Non-targeted metabolomics and explainable artificial intelligence: Effects of processing and color on coniferyl aldehyde levels in Eucommiae cortex.

Eucommia ulmoides, a plant native to China, is valued for its medicinal properties and has applications in food, health products, and traditional Chinese medicine. Processed Eucommiae Cortex (EC) has historically been a highly valued medicine. Ancient doctors had ample experience processing EC, especially with ginger juice, as documented in traditional Chinese medical texts. The combination of EC and ginger juice helps release and transform the active ingredients, strengthening the medicine's effectiveness and improving its taste and shelf life. However, the lack of quality control standards for Ginger-Eucommiae Cortex (G-EC), processed from EC and ginger, presents challenges for its industrial and clinical use. This study optimized G-EC processing using the CRITIC and Box-Behnken methods. Metabolomics showed 517 chemical changes between raw and processed G-EC, particularly an increase in coniferyl aldehyde (CFA). Explainable artificial intelligence techniques revealed the feasibility of using color to CFA content, providing insights into quality indicators.

Effects of Intervention Timing on Health-Related Fake News: Simulation Study.

BACKGROUND: Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. OBJECTIVE: This study investigated the appropriate timing of interventions to minimize the side effects of fake news. METHODS: A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. RESULTS: Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. CONCLUSIONS: Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response.

From Networking to Attitudinal Outcomes: Psychological Capital as a Mediator.

Building on conservation of resources (COR) theory and following recent recommendations, this study investigates the mediating role of Psychological Capital (PsyCap) in the relationships between networking behaviors and attitudinal outcomes (i.e., work engagement and work satisfaction). We propose that networking, as an investment of personal resources to gain access to other resources, contributes to the prediction of attitudinal outcomes. We surveyed 254 employees from a public Belgian administrative company. We use structural equation modelling and the bootstrapping method. PsyCap totally mediates the relationships between networking and both attitudinal outcomes. This study contributes to theoretical development by integrating Networking and PsyCap literatures into COR theory literature, and demonstrates the legitimacy of COR theory to explain these complex variables and their relationships.

Factors influencing self-management behaviors in persons with lupus nephritis: A cross-sectional study.

Background: Patients with lupus nephritis experience disease symptoms and side effects from treatment. Although self-management behaviors are important in patients with this disease, there is limited research on the factors influencing these behaviors. Objective: This study aimed to examine the factors influencing self-management behaviors in patients with lupus nephritis. Methods: This cross-sectional study was conducted in 240 patients with lupus nephritis at a university hospital in Thailand between August 2019 and December 2020 using a random sampling method. Data were collected using a demographic and clinical characteristic questionnaire, Self-Management Behavior Questionnaire, Self-efficacy for Managing Chronic Disease: A 6-item Scale, Knowledge about Lupus Nephritis Questionnaire, Family Support Scale, Social Networks in Adult Life Questionnaire, and Memorial Symptom Assessment Scale for Lupus Nephritis. Descriptive statistics and multiple linear regression analyses were employed. Results: The participants reported a moderate level of self-management behaviors. Multiple regression analyses revealed that disease duration, income, symptoms, self-efficacy, knowledge, family support, social networks, and classes of lupus nephritis significantly explained 21% of the variance in self-management behaviors (R2 = 0.21; F(8,231) = 7.73; p <0.001). Family support (ß = 0.32, p <0.001) and symptoms (ß = -0.23, p <0.001) were significant determinants of self-management behaviors in patients with lupus nephritis. Conclusion: The findings provide valuable insight for nurses to better understand the factors influencing self-management behaviors in patients with lupus nephritis. Patients with low family support and high symptom severity may face difficulty in performing self-management behaviors. Nurses should pay more attention to these patients and provide family-based interventions to optimize self-management behaviors in this population.

Decoding genetic and pathophysiological mechanisms in amyotrophic lateral sclerosis and primary lateral sclerosis: A comparative study of differentially expressed genes and implicated pathways in motor neuron disorders.

