Stepwise Synthesis of Low-Symmetry Hexacationic Pyridinium Organic Cages.

An efficient approach was developed for the synthesis of the well-known BlueCage by pre-bridging two 2,4,6-tris(4-pyridyl)-1,3,5-triazine (TPT) panels with one linker followed by cage formation in a much improved yield and shortened reaction time. Such a stepwise methodology was further applied to synthesize three new pyridinium organic cages, C2, C3, and C4, where the low-symmetry cages C3 and C4 with angled panels demonstrated better recognition properties toward 1,1'-bi-2-naphthol (BINOL) than the high-symmetry analogue C2 featuring parallel platforms.

Functional cellulose-derived epoxy cross-linked with BADGE resin to construct high-performance epoxy composites.

The development of equipped bio-based epoxy materials has been gaining much attention recently. Nevertheless, finding the balance between the structure and properties of materials remains a significant challenge. In this work, cellulose-based epoxy (PHPCEP) with "soft" and "hard" cooperative structures was designed and demonstrated to endow bisphenol A diglycidyl ether (BADGE) with excellent toughness, heat resistance, mechanical strength, glass transition temperature, thermal stability, and solvent resistance. When 5 wt% PHPCEP was incorporated into BADGE composites, the resulting materials exhibited the maximum flexural strength (121.9 MPa) and tensile strength (71.4 MPa), a high glass transition temperature (148.3 °C), and 10 wt% PHPCEP/BADGE demonstrated the highest impact strength (70.5 kJ/m2). These figures are 18.8 %, 16.1 %, 21.5 %, and 254.3 % higher than the corresponding values of neat BADGE. The results of dynamic mechanical properties and heat degradation of the cured specimens also suggest that PHPCEP/BADGE materials have superior stiffness and toughness than neat BADGE, which could be attributed to the strong interaction between PHPCEP and BADGE, delivering better thermal stability for the composites compared to the pristine resin. Considering the remarkable effect, this work provides an effective way of highly efficient utilization of abundant cellulose and a high-performance additive for composite materials.

Relationships among the gut microbiome, brain networks, and symptom severity in schizophrenia patients: A mediation analysis.

The microbiome-gut-brain axis (MGBA) plays a critical role in schizophrenia (SZ). However, the underlying mechanisms of the interactions among the gut microbiome, brain networks, and symptom severity in SZ patients remain largely unknown. Fecal samples, structural and functional magnetic resonance imaging (MRI) data, and Positive and Negative Syndrome Scale (PANSS) scores were collected from 38 SZ patients and 38 normal controls, respectively. The data of 16S rRNA gene sequencing were used to analyze the abundance of gut microbiome and the analysis of human brain networks was applied to compute the nodal properties of 90 brain regions. A total of 1,691,280 mediation models were constructed based on 261 gut bacterial, 810 nodal properties, and 4 PANSS scores in SZ patients. A strong correlation between the gut microbiome and brain networks (r = 0.89, false discovery rate (FDR) -corrected p < 0.05) was identified. Importantly, the PANSS scores were linearly correlated with both the gut microbiome (r = 0.5, FDR-corrected p < 0.05) and brain networks (r = 0.59, FDR-corrected p < 0.05). The abundance of genus Sellimonas significantly affected the PANSS negative scores of SZ patients via the betweenness centrality of white matter networks in the inferior frontal gyrus and amygdala. Moreover, 19 significant mediation models demonstrated that the nodal properties of 7 brain regions, predominately from the systems of visual, language, and control of action, showed significant mediating effects on the PANSS scores with the gut microbiome as mediators. Together, our findings indicated the tripartite relationships among the gut microbiome, brain networks, and PANSS scores and suggested their potential role in the neuropathology of SZ.

Automated exploitation of deep learning for cancer patient stratification across multiple types.

MOTIVATION: Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. RESULTS: To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. RESULTS: To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION: The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prognostic and therapeutic significance of microbial cell-free DNA in plasma of people with acute decompensation of cirrhosis.

