Explore the vast range of titles available.

The rapid, highly sensitive, and accurate detection of bacteria is the focus of various fields, especially food safety and public health. Surface‐enhanced Raman spectroscopy (SERS), with the advantages of being fast, sensitive, and nondestructive, can be used to directly obtain molecular fingerprint information, as well as for the on‐line qualitative analysis of multicomponent samples. It has therefore become an effective technique for bacterial detection. Within this progress report, advances in the detection of bacteria using SERS and other compatible techniques are discussed in order to summarize its development in recent years. First, the enhancement principle and mechanism of SERS technology are briefly overviewed. The second part is devoted to a label‐free strategy for the detection of bacterial cells and bacterial metabolites. In this section, important considerations that must be made to improve bacterial SERS signals are discussed. Then, the label‐based SERS strategy involves the design strategy of SERS tags, the immunomagnetic separation of SERS tags, and the capture of bacteria from solution and dye‐labeled SERS primers. In the third part, several novel SERS compatible technologies and applications in clinical and food safety are introduced. In the final part, the results achieved are summarized and future perspectives are proposed. This progress report focuses on the progress for bacterial detection by surface‐enhanced Raman spectroscopy (SERS) and other compatible techniques, and the contents mainly include the following parts: the enhancement principle and mechanism of SERS, the progress of label‐free strategy and label‐based strategy for SERS detection of bacteria, several novel developed SERS compatible technologies and the application in clinical and food safety.

Background: Extracellular vesicles contain biological molecules specified by cell-type of origin and modified by microenvironmental changes. To conduct reproducible studies on exosome content and function, storage conditions need to have minimal impact on airway exosome integrity. Aim: We compared surface properties and protein content of airway exosomes that had been freshly isolated vs. those that had been treated with cold storage or freezing. Methods: Mouse bronchoalveolar lavage fluid (BALF) exosomes purified by differential ultracentrifugation were analysed immediately or stored at +4°C or −80°C. Exosomal structure was assessed by dynamic light scattering (DLS), transmission electron microscopy (TEM) and charge density (zeta potential, ζ). Exosomal protein content, including leaking/dissociating proteins, were identified by label-free LC-MS/MS. Results: Freshly isolated BALF exosomes exhibited a mean diameter of 95 nm and characteristic morphology. Storage had significant impact on BALF exosome size and content. Compared to fresh, exosomes stored at +4°C had a 10% increase in diameter, redistribution to polydisperse aggregates and reduced ζ. Storage at −80°C produced an even greater effect, resulting in a 25% increase in diameter, significantly reducing the ζ, resulting in multilamellar structure formation. In fresh exosomes, we identified 1140 high-confidence proteins enriched in 19 genome ontology biological processes. After storage at room temperature, 848 proteins were identified. In preparations stored at +4°C, 224 proteins appeared in the supernatant fraction compared to the wash fractions from freshly prepared exosomes; these proteins represent exosome leakage or dissociation of loosely bound \"peri-exosomal\" proteins. In preparations stored at −80°C, 194 proteins appeared in the supernatant fraction, suggesting that distinct protein groups leak from exosomes at different storage temperatures. Conclusions: Storage destabilizes the surface characteristics, morphological features and protein content of BALF exosomes. For preservation of the exosome protein content and representative functional analysis, airway exosomes should be analysed immediately after isolation.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue. Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within. Often, fluorescent dyes or trackers are used that can be detected under the microscope. However, this procedure can sometimes interfere with the biological processes being studied. Now, Guo, Yeh, Folkesson et al. have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label. The technique, called QLIPP, uses the phase and polarization of the light passing through the sample to get information about its makeup. A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human. This way, Guo et al. were able to reveal details that conventional microscopy would have missed. Then, a type of machine learning, known as ‘deep learning’, was used to translate the density and orientation images into fluorescence images, which enabled the researchers to predict specific structures in human brain tissue sections. QLIPP can be added as a module to a microscope and its software is available open source. Guo et al. hope that this approach can be used across many fields of biology, for example, to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection. However, further work in automating other aspects, such as sample preparation and analysis, will be needed to realize the full benefits.

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic β-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients’ GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of β-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface‐enhanced Raman Spectroscopy (SERS), as an innovative non‐invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost‐effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label‐free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed. Label‐free SERS coupled with machine learning algorithms in pathogenic microbial identification: current trends, challenges, and perspectives. This review introduced the applications of label‐free surface‐enhanced Raman scattering (SERS) in bacteria, viruses, fungi, and clinical samples. The four aspects involved in processing SERS data were examined: data preprocessing, unsupervised learning, supervised learning, and performance evaluation. Specifically, data preprocessing includes peak removal, smoothing, baseline correction, and normalization. Unsupervised and supervised learning include clustering, machine learning, and deep learning. Performance evaluation includes accuracy, precision, recall, F1‐score, confusion matrix, and ROC curve.

