Development of an automated Raman system and use of principal component analysis to classify real and counterfeit liquors† † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ra06057h
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SUBMITTER: Chi H
PROVIDER: S-EPMC10642367 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:Objective: Pivotal trials of automated insulin delivery (AID) closed-loop systems have demonstrated a consistent picture of glycemic benefit, supporting approval of multiple systems by the Food and Drug Administration or Conformité Européenne mark receipt. To assess how pivotal trial findings translate to commercial AID use, a systematic review of retrospective real-world studies was conducted. Methods: PubMed and EMBASE were searched for articles published after 2018 with more than five nonpregnant individuals with type 1 diabetes (T1D). Data were screened/extracted in duplicate for sample size, AID system, glycemic outcomes, and time in automation. Results: Of 80 studies identified, 20 met inclusion criteria representing 171,209 individuals. Time in target range 70-180 mg/dL (3.9-10.0 mmol/L) was the primary outcome in 65% of studies, with the majority of reports (71%) demonstrating a >10% change with AID use. Change in hemoglobin A1c (HbA1c) was reported in nine studies (range 0.1%-0.9%), whereas four reported changes in glucose management indicator (GMI) with a 0.1%-0.4% reduction noted. A decrease in HbA1c or GMI of >0.2% was achieved in two-thirds of the studies describing change in HbA1c and 80% of articles where GMI was described. Time below range <70 mg/dL (<3.9 mmol/L) was reported in 16 studies, with all but 1 study showing stable or reduced levels. Most systems had >90% time in automation. Conclusion: With larger and more diverse populations, and follow-up periods of longer duration (∼9 months vs. 3-6 months for pivotal trials), real-world retrospective analyses confirm pivotal trial findings. Given the glycemic benefits demonstrated, AID is rapidly becoming the standard of care for all people living with T1D. Individuals should be informed of these systems and differences between them, have access to and coverage for these technologies, and receive support as they integrate this mode of insulin delivery into their lives.
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Project description:Membrane fouling, i.e. accumulation of unwanted material on the surface of the membrane is a significant problem in filtration processes since it commonly degrades membrane performance and increases operating costs. Therefore, the advantages of early stage monitoring and control of fouling are widely recognized. In this work, the potential of using Raman spectroscopy coupled to chemometrics in order to quantify degree of membrane fouling in real-time was investigated. The Raman data set collected from adsorption experiments with varying pHs and concentrations of model compound vanillin was used to develop a predictive model based on principal component analysis (PCA) for the quantification of the vanillin adsorbed on the membrane. The correspondence between the predicted concentrations based on the PCA model and actual measured concentrations of adsorbed vanillin was moderately good. The model developed was successful in monitoring both adsorption and desorption processes. Furthermore, the model was able to detect abnormally proceeding experiment based on differentiating PCA score and loading values. The results indicated that the presented approach of using Raman spectroscopy combined with a PCA model has potential for use in monitoring and control of fouling and cleaning in membrane processes.
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Project description:Although principal component analysis is frequently used in multivariate/ analysis, it has disadvantages when applied to experimental or diagnostic data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the set. Second, the method is sensitive to experimental noise and bias between sample groups, since it cannot reflect the design of experiments; rather, it estimates the same weight and independence of all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. The resulting components were scaled to unify their size unit. Also, the principal axes were identified using training data sets and shared among experiments. This training data reflects the design of experiments, and its preparation allows noise to be reduced and group bias to be removed. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. Additionally, unknown samples were appropriately classified using pre-arranged axes, and principal axes well reflected the characteristics of groups in the experiments. This SuperSeries is composed of the SubSeries listed below.
2011-08-15 | GSE31375 | GEO
Project description:Ethyl carbamate (EC), a potential carcinogen, can be formed during the fermentation and storage of alcoholic beverages. In this work, quantitative detection of EC in alcoholic beverages by using surface-enhanced Raman spectroscopy (SERS) is reported. Flower-shaped silver nanostructure substrates and silver nanocube substrates were prepared and employed as SERS platform. Flower-like silver substrates had better Raman enhancement effect on EC and were selected for further EC detection. In EC SERS spectra based on flower-shaped silver substrates, the strongest and reproducible characteristic band at 857 cm-1 was chosen for establishing a linear regression model in the concentrations ranging from 10-5 to 10-9 M, which effectively extended the application scope of the quantitative model for determination EC. Furthermore, a real alcoholic beverage was tested to verify the feasibility and reliability of the method.