BACKGROUND & AIMS: Although the effect of bacterial infection on cirrhosis has been well-described, the effect of non-hepatotropic virus (NHV) infection is unknown. This study evaluated the genome fragments of circulating microorganisms using metagenomic next-generation sequencing (mNGS) in individuals with acute decompensation (AD) of cirrhosis, focusing on NHVs, and related the findings to clinical outcomes. METHODS: Plasma mNGS was performed in 129 individuals with AD of cirrhosis in the study cohort. Ten healthy volunteers and 20, 39, and 81 individuals with stable cirrhosis, severe sepsis and hematological malignancies, respectively, were enrolled as controls. Validation assays for human cytomegalovirus (CMV) reactivation were performed in a validation cohort (n = 58) and exploratory treatment was instituted. RESULTS: In the study cohort, 188 microorganisms were detected in 74.4% (96/129) of patients, including viruses (58.0%), bacteria (34.1%), fungi (7.4%) and chlamydia (0.5%). A NHV signature was identified in individuals with AD, and CMV was the most frequent NHV, which correlated with the clinical effect of empirical antibiotic treatment, progression to acute-on-chronic liver failure, and 90-day mortality. The NHV signature in individuals with acute-on-chronic liver failure was similar to that in those with sepsis and hematological malignancies. CMV was detected in 24.1% (14/58) of patients in the validation cohort. Of the 14 cases with detectable CMV by mNGS, nine were further validated by real-time PCR or pp65 antigenemia testing. Three patients with CMV reactivation received ganciclovir therapy in an exploratory manner and experienced clinical resolutions. CONCLUSIONS: The results of this study suggest that NHVs may play a pathogenic role in complicating the course of AD. Further validation is needed to define whether this should be incorporated into the routine management of individuals with AD of cirrhosis. IMPACT AND IMPLICATIONS: A non-hepatotropic virus (NHV) signature, which was similar to that in individuals with sepsis and hematological malignancies, was identified in individuals with acute decompensation of cirrhosis. The detected viral signature had clinical correlates, including clinical efficacy of empirical antibiotic treatment, progression to acute-on-chronic liver failure and short-term mortality. Cytomegalovirus reactivation, which is treatable, may adversely affect clinical outcomes in some individuals with decompensated cirrhosis. Routine screening for NHVs, especially cytomegalovirus, may be useful for the management of individuals with acute decompensation of cirrhosis.

Study of Surface Integrity of Titanium Alloy (TC4) by Belt Grinding to Achieve the Same Surface Roughness Range.

Titanium alloy materials are used in a variety of engineering applications in the aerospace, aircraft, electronics, and shipbuilding industries, and due to the continuous improvement of the contemporary age, surface integrity needs to be improved for engineering applications. Belt grinding parameters and levels directly affect the surface integrity of titanium alloys (TC4), which further affects the fatigue life of the titanium alloys during service. In order to investigate the surface integrity of titanium alloys at different roughness levels, the surfaces were repeatedly ground with the same type and different models of abrasive belts. The results showed that at roughness Ra levels of 0.4 µm to 0.2 µm, the compressive residual stresses decreased with increasing linear velocity and there were problems with large surface morphological defects. At the roughness Ra of 0.2 µm or less, grinding improves the surface morphology, the compressive residual stress increases with increasing feed rate, and the surface hardness decreases with increasing linear velocity. In addition, the research facilitates the engineering of grinding parameters and levels that affect surface integrity under different roughness conditions, providing a theoretical basis and practical reference.

Adaptive coordination assemblies based on a flexible tetraazacyclododecane ligand for promoting carbon dioxide fixation.

Coordination hosts based on flexible ligands have received increasing attention due to their inherent adaptive cavities that often show induced-fit guest binding and catalysis like enzymes. Herein, we report the controlled self-assembly of a series of homo/heterometallic coordination hosts (Me4enPd)2n (ML) n [n = 2/3; M = Zn(ii)/Co(ii)/Ni(ii)/Cu(ii)/Pd(ii)/Ag(i); Me4en: N,N,N',N'-tetramethylethylenediamine] with different shapes (tube/cage) from a flexible tetraazacyclododecane-based pyridinyl ligand (L) and cis-blocking Me4enPd(ii) units. While the Ag(i)-metalated ligand (AgL) gave rise to the formation of a (Me4enPd)4(ML)2-type cage, all other M(ii) ions led to isostructural (Me4enPd)6(ML)3-type tubular complexes. Structural transformations between cages and tubes could be realized through transmetalation of the ligand. The buffering effect on the ML panels endows the coordination tubes with remarkable acid-base resistance, which makes the (Me4enPd)6(ZnL)3 host an effective catalyst for the CO2 to CO3 2- conversion. Control experiments suggested that the integration of multiple active Zn(ii) sites on the tubular host and the perfect geometry match between CO3 2- and the cavity synergistically promoted such a conversion. Our results provide an important strategy for the design of adaptive coordination hosts to achieve efficient carbon fixation.

