Treatment of myelofibrosis with refractory anemia with luspatercept: a multicenter Chinese study.

Myelofibrosis is a rare and often fatal hematological neoplasm, and the treatment of myelofibrosis-associated anemia remains suboptimal, with no improved therapies. Luspatercept was shown to display some efficacy in a phase 2 clinical trial for Myelofibrosis with anemia, yet relevant research are limited. Threrfore, data from patients diagnosed with refractory anemic primary or post-essential thrombocythemia/polycythemia vera myelofibrosis, who were treated with luspatercept for at least 9 weeks, were retrospectively collected. Eighteen patients with myelofibrosis treated with luspatercept were enrolled. Median age was 68 years (range, 44-80 years), and 27.8% were males. Ten (55.6%) were transfusion-dependent. Ten (55.6%) were Dynamic International Prognostic Scoring System intermediate-1, and eight (44.4%) were intermediate-2. The median follow-up was 7 (4-16) months. Erythroid response occurred in eight patients (44.4%) at week 12, four patients (30.8%) at week 24, and nine (50%) at the end of follow-up. Patients who were transfusion-dependent and not transfusion-dependent had similar HI-E responses, at different time points (P > 0.05). Patients had a significantly higher hemoglobin level at 12 weeks, 24 weeks, and at the end of follow-up, than at baseline (P = 0.001, P = 0.021, and P = 0.005, respectively). Treatment-related adverse events occurred in five (16.7%) patients, with no serious adverse events. Two (11.1%) patients relapsed at weeks 15 and 31. One patient progressed to acute myeloid leukemia. No patients had died by the end of follow-up. Luspatercept induced a good response in patients with anemic myelofibrosis, with a low relapse rate and good tolerance.

Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.

BACKGROUND: Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. METHODS: The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). RESULTS: The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p<0.05. CONCLUSION: KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons.

MOTIVATION: To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images. RESULTS: We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer. AVAILABILITY AND IMPLEMENTATION: The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gallbladder disease is associated with the risk of cardiovascular disease among Uyghurs in Xinjiang: a prospective cohort study.

BACKGROUND: Gallbladder disease (GBD) can increase the risk of cardiovascular disease (CVD). However, GBD has rarely been reported in the less developed, rural areas of Xinjiang. This study aimed to determine the prevalence of GBD and incidence of CVD in a prospective cohort study in rural Xinjiang. Moreover, the study aimed to explore the association between GBD and CVD within this cohort. METHODS: The study cohort included 11,444 Uyghur adults in Xinjiang, 3rd division, from the 51st Mission. Study groups were classified according to whether GBD was present or absent at baseline. The occurrence of CVD was the end event. Demographic, anthropometric, and biochemical data were recorded, and the incidence of CVD in the GBD and non-GBD groups analysed. Cox proportional hazards regression models were used to assess the association between GBD and CVD and factors associated with their incidence. Several subgroup analyses were performed to assess CVD incidence in different subgroups. The interaction between GBD and cardiometabolic risk factors, and subsequent risk of developing CVD, was evaluated. RESULTS: Prevalence of GBD in the study cohort was 10.29%. After a median follow-up of 4.92 years, the cumulative incidence of CVD in the study cohort was 10.49%, 8.43% in males and 12.65% in females. CVD incidence was higher in the GBD group (34.04% vs. 7.78%, HR = 4.96, 95% CI: 4.40-5.59). After multivariate adjustment, the risk of CVD remained higher in the GBD group (HR = 2.89, 95% CI: 2.54-3.29). Subgroup analyses showed male sex, smoking, alcohol consumption, lack of exercise, and abnormal renal function were all associated with increased risk of CVD. Moreover, the risk of CVD was markedly higher in GBD combined with cardiometabolic risk factors (hypertension, T2DM, dyslipidaemia, overweight, and abdominal obesity), than in cardiometabolic risk factors alone and this was higher in the GBD group than in the non-GBD group regardless of whether cardiometabolic risk factors were combined. CONCLUSION: GBD is an important independent risk factor for CVD development. Awareness of these associations will raise concerns among clinicians about the risk of cardiovascular disease in patients with GBD.

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.

PURPOSE: With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis. METHODS: Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD. RESULTS: After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang. CONCLUSION: The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning.

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Association between methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and H-type hypertension: A systematic review and meta-analysis.

