Chemical moderate preoxidation for algae-laden water is an economical and prospective strategy for controlling algae and exogenous pollutants, whereas it is constrained by a lack of effective on-line evaluation and quick-response feedback method. Herein, excitation-emission matrix parallel factor analysis (EEM-PARAFAC) was used to identify cyanobacteria fluorophores after preoxidation of sodium hypochlorite (NaClO) at Excitation/Emission wavelength of 260(360)/450 nm, based on which the algal cell integrity and intracellular organic matter (IOM) release were quantitatively assessed. Machine learning modeling of fluorescence spectral data for prediction of moderate preoxidation using NaClO was established. The optimal NaClO dosage for moderate preoxidation depended on algal density, growth phases, and organic matter concentrations in source water matrices. Low doses of NaClO (<0.5 mg/L) led to short-term desorption of surface-adsorbed organic matter (S-AOM) without compromising algal cell integrity, whereas high doses of NaClO (≥0.5 mg/L) quickly caused cell damage. The optimal NaClO dosage increased from 0.2-0.3 mg/L to 0.9-1.2 mg/L, corresponding to the source water with algal densities from 0.1 × 10⁶ to 2.0 × 10⁶ cells/mL. Different growth stages required varying NaClO doses: stationary phase cells needed 0.3-0.5 mg/L, log phase cells 0.6-0.8 mg/L, and decaying cells 2.0-2.5 mg/L. The presence of natural organic matter and S-AOM increased the NaClO dosage limit with higher dissolved organic carbon (DOC) concentrations (1.00 mg/L DOC required 0.8-1.0 mg/L NaClO, while 2.20 mg/L DOC required 1.5-2.0 mg/L). Compared to other predictive models, the machine learning model (Gaussian process regression-Matern (0.5)) performed best, achieving R2 values of 1.000 and 0.976 in training and testing sets. Optimal preoxidation followed by coagulation effectively removed algal contaminants, achieving 91%, 92%, and 92% removal for algal cells, turbidity, and chlorophyll-a, respectively, thereby demonstrating the effectiveness of moderate preoxidation. This study introduces a novel approach to dynamically adjust NaClO dosage by monitoring source water qualities and tracking post-preoxidation fluorophores, enhancing moderate preoxidation technology application in algae-laden water treatment.

The present study aimed to validate the association between core cuproptosis genes (CRGs) and Alzheimer\'s disease (AD) from both bioinformatics and experimental perspectives and also to develop a risk prediction model. To this end, 78 human‑derived temporal back samples were analyzed from GSE109887, and the biological functions of the resulting CRGs were explored by cluster analysis, weighted gene co‑expression network analysis and similar methods to identify the best machine model. Moreover, an external dataset GSE33000 and a nomogram were used to validate the model. The mRNA and protein expression of CRGs were validated using the SH‑SY5Y cell model and the Sprague‑Dawley rat animal model. The RT‑qPCR and western blotting results showed that the mRNA and protein expression content of dihydrolipoamide dehydrogenase, ferredoxin 1, glutaminase and pyruvate dehydrogenase E1 subunit β decreased, and the expression of dihydrolipoamide branched chain transacylase E2 increased in AD, which supported the bioinformatic analysis results. The CRG expression alterations affected the aggregation and infiltration of certain immune cells. The present study also confirmed the accuracy and validity of AD diagnostic models and nomograms, and validated the association between five CRGs and AD, indicating a significant difference between patients with AD and healthy individuals. Therefore, CRGs are expected to serve as relevant biomarkers for the diagnosis and prognostic monitoring of AD.

Alzheimer\'s disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer\'s disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer\'s diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer\'s disease.

Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challenge. Here, based on 254 LW samples, a machine learning (ML) model to predict the methane production performance of pretreated feedstock was constructed using two automated ML platforms (tree-based pipeline optimization tool and neural network intelligence). Furthermore, the interactive effects of pretreatment conditions, feedstock properties, and digestion conditions on methane production of pretreated LW were studied through model interpretability analysis. The optimal ML model performed well on the validation set, and the digestion time, pretreatment agent, and lignin content (LC) were found to be key factors affecting the methane production of pretreated LW. If the LC in the raw LW was lower than 15%, the maximum CMY might be achieved using the NaOH, KOH, and alkaline hydrogen peroxide (AHP) with concentrations of 3.8%, 4.4%, and 4.5%, respectively. On the other hand, if LC was higher than 15%, only high concentrations of AHP exceeding 4% could significantly increase methane production. This study provides valuable guidance for optimizing pretreatment process, comparing different chemical pretreatment approaches, and regulating the operation of large-scale biogas plants.

The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.

BACKGROUND: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies.
OBJECTIVE: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database.
METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction.
RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831.
CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification.
BACKGROUND: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action.

BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications.
OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications.
METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach.
RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80.
CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

The incidence of Alzheimer\'s disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

Bisphenol A diglycidyl ether (BADGE), a derivative of the well-known endocrine disruptor Bisphenol A (BPA), is a potential threat to long-term environmental health due to its prevalence as a micropollutant. This study addresses the previously unexplored area of BADGE toxicity and removal. We investigated, for the first time, the biodegradation potential of laccase isolated from Geobacillus thermophilic bacteria against BADGE. The laccase-mediated degradation process was optimized using a combination of response surface methodology (RSM) and machine learning models. Degradation of BADGE was analyzed by various techniques, including UV-Vis spectrophotometry, high-performance liquid chromatography (HPLC), Fourier transform infrared (FTIR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). Laccase from Geobacillus stearothermophilus strain MB600 achieved a degradation rate of 93.28% within 30 min, while laccase from Geobacillus thermoparafinivorans strain MB606 reached 94% degradation within 90 min. RSM analysis predicted the optimal degradation conditions to be 60 min reaction time, 80°C temperature, and pH 4.5. Furthermore, CB-Dock simulations revealed good binding interactions between laccase enzymes and BADGE, with an initial binding mode selected for a cavity size of 263 and a Vina score of -5.5, which confirmed the observed biodegradation potential of laccase. These findings highlight the biocatalytic potential of laccases derived from thermophilic Geobacillus strains, notably MB600, for enzymatic decontamination of BADGE-contaminated environments.