Integrated gut microbiota and metabolomic analysis reveals immunomodulatory effects of Echinacea extract and Astragalus polysaccharides.

Immunosuppression in different animals increases the susceptibility of various infections caused by pathogenic microorganisms leading to increase risks posed by antibiotics in different animal farming sectors. Therefore, investigation of the interactions between natural medicines and the intestinal environmental ecosystem is of vital importance and crucial. This study for the first time investigated the effects of Echinacea Extract (EE) and Astragalus polysaccharide (APS) on the gut using 16S rRNA and metabolomic analysis approaches in immunosuppressed broiler chickens. There were four groups divided into control (C), immunosuppression (IS), EE, and APS groups. Sequencing of gut microbes showed that immunosuppression decreased the relative abundance of Anaerofustis, Anaeroplasma, Anaerotroncus, and Lachnospira in the gut while increasing that of c_115 and Holdemania. However, EE and APS diminished the effects on the immunosuppression on the microbiota. The results revealed up-regulation of the relative abundance of Enterococcus in broiler chickens. In addition, EE reduced the relative abundance of Ruminococcus and Blautia. The results on metabolomic analysis revealed that immunosuppression mainly affects cyanuric acid metabolism, starch and sucrose metabolism while interconversion of pentose and glucuronide. EE and APS, on the other hand mainly impact butyrate metabolism, alanine, aspartate and glutamate metabolism while the interconversion of pentose and glucuronide, and D-glutamine and D-glutamate metabolism. Results regarding correlation analysis revealed significantly metabolic pathways including TCA cycle, butyrate metabolism, glyoxylate and dicarboxylate metabolism, propionate metabolism, alanine, aspartate and glutamate metabolism associated with Ruminococcus and Blautia. Both EE and APS can antagonize the effects of immunosuppression by modulating the disrupted gut microbiota. Nevertheless, EE might have a bidirectional regulatory functions on the intestinal health and further studies are needed to know the exact and relevant mechanisms of action regarding the effects of EE and APS.

Combinatorial Self-Assembly of Coordination Cages with Systematically Fine-Tuned Cavities for Efficient Co-Encapsulation and Catalysis.

Controllable arrangement of different ligands in a single assembly will not only bring increased complexity but also offers a new route to fine-tune the function of the designed architecture. We report here a combinatorial self-assembly with enPd(NO3 )2 and three different ligands (L1-3 ), which gave rise to a family of six palladium-organic cages (C1-6) with systematically varied shapes and cavities, including three new heteroleptic (Pd5 L1 2 L2 , Pd5 L1 2 L3 , Pd4 L2 L3 ), one new homoleptic (Pd4 L3 2 ) cages, and two known homoleptic (Pd6 L1 4 , Pd4 L2 2 ) cages. Emergent functions due to the fusion of two half cavities on the heteroleptic cages from their parent homoleptic cages have been observed: the heteroleptic cages can form ternary complexes by co-encapsulation of both aromatic and aliphatic guests, while their homoleptic counterparts can only form binary complexes. Such a forced co-encapsulation effect endows the heteroleptic cages with enhanced catalytic power for the Knoevenagel condensation.

Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning.

BACKGROUND: Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency. METHODS: To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, L1 and L2 regularization and dropout layers are added. RESULTS: For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms. CONCLUSIONS: DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity.

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning.

Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

Gut Microbiota Signatures in Gestational Anemia.

