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Background: Osteoporosis is a prevalent disease for the aged population. Chinese herb-derived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throughput approach with the integration of cheminformatics and deep-learning methods to explore their mechanistic action, especially herb/drug-gene interaction networks. Methods: Ten medicinal herbs for clinical osteoporosis treatment were selected. Chemical ingredients of top 10 herbs were retrieved from TCMIO database, and their predicted targets were obtained from SEA server. Anti-osteoporosis clinical drugs and targets were collected from multi-databases. Chemical space, fingerprint similarity, and scaffold comparison of the compounds between herbs and clinical drugs were analyzed by RDKit and SKlearn. A network of herb-ingredient-target were constructed via Gephi, and GO and KEGG enrichment analysis were performed using clusterProfiler. Additionally, the bioactivity of compounds and targets were predicted by DeepScreening. Molecular docking of YYH flavonoids to HSD17B2 was accomplished by AutoDockTools. Results: Cheminformatics result depicts a pharmacological network consisting of 89 active components and 30 potential genes. The chemical structures of plant steroids, flavonoids, and alkaloids are key components for anti-osteoporosis effects. Moreover, bioinformatics result demonstrates that the active components of herbs mainly participate in steroid hormone biosynthesis and the TNF signaling pathway. Finally, deep-learning-based regression models were constructed to evaluate 22 anti-osteoporosis-related protein targets and predict the activity of 1350 chemical ingredients of the 10 herbs. Conclusion: The combination of cheminformatics and deep-learning approaches sheds light on the exploration of medicinal herbs mechanisms, and the identification of novel and active compounds from medical herbs in complex molecular systems.

The encapsulation efficiency (EE) of hydrophobic drug into cubosomes was high by conventional methods, while poor for the hydrophilic drug. In this study, a remote loading method based on transmembrane pH-gradient was applied to prepare hydrophilic drug loaded cubosomes. Several hydrophilic drugs were selected and studied. Results showed just part of the investigated drugs were successfully loaded into cubosomes by the remote loading method, whereas all the drugs failed to be encapsulated by the high-pressure homogenization method. The EE based on remote loading method was affected by the solubility, LogP, number of rings, and polarizability of the drug independent of the number of hydrogen acceptor and hydrogen donor. And the drugs that had high EE by remote loading method were BCS class 1 or 2. In addition, the EE base on remote loading method was significantly affected by the external water pH of cubosomes and drug concentration. The size of drug-loaded cubosomes by remote loading method mainly depended on the pre-formed blank cubosomes, which was bigger than that by high-pressure homogenization method. The preparation method affected the liquid crystalline structure of acidic drug loaded cubosomes, while showed no obvious effect on that of basic drug loaded cubosomes. The release of drug was susceptible to the pH of release medium independent of the preparation method. The drug-loaded cubosomes prepared by different method all showed favorable stability during storage. The remote loading method was a promising approach for the efficient encapsulation of hydrophilic drug into cubosomes. This study laid a foundation for the application of remote loading method on the preparation of hydrophilic drug loaded cubosomes.

To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.

