Characterization on the oncogenic effect of the missense mutations of p53 via machine learning.

Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.

Butyricimonas is a key gut microbiome component for predicting postoperative recurrence of esophageal cancer.

BACKGROUND: Recently, intestinal bacteria have attracted attention as factors affecting the prognosis of patients with cancer. However, the intestinal microbiome is composed of several hundred types of bacteria, necessitating the development of an analytical method that can allow the use of this information as a highly accurate biomarker. In this study, we investigated whether the preoperative intestinal bacterial profile in patients with esophageal cancer who underwent surgery after preoperative chemotherapy could be used as a biomarker of postoperative recurrence of esophageal cancer. METHODS: We determined the gut microbiome of the patients using 16S rRNA metagenome sequencing, followed by statistical analysis. Simultaneously, we performed a machine learning analysis using a random forest model with hyperparameter tuning and compared the data obtained. RESULTS: Statistical and machine learning analyses revealed two common bacterial genera, Butyricimonas and Actinomyces, which were abundant in cases with recurrent esophageal cancer. Butyricimonas primarily produces butyrate, whereas Actinomyces are oral bacteria whose function in the gut is unknown. CONCLUSION: Our results indicate that Butyricimonas spp. may be a biomarker of postoperative recurrence of esophageal cancer. Although the extent of the involvement of these bacteria in immune regulation remains unknown, future research should investigate their presence in other pathological conditions. Such research could potentially lead to a better understanding of the immunological impact of these bacteria on patients with cancer and their application as biomarkers.

Validation of Distinct Bladder Pain Phenotypes Utilizing the MAPP Research Network Cohort.

INTRODUCTION AND HYPOTHESIS: As interstitial cystitis/bladder pain syndrome (IC/BPS) likely represents multiple pathophysiologies, we sought to validate three clinical phenotypes of IC/BPS patients in a large, multi-center cohort using unsupervised machine learning (ML) analysis. METHODS: Using the female Genitourinary Pain Index and O'Leary-Sant Indices, k-means unsupervised clustering was utilized to define symptomatic phenotypes in 130 premenopausal IC/BPS participants recruited through the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network. Patient-reported symptoms were directly compared between MAPP ML-derived phenotypic clusters to previously defined phenotypes from a single center (SC) cohort. RESULTS: Unsupervised ML categorized IC/BPS participants into three phenotypes with distinct pain and urinary symptom patterns: myofascial pain, non-urologic pelvic pain, and bladder-specific pain. Defining characteristics included presence of myofascial pain or trigger points on examination for myofascial pain patients (p = 0.003) and bladder pain/burning for bladder-specific pain patients (p < 0.001). The three phenotypes were derived using only 11 features (fGUPI subscales and ICSI/ICPI items), in contrast to 49 items required previously. Despite substantial reduction in classification features, unsupervised ML independently generated similar symptomatic clusters in the MAPP cohort with equivalent symptomatic patterns and physical examination findings as the SC cohort. CONCLUSIONS: The reproducible identification of IC/BPS phenotypes, distinguishing bladder-specific pain from myofascial and genital pain, using independent ML analysis of a multicenter database suggests these phenotypes reflect true pathophysiologic differences in IC/BPS patients.

Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

BACKGROUND: Advancements in mobile health technologies and machine learning approaches have expanded the framework of behavioral phenotypes in obesity treatment to explore the dynamics of temporal changes. OBJECTIVE: This study aimed to investigate the dynamics of behavioral changes during obesity intervention and identify behavioral phenotypes associated with weight change using a hybrid machine learning approach. METHODS: In total, 88 children and adolescents (ages 8-16 years; 62/88, 71% male) with age- and sex-specific BMI ≥85th percentile participated in the study. Behavioral phenotypes were identified using a hybrid 2-stage procedure based on the temporal dynamics of adherence to the 5 behavioral goals during the intervention. Functional principal component analysis was used to determine behavioral phenotypes by extracting principal component factors from the functional data of each participant. Elastic net regression was used to investigate the association between behavioral phenotypes and weight change. RESULTS: Functional principal component analysis identified 2 distinctive behavioral phenotypes, which were named the high or low adherence level and late or early behavior change. The first phenotype explained 47% to 69% of each factor, whereas the second phenotype explained 11% to 17% of the total behavioral dynamics. High or low adherence level was associated with weight change for adherence to screen time (ß=-.0766, 95% CI -.1245 to -.0312), fruit and vegetable intake (ß=.1770, 95% CI .0642-.2561), exercise (ß=-.0711, 95% CI -.0892 to -.0363), drinking water (ß=-.0203, 95% CI -.0218 to -.0123), and sleep duration. Late or early behavioral changes were significantly associated with weight loss for changes in screen time (ß=.0440, 95% CI .0186-.0550), fruit and vegetable intake (ß=-.1177, 95% CI -.1441 to -.0680), and sleep duration (ß=-.0991, 95% CI -.1254 to -.0597). CONCLUSIONS: Overall level of adherence, or the high or low adherence level, and a gradual improvement or deterioration in health-related behaviors, or the late or early behavior change, were differently associated with weight loss for distinctive obesity-related lifestyle behaviors. A large proportion of health-related behaviors remained stable throughout the intervention, which indicates that health care professionals should closely monitor changes made during the early stages of the intervention. TRIAL REGISTRATION: Clinical Research Information Science KCT0004137; https://tinyurl.com/ytxr83ay.

Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring.

Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow 'green' bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515-554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.

OBJECTIVE: The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS: The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS: The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS: The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Immune Cell-Related Genes in Juvenile Idiopathic Arthritis Identified Using Transcriptomic and Single-Cell Sequencing Data.

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children. The heterogeneity of the disease can be investigated via single-cell RNA sequencing (scRNA-seq) for its gap in the literature. Firstly, five types of immune cells (plasma cells, naive CD4 T cells, memory-activated CD4 T cells, eosinophils, and neutrophils) were significantly different between normal control (NC) and JIA samples. WGCNA was performed to identify genes that exhibited the highest correlation to differential immune cells. Then, 168 differentially expressed immune cell-related genes (DE-ICRGs) were identified by overlapping 13,706 genes identified by WGCNA and 286 differentially expressed genes (DEGs) between JIA and NC specimens. Next, four key genes, namely SOCS3, JUN, CLEC4C, and NFKBIA, were identified by a protein-protein interaction (PPI) network and three machine learning algorithms. The results of functional enrichment revealed that SOCS3, JUN, and NFKBIA were all associated with hallmark TNF-α signaling via NF-κB. In addition, cells in JIA samples were clustered into four groups (B cell, monocyte, NK cell, and T cell groups) by single-cell data analysis. CLEC4C and JUN exhibited the highest level of expression in B cells; NFKBIA and SOCS3 exhibited the highest level of expression in monocytes. Finally, real-time quantitative PCR (RT-qPCR) revealed that the expression of three key genes was consistent with that determined by differential analysis. Our study revealed four key genes with prognostic value for JIA. Our findings could have potential implications for JIA treatment and investigation.

On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis.

One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM2.5 and PM10). This paper aims to develop descriptive-analytical solutions for increasing the accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Photometer instruments DUST TRAK and BS-EN-12341 method were used to determine concentration of PM2.5 and PM10. Sampling was performed on 4 environmental stations of Kerman cement plant in the four seasons. In order to accurate assessment of particulate matter concentration, a new model was proposed to resample cement plant time series data using Pandas in Python. The effect of meteorological parameters including wind speed, relative humidity, air temperature and rainfall on the particulate matter concentration was investigated through statistical analysis. The results indicated that the maximum annual average of 24-h of PM2.5 belonged to the east side (opposite the clinker depot) in 2019 (31.50 µg m-3) and west side (in front of the mine) in 2020 (31.00 µg m-3). Also, maximum annual average of 24-h of PM10 belonged to the west side (in front of the mine) in 2020 (121.00 µg m-3) and east side (opposite the clinker depot) in 2020 (120.75 µg m-3). The PM2.5 and PM10 concentrations are more than the allowable limit. The results demonstrate that particulate matter concentration increases with increasing relative humidity and rainfall. Finally, the SARIMA model was used to predict the particulate matter concentration. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04645-3.

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.

BACKGROUND: Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease. MATERIAL AND METHODS: Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers. RESULTS: A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock. CONCLUSION: Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites.

Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.

A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s.

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas - particularly those areas that fall within more temperate/less arid ecological sub-regions - drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change. Supplementary Information: The online version contains supplementary material available at 10.1007/s11111-022-00399-9.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

BACKGROUND: The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE: This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. METHODS: We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. RESULTS: A higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011). CONCLUSIONS: Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Long-Acting FGF21 Inhibits Retinal Vascular Leakage in In Vivo and In Vitro Models.