Gestational diseases are associated with altered intestinal microbiota in pregnant women. Characterizing the gut microbiota of gestational anemia (GA) may describe a novel role of gut microbial abnormality in GA. In this study, we investigated differences in gut microbiota between GA patients and healthy pregnant women from the first trimester (n = 24 vs. 54) and the third trimester (n = 30 vs. 56) based on the 16S rRNA gene sequencing method. No statistically significant differences in α-diversity were identified between GA patients and controls in the first trimester of pregnancy, whereas the Shannon index and observed OTUs were significantly lower in GA patients than in healthy controls in the third trimester. Distance-based redundancy analysis revealed striking differences in microbial communities in the third trimester between GA patients and controls. Four genera were significantly different in relative abundance between GA patients and healthy controls, while 12 genera differentiated significantly between GA patients and healthy controls in the third trimester. At the operational taxonomic unit (OTU) level, 17 OTUs and 30 OTUs were identified to be different between GA patients and healthy controls in the first and third trimesters, respectively. Changes in gut microbial composition of GA patients suggest a potential relation with GA, and provide insights into the prediction and intervention of gestational anemia.

Controlled Self-Assembly and Multistimuli-Responsive Interconversions of Three Conjoined Twin-Cages.

Stimuli-responsive structural transformations between discrete coordination supramolecular architectures not only are essential to construct smart functional materials but also provide a versatile molecular-level platform to mimic the biological transformation process. We report here the controlled self-assembly of three topologically unprecedented conjoined twin-cages, i.e., one stapled interlocked Pd12L6 cage (2) and two helically isomeric Pd6L3 cages (3 and 4) made from the same cis-blocked palladium corners and a new bis-bidentate ligand (1). While cage 2 features three mechanically coupled cavities, cages 3 and 4 are topologically isomeric helicate-based twin-cages based on the same metal/ligand stoichiometry. Sole formation of cage 2 or a dynamic mixture of cages 3 and 4 can be controlled by changing the solvents employed during the self-assembly. Structural conversions between cages 3 and 4 can be triggered by changes in both temperature/solvent and induced-fit guest encapsulations. Well-controlled interconversion between such topologically complex superstructures may lay a solid foundation for achieving a variety of functions within a switchable system.

Identification of haploinsufficient genes from epigenomic data using deep forest.

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest.

Identification of pan-cancer Ras pathway activation with deep learning.

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

Guest-Reaction Driven Cage to Conjoined Twin-Cage Mitosis-Like Host Transformation.

We report here a guest-reaction-induced mitosis-like host transformation from a known Pd4 L2 cage 1 to a conjoined Pd6 L3 twin-cage 2 featuring two separate cavities. The encapsulation of 1-hydroxymethyl-2-naphthol (G1), a known ortho-quinone methide (o-QMs) precursor, within the hydrophobic cavity of cage 1 is found crucial to realize the cage to twin-cage conversion. Confined G1 molecules within the nanocavity undergo self-coupling dimerization reaction to form 2,2'-dihydroxy-1,1'-dinaphthylmethane (G2) which then triggers the cage to twin-cage mitosis. The same conversion also proceeds, in a much faster rate, via the direct templation of G2, confirming the induced-fit transformation mechanism. The structure of the (G2)2 ⊂2 host-guest complex has been established by X-ray crystallographic study, where cis- to trans- conformational switch on one bridging ligand is revealed.

Systematic analysis of gut microbiota in pregnant women and its correlations with individual heterogeneity.

The woman's gut microbiota during pregnancy may support nutrient acquisition, is associated with diseases, and has been linked to infant health. However, there is limited information on gut microbial characteristics and dependence in pregnant women. In this study, we provide a comprehensive overview of the gut microbial characteristics of 1479 pregnant women using 16S rRNA gene sequencing of fecal samples. We identify a core microbiota of pregnant women, which displays a similar overall structure to that of age-matched nonpregnant women. Our data show that the gestational age-associated variation in the gut microbiota, from the ninth week of gestation to antepartum, is relatively limited. Building upon rich metadata, we reveal a set of exogenous and intrinsic host factors that are highly correlated with the variation in gut microbial community composition and function. These microbiota covariates are concentrated in basic host properties (e.g., age and residency status) and blood clinical parameters, suggesting that individual heterogeneity is the major force shaping the gut microbiome during pregnancy. Moreover, we identify microbial and functional markers that are associated with age, pre-pregnancy body mass index, residency status, and pre-pregnancy and gestational diseases. The gut microbiota during pregnancy is also different between women with high or low gestational weight gain. Our study demonstrates the structure, gestational age-associated variation, and associations with host factors of the gut microbiota during pregnancy and strengthens the understanding of microbe-host interactions. The results from this study offer new materials and prospects for gut microbiome research in clinical and diagnostic fields.

