1,25(OH)2 D3 inhibits Lewis lung cancer cell migration via NHE1-sensitive metabolic reprograming.

High prevalence and metastasis rates are characteristics of lung cancer. Glycolysis provides energy for the development and metastasis of cancer cells. The 1,25-dihydroxy vitamin D3 (1,25(OH)2 D3 ) has been linked to reducing cancer risk and regulates various physiological functions. We hypothesized that 1,25(OH)2 D3 could be associated with the expression and activity of Na+ /H+ exchanger isoform 1 (NHE1) of Lewis lung cancer cells, thus regulating glycolysis as well as migration by actin reorganization. Followed by online public data analysis, Vitamin D3 receptor, the receptor of 1,25(OH)2 D3 has been proved to be abundant in lung cancers. We demonstrated that 1,25(OH)2 D3 treatment suppressed transcript levels, protein levels, and activity of NHE1 in LLC cells. Furthermore, 1,25(OH)2 D3 treatment resets the metabolic balance between glycolysis and OXPHOS, mainly including reducing glycolytic enzymes expression and lactate production. In vivo experiments showed the inhibition effects on tumor growth as well. Therefore, we concluded that 1,25(OH)2 D3 could amend the NHE1 function, which leads to metabolic reprogramming and cytoskeleton reconstruction, finally inhibits the cell migration.

Barriers and facilitators of the nurse providing evidence-based preoperative visit-care for transcatheter aortic valve replacement: a mixed-methods study based on an evidence application setting.

BACKGROUND: Preoperative visit-care for transcatheter aortic valve replacement (TAVR) plays a crucial role in improving the quality of care and patient safety. However, preoperative care for TAVR patients is still in its early stages in China, with the care often being experience-based. The application of relevant evidence in nursing practice is necessary. Little is known regarding the facilitators and barriers to apply and compliance to the evidences about preoperative visit-care for TAVR in nursing. METHODS: The Nurse's Compliance Checklist was used to investigate the evidence-based compliance of nurses (n = 21) who worked in the TAVR team in the evidence-based implementation setting. Meanwhile, an Evidence-Based Practice Beliefs Scale, and Influencing Factors Checklist were used to investigate all nurses (n = 66) who work in the same setting. Stakeholders (Middle and senior-level nursing administrators, frontline clinical nurses, and patients) interview was carried out to further disclose the barriers and facilitators in the process of evidence-based practice. RESULTS: The results of this study showed that only 1 evidence implemented fully (100%) by nurses, 3 evidences with 0% implementation rate, and implementation rate of the other evidences were 9.5∼71.4%. The overall score of nurses' evidence-based nursing belief level was (3.52 ± 0.82). Three domains of barriers were identified: the Context Domain included lack of nursing procedures, inadequate health education materials, insufficient training; the Practitioner Domain included insufficient attention, lack of relevant knowledge, high work pressure and uncertainty of expected results, and Patient Domain included lack of relational knowledge. Facilitating factors included leadership support, nurse' high evidence-based nursing belief, high executive ability and enthusiasm for learning. CONCLUSION: The study indicated that the nurses' compliance of evidence-based practice in preoperative visit-care for TAVR was in lower level. There were some factors influencing the application of the evidences. The study revealed potential modifiable barriers to the successful implementation of evidence-based preoperative visit-care, including a lack of preoperative visit- care routine, related knowledge and training. Leadership support and nurse training should be considered to improve nurses' compliance with evidence-based practice.

Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery.

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data-based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug-target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node's (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.

Chiral Carbon Dots Derived from Serine with Well-Defined Structure and Enantioselective Catalytic Activity.

Carbon dots (C-Dots), with unique properties from tunable photoluminescence to biocompatibility, show wide applications in biotechnology, optoelectronic device and catalysis. Chiral C-Dots are expected to have strongly chirality-dependent biological and catalytic properties. For chiral C-Dots, a clear structure and quantitative structure-property relationship need to be clarified. Here, chiral C-Dots were fabricated by electrooxidation polymerization from serine enantiomers. The oxidized serine has a reversed chiral configuration to serine, which leads to the chiral C-Dots possessing inverse handedness compared with their raw materials. Electron circular dichroism spectrum, together with other diverse characterization techniques and theoretical calculations, confirmed that these chiral C-Dots (2-7 nm) have a well-defined primary structure of polycyclic dipeptide and possess a spatial structure with a c-axis of hexagonal symmetry and two cyclic dipeptides as the spatial structural unit. These chiral C-Dots also show enantioselective catalytic activity on DOPA enantiomers oxidation.

