[A preliminary pharmacophylogenetic study of Solanaceae medicinal plants containing tropane alkaloids].

The Solanaceae plants distributed in China belong to 105 species and 35 varietas of 24 genera. Some medicinal plants of Solanaceae are rich in tropane alkaloids(TAs), which have significant pharmacological activities. In this paper, the geographical distribution, chemical components, traditional therapeutic effect, pharmacological activities, and biosynthetic pathways of TAs in Solanaceous plants were summarized. Besides, the phylogeny of medicinal plants belonging to Solanaceae was visualized by network diagram. Fourteen genera of Solanaceae plants in China contain TAs and have medical records. TAs mainly exist in Datura, Anisodus, Atropa, Physochlaina, and Hyoscyamus. The TAs-containing species were mainly concentrated in Southwest China, and the content of TAs was closely related to plant distribution area and altitude. The Solanaceae plants containing TAs mainly have antispasmodic, analgesic, antiasthmatic, and antitussive effects. Modern pharmacological studies have proved the central sedative, pupil dilating, glandular secretion-inhibiting, and anti-asthma activities of TAs. These pharmacological activities provide a reasonable explanation for the traditional therapeutic efficacy of tropane drugs. In this paper, the geographical distribution, chemical components, traditional therapeutic effect, and modern pharmacological activities of TAs-containing species in Solanaceae were analyzed for the first time. Based on these data, the genetic relationship of TAs-containing Solanaceae species was preliminarily discussed, which provided a scientific basis for the basic research on TAs-containing solanaceous species and was of great significance for the development of natural medicinal plant resources containing TAs.

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.

Understanding large-scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County-level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short-term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county-level corn yields. By conflating heterogeneous phenology-based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology-based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end-of-the-season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root-mean-square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high-dimensional (spectral, spatial, and temporal) input features to achieve a robust county-level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.

Functions of Jasmonic Acid in Plant Regulation and Response to Abiotic Stress.

Jasmonic acid (JA) is an endogenous growth-regulating substance, initially identified as a stress-related hormone in higher plants. Similarly, the exogenous application of JA also has a regulatory effect on plants. Abiotic stress often causes large-scale plant damage. In this review, we focus on the JA signaling pathways in response to abiotic stresses, including cold, drought, salinity, heavy metals, and light. On the other hand, JA does not play an independent regulatory role, but works in a complex signal network with other phytohormone signaling pathways. In this review, we will discuss transcription factors and genes involved in the regulation of the JA signaling pathway in response to abiotic stress. In this process, the JAZ-MYC module plays a central role in the JA signaling pathway through integration of regulatory transcription factors and related genes. Simultaneously, JA has synergistic and antagonistic effects with abscisic acid (ABA), ethylene (ET), salicylic acid (SA), and other plant hormones in the process of resisting environmental stress.

[Variety textual research of Mongolian medicine of "saradma"].

Mongolian medicine is an indispensable part in developing traditional Mongolian medicine. This study is aimed to provide a basis for the formulation of clinical and Mongolian medicinal materials standards by clarifying the original plant and species collation of Mongolia medicine of "saradma". Mongolian herbal medicine, as an important part of Mongolian medicine, is needed to study the authentic Mongolian medicine, in order to exert the best therapeutic effect in the application. The Mongolian medicine of "saradma" is a kind of medicinal material for diuresis, reinforcing kidney, and eliminating edema, for which comes from the roots, stems, leaves, flowers, fruits, seeds and other parts of medicinal plant. The ancient books of Mongolian medicine are the most important reference the research of Mongolian medicine varieties. This review adopts the method of inductive comparison of ancient books in order to summarize the conclusion of Mongolian medicine of "saradma". According to the investigations, Mongolian medicine of "saradma" type is mainly Leguminosae plant, Oxytropis latibracteata, Hedysarum multijugum, Thermopsis barbata, Astragalus membranaceus, Vicia amoena, O. caerulea, Astragalus bhotanensis, Hedysarum sikkimense. Compared with modern works, it is found that the drug has a wide range of resources distribution and application. It can be used for the treatment of cold edema, hot edema, nephrogenic edema, edema, swelling and likes caused by different diseases. Based on the research of Mongolian medicine of "saradma" varieties, it was found that the most commonly used varieties in Inner Mongolia were cayan saradma, xara saradam and sira saradma all of which are all top-grade drugs that reduce swelling.

[Study on traditional processing method of Mongolian medicine and excipient usage based on data mining].

