Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints. Corroborating with in-silico experiments, we establish five spectral features, each corresponding to a structural characteristic: (1) presence/absence of epimers, (2) monosaccharide/cerebroside, (3) saturated/unsaturated cerebroside, (4) glucosyl/galactosyl, and (5) GlcCer or GalCer's carbon chain lengths. Leveraging these insights, we create a fully generalizable framework to identify and quantify cerebrosides at concentrations between 10-4 to 10-10 M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer24:1, and GalCer24:1 using their untrained spectra in the models.

Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra.

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7-7.9 %. We first use plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with <4 % error. Our interpretable NP structural space further elucidates the two higher-order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures' extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on-demand, autonomous synthesis-characterization platforms.

Protectin D1 ameliorates non-compressive lumbar disc herniation through SIRT1-mediated CGRP signaling.

Background. Neuro-inflammatory response promotes the initiation and sustenance of lumbar disc herniation (LDH). Protectin D1 (PD1), as a new type of specialized pro-resolving mediator (SPM), can improve the prognosis of various inflammatory diseases. Recent studies have shown that over representation of calcitonin gene-related peptides (CGRP) may activate nociceptive signaling following nerve injury. Silent information regulator 1 (SIRT1) is ubiquitously expressed in the dorsal horn of the spinal cord and plays a role in the pathogenesis of LDH. In this study, we investigated the analgesic effects of PD1 and elucidated the impact of neurogenic inflammation in the pathogenesis of neuropathic pain induced by non-compressive lumbar disc herniation (NCLDH) in a rat model. Methods. NCLDH models were established by applying protruding autologous nucleus pulposus to the L5 Dorsal root ganglion (DRG). PD1, SIRT1 antagonist or agonist, CGRP or antagonist were administered as daily intrathecal injections for three consecutive days postoperatively. Behavioral tests were conducted to assess mechanical and thermal hyperalgesia. The ipsilateral lumbar (L4-6) segment of the spinal dorsal horn was isolated for further analysis. Alterations in the release of SIRT1 and CGRP were explored using western blot and immunofluorescence. Results. Application of protruded nucleus (NP) materials to the DRG induced mechanical and thermal allodynia symptoms, and deregulated the expression of pro-inflammatory and anti-inflammatory cytokines in rats. Intrathecal delivery of PD1 significantly reversed the NCLDH-induced imbalance in neuro-inflammatory response and alleviated the symptoms of mechanical and thermal hyperalgesia. In addition, NP application to the DGRs resulted the spinal upregulation of CGRP and SIRT1 expression, which was almost restored by intrathecal injection of PD1 in a dose-dependent manner. SIRT1 antagonist or agonist and CGRP or antagonist treatment further confirmed the result. Conclusion. Our findings indicate PD1 has a potent analgesic effect, and can modulate neuro-inflammation by regulating SIRT1-mediated CGRP signaling in NCLDH.

Four new hypogean species of the genus Triplophysa (Osteichthyes, Cypriniformes, Nemacheilidae) from Guizhou Province, Southwest China, based on molecular and morphological data.

Recently described cave species of the genus Triplophysa have been discovered in southwestern China, suggesting that the diversity of the genus is severely underestimated and that there may be many undescribed species. In this work, four new species of the genus Triplophysa are described from southwestern Guizhou Province, China, namely Triplophysacehengensis Luo, Mao, Zhao, Xiao & Zhou, sp. nov. and Triplophysarongduensis Mao, Zhao, Yu, Xiao & Zhou, sp. nov. from Rongdu Town, Ceheng County, Guizhou, Triplophysapanzhouensis Yu, Luo, Lan, Xiao & Zhou, sp. nov. from Hongguo Town, Panzhou City, Guizhou, and Triplophysaanlongensis Song, Luo, Lan, Zhao, Xiao & Zhou, sp. nov. from Xinglong Town, Anlong County, Guizhou. These four new species can be distinguished from all recognized congeners by a combination of morphological characteristics and significant genetic divergences. The discovery of these species increases the number of known cave species within the genus Triplophysa to 39, making the genus the second most diverse group of cave fishes in China after the golden-line fish genus Sinocyclocheilus. Based on the non-monophyletic relationships of the different watershed systems in the phylogenetic tree, this study also discusses the use of cave species of the genus Triplophysa to determine the possible historical connectivity of river systems.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification.

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research.

Apple consumption affects cecal health by regulating 12S-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid (12(S)-HETE) levels through modifying the microbiota in rats.

