Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model

INTRODUCTION

Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture.

OBJECTIVES

In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components.

METHODS

We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method.

RESULTS

The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture.

CONCLUSION

Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

[Evidence mapping of Chinese patent medicines in treatment of premature ventricular contractions]

This study employed evidence mapping to systematically sort out the clinical studies about the treatment of premature ventricular contractions with Chinese patent medicines and to reveal the distribution of evidence in this field. The articles about the treatment of premature ventricular contractions with Chinese patent medicines were searched against PubMed, Cochrane Library, Web of Science, CNKI, Wanfang, and VIP with the time interval from January 2016 to December 2022. Evidence was analyzed and presented by charts and graphs combined with text. According to the inclusion and exclusion criteria, 164 papers were included, including 147 interventional studies, 4 observational studies, and 13 systematic reviews. A total of 27 Chinese patent medicines were involved, in which Shensong Yangxin Capsules and Wenxin Granules had high frequency. There were off-label uses in clinical practice. In recent years, the number of articles published in this field showed a decreasing trend. Eight types of outcome indicators were used in interventional studies. Ambulatory electrocardiography, clinical response rate, safety, and echocardiography had high frequency, while the rate of β-blocker decompensation, major cardiovascular events, and pharmaceutical economic indicators were rarely reported. The evaluation was one-sided. The low quality of the included articles reduced the reliability of the findings. In the future, the clinical use of medicines should be standardized, and the quality of clinical studies should be improved. Comprehensive clinical evaluation should be carried out to provide a sound scientific basis for the treatment of premature ventricular contractions with Chinese patent medicines.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

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.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

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.

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 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.

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

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.

Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks

Some infectious diseases, including COVID-19, can undergo airborne transmission. This may happen at close proximity, but as time indoors increases, infections can occur in shared room air despite distancing. We propose two indicators of infection risk for this situation, that is, relative risk parameter (H_r) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and aerosol-removal rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend that is consistent with airborne infection and enable recommendations to minimize transmission risk. Transmission in typical prepandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, influenza, and tuberculosis were also assessed. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at higher risk parameter values. Because both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on masking, ventilation and aerosol-removal rates, number of occupants, and duration of exposure, to investigate airborne transmission.

Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks

Some infectious diseases, including COVID-19, can undergo airborne transmission. This may happen at close proximity, but as time indoors increases, infections can occur in shared room air despite distancing. We propose two indicators of infection risk for this situation, that is, relative risk parameter (H_r) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and aerosol-removal rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend that is consistent with airborne infection and enable recommendations to minimize transmission risk. Transmission in typical prepandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, influenza, and tuberculosis were also assessed. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at higher risk parameter values. Because both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on masking, ventilation and aerosol-removal rates, number of occupants, and duration of exposure, to investigate airborne transmission.

Serum anti-CRP antibodies differentiate etiology and predict relapse in acute tubulointerstitial nephritis

INTRODUCTION

Acute tubulointerstitial nephritis (ATIN) is a common cause of acute kidney injury with various etiologies. It has been shown that autoimmune-related ATIN (AI-ATIN) has a higher recurrence rate and a greater likelihood of developing into chronic kidney disease compared with drug-induced ATIN, yet misdiagnosis at renal biopsy is not uncommon.

METHODS

Patients who were clinicopathologically diagnosed as ATIN from January 2006 to December 2015 in Peking University First Hospital were enrolled. Clinical, pathological and follow-up data were collected. Serum samples on the day of renal biopsy were collected and tested for anti-C-reactive protein (CRP) antibodies. CRP and its linear peptides were used as coating antigens to detect antibodies. Statistical analysis was used to assess the diagnostic value of the antibodies.

RESULTS

Altogether 146 patients were enrolled. The receiver operating characteristic-area under the curve of the anti-CRP antibody for the identification of late-onset AI-ATIN was 0.750 (95% confidence interval 0.641-0.860, P < 0.001) and the positivity was associated with ATIN relapse (adjusted hazard ratio = 4.321, 95% confidence interval 2.402-7.775, P < 0.001). Antibodies detected by CRP linear peptide 6 (PT6) were superior with regard to differentiating patients with AI-ATIN, while antibodies detected by peptide 17 (PT17) could predict ATIN relapse. Antibodies detected by these two peptides were positively correlated with the severity of tubular dysfunction and pathological injury.

