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Current Research in Food Science 2024Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine...
Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm, 1410-1500 cm fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm, 1410-1500 cm fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm, 1050-1180 cm, 1410-1500 cm fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.
PubMed: 38939610
DOI: 10.1016/j.crfs.2024.100782 -
JACC. Advances Feb 2024With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with...
BACKGROUND
With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest.
OBJECTIVES
This study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases.
METHODS
We harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects.
RESULTS
Among the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance.
CONCLUSIONS
This study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.
PubMed: 38939385
DOI: 10.1016/j.jacadv.2023.100801 -
Frontiers in Oncology 2024Infections represent one of the most frequent causes of death of higher-risk MDS patients, as reported previously also by our group. Azacitidine Infection Risk Model...
INTRODUCTION
Infections represent one of the most frequent causes of death of higher-risk MDS patients, as reported previously also by our group. Azacitidine Infection Risk Model (AIR), based on red blood cell (RBC) transfusion dependency, neutropenia <0.8 × 10/L, platelet count <50 × 10/L, albumin <35g/L, and ECOG performance status ≥2 has been proposed based on the retrospective data to estimate the risk of infection in azacitidine treated patients.
METHODS
The prospective non-intervention study aimed to identify factors predisposing to infection, validate the AIR score, and assess the impact of antimicrobial prophylaxis on the outcome of azacitidine-treated MDS/AML and CMML patients.
RESULTS
We collected data on 307 patients, 57.6 % males, treated with azacitidine: AML (37.8%), MDS (55.0%), and CMML (7.1%). The median age at azacitidine treatment commencement was 71 (range, 18-95) years. 200 (65%) patients were assigned to higher risk AIR group. Antibacterial, antifungal, and antiviral prophylaxis was used in 66.0%, 29.3%, and 25.7% of patients, respectively. In total, 169 infectious episodes (IE) were recorded in 118 (38.4%) patients within the first three azacitidine cycles. In a multivariate analysis ECOG status, RBC transfusion dependency, IPSS-R score, and CRP concentration were statistically significant for infection development ( < 0.05). The occurrence of infection within the first three azacitidine cycles was significantly higher in the higher risk AIR group - 47.0% than in lower risk 22.4% (odds ratio (OR) 3.06; 95% CI 1.82-5.30, < 0.05). Administration of antimicrobial prophylaxis did not have a significant impact on all-infection occurrence in multivariate analysis: antibacterial prophylaxis (OR 0.93; 0.41-2.05, = 0.87), antifungal OR 1.24 (0.54-2.85) ( = 0.59), antiviral OR 1.24 (0.53-2.82) ( = 0.60).
DISCUSSION
The AIR Model effectively discriminates infection-risk patients during azacitidine treatment. Antimicrobial prophylaxis does not decrease the infection rate.
PubMed: 38939343
DOI: 10.3389/fonc.2024.1404322 -
JACC. Advances Jul 2023Reports of long-term mortality and reintervention after transposition of the great arteries with intact ventricular septum treatment, although favorable, are mostly...
BACKGROUND
Reports of long-term mortality and reintervention after transposition of the great arteries with intact ventricular septum treatment, although favorable, are mostly limited to single-center studies. Even less is known about hospital resource utilization (days at hospital) and the impact of treatment choices and timing on outcomes.
OBJECTIVES
The purpose of this study was to describe survival, reintervention and hospital resource utilization after arterial switch operation (ASO) in a national dataset.
METHODS
Follow-up and life status data for all patients undergoing ASO between 2000 and 2017 in England and Wales were collected and explored using multivariable regressions and matching.
RESULTS
A total of 1,772 patients were identified, with median ASO age of 9.5 days (IQR: 6.5-14.5 days). Mortality and cardiac reintervention at 10 years after ASO were 3.2% (95% CI: 2.5%-4.2%) and 10.7% (95% CI: 9.1%-12.2%), respectively. The median time spent in hospital during the ASO spell was 19 days (IQR: 14, 24). Over the first year after the ASO patients spent 7 days (IQR: 4-10 days) in hospital in total, decreasing to 1 outpatient day/year beyond the fifth year. In a subgroup with complete risk factor data (n = 652), ASO age, and balloon atrial septostomy (BAS) use were not associated with late mortality and reintervention, but cardiac or congenital comorbidities, low weight, and circulatory/renal support at ASO were. After matching for patient characteristics, BAS followed by ASO and ASO as first procedure, performed within the first 3 weeks of life, had comparable early and late outcomes, including hospital resource utilization.