Gold nanoparticles (AuNPs) are prospective tools for nano-based medicine that can directly target cellular biological processes to influence cell fate and function. Studies have revealed the essential role of AuNPs in metabolic remodeling for macrophage polarization. Nevertheless, as a hallmark of cancer cells, metabolic changes in tumor cells in response to AuNPs have not yet been reported. In the present study, polymer- and folate-conjugated AuNPs with satisfactory biocompatibility and tumor-targeting activity were synthesized to investigate their underlying roles in tumor metabolism. Tumor cells were significantly suppressed by AuNPs in vitro and in vivo, with little cytotoxicity in non-tumor cells. Subcellular localization showed that AuNPs localized in the mitochondria of tumor cells and impaired their structure and function, leading to excessive oxidative stress and mitochondrial apoptosis. Metabolic stress, with decreased glycolysis and insufficient nutrients, was also caused by AuNPs exposure in tumor cells. Mechanistically, the key enzymes (GLUT1 and HK2) for glycolysis modulation were remarkably reduced by AuNPs in a c-Myc-dependent manner. The present study demonstrated a new mechanism for AuNPs in the inhibition of tumor growth, that is, via directly targeting glycolysis and depriving energy. These findings provide new strategies for the design of nano-based medicines and anti-glycolytic therapeutics to inhibit the development of malignant tumors. STATEMENT OF SIGNIFICANCE: Gold nanoparticles (AuNPs) have acquired ever-increasing interest for applications in cancer treatment and diagnosis due to their high biosafety and facile surface modification. Recent studies have shown that AuNPs can work as active agents to directly target the cellular processes and harbor antitumor properties, while the underlying mechanisms remain largely unknown. From the present findings, the stabilized AuNPs showed direct inhibition effects on tumor growth by glycolysis inhibition and energy deprivation. These results provide new insights of AuNPs for tumor treatments, which will further contribute to the development of promising nano-based medicines and anti-glycolytic therapies.

The unique pathophysiologic contributions of obstructive sleep apnea (OSA) toward pulmonary hypertension and right ventricular (RV) dysfunction still represent an understudied area. We aimed to investigate the impacts of various respiratory parameters on pulmonary hemodynamics and RV performance in OSA.Data of consecutive patients with OSA who completed right heart catheterization for evaluation of pulmonary hemodynamics were retrospectively reviewed and analyzed. Univariable and multivariable regression analyses were used to determine the significant respiratory parameter associated with right heart catheterization metrics.Of 205 OSA patients (43.4% male), 134 (65.4%) had pulmonary hypertension. Among various sleep parameters, the time percentage spent with SpOIncreased mPAP, PVR, and RVSWI were associated with prolonged T90 in patients with OSA. Assessment of OSA with insights into hypoxemic duration may aid in early recognition of impaired pulmonary hemodynamics and RV dysfunction.

The preparation of Ru-based single atom catalysts with SMSI effect.

Early life experiences may have a persistent influence throughout life. However, little is known about the effect of parental absence during infancy and early childhood on offspring's cognition and risk of depression.Data were obtained from the China Family Panel Studies (CFPS). We examined the effect of parental absence during infancy and early childhood on cognitive development and depression in later life using weighted linear regression models. A series of sensitivity analyses and subgroup analyses were also conducted.Parental absence during infancy and early childhood was associated with poor mathematics performance and high depression scores measured 8 years later. The coefficients of association between parental absence and math test performance and depression were -0.012 (95%CI: -0.024, -0.001, P = 0.031) and 0.009 (95%CI: 0.001, 0.017, P = 0.028) in adjusted models, respectively. Moreover, a similar association was observed among children who were females, urban dwellers, and had more years of education.Recall bias may exist for the exposure measure, and the current study cannot control residual confounders.Long parental absence during infancy and early childhood may attenuate the cognitive process and increase risk of depression in later life, indicating the necessity of parental care as an intervention initiative.

Despite the connection of secretory cells to distinct mucus-containing colon cancer histological subtypes and the interaction of secretory cells with immune cells in the pathogenesis of intestinal inflammatory diseases, whether the secretory cell signatures are associated with tumor microenvironment (TME) heterogeneity and can aid in colon cancer patient classification have not been investigated. Here, by performing the principal component analysis and consensus clustering analysis, we identified four distinct expression patterns based on secretory cell signatures which were significantly associated with different clinical behaviors, TME landscape, pathway activation, genomic mutations, and DNA methylation characteristics. Subsequently, a 'SCS score' model was constructed. The high SCS score indicated a pattern of 'secretory cell subtype 2', which was characterized by stromal infiltration and activation, and predicted poor prognosis and low sensitivity to fluorouracil-based chemotherapy and immunotherapy, but high sensitivity to PI3K catalytic subunit inhibitors. In conclusion, our study comprehensively uncovered the tumor heterogeneity related to secretory cell signature expression patterns. Moreover, the SCS score can supplement routine histopathological assessments to guide personalized therapeutic strategies in colon cancer patients.

