Nerve growth factor (Ngf) gene-driven semaphorin 3a (Sema3a) expression exacerbates thoracic aortic aneurysm dissection in mice.

Thoracic aortic aneurysm and dissection (TAAD) is a life-threatening disease and currently there is no pharmacological therapy. Sympathetic nerve overactivity plays an important role in the development of TAAD. Sympathetic innervation is mainly controlled by nerve growth factor (NGF, a key neural chemoattractant) and semaphoring 3A (Sema3A, a key neural chemorepellent), while the roles of these two factors in aortic sympathetic innervation and especially TAAD are unknown. We hypothesized that genetically manipulating the NGF/Sema3A ratio by the Ngf -driven Sema3a expression approach may reduce aortic sympathetic nerve innervation and mitigate TAAD progression. A mouse strain of Ngf gene-driven Sema3a expression (namely NgfSema3a/Sema3a mouse) was established by inserting the 2A-Sema3A expression frame to the Ngf terminating codon using CRISPR/Cas9 technology. TAAD was induced by ß-aminopropionitrile monofumarate (BAPN) both in NgfSema3a/Sema3a mice and wild type (WT) littermates. Contrary to our expectation, the BAPN-induced TAAD was severer in NgfSema3a/Sema3a mice than in wild-type (WT) mice. In addition, NgfSema3a/Sema3a mice showed higher aortic sympathetic innervation, inflammation and extracellular matrix degradation than the WT mice after BAPN treatment. The aortic vascular smooth muscle cells isolated from NgfSema3a/Sema3a mice and pretreated with BAPN in vivo for two weeks showed stronger capabilities of proliferation and migration than that from the WT mice. We conclude that the strategy of Ngf -driven Sema3a expression cannot suppress but worsens the BAPN-induced TAAD. By investigating the aortic phenotype of NgfSema3a/Sema3a mouse strain, we unexpectedly find a path to exacerbate BAPN-induced TAAD which might be useful in future TAAD studies.

Structural abnormality of hepatic glycogen in rat liver with diethylnitrosamine-induced carcinogenic injury.

Growing evidence confirms associations between glycogen metabolic re-wiring and the development of liver cancer. Previous studies showed that glycogen structure changes abnormally in liver diseases such as cystic fibrosis, diabetes, etc. However, few studies focus on glycogen molecular structural characteristics during liver cancer development, which is worthy of further exploration. In this study, a rat model with carcinogenic liver injury induced by diethylnitrosamine (DEN) was successfully constructed, and hepatic glycogen structure was characterized. Compared with glycogen structure in the healthy rat liver, glycogen chain length distribution (CLD) shifts towards a short region. In contrast, glycogen particles were mainly present in small-sized ß particles in DEN-damaged carcinogenic rat liver. Comparative transcriptomic analysis revealed significant expression changes of genes and pathways involved in carcinogenic liver injury. A combination of transcriptomic analysis, RT-qPCR, and western blot showed that the two genes, Gsy1 encoding glycogen synthase and Gbe1 encoding glycogen branching enzyme, were significantly altered and might be responsible for the structural abnormality of hepatic glycogen in carcinogenic liver injury. Taken together, this study confirmed that carcinogenic liver injury led to structural abnormality of hepatic glycogen, which provided clues to the future development of novel drug targets for potential therapeutics of carcinogenic liver injury.

Magnetite-boosted syntrophic conversion of acetate to methane during thermophilic anaerobic digestion.

Using a batch thermophilic anaerobic system established with 60 mL serum bottles, the mechanism on how microbial enrichments obtained from magnetite-amended paddy soil via repeated batch cultivation affected methane production from acetate was investigated. Magnetite-amended enrichments (MAEs) can improve the methane production rate rather than the methane yield. Compared with magnetite-unamended enrichments, the methane production rate in MAE was improved by 50%, concomitant with the pronounced electrochemical response, high electron transfer capacity, and fast acetate degradation. The promoting effects might be ascribed to direct interspecies electron transfer facilitated by magnetite, where magnetite might function as electron conduits to link the acetate oxidizers (Anaerolineaceae and Peptococcaceae) with methanogens (Methanosarcinaceae). The findings demonstrated the potential application of MAE for boosting methanogenic performance during thermophilic anaerobic digestion.

