Porphyromonas gingivalis (P. gingivalis) is the leading causative pathogen for periodontitis and gingivitis. Specific and direct detection of P. gingivalis based on surface-enhanced Raman spectroscopy (SERS) is a challenge, especially in clinical samples. In this paper, we have developed a novel label-free SERS platform to successfully obtain Raman fingerprints of P. gingivalis in simulated and clinical patients. The activated silver nanoparticles was used as the substrate with the addition of reducing agent (sodium borohydride) twice and aggregating agent (Na+) to establish the "hot spot", that accumulated on the surface like the “mask” to capture bacteria. The method broke the bottleneck of poor quantification and low specificity of SERS, and achieved rapid, sensitive, quantifiable and specific determination of bacteria with lower limit, excellent repeatability and stability. Further mechanical learning was combined with effective classification of four different oral bacteria, including P. gingivalis, Enterococcus faecalis (E. faecalis), Staphylococcus aureus (S. aureus) and Streptococcus mutans (S. mutans). The technique could identify P. gingivalis not only in artificial serum, artificial saliva and mixed bacterial samples, but most importantly, enabled non-destructive detection in the saliva of patients with periodontal disease. In addition, antimicrobial experiments have confirmed environmental friendliness that no secondary infections will occur. Therefore, the unlabelled, convenient and green SERS detection tool for oral bacteria holds great potential applications in clinical early diagnosis and prediction disease progression in periodontal disease.

Currently, more than 100 genotypes of human adenovirus (HAdV) are classified into seven species (A-G). The tissue tropism and virulence of HAdV are closely related to the genotypes, different genotypes of HAdV infection lead to different types and severity of the disease. Although traditional genotype identification techniques are widely used, they are time-consuming, complex and low sensitivity. Therefore, a rapid and accurate genotype recognition method for HAdV is necessary. In this study, silver nanoparticles incubated with iodine ions and aggregated with calcium ions were used as the enhanced substrate to form a good "hot spot" suitable for viral nucleic acid. The method could rapidly identify the seven common genotypes of respiratory HAdV characteristic fingerprints, combined with machine learning technology could classify and identify HAdV (500 copies/mL) within 5 min, with an accuracy rate of over 98%. In addition, the concentration dependence curve assessing the relationship between the intensity of the characteristic peak and the nucleic acid concentration showed a good linear relationship, the method can be used to quantitatively detect HAdV nucleic acid. This technique can enhance diagnosis and genotype identification of HAdV infection, thus providing a feasible method for the efficient response to respiratory human adenovirus outbreaks.

Epithelial-mesenchymal transformation (EMT) is one of the important mechanisms of malignancy in endometrial cancer, and detection of EMT targets is a key challenge to explore the mechanism of endometrial carcinoma (EC) malignancy and discover novel therapeutic targets. This study attempts to use surface-enhanced Raman spectroscopy (SERS), a highly sensitive, ultrafast, and highly specific analytical technology, to rapidly detect microRNA-200a-3p and ZEB1 in endometrial cancer cell lines. The silver nanoparticles were decorated with iodine and calcium ions, can capture the SERS fingerprints of microRNA-200a-3p and ZEB1 protein, and effectively avoid the interference of impurity signals. At the same time, the method has high sensitivity for the detection of the above EMT targets, and the lowest detection limits for microRNA-200a-3p and ZEB1 are 4.5 pmol/mL and 10 ng/mL, respectively. At the lowest detection concentration, the method still has high stability. In addition, principal component analysis can not only identify microRNA-200a-3p and ZEB1 protein from a variety of EMT-associated microRNA and proteins but also identify them in the total RNA and total protein of endometrial cancer cell lines and normal endometrial epithelial cell lines. This study modified silver nanoparticles with iodine and calcium ions and for the first time captured the fingerprints of EMT-related targets microRNA-200a-3p and ZEB1 at the same time without label, and the method has high sensitivity and stability. This SERS-based method has immense potential for elucidating the molecular mechanisms of EMT-related EC, as well as identifying biomarkers for malignant degree and prognosis prediction.

Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.