This paper explores Mongolian medicine processing methods and the use regularity of excipient by text mining techniques. Relevant books of Mongolian medicine processing were consulted to collect data on Mongolian medicine processing methods and excipient, and select data based on processing methods and excipient noun frequency statistics. Microsoft Excel 2010 software was used for statistical analysis and mining for the usage regularity of different types of Mongolian medicinal materials in different periods. And Cytoscape 3.6.1 software was used for visual presentation. The topological analysis showed the top five processing methods were net production, development, frying, calcining and cooking, and the top five processing excipient were fresh milk, wine, urine, cream and mineral borax. Frequency analysis showed that the plant medicinal materials were mostly recorded in the 18~(th) and 21~(st) centuries, especially in the 21 st century; the processing methods mostly contained water processing, repair processing and other methods. The mineral medicinal materials were mostly recorded in the 18~(th), 19~(th) and 21~(st) centuries; most of the processing methods were the fire processing method. The animal medicinal materials were recorded in the 18~(th), 19~(th) and 21~(st) century; the fire processing method occupied a major position, and the repair processing and the grinding processing were markedly increased in the 21~(st) century. In the use of excipient, liquid excipient were mostly used in plant medicines. Solid excipient were most commonly used in the 18~(th) century. Animal excipient were mostly used during the processing in the 18~(th) century. The use of liquid excipient gradually increased in the 19~(th) and 21~(st) centuries. This study summarizes the traditional processing methods of Mongolian medicine and the usage regularity of excipient, defines the characteristics of Mongolian medicine processing methods and excipient, and the characteristics of the combination of medicinal materials and excipient, so as to provide reference for the clinical use of Mongolian medicine.

Association between Lifestyle Modification and All-Cause, Cardiovascular, and Premature Mortality in Individuals with Non-Alcoholic Fatty Liver Disease.

BACKGROUND: This study is designed to explore the correlation between multiple healthy lifestyles within the framework of "lifestyle medicine", and the mortality risk of nonalcoholic fatty liver disease (NAFLD). METHODS: The National Health and Nutrition Examination Survey (NHANES) database was employed. The analysis consisted of 5542 participants with baseline NAFLD and 5542 matched non-NAFLD participants from the database. Lifestyle information, including five low risk factors advocated by lifestyle medicine (healthy diet, vigorous physical activity, healthy sleep duration, avoiding smoking, and maintaining a non-depressed psychological status), was collected through a baseline questionnaire. Cox proportional hazards regression models and Kaplan-Meier survival curve were used to evaluate risk of mortality. In addition, subgroups were analyzed according to gender, age, body mass index and waist circumference. RESULTS: In total, 502 deaths (n = 181 deaths from cardiovascular disease (CVD)) were recorded among NAFLD participants after the median follow up duration of 6.5 years. In the multivariate-adjusted model, compared to participants with an unfavorable lifestyle (scoring 0-1), NAFLD participants with a favorable lifestyle (scoring 4-5) experienced a 56% reduction in all-cause mortality and a 66% reduction in CVD mortality. Maintaining an undepressed psychological state and adhering to vigorous exercise significantly reduced CVD mortality risk in NAFLD participants (HR, 0.64 [95% CI, 0.43-0.95]; HR, 0.54 [95% CI, 0.33-0.88]) while maintaining healthy sleep reduced premature mortality due to CVD by 31%. CONCLUSIONS: Healthy lifestyle, characterized by maintaining an undepressed mental state and healthy sleep, significantly mitigates the risk of all-cause, CVD, and premature mortality risk among NAFLD patients, with a particularly pronounced effect observed in female and obese subpopulations.

Antiosteoporosis effect and possible mechanisms of the ingredients of Radix Achyranthis Bidentatae in animal models of osteoporosis: systematic review and meta-analysis of in vivo studies.

