reguloGPT: Harnessing GPT for Knowledge Graph Construction of Molecular Regulatory Pathways.

Motivation: Molecular Regulatory Pathways (MRPs) are crucial for understanding biological functions. Knowledge Graphs (KGs) have become vital in organizing and analyzing MRPs, providing structured representations of complex biological interactions. Current tools for mining KGs from biomedical literature are inadequate in capturing complex, hierarchical relationships and contextual information about MRPs. Large Language Models (LLMs) like GPT-4 offer a promising solution, with advanced capabilities to decipher the intricate nuances of language. However, their potential for end-to-end KG construction, particularly for MRPs, remains largely unexplored. Results: We present reguloGPT, a novel GPT-4 based in-context learning prompt, designed for the end-to-end joint name entity recognition, N-ary relationship extraction, and context predictions from a sentence that describes regulatory interactions with MRPs. Our reguloGPT approach introduces a context-aware relational graph that effectively embodies the hierarchical structure of MRPs and resolves semantic inconsistencies by embedding context directly within relational edges. We created a benchmark dataset including 400 annotated PubMed titles on N6-methyladenosine (m6A) regulations. Rigorous evaluation of reguloGPT on the benchmark dataset demonstrated marked improvement over existing algorithms. We further developed a novel G-Eval scheme, leveraging GPT-4 for annotation-free performance evaluation and demonstrated its agreement with traditional annotation-based evaluations. Utilizing reguloGPT predictions on m6A-related titles, we constructed the m6A-KG and demonstrated its utility in elucidating m6A's regulatory mechanisms in cancer phenotypes across various cancers. These results underscore reguloGPT's transformative potential for extracting biological knowledge from the literature. Availability and implementation: The source code of reguloGPT, the m6A title and benchmark datasets, and m6A-KG are available at: https://github.com/Huang-AI4Medicine-Lab/reguloGPT.

Beyond Triphenylphosphine: Advances on the Utilization of Triphenylphosphine Oxide.

Triphenylphosphine oxide is a well-known industrial waste byproduct, and thousands of tons of it are generated every year. Due to its chemical stability and limited applications, settlement of this waste issue has drawn extensive attention from chemists. The reduction of triphenylphosphine oxide to triphenylphosphine is heretofore the most employed solution, and is well reviewed. In view of our recent studies on the selective and efficient conversion of Ph3P(O) to other valuable organophosphorus chemicals by using sodium, the present perspective mainly highlights the advances on the utilization of Ph3P(O) to prepare a diverse range of functional organophosphorus compounds, except Ph3P, via selective P-C, C-H, and P-O bond cleavages.

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge.

Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

Histone Deacetylase 3-Directed PROTACs Have Anti-inflammatory Potential by Blocking Polarization of M0-like into M1-like Macrophages.

Macrophage polarization plays a crucial role in inflammatory processes. The histone deacetylase 3 (HDAC3) has a deacetylase-independent function that can activate pro-inflammatory gene expression in lipopolysaccharide-stimulated M1-like macrophages and cannot be blocked by traditional small-molecule HDAC3 inhibitors. Here we employed the proteolysis targeting chimera (PROTAC) technology to target the deacetylase-independent function of HDAC3. We developed a potent and selective HDAC3-directed PROTAC, P7, which induces nearly complete HDAC3 degradation at low micromolar concentrations in both THP-1 cells and human primary macrophages. P7 increases the anti-inflammatory cytokine secretion in THP-1-derived M1-like macrophages. Importantly, P7 decreases the secretion of pro-inflammatory cytokines in M1-like macrophages derived from human primary macrophages. This can be explained by the observed inhibition of macrophage polarization from M0-like into M1-like macrophage. In conclusion, we demonstrate that the HDAC3-directed PROTAC P7 has anti-inflammatory activity and blocks macrophage polarization, demonstrating that this molecular mechanism can be targeted with small molecule therapeutics.

PD-L1 expression and prognosis in definitive radiotherapy patients with neuroendocrine cervical carcinoma.