Unraveling bacterial identity through Raman scattering techniques has been persistently challenging due to homogeneously amplified Raman signals across a wide variety of bacterial molecules, predominantly protein- or nucleic acid-mediated. In this study, we present an approach involving the use of silver nanoparticles to completely and uniformly "mask" adsorption on the surface of bacterial molecules through sodium borohydride and sodium chloride. This approach enables the acquisition of enhanced surface-enhanced Raman scattering (SERS) signals from all components on the bacterial surface, facilitating rapid, specific, and label-free bacterial identification. For the first time, we have characterized the identity of a bacterium, including its DNA, metabolites, and cell walls, enabling the accurate differentiation of various bacterial strains, even within the same species. In addition, we embarked on an exploration of the origin and variability patterns of the main characteristic peaks of Gram-positive and Gram-negative bacteria. Significantly, the SERS peak ratio was found to determine the inflection point of accelerated bacterial death upon treatment with antimicrobials. We further applied this platform to identify 15 unique clinical antibiotic-resistant bacterial strains, including five Escherichia coli strains in human urine, a first for Raman technology. This work has profound implications for prompt and accurate identification of bacteria, particularly antibiotic-resistant strains, thereby significantly enhancing clinical diagnostics and antimicrobial treatment strategies.

Due to the background interference from biological samples, detecting viruses using surface-enhanced Raman scattering (SERS) in clinical samples is challenging. This study is based on SERS by reducing sodium borohydride and aggregating silver nanoparticles to develop suitable virus detection "hot spot." The monkeypox virus and human papillomavirus fingerprints were quickly obtained, tested, and identified in serum and artificial vaginal discharge, respectively, by combining the principal component analysis method. Therefore, these viruses were successfully identified in the biological background. In addition, the lowest detection limit was 100 copies/mL showing good reproducibility and signal-to-noise ratio. The concentration-dependent curve of the monkeypox virus had a good linear relationship. This method helps solve the SERS signal interference problem in complex biological samples, with low detection limits and high selectivity in virus characterization and quantitative analysis. Therefore, this method has a reasonable prospect of clinical application.

DNA methylation is an important feature of gene epigenetics that affects the metabolic process of organisms. Although surface-enhanced Raman spectroscopy (SERS) has demonstrated great potential in label-free DNA detection, discriminating the various processes involved in DNA methylation remains a challenge. DNA molecules fold themselves, wrapping the hydrophobic bases, thus making it difficult for traditional methods to detect single-base signals. In this study, we develop a SERS platform for detecting DNA via modifying silver nanoparticles by zirconium ions to obtain the DNA fingerprint information of base methylations (N6-methylated adenine and 5-methylated cytosine). Zirconium ions open the folded DNA molecules, enabling SERS signals of the four DNA bases (A, C, G, T) to be obtained as well as identification of the subtle differences between normal and methylated DNA with single base-level sensitivity. Moreover, the identifying information of DNA methylation was obtained by combining principal component analysis (PCA) with 2D correlation spectroscopy analysis. The findings of this study provide a substantial progress for current platforms for DNA sequencing, genetic testing, and gene-disease treatment.

Pesticide residue poses a significant global public health concern, necessitating improved detection methods. Here, a novel platform was introduced based on surface-enhanced Raman spectroscopy (SERS) to detect ten distinct types of pesticides. Notably, the sensitivity of this approach is exemplified by detecting trace amounts of 50 pM (10 ppt) thiabendazole. The correlation between the characteristic peak intensity of coexisting pesticides and their concentrations displays an exceptional linear relationship (R2 = 0.9999), underscoring its utility for quantitative mixed pesticide detection. Additionally, qualitative analysis of five mixed pesticides was conducted leveraging distinctive peak labeling. Harnessing machine learning techniques, a model for classifying and pre-dicting pesticides on pericarps was developed. Remarkably, the convolutional neural network achieved classi-fication accuracy of 100 % and prediction accuracy of 99.62 %. This innovative approach accurately identifies and quantifies diverse pesticides, thus offering a feasible scheme for in-situ detection of pesticide residues. Ul-timately, this strategy contributes to ensuring food safety and public health.

