Multivariate Curve Research Articles

Unlocking New Capabilities in the Analysis of GC × GC‐TOFMS Data With Shift‐Invariant Multi‐Linearity

ABSTRACTThis paper introduces a novel deconvolution algorithm, shift‐invariant multi‐linearity (SIML), which significantly enhances the analysis of data from two‐dimensional gas chromatography instruments coupled to a time‐of‐flight mass spectrometer (GC × GC‐TOFMS). Designed to address the challenges posed by retention time shifts and high noise levels, SIML incorporates wavelet‐based smoothing and Fourier‐transform based shift‐correction within the multivariate curve resolution‐alternating least squares (MCR‐ALS) framework. We benchmarked the SIML algorithm against non‐negativity constrained MCR‐ALS and parallel factor analysis 2 with flexible coupling (PARAFAC2 × N) using both simulated and real GC × GC‐TOFMS datasets. Our results demonstrate that SIML provides unique solutions with significantly improved robustness, particularly in low signal‐to‐noise ratio scenarios, where it maintains high accuracy in estimating mass spectra and concentrations. The enhanced reliability of quantitative analyses afforded by SIML underscores its potential for broad application in complex matrix analyses across environmental science, food science, and biological research.

Probing the Intramolecular Folding of Nucleic Acids with Native Ion Mobility Mass Spectrometry: Strategies and Caveats.

The goal of native mass spectrometry is to obtain information on noncovalent interactions in solution through mass spectrometry measurements in the gas phase. Characterizing intramolecular folding requires using structural probing techniques such as ion mobility spectrometry. However, inferring solution structures of nucleic acids is difficult because the low-charge state ions produced from aqueous solutions at physiological ionic strength get compacted during electrospray. Here we explored whether native supercharging could produce higher charge states that would better reflect solution folding, and whether the voltage required for collision-induced unfolding (CIU) could reflect preserved intramolecular hydrogen bonds. We studied pH-responsive i-motif structures with different loops, and unstructured controls. We also implemented a multivariate curve resolution procedure to extract physically meaningful pure components from the CIU data and reconstruct unfolding curves. We found that the relative unfolding voltages reflect to some extent, but not always unambiguously, the number of intramolecular hydrogen bonds that were present in solution. Reaching phosphate charging densities over 0.25 makes it easier to discriminate between structures, and the use of native supercharging agents is thus essential. We also uncovered several caveats in data interpretation: (1) when different structures (for example the i-motif with and without hairpin) unfold via different pathways, the unfolding voltages do not necessarily reflect the number of hydrogen bonds, (2) unstructured controls also undergo unfolding, and the base composition influences the unfolding voltage, (3) changing the solution pH also unexpectedly changed the unfolding voltage, and (4) the ion mobility patterns become more complicated when two structures are present simultaneously, such as an i-motif and a harpin, because of opposite effects on the collision cross section upon activation.

Mapping active pharmaceutical ingredients Distributions in tablets using Time-of-Flight secondary ion mass spectrometry with multivariate curve resolution

This study introduces an advanced approach to identifying signals associated with indapamide, amlodipine, and perindopril in solid dosage form using time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate curve resolution (MCR). The primary objective was to obtain and characterize these signals through MCR factors, which then led to identifying unique signals specific to each active pharmaceutical ingredient (API). These unique signals were crucial for constructing detailed 2D and 3D images of the API distribution within the tablet. The results demonstrated that indapamide and amlodipine exhibited multiple unique peaks, while perindopril had only one. Additionally, the study revealed non-homogeneous distribution patterns of APIs and excipients, such as SiO2 and Mg stearate. This work highlights the significance of applying advanced analytical techniques such as ToF-SIMS and MCR in the pharmaceutical field to ensure consistent therapeutic efficacy through a better understanding of API distribution.