Background: Neuroendocrine carcinoma of the cervix (NECC) is more prone to lymphatic infiltration, lymph node involvement, local recurrence, and distant metastasis. Using concurrent chemoradiotherapy (CCRT) with or without adjuvant chemotherapy as the standard treatment for locally advanced NECCs and CCRT for patients with early lesions confined to the cervix. However, the prognosis of NECC patients treated with definitive radiotherapy (RT) is unknown. Immune checkpoint inhibitors are a promising therapeutic strategy for locally advanced cervical cancer. Some reports suggest that the expression of PD-L1 in solid tumors correlates with prognosis. Aim: This study investigates prognostic factors for survival in patients with neuroendocrine cervical carcinoma (NECC) treated with definitive RT and the relationship between PD-L1 expression and prognosis in these patients. Methods: This retrospective study included 66 patients with histologically confirmed NECC who received RT with or without chemotherapy. From January 2015 to December 2020, patients received routine extended-field irradiation (EFI), and PD-L1 expression was assessed by immunohistochemistry. The most commonly used chemotherapy agents were etoposide-platinum and paclitaxel-platinum. Results: PD-L1 expression was positive in 17 of 45 (37.8%) patients. There were 52 cases of pure NECC and 14 cases of mixed carcinoma. Sixty stage IB-III patients received definitive RT. The 3- and 5-year progression-free survival (PFS) was 39.8% and 34.1%, and 3- and 5-year overall survival (OS) was 48.0% and 40.2%, respectively. There was no significant difference in 3 and 5-year PFS and 3 and 5-year OS between patients with pure and mixed carcinoma. Positive PD-L1 expression was associated with higher 3-year PFS in patients with mixed histology. Univariate analysis showed that lymph node metastasis (LNM) and the International Federation of Gynecology and Obstetrics stages predicted 3- and 5-year PFS in patients who received definitive RT. The median OS in patients receiving less than four cycles and at least four cycles of chemotherapy (CT) was 26.0 and 44.0 months, respectively (P = 0.038); moreover, 3- and 5-year PFS was 34.1% and 25.7% in the former and 46.4% and 40.4% in the latter. There were no significant differences in OS and PFS between pelvic irradiation and prophylactic EFI in patients treated with definitive RT. There were no significant differences in para-aortic failure rate after concurrent chemoradiotherapy between patients who underwent pelvic irradiation or prophylactic EFI (P = 0.147). Conclusion: In patients with mixed NECC, positive PD-L1 expression is correlated with higher 3-year PFS. Chemoradiotherapy was effective for NECCs. The LNM and stage predicted PFS. Four or more cycles of chemotherapy improve prognosis. Prophylactic EFI did not significantly improve PFS and OS. Relevance for Patients: This study is relevant to patients as it confirms that chemoradiotherapy is effective for both early and locally advanced NECC and that four or more cycles of chemotherapy improved prognosis. The regimen should be carefully evaluated to ensure that patients receive the most effective radiation therapy for the prophylactic of para-aortic LNM. Potential risk factors for the recurrence of radical radiotherapy should be fully understood to minimize these risks. This study observed that PD-L1 expression positive in patients with mixed NECC types is correlated with higher 3-year PFS.

Full waveform inversion guided wave tomography with a recurrent neural network.

Corrosion quantitative detection of plate or plate-like structure materials is crucial in industrial Non-Destructive Testing (NDT) for determining their remaining life. For doing that, a novel ultrasonic guided wave tomography method, incorporating recurrent neural network (RNN) into full waveform inversion (FWI) called as RNN-FWI, is proposed in this paper. When the wave equation of an acoustic model is solved by a forward model with the cyclic calculation units of an RNN, it is shown that the inversion of the forward model can be obtained iteratively by minimizing a waveform misfit function of quadratic Wasserstein distance between the modeled and measured data. It is also demonstrated that the gradient of the objective function can be obtained by automatic differentiation while the parameters of the waveform velocity model are updated by the adaptive momentum estimation algorithm (Adam). The U-Net deep image prior (DIP) is used as the velocity model regularization in each iteration. The final thickness maps of the plate or plate-like structure materials shown can be archived by the dispersion characteristics of guided waves. Both the numerical simulation and experimental results show that the proposed RNN-FWI tomography method performs better than the conventional time-domain FWI in terms of convergence rate, initial model requirement, and robustness.