1,25(OH)2 D3 attenuates sleep disturbance in mouse models of Lewis lung cancer, in silico and in vivo.

Many clinical studies have reported that patients diagnosed with cancer will suffer from sleep disturbance during their clinical process, especially among lung cancer patients, and this effect will not easily subside. 1,25-dihydroxy-vitamin-D3 [1,25(OH)2 D3 ], the activated form of vitamin D, can participate in neuronal differentiation and prevent damage to the nervous system. However, little is known about the potential therapeutic effects of cancer-related psychiatric symptoms. In light of this, we hypothesized that a low circulating level of vitamin D was related to sleep quality in the presence of a tumor, 1,25(OH)2 D3 may be an effective way to ameliorate sleep disturbance and neurochemical alterations along with the cancer progress. Male C57BL/6 mice were implanted with intracranial transmitters to monitor electroencephalogram and were subcutaneously inoculated with Lewis lung cancer cells. The results demonstrated that on Days 19-20, tumor-bearing mice displayed fragmented sleep, shortened wake phase, prolonged sleep in the non-rapid eye movement phase, and the levels of vitamin D-associated genes in the brain had changed a lot compared to control mice. Importantly, 1,25(OH)2 D3 treatment really effectively saved the sleep quality of tumor-bearing mice. We further explored and confirmed that 1,25(OH)2 D3 repressed tumor-induced neuroinflammation (IL-1ß, TNF-α, IL-6, IL-10, IFN-γ, and IL-2), enhanced neurotrophic factors (brain-derived neurotrophic factor [BDNF], glialcellline-derived neurotrophic factor) and 5-HT system in the hippocampus, hypothalamus or cortex. A molecular docking approah manifested the ability of 1,25(OH)2 D3 to affect the activity of tryptophan hydroxylase 2 and BDNF. Together, our results suggested that 1,25(OH)2 D3 treatment may attenuate sleep disturbance in Lewis lung cancer-bearing mice, and become a promising strategy for treating cancer symptom clusters to ameliorate the quality of life of patients with cancer.

A Feasible Strategy for Identifying Single-Atom Catalysts Toward Electrochemical NO-to-NH3 Conversion.

Combining NO removal and NH3 synthesis, electrochemical NO reduction reaction (NORR) toward NH3 is considered as a novel and attractive approach. However, exploring suitable catalysts for NO-to-NH3 conversion is still a formidable task due to the lack of a feasible method. Herein, utilizing systematic first-principles calculations, a rational strategy for screening efficient single-atom catalysts (SACs) for NO-to-NH3 conversion is reported. This strategy runs the gamut of stability, NO adsorbability, NORR activity, and NH3 selectivity. Taking transition metal atom embedded in C2 N (TM-C2 N) as an example, its validity is demonstrated and Zr-C2 N is selected as a stable NO-adsorbable NORR catalyst with high NH3 selectivity. Therefore, this work has established a theoretical landscape for screening SACs toward NO-to-NH3 conversion, which will contribute to the application of SACs for NORR and other electrochemical reactions.

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach.

Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming. Therefore, prediction models based on diverse approaches have been established in recent decades. Computational prediction of molecular properties has become an important step in drug discovery, aiming to identify potential drug-like candidates and reduce costs. However, limitations related to dataset capacity and algorithm adaptation still place restrictions on the applicability of the related models. In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. Benchmark datasets of aqueous solubility (log S), lipophilicity (log D), and membrane permeability measured using the Caco-2 cell line (log Papp) were constructed by merging and calibrating experimental data from diverse articles and databases. Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. MESN achieves performance improvements over other state-of-the-art methods for the prediction of aqueous solubility, lipophilicity, and membrane permeability. Results were also obtained using several other algorithms and independent validation datasets as a control study. Moreover, a dimension reduction analysis (based on t-distributed stochastic neighbor embedding, t-SNE) and an atomic feature similarity analysis showed that the molecular embeddings extracted from the MESN model exhibit good clustering and diversity. Overall, considering the fundamental role of the data and the superior prediction performance of the model, we highlight the applicability of MESN on benchmark datasets for further utility in drug discovery-related molecular property prediction.

Herbal plants coordinate COVID-19 in multiple dimensions - An insight analysis for clinically applied remedies.