The aim of the current study was to investigate the impact of long-acting fibroblast growth factor 21 (FGF21) on retinal vascular leakage utilizing machine learning and to clarify the mechanism underlying the protection. To assess the effect on retinal vascular leakage, C57BL/6J mice were pre-treated with long-acting FGF21 analog or vehicle (Phosphate Buffered Saline; PBS) intraperitoneally (i.p.) before induction of retinal vascular leakage with intravitreal injection of mouse (m) vascular endothelial growth factor 164 (VEGF164) or PBS control. Five hours after mVEGF164 injection, we retro-orbitally injected Fluorescein isothiocyanate (FITC) -dextran and quantified fluorescence intensity as a readout of vascular leakage, using the Image Analysis Module with a machine learning algorithm. In FGF21- or vehicle-treated primary human retinal microvascular endothelial cells (HRMECs), cell permeability was induced with human (h) VEGF165 and evaluated using FITC-dextran and trans-endothelial electrical resistance (TEER). Western blots for tight junction markers were performed. Retinal vascular leakage in vivo was reduced in the FGF21 versus vehicle- treated mice. In HRMECs in vitro, FGF21 versus vehicle prevented hVEGF-induced increase in cell permeability, identified with FITC-dextran. FGF21 significantly preserved TEER compared to hVEGF. Taken together, FGF21 regulates permeability through tight junctions; in particular, FGF21 increases Claudin-1 protein levels in hVEGF-induced HRMECs. Long-acting FGF21 may help reduce retinal vascular leakage in retinal disorders and machine learning assessment can help to standardize vascular leakage quantification.

Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User's States Assessment.

Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body. Since no works have been found in this direction, the proposed study aimed at overcoming this lack of evidence and seeking analogies between the brain activity and the electrical activity of non-cerebral locations, such as the neck and wrists, to determine if i) cerebral parameters can be estimated from non-cerebral sites, and if ii) non-cerebral sensors can replace cerebral sensors for the evaluation of the users under specific experimental conditions, such as eyes open or closed. In fact, the use of cerebral sensors requires high-qualified personnel, and reliable recording systems, which are still expensive. Therefore, the possibility to use cheaper and easy-to-use equipment to estimate cerebral parameters will allow making some brain-based applications less invasive and expensive, and easier to employ. The results demonstrated the occurrence of significant correlations and analogies between cerebral and non-cerebral electrical activity. Furthermore, the same discrimination and classification accuracy were found in using the cerebral or non-cerebral sites for the user's status assessment.

Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm.

Several regression-based models for predicting outcomes after acute myocardial infarction (AMI) have been developed. However, prediction models that encompass diverse patient-related factors over time are limited. This study aimed to develop a machine learning-based model to predict longitudinal outcomes after AMI. This study was based on a nationwide prospective registry of AMI in Korea (n = 13,104). Seventy-seven predictor candidates from prehospitalization to 1 year of follow-up were included, and six machine learning approaches were analyzed. Primary outcome was defined as 1-year all-cause death. Secondary outcomes included all-cause deaths, cardiovascular deaths, and major adverse cardiovascular event (MACE) at the 1-year and 3-year follow-ups. Random forest resulted best performance in predicting the primary outcome, exhibiting a 99.6% accuracy along with an area under the receiver-operating characteristic curve of 0.874. Top 10 predictors for the primary outcome included peak troponin-I (variable importance value = 0.048), in-hospital duration (0.047), total cholesterol (0.047), maintenance of antiplatelet at 1 year (0.045), coronary lesion classification (0.043), N-terminal pro-brain natriuretic peptide levels (0.039), body mass index (BMI) (0.037), door-to-balloon time (0.035), vascular approach (0.033), and use of glycoprotein IIb/IIIa inhibitor (0.032). Notably, BMI was identified as one of the most important predictors of major outcomes after AMI. BMI revealed distinct effects on each outcome, highlighting a U-shaped influence on 1-year and 3-year MACE and 3-year all-cause death. Diverse time-dependent variables from prehospitalization to the postdischarge period influenced the major outcomes after AMI. Understanding the complexity and dynamic associations of risk factors may facilitate clinical interventions in patients with AMI.

Machine Learning Classification of Self-Organized Surface Structures in Ultrashort-Pulse Laser Processing Based on Light Microscopic Images.

In ultrashort-pulsed laser processing, surface modification is subject to complex laser and scanning parameter studies. In addition, quality assurance systems for monitoring surface modification are still lacking. Automated laser processing routines featuring machine learning (ML) can help overcome these limitations, but they are largely absent in the literature and still lack practical applications. This paper presents a new methodology for machine learning classification of self-organized surface structures based on light microscopic images. For this purpose, three application-relevant types of self-organized surface structures are fabricated using a 300 fs laser system on hot working tool steel and stainless-steel substrates. Optical images of the hot working tool steel substrates were used to learn a classification algorithm based on the open-source tool Teachable Machine from Google. The trained classification algorithm achieved very high accuracy in distinguishing the surface types for the hot working steel substrate learned on, as well as for surface structures on the stainless-steel substrate. In addition, the algorithm also achieved very high accuracy in classifying the images of a specific structure class captured at different optical magnifications. Thus, the methodology proposed represents a simple and robust automated classification of surface structures that can be used as a basis for further development of quality assurance systems, automated process parameter recommendation, and inline laser parameter control.

In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis.

Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary ß-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection.

Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study.

Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.

Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling.

BACKGROUND: Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. METHODS: We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. RESULTS: The XGBoost model had the highest prediction accuracy with an R2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. CONCLUSIONS: In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving.