Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes.

PURPOSE: The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR. METHODS: A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry. RESULTS: The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients. CONCLUSION: Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics.

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

Discrimination and screening of volatile metabolites in atractylodis rhizoma from different varieties using headspace solid-phase microextraction-gas chromatography-mass spectrometry and headspace gas chromatography-ion mobility spectrometry, and ultra-fast gas chromatography electronic nose.

Atractylodis rhizoma is a common bulk medicinal material with multiple species. Although different varieties of atractylodis rhizoma exhibit variations in their chemical constituents and pharmacological activities, they have not been adequately distinguished due to their similar morphological features. Hence, the purpose of this research is to analyze and characterize the volatile organic compounds (VOCs) in samples of atractylodis rhizoma using multiple techniques and to identify the key differential VOCs among different varieties of atractylodis rhizoma for effective discrimination. The identification of VOCs was carried out using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), resulting in the identification of 60 and 53 VOCs, respectively. The orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to screen potential biomarkers and based on the variable importance in projection (VIP ≥ 1.2), 24 VOCs were identified as critical differential compounds. Random forest (RF), K-nearest neighbor (KNN) and back propagation neural network based on genetic algorithm (GA-BPNN) models based on potential volatile markers realized the greater than 90 % discriminant accuracies, which indicates that the obtained key differential VOCs are reliable. At the same time, the aroma characteristics of atractylodis rhizoma were also analyzed by ultra-fast gas chromatography electronic nose (Ultra-fast GC E-nose). This study indicated that the integration of HS-SPME-GC-MS, HS-GC-IMS and ultra-fast GC E-nose with chemometrics can comprehensively reflect the differences of VOCs in atractylodis rhizoma samples from different varieties, which will be a prospective tool for variety discrimination of atractylodis rhizoma.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

LC-MS-based metabolomics reveals metabolite dynamic changes of beef after superchilling early post-mortem.

To explore the underlying mechanisms by which superchilling (SC, -3 °C within 5 h of slaughter) improves beef tenderness, an untargeted metabolomics strategy was employed. M. Longissimus lumborum (LL) muscles from twelve beef carcasses were assigned to either SC or very fast chilling (VFC, 0 °C within 5 h of slaughter) treatments, with conventional chilling (CC, 0 âˆ¼ 4 °C until 24 h post-mortem) serving as the control (6 per group). Biochemical properties and metabolites were investigated during the early post-mortem period. The results showed that the degradation of µ-calpain and caspase 3 occurred earlier in SC treated sample, which might be attributed to the accelerated accumulation of free Ca2+. The metabolomic profiles of samples from the SC and CC treatments were clearly distinguished based on partial least squares-discriminant analysis (PLS-DA) at each time point. It is noteworthy that more IMP and 4-hydroxyproline were found in the comparison between SC and CC treatments. According to the results of metabolic pathways analysis and the correlation analysis between traits related to tenderness and metabolites with significant differences (SC vs. CC), it can be suggested that the tenderization effect of the SC treatment may be related to the alteration of arginine and proline metabolism, and purine metabolism in the early post-mortem phase.

Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis.

The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia.

BACKGROUND: Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. METHODS: Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. RESULTS: A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = -13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden' s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854-0.987). CONCLUSION: The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

Discrimination of Diptera order insects based on their saturated cuticular hydrocarbon content using a new microextraction procedure and chromatographic analysis.

The nature and proportions of hydrocarbons in the cuticle of insects are characteristic of the species and age. Chemical analysis of cuticular hydrocarbons allows species discrimination, which is of great interest in the forensic field, where insects play a crucial role in estimating the minimum post-mortem interval. The objective of this work was the differentiation of Diptera order insects through their saturated cuticular hydrocarbon compositions (SCHCs). For this, specimens fixed in 70 : 30 ethanol : water, as recommended by the European Association for Forensic Entomology, were submitted to solid-liquid extraction followed by dispersive liquid-liquid microextraction, providing preconcentration factors up to 76 for the SCHCs. The final organic extract was analysed by gas chromatography coupled with flame ionization detection (GC-FID), and GC coupled with mass spectrometry was applied to confirm the identity of the SCHCs. The analysed samples contained linear alkanes with the number of carbon atoms in the C9-C15 and C18-C36 ranges with concentrations between 0.1 and 125 ng g-1. Chrysomya albiceps (in its larval stage) showed the highest number of analytes detected, with 21 compounds, while Lucilia sericata and Calliphora vicina the lowest, with only 3 alkanes. Non-supervised principal component analysis and supervised orthogonal partial least squares discriminant analysis were performed and an optimal model to differentiate specimens according to their species was obtained. In addition, statistically significant differences were observed in the concentrations of certain SCHCs within the same species depending on the stage of development or the growth pattern of the insect.

