Modified Unit-Mediated Strand Displacement Reactions for Direct Detection of Single Nucleotide Variants in Active Double-Stranded DNA.

Accurate identification of single nucleotide variants (SNVs) in key driver genes holds a significant value for disease diagnosis and treatment. Fluorescent probes exhibit tremendous potential in specific, high-resolution, and rapid detection of SNVs. However, additional steps are required in most post-PCR assays to convert double-stranded DNA (dsDNA) products into single-stranded DNA (ssDNA), enabling them to possess hybridization activity to trigger subsequent reactions. This process not only prolongs the complexity of the experiment but also introduces the risk of losing target information. In this study, we proposed two strategies for enriching active double-stranded DNA, involving PCR based on obstructive groups and cleavable units. Building upon this, we explored the impact of modified units on the strand displacement reaction (SDR) and assessed their discriminatory efficacy for mutations. The results showed that detection of low variant allele frequencies (VAF) as low as 0.1% can be achieved. The proposed strategy allowed orthogonal identification of 45 clinical colorectal cancer tissue samples with 100% specificity, and the results were generally consistent with sequencing results. Compared to existing methods for enriching active targets, our approach offers a more diverse set of enrichment strategies, characterized by the advantage of being simple and fast and preserving original information to the maximum extent. The objective of this study is to offer an effective solution for the swift and facile acquisition of active double-stranded DNA. We anticipate that our work will facilitate the practical applications of SDR based on dsDNA.

DNA-based molecular classifiers for the profiling of gene expression signatures.

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Building a stable and robust anti-interference DNA dissipation system by eliminating the accumulation of systemic specified errors.

BACKGROUND: The emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. RESULTS: In this study, we introduce strategically designed "trash cans" to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. SIGNIFICANCE: The system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks.

Multi modality fusion transformer with spatio-temporal feature aggregation module for psychiatric disorder diagnosis.

Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work - MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities. Specifically, this method decouples the temporal and spatial dimensions and designs two feature extraction modules for extracting temporal and spatial information separately. Extensive experiments demonstrate the effectiveness of our proposed STFAM in extracting features from rs-fMRI, and prove that our STF2Former can significantly outperform MFFormer and achieve much better results among other state-of-the-art methods.

Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome.

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

Indole alkaloids from endophytic fungus Robillarda sessilis and their antibacterial activity.

Three undescribed indole alkaloids, fusarindoles F and G (1 and 2), and chlamydosporin B (3), together with five known compounds (4-8) were isolated from Robillarda sessilis. Their structures were elucidated based on NMR, UV, HRESIMS, and ECD calculation. Fusarindole F (1) own unusual asymmetric bis-indole structure. Compounds 5, 6, 7 exhibited moderate antibacterial activity against methicillin-resistant Staphylococcus aureus with a MIC value of 12.5 µg/mL. According to molecular docking experiment, the target proteins of compound 7 against methicillin-resistant S. aureus may be ELANE, MAOB and STAT3.

A Spatially Controlled Proximity Split Tweezer Switch for Enhanced DNA Circuit Construction and Multifunctional Transduction.

DNA strand displacement reactions are vital for constructing intricate nucleic acid circuits, owing to their programmability and predictability. However, the scarcity of effective methods for eliminating circuit leakages has hampered the construction of circuits with increased complexity. Herein, a versatile strategy is developed that relies on a spatially controlled proximity split tweezer (PST) switch to transduce the biomolecular signals into the independent oligonucleotides. Leveraging the double-stranded rigidity of the tweezer works synergistically with the hindering effect of the hairpin lock, effectively minimizing circuit leakage compared with sequence-level methods. In addition, the freely designed output strand is independent of the target binding sequence, allowing the PST switch conformation to be modulated by nucleic acids, small molecules, and proteins, exhibiting remarkable adaptability to a wide range of targets. Using this platform, established logical operations between different types of targets for multifunctional transduction are successfully established. Most importantly, the platform can be directly coupled with DNA catalytic circuits to further enhance transduction performance. The uniqueness of this platform lies in its design straightforwardness, flexibility, scalable intricacy, and system compatibility. These attributes pave a broad path toward nucleic acid-based development of sophisticated transduction networks, making them widely applied in basic science research and biomedical applications.

Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features.

Objective.This study aimed to develop an automatic and accurate method for severity assessment and localization of coronary artery disease (CAD) based on an optically pumped magnetometer magnetocardiography (MCG) system.Approach.We proposed spatiotemporal features based on the MCG one-dimensional signals, including amplitude, correlation, local binary pattern, and shape features. To estimate the severity of CAD, we classified the stenosis as absence or mild, moderate, or severe cases and extracted a subset of features suitable for assessment. To localize CAD, we classified CAD groups according to the location of the stenosis, including the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), and separately extracted a subset of features suitable for determining the three CAD locations.Main results.For CAD severity assessment, a support vector machine (SVM) achieved the best result, with an accuracy of 75.1%, precision of 73.9%, sensitivity of 67.0%, specificity of 88.8%, F1-score of 69.8%, and area under the curve of 0.876. The highest accuracy and corresponding model for determining locations LAD, LCX, and RCA were 94.3% for the SVM, 84.4% for a discriminant analysis model, and 84.9% for the discriminant analysis model.Significance. The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance.

Co-registration of OPM-MCG signals with CT using optical scanning.

Magnetocardiography (MCG) can be used to noninvasively measure the electrophysiological activity of myocardial cells. The high spatial resolution of magnetic source localization can precisely determine the location of cardiomyopathy, which is of great significance for the diagnosis and treatment of cardiovascular disease. To perform magnetic source localization, MCG data must be co-registered with anatomical images. We propose a co-registration method that can be applied to OPM-MCG systems. In this method, the sensor array and the trunk of the subject are scanned using structured light-scanning technology, and the scan results are registered with the reconstructed structure using computed tomography (CT). This can increase the number of effective cloud points acquired and reduce the interference from respiratory motion. The scanning bed of the OPM-MCG system was modified to be consistent with the CT device, ensuring that the state of the body remains consistent between the cardiac magnetometry measurements and CT scans.

Unmodificated stepless regulation of CRISPR/Cas12a multi-performance.

As CRISPR technology is promoted to more fine-divided molecular biology applications, its inherent performance finds it increasingly difficult to cope with diverse needs in these different fields, and how to more accurately control the performance has become a key issue to develop CRISPR technology to a new stage. Herein, we propose a CRISPR/Cas12a regulation strategy based on the powerful programmability of nucleic acid nanotechnology. Unlike previous difficult and rigid regulation of core components Cas nuclease and crRNA, only a simple switch of different external RNA accessories is required to change the reaction kinetics or thermodynamics, thereby finely and almost steplessly regulating multi-performance of CRISPR/Cas12a including activity, speed, specificity, compatibility, programmability and sensitivity. In particular, the significantly improved specificity is expected to mark advance the accuracy of molecular detection and the safety of gene editing. In addition, this strategy was applied to regulate the delayed activation of Cas12a, overcoming the compatibility problem of the one-pot assay without any physical separation or external stimulation, and demonstrating great potential for fine-grained control of CRISPR. This simple but powerful CRISPR regulation strategy without any component modification has pioneering flexibility and versatility, and will unlock the potential for deeper applications of CRISPR technology in many finely divided fields.

H/D Exchange Coupled with 2H-labeled Stable Isotope-Assisted Metabolomics Discover Transformation Products of Contaminants of Emerging Concern.

