Machine learning-based study on Xin-Pi simultaneous treatment formula via drug nanodelivery systems regulating macrophage polarization and TEAD2/PKM2 synergistic repair strategy in myocarditis.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB. We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape. Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB. Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Purpose: Based on the risk factors of post stroke cognitive impairment (PSCI), combining the Constitution and Syndrome of Traditional Chinese Medicine, using a variety of Machine learning (ML) algorithms, to construct a prediction model with high accuracy and good fitting degree, so as to provide theoretical and data support for early screening and early prevention of ischemic stroke (IS) patients. Patients and methods: A retrospective analysis was conducted on 85 patients with acute ischemic stroke admitted to the Department of Neurology of a third grade a hospital of integrated Traditional Chinese and Western Medicine (TCM-WM) from June 2019 to January 2020. The patients were divided into three groups: Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), ML algorithms were used to construct the risk prediction model of post-stroke cognitive impairment, and the prediction accuracy and area under curve (AUC) of receiver operating characteristic curve (ROC) were used to evaluate the prediction effect of the three models. Results: The average prediction accuracy of GBDT was 80.77 percent, the highest and the most stable. The average AUC area of GBDT was 0.85, which was larger than that of the other three ML algorithms, and the prediction effect was better. After analyzing the importance of the features obtained from the training of GBDT model, it is concluded that the features with the highest degree of discrimination for PSCI in this data set are as follows: Barthel index, Age, fasting blood glucose (FPG), blood homocysteine (Hcy). Based on GBDT algorithm, four GBDT models were obtained by training 75 percent, 80 percent, 85 percent and 90 percent training sets respectively. It was found that the prediction accuracy of the models with 85 percent and 90 percent training sets could reach 84.62 percent and 88.89 percent, indicating the potential of applying machine learning algorithm to the prediction of cognitive impairment after ischemic stroke. Conclusion: The ML algorithm is used to construct the early prediction model of TCM-WM integration for cognitive impairment after ischemic stroke, and analyze the influencing factors with strong correlation with PSCI, so as to carry out early detection, early diagnosis and early treatment of PSCI, so as to provide basis and reference for researchers who construct a large sample prediction model of cognitive impairment after ischemic stroke.

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

Machine learning prediction of myocardial ischaemia‒reperfusion injury: Clinical features and Qishen Yiqi dripping pills mechanism.

BackgroundPsoriasis is a chronic skin disease affected by genetic and autoimmunity. The traditional Chinese medicine, Compound Qingdai Capsule (CQC), has shown potential benefits in treating psoriasis in clinical settings. Despite its efficacy, the molecular mechanisms underpinning its therapeutic action remain unclear.PurposeThis study aimed to unravel the molecular mechanism of Compound Qingdai Capsule for psoriasis based on the psoriasis pathogenic pathway network, integrating multi-omics analysis, systems pharmacology, machine learning modeling, and animal experimentation.MethodsPsoriasis pathogenic pathway network was constructed through employing bioinformatics analysis and psoriasis-related multi-omics data mining. The ingredients of CQC were detected by UPLC-MS/MS, and target prediction was performed by systems pharmacology. Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. Molecular docking was employed to evaluate the binding affinity between ingredients and core targets. The expression levels of core targets were determined using qRT-PCR and ELISA.ResultsPsoriasis-related datasets GSE201827 and GSE174763 were comprehensively analyzed to obtain 635 psoriasis-related genes. These genes were further enriched to elucidate signaling pathways involved, leading to the construction of psoriasis pathogenic pathway network. Utilizing UPLC-MS/MS, 29 main ingredients of CQC were characterized. CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. Molecular docking confirmed good binding affinity between these targets and ingredients. Imiquimod (IMQ) induced psoriasis-like rat validated the anti-psoriasis effect of CQC by alleviating symptoms, reducing spleen and thymus index, and modulating the expressions of core targets at mRNA and protein levels.ConclusionCQC effectively modulates the expression levels of AURKB, CCNB1, CCNB2, CCNE1, CDK1, and JAK3 through various ingredients, such as astilbin, salvianolic acid A, and engeletin, via multiple pathways, thereby alleviating psoriasis-like symptoms.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.