Role of KOH-activated biochar on promoting anaerobic digestion of biomass from Pennisetumgianteum.

Pennisetum giganteum is a promising non-food crop feedstock for biogas production due to its high productivity and bio-methane potential. However, the accumulation of volatile fatty acids (VFA) usually restricts the conversion efficiency of P. giganteum biomass (PGB) during anaerobic digestion (AD). Here, the role of KOH-activated biochar (KB) in improving the AD efficiency of PGB and the related mechanisms were investigated in detail. The results revealed that KB exhibited excellent electrical conductivity, electron transfer capacity and specific capacitance, which might be related to the decrease in the electron transfer resistance after adding KB to the AD process. In addition, the KB addition not only reinforced metabolisms of energy and VFAs but also promoted the conversion of VFAs to methane, leading to a 52% increase in the methane production rate. Bioinformatics analysis showed that Smithella and Methanosaeta were key players in the KB-mediated AD process of PGB. The stimulatory effect of methanogenesis probably resulted from the establishment of direct interspecies electron transfer (DIET) between VFA-oxidizing acetogens (e.g., Smithella) and Methanosaeta. These findings provided a key step to improve the PGB-based AD process.

Agreement of intraocular pressure measurement with Corvis ST, non-contact tonometer, and Goldmann applanation tonometer in children with ocular hypertension and related factors.

AIM: To access the agreement of intraocular pressure (IOP) values obtained from biomechanically corrected tonometer [Corvis ST (CST)], non-contact tonometer (NCT), and Goldmann applanation tonometer (GAT) in children with NCT measured-IOP (NCT-IOP) values of 22 mm Hg or more, and related factors. METHODS: A total of 51 eyes with NCT-IOP≥22 mm Hg in children aged 7 to 14y were examined and IOP was measured by CST, NCT, and GAT. Based on GAT measured IOP (GAT-IOP), ocular hypertension (OHT) group (≥22 mm Hg, 24 eyes) and the non-OHT group (<22 mm Hg, 27 eyes) were defined. We compared the agreement of the three measurements, i.e., CST measured IOP (CST-IOP), GAT-IOP, and NCT-IOP, and further analyzed the correlation between the differences in tonometry readings, central corneal thickness (CCT), axial length (AL), optic disc rim volume, and age. RESULTS: Compared with the OHT group, thicker CCT, larger rim volume, and higher differences between NCT-IOP and GAT-IOP, were found in the non-OHT group. The differences between CST-IOP and GAT-IOP were lower than the differences between NCT-IOP and GAT-IOP in both groups. The mean differences in CST-IOP and GAT-IOP were 1.26 mm Hg (95% limit of agreement ranged from 0.1 to 2.41 mm Hg, OHT group) and 1.20 mm Hg (95% limit of agreement ranged from -0.5 to 3.00 mm Hg, non-OHT group), and the mean differences in NCT and GAT were 3.90 mm Hg (95% limit of agreement ranged from -0.19 to 9.70 mm Hg, OHT group) and 6.00 mm Hg (95% limit of agreement ranged from 1.50 to 10.50 mm Hg, non-OHT group). The differences between CST-IOP and GAT-IOP were not related to CCT, age, and AL in both groups; while the differences between NCT-IOP and GAT-IOP were related to CCT in the OHT group (r=0.93, P<0.001) and to CCT and AL in the non-OHT group (r=0.66, P<0.001, r=-0.81, P<0.001). CONCLUSION: The accuracy of NCT in the diagnosis of pediatric OHT is low. The agreement of CST-IOP and GAT-IOP was significantly higher in children with and without OHT than in those with NCT-IOP and GAT-IOP. Therefore, CST can be used as a good alternative for IOP measurement in children. The impacts of CCT and AL on NCT measurement need to be fully considered when managing childhood IOP.

Molecular mechanisms of glycogen particle assembly in Escherichia coli.