PURPOSE: The polymorphism of methylenetetrahydrofolate reductase (MTHFR) gene C677T has been linked to H-type hypertension. But the conclusion remained controversial. To elucidate this issue, we performed a comprehensive meta-analysis to analyze the MTHFR C677T polymorphism and H-type hypertension. MATERIALS AND METHODS: The English and Chinese databases were systematically searched to identify relevant studies until November 2020. RevMan 5.3 and Stata 12.0 software were used for meta-analysis. The odds ratio (ORs) and 95% confidence intervals (95% CIs) were used to assess the relationship between the MTHFR C677T polymorphism and H-type hypertension. RESULTS: A total of 14 studies involving 1769 cases and 1443 controls were included. The meta-analysis results showed the association between MTHFR C677T polymorphism and H-type hypertension with the homozygous codominant model (OR = 3.30, 95% CI = 1.94-5.60), heterozygous codominant model (OR = 2.34, 95% CI = 1.53-3.58), dominant model (OR = 1.79, 95% CI = 1.33-2.41), recessive model (OR = 2.70, 95% CI = 1.73-4.21),and the allelic model (OR = 1.82, 95% CI = 1.41-2.35). All p-values were less than 0.05. Therefore, MTHFR C677T polymorphism has a positive correlation with the risk of H-type hypertension. Among them, TT mutation has the greatest impact on the activity of this enzyme, which causes Hcy to rise and leads to H-type hypertension. CONCLUSION: In summary, our results provide sufficient data to support the hypothesis that the MTHFR C677T polymorphism is related to H-type hypertension susceptibility.

Comparison of obesity-related indicators for identifying metabolic syndrome among normal-weight adults in rural Xinjiang, China.

BACKGROUND: This study aimed to compare the ability of certain obesity-related indicators to identify metabolic syndrome (MetS) among normal-weight adults in rural Xinjiang. METHODS: A total of 4315 subjects were recruited in rural Xinjiang. The questionnaire, biochemical and anthropometric data were collected from them. Binary logistic regression was used to analyze the association between the z-score of each index and MetS. The area under the receiver-operating characteristic (ROC) curves were used to compare the diagnostic ability of each index. According to the cut-off value of each index, nomogram models were established and their diagnostic ability were evaluated. RESULTS: After adjusting for confounding factors, each indicator in different genders was correlated with MetS. Triglyceride-glucose index (TyG index) showed the strongest association with MetS in both males (OR = 3.749, 95%CI: 3.173-4.429) and females (OR = 3.521,95%CI: 2.990-4.148). Lipid accumulation product (LAP) showed the strongest diagnostic ability in both males (AUC = 0.831, 95%CI: 0.806-0.856) and females (AUC = 0.842, 95%CI: 0.820-0.864), and its optimal cut-off values were 39.700 and 35.065, respectively. The identification ability of the TyG index in different genders (males AUC: 0.817, females AUC: 0.817) was slightly weaker than LAP. Waist-to-height ratio (WHtR) had the similar AUC (males: 0.717, females: 0.747) to conicity index (CI) (males: 0.734, females: 0.749), whereas the identification ability of a body shape index (ABSI) (males AUC: 0.700, females AUC: 0.717) was relatively weak. Compared with the diagnostic ability of a single indicator, the AUC of the male nomogram model was 0.876 (95%CI: 0.856-0.895) and the AUC of the female model was 0.877 (95%CI: 0.856-0.896). The identification ability had been significantly improved. CONCLUSION: LAP and TyG index are effective indicators for identifying MetS among normal-weight adults in rural Xinjiang. Nomogram models including age, CI, LAP, and TyG index can significantly improve diagnostic ability.

Updating Framingham CVD risk score using waist circumference and estimated cardiopulmonary function: a cohort study based on a southern Xinjiang population.

PURPOSE: To explore the association between waist circumference (WC), estimated cardiopulmonary function (eCRF), and cardiovascular disease (CVD) risk in southern Xinjiang. Update the Framingham model to make it more suitable for the southern Xinjiang population. METHODS: Data were collected from 7705 subjects aged 30-74 years old in Tumushuke City, the 51st Regiment of Xinjiang Production and Construction Corps. CVD was defined as an individual's first diagnosis of non-fatal acute myocardial infarction, death from coronary heart disease, and fatal or non-fatal stroke. The Cox proportional hazards regression analysis was used to analyze the association between WC, eCRF and CVD risk. Restricted cubic spline plots were drawn to describe the association of the two indicators with CVD risk. We update the model by incorporating the new variables into the Framingham model and re-estimating the coefficients. The discrimination of the model is evaluated using AUC, NRI, and IDI metrics. Model calibration is evaluated using pseudo R2 values. RESULTS: WC was an independent risk factor for CVD (multivariate HR: 1.603 (1.323, 1.942)), eCRF was an independent protective factor for CVD (multivariate HR: 0.499 (0.369, 0.674)). There was a nonlinear relationship between WC and CVD risk (nonlinear χ2 = 12.43, P = 0.002). There was a linear association between eCRF and CVD risk (non-linear χ2 = 0.27, P = 0.6027). In the male, the best risk prediction effect was obtained when WC and eCRF were added to the model (AUC = 0.763((0.734,0.792)); pseudo R2 = 0.069). In the female, the best risk prediction effect was obtained by adding eCRF to the model (AUC = 0.757 (0.734,0.779); pseudo R2 = 0.107). CONCLUSION: In southern Xinjiang, WC is an independent risk factor for CVD. eCRF is an independent protective factor for CVD. We recommended adding WC and eCRF in the male model and only eCRF in the female model for better risk prediction.