Motor Neuron Disorders (MNDs), characterized by the degradation and loss of function of motor neurons, are recognized as fatal conditions with limited treatment options and no known cure. The present study aimed to identify the pathophysiological functions and affected genes in patients with MNDs, specifically Amyotrophic Lateral Sclerosis (ALS) and Primary Lateral Sclerosis (PLS). The GSE56808 dataset comprised three sample groups: six patients diagnosed with ALS (GSM1369650, GSM1369652, GSM1369654, GSM1369656, GSM1369657, GSM1369658), five patients diagnosed with PLS (GSM1369648, GSM1369649, GSM1369653, GSM1369655, GSM1369659), and six normal controls (GSM1369642, GSM1369643, GSM1369644, GSM1369645, GSM1369646, and GSM1369647). The application of computational analysis of microarray gene expression profiles enabled us to identify 346 significantly differentially expressed genes (DEGs), 169 genes for the ALS sample study, and 177 genes for the PLS sample study. Enrichment was carried out using MCODE, a Cytoscape plugin. Functional annotation of DEGs was carried out via ClueGO/CluePedia (v2.5.9) and further validated via the DAVID database. NRP2, SEMA3D, ROBO3 and, CACNB1, CACNG2 genes were identified as the gene of interest for ALS and PLS sample groups, respectively. Axonal guidance (GO:0007411) and calcium ion transmembrane transport (GO:0070588) were identified to be some of the significantly dysregulated gene ontology (GO) terms, with arrhythmogenic right ventricular cardiomyopathy (KEGG:05412) to be the top relevant KEGG pathway which is affected in MND patients. ROBO3 gene was observed to have distinctive roles in ALS and PLS-affected patients, hinting towards the differential progression of ALS from PLS. The insights derived from our comprehensive analysis accentuate the distinct variances in the underlying molecular pathogenesis of ALS and PLS. Further research should investigate the mechanistic roles of the identified DEGs and molecular pathways, leading to potential targeted therapies for ALS and PLS.

LC-ESI-MS/MS-based molecular networking, antioxidant, anti-glioma activity and molecular docking studies of Clematis graveolens.

Clematis graveolens Lindl., an indigenous climbing plant found in the Himalayan areas, is used by local communities for the treatment of neck tumors. The objective of this work is to examine the comprehensive metabolomic profile, antioxidant capability, in vitro and in silico anti-glioma effects on U-87 human glioma cell lines of the crude extract and fractions from C. graveolens. Liquid chromatography coupled with mass spectroscopy (LC-MS/MS) was used to establish detailed metabolite profiling of C. graveolens. The assessment of cell cytotoxicity was conducted using MTT cell viability assay on U-87 and BHK-21. Through molecular docking studies, the mode of inhibition and binding interaction between identified compounds and target proteins were also determined to evaluate the in vitro results. The use of LC-MS/MS-based global natural products social (GNPS) molecular networking analysis resulted in the identification of 27 compounds. The crude extract, ethyl acetate fraction, and chloroform fraction exhibited significant inhibitory activity against the U-87 cell lines, with IC50 values of 112.0, 138.1, and 142.7 µg/mL, respectively. The ethyl acetate fraction exhibited significant inhibitory concentration for 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) activity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity and the metal chelation activity with IC50 value of 39.50 µg/mL, 32.27 µg/mL, and 53.46 µg/mL, respectively. The crude extract showed maximum total phenolic, and total flavonoid concentration measuring 338.7 µg GAE/mg, and 177.04 µg QE/mg, respectively. The findings of this study indicate that C. graveolens consists of a diverse range of active phytoconstituents that possess antioxidant and anti-glioma properties.

Factors associated with satisfaction of the australian rural resident medical officer cadetship program: results from a cross-sectional study.