The use of multipronged measures, including traditional Chinese medicine (TCM), has greatly increased in response to the COVID-19 pandemic, and we found the use of TCM and is positively correlated with the regional cure rate in China (R=0.77, P<10-5). We analyzed 185 commonly administered TCM recipes comprised of 210 herbs nationwide to reveal mechanistic insight. Eight out of the 10 most commonly used herbs showed anti-coronavirus potential by intersecting with COVID-19 targets. Intriguingly, 17 compounds from the 5 most commonly used herbs were revealed to have direct anti-SARS-CoV-2 potential by docking with the two core structures [CoV spike (S) glycoprotein (6SVB) and CoV 3CL hydrolase (6LU7)]. Seven reported COVID-19 drugs served as positive controls; among them, retionavir (-7.828 kcal/mol) and remdesivir (-8.738 kcal/mol) performed best with 6VSB and 6LU7, respectively. The top candidate was madreselvin B (6SVB: -8.588 kcal/mol and 6LU7: -9.017 kcal/mol), an appreciable component of Flos Lonicerae. Eighty-six compounds from 22 unlisted herbs were further identified among 2,042 natural compounds, completing our arsenal for TCM formulations. The mechanisms have been implicated as multifactorial, including activation of immunoregulation (Th2, PPAR and IL10), suppression of acute inflammatory responses (IL-6, IL-1α/ß, TNF, COX2/1, etc.), enhancement of antioxidative activity (CAT and SOD1), and modulation of apoptosis (inhibited CASP3). It is of interest to understand the biological mechanisms of TCM recipes. We then analyzed 18 representative remedies based on molecular targets associated with 14 medical conditions over the disease course, e.g., pyrexia, coughing, asthenia, lymphopenia, cytokine storm, etc. The significant level of coherence (SLC) revealed, in part, the potential uses and properties of corresponding TCMs. Thus, herbal plants coordinate to combat COVID-19 in multiple dimensions, casting a light of hope before effective vaccines are developed.

Fructus Ligustri Lucidi modulates estrogen receptor expression with no uterotrophic effect in ovariectomized rats.

BACKGROUND: Accumulating evidence suggests that Fructus Ligustri Lucidi (FLL) plays a beneficial role in preventing the development of osteoporosis. However, the effects of FLL on estrogen receptor (ER) α and ERß expressions remain unknown. Therefore, in the current study we attempted to probe into the effects of FLL on ERα and ERß expressions in femurs, tibias and uteri of ovariectomized (OVX) rats. METHODS: The OVX rats were orally administrated with FLL water extract (3.5 g/kg/day) for 12 weeks. The uteri, femurs, tibias and serum were harvested from rats. The serum levels of estrogen (E2), luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were determined by ELISA. The expressions of ERα and ERß in the femurs and tibias as well as uteri were analysed by western blot and immunohistochemical staining. RESULTS: FLL treatment did not increase uterus relative weight in OVX rats. Further, FLL treatment increased ERα expression in the femurs and tibias, and enhanced ERß expression in the uteri of OVX rats. However, the resulted expression of ERα was stronger than that of ERß in OVX rats in response to FLL treatment. Meanwhile, administration with FLL to OVX rats increased FSH and LH but did not increase E2 level in the serum. CONCLUSION: FLL treatment shows tissue selection on ERα and ERß expressions in the femurs and tibias as well as uteri of OVX rats without uterotrophic effect, which may offer the scientific evidence of the efficiency and safety of its clinical application.

[Effects of crocin on hippocampus rapid kindling epilepsy in mice].

Objective: To investigate the effect of crocin on the progression and generalized seizure of temporal lobe epilepsy in mice. Methods: Hippocampus rapid kindling model was established in C57BL/6J mice. The effects of crocin on seizure stage, afterdischarge duration (ADD), number of stimulation in each stage and final state, the incidence of generalized seizure (GS), average seizure stage and ADD were observed. Results: Crocin (20 mg/kg) significantly retarded behavioral seizure stages ( P<0.05) and shortened cumulative ADD ( P<0.01) during hippocampus rapid kindling acquisition in mice compared with vehicle group. Meanwhile, number of stimulations in stage 1-2 was significantly increased ( P<0.05) and the incidence of fully kindled animals was significantly decreased ( P<0.01). However, 10 or 50 mg/kg crocin showed no significant effect on the above indexes (all P>0.05). Crocin (100 or 200 mg/kg) significantly decreased the incidence of GS (all P<0.01) and reduced average seizure stages (all P<0.01) in fully-kindled mice compared with vehicle group; Fifty mg/kg crocin only reduced average seizure stages ( P<0.05). Conclusion: Low-dose crocin can retard the progression in hippocampus rapid kindling acquisition in mice, while high-dose crocin relieves the GS in fully-kindled mice, which suggests that crocin may be a potential anti-epileptic compound.