CONCLUSIONS

Serum anti-CRP antibody could be used to differentiate late-onset AI-ATIN and predict relapse of ATIN at the time of renal biopsy. The CRP linear peptides PT6 and PT17 could be used as coating antigens to detect anti-CRP antibodies, which may provide more information for the clinical assessment of ATIN.

Effects of NaCl Concentrations on Growth Patterns, Phenotypes Associated With Virulence, and Energy Metabolism in Escherichia coli BW25113

According to the sit-and-wait hypothesis, long-term environmental survival is positively correlated with increased bacterial pathogenicity because high durability reduces the dependence of transmission on host mobility. Many indirectly transmitted bacterial pathogens, such as Mycobacterium tuberculosis and Burkhoderia pseudomallei, have high durability in the external environment and are highly virulent. It is possible that abiotic stresses may activate certain pathways or the expressions of certain genes, which might contribute to bacterial durability and virulence, synergistically. Therefore, exploring how bacterial phenotypes change in response to environmental stresses is important for understanding their potentials in host infections. In this study, we investigated the effects of different concentrations of salt (sodium chloride, NaCl), on survival ability, phenotypes associated with virulence, and energy metabolism of the lab strain Escherichia coli BW25113. In particular, we investigated how NaCl concentrations influenced growth patterns, biofilm formation, oxidative stress resistance, and motile ability. In terms of energy metabolism that is central to bacterial survival, glucose consumption, glycogen accumulation, and trehalose content were measured in order to understand their roles in dealing with the fluctuation of osmolarity. According to the results, trehalose is preferred than glycogen at high NaCl concentration. In order to dissect the molecular mechanisms of NaCl effects on trehalose metabolism, we further checked how the impairment of trehalose synthesis pathway (otsBA operon) via single-gene mutants influenced E. coli durability and virulence under salt stress. After that, we compared the transcriptomes of E. coli cultured at different NaCl concentrations, through which differentially expressed genes (DEGs) and differential pathways with statistical significance were identified, which provided molecular insights into E. coli responses to NaCl concentrations. In sum, this study explored the in vitro effects of NaCl concentrations on E. coli from a variety of aspects and aimed to facilitate our understanding of bacterial physiological changes under salt stress, which might help clarify the linkages between bacterial durability and virulence outside hosts under environmental stresses.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Sarsasapogenin restores podocyte autophagy in diabetic nephropathy by targeting GSK3β signaling pathway

Podocyte injury following abnormal podocyte autophagy plays an indispensable role in diabetic nephropathy (DN), therefore, restoration of podocyte autophagy is considered as a feasible strategy for the treatment of DN. Here, we investigated the preventive effects of sarsasapogenin (Sar), the main active ingredient in Anemarrhena asphodeloides Bunge, on the podocyte injury in diabetic rats, and tried to illustrate the mechanisms underlying the effects in high glucose (HG, 40 mM)-treated podocytes (MPs). Diabetes model was established in rats with single streptozocin (60 mg· kg^-1) intraperitoneal administration. The rats were then treated with Sar (20, 60 mg· kg^-1· d^-1, i.g.) or a positive control drug insulin (INS) (40 U· kg^-1· d^-1, i.h.) for 10 weeks. Our results showed that both Sar and insulin precluded the decreases of autophagy-related proteins (ATG5, Beclin1 and LC3B) and podocyte marker proteins (podocin, nephrin and synaptopodin) in the diabetic kidney. Furthermore, network pharmacology was utilized to assess GSK3β as the potential target involved in the action of Sar on DN and were substantiated by significant changes of GSK3β signaling in the diabetic kidney. The underlying protection mechanisms of Sar were explored in HG-treated MPs. Sar (20, 40 μM) or insulin (50 mU/L) significantly increased the expression of autophagy- related proteins and podocyte marker proteins in HG-treated MPs. Furthermore, Sar or insulin treatment efficiently regulatedphosphorylation at activation and inhibition sites of GSK3β. To sum up, this study certifies that Sar meliorates experimental DN through targeting GSK3β signaling pathway and restoring podocyte autophagy.

Ethanol extract of mulberry leaves partially restores the composition of intestinal microbiota and strengthens liver glycogen fragility in type 2 diabetic rats

BACKGROUND

Mulberry leaf as a traditional Chinese medicine is able to treat obesity, diabetes, and dyslipidemia. It is well known that diabetes leads to intestinal microbiota dysbiosis. It is also recently discovered that liver glycogen structure is impaired in diabetic animals. Since mulberry leaves are able to improve the diabetic conditions through reducing blood glucose level, it would be interesting to investigate whether they have any positive effects on intestinal microbiota and liver glycogen structure.

