Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Peptidomic profile of human milk as influenced by fortification with different protein sources: An in vitro dynamic digestion simulation.

Fortification of human milk (HM) is often necessary to meet the nutritional requirements of preterm infants. The present experiment aimed to establish whether the supplementation of HM with either an experimental donkey milk-derived fortifier containing whole donkey milk proteins, or with a commercial bovine milk-derived fortifier containing hydrolyzed bovine whey proteins, affects peptide release differently during digestion. The experiment was conducted using an in vitro dynamic system designed to simulate the preterm infant's digestion followed by digesta analysis by means of LC-MS-MS. The different fortifiers did not appear to influence the cumulative intensity of HM peptides. Fortification had a differential impact on the release of either donkey or bovine bioactive peptides. Donkey milk peptides showed antioxidant/ACE inhibitory activities, while bovine peptides showed opioid, dipeptil- and propyl endo- peptidase inhibitory and antimicrobial activity. A slight delay in peptide release from human lactoferrin and α-lactalbumin was observed when HM was supplemented with donkey milk-derived fortifier.

Multivariate analysis of structural and functional properties of soluble dietary fiber from corn bran using different modification methods.

This study comprehensively investigated the effects of high-temperature cooking (HT), complex enzyme hydrolysis (CE), and high-temperature cooking combined enzymatic hydrolysis (HE) on the chemical composition, microstructure, and functional attributes of soluble dietary fiber (SDF) extracted from corn bran. The results demonstrated that HE-SDF yielded the highest output at 13.80 ± 0.20 g/100 g, with enhancements in thermal stability, viscosity, hydration properties, adsorption capacity, and antioxidant activity. Cluster analysis revealed three distinct categories of SDF's physicochemical properties. Principal component analysis (PCA) confirmed the superior functional properties of HE-SDF. Correlation analysis showed positive relationships between the monosaccharide composition, purity, and viscosity of SDF and most of its functional attributes, whereas particle size and zeta potential were inversely correlated. Furthermore, a highly significant positive correlation was observed between crystallinity and thermal properties. These findings suggest that the HE method constitutes a viable strategy for enhancing the quality of SDF sourced from corn bran.

Label-free detection of kidney stones urine combined with SERS and multivariate statistical algorithm.

Kidney stones are a common urological disease with an increasing incidence worldwide. Traditional diagnostic methods for kidney stones are relatively complex and time-consuming, thus necessitating the development of a quicker and simpler diagnostic approach. This study investigates the clinical screening of kidney stones using Surface-Enhanced Raman Scattering (SERS) technology combined with multivariate statistical algorithms, comparing the classification performance of three algorithms (PCA-LDA, PCA-LR, PCA-SVM). Urine samples from 32 kidney stone patients, 30 patients with other urinary stones, and 36 healthy individuals were analyzed. SERS spectra data were collected in the range of 450-1800 cm-1 and analyzed. The results showed that the PCA-SVM algorithm had the highest classification accuracy, with 92.9 % for distinguishing kidney stone patients from healthy individuals and 92 % for distinguishing kidney stone patients from those with other urinary stones. In comparison, the classification accuracy of PCA-LR and PCA-LDA was slightly lower. The findings indicate that SERS combined with PCA-SVM demonstrates excellent performance in the clinical screening of kidney stones and has potential for practical clinical application. Future research can further optimize SERS technology and algorithms to enhance their stability and accuracy, and expand the sample size to verify their applicability across different populations. Overall, this study provides a new method for the rapid diagnosis of kidney stones, which is expected to play an important role in clinical diagnostics.

Is visual information use during facial emotion recognition related to eating disorder symptoms in college-aged men and women? An experimental study.