It has been reported that glycogen in Escherichia coli has two structural states, that is, fragility and stability, which alters dynamically. However, molecular mechanisms behind the structural alterations are not fully understood. In this study, we focused on the potential roles of two important glycogen degradation enzymes, glycogen phosphorylase (glgP) and glycogen debranching enzyme (glgX), in glycogen structural alterations. The fine molecular structure of glycogen particles in Escherichia coli and three mutants (ΔglgP, ΔglgX and ΔglgP/ΔglgX) were examined, which showed that glycogen in E. coli ΔglgP and E. coli ΔglgP/ΔglgX were consistently fragile while being consistently stable in E. coli ΔglgX, indicating the dominant role of GP in glycogen structural stability control. In sum, our study concludes that glycogen phosphorylase is essential in glycogen structural stability, leading to molecular insights into structural assembly of glycogen particles in E. coli.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms.

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Fractional extraction and structural characterization of glycogen particles from the whole cultivated caterpillar fungus Ophiocordyceps sinensis.

Ophiocordyceps sinensis (syn. Cordyceps sinensis) is a valuable medicinal fungus in traditional Chinese medicine, and one or more polysaccharides are the key constituents with important medical effects. Glycogen as a functional polysaccharide is widely identified in eukaryotes including fungi. However, there is no definitive report of glycogen presence in O. sinensis. In this study, we carefully fractionated polysaccharides from cultivated caterpillar fungus O. sinensis, which were then characterized via methods for glycogen analysis. According to the results, 1.03 ± 0.43 % of polysaccharides were quantified via amyloglucosidase digestion in the whole cultivated caterpillar fungus, which had a typical spherical shape under transmission electron microscope with an average peak radius of 37.63 ± 0.57 nm via size exclusion chromatography and an average chain length of 12.47 ± 0.94 degree of polymerization via fluorophore-assisted capillary electrophoresis. Taken together, this study confirmed that the polysaccharides extracted form O. sinensis were mostly glycogen.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.

BACKGROUND: The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential. AIM OF REVIEW: Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings. KEY SCIENTIFIC CONCEPTS OF REVIEW: The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

Nano magnetite-loaded biochar boosted methanogenesis through shifting microbial community composition and modulating electron transfer.

A batch anaerobic fermentation system was employed to clarify how nano magnetite-loaded biochar can improve methanogenic performance of the propionate-degrading consortia (PDC). The nano magnetite-loaded biochar was prepared in a sequential hydrothermal and pyrolysis procedure using the household waste (HW), biogas residue (BR) and Fe (NO3)3 as pristine materials. Comprehensive characterization showed that the nano magnetite-loaded biochar ameliorated the biochar properties with large specific surface area, high electrochemical response and low electron transfer resistance. PDC supplemented with the magnetite/BR-originated biochar composites displayed excellent methanogenic performance, where the methane production rate was enhanced by 1.6-fold compared with the control. The nano magnetite-loaded biochar promoted methane production probably by promoting direct interspecies electron transfer between syntrophic bacteria (e.g., Syntrophobacter and Thauera) and their partners (e.g., Methanosaeta). In this process, magnetite might be responsible for triggering rapidly extracellular electron release, whereas both external functional groups and intrinsic graphitic matrices of biochar might work as electron bridges for electron transport.

Research progress on vegetation restoration of road slopes in China.

China is the largest country in road construction due to rapid economy growth, which results in a large number of exposed slopes. Vegetation restoration of these road slopes has become the dominant method in ecological restoration. We reviewed research progress from three aspects, including key technologies for road slope vegetation restoration, application of vegetation restoration engineering, and factors influencing the vegetation restoration efforts. The slope protection technologies commonly used in road slope vegetation restoration include soil spraying technology, vegetation concrete slope protection technology, thick base material technology, and hydraulic spraying technology. In engineering applications, slope vegetation has the functions such as soil and water conservation, air purification, and landscape restoration. Currently, the most common community configuration is shrub and grass configuration. The main influencing factors of vegetation restoration on road slopes are climate, soil substrate, slope direction, plant species and community configuration used, human factors, and other natural factors (such as hydrology, altitude, microtopography, and wildlife). Future researches should focus on the mechanisms of different factors affecting road slope vegetation restoration, and study ecological substrates and slope protection technologies, plant species and diverse community configuration models suitable for road slope restoration in different climatic regions and site conditions.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms.

The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. IMPORTANCE In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. Taken together, we concluded that the SERS technique held a promising potential for fast bacterial pathogen diagnosis in clinical laboratories with the integration of deep learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.