Predictive power of a body shape index and traditional anthropometric indicators for cardiovascular disease: a cohort study in rural Xinjiang, China.

BACKGROUND: A body shape index (ABSI) has been proven to be related to a population's CVD incidence. However, the application of this indicator has produced different results. AIM: This study aimed to evaluate the applicability of the ABSI in predicting the incidence of CVD in rural Xinjiang, China, and compare it with waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and body mass index (BMI). SUBJECTS AND METHODS: 5375 people aged 18 years or older were included in the study. We used the Cox proportional hazard model to evaluate the relationship between WC, WHR, WHtR, BMI, and ABSI and the incidence of CVD, the area under the curve (AUC) to evaluate the predictive power of each anthropometric index for the incidence of CVD, and restricted cubic splines are used to analyse the trend relationship between anthropometric indicators and the incidence of CVD. RESULTS: After multivariate adjustment, standardised WC, WHR, WHtR, BMI, and ABSI all positively correlated with the incidence of CVD. WC had the highest HR (95% CI) value, 1.64 (1.51-1.78), and AUC (95% CI) value, 0.7743 (0.7537-0.7949). ABSI had the lowest HR (95% CI) value, 1.21(1.10-1.32), and AUC (95% CI) value, 0.7419 (0.7208-0.7630). In the sex-specific sensitivity analysis, the predictive ability of traditional anthropometric indicators for the incidence of CVD is higher than that of ABSI. CONCLUSIONS: In the rural areas of Xinjiang, the traditional anthropometric indicators of WC had better ability to predict the incidence of CVD than ABSI.

CARS Imaging Advances Early Diagnosis of Cardiac Manifestation of Fabry Disease.

Vibrational spectroscopy can detect characteristic biomolecular signatures and thus has the potential to support diagnostics. Fabry disease (FD) is a lipid disorder disease that leads to accumulations of globotriaosylceramide in different organs, including the heart, which is particularly critical for the patient's prognosis. Effective treatment options are available if initiated at early disease stages, but many patients are late- or under-diagnosed. Since Coherent anti-Stokes Raman (CARS) imaging has a high sensitivity for lipid/protein shifts, we applied CARS as a diagnostic tool to assess cardiac FD manifestation in an FD mouse model. CARS measurements combined with multivariate data analysis, including image preprocessing followed by image clustering and data-driven modeling, allowed for differentiation between FD and control groups. Indeed, CARS identified shifts of lipid/protein content between the two groups in cardiac tissue visually and by subsequent automated bioinformatic discrimination with a mean sensitivity of 90-96%. Of note, this genotype differentiation was successful at a very early time point during disease development when only kidneys are visibly affected by globotriaosylceramide depositions. Altogether, the sensitivity of CARS combined with multivariate analysis allows reliable diagnostic support of early FD organ manifestation and may thus improve diagnosis, prognosis, and possibly therapeutic monitoring of FD.

Monitoring Deuterium Uptake in Single Bacterial Cells via Two-Dimensional Raman Correlation Spectroscopy.

Raman-stable isotope labeling using heavy water (Raman-D2O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen-deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C-H stretching signals decreased in intensity over time, prior to the formation of the C-D stretching vibration signals. The intensity of the C-D signal gradually increased over time, and the shape of the C-D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C-D signal encompassed a combination of symmetric and antisymmetric CD2 and CD3 stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D2O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.

Effects of Chiral Molecule Modification on Surface Biosorption Behavior.