METHODS

In this study, we first determined the bioactive components of ethanol extract of mulberry leaves via high-performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC/MS). Murine animal models were divided into three groups, normal Sprague-Dawley (SD) rats, high-fat diet (HFD) and streptozotocin (STZ) induced type 2 diabetic rats, and HFD/STZ-induced rats administered with ethanol extract of mulberry leaves (200 mg/kg/day). Composition of intestinal microbiota was analyzed via metagenomics by sequencing the V3-V4 region of 16S rDNAs. Liver glycogen structure was characterized through size exclusion chromatography (SEC). Both Student's t-test and Tukey's test were used for statistical analysis.

RESULTS

A group of type 2 diabetic rat models were successfully established. Intestinal microbiota analysis showed that ethanol extract of mulberry leaves could partially change intestinal microbiota back to normal conditions. In addition, liver glycogen was restored from fragile state to stable state through administration of ethanol extract of mulberry leaves.

CONCLUSIONS

This study confirms that the ethanol extract of mulberry leaves (MLE) ameliorates intestinal microbiota dysbiosis and strengthens liver glycogen fragility in diabetic rats. These finding can be helpful in discovering the novel therapeutic targets with the help of further investigations.

From Prokaryotes to Eukaryotes: Insights Into the Molecular Structure of Glycogen Particles

Glycogen is a highly-branched polysaccharide that is widely distributed across the three life domains. It has versatile functions in physiological activities such as energy reserve, osmotic regulation, blood glucose homeostasis, and pH maintenance. Recent research also confirms that glycogen plays important roles in longevity and cognition. Intrinsically, glycogen function is determined by its structure that has been intensively studied for many years. The recent association of glycogen α-particle fragility with diabetic conditions further strengthens the importance of glycogen structure in its function. By using improved glycogen extraction procedures and a series of advanced analytical techniques, the fine molecular structure of glycogen particles in human beings and several model organisms such as Escherichia coli, Caenorhabditis elegans, Mus musculus, and Rat rattus have been characterized. However, there are still many unknowns about the assembly mechanisms of glycogen particles, the dynamic changes of glycogen structures, and the composition of glycogen associated proteins (glycogen proteome). In this review, we explored the recent progresses in glycogen studies with a focus on the structure of glycogen particles, which may not only provide insights into glycogen functions, but also facilitate the discovery of novel drug targets for the treatment of diabetes mellitus.

Dismantling myths on the airborne transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)

The coronavirus disease 2019 (COVID-19) pandemic has caused untold disruption throughout the world. Understanding the mechanisms for transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is key to preventing further spread, but there is confusion over the meaning of ‘airborne’ whenever transmission is discussed. Scientific ambivalence originates from evidence published many years ago which has generated mythological beliefs that obscure current thinking. This article collates and explores some of the most commonly held dogmas on airborne transmission in order to stimulate revision of the science in the light of current evidence. Six ‘myths’ are presented, explained and ultimately refuted on the basis of recently published papers and expert opinion from previous work related to similar viruses. There is little doubt that SARS-CoV-2 is transmitted via a range of airborne particle sizes subject to all the usual ventilation parameters and human behaviour. Experts from specialties encompassing aerosol studies, ventilation, engineering, physics, virology and clinical medicine have joined together to produce this review to consolidate the evidence for airborne transmission mechanisms, and offer justification for modern strategies for prevention and control of COVID-19 in health care and the community.

[The mechanism of hyperbaric oxygen regulating HMGB1 in the prevention and treatment of encephalopathy after acute CO poisoning]