Stable isotope-assisted metabolomics (SIAM) is a powerful tool for discovering transformation products (TPs) of contaminants. Nevertheless, the high cost or lack of isotope-labeled analytes limits its application. In-house H/D (hydrogen/deuterium) exchange reactions enable direct 2H labeling to target analytes with favorable reaction conditions, providing intuitive and easy-to-handle approaches for environmentally relevant laboratories to obtain cost-effective 2H-labeled contaminants of emerging concern (CECs). We first combined the use of in-house H/D exchange and 2H-SIAM to discover potential TPs of 6PPD (N-1,3-dimethylbutyl-N'-phenyl-p-phenylenediamine), providing a new strategy for finding TPs of CECs. 6PPD-d9 was obtained by in-house H/D exchange with favorable reaction conditions, and the impurities were carefully studied. Incomplete deuteride, for instance, 6PPD-d8 in this study, constitutes a major part of the impurities. Nevertheless, it has few adverse effects on the 2H-SIAM pipeline in discovering TPs of 6PPD. The 2H-SIAM pipeline annotated 9 TPs of 6PPD, and commercial standards further confirmed the annotated 6PPDQ (2-anilino-5-(4-methylpentan-2-ylamino)cyclohexa-2,5-diene-1,4-dione) and PPPD (N-phenyl-p-phenylenediamine). Additionally, a possible new formation mechanism for 6PPDQ was proposed, highlighting the performance of the strategy. In summary, this study highlighted a new strategy for discovering the TPs of CECs and broadening the application of SIAM in environmental studies.

Magnetocardiography using optically pumped magnetometers array to detect acute myocardial infarction and premature ventricular contractions in dogs.

Objective. Optically pumped magnetometers (OPMs) are recently developed magnetocardiography (MCG) sensors that can detect cardiac diseases. This is of great clinical significance for detecting acute myocardial infarction (AMI) and premature ventricular contractions (PVC). This study investigates the use of an array of OPMs to detect heart disease in animals.Approach.An array of OPMs was used to detect the MCG of AMI and PVC in dogs. We used four dogs in this study, and models of AMI with different degrees of severity were established by ligating the middle and proximal segments of the left anterior descending coronary artery. The dogs had PVC at the time of AMI. Continuous MCG time series with corresponding electrocardiograms (ECGs) and average MCG for each dog in different states are presented. The MCG features were extracted from the MCG butterfly diagram, magnetic field map, and pseudo current density map. The MCG features were used to quantify and compare with the gold-standard ECG measures.Main results.The results show that MCG features can accurately distinguish different states of dogs. That is, an array of OPMs can effectively detect AMI and PVC in dogs.Significance.We conclude that the array of OPMs can detect heart diseases in animals. Moreover, OPMs can complement or even replace superconducting quantum interference devices for MCG measurement in animals and diagnosis of human heart diseases in the future.

Universal probe-based SNP genotyping with visual readout: a robust and versatile method.

Detection of single nucleotide polymorphisms (SNPs) is critical for personalized clinical diagnosis, treatment, and medication. Current clinical detection methods suffer from primer dimerization and require the redesigning of reaction systems for different targets, resulting in a time-consuming and laborious process. Here, we present a robust and versatile method for SNP typing by using tailed primers and universal small molecule probes in combination with a visualized lateral flow assay (LFA). This approach enables not only rapid typing of different targets, but also eliminates the interference of primer dimers and enhances the accuracy and reliability of the results. Our proposed universal assay has been successfully applied to the typing of four SNP loci of clinical samples to verify the accuracy and universality, and the results are consistent with those obtained by Sanger sequencing. Therefore, our study establishes a new universal "typing formula" using nucleic acid tags and small molecule probes that provides a powerful genotyping platform for genetic analysis and molecular diagnostics.

Changes in Inflammatory Biomarkers in Patients with Schizophrenia: A 3-Year Retrospective Study.

