Spatially confined CuFe2O4 nanosphere in N/O-codoped porous carbon mimetics for triple-mode sensing of antibiotics and visual detection of neurotransmitters in biofluids.

BACKGROUND: Carbon-based nanozymes have recently received enormous concern, however, there is still a huge challenge for inexpensive and large-scale synthesis of magnetic carbon-based "Two-in-One" mimics with both peroxidase (POD)-like and laccase-like activities, especially their potential applications in multi-mode sensing of antibiotics and neurotransmitters in biofluids. Although some progresses have been made in this field, the feasibility of biomass-derived carbon materials with both POD-like and laccase-like activities by polyatomic doping strategy is still unclear. In addition, multi-mode sensing platform can provide a more reliable result because of the self-validation, self-correction and mutual agreement. Nevertheless, the use of magnetic carbon-based nanozyme sensors for the multi-mode detection of antibiotics and neurotransmitters have not been investigated. RESULTS: We herein report a shrimp shell-derived N, O-codoped porous carbon confined magnetic CuFe2O4 nanosphere with outstanding laccase-like and POD-like activities for triple-mode sensing of antibiotic d-penicillamine (D-PA) and chloramphenicol (CPL), as well as colorimetric detection of neurotransmitters in biofluids. The magnetic CuFe2O4/N, O-codoped porous carbon (MCNPC) armored mimetics was successfully fabricated using a combined in-situ coordination and high-temperature crystallization method. The synthesized MCNPC composite with superior POD-like activity can be used for colorimetric/temperature/smartphone-based triple-mode detection of D-PA and CPL in goat serum. Importantly, the MCNPC nanozyme can also be used for colorimetric analysis of dopamine and epinephrine in human urine. SIGNIFICANCE: This work not only offered a novel strategy to large-scale, cheap synthesize magnetic carbon-based "Two-in-One" armored mimetics, but also established the highly sensitive and selective platforms for triple-mode monitoring D-PA and CPL, as well as colorimetric analysis of neurotransmitters in biofluids without any tanglesome sample pretreatment.

Risk factors for postoperative bleeding following endoscopic submucosal dissection in early gastric cancer: A systematic review and meta-analysis.

BACKGROUND: Early gastric cancer (EGC) presents a significant challenge in surgical management, particularly concerning postoperative bleeding following endoscopic submucosal dissection. Understanding the risk factors associated with postoperative bleeding is crucial for improving patient outcomes. METHODS: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review and meta-analysis were conducted across PubMed, Embase, Web of Science, and the Cochrane Library without publication date restrictions. The inclusion criteria encompassed observational studies and randomized controlled trials focusing on EGC patients undergoing endoscopic submucosal dissection and their risk factors for postoperative bleeding. The Newcastle-Ottawa Scale was utilized for quality assessment. The effect size was calculated using random or fixed-effects models based on the observed heterogeneity. We assessed the heterogeneity between studies and conducted a sensitivity analysis. RESULTS: In our meta-analysis, 6 studies involving 4868 EGC cases were analyzed. The risk of postoperative bleeding was notably increased with intraoperative ulcer detection (odds ratio: 1.97, 95% confidence interval [CI]: 1.03-3.76, I2 = 61.0%, P = .025) and antithrombotic medication use (odds ratio: 2.02, 95% CI: 1.16-3.51, I2 = 57.2%, P = .039). Lesion resection size showed a significant mean difference (5.16, 95% CI: 2.97-7.98, P < .01), and longer intraoperative procedure time was associated with increased bleeding risk (mean difference: 11.69 minutes, 95% CI: 1.82-26.20, P < .05). Sensitivity analysis affirmed the robustness of these findings, and publication bias assessment indicated no significant bias. CONCLUSIONS: In EGC treatment, the risk of post-endoscopic submucosal dissection bleeding is intricately linked to factors like intraoperative ulcer detection, antithrombotic medication use, the extent of lesion resection, and the length of the surgical procedure. These interwoven risk factors necessitate careful consideration and integrated management strategies to enhance patient outcomes and safety in EGC surgeries.

Biotemplated fabrication of N/O co-doped porous carbon confined spinel NiFe2O4 heterostructured mimetics for triple-mode sensing of antioxidants and ameliorating packaging properties.

In this work, shrimp shell-derived magnetic NiFe2O4/N, O co-doped porous carbon nanozyme with superior oxidase (OXD)-like activity was prepared and used for colorimetric/photothermal/smartphone dual-signal triple-mode detection of antioxidants in fruits and beverages. The magnetic NiFe2O4/N, O co-doped porous carbon (MNPC) material was triumphantly fabricated using a combined in-situ surface chelation and pyrolysis method. The resultant MNPC composite exhibits a superior OXD-like activity, which can effectively oxidize 3,3',5,5'-tetramethylbenzidine (TMB) for yielding colorimetric/temperature dual-signal (CTDS) in absence of H2O2. This CTDS output sensor was successfully used for the determination of ascorbic acid and tannic acid. The proposed CTDS sensor with good specificity and high sensitivity can satisfy different on-site analysis requirements. Interestingly, the MNPC as a sustainable filler was further used for improving packaging properties of polyvinyl alcohol film. In short, this work offers a large-scale and cheap method to fabricate magnetic carbon-based nanozyme for monitoring antioxidants and ameliorating packaging properties.