BACKGROUND: The effect and mechanisms of the ingredients (IRAB) of Radix Achyranthis Bidentatae (RAB) on treating osteoporosis (OP) remains debated. We aimed to summary the evidence to evaluate the efficacy of IRAB for animal model OP and elucidate the potential mechanism of IRAB in the treatment of OP. METHODS: In this review and meta-analysis, we searched PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang, Chinese Biomedical Literature Database, as well as Chinese VIP databases for targeting articles published from inception to March 2023 in English or Chinese. All randomized controlled animal trials that assessed the efficacy and safety of IRAB for OP were included. We excluded trials according to exclusion criteria. The CAMARADES 10-item quality checklist was utilized to test the risk of potential bias for each including study and modifications were performed accordingly. The primary outcome measures were bone mineral density of the femoral neck (F-BMD), serum calcium (Ca), serum phosphorus (P), serum alkaline phosphatase (ALP), bone gla protein (BGP), bone maximum stress (M-STRESS). The secondary outcome measure was the antiosteoporosis mechanisms of IRAB. RESULTS: Data from nine articles were included in the systematic review and meta-analysis, which focused on 196 animals. Egger's test revealed the presence of publication bias in various studies regarding the primary outcome. Administration of IRAB or RAB could significantly increases the F-BMD (SMD = 2.09; 95% CI = 1.29 to 2.89; P < 0.001, I2 = 76%), Ca (SMD = 0.86; 95% CI = 0.39to1.34; P = 0.07, I2 = 49%); P (SMD = 1.01; 95% CI = 0.45-4.57; P = 0.08, I2 = 50%), BGP (SMD = 2.13; 95% CI = 1.48 to 2.78; I2 = 46%, P = 0.10), while the ALP (SMD = - 0.85; 95% CI = - 1.38 to - 0.31; I2 = 46%, P = 0.10) was remarkably decreased in OP model animals. Moreover, the bone biomechanical indicator M-STRESS (SMD = 2.39; 95% CI = 1.74-3.04; I2 = 32%, P = 0.21) was significantly improved. CONCLUSION: Collectively, the findings suggest that the RAB or IRAB could be an effective drug or an ingredient in diet for the clinical treatment of OP in future.

Exploring the mechanism of MP gel against skin photoaging based on network pharmacology, molecular docking, and experimental validation.

OBJECTIVE: Long-term and high exposure to UV radiation can lead to the development of skin photoaging diseases. Therefore, there is an ongoing need for more natural and safe drugs to prevent or treat skin photoaging diseases. METHODS: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database were used to collect the active compounds and corresponding targets of Cnidii Fructus, Arnebiae Radix, Angelicae Sinensis Radix, Poria, and Borneolum. The GeneCards database and the NCBI Gene database were used to collect the targets of skin photoaging diseases. The STRING database was used to construct a protein-protein interaction network formed by the intersecting targets of drugs and diseases. The Metascape database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the targets. Molecular docking between active compounds and targets was verified by Autodock. After that, the skin photoaging model of mice was established and treated with MP gel. The skin characterization on the back of mice was observed, and the ameliorative effect of MP gel on skin photoaging was evaluated by histological and epidermal thickness assays. The MDA content and SOD activity were measured. Caspase-3 expression in mouse skin tissues was detected by immunohistochemistry, quantitative real-time polymerase chain reaction assay, and Western blot. RESULTS: The results of network pharmacology experiments showed that the natural drugs have multi-component, multi-target therapeutic disease characteristics. The results of animal studies showed that MP gel improved the health of photoaged skin, promoted skin structural integrity, had antioxidant properties and significantly inhibited caspase-3 expression. CONCLUSION: The experimental validation of the results of the preliminary network pharmacology analysis was carried out in animal experiments, which confirmed part of the mechanism of action of MP gel in the prevention and treatment of skin photoaging.

Advances and challenges in medicinal plant breeding.

Large-scale cultivation of medicinal plants is the most rapid and effective means of addressing the disparity between the supply and demand of medicinal plants. To achieve this scale of production, breeding studies are necessary for further development of medicinal plant cultivation. Although advances have been made in the breeding of some medicinal plants, a number of challenges remain, owing to the particularity and complexity in determining the breeding target. Additionally, there are limitations associated with research on traditional and modern breeding methods for medicinal plants. In this review, we summarize and analyze the selection strategies for breeding direction and breeding models, and emphasize the importance of breeding research in promoting the breeding of medicinal plants.

Understanding the learning mechanism of convolutional neural networks in spectral analysis.

Deep learning approaches, especially convolutional neural network (CNN) models, have achieved excellent performances in vibrational spectral analysis. The critical drawback of the CNN approach is the lack of interpretation, and it is regarded as a black box. Interpreting the learning mechanism of chemometric models is critical for intuitive understanding and further application. In this study, an interpretable CNN model with a global average pooling layer is presented for Raman and mid-infrared spectral data analysis. A class activation mapping (CAM)-based approach is leveraged to visualize the active variables in the whole spectrum. The visualization of active variables shows a discriminative pattern in which the most contributed variables peaked around theoretical chemical characteristic bands. The visualization of the feature maps by three convolutional layers demonstrates the data transformation pipeline and how the CNN model hierarchically extracts informative spectral features. The first layer acts as a Savitzky-Golay filter and learns spectral shape characteristics, while the second layer learns enhanced patterns from typical spectral peaks on a few correlated variables. The third layer shows stable activations on critical spectral peaks. A partial least squares - linear discriminant analysis (PLS-LDA) model is presented for comparison on classification accuracy and model interpretation. The CNN model yields mean classification accuracies of 99.01 and 100% for E. coli and meat datasets on the test set, while the PLS-LDA models obtain accuracies of 98.83 and 100%. Both the CNN and PLS-LDA models demonstrate stable patterns on active variables while CNN models are more stable than PLS-LDA models on classification performances for various dataset partitions with Monte-Carlo cross-validation.