Objective: Accumulating evidence suggested that immune system activation might be involved in the pathophysiology of schizophrenia. The neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) can measure inflammation. This study aimed to investigate the inflammatory state in patients with schizophrenia by using these indicators. Methods: In this study, the complete blood count data for 187 continuing hospitalized patients with schizophrenia and 187 age- and sex-matched healthy participants was collected annually from 2017 to 2019. Platelet (PLT), lymphocyte (LYM), monocyte (MON) and neutrophil (NEU) counts were aggregated and NLR, MLR, PLR, and SII were calculated. Using a generalized linear mixed model, we assessed the impact of age, sex, diagnosis, and sampling year on the above indicators and evaluated the interaction between the factors. Results: According to the estimation results of the generalized linear mixed model, the NLR increased by 0.319 (p = 0.004), the MLR increased by 0.037 (p < 0.001), and the SII increased by 57.858 (p = 0.018) in patients with schizophrenia. Data after two years of continuous antipsychotic treatment showed that the NLR and MLR were higher in patients with schizophrenia than those in healthy controls, while the PLT and LYM counts were decreased in patients with schizophrenia. The schizophrenia diagnosis was correlated to the MON and LYM count, NLR, MLR, and SII (p < 0.05). Conclusion: The differences in these markers were stable and cannot be eliminated by a full course of treatment. This study provides impetus for the inflammatory hypothesis of schizophrenia.

Neutrophil/lymphocyte, platelet/lymphocyte, monocyte/lymphocyte ratios and systemic immune-inflammation index in patients with depression.

BACKGROUND: Evidence suggest immunity abnormalities and inflammation might play an important role in the pathophysiology of depression. This study explored the relationship between inflammation and depression using neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) as inflammatory markers. METHODS: We collected the full blood count results of 239 patients with depression and 241 healthy controls. Patients were divided into three diagnostic subtype groups: severe depressive disorder with psychotic symptoms, severe depressive disorder without psychotic symptoms, and moderate depressive disorder. We analyzed the Participants' neutrophil (NEU), lymphocyte (LYM), monocyte (MON), and platelet (PLT) counts, compared the differences in NLR, MLR, PLR and SII, and explored the relationships between depression and these indicators. RESULTS: There were significant differences in PLT, MON, NEU, MLR, and SII among the four groups. MON and MLR were significantly higher in three groups of depressive disorders. SII was significantly increased in two severe depressive disorder groups, while the SII in the moderate depressive disorder group showed an increasing trend. CONCLUSION: Increased MON, MLR and SII, as signs of inflammatory response, were not different among three subtypes of depressive disorders, and may be biological indictors of depressive disorders (Tab. 1, Ref. 17). Text in PDF www.elis.sk Keywords: depression, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII).

A hairpin probe-mediated exponential amplification reaction for highly sensitive and specific detection of microRNAs.

In this work, we propose a hairpin probe-mediated exponential amplification reaction (HEAR) strategy that combines DNA strand displacement with a "who triggers, who gets generated" mode, providing excellent single-base discrimination and a reduced background signal. The detection limit is 19 aM, which is reduced by 3 orders of magnitude compared to traditional exponential amplification approaches. This one-pot strategy also exhibits a wide dynamic range, high specificity and short detection time. It is expected to become a powerful tool for clinical diagnosis.

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks.

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.

Deep learning based approach for automated characterization of large marine microplastic particles.

The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic particles are normally subjected to visual characterization prior to more sophisticated chemical analyses. However, the misidentification rate of current visual inspection approaches remains high. This study proposed a state-of-the-art deep learning-based approach, Mask R-CNN, to locate, classify, and segment large marine microplastic particles with various shapes (fiber, fragment, pellet, and rod). A microplastic dataset including 3000 images was established to train and validate this Mask R-CNN algorithm, which was backboned by a Resnet 101 architecture and could be tuned in less than 8 h. The fully trained Mask R-CNN algorithm was compared with U-Net in characterizing microplastics against various backgrounds. The results showed that the algorithm could achieve Precision = 93.30%, Recall = 95.40%, F1 score = 94.34%, APbb (Average precision of bounding box) = 92.7%, and APm (Average precision of mask) = 82.6% in a 250 images test dataset. The algorithm could also achieve a processing speed of 12.5 FPS. The results obtained in this study implied that the Mask R-CNN algorithm is a promising microplastic characterization method that can be potentially used in the future for large-scale surveys.