Icariside II protects cardiomyocytes from hypoxia­induced injury by upregulating the miR­7­5p/BTG2 axis and activating the PI3K/Akt signaling pathway.

Icariside II (ICS II) has been reported to have protective effects against oxidative stress. However, whether ICS II protects cardiomyocytes from myocardial infarction (MI), and the associated underlying mechanisms, remain to be elucidated. Therefore, the current study investigated the effects of ICS II on hypoxia­injured H9c2 cells, as well as the associated molecular mechanisms. A hypoxic injury model was established to emulate the effects of MI. The effects of ICS II on the proliferation of rat cardiomyocyte H9c2 cells were assessed with cell counting kit­8 assays. The apoptotic status of the cells was assessed by flow cytometry, and the expression of apoptosis­related proteins was analyzed by western blotting. A microRNA (miRNA/miR) microarray was used to quantify the differential expression of miRNAs after ICS II treatment, and the levels of miR­7­5p were further quantified by reverse transcription­quantitative PCR. Whether ICS II affected hypoxia­injured cells via miR­7­5p was subsequently examined, and the target of miR­7­5p was also investigated by bioinformatics analysis and luciferase reporter assays. The effects of ICS II on the PI3K/Akt pathway were then evaluated by western blot analysis. Hypoxia treatment decreased viability and the migration and invasion abilities of H9c2 cells, and also induced apoptosis. ICS II significantly increased viability and reduced hypoxia­associated apoptosis. Moreover, ICS II treatment led to the upregulation of miR­7­5p, and the protective effects of ICS II were found to rely on miR­7­5p. Moreover, BTG anti­proliferation factor (BTG2) was identified as a direct target of miR­7­5p, and overexpression of BTG2 inhibited the protective effects of miR­7­5p. Finally, ICS II treatment resulted in the activation of the PI3K/Akt signaling pathway, which is essential for the survival of H9c2 cells under hypoxic conditions. In summary, ICS II reduces hypoxic injury in H9c2 cells via the miR­7­5p/BTG2 axis and activation of the PI3K/Akt signaling pathway.

Gut Microbiota Profiling in Patients With HER2-Negative Metastatic Breast Cancer Receiving Metronomic Chemotherapy of Capecitabine Compared to Those Under Conventional Dosage.

Purpose: Low-dose metronomic chemotherapy can achieve disease control with reduced toxicity compared to conventional chemotherapy in maximum tolerated dose. Characterizing the gut microbiota of cancer patients under different dosage regimens may describe a new role of gut microbiota associated with drug efficacy. Therefore, we evaluated the composition and the function of gut microbiome associated with metronomic capecitabine compared to conventional dosage. Methods: The fecal samples of HER2-negative metastatic breast cancer patients treated with capecitabine as maintenance chemotherapy were collected and analyzed by 16S ribosome RNA gene sequencing. Results: A total of 15 patients treated with metronomic capecitabine were compared to 16 patients under a conventional dose. The unweighted-unifrac index of the metronomic group was statistically significantly lower than that of the routine group (P = 0.025). Besides that, the Bray-Curtis distance-based redundancy analysis illustrated that the microbial genera between the two groups can be separated partly. Nine Kyoto Encyclopedia of Genes and Genomes (KEGG) modules were enriched in the metronomic group, while no KEGG modules were significantly enriched in the routine group. Moreover, univariate and multivariate analyses suggested that the median progression-free survival (PFS) was significantly shorter in patients with the gut microbial composition of Slackia (9.2 vs. 32.7 months, P = 0.004), while the patients with Blautia obeum had a significantly prolonged PFS than those without (32.7 vs. 12.9 months, P = 0.013). Conclusions: The proof-of-principle study suggested that the gut microbiota of patients receiving metronomic chemotherapy was different in terms of diversity, composition, and function from those under conventional chemotherapy, and the presence of specific bacterial species may act as microbial markers associated with drug resistance monitoring and prognostic evaluation.