Spider and scorpion knottins targeting voltage-gated sodium ion channels in pain signaling.

In sensory neurons that transmit pain signals, whether acute or chronic, voltage-gated sodium channels (VGSCs) are crucial for regulating excitability. NaV1.1, NaV1.3, NaV1.6, NaV1.7, NaV1.8, and NaV1.9 have been demonstrated and defined their functional roles in pain signaling based on their biophysical properties and distinct patterns of expression in each subtype of sensory neurons. Scorpions and spiders are traditional Chinese medicinal materials, belonging to the arachnid class. Most of the studied species of them have evolved venom peptides that exhibit a wide variety of knottins specifically targeting VGSCs with subtype selectivity and conformational specificity. This review provides an overview on the exquisite knottins from scorpion and spider venoms targeting pain-related NaV channels, describing the sequences and the structural features as well as molecular determinants that influence their selectivity on special subtype and at particular conformation, with an aim for the development of novel research tools on NaV channels and analgesics with minimal adverse effects.

Zeolite-encapsulated copper(II) complexes with NNO-tridentate Schiff base ligands: catalytic activity for methylene blue (MB) degradation under near neutral conditions.

Three novel copper Schiff base complexes, L1Cu(OAc)-L3Cu(OAc), bearing NNO tridentate ligands were synthesized and successfully entrapped in zeolite. All free and encapsulated complexes were fully characterized through experiments combined with theoretical calculations, and were subsequently employed as catalysts to activate H2O2 for degradation of methylene blue (MB). The catalytic activity of free complexes was tunable by substitution effects. The complex L3Cu(OAc) displayed enhanced efficiency by adopting bulky and donor substitutions due to the lower oxidation states. However, the free complexes exhibited modified structural and catalytic properties upon encapsulation into the zeolite. The constraint from the zeolite holes and coordination geometry caused the alteration of electronic structures and subsequently modified the reactivity. This study revealed that upon encapsulation, the larger molecular dimension of L3Cu(OAc) resulted in additional distorted geometry, leading to higher catalytic efficiency for MB degradation with more blue shifts in the UV-Vis spectrum. There was high catalytic activity by LnCu(OAc)-Y compared to that of the free complex, and high recyclability under near neutral conditions. In addition, the catalytic efficiency of L3Cu(OAc)-Y was higher or equivalent compared to other catalysts. This work provides new complexes with NNO tridentate ligands encapsulated inside zeolite and explains the relationship between the modified structure and functionality.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning.

Background: Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF. Methods: We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS. Results: Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4. Conclusion: In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Model informed precision medicine of Chinese herbal medicines formulas-A multi-scale mechanistic intelligent model.

Recent trends suggest that Chinese herbal medicine formulas (CHM formulas) are promising treatments for complex diseases. To characterize the precise syndromes, precise diseases and precise targets of the precise targets between complex diseases and CHM formulas, we developed an artificial intelligence-based quantitative predictive algorithm (DeepTCM). DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling, molecular and theoretical levels of traditional Chinese medicine (TCM). As an example, our model simulated the optimal CHM formulas for the treatment of coronary heart disease (CHD) with depression, and through model sensitivity analysis, we calculated the balanced scoring of the formulas. Furthermore, we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions. Finally, we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice. This novel multiscale model opened up a new avenue to combine "disease syndrome" and "macro micro" system modeling to facilitate translational research in CHM formulas.

Toxicity and decomposition activity inhibition of VO2 micro/nanoparticles to white rot fungus Phanerochaete chrysosporium.

Vanadium dioxide (VO2) is an excellent phase transition material widely used in various applications, and thus inevitably enters the environment via different routes and encounters various organisms. Nonetheless, limited information is available on the environmental hazards of VO2. In this study, we investigated the impact of two commercial VO2 particles, nanosized S-VO2 and micro-sized M-VO2 on the white rot fungus Phanerochaete chrysosporium. The growth of P. chrysosporium is significantly affected by VO2 particles, with S-VO2 displaying a higher inhibitory effect on weight gain. In addition, VO2 at high concentrations inhibits the formation of fungal fibrous hyphae and disrupts the integrity of fungus cells as evidenced by the cell membrane damage and the loss of cytoplasm. Notably, at 200 µg/mL, S-VO2 completely alters the morphology of P. chrysosporium, while the M-VO2 treatment does not affect the mycelium formation of P. chrysosporium. Additionally, VO2 particles inhibit the laccase activity secreted by P. chrysosporium, and thus prevent the dye decoloration and sawdust decomposition by P. chrysosporium. The mechanism underlying this toxicity is related to the dissolution of VO2 and the oxidative stress induced by VO2. Overall, our findings suggest that VO2 nanoparticles pose significant environmental hazards and risks to white rot fungi.

