A new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning.

Low-density lipoprotein cholesterol (LDL-C) is a crucial marker of cardiovascular system damage. In the Chinese population, the estimation of LDL-C concentration by Friedewald, Martin-Hopkins or Sampson equations is not accurate. The aim of this study was to develop a group of new equations for calculating LDL-C concentration using machine learning techniques and to evaluate their efficacy. A total of 182,901 patient samples were collected with standard lipid panel measurements. These samples were collated and randomly divided into a training set and a test set. In the training set, a new equation was constructed using polynomial ridge-regression and compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations in the test set. Subsequently, an additional set of 17,285 patient samples were collected to evaluate the performance of the new equation in clinical practice. The new equation, a ternary cubic equation, was accurate and easy to use, with a goodness-of-fit R2 of 0.9815 and an uncertainty MSE of 37.4250 on the testing set. The difference between the calculated value by the new equation and the measured value of LDL-C was small (0.0424 ±â€…5.1161 vs Friedewald equation: -13.3647 ±â€…17.9198, vs Martin/Hopkins and extended Martin/Hopkins equation: -6.4737 ±â€…8.1036, vs Sampson equation: -8.9252 ±â€…12.6522, P < .001). It could accurately calculate LDL-C concentration even at high triglyceride and low LDL-C. Furthermore, the new equation could also precisely calculate LDL-C concentration in actual clinical use (R2 = 0.9780, MSE = 24.8482). The new equation developed in this study can accurately calculate LDL-C concentration within the full concentration range of triglyceride and LDL-C, and can serve as a supplement to the direct determination of LDL-C concentration for the prevention, treatment, evaluation, and monitoring of atherosclerotic diseases, compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations.

[Screening for Characteristic Genes of Different Traditional Chinese Medicine Syndromes of Psoriasis Vulgaris: A Study Based on Bioinformatics and Machine Learning]. / ç”Ÿç‰©ä¿¡æ¯å­¦å’Œæœºå™¨å­¦ä¹ ç­›é€‰é“¶å±‘ç— ä¸­åŒ»è¯åž‹ç‰¹å¾åŸºå› .

Objective: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types. Methods: The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO). The limma package was used to screen for the differentially expressed genes (DEGs) of PV, BHS, BSS, and BDS in PV patients and healthy populations. In addition, KEGG (Kyoto Encyclopedia of Genes and Genes) pathway enrichment analysis was performed. The DEGs associated with PV, BHS, BSS, and BDS were identified in the screening and were intersected separately to obtain differentially characterized genes. Out of two algorithms, the support vector machine (SVM) and random forest (RF), the one that produced the optimal performance was used to analyze the characteristic genes and the top 5 genes were identified as the key characteristic genes. The receiver operating characteristic (ROC) curves of the key characteristic genes were plotted by using the pROC package, the area under curve (AUC) was calculated, and the diagnostic performance was evaluated, accordingly. Results: The numbers of DEGs associated with PV, BHS, BSS, and BDS were 7699, 7291, 7654, and 6578, respectively. KEGG enrichment analysis was focused on Janus kinase (JAK)/signal transducer and activator of transcription (STAT), cyclic adenosine monophosphate (cAMP), mitogen-activated protein kinase (MAPK), apoptosis, and other pathways. A total of 13 key characteristic genes were identified in the screening by machine learning. Among the 13 key characteristic genes, malectin (MLEC), TUB like protein 3 (TULP3), SET domain containing 9 (SETD9), nuclear envelope integral membrane protein 2 (NEMP2), and BTG anti-proliferation factor 3 (BTG3) were the key characteristic genes of BHS; phosphatase 15 (DUSP15), C1q and tumor necrosis factor related protein 7 (C1QTNF7), solute carrier family 12 member 5 (SLC12A5), tripartite motif containing 63 (TRIM63), and ubiquitin associated protein 1 like (UBAP1L) were the key characteristic genes of BSS; recombinant mouse protein (RRNAD1), GTPase-activating protein ASAP3 Protein (ASAP3), and human myomesin 2 (MYOM2) were the key characteristic genes of BDS. Moreover, all of them showed high diagnostic efficacy. Conclusion: There are significant differences in the characteristic genes of different PV syndromes and they may be potential biomarkers for diagnosing TCM syndromes of PV.

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.

OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan. METHODS: This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. RESULTS: There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features' clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. CONCLUSIONS: The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.