Since two‐dimensionalal (2D) materials have distinct chemical and physical properties, they are widely used in various sectors of modern technologies. In the domain of diagnostic biodevices, particularly for point‐of‐care (PoC) biomedical diagnostics, 2D‐based field‐effect transistor biosensors (bio‐FETs) demonstrate substantial potential. Here, in this review article, the operational mechanisms and detection capabilities of biosensing devices utilizing graphene, transition metal dichalcogenides (TMDCs), black phosphorus, and other 2D materials are addressed in detail. The incorporation of these materials into FET‐based biosensors offers significant advantages, including low detection limits (LOD), real‐time monitoring, label‐free diagnosis, and exceptional selectivity. The review also highlights the diverse applications of these biosensors, ranging from conventional to wearable devices, underscoring the versatility of 2D material‐based FET devices. Additionally, the review provides a comprehensive assessment of the limitations and challenges faced by these devices, along with insights into future prospects and advancements. Notably, a detailed comparison of FET‐based biosensors is tabulated along with various other biosensing platforms and their working mechanisms. Ultimately, this review aims to stimulate further research and innovation in this field while educating the scientific community about the latest advancements in 2D materials‐based biosensors. This article explores the working mechanism and detecting capacities of FET biosensors made from diverse 2D materials such as graphene, TMDCs, and black phosphorus. These materials enable the sensors to achieve low detection limits, real‐time monitoring, label‐free diagnostics, and great selectivity. It also discusses constraints and future directions, paving the path for advances in biosensor technology.

There is a strong correlation between the composition of Deinagkistrodon acutus venom proteins and their potential pharmacological effects. The proteomic analysis revealed 103 proteins identified through label-free proteomics from 30 different snake venom families. Phospholipase A2 (30.0%), snaclec (21.0%), antithrombin (17.8%), thrombin (8.1%) and metalloproteinases (4.2%) were the most abundant proteins. The main toxicity of Deinagkistrodon acutus venom is hematotoxicity and neurotoxicity, and it acts on the lung. Deinagkistrodon acutus venom may have anticoagulant and antithrombotic effects. In summary, the protein profile and related toxicity and pharmacological activity of Deinagkistrodon acutus venom from southwest China were put forward for the first time. In addition, we revealed the relationship between the main toxicity, pharmacological effects, and the protein components of snake venom.

The incidence of prostate cancer (PCa) in men globally increases as the standard of living improves. Blood serum biomarker prostate‐specific antigen (PSA) detection is the gold standard assay that do not meet the requirements of early detection. Herein, a solution‐gated graphene transistor (SGGT) biosensor for the ultrasensitive and rapid quantification detection of the early prostate cancer‐relevant biomarker, miRNA‐21 is reported. The designed single‐stranded DNA (ssDNA) probes immobilized on the Au gate can hybridize effectively with the miRNA‐21 molecules targets and induce the Dirac voltage shifts of SGGT transfer curves. The limit of detection (LOD) of the sensor can reach 10−20 M without amplification and any chemical or biological labeling. The detection linear range is from 10−20 to 10−12 M. The sensor can realize real‐time detection of the miRNA‐21 molecules in less than 5 min and can well distinguish one‐mismatched miRNA‐21 molecule. The blood serum samples from the patients without RNA extraction and amplification are measured. The results demonstrated that the biosensor can well distinguish the cancer patients from the control group and has higher sensitivity (100%) than PSA detection (58.3%). Contrastingly, it can be found that the PSA level is not directly related to PCa. An ultrasensitive and rapid quantification detection of the early prostate cancer‐relevant biomarker miRNA‐21 is designed based on gate‐functionalized SGGT. The LOD can reach 10−20 M and the linear range is from 10−20 to 10−12 M. The fastest response time is 3 min. The biosensor directly detects miRNA‐21 in clinical serum samples to discriminate prostate cancer patients.

Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active‐Matrix Digital Microfluidics (AM‐DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi‐target, collision‐free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL‐60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM‐DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology. This paper presents a novel label‐free cell sorting method using Active‐Matrix Digital Microfluidics and YOLOv8 object detection model. The method demonstrates high purity and precision in sorting HeLa cells, red blood cells, and white blood cell subtypes, showcasing its potential for applications in cell sorting, omics research, single cell research, and drug development.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface‐enhanced Raman spectroscopy (SERS) is a non‐invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost‐effective identification of closely associated S. enterica serovars.