Abstract:As part of a larger study to elucidate the presence of hydrated minerals on asteroid surfaces, we are developing a robust taxonomic classification system using spectroscopic observations in the vicinity of 3 &amp;#956;m. We have constructed a Python algorithm to identify band centers and band depths near 3 &amp;#181;m for a set of normalized, thermally-corrected asteroid spectra for use to serve as inputs to Python&amp;#8217;s Scikit-Learn library of Machine Learning (ML) algorithms. We anticipate a thorough investigation of both Principal Component Analysis and ML (supervised, unsupervised, and Artificial Neural Network) techniques to assess which technique is likely to be better suited for classifying the 3-&amp;#181;m data. At this writing, we have run tests using Python&amp;#8217;s Agglomerative clustering ML algorithm to examine possible clustering scenarios. These initial steps have given us some familiarity with the mechanics of using ML on the 3-&amp;#181;m dataset as well as serving to identify some possible pitfalls or cul-de-sacs. Presented here are the preliminary results we have obtained.Introduction:Although various techniques have been used, asteroid classification has typically been done via Principal Component Analysis (PCA: [1,2]). PCA is a statistical technique that reduces the dimensionality of a dataset by identifying the most important parameters within a dataset based on their variance. Parameters that exhibit the greatest amount of variance are considered to be of greater importance while parameters with the least amount of variance are considered to be of lower importance. While the PCA technique produces better visualizations of the data by reducing the dimensionality of a dataset, the PCA technique comes with some drawbacks. Disadvantages such as its dependence on scale and information loss due to the orthogonal property of PCA can cause interpretation of PCA results to prove to be a more critical and time-consuming process. Therefore, exploring other means of classification may prove to be worthwhile.Machine Learning (ML) algorithms have had a significant impact on the way in which data is analyzed and interpreted, and have already proven to be a powerfully reliable resource in the field of planetary science. Accordingly, the application of ML to an asteroid taxonomy has the potential to be more efficient, objective, and easy-to-implement than PCA. ML algorithms can be supervised, in which the program &amp;#8220;learns&amp;#8221; from training data and is able to classify new inputs, or unsupervised, in which the program analyzes the dataset to determine patterns such as clusters. [3] used an Artificial Neural Network (ANN, a subset of ML) to classify asteroids, work followed up by [4]. Recent explorations of supervised ML for asteroid taxonomy are promising, and have applied training sets from existing databases to new visible and/or NIR photometric data (e.g. [5,6,7]).We seek to explore the benefits of ML algorithms, as well as compare and contrast to the PCA technique, in the production of an asteroid taxonomy. Our initial exploration has utilized a set of normalized, thermally-corrected asteroid spectra in the vicinity of 3 &amp;#181;m. We have identified band centers and band depths and served this parameter space as inputs to Python&amp;#8217;s Agglomerative clustering ML algorithm.Methodology:Thermal corrections of the asteroid spectra were performed via a forward model that uses a modified version of the Standard Thermal Model (STM: [8]). The forward model treats the beaming parameter as a free parameter adjusting its value for each iteration of the STM until it converges onto a value that yields expected long-wavelength continuum behavior. Spectra were then normalized to unity at a wavelength of 2.3 &amp;#181;m, followed by identification of band centers and band depths near 3 &amp;#181;m using both polynomial and Gaussian fits. In addition, band depths were measured at wavelengths of 2.9 &amp;#181;m and 3.2 &amp;#181;m to gather more information on asteroid band shapes. Lastly, the aforementioned calculated spectral features were input into Python&amp;#8217;s Agglomerative clustering algorithm to determine which asteroid spectra shared similar features.Summary:As part of a larger investigation to better understand hydrated mineralogies as they apply to asteroids, we have begun work towards developing a quantitative taxonomic framework derived from asteroid spectra in the wavelength range from 2.0-4.0 &amp;#181;m. Our exploration thus far of Python&amp;#8217;s Agglomerative clustering algorithm has proven to be fruitful. Minor changes to the parameterization of this algorithm can yield very different results, which naturally can lead to different interpretations. The Agglomerative clustering algorithm is one of many the powerful ML algorithms we will explore against the PCA technique, all of which we will be discussing in our presentation.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