Comparison of release and penetration of tacrolimus ointment reference and trial formulation after dermal application to pigs by liquid chromatography-electrospray ionization tandem mass spectrometry.

To investigate the consistency and bioequivalence of tacrolimus ointment reference and trial formulation, the tacrolimus concentrations in blood and skin were determined by HPLC-ESI-MS/MS following topical application of two kinds of ointment in porcine skin in a parallel, cross-over trial. The plasma protein of blood was precipitated by acetonitrile and the tacrolimus in skin was extracted by acetonitrile before HPLC-ESI-MS/MS analysis. The internal calibration method (diazepam was the internal standard) was used for quantification analysis (R(2) > 0.9999), with linear range from 0.05 to 5 ng/mL for blood samples and from 1 to 200 ng/mL for skin samples. The limits of detection for the porcine blood and skin were 0.005 and 0.5 ng/mL, respectively. The average recoveries for the porcine blood and skin spiked at three levels were 97.56-109.53 and 96.48-103.57%, respectively. The precision expressed in RSDs was from 3.43 to 10.83% for porcine blood and from 3.10 to 8.69% for porcine skin. For the same pig, the tacrolimus concentrations and variation with time of the two kinds of ointment in porcine skin were similar, although variation occurred with different individuals. These results showed that the release and penetration of tacrolimus from the reference and trial formulation are similar.

Silencing of RAB42 down-regulated PD-L1 expression to inhibit the immune escape of hepatocellular carcinoma cells through inhibiting the E2F signaling pathway.

OBJECTIVE: To investigate the mechanistic role of RAB42 and corresponding regulatory path in hepatocellular carcinoma (HCC). METHODS: The expression of RAB42 in HCC tissue was checked by RT-qPCR and immunohistochemical staining assay. Cell proliferation was checked by colony formation and CCK-8 assay. Cell apoptosis and cycle distribution were analyzed with flow cytometry. The relevance of RAB42 and PD-L1 was analyzed from TCGA database. The binding of E2F1 to PD-L1 was detected by JASPAR database, luciferase and ChIP assay. The expression of PD-L1, cell apoptosis- and E2F pathway-related proteins were checked by western blotting. RESULTS: RAB42 was highly expressed in HCC tissue. RAB42 silencing could inhibit proliferation and induce G1 phase arrest and apoptosis of HCC cells. TCGA database disclosed that PD-L1 was highly associated with RAB42 expression. Silencing of RAB42 could retard PD-L1 expression in HCC cells. GSEA analysis showed RAB42 could activate E2F signaling pathway. Silencing of RAB42 could observably weaken the expression of E2F1, CDK1 and CDC20 in HCC cells. JASPAR database predicted the binding site between E2F1 and PD-L1, and E2F1 overexpression could promote PD-L1 expression. Overexpression of E2F1 could reverse the biological function of RAB42 silencing in HCC cells. CONCLUSION: Silencing of RAB42 could down-regulate PD-L1 expression to inhibit immune escape through inhibiting E2F signaling pathway in HCC cells. RAB42 may become a novel clinical diagnostic and therapy marker for HCC.

Myopenic Obesity Determined by Fat Mass Percentage Predicts Risk of Aspirin-Induced Bleeding in Chinese Older Adults.