Apples are rich in many nutrients and functional components. However, the mechanism of the effect of fresh apple consumption on rats remains unclear. In the present study, fresh apples (10 g kg-1) were added to the diet of Wistar rats, and changes in the microbiota and metabolite content of the cecum were analyzed after 28 days of feeding, and changes in the 12S-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid (12(S)-HETE) content and indicators related to inflammation, oxidative stress, and apoptosis were detected. Subsequently, a fecal microbiota transplantation (FMT) protocol was designed and carried out to verify the relationship between the microbiota and 12(S)-HETE, the cecal structure, and inflammatory factors. The results show that apple consumption significantly reduced the serum levels of alanine aminotransferase (ALT) and immunoglobulin G (IgG), altered the cecal histomorphology, and significantly upregulated the gene expression of claudin-1 and zonula occludens-1 (ZO-1), which encode tight junction proteins. Apple consumption also changed the structure of the cecal microbiota, increasing the abundance of some species (such as Shuttleworthia) and decreasing the abundance of others (such as Alphaproteobacteria). Metabolomic screening identified 64 significantly different metabolites. The FMT results showed that apple consumption reduced 12(S)-HETE metabolite levels in the cecal contents, improved the intestinal structure, and reduced the levels of proinflammatory factor expression by altering the cecal microbiota. In conclusion, this study provides further insight into the effects of apples on animals using rats as experimental animals. It provides basic data for future exploration of the mechanisms of the effect of apple consumption on humans.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

SUMOylation of Smad2 mediates TGF-ß-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-ß) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-ß signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear. Here, we show that Smad2 is predominantly modified by SUMO1 at two major SUMOylation sites with PIAS2α as the primary E3 ligase, whereas SENP1 (sentrin/SUMO-specific protease 1) mediates the deSUMOylation of Smad2. In addition, we identified that SUMOylation significantly enhances the transcriptional activity and protein stability of Smad2, regulating the expression of downstream target genes. SUMOylation increases the phosphorylation of Smad2 and the formation of the Smad2-Smad4 complex, thus promoting the nuclear translocation of Smad2. Ultimately, the wildtype, but not SUMOylation site mutant Smad2 facilitated the EndoMT process. More importantly, TGF-ß enhances the nuclear translocation of Smad2 by enhancing its SUMOylation and promoting the EndoMT process. These results demonstrate that SUMOylation of Smad2 plays a critical role in the TGF-ß-mediated EndoMT process, providing a new theoretical basis for the treatment and potential drug targets of EndoMT-related clinical diseases.

Modular Click Assembly DNA-Encoded Glycoconjugate Libraries with on-DNA Functional Group Transformations.

Carbohydrates are an important class of naturally active products and play vital roles in regulating various physiological activities. To meet the demand for carbohydrate-based libraries used for the identification of potential drug candidates for pharmaceutical-related targets, we developed a set of on-DNA protocols to construct the DNA-encoded glycoconjugates, including Seyferth-Gilbert homologation, anomeric azidation, and CuAAC cyclization. These on-DNA chemistries enable the generation and modification of DNA-linked glycosyl compounds with good conversions and broad substrate scope. Finally, three DNA-linked glycoconjugate libraries were successfully generated to demonstrate their applicability and feasibility in library preparation.

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT. METHODS: One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was applied to the polarmaps of LVMD to extract new predictors characterizing LVMD. Pearson correlation analysis was used to explain the relationships between new predictors and existing clinical parameters. Patients from the IAEA VISION-CRT trial were used for an external validation. Heatmaps were used to interpret the AE-extracted feature. RESULTS: Complete data were obtained in 130 patients, and 68.5% of them were classified as CRT responders. After variable selection by feature importance ranking and correlation analysis, one AE-extracted LVMD predictor was included in the statistical analysis. This new AE-extracted LVMD predictor showed statistical significance in the univariate (OR 2.00, P = .026) and multivariate (OR 1.11, P = .021) analyses, respectively. Moreover, the new AE-extracted LVMD predictor not only had incremental value over PBW and significant clinical variables, including QRS duration and left ventricular end-systolic volume (AUC 0.74 vs 0.72, LH 7.33, P = .007), but also showed encouraging predictive value in the 165 patients from the IAEA VISION-CRT trial (P < .1). The heatmaps for calculation of the AE-extracted predictor showed higher weights on the anterior, lateral, and inferior myocardial walls, which are recommended as LV pacing sites in clinical practice. CONCLUSIONS: AE techniques have significant value in the discovery of new clinical predictors. The new AE-extracted LVMD predictor extracted from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.

The efficacy of the combination of radiofrequency ablation and endoscopic metal stent in the treatment of 44 patients with unresectable cholangiocarcinoma / 中华消化杂志