Clinical implications of interleukins-31, 32, and 33 in gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignancies in China with a high morbidity and mortality. AIM: To determine whether interleukin (IL)-31, IL-32, and IL-33 can be used as biomarkers for the detection of GC, via evaluating the correlations between their expression and clinicopathological parameters of GC patients. METHODS: Tissue array (n = 180) gastric specimens were utilised. IL-31, IL-32, and IL-33 expression in GC and non-GC tissues was detected immunohistochemically. The correlations between IL-31, IL-32, and IL-33 expression in GC and severity of clinicopathological parameters were evaluated. Survival curves were plotted using the Kaplan-Meier method/Cox regression. Circulating IL-31, IL-32, and IL-33 were detected by ELISA. RESULTS: We found that the expression levels of IL-31, IL-32, and IL-33 were all lower in GC than in adjacent non-GC gastric tissues (P < 0.05). IL-33 in peripheral blood of GC patients was significantly lower than that of healthy individuals (1.50 ± 1.11 vs 9.61 ± 8.00 ng/mL, P <0.05). Decreased IL-31, IL-32, and IL-33 in GC were observed in younger patients (< 60 years), and IL-32 and IL-33 were lower in female patients (P < 0.05). Higher IL-32 correlated with a longer survival in two GC subgroups: T4 invasion depth and TNM I-II stage. Univariate/multivariate analysis revealed that IL-32 was an independent prognostic factor for GC in the T4 stage subgroup. Circulating IL-33 was significantly lower in GC patients at TNM stage IV than in healthy people (P < 0.05). CONCLUSION: Our findings may provide new insights into the roles of IL-31, IL-32, and IL-33 in the carcinogenesis of GC and demonstrate their relative usefulness as prognostic markers for GC. The underlying mechanism of IL-31, IL-32, and IL-33 actions in GC should be further explored.

Comparative transcriptome analysis of diurnal alterations of liver glycogen structure: A pilot study.

Molecular mechanisms behind structural alterations between fragile and stable glycogen α particles in liver are not clear yet. In this pilot study, we re-examined the diurnal alterations of glycogen structure from the perspective of liver tissue transcriptome. By comparing the structures of liver glycogen from mice at 12 am, 8 am, 12 pm, and 8 pm (light-on: 6 am; light-off: 6 pm), we re-confirmed that the liver glycogen was fragile at 12 am and 8 am and stable at 12 pm and 8 pm as previously reported. The structural differences of glycogen particles at 12 am and 12 pm were thoroughly compared via transcriptomics. Differentially expressed genes (DEGs) with statistical significance were identified, while expression level of the gene ppp1r3g (log2Fold_Change = -6.368, P-value = 2.89E-04) that encoded PPP1R3G with glycogen binding domain was most significantly changed, which provided preliminary clues to the structural alterations of glycogen α particles during the diurnal cycle.

Global profiling of protein lysine malonylation in mouse cardiac hypertrophy.

Lysine malonylation, a novel identified protein posttranslational modification (PTM), is conservative and present in both eukaryotic and prokaryotic cells. Previous studies have reported that malonylation plays an important role in inflammation, angiogenesis, and diabetes. However, its potential role in cardiac remodeling remains unknown. Here, we observed a reduced lysine malonylation in hypertrophic mice hearts created by transverse aortic constriction (TAC) for 8 weeks. We also detected a decreased lysine malonylation in hypertrophic H9C2 cardiomyocytes induced by angiotensin II for 48 h. Using a proteomic method based on affinity purification and LC-MS/MS, we identified total 679 malonylated sites in 330 proteins in the hearts of sham mice and TAC mice. Bioinformatic analysis of the proteomic data revealed enrichment of malonylated proteins involved in cardiac structure and contraction, cGMP-PKG pathway, and metabolism. Specifically, we detected a decreased lysine malonylation in myocardial isocitrate dehydrogenase 2 (IDH2) by immunoprecipitation coupled with Western blotting both in vivo and in vitro. Together, our work suggests an important role and implication of protein lysine malonylation in cardiac hypertrophy, especially the IDH2. SIGNIFICANCE: Heart failure is the terminal stage of cardiac hypertrophy, which imposes an enormous clinical and economic burden worldwide. Despite our knowledge on the pathophysiology of the disease, current therapeutic approaches are still largely limited. Cardiac hypertrophy can be regulated at post-translational modifications (PTMs), and several PTMs have been reported in cardiac hypertrrophy and heart failure. In our study, we first reported a novel PTMs, lysine malonylation, in cardiac hypertophy. we found a reduced lysine malonylation in hypertrophic mice hearts in vivo and H9C2 cardiomyocytes after stimulating with angiotensinII for 48 h in vitro. Using affinity purification and LC-MS/MS, we identified 679 malonylated sites in 330 proteins in the hearts of sham and TAC mice. Compared to the sham group, 5 sites in 2 proteins were quantified as downregulated targets using a 2-fold threshold (downregulation <0.5-fold, P < 0.05). Functional analysis showed a significant enrichment in cardiac structure and contraction, cGMP-PKG pathway and metabolism. Notably, we identified a decreased Kmal level in isocitrate dehydrogenase 2 (IDH2), but the protein level of IDH2 has no changed in cardiac hypertrophy, These results highlight that lysine malonylation is associated with cardiac hypertrophy, and may be a new therapeutic target of the disease.