Background: Body mass index (BMI) correlates with aspirin-induced bleeding risk. However, skeletal muscle mass (SMM) loss and fat gain commonly occur with aging, making BMI not a reasonable marker of bleeding risk in older individuals. In the present study, we aimed to investigate the prognostic value of myopenic obesity based on the percent of fat mass (%FM) for aspirin-induced bleeding in Chinese patients over 60 years old. Methods: We prospectively analyzed 185 patients taking aspirin for primary and secondary prevention of cardiovascular diseases. Body composition parameters were estimated using bioelectrical impedance analysis. We defined myopenic obesity (MO) as a height-adjusted appendicular SMM <7.0 kg/m2 in males and <5.7 kg/m2 in females with a %FM >29% in males and >41% in females or a BMI ≥25 kg/m2. The patients were categorized into four groups by the presence or absence of myopenia and obesity. Results: Based on the %FM grouping, the bleeding risk was significantly higher in the MO group, followed by the nonmyopenic obesity, myopenic nonobesity, and nonmyopenic nonobesity groups (P = 0.044). No statistically significant differences in the probability of bleeding events were observed among the four BMI-based groups (P = 0.502). Multivariate Cox analysis indicated that MO (hazard ratio [HR] 2.724, 95% confidence interval [CI] 1.073-6.918, P = 0.035), aspirin dose (100 vs 50 mg/day, HR 2.609, 95% CI 1.291-5.273, P = 0.008), concomitant use of histamine-2 receptor antagonists and proton pump inhibitors (HR 1.777, 95% CI 1.007-3.137, P = 0.047), and hemorrhage history (HR 2.576, 95% CI 1.355-4.897, P = 0.004) were associated with bleeding events independently. Conclusion: %FM-based MO was an independent predictor of aspirin-induced bleeding in older Chinese individuals. Reducing %FM rather than BMI should be an optimal strategy for the management of myopenic obesity.

Gypenosides suppress fibrosis of the renal NRK-49F cells by targeting miR-378a-5p through the PI3K/AKT signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The incidence of renal fibrosis caused by chronic kidney disease is increasing year by year. Preventing the activation and conversion of kidney-intrinsic fibroblasts to a myofibroblast phenotype is an important target for blocking the development of renal interstitial fibrosis. Our team established a stable renal interstitial fibrosis cell model in the early stage, and the screening results showed that GPs has good anti-fibrosis potential. At this stage, only a few literatures have reported its anti-fibrosis effect, and the mechanism of action is still unclear. AIM OF THE STUDY: The massive synthesis and secretion of extracellular-matrix (ECM) components by activated fibroblasts in the kidneys causes irreversible renal interstitial fibrosis. Gypenosides (GPs) have been shown to decelerate this process, in which micro RNAs (miRNAs) play an important regulatory role. This study aimed to evaluate the mechanism underlying the suppressive effect of GPs on renal fibrosis. MATERIALS AND METHODS: This study used TGF-ß1-stimulated NRK-49F renal cells as an in-vitro model of renal interstitial fibrosis. First, the concentration range of GPs that significantly affects the cytoactive was determined. Then, the anti-fibrotic effects of various concentrations of GPs in the in-vitro model were assessed via immunofluorescence, western blotting, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Non-coding-RNA sequencing combined with bioinformatics was used to predict the mechanistic basis of the anti-fibrotic effect of GPs, and qRT-PCR was used to verify the sequencing results and bioinformatic predictions. The identified relationships of the anti-fibrotic effect of GPs with miR-378a-5p and the PI3K/AKT signaling were evaluated using a miR-NC mimic and the PI3K inhibitor LY294002 as controls, respectively. RESULTS: TGF-ß1 stimulation up-regulated α-SMA, COL1, and COL3 in NRK-49F cells, and this effect was suppressed by GPs. Additionally, TGF-ß1 stimulation significantly changed the expression levels of 151 miRNAs, and GPs significantly suppressed the effect of TGF-ß1 on the levels of 18 of these miRNAs. Among them, miR-3588 and miR-378a-5p were down-regulated, and miR-135b-5p and miR-3068-5p were up-regulated upon TGF-ß1 induction. Of these miRNAs, miR-378a-5p was predicted to target the mRNAs of numerous proteins mainly enriched in the PI3K/AKT signaling pathway. The miRNA transfection experiments with the miR-NC mimic and PI3K inhibitor as controls showed that miR-378a-5p overexpression could suppress the TGF-ß1-induced up-regulation of α-SMA, COL1, PI3K, and AKT, including the phosphorylated form (p-AKT). CONCLUSION: GPs inhibit the PI3K/AKT signaling by up-regulating miR-378a-5p in TGF-ß1-stimulated NRK-49F cells and thereby reduce their massive secretion of ECM components. Given that this in-vitro model of renal interstitial fibrosis closely mimics the in-vivo pathogenesis, our results most likely apply to the in-vivo conditions.

MantaID: a machine learning-based tool to automate the identification of biological database IDs.

The number of biological databases is growing rapidly, but different databases use different identifiers (IDs) to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID, a data-driven, machine learning-based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within 2 min. MantaID supports the discovery and exploitation of ID from large quantities of databases (e.g. up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application and application programming interfaces were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate and comprehensive identification of large quantities of IDs and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.