Effect of Principal Component Analysis Centering and Scaling on Classification of Mycobacteria from Raman Spectra

Discrimination of cis-diol-containing molecules using fluorescent boronate affinity probes by principal component analysis

Fluorescent boronate affinity molecules have gained increasing attention in the field of fluorescence sensing and detection due to their selective recognition capability towards cis-diol-containing molecules (cis-diols). However, the conventional fluorescent boronate affinity molecules face a challenge in differentiating the type of cis-diol only by their fluorescence responses. In this study, a simple method was used to discriminate different types of cis-diols, including nucleosides, nucleotides, sugars, and glycoproteins based on the phenylboronic acid-functionalized fluorescent molecules combined with principal component analysis (PCA). Both fluorescent molecules were simply synthesized by the covalent interaction between the amino group in 3-aminophenyl boronic acid and the isothiocyanate group in fluorescein or rhodamine B. In view of their fluorescence-responsive behaviors to these cis-diols directly, it is impossible to differentiate their types even under the optimized experimental conditions. When PCA was employed to treat the fluorescence response data and the quenching constants with their molecular weight, different types of cis-diols can be distinguished successfully. As a result, by integrating the fluorescence response of the boronate affinity probes with PCA, it can greatly improve the specific recognition capability of the boronic acids, providing a simple and direct way to distinguish and identify different types of cis-diols.

Dual-Principal Component Analysis of the Raman Spectrum Matrix to Automatically Identify and Visualize Microplastics and Nanoplastics

As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.

Identification and visualisation of microplastics via PCA to decode Raman spectrum matrix towards imaging

To visualise microplastics and nanoplastics via Raman imaging, we need to scan the sample surface over a pixel array to collect Raman spectra as a matrix. The challenge is how to decode this spectrum matrix to map accurate and meaningful Raman images. This study compares two decoding approaches. The first approach is used when the sample contains several known types of microplastics whose standard spectra are available. We can map the Raman intensity at selected characteristic peaks as images. In order to increase the image certainty, we employ a logic-based algorithm to merge several images that are simultaneously mapped at several characteristic peaks to one image. However, the rest of the signals other than the selected peaks are ignored, meaning a low signal-noise ratio. The second approach for decoding is used when samples are complicated and standard spectra are not available. We employ principal component analysis (PCA) to decode the spectrum matrix. By selecting principal components (PC) and generating PC score curves to mimic the Raman spectrum, we can justify and assign the suspected items to microplastics and other materials. By mapping the PC loadings as images, microplastics and other materials can be simultaneously visualised. We analyse a sample containing two known microplastics to validate the effectiveness of the PCA-based algorithm. We then apply this method to analyse "unknown" microplastics printed on paper to extract Raman spectra from the complicated background and individually assign the images to paper fabric/additive, black carbon and microplastics, etc. Overall, the PCA-based algorithm shows some advantages and suggests a further step to decode Raman spectrum matrices towards machine learning.

Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Towards normalization selection of Raman data in the context of protein glycation: application of validity indices to PCA processed spectra

Vibrational data of biological samples require appropriate pre-processing for ensuring relevant interpretation. Here, mathematical criteria (validity indices) are used to select how to normalize Raman data collected in the protein glycation context.

Effect of growth media and phase on Raman spectra and discrimination of mycobacteria

When developing a Raman spectral library to identify bacteria, differences between laboratory and real world conditions must be considered. For example, culturing bacteria in laboratory settings is performed under conditions for ideal bacteria growth. In contrast, culture conditions in the human body may differ and may not support optimized bacterial growth. To address these differences, researchers have studied the effect of conditions such as growth media and phase on Raman spectra. However, the majority of these studies focused on Gram-positive or Gram-negative bacteria. This article focuses on the influence of growth media and phase on Raman spectra and discrimination of mycobacteria, an acid-fast genus. Results showed that spectral differences from growth phase and media can be distinguished by spectral observation and multivariate analysis. Results were comparable to those found for other types of bacteria, such as Gram-positive and Gram-negative. In addition, the influence of growth phase and media had a significant impact on machine learning models and their resulting classification accuracy. This study highlights the need for machine learning models and their associated spectral libraries to account for various growth parameters and stages to further the transition of Raman spectral analysis of bacteria from laboratory to clinical settings.

In vitro model for predicting bioavailability of subcutaneously injected monoclonal antibodies

Monoclonal antibodies (mAbs), which are now more frequently administered by subcutaneous (SC) injection rather than intravenously, have become a tremendously successful drug format across a wide range of therapeutic areas. Preclinical evaluations of mAbs to be administered by SC injection are typically performed in species such as mice, rats, minipigs, and cynomolgus monkeys to obtain critical information regarding formulation performance and prediction of PK/PD outcomes needed to select clinical doses for first-in-human studies. Despite extensive efforts, no preclinical model has been identified to date that accurately predicts clinical outcomes for these SC injections. We have addressed this deficiency with a novel in vitro instrument, termed Scissor, to model events occurring at the SC injection site and now further validated this approach using a set of eight mAbs for which clinical PK/PD outcomes have been obtained. Diffusion of these mAbs from the Scissor system injection cartridge into a large volume physiological buffer, used to emulate mAb movement from the SC injection site into the systemic circulation, provided distinct profiles when monitored over a 6h period. Curve-fitting analysis of these profiles using the Hill equation identified parameters that were used, along with physicochemical properties for each mAb, in a partial least squares analysis to define a relationship between molecule and formulation properties with clinical PK outcomes. The results demonstrate that parameters of protein charge at neutral pH and isoelectric point (pI) along with combined formulation properties such as viscosity and mAb concentration can dictate the movement of the mAb from the injection cartridge to infinite sink compartment. Examination of profile characteristics of this movement provided a strong predictive correlation for these eight mAbs. Together, this approach demonstrates the feasibility of this in vitro modelling strategy as a tool to identify drug and formulation properties that can define the performance of SC injected medicines and provide the potential for predicting clinical outcomes that could be useful for formulation selection and a first-in-human clinical dosing strategy.