Abnormal cortisol profile during psychosocial stress among patients with schizophrenia in a Chinese population.

Accumulating evidence suggests that hypothalamic-pituitary-adrenal axis dysfunction might play an important role in the pathophysiology of schizophrenia. The aim of this study was to explore the cortisol response to psychological stress in patients with schizophrenia. In this study, patients with schizophrenia (n = 104) and healthy volunteers (n = 59) were asked to complete psychological stress challenge tasks, which included the Paced Auditory Serial Addition Task and Mirror-Tracing Persistence Task, and pre- and post-task saliva samples were collected to measure cortisol levels. Emotions and psychopathology were assessed by the Positive and Negative Affect Schedule and Positive and Negative Syndrome Scale. The results showed (1) that the cortisol response and negative emotions in patients with schizophrenia differed significantly from those in healthy volunteers, (2) there were significant interactions between the sampling time and diagnosis for saliva cortisol levels, (3) there were significant interactions between the scoring time and diagnosis for the negative affect score of the PANAS, and (4) the changes in salivary cortisol levels and negative affect scores before and after the psychological stress challenge tasks were not correlated with clinical symptoms in patients with schizophrenia. These findings indicated an abnormal cortisol profile in patients with schizophrenia, which might be a biological characteristic of the disease.

Infection-activated lipopeptide nanotherapeutics with adaptable geometrical morphology for in vivo bacterial ablation.

The nonselective membrane disruption of antimicrobial peptides (AMPs) helps in combating the antibacterial resistance. But their overall positive charges lead to undesirable hemolysis and toxicity toward normal living cells, as well as the rapid clearance from blood circulation. In consequence, developing smart AMPs to optimize the antimicrobial outcomes is highly urgent. Relying on the local acidity of microbial infection sites, in this work, we designed an acidity-triggered charge reversal nanotherapeutics with adaptable geometrical morphology for bacterial targeting and optimized therapy. C16-A3K4-CONH2 was proposed and the ε-amino groups in lysine residues were acylated by dimethylmaleic amide (DMA), enabling the generated C16-A3K4(DMA)-CONH2 to self-assemble into negatively charged spherical nanostructure, which relieved the protein adsorption and prolonged blood circulation in vivo. After the access of C16-A3K4(DMA)-CONH2 into the microbial infection sites, acid-sensitive ß-carboxylic amide would hydrolyze to regenerate the positive C16-A3K4-CONH2 to destabilize the negatively charged bacterial membrane. In the meanwhile, attractively, the self-assembled spherical nanoparticle transformed to rod-like nanostructure, which was in favor of the efficient binding with bacterial membranes due to the larger contact area. Our results showed that the acid-activated AMP nanotherapeutics exhibited strong and broad-spectrum antimicrobial activities against Yeast, Gram-positive Staphylococcus aureus, Gram-negative Escherichia coli, and methicillin-resistant Staphylococcus aureus (MRSA). Moreover, the biocompatible lipopeptide nanotherapeutics dramatically improved the dermapostasis caused by bacterial infection. The strategy of merging pathology-activated therapeutic function and morphological adaptation to augment therapeutic outcomes shows the great potential for bacterial inhibition. STATEMENT OF SIGNIFICANCE: The overall positive charges of antimicrobial peptides (AMPs) lead to undesirable hemolysis and nonselective toxicity, as well as the rapid clearance from blood circulation. Infection-activated lipopeptide nanotherapeutics with adaptable geometrical morphology were developed to address these issues. The self-assembled lipopeptide was pre-decorated to reverse the positive charge to reduce the hemolysis and nonselective cytotoxicity. After accessing the acidic infection sites, the nanotherapeutics recovered the positive charge to destabilize negatively charged bacterial membranes. Meanwhile, the morphology of self-assembled nanotherapeutics transformed from spherical nanoparticles to rod-like nanostructures in the lesion site, facilitating the improved association with bacterial membranes to boost the therapeutic efficiency. These results provide new design rationale for AMPs developed for bacterial inhibition.