Classification of two species of Gram-positive bacteria through hyperspectral microscopy coupled with machine learning

Recent advancement and trends in the development of membranes having bactericidal attributes via direct ink writing

The necessity for orthopedic prostheses, implants, and membranes to treat diseases, trauma, and other disasters has increased as the risk of survive through various factors has intensified exponentially. Considering exponential growth in demand, it has been observed that the traditional technology of grafts and membranes lags to fulfill the demand and effectiveness simultaneously. These challenges in traditional methodologies prompted a revolutionary shift in the biomedical industry when additive manufacturing (AM) emerged as an alternative fabrication technique for medical equipments such as prostheses, implants, and membranes. However these techniques were fast and precise the major attributes of the biomedical materials were the processability, bactericidal nature, biocompatibility, biodegradability, and nontoxicity together with good mechanical properties. Major challenges faced by researchers in the present-day scenario regarding materials are the lack of bactericidal attributes in tailored material, though having better mechanical as well as biocompatible properties, which, on the other hand, are primary critical factors too, in the healthcare sector. Hence considering the advantages of AM and need for membranes with bacteriacidal attributes this present review will highlight the studies based on the manufacturing of membranes with bacteria-resistant properties majorly using direct ink writing and some AM techniques and the reasoning behind the antibacterial attributes of those composite materials.

Hyperspectral upgrade solution for biomicroscope combined with Transformer network to classify infectious bacteria

Infectious diseases caused by bacterial pathogens pose a significant public health threat, emphasizing the need for swift and accurate bacterial species detection methods. Hyperspectral microscopic imaging (HMI) offers nondestructive, rapid, and data-rich advantages, making it a promising tool for microbial detection. In this research, we present a highly compatible and cost-effective approach to extend a standard biomicroscope system into a hyperspectral biomicroscope using a prism-grating-prism configuration. Using this prototype, we generate 600 hyperspectral data cubes for Listeria, Bacillus typhi, Bacillus pestis, and Bacillus anthracis. Additionally, we propose a Transformer-based classification network that achieves a 99.44% accuracy in classifying these infectious pathogens, outperforming traditional methods. Our results suggest that the successful combination of HMI and the optimized Transformer-based classification network highlights the potential for rapid and precise detection of infectious disease pathogens .

Rapid and robust bacterial species identification using hyperspectral microscopy and gram staining techniques

Gram staining can classify bacterial species into two large groups based on cell wall differences. Our study revealed that within the same gram group (gram-positive or gram-negative), subtle cell wall variations can alter staining outcomes, with the peptidoglycan layer and lipid content significantly influencing this effect. Thus, bacteria within the same group can also be differentiated by their spectra. Using hyperspectral microscopy, we identified six species of intestinal bacteria with 98.1% accuracy. Our study also demonstrated that selecting the right spectral band and background calibration can enhance the model's robustness and facilitate precise identification of varying sample batches. This method is suitable for analyzing bacterial community pathologies.

Understanding bacterial pathogenicity: a closer look at the journey of harmful microbes

Bacteria are the most prevalent form of microorganisms and are classified into two categories based on their mode of existence: intracellular and extracellular. While most bacteria are beneficial to human health, others are pathogenic and can cause mild to severe infections. These bacteria use various mechanisms to evade host immunity and cause diseases in humans. The susceptibility of a host to bacterial infection depends on the effectiveness of the immune system, overall health, and genetic factors. Malnutrition, chronic illnesses, and age-related vulnerabilities are the additional confounders to disease severity phenotypes. The impact of bacterial pathogens on public health includes the transmission of these pathogens from healthcare facilities, which contributes to increased morbidity and mortality. To identify the most significant threats to public health, it is crucial to understand the global burden of common bacterial pathogens and their pathogenicity. This knowledge is required to improve immunization rates, improve the effectiveness of vaccines, and consider the impact of antimicrobial resistance when assessing the situation. Many bacteria have developed antimicrobial resistance, which has significant implications for infectious diseases and favors the survival of resilient microorganisms. This review emphasizes the significance of understanding the bacterial pathogens that cause this health threat on a global scale.

Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology

To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data. To address the multi-scale features and the need for the rapid analysis of directly smeared data, a multi-scale buffered convolutional neural network, MBNet, was introduced, which included three convolutional combination units and four buffer units to extract the spectral features of directly smeared data from different dimensions. The focus was on studying the differences in spectral features between positive and negative bacterial infection, as well as the temporal correlation between positive-negative determination and short-term cultivation. The experimental results demonstrate that the joint determination model achieved an accuracy of 97.29%, a Positive Predictive Value (PPV) of 97.17%, and a Negative Predictive Value (NPV) of 97.60% in the directly smeared urine dataset. This result outperformed the single MBNet model, indicating the effectiveness of the multi-scale buffered architecture for global and large-scale features of directly smeared data, as well as the high sensitivity of spectral database matching for single bacterial targets. The rapid determination solution of the whole process, which combines directly smeared sample preparation, joint determination model, and software analysis integration, can provide a preliminary report of bacterial infection within 10 min, and it is expected to become a powerful supplement to the existing technologies of rapid bacterial detection.

SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers

We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.

Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study

We aimed to automate Gram-stain analysis to speed up the detection of bacterial strains in patients suffering from infections. We performed comparative analyses of visual transformers (VT) using various configurations including model size (small vs. large), training epochs (1 vs. 100), and quantization schemes (tensor- or channel-wise) using float32 or int8 on publicly available (DIBaS, n = 660) and locally compiled (n = 8500) datasets. Six VT models (BEiT, DeiT, MobileViT, PoolFormer, Swin and ViT) were evaluated and compared to two convolutional neural networks (CNN), ResNet and ConvNeXT. The overall overview of performances including accuracy, inference time and model size was also visualized. Frames per second (FPS) of small models consistently surpassed their large counterparts by a factor of 1-2×. DeiT small was the fastest VT in int8 configuration (6.0 FPS). In conclusion, VTs consistently outperformed CNNs for Gram-stain classification in most settings even on smaller datasets.

Graphene in nanomedicine: A review on nano-bio factors and antibacterial activity

Graphene-based nanomaterials possess potent antibacterial activity and have engrossed immense interest among researchers as an active armour against pathogenic microbes. A comprehensive perception of the antibacterial activity of these nanomaterials is critical to the fabrication of highly effective antimicrobial nanomaterials, which results in highly efficient and enhanced activity. These materials owing to their antimicrobial activity are utilized as nanomedicine against various pathogenic microbes. The present article reviews the antimicrobial activity of graphene and its analogs such as graphene oxide, reduced graphene oxide as well as metal, metal oxide and polymeric composites. The review draws emphasis on the effect of various nano-bio factors on the antibacterial capability. It also provides an insight into the antibacterial properties of these materials along with a brief discussion on the discrepancies in their activities as evidenced by the scientific communities. In this way, the review is expected to shed light on future research and development in graphene-based nanomedicine.

Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to the use of biological cells or microorganisms in optics and photonics. The technologies demonstrated here aim to address challenges to meeting the optical imaging, sensing, manipulating and therapy needs in a natural or even endogenous manner. This collection of 15 papers includes the novel results on designs of optical systems or photonic devices, image-assisted diagnosis and treatment, and manipulation or sensing methods, with applications for both ex vivo and in vivo use. These works portray the opportunities for exploring the field crossing biology and photonics in which a natural element can be functionalized for biomedical applications.