A strategy to develop chemotherapeutic agents by combining several active groups into a single molecule as a conjugate that can modulate multiple cellular pathways may produce compounds having higher efficacy compared to that of single-target drugs. In this article, we describe the synthesis and evaluation of an array of dual-acting ER and histone deacetylase inhibitors. These novel hybrid compounds combine an indirect antagonism structure motif of ER (OBHS, oxabicycloheptene sulfonate) with the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA). These OBHS-HDACi conjugates exhibited good ER binding affinity and excellent ERα antagonistic activity, and they also exhibited potent inhibitory activities against HDACs. Compared with the approved drug tamoxifen, these conjugates exhibited higher antitumor potency in ERα-positive breast cancer cells (MCF-7). Moreover, these conjugates not only showed selective anticancer activity that was more potent against MCF-7 cells than DU 145 (prostate cancer), but they had no toxicity toward normal cells.

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BackgroundAntibiotic-associated diarrhea (AAD) is a risk factor for exacerbating the outcome of critically ill patients. Dysbiosis induced by the exposure to antibiotics reveals the potential therapeutic role of fecal microbiota transplantation (FMT) in these patients. Herein, we aimed to evaluate the safety and potential benefit of rescue FMT for AAD in critically ill patients.MethodsA series of critically ill patients with AAD received rescue FMT from Chinese fmtBank, from September 2015 to February 2019. Adverse events (AEs) and rescue FMT success which focused on the improvement of abdominal symptoms and post-ICU survival rate during a minimum of 12 weeks follow-up were assessed.ResultsTwenty critically ill patients with AAD underwent rescue FMT, and 18 of them were included for analysis. The mean of Acute Physiology and Chronic Health Evaluation (APACHE) II scores at intensive care unit (ICU) admission was 21.7 ± 8.3 (range 11–37). Thirteen patients received FMT through nasojejunal tube, four through gastroscopy, and one through enema. Patients were treated with four (4.2 ± 2.1, range 2–9) types of antibiotics before and during the onset of AAD. 38.9% (7/18) of patients had FMT-related AEs during follow-up, including increased diarrhea frequency, abdominal pain, increased serum amylase, and fever. Eight deaths unrelated to FMT occurred during follow-up. One hundred percent (2/2) of abdominal pain, 86.7% (13/15) of diarrhea, 69.2% (9/13) of abdominal distention, and 50% (1/2) of hematochezia were improved after FMT. 44.4% (8/18) of patients recovered from abdominal symptoms without recurrence and survived for a minimum of 12 weeks after being discharged from ICU.ConclusionIn this case series studying the use of FMT in critically ill patients with AAD, good clinical outcomes without infectious complications were observed. These findings could potentially encourage researchers to set up new clinical trials that will provide more insight into the potential benefit and safety of the procedure in the ICU.Trial registrationClinicalTrials.gov, Number NCT03895593. Registered 29 March 2019 (retrospectively registered).

Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design supervised computational methods for predicting their structures and functions. Lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, protein-RNA binding preference modeling, and 5' UTR-based mean ribosome loading prediction. The comprehensive experiments show that the proposed method improves the RNA structural and functional modeling results significantly and consistently. Despite only trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

In this work, we developed a small library of novel OBHS-RES hybrid compounds with dual inhibition activities targeting both the estrogen receptor α (ERα) and NF-κB by incorporating resveratrol (RES), a known inhibitor of NF-κB, into a privileged indirect antagonism structural motif (OBHS, oxabicycloheptene sulfonate) of estrogen receptor (ER). The OBHS-RES conjugates could bind well to ER and showed remarkable ERα antagonistic activity, and they also exhibited excellent NO inhibition in macrophage RAW 264.7 cells. Compared with 4-hydroxytamoxifen, some of them showed better antiproliferative efficacy in MCF-7 cell lines with IC up to 3.7 μM. In vivo experiments in a MCF-7 breast cancer model in Balb/c nude mice indicated that compound 26a was more potent than tamoxifen. Exploration of the compliancy of the structure against ER specificity utilizing these types of isomeric three-dimensional ligands indicated that one enantiomer had much better biological activity than the other.

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Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR) and multipath combinations of BeiDou Navigation Satellite System (BDS), as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided the same elevation, the CNR of B1 observables is the lowest among the three BDS frequencies, while B3 is the highest. The code multipath combinations of BDS inclined geosynchronous orbit (IGSO) and medium Earth orbit (MEO) satellites are remarkably correlated with elevation, and the systematic “V” shape trends could be eliminated through between-station-differencing or modeling correction. Daily periodicity was found in the geometry-free ionosphere-free (GFIF) combinations of both BDS geostationary Earth orbit (GEO) and IGSO satellites. The variation range of carrier phase GFIF combinations of GEO satellites is −2.0 to 2.0 cm. The periodicity of carrier phase GFIF combination could be significantly mitigated through between-station differencing. Carrier phase GFIF combinations of BDS GEO and IGSO satellites might also contain delays related to satellites. Cross-correlation suggests that the GFIF combinations’ time series of some GEO satellites might vary according to their relative geometries with the sun.

RNA structure determination and prediction can promote RNA-targeted drug development and engineerable synthetic elements design. But due to the intrinsic structural flexibility of RNAs, all the three mainstream structure determination methods (X-ray crystallography, NMR, and Cryo-EM) encounter challenges when resolving the RNA structures, which leads to the scarcity of the resolved RNA structures. Computational prediction approaches emerge as complementary to the experimental techniques. However, none of the de novo approaches is based on deep learning since too few structures are available. Instead, most of them apply the time-consuming sampling-based strategies, and their performance seems to hit the plateau. In this work, we develop the first end-to-end deep learning approach, E2Efold-3D, to accurately perform the de novo RNA structure prediction. Several novel components are proposed to overcome the data scarcity, such as a fully-differentiable end-to-end pipeline, secondary structure-assisted self-distillation, and parameter-efficient backbone formulation. Such designs are validated on the independent, non-overlapping RNA puzzle testing dataset and reach an average sub-4 Å root-mean-square deviation, demonstrating its superior performance compared to state-of-the-art approaches. Interestingly, it also achieves promising results when predicting RNA complex structures, a feat that none of the previous systems could accomplish. When E2Efold-3D is coupled with the experimental techniques, the RNA structure prediction field can be greatly advanced.