Objective: To study the expression of high mobility group protein 1 (HMGB1) in the brain of rats after hyperbaric oxygen (HBO) treatment of acute carbon monoxide poisoning (DEACMP) , and to explore the mechanism of HBO in the prevention and treatment of DEACMP pathological process by regulating HMGB1. Methods: 108 SD rats were randomly divided into control group (NC group) and co group (CO group) . HBO treatment group (HBO group) , 48 rats in each group. Co group and HBO group were used to establish CO poisoning model, HBO group were treated with hyperbaric oxygen once a day. Water maze test was used to detect and analyze the memory retention ability of three groups of rats in 3 d, 7 d, 14 d. ELISA was used to detect the plasma concentration of HMGB1、IL-6、TNF-α in three groups of rats on the 1 d, 3 d, 7 d, 14 d, 21 d Concentration. Western blotting was used to detect the expression of HMGB1 and Caspase-3 in the brain of the three groups on the 1 d, 3 d, 7 d, 14 d, 21 d. TUNEL staining was used to detect the apoptosis of hippocampal neurons in the three groups. Results: Compared with NC group, the average escape latency of rats in CO group and HBO group was significantly prolonged, and the activity time of platform quadrant in CO group was significantly shortened on 14 d and 21 d (P<0.05) ; compared with CO group, the average escape latency of HBO group on 7 d, 14 d and 21 d was significantly shortened (P<0.05) . Compared with NC group, plasma HMGB1 in CO group and HBO group were significantly increased (P<0.05) ; after 3 days, HBO group was significantly lower than co group, the difference was statistically significant (P<0.05) . The levels of IL-6 and TNF-α in HBO group and co group increased rapidly and then decreased gradually. The increased levels of IL-6 and TNF-α in HBO group were significantly lower than those in CO group (P<0.05) . Compared with NC group, the expression of HMGB1 and Caspase-3 in CO group was significantly increased on 3 d, 7 d and 14 d (P<0.05) ; the expression of HMGB1 and Caspase-3 in HBO group was significantly increased on 3 d, 7 d, 14 d and 21 d (P<0.05) ; compared with CO group, the expression of HMGB1 and Caspase-3 in HBO group decreased significantly on 3 d, 7 d, 14 d and 21 d (P<0.05) . The apoptotic index of nerve cells in CO group began to increase at 3 days, which was significantly different from that of NC group (P<0.05) , and the difference was still statistically significant on 21 d (P<0.05) ; the apoptotic index of nerve cells in HBO group was slightly increased, but there was no significant difference compared with NC group (P>0.05) , and the apoptotic index of 3 d, 7 d, 14 d and 21 d in HBO group was significantly lower than that in CO group (P<0.05) . Conclusion: acute CO poisoning can induce the release of HMGB1 and a variety of inflammatory factors. HMGB1 can promote the apoptosis of nerve cells after acute CO poisoning by up regulating the expression of caspase-3 protein, and participate in the pathological process of DEACMP. HBO can down regulate the expression of HMGB1, IL-6, TNF-α and caspase-3 protein, inhibit the apoptosis of nerve cells, and play a protective role on nerve cells.

CRISPR/Cas9-Based Genome Editing in the Filamentous Fungus Glarea lozoyensis and Its Application in Manipulating gloF

Glarea lozoyensis is an important industrial fungus that produces the pneumocandin B0, which is used for the synthesis of antifungal drug caspofungin. However, because of the limitations and complications of traditional genetic tools, G. lozoyensis strain engineering has been hindered. In this study, we established an efficient CRISPR/Cas9-based gene editing tool in G. lozoyensis SIPI1208. With this method, gene mutagenesis efficiency in the target locus can be up to 80%, which enables the rapid gene knockout. According to the reports, GloF and Ap-HtyE, proline hydroxylases involved in pneumocandin and Echinocandin B biosynthesis, respectively, can catalyze the proline to generate different ratios of trans-3-hydroxy-l-proline to trans-4-hydroxy-l-proline. Heterologous expression of Ap-HtyE in G. lozoyensis decreased the ratio of pneumocandin C0 to (pneumocandin B0 + pneumocandin C0) from 33.5% to 11% without the addition of proline to the fermentation medium. Furthermore, the gloF was replaced by ap-htyE to study the production of pneumocandin C0. However, the gene replacement has been hampered by traditional gene tools since gloF and gloG, two contiguous genes indispensable in the biosynthesis of pneumocandins, are cotranscribed into one mRNA. With the CRISPR/Cas9 strategy, ap-htyE was knocked in and successfully replaced gloF, and results showed that the knock-in strain retained the ability to produce pneumocandin B0, but the production of pneumocandin C0 was abolished. Thus, this strain displayed a competitive advantage in the industrial production of pneumocandin B0. In summary, this study showed that the CRISPR/Cas9-based gene editing tool is efficient for manipulating genes in G. lozoyensis.