Techniques for in vivo serotonin detection in the brain: State of the art.

Neuronal circuits in the brain that utilize the neurotransmitter serotonin are essential to the regulation of mood and emotional expression. Disruptions in serotonin signaling underlie neuropsychiatric conditions such as depression and anxiety. However, the cellular mechanisms that regulate serotonergic signaling in the brain in healthy and diseased states remain to be better understood. In particular, as more is learned about serotonin in the brain, we recognize an urgent need to develop techniques capable of mapping its complex spatiotemporal dynamics in awake, behaving animals. Notably, analytical methods to detect serotonin in situ, including tomography, are widely used but still recognized as limited in terms of their spatiotemporal resolution, their methodological caveats, and their technical limitations when cross-referenced with behavioral studies. To overcome such limitations, genetically encoded serotonin indicators were developed, leading to the introduction of novel imaging modalities that enable researchers to achieve remarkable spatiotemporal resolution in the study of serotonergic circuits in preclinical models of neuropsychiatric disorders. These novel approaches, while remarkably powerful, are also not without limitations. Here, we review the current techniques for detecting and quantifying serotonin in vivo within the brain and discuss how novel approaches such as genetically encoded serotonin indicators will lead to new insights into the roles of serotonergic circuits in health and disease.

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction.

Single-cell RNA sequencing (scRNA-seq) technology has become an effective tool for high-throughout transcriptomic study, which circumvents the averaging artifacts corresponding to bulk RNA-seq technology, yielding new perspectives on the cellular diversity of potential superficially homogeneous populations. Although various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material, the technical noise and biological variation are inevitably introduced into experimental process, resulting in high dropout events, which greatly hinder the downstream analysis. Considering the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data, we propose a customized autoencoder based on a two-part-generalized-gamma distribution (AE-TPGG) for scRNA-seq data analysis, which takes mixed discrete-continuous random variables of scRNA-seq data into account using a two-part model and utilizes the generalized gamma (GG) distribution, for fitting the positive and right-skewed continuous data. The adopted autoencoder enables AE-TPGG to captures the inherent relationship between genes. In addition to the ability of achieving low-dimensional representation, the AE-TPGG model also provides a denoised imputation according to statistical characteristic of gene expression. Results on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11704-022-2011-y.

DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) can reveal differences in genetic material at the single-cell level and is widely used in biomedical studies. However, the minute RNA content within individual cells often results in a high number of dropouts and introduces random noise of scRNA-seq data, concealing the original gene expression pattern. Therefore, data normalization is critical in the analysis pipeline to adjust for unexpected biological and technical effects, leading to a particular bimodal expression pattern exhibited in the semi-continuous normalized data. We further find the positive continuous expression presents a right-skewed distribution, which is still under-explored by mainstream dimensionality reduction and imputation methods. We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data. DAE-TPGM uses a two-part-gamma model to capture the statistical characteristics of semi-continuous normalized data and adaptively explores the potential relationships between genes for promoting data imputation by deep autoencoder. Just as the classic application scenarios that use an autoencoder in dimensionality reduction, our personalized autoendoer can capture phenotypic information on the peripheral blood mononuclear cells (PBMC) better and clearly infer continuous phenotype information for hematopoiesis in mice. Compared with that of mainstream imputation methods such as MAGIC, SAVER, scImpute and DCA, the new model achieved substantial improvement on the recognition of cellular phenotypes in two real datasets, and the comprehensive analyses on synthetic "ground truth" data demonstrated that our method obtains competitive advantages over other imputation methods in discovering underlying gene expression patterns in time-course data.

DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.

Here, we report a sensitive DocMF system that uses next-generation sequencing chips to profile protein-DNA interactions. Using DocMF, we successfully identified a variety of endonuclease recognition sites and the protospacer adjacent motif (PAM) sequences of different CRISPR systems. DocMF can simultaneously screen both 5' and 3' PAMs with high coverage. For SpCas9, we found noncanonical 5'-NAG-3' (~5%) and 5'-NGA-3' (~1.6%), in addition to its common PAMs, 5'-NGG-3' (~89.9%). More relaxed PAM sequences of two uncharacterized Cas endonucleases, VeCas9 and BvCas12a, were extensively characterized using DocMF. Moreover, we observed that dCas9, a DNA binding protein lacking endonuclease activity, preferably bound to the previously reported 5'-NGG-3' sequence. In summary, our studies demonstrate that DocMF is the first tool with the capacity to exhaustively assay both the binding and the cutting properties of different DNA binding proteins.