Hyperspectral Raman imaging with multivariate curve resolution-alternating least square (MCR-ALS) analysis for xylazine-containing drug mixtures

Xylazine, increasingly implicated in illicit opioid overdose deaths, poses a significant public health threat due to its synergistic effects with fentanyl and resistance to naloxone reversal. Despite its rising prevalence, xylazine is not classified as a controlled substance, leading to its exclusion from routine forensic screening. This study introduces a novel analytical method combining Raman hyperspectral imaging with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to detect xylazine in drug mixtures containing common excipients such as acetaminophen, dipyrone, and mannitol. Utilizing only non-negativity constraints, MCR-ALS successfully resolved the Raman spectrum of xylazine at levels as low as 5 % without reference spectra. The method demonstrated robust performance, with percent variance explained (R²) values of 99.60 %, 99.80 %, and 99.91 % for the drug mixtures containing 25 %, 10 %, and 5 % xylazine, respectively.

The Association Between Body Mass Index and 28-day Mortality in Patients With Sepsis: A Retrospective Cohort Study.

Sepsis is a severe clinical syndrome with high morbidity and mortality in intensive care units (ICUs). Body Mass Index (BMI) shows a rising trend of obese patients being admitted to ICUs. The relationship between BMI and the clinical outcome of sepsis remains highly debated. The data used in this study were sourced from the Intensive Care Information Center IV (MIMIC-IV) database. Baseline information extracted within 24 hours of ICU admission was categorized according to World Health Organization (WHO)'s BMI classifications. A multivariate Cox regression model and curve fitting assessed the independent correlation between BMI and the primary outcome. A total of 7836 patients were included in the study and categorized into five groups based on BMI. The overall 28-day mortality rate was 21.94% (1719/7836). Class I obesity (17.14%) and class II/III obesity (13.49%) individuals tended to be younger and male. Compared to patients with normal BMI (32.55%), those with low BMI (5.79%) had a 47% increased risk of 28-day mortality (HR 1.47, 95% CI 1.16-1.85, P = 0.0013), while class II/III obesity patients had a 17% lower 28-day mortality rate (HR 0.83, 95% CI 0.71-0.97, P = 0.0218). Curve fitting revealed a nonlinear relationship between BMI and 28-day mortality. The Kaplan-Meier survival analysis highlighted variations in survival rates across the five groups (P = 0.0123), with underweight patients exhibiting poorer survival outcomes. In sepsis patients, a low BMI is related to higher 28-day mortality compared to those with a normal BMI. Conversely, patients with a BMI≥35kg/m2 have significantly reduced mortality risks.

Development of an Attenuated Total Reflectance-Ultraviolet-Visible Probe for the Online Monitoring of Dark Solutions.

Optical spectroscopy is a valuable tool for online monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy can monitor the concentration of analytes as well as identify the speciation and oxidation state. However, it can be difficult or impossible to employ UV-vis-based sensors in chemical systems that are very dark (i.e., have a high optical density), requiring exceedingly short path lengths (for transmission approaches) or an effective means of backscattering (for reflectance approaches). Examples of processes that produce highly absorbing solutions and that would benefit significantly from the diagnostic potential of optical sensors include used nuclear fuel recycling and molten salt systems with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for various processes can be time-consuming and expensive. Here, we evaluate the ability of a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible "path lengths" by altering the length of the ATR fiber that was submerged within solution during spectral measurements. Measurements within the ATR sensor cell were compared to measurements gathered by transmission UV-vis of samples within a commercially available 1 cm optical cuvette. The ATR-UV-vis probe was capable of measuring absorbance of solutions with a chromophore concentration 600 times greater than that in the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the investigation of dark solutions is a promising avenue for use in online monitoring of nuclear processes.

Chemometric modeling of spectroscopic data for characterizing the visible-light-driven photocatalytic N-dealkylation of rhodamine B on a TiO2 film.