The conventional detection methods cannot satisfy the need for early and rapid detection of monkeypox virus (MPXV) infection. This is due to complicated pretreatment, time consumption, and complex operation of the diagnostic tests. Based on surface-enhanced Raman spectroscopy (SERS), this study attempted to capture the characteristic fingerprints of the MPXV genome and multiple antigenic proteins without the need to design specific probes. The minimum detection limit of this method is 100 copies/mL, with good reproducibility and signal-to-noise ratio. Therefore, the relationship between characteristic peak intensity and the protein and nucleic acid concentration can be used to construct a concentration-dependent spectral line with a good linear relationship. Additionally, principal component analysis (PCA) could identify the SERS spectra of four different MPXV proteins in serum. Therefore, this rapid detection method in the current outbreak of monkeypox control and the future response to possible new outbreaks has broad application prospects.

The detection of biomolecules is the key to basic molecular research, diagnostics, drug screening, and other biomedical applications. However, the existing detection techniques can only detect single classes of biomolecules, which warrant the development of a versatile biomolecule detection platform. Here, we developed a universal method for label-free detection of biomolecules via surface-enhanced Raman spectroscopy (SERS) by using sulfhydryl-modified gold nanoparticles as the substrate. The biomolecules can be adsorbed on the surface of gold nanoparticles cleaned by bromide ions to obtain initially enhanced Raman signals, and the aggregator (calcium ion) was further added to form a "hot spot", which enhanced the biomolecular signal again. Through the "two-step enhancement method", we were able to obtain fingerprints of DNA, RNA, amino acids, peptides, proteins, viruses, bacteria, and lipid molecules. This low-toxic, highly sensitive, and widely applicable technique has potential applications in biomedical research, clinical testing, and disease diagnosis and lays the foundation for the development of SERS technology in various fields.

Nearly 200 million people have been diagnosed with COVID-19 since the outbreak in 2019, and this disease has claimed more than 5 million lives worldwide. Currently, researchers are focusing on vaccine development and the search for an effective strategy to control the infection source. This work designed a detection platform based on Surface-Enhanced Raman Spectroscopy (SERS) by introducing acetonitrile and calcium ions into the silver nanoparticle reinforced substrate system to realize the rapid detection of novel coronavirus. Acetonitrile may amplify the calcium-induced hot spots of silver nanoparticles and significantly enhanced the stability of silver nanoparticles. It also elicited highly sensitive SERS signals of the virus. This approach allowed us to capture the characteristic SERS signals of SARS-CoV-2, Human Adenovirus 3, and H1N1 influenza virus molecules at a concentration of 100 copies/test (PFU/test) with upstanding reproduction and signal-to-noise ratio. Machine learning recognition technology was employed to qualitatively distinguish the three virus molecules with 1000 groups of spectra of each virus. Acetonitrile is a potent internal marker in regulating the signal intensity of virus molecules in saliva and serum. Thus, we used the SERS peak intensity to quantify the virus content in saliva and serum. The results demonstrated a satisfactory linear relationship between peak intensity and protein concentration. Collectively, this rapid detection method has a broad application prospect in clinical diagnosis of viruses, management of emergent viral infectious diseases, and exploration of the interaction between viruses and host cells.

Accurate and sensitive detection of SARS-CoV-2 is an effective strategy for preventing the COVID-19 pandemic in the current absence of specific drug therapy. This study presents a novel enhanced substrate for label-free detection of respiratory viruses using surface-enhanced Raman Scattering. Sodium borohydride reduces silver ions to clustered silver nanoparticles to eliminate the disorganized peak signal of the traditional citrate reducing agent. Meanwhile, the study obtained the fingerprints and concentration-dependent curves of many respiratory viruses, including SARS-CoV-2, human adenovirus type 7, and H1N1 virus, with good linear relationships. The three viruses were also identified in serum and saliva within two minutes, combined with linear discriminant diagnostic analysis. Therefore, establishing this enhanced substrate is greatly valuable for the global response to the COVID-19 pandemic.