BACKGROUND: Australian Rural Resident Medical Officer Cadetships are awarded to medical students interested in a rural medical career. The Rural Residential Medical Officer Cadetship Program (Cadetship Program) is administered by the Rural Doctors Network on behalf of the NSW Ministry of Health. This study aimed to assess the overall experience of medical students and key factors that contributed to their satisfaction with the Cadetship Program. METHODS: A quantitative cross-sectional study was conducted among 107 former cadets who had completed the Cadetship Program. Data on medical students' experience with the Cadetship Program (outcome variable) and potential explanatory variables were collected using a structured self-administered questionnaire. Explanatory variables included gender, geographical location, rural health club membership, rural clinical school attendance, financial support, mentorship benefits, networking opportunities, influence on career decisions, opportunity for preferential placements, and relocation. Both bivariate (Pearson's chi-squared test) and multiple logistic regression analysis were employed to identify the factors associated with medical students' overall experience with the Cadetship Program. The non-linear analysis was weighted to represent the rural/remote health workforce, in Stata/SE 14.1. RESULTS: Our results indicate that 91% of medical students were satisfied with the Cadetship Program. The logistic regression model identified two significant predictors of a positive experience with the Cadetship Program. Medical students who perceived financial support as beneficial were significantly more likely to report a satisfactory program experience (aOR = 6.22, 95% CI: 1.36-28.44, p = 0.019) than those who perceived financial support as not beneficial. Similarly, those who valued networking opportunities were more likely to have a positive view of their cadetship experience (aOR = 10.06, 95% CI: 1.11-91.06, p = 0.040) than their counterparts. CONCLUSION: Our study found that students who valued financial support and networking opportunities had the most positive views of the Cadetship Program. These findings demonstrate that the Cadetship Program may be most helpful for those who need financial support and for students who seek networking opportunities. These findings increase our knowledge about the characteristics of medical students who have the most positive experiences with the Cadetship Program. They help us to understand the mechanisms of influence of such programs on individuals' decisions to be part of the future rural health workforce.

Relationships between social networking sites use and subjective well-being--- a meta-analysis and meta-analytic structural equation model.

During the last decade, the amount of research on the relationship between social networking sites (SNS) use and users' subjective well-being (SWB) has increased, leading to discrepancies regarding the results. Our review of the literature generated 73 independent samples and indicated that considerable inconsistent results may be attributed to different measurements of SNS use, moderation effects, or media response states and their effects. In this study, meta-analytic procedures were used to assess the strength of the relationships between SNS use indicators, perceived social support (PSS), self-esteem and SWB. The results showed that PSS and self-esteem had stronger effects on SWB than SNS use indicators. Furthermore, a meta-analytic structural equation model was conducted to assess the strength of the relationships between SNS use indicators, PSS, self-esteem, and SWB. The results did not support the proposition SNS use is associated with SWB. Compared with SNS use indicators, media response states such as self-esteem and PSS, had more effects on SWB.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Spatial metabolomics method to reveal the differences in chemical composition of raw and honey-fried Stemona tuberosa Lour. by using UPLC-Orbitrap Fusion MS and desorption electrospray ionization mass spectrometry imaging.

INTRODUCTION: Stemona tuberosa Lour. (ST) is a significant traditional Chinese medicine (TCM) renowned for its antitussive and insecticidal properties. ST is commonly subjected to processing in clinical practice before being utilized as a medicinal substance. Currently, the customary technique for processing ST is honey-fried. Nevertheless, the specific variations in chemical constituents of ST before and after honey-fried remain unclear. OBJECTIVE: This work aimed to analyze the variations in chemical constituents of ST before and after honey-fried and to study the distribution of differential markers in the roots. METHODS: UPLC-Orbitrap Fusion MS combined with molecular network analysis was used to analyze the metabolome of ST and honey-fried ST (HST) and to screen the differential metabolites by multivariate statistical analysis. Spatial metabolomics was applied to study the distribution of differential metabolites by desorption electrospray ionization mass spectrometry imaging (DESI-MSI). RESULTS: The ST and HST exhibited notable disparities, with 56 and 61 chemical constituents found from each, respectively. After processing, the types of alkaloids decreased, and 12 differential metabolites were screened from the common compounds. The notable component variations were epibisdehydro-tuberostemonine J, neostenine, tuberostemonine, croomine, neotuberostemonine, and so forth. MSI visualized the spatial distribution of differential metabolites. CONCLUSIONS: Our research provided a rapid and effective visualization method for the identification and spatial distribution of metabolites in ST. Compared with the traditional method, this method offered more convincing data supporting the processing mechanism investigations of Stemona tuberosa from a macroscopic perspective.

Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach.

Due to the emergence of drug-resistant microorganisms, the search for broad-spectrum antimicrobial compounds has become extremely crucial. Natural sources like plants and soils have been explored for diverse metabolites with antimicrobial properties. This study aimed to identify microorganisms from agricultural soils exhibiting antimicrobial effects against known human pathogens, and to highlight the chemical space of the responsible compounds through the computational metabolomics-based bioprospecting approach. Herein, bacteria were extracted from soil samples and their antimicrobial potential was measured via the agar well diffusion method. Methanolic extracts from the active bacteria were analyzed using the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique, and the subsequent data was further analyzed through molecular networking approach which aided in identification of potential anti-microbial compounds. Furthermore, 16S rRNA gene sequencing enabled identification of the active bacterial isolates, where isolate 1 and 2 were identified as strains of Bacillus pumilus, whilst isolate 3 was found to be Bacillus subtilis. Interestingly, isolate 3 (Bacillus subtilis) displayed wide-ranging antimicrobial activity against the tested human pathogens. Molecular networking revealed the presence of Diketopiperazine compounds such as cyclo (D-Pro-D-Leu), cyclo (L-Tyr-L-Pro), cyclo (L-Pro-D-Phe), and cyclo (L-Pro-L-Val), alongside Surfactin C, Surfactin B, Pumilacidin E, and Isarrin D in the Bacillus strains as the main anti-microbial compounds. The application of the molecular networking approach represents an innovation in the field of bio-guided bioprospection of microorganisms and has proved to be an effective and feasible towards unearthing potent antimicrobial compounds. Additionally, the (computational metabolomics-based) approach accelerates the discovery of bioactive compounds and isolation of strains which offer a promising avenue for discovering new clinical antimicrobials. Finally, soil microbial flora could serve an alternative source of anti-microbial compounds which can assist in the fight against emergence of multi-drug resistance bacterial pathogens.

Radiotolerant endophytic bacteria and analysis of the effects of 137Cesium on the metabolome of Pantoea sp.

Some bacteria have developed mechanisms to withstand the stress caused by ionizing radiation. The ability of these radioresistant microorganisms to survive high levels of radiation is primarily attributed to their DNA repair mechanisms and the production of protective metabolites. To determine the effect of irradiation on bacterial growth, we propose to compare the metabolites produced by the irradiated isolates to those of the control (non-irradiated isolates) using mass spectrometry, molecular networking, and chemometric analysis. We identified the secondary metabolites produced by these bacteria and observed variations in growth following irradiation. Notably, after 48 h of exposure to radiation, Pantoea sp. bacterial cells exhibited a significant 6-log increase compared to non-irradiated cells. Non-irradiated cells produce exclusively Pyridindolol, 1-hydroxy-4-methylcarbostyril, N-alkyl, and N-2-alkoxyethyl diethanolamine, while 5'-methylthioadenosine was detected only in irradiated cells. These findings suggest that the metabolic profile of Pantoea sp. remained relatively stable. The results obtained from this study have the potential to facilitate the development of innovative strategies for harnessing the capabilities of endophytic bacteria in radiological protection and bioremediation of radionuclides.

Acaricidal activity of Erigeron acer L. root against Haemaphysalis longicornis and phytochemical profiling by liquid chromatography-tandem mass spectrometry.

The present study is focused on evaluating acaricidal activity and chemical compositions of Erigeron acer root, which was identified as a promising candidate among fifteen Mongolian plant extracts tested for acaricidal activity. The acaricidal effect was evaluated against Haemaphysalis longicornis, assessed for toxicity to normal human skin fibroblast, and analyzed for its chemical constituents. The acetone extract of E. acer root showed significant activity against H. longicornis, with a lethal concentration (LC50) of 5.31 mg/mL and low toxicity, evidenced by a cytotoxic concentration (CC50) of 267.00 µg/mL. Using liquid chromatography-tandem mass spectrometry and molecular networking, thirteen natural compounds were identified, including pyrrolidines, alkaloids, fatty acids, and flavonoids, highlighting the efficacy of E. acer root extract as an effective acaricide against H. longicornis and offering insights for developing new tick control solutions.

