This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS beh...This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS behaviors was synthesized using a seed-mediated growth method and the dimensions of the Au NTOH was easily tuned.In addition,the influence of size on the SERS performance of their monolayers was systematically investigated,and the Au NTOH with the size of 61 nm possessed the best SERS performance.Importantly,a hydrophilic-substrateassisted interfacial self-assembled monolayer transfer technique was proposed to transfer Au NTOH onto PDMS films,resulting in forming flexible and transparent Au NTOH@PDMS substrates.Furthermore,the excellent signal homoge⁃neity of this substrate was demonstrated and the sensitivity was verified by a measurement of crystal violet(CV)as low as 1×10^(-8) mol/L.As a result,this SERS sensor is progressing for applying in the identification of trace contaminants in broad fields.展开更多
Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amo...Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.展开更多
Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a...Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a novel,noninvasive,and accurate diagnostic method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning to analyze seminal plasma exosomes.Semen samples from healthy controls(n=32)and azoospermic patients(n=22)were collected,and their exosomal SERS spectra were obtained.Machine learning algorithms were employed to distinguish between the SERS pro files of healthy and azoospermic samples,achieving an impressive sensitivity of 99.61%and a speci ficity of 99.58%,thereby highlighting signi ficant spectral differences.This integrated SERS and machine learning approach offers a sensitive,label-free,and objective diagnostic tool for early detection and monitoring of azoospermia,potentially enhancing clinical outcomes and patient management.展开更多
Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical...Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time.In this work,we developed a 4-mercaptophenylboronic acid gold nanoparticles(4-MPBA-AuNPs)-functionalized hydrogel microneedle(MPBA-H-MN)for bacteria detection in skin interstitial fluid.MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min.Surface enhanced Raman spectroscopy(SERS)detection was then performed and combined with machine learning technology to distinguish and identify a variety of bacteria.Overall,the capture efficiency of this method exceeded 50%.In the concentration range of 1×10_(7) to 1×10^(10) colony-forming units/mL(CFU/mL),the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration.Using random forest(RF)-based machine learning,bacteria were effectively distinguished with an accuracy of 97.87%.In addition,the harmless disposal of used MNs by photothermal ablation was convenient,environmentally friendly,and inexpensive.This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites.展开更多
基金The National Natural Science Foundation of China(12274055)the Fundamental Research Funds for the Central Universities(04442024072)the Training Program of Innovation and Entrepreneurship for Undergraduates in Dalian Minzu University(202312026063)。
文摘This article presented a facile fabrication process for polydimethylsiloxane(PDMS)composite gold nanotris⁃octahedra(Au NTOH)for a flexible SERS sensor with high sensitivity.Specifically,Au NTOH with excellent SERS behaviors was synthesized using a seed-mediated growth method and the dimensions of the Au NTOH was easily tuned.In addition,the influence of size on the SERS performance of their monolayers was systematically investigated,and the Au NTOH with the size of 61 nm possessed the best SERS performance.Importantly,a hydrophilic-substrateassisted interfacial self-assembled monolayer transfer technique was proposed to transfer Au NTOH onto PDMS films,resulting in forming flexible and transparent Au NTOH@PDMS substrates.Furthermore,the excellent signal homoge⁃neity of this substrate was demonstrated and the sensitivity was verified by a measurement of crystal violet(CV)as low as 1×10^(-8) mol/L.As a result,this SERS sensor is progressing for applying in the identification of trace contaminants in broad fields.
基金supported by the National Key Research and Development Program of China(No.2023YFC3402900)the National Nature Science of Foundation(No.61875131)+1 种基金Shenzhen Key Laboratory of Photonics and Biophotonics(No.ZDSYS20210623092006020)Shenzhen Science and Technology Innovation Program(No.20231120175730001)。
文摘Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.
基金support from the National Natural Science Foundation of China(No.62275049)the Natural Science Foundation of Fujian Province,China(No.2022J02024)the Fujian Province Joint Fund Project for Scientific and Technological Innovation(2023Y9383).
文摘Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a novel,noninvasive,and accurate diagnostic method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning to analyze seminal plasma exosomes.Semen samples from healthy controls(n=32)and azoospermic patients(n=22)were collected,and their exosomal SERS spectra were obtained.Machine learning algorithms were employed to distinguish between the SERS pro files of healthy and azoospermic samples,achieving an impressive sensitivity of 99.61%and a speci ficity of 99.58%,thereby highlighting signi ficant spectral differences.This integrated SERS and machine learning approach offers a sensitive,label-free,and objective diagnostic tool for early detection and monitoring of azoospermia,potentially enhancing clinical outcomes and patient management.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204340,82173954,and 82073815)the Natural Science Foundation of Jiangsu Province,China(Grant No.:BK20221048)+1 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent,China(Grant No.:2022ZB295)Key Laboratory Project of Quality Control of Chinese Herbal Medicines and Decoction Pieces,Gansu Institute for Drug Control,China(Grant No.:2024GSMPA-KL02).
文摘Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly diagnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time.In this work,we developed a 4-mercaptophenylboronic acid gold nanoparticles(4-MPBA-AuNPs)-functionalized hydrogel microneedle(MPBA-H-MN)for bacteria detection in skin interstitial fluid.MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min.Surface enhanced Raman spectroscopy(SERS)detection was then performed and combined with machine learning technology to distinguish and identify a variety of bacteria.Overall,the capture efficiency of this method exceeded 50%.In the concentration range of 1×10_(7) to 1×10^(10) colony-forming units/mL(CFU/mL),the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration.Using random forest(RF)-based machine learning,bacteria were effectively distinguished with an accuracy of 97.87%.In addition,the harmless disposal of used MNs by photothermal ablation was convenient,environmentally friendly,and inexpensive.This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites.
