreased functional connectivity between the visual and parietal regions, whereas, a significant increase was observed between the anterior cingulate cortex and insula. Moreover, changes in functional connectivity in the anterior cingulate cortex and insula showed a significant positive correlation with the response time to PVT. Conclusion: Our results suggest that 36 h of TSD impaired vigilant visual attention, resulting in slower reaction times. The decrease in visual-parietal functional connectivity may be related to the decrease in the reception of information in the brain. Enhanced functional connectivity of the anterior cingulate cortex with the insula revealed that the brain network compensation occurs mainly in executive function.

Tetrahedral DNA Supported Walking Nanomachine for Ultrasensitive miRNA Detection in Cancer Cells and Serums.

A three-dimensional DNA tetrahedral nanostructure is constructed to support a walker strand on top and multiple track strands around it via the assembly of triplex-forming oligonucleotide (TFO). This design facilitates the regeneration of the sensing interface by simply adjusting pH conditions. On the basis of the tetrahedral DNA supported walking nanomachine, ultrasensitive electrochemical analysis of miRNA (miR-141) is achieved. Target miRNA assists the formation of three-way junction nanostructure. It contains a duplex region (hybridized by track and walker strands) that could be specially recognized and digested by certain nicking endonuclease. As a result, walker strand and target miRNA are released and move around the attached tracks for continuous cleavage reactions, releasing a larger number of signal reporters. By measuring the variation of signal responses, ultrasensitive analysis of miRNA is achieved. The limit of detection (LOD) is calculated to be 4.9 aM, which is rather low. In addition, the proposed method is successfully applied for the detection of miRNA in cell and serum samples, which could distinguish pathological information from healthy controls.

Design, Synthesis, and Application of Carbon Dots With Synergistic Antibacterial Activity.

The diversity of bacteria and their ability to acquire drug resistance lead to many challenges in traditional antibacterial methods. Photothermal therapies that convert light energy into localized physical heat to kill target microorganisms do not induce resistance and provide an alternative for antibacterial treatment. However, many photothermal materials cannot specifically target bacteria, which can lead to thermal damage to normal tissues, thus seriously affecting their biological applications. Here, we designed and synthesized bacteria-affinitive photothermal carbon dots (BAPTCDs) targeting MurD ligase that catalyzes the synthesis of peptidoglycan (PG) in bacteria. BAPTCDs presented specific recognition ability and excellent photothermal properties. BAPTCDs can bind to bacteria very tightly due to their chiral structure and inhibit enzyme activity by competing with D-glutamic acid to bind to MurD ligases, thus inhibiting the synthesis of bacterial walls. It also improves the accuracy of bacteria treatment by laser irradiation. Through the synergy of biochemical and physical effects, the material offers outstanding antibacterial effects and potentially contributes to tackling the spread of antibiotic resistance and facilitation of antibiotic stewardship.

A visual method for determination of hepatitis C virus RNAs based on a 3D nanocomposite prepared from graphene quantum dots.

Sensitive and specific assay of hepatitis C virus (HCV) RNA has a great clinical significance for the diagnosis of hepatitis C in window period. A colorimetric assay is described for determination of the RNA of hepatitis C virus. The method is based on the in situ synthesis of graphene quantum dot-silver nano-composites (GQD/Ag NCs), magnetic separation and catalyzed hairpin assembly reaction (CHA). The silver component of GQD/Ag NCs react with H2O2 generated by glucose oxidase, which are induced via CHA. Then the light-yellow solution turns into colorless transparent in the presence of the target RNAs. The method has a linear response in the 25-500 pM RNA concentration range, and the detection limit is 24.84 pM. The method is simple, stable and sensitive and might be extend to the assay of other RNA viruses. The retro-transcription free and visual distinguishable properties endow this strategy a promising application in the screening of HCV.

Low Cost, Easily-Assembled Centrifugal Buoyancy-Based Emulsification and Digital PCR.

Microfluidic-based droplet generation approaches require the design of microfluidic chips and a precise lithography process, which require skilled technicians and a long manufacturing time. Here we developed a centrifugal buoyancy-based emulsification (CBbE) method for producing droplets with high efficiency and minimal fabrication time. Our approach is to fabricate a droplet generation module that can be easily assembled using syringe needles and PCR tubes. With this module and a common centrifuge, high-throughput droplet generation with controllable droplet size could be realized in a few minutes. Experiments showed that the droplet diameter depended mainly on centrifugal speed, and droplets with controllable diameter from 206 to 158 µm could be generated under a centrifugal acceleration range from 14 to 171.9 g. Excellent droplet uniformity was achieved (CV < 3%) when centrifugal acceleration was greater than 108 g. We performed digital PCR tests through the CBbE approach and demonstrated that this cost-effective method not only eliminates the usage of complex microfluidic devices and control systems but also greatly suppresses the loss of materials and cross-contamination. CBbE-enabled droplet generation combines both easiness and robustness, and breaks the technical challenges by using conventional lab equipment and supplies.

Ratiometric Electrochemical Switch for Circulating Tumor DNA through Recycling Activation of Blocked DNAzymes.

Circulating tumor DNA (ctDNA) serves as a powerful noninvasive and viable biomarker for the diagnosis of cancers. The abundance of ctDNA in patients with advanced stages is significantly higher than that in patients with early stages. Herein, a ratiometric electrochemical biosensor for the detection of ctDNA is developed by smart design of DNA probes and recycles of DNAzyme activation. The conformational variation of DNA structures leads to the changes of two types of electrochemical species. This enzyme-free sensing strategy promotes excellent amplification efficiency upon target recognition. The obtained results assure good analytical performances and a limit of detection as low as 25 aM is achieved. Additionally, this method exhibits outstanding selectivity and great application prospects in biological sample analysis.