Objective:To explore the efficacy of the combination of radiofrequency ablation(RFA) and endoscopic metal stent in the treatment of patients with unresectable cholangiocarcinoma.Methods:From January 3, 2012 to June 30, 2019, at the Department of Endoscopic of the Third Affiliated Hospital of Naval Medical University, the clinical data of 44 patients with unresectable cholangiocarcinoma who were treated by the combination of RFA and endoscopic metal stent were retrospectively collected, which included age, gender, location of cholangiocarcinoma(hilar cholangiocarcinoma and distal cholangiocarcinoma), etc. Postoperative evaluation was conducted based on the follow-up, including clinical success rate, postoperative complication rate, time of stent patency and overall survival time (OS). The Kaplan-Meier method and log-rank test were used to analyze the difference of OS between patients with hilar cholangiocarcinoma and distal cholangiocarcinoma. Mann-Whitney U test was used for statistical analysis. Results:The age of the 44 patients with cholangiocarcinoma was (70.3±11.6) years old, with 20 males (45.5%). There were 22 patients (50.0%) with hilar cholangiocarcinoma and 22 patients (50.0%) with distal cholangiocarcinoma. The clinical success rate of 44 patients was 93.2%(41/44). A total of 5 patients(11.4%) had postoperative complications, which were all improved by appropriate treatment. The median time of follow-up of the 44 patient was 9.2 months(ranged from 3.1 to 57.6 months), the median time of stent patency was 7.0 months (ranged from 5.8 to 8.2 months). Thirty-two patients (72.7%) died during the follow-up, and the median OS was 10.9 months(ranged from 9.0 to 12.8 months). The median OS of patients with hilar cholangiocarcinoma was 7.8 months(ranged from 4.6 to 11.0 months) and that of patients with distal cholangiocarcinoma was 12.5 months(ranged from 5.7 to 19.4 months), and there was no statistically significant difference( P>0.05). Conclusion:RFA combined with endoscopic metal stent is safe and effective in the treatment of patients with unresectable cholangiocarcinoma.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring.

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X.

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Inducing Ring Complexation for Efficient Capture and Detection of Small Gaseous Molecules Using SERS for Environmental Surveillance.

Gas-phase surface-enhanced Raman scattering (SERS) remains challenging due to poor analyte affinity to SERS substrates. The reported use of capturing probes suffers from concurrent inconsistent signals and long response time due to the formation of multiple potential probe-analyte interaction orientations. Here, we demonstrate the use of multiple non-covalent interactions for ring complexation to boost the affinity of small gas molecules, SO2 and NO2 , to our SERS platform, achieving rapid capture and multiplex detection down to 100 ppm. Experimental and in-silico studies affirm stable ring complex formation, and kinetic investigations reveal a 4-fold faster response time compared to probes without stable ring complexation capability. By synergizing spectral concatenation and support vector machine regression, we achieve 91.7 % accuracy for multiplex quantification of SO2 and NO2 in excess CO2 , mimicking real-life exhausts. Our platform shows immense potential for on-site exhaust and air quality surveillance.

Air-stable plasmonic bubbles as a versatile three-dimensional surface-enhanced Raman scattering platform for bi-directional gas sensing.

Harnessing large hotspot volumes is key for enhanced gas-phase surface-enhanced Raman scattering (SERS) sensing. Herein, we introduce versatile, air-stable 3D 'Plasmonic bubbles' with bi-directional sensing capabilities. Our Plasmonic bubbles are robust, afford strong and homogenous SERS signals, and can swiftly detect both encapsulated and surrounding 4-methylbenzenethiol vapors.

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution.

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm.

Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min.

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.

[Effects of Butylphthalide on the expressions of HMGB1 and RAGE in frontal lobe of rats after chronic sleep deprivation].

OBJECTIVE: To investigate the effects of Butylphthalide on the expressions of HMGB1 and RAGE in frontal lobe of rats after chronic sleep deprivation. METHODS: Chronic sleep deprivation and butylphthalide treatment was performed in Sprague Dawley(SD)rats and the rats were divided into three groups (n＝6): platform control group, chronic sleep deprivation group and chronic sleep deprivation + butylphthalide intervention group. Rats suffering chronic sleep deprivation were put in multiple platforms box for 18 h per day and sleep deprivation lasted for 28 days. Rats in butylphthalide intervention group were intraperitoneally injected with butylphthalide 100 mg/(kg·d) for 14 days after sleep deprivation. After collecting brains, high-mobility group box (HMGB1) and nuclear transcription factor kappB (NF-κB)p65 were detected by immunohistochemistry. The expression of HMGB1, silent information regulator of transcription 1 (SIRT1), receptor for advanced glycation end-products (RAGE) and NF-κB in frontal lobe were determinated by Western blot. RESULTS: Compared with platform control group, the expression levels of HMGB1, RAGE and nuclear NF-κB p65 were increased significantly, while the expression of SIRT1 was decreased siginificantly in frontal lobe of chronic sleep deprivation group (all P＜0.05). Compared with chronic sleep deprivation group, the expression levels of of HMGB1, RAGE and nuclear NF-κB p65 were decreased significantly, while the expression of SIRT1 was increased significantly in chronic sleep deprivation + butylphthalide intervention group (all P＜0.05). CONCLUSION: Butylphthalide can inhibit HMGB1/RAGE/NF-κB pathway in frontal lobe of rats after chronic sleep deprivation by changing the expression of HMGB1 and RAGE, and reducing the nuclear translocation of NF-κBp65.