Machine learning model based on RCA-PDCA nursing methods and differentiating factors to predict hypotension during cesarean section surgery.

BACKGROUND: Intraoperative hypotension during cesarean section has become a serious complication for maternal and fetal healthy. It is commonly encountered by subarachnoid anesthesia. However, currently used control methods have varying degrees of side effects, such as drugs. The Root Cause Analysis (RCA) - Plan, Do, Check, Act (PDCA) is a new model of care that identifies the root causes of problems. The study aimed to demonstrate the usefulness of RCA-PDCA nursing methods in preventing intraoperative hypotension during cesarean section and to predict the occurrence of intraoperative hypotension through a machine learning model. METHODS: Patients who underwent cesarean section at Traditional Chinese Medicine of Southwest Medical University from January 2023 to December 2023 were retrospectively screened, and the data of their gestational times, age, height, weight, history of allergies, intraoperative vital signs, fetal condition, operative time, fluid out and in, adverse effects, use of vasopressor drugs, anxiety-depression-pain scores, and satisfaction were collected and analyzed. The statistically different features were screened and five machine learning models were used as predictive models to assess the usefulness of the RCA-PDCA model of care. RESULTS: (1) Compared with the general nursing model, the RCA-PDCA nursing model significantly reduces the incidence of intraoperative hypotension and postoperative complications in cesarean delivery, and the patient experience is comfortable and satisfactory. (2) Among the five machine learning models, the RF model has the best predictive performance, and the accuracy of the random forest model in preventing intraoperative hypotension is as high as 90%. CONCLUSION: Through computer machine learning model analysis, we prove the importance of the RCA-PDCA nursing method in the prevention of intraoperative hypotension during cesarean section, especially the Random Forest model which performed well and promoted the application of artificial intelligence computer learning methods in the field of medical analysis.

Warfarin-A natural anticoagulant: A review of research trends for precision medication.

BACKGROUND: Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE: The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS: A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS: Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION: Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Elucidating the molecular mechanisms of sepsis: Identifying key aging-related biomarkers and potential therapeutic targets in the treatment of sepsis.

BACKGROUND: Sepsis remains a crucial global health issue characterized by high mortality rates and a lack of specific treatments. This study aimed to elucidate the molecular mechanisms underlying sepsis and to identify potential therapeutic targets and compounds. METHODS: High-throughput sequencing data from the GEO database (GSE26440 as the training set and GSE13904 and GSE32707 as the validation sets), weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, alongside a combination of PPI and machine learning methods (LASSO and SVM) were utilized. RESULTS: WGCNA identified the black module as positively correlated, and the green module as negatively correlated with sepsis. Further intersections of these module genes with age-related genes yielded 57 sepsis-related genes. GO and KEGG pathway enrichment analysis, PPI, LASSO, and SVM selected six hub aging-related genes: BCL6, FOS, ETS1, ETS2, MAPK14, and MYC. A diagnostic model was constructed based on these six core genes, presenting commendable performance in both the training and validation sets. Notably, ETS1 demonstrated significant differential expression between mild and severe sepsis, indicating its potential as a biomarker of severity. Furthermore, immune infiltration analysis of these six core genes revealed their correlation with most immune cells and immune-related pathways. Additionally, compounds were identified in the traditional Chinese medicine Danshen, which upon further analysis, revealed 354 potential target proteins. GO and KEGG enrichment analysis of these targets indicated a primary enrichment in inflammation and immune-related pathways. A Venn diagram intersects these target proteins, and our aforementioned six core genes yielded three common genes, suggesting the potential efficacy of Danshen in sepsis treatment through these genes. CONCLUSIONS: This study highlights the pivotal roles of age-related genes in the molecular mechanisms of sepsis, offers potential biomarkers, and identifies promising therapeutic compounds, laying a robust foundation for future studies on the treatment of sepsis.

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Quantification of caffeine in coffee cans using electrochemical measurements, machine learning, and boron-doped diamond electrodes.