BACKGROUND: Previous studies of emotion recognition abilities of people with eating disorders used accuracy to identify performance deficits for these individuals. The current study examined eating disorder symptom severity as a function of emotion categorization abilities, using a visual cognition paradigm that offers insights into how emotional faces may be categorized, as opposed to just how well these faces are categorized. METHODS: Undergraduate students (N = 87, 50 women, 34 men, 3 non-binary) completed the Bubbles task and a standard emotion categorization task, as well as a set of questionnaires assessing their eating disorder symptomology and comorbid disorders. We examined the relationship between visual information use (assessed via Bubbles) and eating disorder symptomology (EDDS) while controlling for anxiety (STAI), depression (BDI-II), alexithymia (TAS), and emotion regulation difficulties (DERS-sf). RESULTS: Overall visual information use (i.e. how well participants used facial features important for accurate emotion categorization) was not significantly related to eating disorder symptoms, despite producing interpretable patterns for each emotion category. Emotion categorization accuracy was also not related to eating disorder symptoms. CONCLUSIONS: Results from this study must be interpreted with caution, given the non-clinical sample. Future research may benefit from comparing visual information use in patients with an eating disorder and healthy controls, as well as employing designs focused on specific emotion categories, such as anger.

Men and women with severe eating disorder symptoms may find it harder to identify and describe emotions than people with less severe eating disorder symptoms. However, previous work makes it difficult to determine why emotion recognition deficits exist, and what underlying abilities or strategies are actually different due to a deficit. In addition to a typical emotion recognition task (emotion categorization), this study used the Bubbles task, which allowed us to determine which parts of an image are important for emotion recognition, and whether participants used these parts during the task. In 87 undergraduate students (47 female; 49 with clinically-significant eating disorder symptoms), there was no significant relationship between task performance and eating disorder symptom severity, before and after controlling for the relationship with other comorbid disorders. Our results imply that emotion recognition deficits are unlikely to be an important mechanism underlying eating disorder pathology in participants with a range of eating disorders symptoms.

An In-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience.

Functional magnetic resonance imaging (fMRI) has been a cornerstone of cognitive neuroscience since its invention in the 1990s. The methods that we use for fMRI data analysis allow us to test different theories of the brain, thus different analyses can lead us to different conclusions about how the brain produces cognition. There has been a centuries-long debate about the nature of neural processing, with some theories arguing for functional specialization or localization (e.g., face and scene processing) while other theories suggest that cognition is implemented in distributed representations across many neurons and brain regions. Importantly, these theories have received support via different types of analyses; therefore, having students implement hands-on data analysis to explore the results of different fMRI analyses can allow them to take a firsthand approach to thinking about highly influential theories in cognitive neuroscience. Moreover, these explorations allow students to see that there are not clearcut "right" or "wrong" answers in cognitive neuroscience, rather we effectively instantiate assumptions within our analytical approaches that can lead us to different conclusions. Here, I provide Python code that uses freely available software and data to teach students how to analyze fMRI data using traditional activation analysis and machine-learning-based multivariate pattern analysis (MVPA). Altogether, these resources help teach students about the paramount importance of methodology in shaping our theories of the brain, and I believe they will be helpful for introductory undergraduate courses, graduate-level courses, and as a first analysis for people working in labs that use fMRI.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme.

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Detecting context dependence in the expression of life history trade-offs.

Life history trade-offs are one of the central tenets of evolutionary demography. Trade-offs, depicting negative covariances between individuals' life history traits, can arise from genetic constraints, or from a finite amount of resources that each individual has to allocate in a zero-sum game between somatic and reproductive functions. While theory predicts that trade-offs are ubiquitous, empirical studies have often failed to detect such negative covariances in wild populations. One way to improve the detection of trade-offs is by accounting for the environmental context, as trade-off expression may depend on environmental conditions. However, current methodologies usually search for fixed covariances between traits, thereby ignoring their context dependence. Here, we present a hierarchical multivariate 'covariance reaction norm' model, adapted from Martin (2023), to help detect context dependence in the expression of life-history trade-offs using demographic data. The method allows continuous variation in the phenotypic correlation between traits. We validate the model on simulated data for both intraindividual and intergenerational trade-offs. We then apply it to empirical datasets of yellow-bellied marmots (Marmota flaviventer) and Soay sheep (Ovis aries) as a proof-of-concept showing that new insights can be gained by applying our methodology, such as detecting trade-offs only in specific environments. We discuss its potential for application to many of the existing long-term demographic datasets and how it could improve our understanding of trade-off expression in particular, and life history theory in general.