Radiomics-based discriminant analysis of principal components to stratify the treatment response of lung metastases following stereotactic body radiation therapy.

PURPOSE: Discriminant analysis of principal components (DAPC) was introduced to describe the clusters of genetically related individuals focusing on the variation between the groups of individuals. Borrowing this approach, we evaluated the potential of DAPC for the evaluation of clusters in terms of treatment response to SBRT of lung lesions using radiomics analysis on pre-treatment CT images. MATERIALS AND METHODS: 80 pulmonary metastases from 56 patients treated with SBRT were analyzed. Treatment response was stratified as complete, incomplete and null responses. For each lesion, 107 radiomics features were extracted using the PyRadiomics software. The concordance correlation coefficients (CCC) between the radiomics features obtained by two segmentations were calculated. DAPC analysis was performed to infer the structure of "radiomically" related lesions for treatment response assessment. The DAPC was performed using the "adegenet" package for the R software. RESULTS: The overall mean CCC was 0.97 ± 0.14. The analysis yields 14 dimensions in order to explain 95 % of the variance. DAPC was able to group the 80 lesions into the 3 different clusters based on treatment response depending on the radiomics features characteristics. The first Linear Discriminant achieved the best discrimination of individuals into the three pre-defined groups. The greater radiomics loadings who contributed the most to the treatment response differentiation were associated with the "sphericity", "correlation" and "maximal correlation coefficient" features. CONCLUSION: This study demonstrates that a DAPC analysis based on radiomics features obtained from pretreatment CT is able to provide a reliable stratification of complete, incomplete or null response of lung metastases following SBRT.

Early differential diagnosis of pancytopenia related diseases based on serum surface-enhanced Raman spectroscopy.

Pancytopenia is a common blood disorder defined as the decrease of red blood cells, white blood cells and platelets in the peripheral blood. Its genesis mechanism is typically complex and a variety of diseases have been found to be capable of causing pancytopenia, some of which are featured by their high mortality rates. Early judgement on the cause of pancytopenia can benefit timely and appropriate treatment to improve patient survival significantly. In this study, a serum surface-enhanced Raman spectroscopy (SERS) method was explored for the early differential diagnosis of three pancytopenia related diseases, i.e., aplastic anemia (AA), myelodysplastic syndrome (MDS) and spontaneous remission of pancytopenia (SRP), in which the patients with those pancytopenia related diseases at initial stage exhibited same pancytopenia symptom but cannot be conclusively diagnosed through conventional clinical examinations. The SERS spectral analysis results suggested that certain amino acids, protein substances and nucleic acids are expected to be potential biomarkers for their early differential diagnosis. In addition, a diagnostic model was established based on the joint use of partial least squares analysis and linear discriminant analysis (PLS-LDA), and an overall accuracy of 86.67 % was achieved to differentiate those pancytopenia related diseases, even at the time that confirmed diagnosis cannot be made by routine clinical examinations. Therefore, the proposed method has demonstrated great potential for the early differential diagnosis of pancytopenia related diseases, thus it has significant clinical importance for the timely and rational guidance on subsequent treatment to improve patient survival.

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality of environmental water and assessing the potential risks associated with industrial waste. This study introduces a single-well fluorometric sensor that utilizes orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs) to detect and differentiate between various chromium species, such as Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-). The variations of fluorescence spectra of the proposed probe upon chromium species addition were analyzed using machine learning techniques such as linear discriminant analysis and partial least squares regression as a classification and multivariate calibration technique, respectively. Linear discriminant analysis (LDA) demonstrated exceptional accuracy in differentiating single-component and bicomponent samples. Additionally, the findings from the partial least squares regression (PLSR) showed that the sensor created has strong linearity within the 1.0-100.0, 1.0-100.0, and 0.1-15 µM range for Cr2O72-, CrO42-, and Cr3+, respectively. Furthermore, appropriate detection limits were successfully achieved, which were 2.6, 2.9, and 0.7 µM for Cr2O72-, CrO42-, and Cr3+, respectively. Ultimately, the successful capability of the sensing platform in the identification and quantification of chromium species in environmental water samples provides innovative insights into general speciation analytics.

Developing an interpretation model for body fluid identification.