Reduction of Triphenylphosphine Oxide to Triphenylphosphine by Phosphonic Acid.

A novel method for the iodine-mediated reduction of phosphine oxides (sulfides) to phosphines using phosphonic acid under solvent-free conditions is described. By using a combination of H3PO3 and I2, both tertiary monophosphine oxides and bis-phosphine oxides were reduced under this system, readily producing monodentate and bidentate phosphines, respectively, in good yields. Notably, chiral (R)-(+)-2,2'-bis(diphenylphosphino)-1,1'-binaphthyl dioxide could be also tolerated without racemization. This new approach is inexpensive and features simple conditions and a wide substrate scope.

Exploring Factor Structures Using Variational Autoencoder in Personality Research.

An accurate personality model is crucial to many research fields. Most personality models have been constructed using linear factor analysis (LFA). In this paper, we investigate if an effective deep learning tool for factor extraction, the Variational Autoencoder (VAE), can be applied to explore the factor structure of a set of personality variables. To compare VAE with LFA, we applied VAE to an International Personality Item Pool (IPIP) Big 5 dataset and an IPIP HEXACO (Humility-Honesty, Emotionality, Extroversion, Agreeableness, Conscientiousness, Openness) dataset. We found that LFA tends to break factors into ever smaller, yet still significant fractions, when the number of assumed latent factors increases, leading to the need to organize personality variables at the factor level and then the facet level. On the other hand, the factor structure returned by VAE is very stable and VAE only adds noise-like factors after significant factors are found as the number of assumed latent factors increases. VAE reported more stable factors by elevating some facets in the HEXACO scale to the factor level. Since this is a data-driven process that exhausts all stable and significant factors that can be found, it is not necessary to further conduct facet level analysis and it is anticipated that VAE will have broad applications in exploratory factor analysis in personality research.

A Robust Lamb Wave Imaging Approach to Plate-Like Structural Health Monitoring of Materials With Transducer Array Position Errors.

The Lamb-wave-based damage imaging via beamforming techniques, which can visualize the location of damage in the structure intuitively, is one of the most promising methods in the field of structural health monitoring (SHM). However, transducer array position errors are inevitable in practical application, which may lead to serious degradation in imaging performance. In this study, it is shown that the uncertainty of the steering vectors led by the imprecise position of transducers in an array can be suppressed by the doubly constrained robust Capon beamformer (DCRCB). After the unwanted side lobes are restrained by the DCRCB-based coherence factor (CF) weighting, an effective adaptive beamforming damage imaging method robust to transducer position errors is proposed. The numerical simulation and imaging experiment of damage on an aluminum plate are carried out to verify the effectiveness of the proposed algorithm. The results show that the proposed Lamb wave damage imaging method performs better than the reported beamforming ones in literature in terms of resolution, contrast, and robustness to transducer position errors.

lncRNA GAS5 Sensitizes Breast Cancer Cells to Ionizing Radiation by Inhibiting DNA Repair.

Radioresistance of breast cancer is a major reason for therapeutic failure and limits further increases in the dose of radiation due to severe adverse effects. Recently, long noncoding RNAs (lncRNAs) have been shown to regulate cancer proliferation, chemoresistance, and radioresistance. Among these lncRNAs, lncRNA GAS5 expression was shown to be downregulated in breast cancer and related to trastuzumab resistance. However, its role in the radiation response is unclear. In this study, we demonstrated that lncRNA GAS5 expression was reduced in irradiated cells and that overexpression of GAS5 reduced cell viability and promoted cell apoptosis after irradiation. Moreover, overexpression of GAS5 resulted in increased G2/M arrest and unrepaired DNA damage, indicating a radiosensitizing role of GAS5 in breast cancer cells. Finally, we found that a GAS5-interacting miRNA, miR-21, reversed the radiosensitizing effects of GAS5 by inhibiting the apoptotic pathway. In conclusion, we found that lncRNA GAS5 sensitized breast cancer cells to ionizing radiation by inhibiting DNA repair and suppressing miR-21, identifying novel targets for breast cancer radiosensitization.