[Multivariate analysis of psychological health among hospital infection prevention and control practitioners in Tianjin].

Objective: To understand the occupational stress and mental health status of hospital infection prevention and control practitioner (HIPCPs) in medical institutions, and analyze their main influencing factors. Methods: In November 2021, 550 nosocomial infection managers in Tianjin were randomly selected to conduct a questionnaire survey using the Concise Occupational Stress Questionnaire, Depression Screening Scale (PHQ-9) and Self-Rating Anxiety Scale (SAS). 497 valid questionnaires were obtained, and the total recovery efficiency was 90.36%. Single factor analysis and multivariate logistic regression method were used to analyze the main influencing factors of occupational stress and mental health status of psychiatric managers. Results: The detection rate of anxiety and depression among 497 HIPCPs was 22.73% (113/497) and 58.95% (293/497), respectively. Gender and major were the influencing factors of depression (P=0.000, 0.001). Average working hours>52 hours per week and night shift days>1 days per week were the influencing factors of anxiety (P=0.035, 0.014). Average working hours>52 h per week, night shift days >1 d per week and different majors were the influencing factors of occupational stress (P=0.000, 0.025, 0.010). Multivariate logistic regression results showed that the risk of anxiety in those who worked more than 52 hours per week was 1.753 times that of those who worked less than 52 hours per week (P=0.038), and the risk of depression in women was 3.071 times that of men (P=0.006) . Conclusion: Working hours are an important influencing factor for occupational stress and anxiety among HIPCPs. In order to reduce the occurrence of occupational stress and mental health problems, it is necessary to strengthen psychological counseling for HIPCPs and balance work and rest.

Hydrogeochemical processes controlling surface water quality for irrigation in a Mediterranean wetland ecosystem, Northeast Algeria.

Fetzara Lake, considered one of the most important wetlands in northeastern Algeria, was designated a Ramsar site in 2002. The waters in its watershed are affected by salinity, which influences their suitability for irrigation. To identify the factors influencing the quality of these surface waters, geochemical and statistical analyses were carried out on the basis of the results of chemical analyses of 51 samples collected, during two monitoring campaigns, from all the tributaries in the watershed. The findings show the dominance of three hydrochemical facies over the two campaigns: Na-Cl facies (55.17% and 22.73%) characterizes the waters water from Fetzara Lake outlet (drainage channel and wadi Meboudja), in relation to the influx of saliferous elements due to water evaporation in the lake. Ca-Mg-Cl (27.59% and 40.91%) and Ca-Mg-HCO3 (13.79%. and 13.79%) facies characterize the waters of the remaining tributaries, reflecting the dissolution of carbonate formations and the alteration of the Edough metamorphic basement. Multivariate statistical analysis, using principal component analysis (PCA), shows three water types: highly mineralized (EC > 3000 µS/cm), moderately mineralized (1000 < EC < 3000 µS/cm), and weakly mineralized (EC < 1000 µS/cm). Evaporation and silicate weathering are the main mechanisms controlling water mineralization according to the different bivariate plots. Furthermore, cation exchange indices (CAI-I and CAI-II) reveal that these reactions involve the adsorption of Na+ and K+ onto clay minerals, as well as the simultaneous release of Ca2+ and Mg2+ ions. Finally, the various quality indices (SAR, %Na, RSC and KR) revealed that the water in 36% of tributaries is unsuitable for irrigation. These findings will provide important information on surface water quality in the study area, particularly for irrigation purposes, and will contribute to the thoughtful and sustainable management of this resource.

Disturbed Dynamic Brain Activity and Neurovascular Coupling in End-Stage Renal Disease Assessed With MRI.