Diagnostic roles of urinary kidney injury molecule 1 and soluble C5b-9 in acute tubulointerstitial nephritis

Acute tubulointerstitial nephritis (ATIN) is a common cause of acute kidney injury characterized by inflammatory cells infiltrating in the interstitium. The present study aimed to explore noninvasive biomarkers that might indicate activity of pathological injuries and help direct treatment. Fifty-four patients with clinical-pathologically diagnosed ATIN from January 1, 2014, to June 30, 2016, at Peking University First Hospital were enrolled. Urine samples were collected on the morning of renal biopsy and assessed for urinary kidney injury molecule-1 (KIM-1) and urinary soluble C5b-9 (sC5b-9). Immunofluorescence staining for KIM-1 and C5b-9 was performed in biopsied kidney sections from ATIN cases. The clinical and pathological relevance of the two urinary biomarkers was analyzed. Both urinary KIM-1 and sC5b-9 values were significantly elevated in patients with ATIN compared with healthy controls. The urinary KIM-1 level positively correlated with urinary N-acetyl-β-d-glucosaminidase (r = 0. 542, P = 0.001) and the pathological tubular injury score (r = 0.469, P < 0.001), whereas the urinary sC5b-9 level was related to pathological activity scores for tubular injury (r = 0.413, P = 0.002), interstitial inflammation (r = 0.388, P = 0.004), and treatment response (r = 0.564, P < 0.001). Urinary KIM-1 tended to have better diagnostic value for tubular injury than urinary sC5b-9, whereas only urinary sC5b-9 was able to demonstrate severe interstitial inflammation. A combination of urinary KIM-1 and sC5b-9 had an area under the receiver-operating characteristic curve of 0.864 (95% confidence interval: 0.766-0.963, P < 0.001, sensitivity: 75%, specificity: 88%) for acute tissue injury in ATIN. KIM-1 expression was markedly increased in renal tubular cells in both ATIN and acute tubular necrosis conditions, whereas a significant upregulation of C5b-9 was only detected in the tubular cells and interstitial cells in ATIN cases. Urinary KIM-1 is a specific biomarker for renal tubular injury in ATIN, whereas urinary sC5b-9 is valuable in demonstrating severe interstitial inflammation. The combination of these two biomarkers helps identify patients at an acute injury stage and, therefore, might facilitate clinical evaluation and guide immunosuppressive therapy.

Evaluating the aptima HIV-1 quant Dx, HCV quant Dx and HBV quant assays against the Abbott HIV-1, HCV and HBV RealTime assays

BACKGROUND

Viral load measurement is routine for the management of patients infected with HIV, HCV or HBV, using sensitive, quantitative, nucleic acid amplification tests (NAATs). However, platforms are not equivalent in terms of turnaround time, random access capability and operator hands-on time.

OBJECTIVES AND STUDY DESIGN

We compared the performance of the Hologic Panther transcription-mediated amplification based Aptima assays for HIV, HBV and HCV viral load measurement with the corresponding Abbott m2000 RealTime assays. All Aptima assays were run according to the manufacturer's instructions, on archived patient samples for HIV (n = 251 including subtypes 01_AE, A (A1), B, BG recombinant, C CRF02_AG and G), HBV (n = 117 including genotypes A, B, C and D) and HCV (n = 82 including genotypes 1, 2, 3 and 4). Additional testing was performed with WHO international standards and expanded HIV-1 subtype and HCV NIBSC genotype panels.

RESULTS

The Hologic Panther Aptima assays for HIV-1 RNA, HBV DNA and HCV RNA viral load measurements performed equivalently to the Abbott m2000 RealTime assays. The performance of the Aptima assays was also acceptable on the WHO and NIBSC standards and subtype/genotype panels.

CONCLUSIONS

The Aptima Panther platform offers equivalent clinical performance for the viral load measurement of these three viruses, with the added benefits of reduced turnaround time, random access capability and reduced 'hands-on time' for staff, resulting in a potentially superior workflow for diagnostic laboratories.

Resource impact of managing suspected Middle East respiratory syndrome patients in a UK teaching hospital

Clinical challenges exist in the management of hospitalized patients returning to the UK with potential Middle East respiratory syndrome coronavirus (MERS-CoV) infection, particularly with its clinical overlap with influenza, as demonstrated in this case-series and cost-analysis review of returning Hajj pilgrims. These patients were hospitalized with acute febrile respiratory illness, initially managed as potential MERS-CoV infections, but were eventually diagnosed with influenza. Additional costs were small, yet enhanced infection prevention measures created significant burdens on isolation rooms and staff time. Planning for predictable events such as Hajj is important for resource management. Here, in-house MERS-CoV diagnostic testing would have facilitated earlier diagnosis and discharge.