Application of Causal Discovery Algorithms in Studying the Nephrotoxicity of Remdesivir Using Longitudinal Data from the EHR.

Remdesivir has been widely used for the treatment of Coronavirus (COVID) in hospitalized patients, but its nephrotoxicity is still under investigation1. Given the paucity of knowledge regarding the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID, we analyzed the role of remdesivir and built multifactorial causal models of COVID-AKI by applying causal discovery machine learning techniques. Risk factors of COVID-AKI and renal function measures were represented in a temporal sequence using longitudinal data from EHR. Our models successfully recreated known causal pathways to changes in renal function and interactions with each other and examined the consistency of high-level causal relationships over a 4-day course of remdesivir. Results indicated a need for assessment of renal function on day 2 and 3 use of remdesivir, while uncovering that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease.

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

Human circular RNA hsa_circRNA_101705 (circTXNDC11) regulates renal cancer progression by regulating MAPK/ERK pathway.

Circular RNAs (circRNAs) play essential roles in the progression of human tumors, including renal cell carcinoma (RCC). The present study aimed to explore the functions and potential mechanisms of human circular RNA hsa_circRNA_101705 (circTXNDC11) in RCC. Quantitative real-time polymerase chain reaction (qRT-PCR) was applied to measure circTXNDC11 expression in RCC tissues and cell lines. RNase R and actinomycin D assays were conducted to analyze the characteristic of circTXNDC11. Cell Counting Kit-8 (CCK-8) assay, colony formation assay, and transwell invasion assay were performed to assess cell proliferation and invasion abilities. Western blotting was applied to assess the levels of MEK and ERK proteins in RCC cells. Murine xenograft model assay was conducted to deduce the role of circTXNDC11 in vivo. The current data showed that circTXNDC11 was overexpressed in RCC tissues and cells. The overexpression of circTXNDC11 is linked to advanced TNM stage and lymph node metastasis of renal cancer. Knocking down circTXNDC11 suppressed cell proliferation and invasion in vitro and reduced tumor growth in vivo. Mechanistically, circTXNDC11 promoted RCC growth and invasion by activating the MAPK/ERK pathway. Thus, the current findings identified circTXNDC11 as a novel regulator of RCC tumorigenesis through the regulation of the MAPK/ERK pathway, offering a potential therapeutic target for RCC treatment.

Validation of Administrative Coding and Clinical Notes for Hospital-Acquired Acute Kidney Injury in Adults.

Acute kidney injury (AKI) is potentially catastrophic and commonly seen among inpatients. In the United States, the quality of administrative coding data for capturing AKI accurately is questionable and needs to be updated. This retrospective study validated the quality of administrative coding for hospital-acquired AKI and explored the opportunities to improve the phenotyping performance by utilizing additional data sources from the electronic health record (EHR). A total of34570 patients were included, and overall prevalence of AKI based on the KDIGO reference standard was 10.13%, We obtained significantly different quality measures (sensitivity.-0.486, specificity:0.947, PPV.0.509, NPV:0.942 in the full cohort) of administrative coding from the previously reported ones in the U.S. Additional use of clinical notes by incorporating automatic NLP data extraction has been found to increase the AUC in phenotyping AKI, and AKI was better recognized in patients with heart failure, indicating disparities in the coding and management of AKI.

Discovery of novel benzofuran scaffold as 4-hydroxyphenylpyruvate dioxygenase inhibitors.

BACKGROUND: 4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays an important role in addressing the issue of plant protection research. This study sheds new light on the differences in molecular scaffold from commercialized HPPD inhibitors. RESULTS: The compounds A1-A18 and B1-B27 were synthesized for in vitro and greenhouse experiments. The greenhouse experiment data indicated that compounds B14 and B18 displayed excellent herbicidal activity, which was higher compared to that of mesotrione. In vitro testing indicated that the compounds were HPPD inhibitors. Moreover, molecular simulation results show that the compounds B14, B18, and mesotrione shared similar interplay with surrounding residues, which led to a perfect interaction with the active site of Arabidopsis thaliana HPPD. Based on crop selectivity results, compounds B14 and B18 were selected for maize studies (injury≤10%), indicating its potential for weed control in maize fields. CONCLUSION: These results showed that the pyrazole-benzofuran structure could be used as possible lead compounds for the development of HPPD inhibitors. © 2020 Society of Chemical Industry.

Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks.

Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m6A, m1A, m1G, m2G, m5C, m5U, 2'-O-Me, Pseudouridine (Ψ) and Dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m6A site prediction algorithm and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.

Selective C-P(O) Bond Cleavage of Organophosphine Oxides by Sodium.

Sodium exhibits better efficacy and selectivity than Li and K for converting Ph3P(O) to Ph2P(OM). The destiny of PhNa co-generated is disclosed. A series of alkyl halides R4X and aryl halides ArX all react with Ph2P(ONa) to produce the corresponding phosphine oxides in good to excellent yields.

Rana chensinensis Ovum Oil Based on CO2 Supercritical Fluid Extraction: Response Surface Methodology Optimization and Unsaturated Fatty Acid Ingredient Analysis.

Rana chensinensis ovum oil (RCOO) is an emerging source of unsaturated fatty acids (UFAs), but it is lacking in green and efficient extraction methods. In this work, using the response surface strategy, we developed a green and efficient CO2 supercritical fluid extraction (CO2-SFE) technology for RCOO. The response surface methodology (RSM), based on the Box-Behnken Design (BBD), was used to investigate the influence of four independent factors (pressure, flow, temperature, and time) on the yield of RCOO in the CO2-SFE process, and UPLC-ESI-Q-TOP-MS and HPLC were used to identify and analyze the principal UFA components of RCOO. According to the BBD response surface model, the optimal CO2-SFE condition of RCOO was pressure 29 MPa, flow 82 L/h, temperature 50 °C, and time 132 min, and the corresponding predicted optimal yield was 13.61%. The actual optimal yield obtained from the model verification was 13.29 ± 0.37%, and the average error with the predicted value was 0.38 ± 0.27%. The six principal UFAs identified in RCOO included eicosapentaenoic acid (EPA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA), and oleic acid (OA), which were important biologically active ingredients in RCOO. Pearson correlation analysis showed that the yield of these UFAs was closely related to the yield of RCOO (the correlation coefficients were greater than 0.9). Therefore, under optimal conditions, the yield of RCOO and principal UFAs always reached the optimal value at the same time. Based on the above results, this work realized the optimization of CO2-SFE green extraction process and the confirmation of principal bioactive ingredients of the extract, which laid a foundation for the green production of RCOO.

Circular RNA DHX33 promotes malignant behavior in ccRCC by targeting miR-489-3p/MEK1 axis.

Mounting evidence indicates that circular RNAs modulate the initiation of clear cell renal cell carcinoma (ccRCC). However, their specific roles in the malignancy of ccRCC is understudied. Here, we present a novel circular RNA, circDHX33, that is up-regulated in ccRCC cell lines and tissues. Upregulated circDHX33 in ccRCC patients significantly correlates with advanced TNM stage and metastasis. Suppressing circDHX33 expression inhibits the proliferation and invasion of cultured cells, and suppresses tumor growth in vivo. Mechanistically, we show that circDHX33 promotes ccRCC progression by sponging miR-489-3p and modulating MEK1 expression. In conclusion, our findings suggest that circDHX33 plays a role in promoting ccRCC via the miR-489-3p/MEK1 axis and may serve as a novel therapeutic target for the treatment of ccRCC patients.

Chlorosilane-Catalyzed Coupling of Hydrogen Phosphine Oxides with Acyl Chlorides Generating Acylphosphine Oxides.

We report a new method for the synthesis of acylphosphine oxides by the direct coupling of hydrogen phosphine oxides and acyl chlorides mediated by chlorosilanes. This new protocol is greener and safer, because it precludes the generation of volatile haloalkanes and the use of oxidants employed in the conventional methods. Moreover, moisture-unstable acylphosphine oxides that are difficult to prepare via the conventional methods can be generated using this new method.