Fluorescence imaging in the second infrared windows (1000–1700 nm) has emerged as a promising approach to tumor diagnostic. However, the currently available NIR-II imaging agents are based on “alwa...

Metastasis and drug resistance are the leading causes of poor prognosis in patients with osteosarcoma. Identifying the relevant factors that drive metastasis and drug resistance is the key to improving the therapeutic outcome of osteosarcoma. Here, we reported that autophagy was highly activated in metastatic osteosarcoma. We found increased autophagolysosomes in metastatic osteosarcoma cell lines by using electron microscopy, Western blot, and immunofluorescence experiments. We further examined the expression of the autophagy-related genes Beclin1 and LC3B in 82 patients through immunohistochemistry and found that Beclin1 and LC3B were highly related to unfavorable prognosis of osteosarcoma. Knockdown of Beclin1 and LC3B reduced invasion, metastasis, and proliferation in metastatic osteosarcoma cells. In vitro and in vivo studies also demonstrated that inhibiting by 3-MA inhibited cell growth and metastasis. Moreover, we demonstrated that autophagy-related genes were activated by SEs and that the inhibition of SEs by JQ-1 decreased the metastasis of osteosarcoma. Overall, our findings highlighted the association of autophagy with osteosarcoma progression and shed new light on autophagy-targeting therapy for osteosarcoma.

Background Chronic kidney disease (CKD) is an important contributor to morbidity and mortality from noncommunicable diseases. We aimed to examine the longitudinal trajectories in risk factors, estimate their impact on CKD burden in China from 1991 to 2011, and project trends in the next 20 years. Methods We used data from a cohort of the China Health and Nutrition Survey and applied the comparative risk assessment method to estimate the number of CKD events attributable to all non-optimal levels of each risk factors. Results In 2011, current smoking was the leading individual attributable factor for CKD burden in China responsible for 7.9 (95% CI, 7.5-8.3) million CKD cases with a population-attributable fraction of 8.7% (95% CI, 6.0-11.6), while the rates of smoking have reduced and may have mitigated the increase in CKD. High triglyceride (TG) and high systolic blood pressure (SBP) were the leading metabolic risk factors responsible for 6.8 (95% CI, 6.4-7.1) million and 5.8 (95% CI, 5.5-6.1) million CKD-attributable cases, respectively. Additionally, the number of CKD cases associated with high body mass index (BMI), high diastolic blood pressure (DBP), high plasma glucose, and low high-density lipoprotein cholesterol (HDL-C) was 5.4 (95% CI, 5.1-5.6), 3.9 (95% CI, 3.7-4.1), 3.0 (95% CI, 2.8-3.1) and 2.6 (95% CI, 2.5-2.8) million, respectively. Conclusions Current smoking, high TG, and high SBP were the top three risk factors that contributed to CKD burden in China. Increased BMI, DBP, plasma glucose, and decreased HDL-C were also associated with the increase in CKD burden.

Reinventing the tetraploid potato into a seed-propagated, diploid, hybrid potato would significantly accelerate potato breeding. In this regard, the development of highly homozygous inbred lines is a prerequisite for breeding hybrid potatoes, but self-incompatibility and inbreeding depression present challenges for developing pure inbred lines. To resolve this impediment, we developed a doubled haploid (DH) technology, based on mutagenesis of the potato DOMAIN OF UNKNOWN FUNCTION 679 membrane protein (StDMP) gene. Here, we show that a deficiency in StDMP allows the generation of maternal haploids for generating diploid potato lines. An exercisable protocol, involving hybridization, fluorescent marker screening, molecular and flow cytometric identification, and doubling with colchicine generates nearly 100% homozygous diploid potato lines. This dmp-triggered haploid induction (HI) system greatly shortens the breeding process and offers a robust method for generating diploid potato inbred lines with high purity.