Criminal investigations, particularly sexual assaults, frequently require the identification of body fluid type in addition to body fluid donor to provide context. In most cases this can be achieved by conventional methods, however, in certain scenarios, alternative molecular methods are required. An example of this is the detection of menstrual fluid and vaginal material, which are not able to be identified using conventional techniques. Endpoint reverse-transcription PCR (RT-PCR) is currently used for this purpose to amplify body fluid specific messenger RNA (mRNA) transcripts in forensic casework. Real-time quantitative reverse-transcription PCR (RT-qPCR) is a similar method but utilises fluorescent markers to generate quantitative results in the form of threshold cycle (Cq) values. Despite the uncertainty surrounding body fluid identification, most interpretation guidelines utilise categorical statements. Probabilistic modelling is more realistic as it reflects biological variation as well as the known performance of the method. This research describes the application of various machine learning models to single-source mRNA profiles obtained by RT-qPCR and assesses their performance. Multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) were used to discriminate between the following body fluid categories: saliva, circulatory blood, menstrual fluid, vaginal material, and semen. We identified that the performance of MLR was somewhat improved when the quantitative dataset of the original Cq values was used (overall accuracy of approximately 0.95) rather than presence/absence coded data (overall accuracy of approximately 0.94). This indicates that the quantitative information obtained by RT-qPCR amplification is useful in assigning body fluid class. Of the three classification methods, MLR performed the best. When we utilised receiver operating characteristic curves to observe performance by body fluid class, it was clear that all methods found difficulty in classifying menstrual blood samples. Future work will involve the modelling of body fluid mixtures, which are common in samples analysed as part of sexual assault investigations.

Fingerprinting of monovarietal olive oils from Argentina and Uruguay via stable isotope, fatty acid profile, and chemometric analyses.

Tracing methods of non-European EVOOs commercialized worldwide are becoming crucial for effective authenticity controls. Limited analytical studies of these oils are available on a global scale, similar to those of European EVOOs. We report for the first time the fatty acid concentrations, bulk-oil 2H/1H, 13C/12C, and 18O/16O ratios and fatty acid 13C/12C ratios of 43 authentic monovarietal EVOOs from different geographical regions in Argentina and Uruguay. The samples were obtained from a wide range of latitudes and altitudes along an E-W profile, from lowlands near the Atlantic Ocean to the pre-Andean highlands near the Pacific Ocean. Principal component scores were used to cluster EVOOs into three groups- central-western Argentina, central Argentina, and Uruguay-based on nine stable isotope ratios and the oleic-linoleic acid concentration ratio. The bulk 2H/1H and 18O/16O values and 13C/12C of palmitoleic and linoleic acids provide good tools for differentiating these oils via linear discriminant analysis.

Investigating isotopic markers for hazelnut geographical authentication: Promising variables and potential applications.

Hazelnuts' features and price are influenced by their geographical origin, making them susceptible to fraud, especially counterfeit claims regarding their provenance. Stable isotope analysis is a recognised approach to establish the geographical origin of foods, yet its potential in hazelnut authentication remains unexplored. In this prospective study, we assessed multiple isotopic markers in hazelnuts from different origins and evaluated the most promising variables for geographical authentication by chemometric tools. Our findings indicate that bulk δ18O, along with δ2H and δ13C in the main fatty acid methyl esters, exhibit significant potential in discriminating geographical origins, and 87Sr/86Sr analysis could serve as a proficient confirmatory tool. Though no single marker alone can differentiate between all the studied origins, employing a multi-isotopic approach based on PLS-DA models achieved up to 92.5 % accuracy in leave-10 %-out cross-validation. These findings will probably lay the groundwork for developing robust models for hazelnut geographical authentication based on larger datasets.

Discriminatory power of MALDI-TOF MS protein profiling analysis of pork meat and meat products.

Forty different sample preparation methods were tested to obtain the most informative MALDI-TOF MS protein profiles of pork meat. Extraction by 25% formic acid with the assistance of zirconia-silica beads followed by defatting by methanol:chloroform mixture (1:1, v/v) and deposition by using the layer-by-layer method was determined as the optimum sample preparation protocol. The discriminatory power of the method was then examined on samples of pork meat and meat products. The method was able to discriminate between selected salami based on the production method and brand and was able to monitor the ripening process in salami. However, it was not able to differentiate between different brands of pork ham or closely located parts of pork meat. In the latter case, a more comprehensive analysis using LC-MS/MS was used to assess the differences in protein abundance and their relation to the outputs of MALDI - TOF MS profiling.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy.

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm-1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm-1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sampling to converge the folding free energy landscape of lasso peptides, a unique class of natural products with knot-like tertiary structures. This knotted scaffold imparts remarkable stability, making lasso peptides resistant to proteolytic degradation, thermal denaturation, and extreme pH conditions. Although their direct synthesis would enable therapeutic design, it has not yet been possible due to the improbable occurrence of spontaneous lasso folding. Thus, simulations characterizing the folding propensity are needed to identify strategies for increasing access to the lasso architecture by stabilizing the pre-lasso ensemble before isopeptide bond formation. Herein, harmonic linear discriminant analysis (HLDA) is combined with metadynamics-enhanced sampling to discover CVs capable of distinguishing the pre-lasso fold and converging the folding propensity. Intuitive CVs are compared to iterative rounds of HLDA to identify CVs that not only accomplish these goals for one lasso peptide but also seem to be transferable to others, establishing a protocol for the identification of folding reaction coordinates for lasso peptides.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys.

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.