The receptor-binding domain of the SARS-CoV-2 spike mediates the key to binding the virus to the host receptor, but capturing the molecular signal of this spike RBD remains a formidable challenge. Here, we report a new surface-enhanced Raman spectroscopy (SERS) approach, which used gold nanoparticles prepared by low-speed constant-temperature centrifugation by bromine and calcium ions in two cleaning steps as the enhanced substrate to rapidly and accurately detect spike RBD large protein molecules in body fluids. The detection signal was extremely stable, and the orientation of the spike RBD on the enhanced substrate surface was also determined. This approach was specific in distinguishing different SARS-CoV-2 variants of spike RBD, including Delta, Beta, Gamma, and Omicron. Additionally, the enhanced substrate can identify biologically active or inactive spike RBD. This two-step cleaning enhanced substrate opens up opportunities not only for early diagnostics of SARS-CoV-2 virus but also for developing targeted drugs against viruses.

Surface enhanced Raman spectroscopy (SERS) is widely used in biomolecular detection. However, maintaining the native structure of proteins while obtaining sensitive and reproducible SERS signals of unlabeled proteins remains a challenge. In this study, dichloromethane (DCM) and CaCl2 were used to optimize the aggregation of Ag nanoparticles (AgNPs), and several proteins were analyzed comprehensively. Calcium ions removed citrate ions outside AgNPs, inducing hot spots and achieving high-sensitivity SERS signals of proteins. Furthermore, 20 random samples of 0.5 μg/mL hemoglobin were analyzed by this method. The obtained spectra showed good repeatability and a high quality. Using the peak intensity of DCM as internal parameter, the differences in peak intensities at the same position were analyzed to distinguish different proteins and evaluate changes in protein structure. Subsequently, the protein content in protein mixtures and serum was quantified and a good linear relationship between peak intensity and protein concentration was obtained. This method shows great promise in the fields of food testing and clinical diagnosis.

Aim: To reconstruct the ancestral sequence of human adenoviral hexon protein by combining sequence variations and structural information. And to provide a candidate hexon protein for developing new adenoviral vector capable of escaping the pre-existing immunity in healthy populations. Methods: The sequences of 74 adenovirus-type strains were used to predict the ancestral sequence of human adenovirus hexon protein using FastML and MEGA software. The three-dimensional structure model was built using homology modeling methods. The immunological features of ancestral loop 1 and loop 2 regions of sequences were tested using protein segments expressed in a prokaryotic expression system and polypeptides synthesized with human serum samples. Results: The tower region of the hexon protein had the highest sequence variability, while the neck and base regions remained constant among different types. The modern strains successfully predicted the common ancestral sequence of the human adenovirus hexon. The positive sera against neutralizing epitopes on the common ancestor of adenoviral hexon were relatively rare among healthy adults. Conclusion: The existing strains inferred the common ancestor of human adenoviruses, with epitopes never observed in the current human strains. The predicted common ancestor hexon is a good prospect in the improvement of adenovirus vectors.

人腺病毒是一种能引起人类多种疾病的DNA病毒,迄今为止发现了包括A～G等7个种、80多个型别.本文对腺病毒的结构、分类标准进行了介绍,同时对新型别人腺病毒及其分型鉴定方法、产生机制进行综述.对人腺病毒多方面的了解有助于更好的探究其进化机制、组织嗜性及致病性,加强对腺病毒感染的预防和治疗.

目的 了解哈尔滨地区住院患者呼吸道病毒感染状况及流行趋势,为疾病防治和腺病毒流行监测提供参考.方法 采用直接免疫荧光法,检测2017年3月 ～2018年2月哈尔滨地区住院患者痰液标本呼吸道病毒的感染情况.选择1例腺病毒阳性标本利用空斑实验进行分离纯化,利用PCR扩增六邻体L1区、纤突和E1A区并进行PCR产物序列测定和Blast比对分析鉴定型别.结果 七种常见呼吸道病毒检测总体阳性率为14.36％.呼吸道合胞病毒阳性率最高为4.18％,腺病毒阳性率为1.82％.不同季节病毒检测阳性率差异有统计学意义(P<0.05).从腺病毒检测阳性标本中分离到一株人腺病毒毒株,进行PCR产物测序和分析,初步鉴定该毒株为人腺病毒3型.结论 哈尔滨地区呼吸道病毒感染的病原体主要以呼吸道合胞病毒为主.呼吸道病毒感染流行季节为冬春季节,应加强哈尔滨地区呼吸道病毒流行情况监测.