The prognosis is poor for patients with pulmonary artery stenosis-associated pulmonary hypertension (PAS-PH). Identifying predictors of prognosis in PAS-PH is crucial to preventing premature death, which has rarely been investigated. We aimed to explore the cardiopulmonary exercise testing (CPET) parameters to predict the prognosis of these patients.We prospectively included all patients with PAS-PH who underwent CPET between September 2014 and June 2021 in Fuwai Hospital (ClinicalTrials.gov ID: NCT02061787). The primary outcome was clinical worsening, including death, rehospitalization for heart failure, or deterioration of PH.Seventy-two patients were included in this study. A median of 2-year follow-up revealed that 18 (25%) patients experienced clinical worsening. The 1-year, 3-year, and 5-year event-free survival rates were 92.5%, 81.7%, and 62.7%, respectively. Patients with clinical worsening demonstrated significantly worse baseline haemodynamics and poorer exercise capacity than their counterparts. Multivariable Cox regression identified that peak OThe prognosis is poor for PAS-PH, and exercise intolerance and ventilation inefficiency are commonly observed. Peak O

Blocking the interaction of MTDH/SND1 complex is an attractive strategy for cancer therapeutics. In this work, we designed and obtained a novel class of potent stabilized peptide inhibitors derived from MTDH sequence to disrupt MTDH/SND1 interaction. Through structure-based optimization and biological evaluation, stabilized peptides were obtained with tight binding affinity, improved cell penetration, and antitumor effects in the triple-negative breast cancer (TNBC) cells without nonspecific toxicity. To date, our study was the first report to demonstrate that stabilized peptides truncated from MTDH could serve as promising candidates to disrupt the MTDH/SND1 interaction for potential breast cancer treatment.

Chemo-immunotherapy has gained increasing attention as one of the most promising combination therapy strategies to battle cancer. In this study, the therapeutic nanoparticles (TNPs) co-delivering doxorubicin (DOX) and IL-12 (IL-12) were developed for chemo-immunotherapy combination therapy on liver cancer. TNPs were synthesized based on the ionic interactions between cationic chitosan (Ch) and anionic poly-(glutamic acid) (PGA). DOX and IL-12 loaded in TNPs presented prolonged circulation in blood, efficient accumulation in tumors, and internalization in tumor cells. After that, DOX and IL-12 were co-released in the tumor microenvironment. The locally responsive property of TNPs could subsequently re-educate macrophages. More significantly, TNPs with no obvious side effects can remarkably inhibit the H22 tumor growth in vivo. A low dosage of loaded IL-12 in TNPs can effectively polarize macrophages toward the M1 phenotype to reduce tumor burden, further enhancing the antitumor efficacy. Our results suggest that the self-stabilized TNPs could be a secure and effective drug carrier for intravenous administration when deprived of protective agents.

Multi-Target Tracking Guidance (MTTG) in unknown environments has great potential values in applications for Unmanned Aerial Vehicle (UAV) swarms. Although Multi-Agent Deep Reinforcement Learning (MADRL) is a promising technique for learning cooperation, most of the existing methods cannot scale well to decentralized UAV swarms due to their computational complexity or global information requirement. This paper proposes a decentralized MADRL method using the maximum reciprocal reward to learn cooperative tracking policies for UAV swarms. This method reshapes each UAV's reward with a regularization term that is defined as the dot product of the reward vector of all neighbor UAVs and the corresponding dependency vector between the UAV and the neighbors. And the dependence between UAVs can be directly captured by the Pointwise Mutual Information (PMI) neural network without complicated aggregation statistics. Then, the experience sharing Reciprocal Reward Multi-Agent Actor-Critic (MAAC-R) algorithm is proposed to learn the cooperative sharing policy for all homogeneous UAVs. Experiments demonstrate that the proposed algorithm can improve the UAVs’ cooperation more effectively than the baseline algorithms, and can stimulate a rich form of cooperative tracking behaviors of UAV swarms. Besides, the learned policy can better scale to other scenarios with more UAVs and targets.