Systemic pro-inflammatory response facilitates the development of cerebral edema during short hypoxia

BACKGROUND

High-altitude cerebral edema (HACE) is the severe type of acute mountain sickness (AMS) and life threatening. A subclinical inflammation has been speculated, but the exact mechanisms underlying the HACE are not fully understood.

METHODS

Human volunteers ascended to high altitude (3860 m, 2 days), and rats were exposed to hypoxia in a hypobaric chamber (5000 m, 2 days). Human acute mountain sickness was evaluated by the Lake Louise Score (LLS), and plasma corticotrophin-releasing hormone (CRH) and cytokines TNF-α, IL-1β, and IL-6 were measured in rats and humans. Subsequently, rats were pre-treated with lipopolysaccharide (LPS, intraperitoneal (ip) 4 mg/kg, 11 h) to induce inflammation prior to 1 h hypoxia (7000 m elevation). TNF-α, IL-1β, IL-6, nitric oxide (NO), CRH, and aquaporin-4 (AQP4) and their gene expression, Evans blue, Na(+)-K(+)-ATPase activity, p65 translocation, and cell swelling were measured in brain by ELISA, Western blotting, Q-PCR, RT-PCR, immunohistochemistry, and transmission electron micrography. MAPKs, NF-κB pathway, and water permeability of primary astrocytes were demonstrated. All measurements were performed with or without LPS challenge. The release of NO, TNF-α, and IL-6 in cultured primary microglia by CRH stimulation with or without PDTC (NF-κB inhibitor) or CP154,526 (CRHR1 antagonist) were measured.

RESULTS

Hypobaric hypoxia enhanced plasma TNF-α, IL-1β, and IL-6 and CRH levels in human and rats, which positively correlated with AMS. A single LPS injection (ip, 4 mg/kg, 12 h) into rats increased TNF-α and IL-1β levels in the serum and cortex, and AQP4 and AQP4 mRNA expression in cortex and astrocytes, and astrocyte water permeability but did not cause brain edema. However, LPS treatment 11 h prior to 1 h hypoxia (elevation, 7000 m) challenge caused cerebral edema, which was associated with activation of NF-κB and MAPKs, hypoxia-reduced Na(+)-K(+)-ATPase activity and blood-brain barrier (BBB) disruption. Both LPS and CRH stimulated TNF-α, IL-6, and NO release in cultured rat microglia via NF-κB and cAMP/PKA.

CONCLUSIONS

Preexisting systemic inflammation plus a short severe hypoxia elicits cerebral edema through upregulated AQP4 and water permeability by TLR4 and CRH/CRHR1 signaling. This study revealed that both infection and hypoxia can cause inflammatory response in the brain. Systemic inflammation can facilitate onset of hypoxic cerebral edema through interaction of astrocyte and microglia by activation of TLR4 and CRH/CRHR1 signaling. Anti-inflammatory agents and CRHR1 antagonist may be useful for prevention and treatment of AMS and HACE.

Influenza virus survival in aerosols and estimates of viable virus loss resulting from aerosolization and air-sampling

Using a Collison nebulizer, aerosols of influenza (A/Udorn/307/72 H3N2) were generated within a controlled experimental chamber, from known starting virus concentrations. Air samples collected after variable suspension times were tested quantitatively using both plaque and polymerase chain reaction assays, to compare the proportion of viable virus against the amount of detectable viral RNA. These experiments showed that whereas influenza RNA copies were well preserved, the number of viable viruses decreased by a factor of 10(4)-10(5). This suggests that air-sampling studies for assessing infection control risks that detect only influenza RNA may greatly overestimate the amount of viable virus available to cause infection.