CONCLUSIONS
Mortality and hospital resource utilization are low, while reintervention remains relatively frequent. Early ASO and individualized use of BAS allows for flexibility in treatment choices and a focus on at-risk patients.
PubMed: 38939004
DOI: 10.1016/j.jacadv.2023.100407 -
Frontiers in Neurology 2024Hemorrhagic transformation (HT) in acute ischemic stroke is likely to occur in patients treated with intravenous thrombolysis (IVT) and may lead to neurological...
BACKGROUND
Hemorrhagic transformation (HT) in acute ischemic stroke is likely to occur in patients treated with intravenous thrombolysis (IVT) and may lead to neurological deterioration and symptomatic intracranial hemorrhage (sICH). Despite the complex inclusion and exclusion criteria for IVT and some useful tools to stratify HT risk, sICH still occurs in approximately 6% of patients because some of the risk factors for this complication remain unknown.
OBJECTIVE
This study aimed to explore whether there are any differences in circulating microRNA (miRNA) profiles between patients who develop HT after thrombolysis and those who do not.
METHODS
Using qPCR, we quantified the expression of 84 miRNAs in plasma samples collected prior to thrombolytic treatment from 10 individuals who eventually developed HT and 10 patients who did not. For miRNAs that were downregulated (fold change (FC) <0.67) or upregulated (FC >1.5) with < 0.10, we investigated the tissue specificity and performed KEGG pathway annotation using bioinformatics tools. Owing to the small patient sample size, instead of multivariate analysis with all major known HT risk factors, we matched the results with the admission NIHSS scores only.
RESULTS
We observed trends towards downregulation of miR-1-3p, miR-133a-3p, miR-133b and miR-376c-3p, and upregulation of miR-7-5p, miR-17-3p, and miR-296-5p. Previously, the upregulated miR-7-5p was found to be highly expressed in the brain, whereas miR-1, miR-133a-3p and miR-133b appeared to be specific to the muscles and myocardium.
CONCLUSION
miRNA profiles tend to differ between patients who develop HT and those who do not, suggesting that miRNA profiling, likely in association with other omics approaches, may increase the current power of tools predicting thrombolysis-associated sICH in acute ischemic stroke patients. This study represents a free hypothesis-approach pilot study as a continuation from our previous work. Herein, we showed that applying mathematical analyses to extract information from raw big data may result in the identification of new pathophysiological pathways and may complete standard design works.
PubMed: 38938784
DOI: 10.3389/fneur.2024.1399345 -
Frontiers in Plant Science 2024Chlorophyll fluorescence, a sensitive and cost-effective probe, is widely used in photosynthetic research. Its rapid phase, occurring within 1 second under intense...
Chlorophyll fluorescence, a sensitive and cost-effective probe, is widely used in photosynthetic research. Its rapid phase, occurring within 1 second under intense illumination, displays complex O-J-I-P transients, providing valuable insights into various aspects of photosynthesis. In addition to employing experimental approaches to measure the rapid Fluorescence Induction (FI) kinetics, mathematical modeling serves as a crucial tool for understanding the underlying mechanisms that drive FI dynamics. However, the significant uncertainty and arbitrary nature of selecting model parameters amplify concerns about the effectiveness of modeling tools in aiding photosynthesis research. Therefore, there is a need to gain a deeper understanding of how these models operate and how arbitrary parameter choices may influence their outcomes. In this study, we employed the Morris method, a global Sensitivity Analysis (SA) tool, to assess the significance of rate constants employed in an existing fluorescence model, particularly those linked to the entire electron transport chain, in shaping the rapid FI dynamics. In summary, utilizing the insights gained from the Morris SA allows for targeted refinement of the photosynthesis model, thereby improving our understanding of the complex processes inherent in photosynthetic systems.
PubMed: 38938638
DOI: 10.3389/fpls.2024.1396309 -
Frontiers in Endocrinology 2024Previous studies have confirmed that the triglyceride glucose (TyG) index, recognized as a reliable marker of insulin resistance, is an important risk factor for...