The green-light driven photocatalytic N-deethylation reaction of Rhodamine B (RhB) on a TiO2 film was investigated by UV-vis absorption and fluorescence emission spectroscopies, in addition to HPLC and HR-MS, to ascertain the nature of the reaction products. The evolution of the photocatalytic reaction was chemometrically analyzed using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of the spectroscopic data to obtain the kinetic and spectral decomposition of the RhB derivatives involved in the reaction. This was then compared with the results obtained by standard HPLC analysis. The MCR-ALS analysis yielded satisfactory spectral and kinetic profiles for RhB and the fully deethylated product, Rhodamine 110. However, the spectral profiles for the N-triethyl (3EtRh) and the mixture of the two isomeric N-diethyl (2EtRh) derivatives exhibited some spectral distortions due to significant spectral overlap between these compounds. In contrast to the HPLC analysis, the MCR-ALS could not resolve the N-ethylrhodamine (EtRh) derivative. The deethylation reactions occurred via independent zero-order steps at the surface of TiO2, indicating that the RhB degradation reaction is governed by the adsorption-desorption equilibrium of the dye and derivatives on the photocatalyst surface, thereby enhancing the diffusion of compounds on the surface.

Simultaneously quantifying a novel five-component anti- migraine formulation containing ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine using UV spectrophotometry and chemometric models

This study presents a new method for simultaneously quantifying a complex anti-migraine formulation containing five components (ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine) using UV spectrophotometry and chemometric models. The formulation presents analytical challenges due to the wide variation in component concentrations (ERG: PRO: CAF: CAM: MEC ratio of 0.075:20:8:5:4) and highly overlapping UV spectra. To create a comprehensive validation dataset, the Kennard-Stone Clustering Algorithm was used to address the limitations of arbitrary data partitioning in chemometric methods. Three different chemometric models were evaluated: Classical Least Squares (CLS), Partial Least Squares (PLS), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Among these, MCR-ALS demonstrated excellent performance, achieving recovery values of 98–102% for all components, accompanied by minimal root mean square errors of calibration (0.072–0.378) and prediction (0.077–0.404). Moreover, the model exhibited high accuracy, with relative errors ranging from 1.936 to 3.121%, bias-corrected mean square errors between 0.074 and 0.389, and a good sensitivity (0.2097–1.2898 μg mL−1) for all components. The Elliptical Joint Confidence Region analysis further confirmed the predictive performance of the models, with MCR-ALS consistently showing the smallest ellipses closest to the ideal point (slope = 1, intercept = 0) for most analytes, indicating superior accuracy and precision. The approach's sustainability was rigorously assessed using six advanced metrics, validating its environmental friendliness, economic viability, and practical application. This approach effectively resolves complex pharmaceutical formulations, contributing to sustainable development objectives in quality control processes.

Preoperative D‑dimer level and prognosis in patients diagnosed with gastric cancer and undergoing gastric cancer radical surgery: A retrospective cohort study.

Gastric cancer, one of the most aggressive tumors affecting the digestive tract, has seen a rise in both incidence and mortality rates in recent years, with a lack of clear prognostic indicators. D-dimer, a byproduct of fibrin degradation, is a conventional coagulation biomarker that has been investigated as a potential adverse prognostic factor for postoperative cancer patients. However, conflicting results exist, with some studies suggesting that D-dimer is an independent risk factor for the postoperative prognosis of gastric cancer, and others suggesting that there is no significant association between D-dimer level and gastric cancer prognosis. This controversy underscores the need for further research to determine the true influence of D-dimer level on the prognosis of postoperative cancer patients, including exploring the linear and non-linear associations, and identifying optimal critical values before surgery. The present study aimed to assess the association between preoperative D-dimer levels and 5-year overall survival (OS) rate in patients undergoing radical surgery for gastric cancer. Data was analyzed from patients diagnosed with gastric cancer who underwent a radical gastrectomy at Shanxi Cancer Hospital (Taiyuan, China) in 2017 and were followed up for 5 years until December 2022. Preoperative D-dimer levels and tumor-related indicators were collected as covariates from hospital records, with patient follow-up information obtained from the hospital's tracking system. Utilizing multivariate Cox regression, curve fitting and inflection point analysis, the present study sought to investigate the link between preoperative D-dimer levels and 5-year OS rate following radical surgery for gastric cancer. After strict admission and exclusion procedures, a total of 133 patients were included in the study and were then classified based on D-dimer level. Based on the mean value, the D-dimer levels were segregated into two distinct cohorts: The D-dimer_high group (n=66) and the D-dimer_low group (n=67). Cox multiple regression analysis was conducted using both continuous and binary variables. The results showed no effect in terms of the continuous variables, but higher preoperative D-dimer levels were significantly associated with a higher 5-year overall survival compared with lower preoperative D-dimer levels in the categorical variables. Therefore, curve fitting was performed, which indicated that the association between D-dimer level and 5-year survival rate after radical gastrectomy for cancer showed a U-shaped curve, with P<0.001, high and low D-dimer levels both indicated poorer prognosis. A curvilinear correlation was demonstrated between preoperative D-dimer levels and the 5-year survival rate following radical gastrectomy for cancer. Subsequently, an inflection point analysis found that the inflection point was located at 100-200 ng/ml. In conclusion, in patients undergoing radical gastrectomy for gastric cancer, preoperative D-dimer can be used to indicate the current state of the tumor to some extent through blood coagulation status. Notably, a U-shaped association exists between D-dimer levels and 5-year postoperative survival rate in gastric cancer. This association demonstrates varying effects across different intervals. Specifically, D-dimer levels <100 ng/ml are associated with a worse prognosis, whereas levels >200 ng/ml are associated with a better prognosis.