DNA Hairpins and Dumbbell-Wheel Transitions Amplified Walking Nanomachine for Ultrasensitive Nucleic Acid Detection.

Nucleic acids, including circulating tumor DNA (ctDNA), microRNA, and virus DNA/RNA, have been widely applied as potential disease biomarkers for early clinical diagnosis. In this study, we present a concept of DNA nanostructures transitions for the construction of DNA bipedal walking nanomachine, which integrates dual signal amplification for direct nucleic acid assay. DNA hairpins transition is developed to facilitate the generation of multiple target sequences; meanwhile, the subsequent DNA dumbbell-wheel transition is controlled to achieve the bipedal walker, which cleaves multiple tracks around electrode surface. Through combination of strand displacement reaction and digestion cycles, DNA monolayer at the electrode interface could be engineered and target-induced signal variation is realized. In addition, pH-assisted detachable intermolecular DNA triplex design is utilized for the regeneration of electrochemical biosensor. The high consistency between this work and standard quantitative polymerase chain reaction is validated. Moreover, the feasibilities of this biosensor to detect ctDNA and SARS-CoV-2 RNA in clinical samples are demonstrated with satisfactory accuracy and reliability. Therefore, the proposed approach has great potential applications for nucleic acid based clinical diagnostics.

A study on the detection of free and bound biotin based on TR-FRET technology.

Biotin is widely used in biological applications due to its highly selective and stable interaction with avidin, which highlights the great potential value of the quantitative determination of biotin concentration. However, the currently reported methods have many defects such as complicated operation processes and low sensitivity. Here, the time-resolved fluorescence resonance energy transfer (TR-FRET) assay is introduced to establish a convenient, rapid and sensitive biotin quantitative detection strategy. Europium cryptate (Eu3+) acts as an energy donor to label streptavidin, while APC acts as an energy acceptor to label biotin. Biotin in aqueous solution interacts with streptavidin in a competition mode. The obtained biotin detection range is 0.05-100 nM and the optimal limit of detection (LOD) of 0.03 nM biotin is obtained. Furthermore, an enzyme digestion test and a competition mode test were performed to analyze biotin in different states. The method used in this work has greatly improved the sensitivity of biotin quantitative detection and it's for the first time that a systematic study on the difference between free and bound biotin based on concentration results is conducted. It can be further extended to the detection of other biological molecules or multiplex detection of other small molecules.

Frequency-spatial domain joint optimization for improving super-resolution images of nonlinear structured illumination microscopy.

Introducing nonlinear fluorophore excitation into structured illumination microscopy (SIM) can further extend its spatial resolution without theoretical limitation. However, it is a great challenge to recover the weak higher-order harmonic signal and reconstruct high-fidelity super-resolution (SR) images. Here, we proposed a joint optimization strategy in both the frequency and spatial domains to reconstruct high-quality nonlinear SIM (NL-SIM) images. We demonstrate that our method can reconstruct SR images with fewer artifacts and higher fidelity on the BioSR dataset with patterned-activation NL-SIM. This method could robustly overcome one of the long-lived obstacles on NL-SIM imaging, thereby promoting its wide application in biology.

3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.

The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spiculation or lobulation of pulmonary nodules in CT images. However, this type of evaluation is subjective and time-consuming, which may result in poor consistency and low efficiency of diagnoses. In this study, a 3D Multi-scale, Multi-task, and Multi-label classification network (3M-CN) was proposed to predict LN metastasis, as well as evaluate multiple related signs of pulmonary nodules in order to improve the accuracy of LN metastasis prediction. The following key approaches were adapted for this method. First, a multi-scale feature fusion module was proposed to aggregate the features from different levels for which different labels be best modeled at different levels; second, an auxiliary segmentation task was applied to force the model to focus more on the nodule region and less on surrounding unrelated structures; and third, a cross-modal integration module called the refine layer was designed to integrate the related risk factors into the model to further improve its confidence level. The 3M-CN was trained using data from 401 cases and then validated on both internal and external datasets, which consisted of 100 cases and 53 cases, respectively. The proposed 3M-CN model was then compared with existing state-of-the-art methods for prediction of LN metastasis. The proposed model outperformed other methods, achieving the best performance with AUCs of 0.945 and 0.948 in the internal and external test datasets, respectively. The proposed model not only obtain strong generalization, but greatly enhance the interpretability of the deep learning model, increase doctors' confidence in the model results, conform to doctors' diagnostic process, and may also be transferable to the diagnosis of other diseases.

Cascade Strand Displacement and Bipedal Walking Based DNA Logic System for miRNA Diagnostics.

DNA logic gated operations empower the highly efficient analysis of multiplex nucleic acid inputs, which have attracted extensive attention. However, the integration of DNA logic gates with abundant computational functions and signal amplification for biomedical diagnosis is far from being fully achieved. Herein, we develop a bipedal DNA walker based amplified electrochemical method for miRNA detection, which is then used as the basic unit for the construction of various logic circuits, enabling the analysis of multiplex miRNAs. In the bipedal walking process, target triggered strand displacement polymerization is able to produce a large number of strands for the fabrication of three-way junction-structured bipedal walkers. The following catalytic hairpin assembly ensures the walking event and the immobilization of signal probes for output. Ultrahigh sensitivity is realized due to the integration of dual signal amplification. In addition, under logic function controls by input triggered cascade strand displacement reactions, NOT, AND, OR, NAND, NOR, XOR, and XNOR logic gates are successfully established. The as-developed DNA logic system can also be extended to multi-input modes, which holds great promise in the fields of DNA computing, multiplex analysis, and clinical diagnosis.