BACKGROUND
Previous studies have confirmed that the triglyceride glucose (TyG) index, recognized as a reliable marker of insulin resistance, is an important risk factor for diabetic kidney disease (DKD). However, it is still unclear whether the DKD risk continues to increase linearly with the elevation of TyG index. This study aimed to thoroughly investigated the intrinsic relationship between TyG index and DKD risk in type 2 diabetes (T2D).
METHODS
This cross-sectional study included 933 patients with T2D in China, who were categorized into DKD and non-DKD groups and stratified by TyG index levels. Logistic regression analysis identified the independent risk factors for DKD. The association between DKD risk and TyG index was evaluated using the restricted cubic spline (RCS) curves analysis. The R package 'CatPredi' was utilized to determine the optimal cut-off point for the relationship between DKD risk and TyG index, followed by threshold effect analysis.
RESULTS
The prevalence of DKD was 33.01%. After adjusting for confounding factors, TyG index was identified as a prominent clinical risk factor for DKD, showing the highest odds ratio (OR 1.57 (1.26 - 1.94), P<0.001). RCS analysis revealed a non-linear relationship with a threshold interval effect between the TyG index and DKD risk. When TyG index ≤ 9.35, DKD risk plateaued at a low level; however, when TyG index > 9.35, DKD risk increased gradually with rising TyG index. Among patients with TyG index > 9.35, each 1-unit increase was associated with a 1.94-fold increased DKD risk (OR=1.94 (1.10 - 3.43), P=0.022).
CONCLUSION
The DKD risk presented a threshold effect with the increase of TyG index, initially stable at a low level, and then gradually rising when the TyG index is above 9.35.
Topics: Humans; Diabetes Mellitus, Type 2; Male; Middle Aged; Cross-Sectional Studies; Female; Diabetic Nephropathies; Triglycerides; Blood Glucose; Risk Factors; China; Aged; Biomarkers; Insulin Resistance; Adult; Nonlinear Dynamics; Prevalence
PubMed: 38938513
DOI: 10.3389/fendo.2024.1411486 -
Journal of Evolutionary Biology Jun 2024Death feigning, a state of immobility observed in many animals in response to external stimuli, is an anti-predator behaviour. Although previous studies showed that...
Death feigning, a state of immobility observed in many animals in response to external stimuli, is an anti-predator behaviour. Although previous studies showed that death-feigning behaviours are quantitative genetic traits, the knowledge of the heritable basis of death-feigning behaviour is lacking. To investigate the heritable basis of death-feigning behaviour, we used three laboratory strains of a braconid parasitoid wasp, Heterospilus prosopidis. The heritable basis using half-sib analysis, and the effects of different geographical backgrounds, rearing conditions in the laboratory, and host age were evaluated. The results of the half-sib analysis showed that the frequency of death feigning varied among sires, suggesting a certain extent of additive genetic variance. Also, the frequency of death feigning varied between geographical backgrounds and among strains. Death-feigning frequency was not affected by the age of the host. Our findings highlight the importance of genetic factors underlying the basis of the death-feigning behaviour and provide support for the genetic alterations of traits from the perspective of evolution in various animal species.
PubMed: 38938076
DOI: 10.1093/jeb/voae079 -
Journal of Translational Medicine Jun 2024Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely...
BACKGROUND
Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis.
METHODS
To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based).
RESULTS
Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580).
CONCLUSIONS
By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.
Topics: Humans; Liver Cirrhosis; Male; Female; Cluster Analysis; Middle Aged; Prognosis; Acute Disease; Algorithms; Aged; Cohort Studies
PubMed: 38937846
DOI: 10.1186/s12967-024-05386-2 -
Parasites & Vectors Jun 2024Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital...
BACKGROUND
Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis.
METHODS
A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping.
RESULTS
A significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected (log risk ratio - 0.83, 95% CrI - 1.57, - 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio - 1.42, 95% CrI - 3.09, 0.10). Analyses of all other environmental data were considered non-significant.
CONCLUSIONS
The spatial range in which interpolation of snail distributions is possible appears < 10km owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities.
Topics: Animals; Malawi; Lakes; Biomphalaria; Bulinus; Schistosomiasis; Spatial Analysis; Humans; Bayes Theorem; Snails; Disease Vectors
PubMed: 38937778
DOI: 10.1186/s13071-024-06353-y