Application of excitation-emission matrix fluorescence spectroscopy and chemometrics for quantitative analysis of emulsified oil concentration

Emulsified oil concentration is an important index for quantitative analysis of sea surface oil spill pollution, and the development of a fast and effective quantitative analysis method for emulsified oil concentration plays a crucial role in the estimation of oil spill volume and post-spill assessment. A quantitative analysis method for emulsified oil concentration based on excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics was proposed. Firstly, the EEM fluorescence spectra of two emulsified oils were measured using a FLS1000 fluorescence spectrometer. Then, the measured EEM fluorescence spectra were decomposed by parallel factor analysis (PARAFAC), and several key excitation wavelengths were filtered from the loading matrix obtained from the decomposition. Subsequently, the three-band fluorescence index (TBFI) at these excitation wavelengths was calculated and combined with the optimal band selection algorithm, from which the optimal emission band combinations were selected. Finally, the selected optimal emission bands were combined with partial least squares regression (PLSR) to establish a prediction model for emulsified oil concentration. By comparing the prediction results with those based on PARAFAC-PLSR and multivariate curve resolved-alternating least squares (MCR-ALS)-PLSR models, the TBFI-PLSR model showed the best results in the quantitative analysis of emulsified oil concentration. The coefficient of determination, mean square relative error, and ratio of performance to interquartile distance for the gasoline and diesel fuel emulsion validation sets were 0.93, 3.67%, 4.72, and 0.93, 3.72%, 4.60, respectively.

Comparative insights into the inhibition of pancreatic lipase by quercetin using Mg-MOF-74 as nano-carrier: Inhibitory interaction, inhibition activity and conformational change

As one of the focus causes of death in the world, obesity has attracted much attention in recent years. Pancreatic lipase (PL), an outstanding target for obesity, is an enzyme which can decompose dietary lipids. Based on the good performance and drug loading effect of Mg-MOF-74, the interaction mechanism and inhibition mechanism of quercetin (Que) loaded with Mg-MOF-74 on PL were studied by multispectral technology and enzyme inhibition kinetic analysis. The effect of Mg-MOF-74 loaded Que on PL is a mix quenching process dominated by hydrophobic force. Fluorescence spectral data of the nanocomposite system were resolved by multivariate curve resolution-alternating least squares. The complex system equilibrated at a [Que]:[PL] ratio nearly 2. The conformation of PL changes under the influence of Mg-MOF-74 and Mg-MOF-74-Que were demonstrated through 3D fluorescence, Fourier transform infrared spectroscopy and circular dichroism spectrum. Molecular docking studies showed that more hydrogen bonds were formed and confirmed the stronger binding affinity of PL when Que loaded on Mg-MOF-74 (−103.17 kcal mol−1). Further analysis of the inhibition kinetics showed that PL-Que belonged to the mixed inhibition type, PL-Mg-MOF-74-Que belonged to the non-competitive inhibition type, and the inhibition constants were Ki, Que (16.63 ± 7.49 × 10−6 mol L−1) > Ki, Mg-MOF-74-Que (10.68 ± 2.04 × 10−6 mol L−1). Through investigating the inhibitory mechanism and interaction between Mg-MOF-74-Que and PL on a molecular level, it affords a new idea for the design of new PL inhibitors and a new method for the potential treatment of obesity.