Bufadienolide-Fatty Acid Conjugates from the Fertilized Eggs of Toad Bufo gargarizans: Isolation, Characterization, Toxicity, and Antiproliferative Evaluation.

Bufadienolides (BDs) are a class of naturally occurring toxins present in amphibian toads. Serving as the chemical weapons, they exist not only in the adult toads but also in toad eggs. Guided by mass spectrometry (MS)-based component analysis and feature-based molecular networking (FBMN), 30 bufadienolide-fatty acid conjugates (BDFs) were isolated from the fertilized eggs of toad Bufo gargrizans, including 25 previously undescribed compounds (1-25). Their chemical structures were elucidated by extensive spectroscopic analysis, chemical methods, and GC-MS. The toxicities of all BDFs and their corresponding free BDs were assessed using the zebrafish model. The structure-toxicity relationship analysis showed that the modification of BDs by hydroxy fatty acids can cause a significant increase of the toxicity. Furthermore, all the isolated compounds were evaluated for their antiproliferative activities in pancreatic cancer cell lines ASPC-1 and PANC10.05. The structure-activity relationship (SAR) analysis revealed that BDFs with hellebrigenin as the bufogenin moiety (6 and 7) exhibited the most potent antiproliferative effect. Further investigation into their functional mechanism demonstrated that 6 and 7 induced apoptosis in pancreatic cancer cells PANC10.05 and significantly suppressed the expression of the apoptosis-related gene c-MYC. In addition, 6 and 7 effectively inhibited the expression of the PI3K/Akt/mTOR pathway in PANC10.05. Moreover, we assessed the efficacy of 6 and 7 on cancer cells from various tissues and observed their broad-spectrum antiproliferative activity.

Inter-generational impact: Exploring the influence of older sister-in-law's contraceptive choices on her peer contraception adoption in India.

OBJECTIVE: Social network-based reproductive decisions are critical. This study compares the effect of an older peer contraceptionuse on her younger peer's use, as they share the closest social network within the household, and tend to influence one another for reproductive decisions. In this study we considered peers as two sister-in-law living in the same household. METHODS: We used data from the fifth round of National Family Health Survey which was conducted between the year 2019 and 2021. We deployed multinomial multivariable logistic regression to find the association between older women contraception use on her younger peer. Also, an attempt has been made to determine contraceptive clustering within households in India and select states. RESULTS: The multinomial analysis found that all the women in the household used the similar method, but still relied only on the female sterilization as the sole method for their family planning. The multinomial multivariable method found that younger peers were 3.42 time odds more likely to use permanent method if her older peer had used it previously. Also, it was found 11% increase in any modern contraception use of younger peer if all her older peer will use any modern contraceptives in India. For all the states, the contraception clustering within household ranged from 5% to 14%, with highest in Himachal (14%). CONCLUSION: Empowering older women as peer educators in contraception workshops can foster informed discussions, dispel myths, and promote the use of modern contraceptive methods among younger peers. Integrating such initiatives into government existing policies like Mission Parivar Vikas is crucial for improving contraceptive uptake, with ASHA workers and community health volunteers playing a pivotal role in delivering targeted education within households.

RSM and ANN-based Optimization of Ultrasound-assisted Extraction of Functional Components from Olive Fruit (cv Arbequina): Assessment of Antioxidant Attributes and GC-MS Profiling.

The current study proposes an optimized ethanolic extraction for the efficient recovery of high-value components from Pakistani olives (cv. Arbequina) using response surface methodology (RSM) and artificial neural networking (ANN). The extracts were investigated for antioxidant properties and GC-MS metabolite profiling.  Four factors such as time, temperature, solvent concentration, and solute weight (g/100 mL) were utilized as independent variables for determining the response (% yield).  The results obtained under optimum extraction conditions such as duration (25 min), temperature (45°C), solvent concentration (65%; ethanol: water v/v), and solute (7.50 g) indicated an extract yield of 40.96% from Arbiquina olives. The analysis of variance (ANOVA) for the RSM model showed significant p-values and a correlation coefficient (R2) of 0.9960, confirming model reliability.  The multilayer perceptron architecture was used in ANN, and the results were fairly consistent with the experimental findings. Arbequina olive extract (AOE) demonstrated significant antioxidant ability in terms of total phenolics, total flavonoid concentration, and DPPH radical scavenging. The GC-MS analysis of AOE revealed the presence of several bioactives, including oleic acid (36.22%), hydroxytyrosol (3.95%), tyrosol (3.32%), ß-sitosterol (2.10%), squalene (1.10%), sinapic acid (0.67%), α-tocopherol (0.66%), vanillic acid (0.56%), 3,5-di-tert-butylcatechol (0.31%), and quercetin.