Antifouling materials have many important applications in biomedical devices and marine coating. Oligo(ethylene glycol) (OEG) or poly(ethylene glycol) (PEG) exhibit promising antifouling properties and are widely used in biomedical engineering. Chiral selection is an important phenomenon in biological processes. Because of the influence of steric hindrance, the modification of chiral molecules with different chirality at interfaces will affect the intermolecular interaction at the interfaces and lead to different structures of interfacial molecules. The difference of surface structures such as surface hydration structure would impact the adsorption of biomolecules on the surface, thus causing different varieties of cell adhesion and cell growth. In this study, the influence on surface hydration and surface cell adhesion of OEG self-assembled monolayers (SAMs) modified with cysteine showing different chirality are explored. The water structure at the interfaces of OEG/water in different conditions was probed with sum frequency generation vibrational spectroscopy (SFG-VS). The results show that the interfacial water structure can change significantly with either d-cysteine or l-cysteine modification on OEG. Water molecules are more ordered at the OEG/water interface under the d-cysteine modification on OEG SAMs, which improves the protein adsorption resistance of the surface. In contrast, l-cysteine modification would make the water less ordered at the OEG/protein solution interface and enhance the protein adsorption. Additionally, optical micrographs indicate that l-cysteine can significantly promote the OEG SAMs cell adhesion and growth, while d-cysteine exhibits an inhibitory effect, which is consistent with the results of SFG-VS experiments.

Metabolically healthy obesity and unhealthy normal weight rural adults in Xinjiang: prevalence and the associated factors.

BACKGROUND: This study aimed to describe the prevalence of metabolically healthy obesity (MHO) and metabolically unhealthy normal weight (MUNW) rural adults in Xinjiang and to explore their influencing factors. METHODS: We selected 13,525 Uyghur, Kazakh and Han participants in Kashi, Yili and Shihezi areas in Xinjiang from 2009 to 2010. Weight status was classified according to body mass index. Metabolic phenotype was further defined based on the National Cholesterol Education Program Adult Treatment Panel III criteria. RESULTS: The prevalence of normal weight, overweight, and obesity were 51.6, 30.2, and 14.4%, respectively. The mean age of the population was 45.04 years. The prevalence of MHO was 5.5% overall and was 38.5% among obese participants. The prevalence of MUNW was 15.5% overall and was 30.1% among normal weight participants. A metabolically healthy phenotype among obese individuals was positively associated with females and vegetable consumption ≥4 plates per week. However, this was inversely associated with higher age, red meat consumption ≥2 kg per week, and larger waist circumference (WC). Conversely, a metabolically unhealthy phenotype among normal-weight individuals was positively associated with higher age, red meat consumption ≥2 kg per week, and larger WC; this was however inversely associated with vegetable consumption ≥4 plates per week. CONCLUSIONS: The prevalence of MHO among obese adults in Xinjiang is higher than that of Han adults, while the prevalence of MUNW among normal weight adults is lower than that among Han adults. In obese and normal weight participants, higher age, more red meat consumption, and larger WC increase the risk of metabolic abnormality, and more vegetable consumption reduces the risk of metabolic abnormality.

The Association of Metabolic Syndrome with the development of cardiovascular disease among Kazakhs in remote rural areas of Xinjiang, China: a cohort study.

BACKGROUND: Metabolic syndrome (MS) can promote the development of cardiovascular disease (CVD). The objective of this study was to examine the association of MS and its components with CVD, to further prevent and control CVD in Kazakhs. METHODS: In the cohort study, a total of 2644 participants completed the baseline survey between April 2010 and December 2012.The follow-up survey was conducted from April 2016 to December 2016 and was completed by 2286 participants (86.46% follow-up rate). Cox regression was used to evaluate the association of each component and the number of combinations of MS components on the development of CVD. RESULTS: A total of 278 CVD patients were enrolled from rural residents of Xinjiang. The average age of the MS and non-MS groups was 46.33 and 38.71 years, respectively. Independent associations with CVD were found for elevated blood pressure (BP) (adjusted hazard ratio (HR) [aHR] = 1.50,95%confidence interval [CI]: 1.08-2.08), elevated waist circumference (WC) (aHR = 1.60, 95%CI: 1.19-2.15), and elevated triglycerides (TG) (aHR = 1.44, 95%CI: 1.04-2.01). Participants with one to 5 MS components had an increased HR for developing CVD, from 1.82to 8.59 (P for trend < 0.001), compared with those with no MS components. The risk of developing CVD increased when TG and WC coexisted (aHR = 2.16, 95%CI: 1.54-3.04)), when TG and BP coexisted ((aHR = 1.92, 95%CI: 1.32-2.79), and when WC and BP coexisted (aHR = 1.93, 95%CI: 1.33-2.82)). However, no significant interactions were found between BP, WC, and TG. CONCLUSIONS: Elevations of BP, WC, and TG were independent risk factors for CVD in Kazakhs. Control of these factors is important to prevent CVD in this population.

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application.

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient's plasma and serum sample using vibrational spectroscopy.