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Project description:Microbial natural product discovery programs face two main challenges today: rapidly prioritizing strains for discovering new molecules and avoiding the rediscovery of already known molecules. Typically, these problems have been tackled using biological assays to identify promising strains and techniques that model variance in a dataset such as PCA to highlight novel chemistry. While these tools have shown successful outcomes in the past, datasets are becoming much larger and require a new approach. Since PCA models are dependent on the members of the group being modeled, large datasets with many members make it difficult to accurately model the variance in the data. Our tool, hcapca, first groups strains based on the similarity of their chemical composition, and then applies PCA to the smaller sub-groups yielding more robust PCA models. This allows for scalable chemical comparisons among hundreds of strains with thousands of molecular features. As a proof of concept, we applied our open-source tool to a dataset with 1046 LCMS profiles of marine invertebrate associated bacteria and discovered three new analogs of an established anticancer agent from one promising strain.
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Project description:IntroductionThe Japanese government has promoted policies ensuring standardized medical care across the secondary medical care areas (SMCAs); however, these efforts have not been evaluated, making the current conditions unclear. Multidimensional indicators could identify these differences; thus, this study examined the regional characteristics of the medical care provision system for 21 SMCAs in Hokkaido, Japan, and the changes from 1998 to 2018.Materials and methodsThis study evaluated the characteristics of SMCAs by principal component analysis using multidimensional data related to the medical care provision system. Factor loadings and principal component scores were calculated, with the characteristics of each SMCA visually expressed using scatter plots. Additionally, data from 1998 to 2018 were analyzed to clarify the changes in SMCAs' characteristics.ResultsThe primary and secondary principal components were Medical Resources and Geographical Factors, respectively. The Medical Resources components included the number of hospitals, clinics, and doctors, and an area's population of older adults, accounting for 65.28% of the total variance. The Geographical Factors components included the number of districts without doctors and the population and a land area of these districts, accounting for 23.20% of the variance. The accumulated proportion of variance was 88.47%. From 1998 to 2018, the area with the highest increase in Medical Resources was Sapporo, with numerous initial medical resources (-9.283 to -10.919).DiscussionPrincipal component analysis summarized multidimensional indicators and evaluated SMCAs in this regional assessment. This study categorized SMCAs into four quadrants based on Medical Resources and Geographical Factors. Additionally, the difference in principal component scores between 1998 and 2018 emphasized the expanding gap in the medical care provision system among the 21 SMCAs.
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Project description:Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.
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Project description: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.
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Project description:The polymorphic quantitative analysis of explosives is very important for national defense and security inspection. However, conventional analytical methods are inaccurate and time-consuming because of the complexity of the polymorphic explosive samples. In this paper, we established a new method of polymorphic quantitative determination in a simple, sensitive, and accurate way. High quality spectra of the four phases of the explosive CL-20 were obtained using a compact Raman spectrometer, and QM calculations were performed to confirm the tentative assignment of the most predominant Raman peaks. Principal component analysis (PCA) of the data was performed to understand the factors affecting the spectral variation across the entire Raman region of the four phases of CL-20 and to calculate the characteristic Raman shift region. In addition, different characteristic peaks were selected according to the PCA and QM calculation results, and a new method for the quantitative determination of polymorphic impurities in ε-CL-20 was also set up. The detection level for the polymorphic impurities was determined to be below 1%, and the standard deviation was less than ±0.5%. This new method is of significant importance for the quality control of synthesis and production not only in explosives, but also in pharmaceuticals, agrochemicals, and optics industries.
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Project description:BackgroundMultiple system atrophy (MSA) is an intractable neurodegenerative disorder with poorly understanding of prognostic factors.ObjectiveThe purpose of this retrospective longitudinal study was to explore the main predictors of survival of MSA patients with new clinical subtypes based on cluster analysis.MethodsA total of 153 Chinese MSA patients were recruited in our study. The basic demographic data and motor and nonmotor symptoms were assessed. Cluster and principal component analysis (PCA) were used to eliminate collinearity and search for new clinical subtypes. The multivariable Cox regression was used to find factors associated with survival in MSA patients.ResultsThe median survival time from symptom onset to death (estimated using data from all patients by Kaplan-Meier analysis) was 6.3 (95% CI = 6.1-6.7) years. The survival model showed that a shorter survival time was associated with motor principal component (PC)1 (HR = 1.71, 95% CI: 1.26-2.30, p < 0.001) and nonmotor PC3 (HR = 1.68, 95% CI: 1.31-2.10, p < 0.001) through PCA. Four clusters were identified: Cluster 1 (mild), Cluster 2 (mood disorder-dominant), Cluster 3 (axial symptoms and cognitive impairment-dominant), and Cluster 4 (autonomic failure-dominant). Multivariate Cox regression indicated that Cluster 3 (HR = 4.15, 95% CI: 1.73-9.90, p = 0.001) and Cluster 4 (HR = 4.18, 95% CI: 1.73-10.1, p = 0.002) were independently associated with shorter survival time.ConclusionMore serious motor symptoms, axial symptoms such as falls and dysphagia, orthostatic hypotension, and cognitive impairment were associated with poor survival in MSA via PCA and cluster analysis.
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