The Pharmacological Effects and Health Benefits of Platycodon grandiflorus-A Medicine Food Homology Species.

Platycodon grandiflorus is a widely used edible, traditional Chinese medicinal herb. It is rich in saponins, flavonoids, phenolic acids, and other compounds. It contains a large number of fatty acids such as linoleic acid (up to 63.24%), a variety of amino acids, vitamins, and multiple essential trace elements. P. grandiflorus has several biological applications, such as in hypotension, lipid reduction, atherosclerosis, inflammation, relieving cough and phlegm, promoting cholic acid secretion, and as an antioxidant. Further, P. grandiflorus is often used in the development of cold mixed vegetables, canned vegetables, preserved fruit, salted vegetables, and cosmetics in northeast China, South Korea, Japan, and Korea. In this paper, the active chemical components and the health benefits of P. grandiflorus have been reviewed, providing new ideas for the further development of nutraceutical products to prevent and manage chronic diseases.

DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis.

Learning patterns from spectra is critical for the development of chemometric analysis of spectroscopic data. Conventional two-stage calibration approaches consist of data preprocessing and modeling analysis. Misuse of preprocessing may introduce artifacts or remove useful patterns and result in worse model performance. An end-to-end deep learning approach incorporated Inception module, named DeepSpectra, is presented to learn patterns from raw data to improve the model performance. DeepSpectra model is compared to three CNN models on the raw data, and 16 preprocessing approaches are included to evaluate the preprocessing impact by testing four open accessed visible and near infrared spectroscopic datasets (corn, tablets, wheat, and soil). DeepSpectra model outperforms the other three convolutional neural network models on four datasets and obtains better results on raw data than in preprocessed data for most scenarios. The model is compared with linear partial least square (PLS) and nonlinear artificial neural network (ANN) methods and support vector machine (SVR) on raw and preprocessed data. The results show that DeepSpectra approach provides improved results than conventional linear and nonlinear calibration approaches in most scenarios. The increased training samples can improve the model repeatability and accuracy.

Deep learning for vibrational spectral analysis: Recent progress and a practical guide.

The development of chemometrics aims to provide an effective analysis approach for data generated by advanced analytical instruments. The success of existing analytical approaches in spectral analysis still relies on preprocessing and feature selection techniques to remove signal artifacts based on prior experiences. Data-driven deep learning analysis has been developed and successfully applied in many domains in the last few years. How to integrate deep learning with spectral analysis received increased attention for chemometrics. Approximately 20 recently published studies demonstrate that deep neural networks can learn critical patterns from raw spectra, which significantly reduces the demand for feature engineering. The composition of multiple processing layers improves the fitting and feature extraction capability and makes them applicable to various analytical tasks. This advance offers a new solution for chemometrics toward resolving challenges related to spectral data with rapidly increased sample numbers from various sources. We further provide a practical guide to the development of a deep convolutional neural network-based analytical workflow. The design of the network structure, tuning the hyperparameters in the training process, and repeatability of results is mainly discussed. Future studies are needed on interpretability and repeatability of the deep learning approach in spectral analysis.

A Targeted "Capture" and "Removal" Scavenger toward Multiple Pollutants for Water Remediation based on Molecular Recognition.

For the water remediation techniques based on adsorption, the long-standing contradictories between selectivity and multiple adsorbability, as well as between affinity and recyclability, have put it on weak defense amid more and more severe environment crisis. Here, a pollutant-targeting hydrogel scavenger is reported for water remediation with both high selectivity and multiple adsorbability for several pollutants, and with strong affinity and good recyclability through rationally integrating the advantages of multiple functional materials. In the scavenger, aptamers fold into binding pockets to accommodate the molecular structure of pollutants to afford perfect selectivity, and Janus nanoparticles with antibacterial function as well as anisotropic surfaces to immobilize multiple aptamers allow for simultaneously handling different kinds of pollutants. The scavenger exhibits high efficiencies in removing pollutants from water and it can be easily recycled for many times without significant loss of loading capacities. Moreover, the residual concentrations of each contaminant are well below the drinking water standards. Thermodynamic behavior of the adsorption process is investigated and the rate-controlling process is determined. Furthermore, a point of use device is constructed and it displays high efficiency in removing pollutants from environmental water. The scavenger exhibits great promise to be applied in the next generation of water purification systems.