Spatiotemporal Organization of Biofilm Matrix Revealed by Confocal Raman Mapping Integrated with Non-negative Matrix Factorization Analysis.

Biofilms are microbial aggregates of microorganisms surrounded by a hydrogel-like matrix formed by extracellular polymeric substances (EPS). The formation of biofilms is intrinsically complex, from the attachment of microbial cells to the dispersion of the biofilm. Meanwhile, the three-dimensional framework built up by EPS changes with time and protects the microorganisms against environmental stress. Simultaneously acquiring chemical and structural information within the biofilm matrix is vital for the cognition and regulation of biofilms, yet it remains a great challenge due to the sample complexity and the limited approaches. In this study, confocal Raman microscopy and non-negative matrix factorization (NMF) analysis were combined to investigate spatiotemporal organization of Escherichia coli biofilms during development at molecular-level detail. The alternating non-negative least-squares (ANLS) approach was incorporated with the sequential coordinate-wise descent (SCD) algorithm to realize the NMF analysis for the large-scale hyperspectral data set. As a result, three components, including bacteria, protein, and polyhydroxybutyrate (PHB), were successfully resolved from the spectra of E. coli biofilm. Furthermore, the structural changes of biofilms could be visualized and quantified by their abundances derived from the NMF analysis, which might be related to the nutrient and oxygen gradient and physiological functions. This methodology provides a comprehensive understanding of the chemical constituents and their spatiotemporal distribution within the biofilm matrix. Furthermore, it also shows great potential for the analysis of unknown and complex biological samples with 3D Raman mapping.

Influence of Carbon Sources on Quantification of Deuterium Incorporation in Heterotrophic Bacteria: A Raman-Stable Isotope Labeling Approach.

A rapid and reliable method for the differentiation between active and inactive bacteria at single cell level is urgently needed in many fields including clinical diagnosis and environmental microbiology, to understand the contribution of metabolically active bacteria in fundamental processes triggering environmental and public health risks. Here, using heavy water (D2O) with Raman-stable isotope labeling (Raman-D2O), we evaluated the reliability of the quantification of deuterium uptake, a well-known indicator for the general metabolic activity of bacteria. For this purpose, we based our study on the quantification of deuterium assimilation from heavy water into single bacterial cells to check the influence of carbon source and bacterial identity on the deuterium uptake. We show that compared to complex carbon substrates, the deuterium assimilation is higher in the presence of simpler substrates such as sugars but differs significantly among bacterial isolates. Despite this variability, the developed classification models could differentiate deuterium labeled and nonlabeled single cells with high sensitivity and specificity. Highlighting the variability between single bacterial cells, the study emphasizes the challenges in establishing a threshold in terms of deuterium uptake to distinguish deuterium labeled and nonlabeled cells. Overall, we show that the Raman-D2O approach, when coupled with chemometrics, constitutes a powerful approach for monitoring single bacterial cells.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Deep learning for 'artefact' removal in infrared spectroscopy.

It has been well recognized that infrared spectra of microscopically heterogeneous media do not merely reflect the absorption of the sample but are influenced also by geometric factors and the wave nature of light causing scattering, reflection, interference, etc. These phenomena often occur simultaneously in complex samples like tissues and manifest themselves as intense baseline profiles, fringes, band distortion and band intensity changes in a measured IR spectrum. The information on the molecular level contained in IR spectra is thus entangled with the geometric structure of a sample and the optical model behind it, which largely hinders the data interpretation and in many cases renders the Beer-Lambert law invalid. It is required to recover the pure absorption (i.e., absorbance) of the sample from the measurement (i.e., apparent absorbance), that is, to remove the 'artefacts' caused merely by optical influences. To do so, we propose an artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape convolutional neural network (1D U-Net), and based our study on poly(methyl methacrylate) (PMMA) as materials. To start, a simulated dataset composed of apparent absorbance and absorbance pairs was generated according to the Mie-theory for PMMA spheres. After a data augmentation procedure, this dataset was utilized to train the 1D U-Net aiming to transform the input apparent absorbance into the corrected absorbance. The performance of the artefact removal was evaluated by the hit-quality-index (HQI) between the corrected and the true absorbance. Based on the prediction and the HQI of two experimental and one simulated independent testing datasets, we could demonstrate that the network was able to retrieve the absorbance very well, even in cases where the absorbance is completely overwhelmed by extremely large 'artefacts'. As the testing datasets bear different patterns of absorbance and 'artefacts' to the training data, the promising correction also indicated a good generalization performance of the 1D U-Net. Finally, the reliability and computational mechanism of the trained network were illustrated via two interpretation approaches including a direct visualization of layer-wise outputs as well as a saliency-based method.