Modifiable factors for somatic symptom persistence in patients with somatic symptom disorder: study protocol for a longitudinal cohort with an embedded ecologically momentary assessment (SOMA.SSD)

IntroductionSomatic symptom disorder (SSD) is a distressing mental disorder characterised by the presence of at least one persistent somatic symptom and a significant psychological burden due to the symptom(s), as well as excessive thoughts, feelings or behaviours related to them or associated health concerns. The primary aim of our study is to deepen the understanding of risk factors and mechanisms for somatic symptom persistence. Specifically, we will investigate the role of patients’ expectations and somatic comorbidity regarding symptom persistence.Methods and analysisIn a prospective 12-month cohort study, n=240 patients with SSD will be recruited from the outpatient clinics of the Department of Psychosomatic Medicine and Psychotherapy at the University Medical Center Hamburg-Eppendorf. The roles of two hypothesised predictors (ie, expectations and comorbidity) will be analysed together with known predisposing and maintaining factors of persistent somatic symptoms. The study entails macrolevel and microlevel assessments to investigate symptom severity over different timespans. Assessments at the macrolevel take place at baseline, 6 and 12 months. At the microlevel, a 10-day ecological momentary assessment study will be implemented to elucidate patients’ dynamic experience with somatic symptoms in their natural environments. In order to elicit possible framing effects on the 10-day course of symptom severity, participants are randomised to either a positive or a negative expectation framing group. Cross-lagged panel models will be used to analyse data at the macrolevel. At the microlevel, multivariate latent growth curve models will be conducted to relate temporal changes in predictor and outcome variables to one another.Ethics and disseminationThe study was approved by the Ethics Committee of the Medical Associations Hamburg, Germany (2020-10197-BO-ff). The results will enable us to draw conclusions regarding the role of expectations for future targeted treatment options and regarding the utility of somatic comorbidity as a potential diagnostic specifier of SSD. Dissemination of our results will be achieved through scientific publications, and lay summaries for study participants, patient advocacy groups and the general public.Trial registration numberISRCTN36251388.

Non-targeted volatilomics for the authentication of saffron by gas chromatography-ion mobility spectrometry and multivariate curve resolution

Non-targeted volatilomics for the authentication of saffron by gas chromatography-ion mobility spectrometry and multivariate curve resolution

Use of a rugged mid-infrared spectrometer for in situ process analysis of liquids

The widespread application of mid-infrared (MIR) spectroscopy for process monitoring is currently limited by the poor transmission of MIR light through fibre optics. In this work, the performance of a novel and robust MIR spectrometer has been evaluated for practical deployment in a pilot plant or production environment. The spectrometer utilises a Sagnac interferometer design containing no moving parts and is directly attached to an attenuated total reflectance probe, eliminating the need for fibre optics. The quantitative performance of the spectrometer for the in situ analysis of ternary solvent mixtures was assessed. The predictions obtained by partial least squares were accurate (root mean square error of prediction of < 1% w/w) and comparable to those of a benchmark Michelson-based spectrometer with a fibre-coupled probe, which is more amenable to process development in a laboratory or pilot plant. Calibration transfer between the two spectrometers was performed using the spectral space transformation method to mimic the scenario of the scale-up of a process from the laboratory to pilot scale or from a pilot plant to production scale, where the two different MIR instruments might be deployed. The ability to perform in situ reaction monitoring with the robust Sagnac-based spectrometer was then demonstrated. Spectra acquired during an esterification reaction were resolved using multivariate curve resolution, to produce concentration profiles of each component. These results demonstrate the suitability of this rugged spectrometer for quantitative in situ monitoring of liquid processes, opening up new opportunities for process monitoring in the MIR region.