Cytoplasmic expression of G protein-coupled estrogen receptor 1 correlates with poor postoperative prognosis in non-small cell lung cancer.

Background: A hormonal role in the development of non-small cell lung cancer (NSCLC) has been well documented, and the classic estrogen receptors (ERs)-ERα and ERß have been extensively investigated over the past decade. The expression of ERß was found to be high and display biological activity in NSCLC, but anti-estrogen therapy targeting this receptor has shown limited efficacy for the disease. The third estrogen receptor, G protein-coupled estrogen receptor 1 (GPER1/GPR30), was recently found to be highly expressed in NSCLC. Herein, we aimed to investigate the expression profile of GPER1 and correlate it with clinicopathological factors as well as postoperative prognosis in NSCLC. Methods: We examined GPER1 and ERß expression using immunohistochemistry among 183 NSCLC cases, including 132 lung adenocarcinoma (LUAD) with identified epidermal growth factor receptor (EGFR) mutation status and 51 squamous cell carcinoma (SCC) patients. We then conducted correlation analysis between the expression of GPER1 and clinicopathological factors and patients' postoperative prognosis. Results: Positive expression of GPER1 was categorized into 2 main classes: nuclei-GPER1 (nGPER1) and concurrent nuclei-and cytoplasm-GPER1 (n/cGPER1), according to its subcellular localization. The LUAD with wild-type EGFR (wt-EGFR) had a higher frequency of n/cGPER1 (50%) but a lower frequency of nGPER1 (31%) when compared with those with mutated EGFR (n/cGPER1: 31%, nGPER1: 41%, respectively). The expression of GPER1, regardless of subcellular localization, was positively correlated with tumor stage and lymph node metastasis. The median recurrence-free survival (mRFS) and overall survival (OS) were significantly worse in participants with n/cGPER1 expression than in those with nGPER1 or without GPER1 expression. Conclusions: This study revealed that GPER1 is aberrantly highly expressed and presents a unique GPER1 expression profile in NSCLC. The n/cGPER1 expression was significantly associated with EGFR mutation status, tumor stage, lymph node metastasis, and poor postoperative prognosis in NSCLC.

Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra.

With its low-cost, label-free and non-destructive features, Raman spectroscopy is becoming an attractive technique with high potential to discriminate the causative agent of bacterial infections and bacterial infections per se. However, it is challenging to achieve consistency and accuracy of Raman spectra from numerous bacterial species and phenotypes, which significantly hinders the practical application of the technique. In this study, we analyzed surfaced enhanced Raman spectra (SERS) through machine learning algorithms in order to discriminate bacterial pathogens quickly and accurately. Two unsupervised machine learning methods, K-means Clustering (K-Means) and Agglomerative Nesting (AGNES) were performed for clustering analysis. In addition, eight supervised machine learning methods were compared in terms of bacterial predictions via Raman spectra, which showed that convolutional neural network (CNN) achieved the best prediction accuracy (99.86%) with the highest area (0.9996) under receiver operating characteristic curve (ROC). In sum, machine learning methods can be potentially applied to classify and predict bacterial pathogens via Raman spectra at general level.