Can Stress Put Digital Privacy at Risk? Evidence from a Controlled Experiment Examining the Impact of Acute Stress on Privacy Decisions on a Simulated Social Network Site.

Internet and Communication Technologies (ICTs) can foster efficient communication and knowledge acquisition, but there are also tradeoffs in terms of risks to one's privacy. Previous research, including work with the privacy calculus framework, indicates that factors such as perceived risks and benefits of using ICTs, ICT trust, and general privacy concerns can influence individuals' digital privacy-related decisions. One pervasive psychological factor that may potentially alter such privacy-related behaviors is acute stress. Acute stress can promote risk-seeking behaviors and a tendency to prefer immediate rewards over delayed, greater value rewards. However, the effect of acute stress in the applied context of privacy decision making is relatively unknown. Participants (N = 143) in this study were randomly assigned to either an acute stress task (socially evaluated cold pressor task) or an active control task (lukewarm water alone). Results revealed that acute stress condition increased information disclosure, as indexed by accepting more online cookies, sharing one's location more frequently, and revealing greater willingness to self-disclose personal information. In addition, the impact of individuals' levels of perceived risk and benefits, trust, and privacy concern on privacy decision making was examined. However, none of these constructs consistently influenced privacy decisions over and above the effect of stress. Overall, our findings suggest that acute stress has robust, independent influence on privacy decision making.

Anti-tumor withanolides as signal transducers and activators of transcription 3 (STAT3)-inhibition from Withania obtusifolia.

The Solanaceae family and the Withania genus specifically are rich sources of medicinal plants. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS/MS) revealed a predominance of withanolides from an organic extract of Withania obtusifolia. A constructed molecular network uncovered the presence of potentially novel withanolides. A series of withanolides were then isolated and structurally characterized from the extract including two new withanolides (withafolia A and withafolia B) and seven previously reported metabolites. Of the isolated compounds, cytotoxicity of withanolide J, physaperuvin G, and a commercial STAT3 inhibitor (S3I-201) were assessed against a human leukemia HL-60 cell line resulting in IC50 values of 26, 29, and 120 µM, respectively. In silico molecular docking simulations indicate that withanolide J and physaperuvin G can bind as an inhibitor in the active site of STAT3 with docking scores comparable to the selective STAT3 inhibitor, S3I-201.

New Cytotoxic γ-Lactam Alkaloids from the Mangrove-Derived Fungus Talaromyces hainanensis sp. nov. Guided by Molecular Networking Strategy.

The fungus Talaromyces hainanensis, isolated from the mangrove soil, was characterized as a novel species by morphology observation and phylogenetic analyses. Four new γ-lactam alkaloids talaroilactams A-D (1-4) and two reported compounds harzianic acid (5) and isoharzianic acid (6) were identified from the fungus T. hainanensis WHUF0341, assisted by OSMAC along with molecular networking approaches. Their structures were determined through ECD calculations and spectroscopic analyses. Moreover, the biosynthetic route of 1-4 was also proposed. Compound 1 displayed potent cytotoxicity against HepG2 cell lines, with an IC50 value of 10.75 ± 1.11 µM. In addition, network pharmacology was employed to dissect the probable mechanisms contributing to the antihepatocellular carcinoma effects of compound 1, revealing that cytotoxicity was mainly associated with proteolysis, negative regulation of autophagy, inflammatory response, and the renin-angiotensin system. These results not only expanded the chemical space of natural products from the mangrove associated fungi but also afforded promising lead compounds for developing the antihepatocellular carcinoma agents.