Early plant disease diagnosis through handheld UV-Vis transmittance spectrometer with DD-SIMCA one-class classification and MCR-ALS bilinear decomposition

A novel non-destructive analytical method for early diagnosis of two bacterial diseases, Pseudomonas syringae and Xanthomonas euvesicatoria, in tomato plants, using ultraviolet-visible (UV-Vis) transmittance spectroscopy and chemometric tools, is developed. Plant-pathogen interactions caused tissue damage that generated non-linear data patterns compared to the control set (healthy samples), which complicates traditional discrimination models, even when employing non-linear discriminant approaches. Alternatively, an authentication task to conduct one-class classification relying on a data-driven version of soft independent modeling of class analogy (DD-SIMCA) is a wise choice due to its quadratic approach, proper to deal with non-linear data. DD-SIMCA detached the target class (control healthy plant leaflet tissues) from all other samples (target class and non-target class of plant leaflet tissues inoculated with two bacteria, even before the manifestation of macroscopic lesions associated with the diseases) by capturing the main similarities within the samples of the target class through the full distance that acts as a classification analytical signal, reaching 100% sensitivity in the training and validation sets. Multivariate curve resolution constrained analysis allowed the description of the bacterial inoculation process on diseased tissues through pure spectral signatures. DD-SIMCA results indicate that non-target class of samples with higher proximity to the acceptance boundary suggested that they were at earlier stages of infection compared to more distant ones, presenting lower full distance values. These findings reveal that a handheld UV-Vis transmittance spectrometer is sufficiently sensitive to be used in acquiring biological data with suitable chemometric modeling for early disease diagnosis and prompt intervention.

Modeling Soil Organic Carbon Content Using Mid-Infrared Absorbance Spectra and a Nonnegative MCR-ALS Analysis

A new approach based on mid-IR absorbance spectra is proposed for modeling total organic carbon (TOC) content of soils. This approach involves a first-time bilinear decomposition of soil mid-IR absorbance spectra using nonnegative multivariate curve resolution (MCR) with an alternating least squares (ALS) algorithm. An MCR-ALS-derived component signifies a chemically meaningful combination of soil constituents. A new mechanistic model has been developed to link the soil composition, expressed in terms of ratios of MCR-ALS-based concentration scores of the identified components, to soil TOC value. Nonnegative MCR-ALS decomposition, performed for 213 mid-IR absorbance spectra of soil samples collected in the north and south of Israel, yielded four components. Fitting the mechanistic model-derived TOC to the experimental TOC values exhibited a TOC content threshold that affected model performance. TOC content <1.0 % w w-1 was represented by the root mean square deviation of 0.18% and 62% of the variance explained, whereas for larger TOC values, a sharp decline in model performance was observed. The existence of this TOC threshold in determining model performance suggested that successful TOC modeling (below 1%) could be indirect and related to IR spectral fingerprints of minerals binding soil organic matter (SOM) and forming organo-mineral complexes. Thus, a SOM fraction having weak interactions with soil minerals was poorly accounted for in a particular set of soil samples. This dependency of the model performance on soil TOC range proposes that it might be possible to differentiate between soil samples based on their different dominating SOM pools, mineral-associated ones and those having weak interactions with minerals. Further studies, especially in soils with high SOM content, are needed to validate our findings.

Quantitative Standardized Expansion Assay: An Artificial Intelligence-Powered Morphometric Description of Blastocyst Expansion and Zona Thinning Dynamics.