Electrochemical measurements, which exhibit high accuracy and sensitivity under low contamination, controlled electrolyte concentration, and pH conditions, have been used in determining various compounds. The electrochemical quantification capability decreases with an increase in the complexity of the measurement object. Therefore, solvent pretreatment and electrolyte addition are crucial in performing electrochemical measurements of specific compounds directly from beverages owing to the poor measurement quality caused by unspecified noise signals from foreign substances and unstable electrolyte concentrations. To prevent such signal disturbances from affecting quantitative analysis, spectral data of voltage-current values from electrochemical measurements must be used for principal component analysis (PCA). Moreover, this method enables highly accurate quantification even though numerical data alone are challenging to analyze. This study utilized boron-doped diamond (BDD) single-chip electrochemical detection to quantify caffeine content in commercial beverages without dilution. By applying PCA, we integrated electrochemical signals with known caffeine contents and subsequently utilized principal component regression to predict the caffeine content in unknown beverages. Consequently, we addressed existing research problems, such as the high quantification cost and the long measurement time required to obtain results after quantification. The average prediction accuracy was 93.8% compared to the actual content values. Electrochemical measurements are helpful in medical care and indirectly support our lives.

A novel hybrid sensor array based on the polyphenol oxidase and its nanozymes combined with the machine learning based dual output model to identify tea polyphenols and Chinese teas.

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine (4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Accumulation mechanism of metabolite markers identified by machine learning between Qingyuan and Xiushui counties in Polygonatum cyrtonema Hua.

Polygonatum cyrtonema Hua is a traditional Chinese medicinal plant acclaimed for its therapeutic potential in diabetes and various chronic diseases. Its rhizomes are the main functional parts rich in secondary metabolites, such as flavonoids and saponins. But their quality varies by region, posing challenges for industrial and medicinal application of P. cyrtonema. In this study, 482 metabolites were identified in P. cyrtonema rhizome from Qingyuan and Xiushui counties. Cluster analysis showed that samples between these two regions had distinct secondary metabolite profiles. Machine learning methods, specifically support vector machine-recursive feature elimination and random forest, were utilized to further identify metabolite markers including flavonoids, phenolic acids, and lignans. Comparative transcriptomics and weighted gene co-expression analysis were performed to uncover potential candidate genes including CHI, UGT1, and PcOMT10/11/12/13 associated with these compounds. Functional assays using tobacco transient expression system revealed that PcOMT10/11/12/13 indeed impacted metabolic fluxes of the phenylpropanoid pathway and phenylpropanoid-related metabolites such as chrysoeriol-6,8-di-C-glucoside, syringaresinol-4'-O-glucopyranosid, and 1-O-Sinapoyl-D-glucose. These findings identified metabolite markers between these two regions and provided valuable genetic insights for engineering the biosynthesis of these compounds.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review.

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

A novel method for detecting credit card fraud problems.

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing techniques such as oversampling of minority classes are commonly used to address this issue, they often generate unrealistic or overgeneralized samples. This paper proposes a method called autoencoder with probabilistic xgboost based on SMOTE and CGAN(AE-XGB-SMOTE-CGAN) for detecting credit card frauds.AE-XGB-SMOTE-CGAN is a novel method proposed for credit card fraud detection problems. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. Autoencoder (AE) is used to extract relevant features from the dataset, enhancing the ability of feature representation learning, and are then fed into xgboost for classification according to the threshold. Additionally, in this study, we propose a novel approach that hybridizes Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to tackle class imbalance problems. Our two-phase oversampling approach involves knowledge transfer and leverages the synergies of SMOTE and GAN. Specifically, GAN transforms the unrealistic or overgeneralized samples generated by SMOTE into realistic data distributions where there is not enough minority class data available for GAN to process effectively on its own. SMOTE is used to address class imbalance issues and CGAN is used to generate new, realistic data to supplement the original dataset. The AE-XGB-SMOTE-CGAN algorithm is also compared to other commonly used machine learning algorithms, such as KNN and Light GBM, and shows an overall improvement of 2% in terms of the ACC index compared to these algorithms. The AE-XGB-SMOTE-CGAN algorithm also outperforms KNN in terms of the MCC index by 30% when the threshold is set to 0.35. This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew's correlation coefficient, making it a promising method for detecting credit card fraud.

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots.

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods.

While onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha-1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models' ability to generalize to growers' fields.

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types. Then, electrical features are extracted from the time domain, frequency domain, and nonlinear perspective respectively, following which typical machine learning algorithms (SVM, RF, KNN, NB, and XGBoost) are demonstrated to recognize APs and non-APs. The results demonstrate XGBoost with the best precision of 66.38%. Moreover, we also quantify the impacts of the differences among AP types and individuals, and propose a pairwise feature generation method to weaken the impacts on recognition precision. By using generated pairwise features, the recognition precision could be improved by 7.17%. The research systematically realizes the automatic recognition of APs and non-APs, and is conducive to pushing forward the intelligent development of APs and Traditional Chinese Medicine theories.

Meta-analysis and machine learning reveal the antiobesity effects of melatonin on obese rodents.

Melatonin appears to be a promising supplement for obesity treatment. The antiobesity effects of melatonin on obese rodents are influenced by various factors, including the species, sex, the dosage of melatonin, treatment duration, administration via, daily treatment time, and initial body weight (IBW). Therefore, we conducted a meta-analysis and machine learning study to evaluate the antiobesity effect of melatonin on obese mice or rats from 31 publications. The results showed that melatonin significantly reduced body weight, serum glucose (GLU), triglycerides (TGs), low-density lipoprotein (LDL), and cholesterol (TC) levels in obese mice or rats but increased high-density lipoprotein (HDL) levels. Melatonin showed a slight positive effect on clock-related genes, although the number of studies was limited. Meta-regression analysis and machine learning indicated that the dosage of melatonin was the primary factor influencing body weight, with higher melatonin dosages leading to a stronger weight reduction effect. Together, male obese C57BL/6 mice and Sprague-Dawley rats with an IBW of 100-200 g showed better body weight reduction when supplemented with a dose of 10-30 mg/kg melatonin administered at night via injection for 5-8 weeks.

Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: Machine learning analysis using national health insurance data.

This study uses machine learning and population data to analyze major determinants of blood transfusion among patients with hip arthroplasty. Retrospective cohort data came from Korea National Health Insurance Service claims data for 19,110 patients aged 65 years or more with hip arthroplasty in 2019. The dependent variable was blood transfusion (yes vs no) in 2019 and its 31 predictors were included. Random forest variable importance and Shapley Additive Explanations were used for identifying major predictors and the directions of their associations with blood transfusion. The random forest registered the area under the curve of 73.6%. Based on random forest variable importance, the top-10 predictors were anemia (0.25), tranexamic acid (0.17), age (0.16), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.04), dementia (0.03), iron (0.02), and congestive heart failure (0.02). These predictors were followed by their top-20 counterparts including cardiovascular disease, statin, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, peripheral vascular disease, liver disease, solid tumor, myocardial infarction and hypertension. In terms of max Shapley Additive Explanations values, these associations were positive, e.g., anemia (0.09), tranexamic acid (0.07), age (0.09), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.02), dementia (0.03), iron (0.04), and congestive heart failure (0.03). For example, the inclusion of anemia, age, tranexamic acid or spinal anesthesia into the random forest will increase the probability of blood transfusion among patients with hip arthroplasty by 9%, 7%, 9% or 5%. Machine learning is an effective prediction model for blood transfusion among patients with hip arthroplasty. The high-risk group with anemia, age and comorbid conditions need to be treated with tranexamic acid, iron and/or other appropriate interventions.

Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate.

Machine learning (ML), a powerful artificial intelligence tool, can effectively assist and guide the production of bio-oil from hydrothermal liquefaction (HTL) of wet biomass. However, for hydrothermal co-liquefaction (co-HTL), there is a considerable lack of application of experimentally verified ML. In this work, two representative wet biomasses, sewage sludge and algal biomass, were selected for co-HTL. The Gradient Boosting Regression (GBR) and Random Forest (RF) algorithms were employed for regression and feature analyses on yield (Yield_oil, %), nitrogen content (N_oil, %), and energy recovery rate (ER_oil, %) of bio-oil. The single-task results revealed that temperature (T, °C) was the most significant factor. Yield_oil and ER_oil reached their maximum values around 350 °C, while that of N_oil was around 280 °C. The multi-task results indicated that the GBR-ML model of the dataset#4 (n_estimators = 40, and max_depth = 7,) owed the highest average test R2 (0.84), which was suitable for developing a prediction application. Subsequently, through experimental validation with actual biomass, the best GBR multi-task ML model (T ≥ 300 °C, Yield_oil error < 11.75 %, N_oil error < 2.40 %, and ER_oil error < 9.97 %) based on the dataset#6 was obtained for HTL/co-HTL. With these steps, we developed an application for predicting the multi-object of bio-oil, which is scarcely reported in co-hydrothermal liquefaction studies.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation.

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.