Abstract. Introduction: Traditional herbal medicine has been practiced for centuries and continues to play a significant role in healthcare systems worldwide. However, the efficacy and therapeutic potential of herbal remedies can be limited due to various factors such as poor bioavailability, instability, and non-specific targeting. In recent years, nanotechnology has emerged as a promising approach to overcome these limitations and revolutionize the field of herbal medicine. This review explores the application of nano drug delivery systems in enhancing the effectiveness of herbal therapeutics. The utilization of nanotechnology in the context of herbal medicine involves the design and development of nano-sized carriers that can encapsulate and deliver herbal bioactive compounds to the target sites in a controlled and targeted manner. Various types of nanocarriers, such as liposomes, polymeric nanoparticles, solid lipid nanoparticles, and nanoemulsions, have been extensively investigated for their potential in improving the bioavailability, stability, and controlled release of herbal compounds. The integration of nanotechnology with herbal medicine offers several advantages, including enhanced solubility, protection against degradation, prolonged circulation time, and specific targeting to diseased tissues or cells. Furthermore, nano drug delivery systems can also facilitate the combination of multiple herbal ingredients, enabling synergistic effects and customized therapeutic approaches. This review provides an overview of the recent advancements in nano drug delivery systems for herbal medicine, highlighting their potential applications in various therapeutic areas, such as cancer treatment, neurodegenerative disorders, cardiovascular diseases, and inflammatory conditions. Additionally, challenges and future perspectives regarding the clinical translation of these nanotechnological approaches are discussed. In conclusion, the integration of nanotechnology with herbal medicine holds great promise in revolutionizing the field of healthcare. The development of efficient and targeted nano drug delivery systems can significantly enhance the therapeutic efficacy of herbal remedies, leading to improved patient outcomes and the potential for personalized medicine. Further research and collaborations between scientists, herbalists, and clinicians are needed to unlock the full potential of nano drug delivery systems in herbal medicine. Keywords: Revolutionizing, Herbal medicine, Nano drug delivery systems, Bioavailability, Stability, Nanotechnology, Nanocarriers, Liposomes, Polymeric nanoparticles, Solid lipid nanoparticles, Nanoemulsions

Utilization of synthetic minority oversampling technique for improving potato yield prediction using remote sensing data and machine learning algorithms with small sample size of yield data

ObjectiveMyocardial ischemia-reperfusion injury (MIRI) is a critical clinical challenge in cardiovascular disease management. Sanshen Dan (SSD), a clinically validated traditional Chinese medicine formula, exerts therapeutic effects on MIRI, but its chemical composition and underlying mechanism remain unclear. This study aimed to systematically elucidate the cardioprotective mechanism of SSD against MIRI using an integrated strategy.MethodsAn integrated approach combining serum pharmacochemistry, network pharmacology, machine learning, molecular docking, and molecular dynamics simulation was adopted. 1. Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was used to identify blood-absorbed components of SSD after oral administration to Sprague-Dawley rats. 2. Potential targets of these components were predicted via public databases, and overlapping targets with MIRI-related genes were screened to construct a compound-target network, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. 3. Three machine learning algorithms (random forest, SVM-RFE, LASSO regression) were applied to identify core therapeutic targets. 4. Molecular docking and 100 ns molecular dynamics simulations were performed to verify the binding affinity and stability of ligand-receptor complexes.ResultsA total of 44 blood-absorbed active components of SSD were identified, including flavonoids, saponins, lignans, and phenolic acids. A total of 392 potential therapeutic targets were screened out, which were mainly enriched in apoptosis, HIF-1, TNF-α, and cAMP signaling pathways. Machine learning analysis identified Mmp14, Htr2b, and Ctnnb1 as core targets, and the constructed nomogram model showed excellent predictive performance (AUC = 1, C-index = 1). Molecular docking indicated that 6 core components (eg, Kaempferide-4′-methyl ether-3-glucoside, Licurazide) exhibited strong binding affinity to the core targets, and molecular dynamics simulations confirmed the high stability of these complexes, with mean binding free energy ranging from −20.58 kcal/mol to −34.31 kcal/mol.ConclusionSSD exerts cardioprotective effects against MIRI via a “multi-component, multi-target, multi-pathway” mode. Its core blood-absorbed components may alleviate MIRI by regulating core targets including Mmp14 and Ctnnb1, and modulating key signaling pathways such as TNF-α and HIF-1. This study provides a scientific basis for the clinical application of SSD and further exploration of traditional Chinese medicine compound formulas.