Artificial intelligence applied to time-lapse microscopy may revolutionize embryo selection in IVF by automating data collection and standardizing the assessments. In this context, blastocyst expansion dynamics, although being associated with reproductive fitness, have been poorly studied. This retrospective study (N = 2184 blastocysts from 786 cycles) exploited both technologies to picture the association between embryo and inner-cell-mass (ICM) area in µm2, the ICM/Trophectoderm ratio, and the zona pellucida thickness in µm (zp-T) at sequential blastocyst expansion stages, with (i) euploidy and (ii) live-birth per transfer (N = 548 transfers). A quantitative-standardized-expansion-assay (qSEA) was also set-up; a novel approach involving automatic annotations of all expansion metrics every 30 min across 5 h following blastulation. Multivariate regressions and ROC curve analyses were conducted. Aneuploid blastocysts were slower, expanded less and showed thicker zp. The qSEA outlined faster and more consistent zp thinning processes among euploid blastocysts, being more or as effective as the embryologists in ranking euploid embryo as top-quality of their cohorts in 69% of the cases. The qSEA also outlined faster and more consistent blastocyst expansion and zp thinning dynamics among euploid implanted versus not implanted blastocysts, disagreeing with embryologists' priority choice in about 50% of the cases. In conclusion, qSEA is a promising objective, quantitative, and user-friendly strategy to predict embryo competence that now deserves prospective validations.

A risk score for ventricular arrhythmia in mitral valve prolapse

Abstract Background Prior studies have identified that complex and frequent ventricular ectopy (cfVE) predicts sudden death in patients with mitral valve prolapse (MVP). Purpose To develop a risk stratification score which predicts rates of cfVE in patients with MVP based on clinical, ECG and echocardiographic features. Methods Data was collected on 637 patients aged 18–90 years with MVP at our institution between 2016-2019. Complex and frequent ventricular ectopy was defined as the presence of &amp;gt; 1% premature ventricular contraction (PVC) burden or non-sustained ventricular tachycardia on Holter monitoring or &amp;gt; 1 PVC on 12-lead ECG. Univariate analysis was performed to identify independent predictors of cfVE. Factors found to be statistically significant on univariate analysis were then evaluated using multivariate logistic regression. Variables analyzed included age greater than 65, moderate or greater mitral regurgitation, female gender, African American race, atrial fibrillation, coronary artery disease, congestive heart failure, inferior or lateral T wave inversion, bileaflet (vs. unileaflet) prolapse, myxomatous mitral valve morphology, mitral annular dysjunction, left ventricular ejection fraction less than 40%, left ventricular hypertrophy, diabetes, and hypertension. Risk points were attributed to multivariate predictors of cfVE using their odds ratios. Predicted probability of cfVE, derived by logistic regression, was used to categorize scores into low, moderate, and high risk. Rates of cfVE were compared between risk groups using Pearson’s Chi-squared test. All-cause mortality was compared between risk score groups with Kaplan-Meier analysis. Results Complex and frequent ventricular ectopy was present in 182 (28.6%) patients. Multivariate predictors of cfVE were mitral annular disjunction (MAD, OR 3.2, 95% CI 1.9-5.4), bileaflet (vs. unileaflet) prolapse (OR 2.1, 95% CI 1.4-3.3), congestive heart failure (OR 2.2 95% CI 1.3-3.7) and inferior or lateral T wave inversion (TWI, OR 2.1, 95% CI 1.3-3.2). Risk points of 1.5 for MAD and 1 for bileaflet prolapse, CHF and TWI were totaled to group patients into low (score 0, n=274, 43.0%), moderate (score 1-2, n=276 ,43.3%) and high (score 2.5-4.5, n=87,13.7%) risk groups. Rates of cfVE were 11.7% for low, 35.5% for moderate and 59.8% for high-risk groups (p&amp;lt;0.0001). On Kaplan Meier analysis, risk group significantly correlated with mortality (p&amp;lt;0.0008). Conclusion In patients with MVP this risk stratification tool, using the presence of mitral annular disjunction, bileaflet prolapse, history of congestive heart failure and T-wave inversion, represents a viable method of predicting risk of cfVE, which correlates with all-cause mortality.Univariate and multivariate analysisKaplan-Meier Curve

Comparative analysis of a novel preoperative stratification method versus traditional scores in patients with severe aortic stenosis undergoing aortic valve surgery