Inactivation of corticotropin-releasing hormone-induced insulinotropic role by high-altitude hypoxia

We have shown that hypoxia reduces plasma insulin, which correlates with corticotropin-releasing hormone (CRH) receptor 1 (CRHR1) in rats, but the mechanism remains unclear. Here, we report that hypobaric hypoxia at an altitude of 5,000 m for 8 h enhances rat plasma CRH, corticosterone, and glucose levels, whereas the plasma insulin and pancreatic ATP/ADP ratio is reduced. In islets cultured under normoxia, CRH stimulated insulin release in a glucose- and CRH-level-dependent manner by activating CRHR1 and thus the cAMP-dependent protein kinase pathway and calcium influx through L-type channels. In islets cultured under hypoxia, however, the insulinotropic effect of CRH was inactivated due to reduced ATP and cAMP and coincident loss of intracellular calcium oscillations. Serum and glucocorticoid-inducible kinase 1 (SGK1) also played an inhibitory role. In human volunteers rapidly ascended to 3,860 m, plasma CRH and glucose levels increased without a detectable change in plasma insulin. By contrast, volunteers with acute mountain sickness (AMS) exhibited a marked decrease in HOMA insulin sensitivity (HOMA-IS) and enhanced plasma CRH. In conclusion, hypoxia may attenuate the CRH-insulinotropic effect by reducing cellular ATP/ADP ratio, cAMP and calcium influx, and upregulated SGK1. Hypoxia may not affect HOMA-IS in healthy volunteers but reduces it in AMS volunteers.

Effects of Replacement of Soybean Meal by Fermented Cottonseed Meal on Growth Performance, Serum Biochemical Parameters and Immune Function of Yellow-feathered Broilers

The study was conducted to examine the effects of partially replacing soybean meal (SBM) by solid-state fermented cottonseed meal (FCSM) on growth performance, serum biochemical parameters and immune function of broilers. After inoculated with Bacillus subtilis BJ-1 for 48 h, the content of free gossypol in cottonseed meal was decreased from 0.82 to 0.21 g/kg. A total of 600, day-old male yellow-feathered broilers were randomly divided into four groups with three replicates of 50 chicks each. A corn-SBM based control diet was formulated and the experimental diets included 4, 8 or 12% FCSM, replacing SBM. Throughout the experiment, broilers fed 8% FCSM had higher (p<0.05) body weight gain than those fed 0, 4 and 12% FCSM. The feed intake in 8% FCSM group was superior (p<0.05) to other treatments from d 21 to 42. On d 21, the concentration of serum immunoglobin M in the 4% and 8% FCSM groups, as well as the content of complements (C3, C4) in 8% FCSM group were greater (p<0.05) than those in the SBM group. Besides, birds fed 8% FCSM had increased (p<0.05) serum immunoglobin M, immunoglobulin G and complement C4 levels on d 42 compared with bird fed control diet. No differences (p>0.05) were found between treatments regarding the serum biochemical parameters and the relative weights of immune organs. In conclusion, FCSM can be used in broiler diets at up to 12% of the total diet and an appropriate replacement of SBM with FCSM may improve growth performance and immunity in broilers.

Molecular analysis of intestinal bacterial microbiota of broiler chickens fed diets containing fermented cottonseed meal

The study was conducted to investigate the effects of dietary inclusion of fermented cottonseed meal (FCM) on the ileal and cecal bacterial microbiota of broiler chickens. A total of 300 newborn yellow-feathered broiler chickens were randomly divided into 2 treatments with 3 replicates each (50 birds per replicate): control and 80 g/kg of FCM group. The feeding trial lasted for 42 d. Ileal and cecal digesta samples were collected from 8 chicks per replicate at 21 and 42 d of age to determine the composition of bacterial microbiota using denaturing gradient gel electrophoresis, cloning, sequencing, and real-time quantitative PCR analysis. The results demonstrated that the microbial composition in the ileum and cecum were considerably affected by the diet. The similarity dendrogram of banding profiles showed a more rapid stabilization of intestinal bacterial microbiota in broilers fed diets supplemented with FCM, compared with that of the birds fed the control diet. No significant difference was observed in total number of bands and Shannon-Weaver index, indicating that FCM had no effects on bacterial diversity. However, enumeration of bacteria in the ileal and cecal contents by quantitative PCR showed an increased (P < 0.05) population of lactobacilli, as well as a decreased (P < 0.05) Escherichia coli number by the dietary inclusion of FCM. In summary, dietary inclusion of FCM did not affect the intestinal microbial diversity but shifted intestinal microbiota, with a more homogenous population and an increased colonization of lactobacilli. The results also support the concept that dietary FCM inclusion could promote the beneficial bacteria in the intestinal tract.