BACKGROUND: Pathophysiological mechanisms underlying cognitive impairment in end-stage renal disease (ESRD) remain unclear, with limited studies on the temporal variability of neural activity and its coupling with regional perfusion. PURPOSE: To assess neural activity and neurovascular coupling (NVC) in ESRD patients, evaluate the classification performance of these abnormalities, and explore their relationships with cognitive function. STUDY TYPE: Prospective. POPULATION: Exactly 33 ESRD patients and 35 age, sex, and education matched healthy controls (HCs). FIELD STRENGTH/SEQUENCE: The 3.0T/3D pseudo-continuous arterial spin labeling, resting-state functional MRI, and 3D-T1 weighted structural imaging. ASSESSMENT: Dynamic (dfALFF) and static (sfALFF) fractional amplitude of low-frequency fluctuations and cerebral blood flow (CBF) were assessed. CBF-fALFF correlation coefficients and CBF/fALFF ratio were determined for ESRD patients and HCs. Their ability to distinguish ESRD patients from HCs was evaluated, alongside assessment of cerebral small vessel disease (CSVD) MRI features. All participants underwent blood biochemical and neuropsychological tests to evaluate cognitive decline. STATISTICAL TESTS: Chi-squared test, two-sample t-test, Mann-Whitney U tests, covariance analysis, partial correlation analysis, family-wise error, false discovery rate, Bonferroni correction, area under the receiver operating characteristic curve (AUC) and multivariate pattern analysis. P < 0.05 denoted statistical significance. RESULTS: ESRD patients exhibited higher dfALFF in triangular part of left inferior frontal gyrus (IFGtriang) and left middle temporal gyrus, lower CBF/dfALFF ratio in multiple brain regions, and decreased CBF/sfALFF ratio in bilateral superior temporal gyrus (STG). Compared with CBF/sfALFF ratio, dfALFF, and sfALFF, CBF/dfALFF ratio (AUC = 0.916) achieved the most powerful classification performance in distinguishing ESRD patients from HCs. In ESRD patients, decreased CBF/fALFF ratio correlated with more severe renal impairment, increased CSVD burden, and cognitive decline (0.4 < |r| < 0.6). DATA CONCLUSION: ESRD patients exhibited abnormal dynamic brain activity and impaired NVC, with dynamic features demonstrating superior discriminative capacity and CBF/dfALFF ratio showing powerful classification performance. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection.

The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45 % and 96.53-98.10 % respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.

Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array.

Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.

The Role of Platelet Distribution Width in the Association Between Blood Glucose and Neurological Impairment Severity in Acute Ischemic Stroke: A Moderated Mediation Model.

Objective: Fasting blood glucose (FBG) is a recognized risk factor for Ischemic Stroke, but little research has examined the interaction among FBG, Platelet Distribution Width (PDW) and the severity of neuronal damage. Thus, the present study constructs a moderated mediation model aimed to elucidate the relationships among FBG, PDW, and NIHSS scores in patients with acute ischemic stroke (AIS). Methods: We conducted a cross-sectional study on 431 AIS patients. Upon hospital admission, we assessed the patients' NIHSS scores and collected blood samples to measure FBG and PDW levels. The relationship between FBG and NIHSS scores moderated by PDW was analyzed by linear curve fitting analysis, multiple linear regression analysis, and moderated mediation analysis respectively. Results: In the tertile grouping based on FBG, both PDW and NIHSS scores of AIS patients demonstrated an increase corresponding with rising levels of FBG (p<0.001 for both). Multiple linear regression analysis revealed that, the ß coefficients (95% CI) for the relationship between FBG and NIHSS scores were 1.49 (1.27-1.71, p<0.01) post-adjustment for potential confounders. The ß coefficients (95% CI) for the relationship between FBG and PDW were 0.02 (0.01-0.04, p<0.01) post-adjustment. Likewise, for the relationship between PDW and NIHSS scores, the ß coefficients (95% CI) were 4.33 (3.07-5.59, p<0.01) after adjustment. These positive association remained consistent in sensitivity analysis and hierarchical analysis. Smoothed plots suggested that there are linear relationships between FBG and PDW and NIHSS scores respectively. Further mediation analysis indicated that increased PDW significantly (p<0.01) mediated 5.91% of FBG-associated increased NIHSS scores. Conclusion: This study suggested that FBG levels were associated with NIHSS scores, and the FBG-associated neurological impairment may be partially mediated by PDW. These findings underscore the importance of monitoring FBG and PDW levels in AIS patients, potentially guiding risk intervention strategies.

On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data.

Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate. The present paper is about the implementation and interpretation of the Addams family of discrete frailty distributions. We propose methods of estimation for this family of densities in the context of shared frailty models for the hazard rates for case I interval-censored data. Our optimization framework allows for stratification of random effect distributions by covariates. We highlight interpretational advantages of the Addams family of discrete frailty distributions and theK-point distribution as compared to other frailty distributions. A unique feature of the Addams family and the K-point distribution is that the support of the frailty distribution depends on its parameters. This feature is best exploited by imposing a model on the distributional parameters, resulting in a model with non-homogeneous covariate effects that can be analyzed using standard measures such as the hazard ratio. Our methods are illustrated with applications to multivariate case I interval-censored infection data.

Molecular age prediction using skull bone samples from individuals with and without signs of decomposition: a multivariate approach combining analysis of posttranslational protein modifications and DNA methylation.

The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4-11.8 years, RMSE: 10.4-15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6-12 years, RMSE: 8.1-15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases.

Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Prognostic Nomogram, Demographics and Comparative Analysis of Urinary Bladder Small Cell and Large Cell Neuroendocrine Carcinoma.

BACKGROUND: This retrospective study aims to provide a comprehensive analysis of the demographics, survival rates, and therapeutic approaches of small-cell neuroendocrine carcinoma (SCNEC) and large-cell neuroendocrine carcinoma (LCNEC) while highlighting key differences compared to common urinary bladder cancers. METHODS: Our analysis utilized the Surveillance, Epidemiology, and End Results database (SEER), and data was collected from 2000-2020. RESULTS: A total of 1040 cases of urinary bladder SCNEC and LCNEC were identified. Most patients were over the age of 80 years (33.2%), male (78.9%), and Caucasian (83.6%). Most tumors were over 4.1cm (47.4%) and in the lateral wall of the bladder (37.8%). The overall 5-year survival was 22.1% (95% confidence interval (95% CI):20.7-23.5). The 5-year survival by sex was greatest for the female population (28.0%; (95% CI: 24.5-35.0). For treatment modality, the 5-year survival for each was as follows: surgery, 12.5% (95% CI: 10.5-14.5) multimodality therapy (surgery and chemotherapy), 31.1% (95% CI: 28.5-33.7) and combination (surgery, chemotherapy, and radiation) 32.8% (95% CI: 29.1-36.5). On multivariable analysis, positive nodal status hazar ratio (HR)(HR3.65 [95% CI: 2.34-5.71], P < .001) was identified as a negative predictor for survival, and increasing age was nearly significant for a worse prognosis (P = .052). The prognostic nomogram that was created to predict patient survivability mirrored the findings from the statistical analysis, with a statistically significant difference found in race, treatment modality, and tumor stage. CONCLUSIONS: SCNEC and LCNEC are rare yet highly intrusive subtypes of bladder cancer that usually affect Caucasian males over the age of 80 years old. The study identifies older age and positive nodal status as adverse prognostic indicators. Our findings offer crucial insights that can inform future clinical guidelines and serve as a basis for more tailored treatment strategies for these aggressive subtypes of bladder cancer.

Spatial clustering based on geographically weighted multivariate generalized gamma regression.

Geographically Weighted Regression (GWR) is one of the local statistical models that can capture the effects of spatial heterogeneity. This model can be used for both univariate and multivariate responses. However, it should be noted that GWR models require the assumption of error normality. To overcome this problem, we propose a GWR model for generalized gamma distributed responses that can capture the phenomenon of some special continuous distributions. The proposed model is known as Geographically Weighted Multivariate Generalized Gamma Regression (GWMGGR). Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method optimized with the Bernt-Hall-Hall-Haussman (BHHH) algorithm. To determine the significance of the spatial heterogeneity effect, a hypothesis test was conducted using the Maximum Likelihood Ratio Test (MLRT) approach. We made a spatial cluster based on the estimated model parameters for each response using the k-means clustering method to interpret the obtained results. Some highlights of the proposed method are:•A new model for GWR with multivariate generalized gamma distributed responses to overcome the assumption of normally distributed errors.•Goodness of fit test to test the spatial effects in GWMGGR model.•Spatial clustering of districts/cities in Central Java based on three dimensions of educational indicators.