Abstract Introduction It is crucial to consider factors that complicate the indication of transcatheter aortic valve replacement (TAVR) in patients with aortic stenosis (AS), such as narrow valve ring, extensive calcification or inadequate vascular access. (1-4) Thus, aortic valve replacement surgery (SAVR) remains a recommended indication, with its pre-surgical stratification being a challenge. (1-3) Despite the widespread use of scores like the STS and EuroSCORE II, they exhibit limitations, being validated primarily for populations outside Latin America and focusing predominantly on individual factors while often neglecting structural parameters like left ventricular hypertrophy, diastolic dysfunction, or left atrium volume. (3-5) Purpose This study aimed to assess whether a novel modified Généreux stratification method offers superior predictive value for postoperative mortality in AS patients compared to traditional scoring systems. Methods We conducted a retrospective, single-center study involving patients with confirmed severe AS who underwent aortic valve replacement surgery between January 2017 and December 2021. The new stratification method categorized patients into three stages based on echocardiographic parameters (see Figure 1). Univariate and multivariate Cox regression analyses were conducted to identify predictors of mortality, with survival analysis performed using Kaplan-Meier curves. A p-value &amp;lt;0.05 was considered statistically significant. Results A total of 508 patients were included in the study. Stage 1 patients had a lower median age (62 years vs. 67 years in stages 2 and 3). The median EuroSCORE and STS were 2.75 and 2.62%, respectively, with stage 3 showing significantly higher scores (p ≤0.001). Over a median follow-up of 81 months, 56 deaths occurred (11%). Kaplan-Meier curve analysis revealed significant differences in all-cause mortality among the three groups (HR 4.073, log-rank p≤0.001), as illustrated in Figure 1. Multivariate analysis identified the three preoperative stages (HR 3.22, [95% CI 1.44-7.20], p=0.004) and mean transaortic gradient (HR 0.96, [95% CI 0.92-0.99], p=0.021) as independent predictors of mortality. The new stages demonstrated a superior area under the ROC curve of 0.758 compared to traditional scores (see Figure 1). Conclusions The novel three-stage preoperative classification and low transaortic gradient serve as significant predictors of mortality in patients undergoing SAVR. These stages exhibit robust predictive capability for mortality compared to EuroSCORE II and STS.Baseline and multivariate analysisKaplan-meier and ROC curve

Impact of progesterone concentration on hCG trigger day on clinical outcomes with cleavage-stage embryo transfer in in vitro fertilization cycles with an antagonist protocol

Objective: To investigate the impact of the progesterone concentration on human chorionic gonadotropin (hCG) trigger day in fresh cycles versus thawed transfer cycles (the freeze-all strategy) with an antagonist protocol, and to compare the differences in clinical outcomes. Methods: This retrospective cohort study included a total of 2 165 cycles conducted at Henan Provincial People's Hospital with cleavage-stage embryo (at least one top-quality) transfer between January 2017 and December 2023, with serum progesterone levels on hCG trigger day all≤6.34 nmol/L (i.e. 2 ng/ml). Multivariate logsitic regression analysis and curve fitting were performed based on different serum progesterone levels on hCG trigger day [≤3.17 nmol/L (i.e. 1 ng/ml) or 1-2 ng/ml]. Results: Multivariate regression analysis, by using cycle type (either fresh or frozen-thawed cycle) as the exposure variable, showed that the clinical pregnancy rate (≤1 ng/ml: OR=0.93, 95%CI: 0.75-1.14; 1-2 ng/ml: OR=1.05, 95%CI: 0.58-1.87) and live birth rate (≤1 ng/ml: OR=0.90, 95%CI: 0.71-1.13; 1-2 ng/ml: OR=1.53, 95%CI: 0.79-3.00) had no statistically significant differences in group of progesterone concentration ≤1 ng/ml or in group of 1-2 ng/ml. Using serum progesterone levels on hCG trigger day as a continuous variable for curve fitting analysis, the clinical pregnancy rate in fresh or thawed cycles showed no significant changes with increasing progesterone levels. Conclusions: In the antagonist protocol with cleavage-stage embryo transfer (at least one top-quality), when the serum progesterone level on hCG day is ≤2 ng/ml, there are no significant differences in clinical outcomes between thawed cycles and fresh cycles, including clinical pregnancy rate and live birth rate. Transferred in fresh cycles or choosing the freeze-all strategy could be selected based on the actual situation of the patients.