De novo protein design by citizen scientists.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now-starting from an extended polypeptide chain-generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds-including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem.

Bakuchiol inhibits lung cancer by modulating tumor microenvironment and the expression of PD-L1.

Immune checkpoint therapy is an emerging frontier in cancer therapy. With the aim to develop an efficient herb derived compound to facilitate immune checkpoint therapy, here we investigate if a herb-derived compound, Bakuchiol (BAK), can be used to treat lung cancer and elucidate if BAK could serve as a PD-L1 regulator. To this end, a murine lung cancer model was established by subcutaneously inoculating murine Lewis lung carcinoma (LLC) cells. BAK of 5 to 40 mg/kg was used for treatment in vivo for 15 days. On Day 15, the population of CD4+ and CD8+ T cells, Treg cells. BAK could effectively inhibit tumor growth by starting treatment either on Day 0 or 6 after tumor inoculation at doses of 5-40 mg/kg. BAK treatment increased the population of cytotoxic immune cells (i.e., CD8+ T cells, and M1 macrophages), meanwhile decreasing pro-tumor immune cells (i.e., CD3+ T cells, Treg cells, and M2 macrophages). Anti-inflammatory cytokines, including IL1ß, IL2, IFNγ, TNF-α, IL4 and IL10 were upregulated by BAK. PD-L1 expression in the tumor was also lowered by BAK. AKT and STAT3 signaling were inhibited by BAK. BAK is an efficient agent in reducing LLC tumor growth. These data support the potential of BAK as a new drug for treating lung cancer by serving as a PD-L1 inhibitor that suppresses the activation of AKT and STAT3.

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines.

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

PRPS2 Enhances Resistance to Cisplatin via Facilitating Exosomes-mediated Macrophage M2 Polarization in Non-small Cell Lung Cancer.

BACKGROUND: Phosphoribosyl pyrophosphate synthetases 2 (PRPS2) is reported as an oncogene in various cancers. However, the role of PRPS2 in cisplatin (DDP) resistance of non-small cell lung cancer (NSCLC) remains unclear. The present study aimed to explore the effect of PRPS2 in DDP resistance of NSCLC. METHODS: mRNA expression levels of genes were detected by RT-PCR. Enzyme-linked immunosorbent assay (ELISA) and Western blot were used to detect protein expression levels. Cell viability was determined by the MTT assay and colony formation assay. Cell apoptosis was detected using nucleosome ELISA assay and caspase-3 activity assay. PRPS2 silencing was achieved using siRNA transfection. Exosomes of cultured cells were isolated through ultracentrifugation. RESULTS: Elevated PRPS2 was correlated with DDP resistance and poor prognosis in NSCLC patients. PRPS2 silencing enhanced sensitivity of DDP-resistant cells to DDP treatment. NSCLC cell-derived exosome induced M2 macrophage polarization. PRPS2 was enriched in the exosomes of NSCLC cells. Exosomal PRPS2 mediated M2 macrophage polarization to promote DDP resistance of NSCLC cells. CONCLUSIONS: In conclusion, PRPS2 potentiates resistance to DDP by promoting exosome-mediated macrophage M2 polarization in NSCLC.

Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.

NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR-derived contacts for an integral membrane protein (T1088). For the three targets with NMR-based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of "inverse structure determination," in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238-residue integral membrane porin, was also used to assess several NMR-assisted prediction methods. Most groups involved in this exercise generated similar beta-barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

A de novo protein binding pair by computational design and directed evolution.

The de novo design of protein-protein interfaces is a stringent test of our understanding of the principles underlying protein-protein interactions and would enable unique approaches to biological and medical challenges. Here we describe a motif-based method to computationally design protein-protein complexes with native-like interface composition and interaction density. Using this method we designed a pair of proteins, Prb and Pdar, that heterodimerize with a Kd of 130 nM, 1000-fold tighter than any previously designed de novo protein-protein complex. Directed evolution identified two point mutations that improve affinity to 180 pM. Crystal structures of an affinity-matured complex reveal binding is entirely through the designed interface residues. Surprisingly, in the in vitro evolved complex one of the partners is rotated 180° relative to the original design model, yet still maintains the central computationally designed hotspot interaction and preserves the character of many peripheral interactions. This work demonstrates that high-affinity protein interfaces can be created by designing complementary interaction surfaces on two noninteracting partners and underscores remaining challenges.

Inhibitor Bound Dengue NS2B-NS3pro Reveals Multiple Dynamic Binding Modes.

Dengue virus poses a significant global health threat as the source of increasingly deleterious dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. As no specific antiviral treatment exists for dengue infection, considerable effort is being applied to discover therapies and drugs for maintenance and prevention of these afflictions. The virus is primarily transmitted by mosquitoes, and infection occurs following viral endocytosis by host cells. Upon entering the cell, viral RNA is translated into a large multisubunit polyprotein which is post-translationally cleaved into mature, structural and nonstructural (NS) proteins. The viral genome encodes the enzyme to carry out cleavage of the large polyprotein, specifically the NS2B-NS3pro cofactor-protease complex-a target of high interest for drug design. One class of recently discovered NS2B-NS3pro inhibitors is the substrate-based trifluoromethyl ketone containing peptides. These compounds interact covalently with the active site Ser135 via a hemiketal adduct. A detailed picture of the intermolecular protease/inhibitor interactions of the hemiketal adduct is crucial for rational drug design. We demonstrate, through the use of protein- and ligand-detected solution-state 19F and 1H NMR methods, an unanticipated multibinding mode behavior of a representative of this class of inhibitors to dengue NS2B-NS3pro. Our results illustrate the highly dynamic nature of both the covalently bound ligand and protease protein structure, and the need to consider these dynamics when designing future inhibitors in this class.

Antiparallel Coiled-Coil Interactions Mediate the Homodimerization of the DNA Damage-Repair Protein PALB2.

Deficits in DNA damage-repair pathways are the root cause of several human cancers. In mammalian cells, DNA double-strand break repair is carried out by multiple mechanisms, including homologous recombination (HR). The partner and localizer of BRCA2 (PALB2), which is an essential factor for HR, binds to the breast cancer susceptibility 1 (BRCA1) protein at DNA double-strand breaks. At the break site, PALB2 also associates with the breast cancer susceptibility 2 (BRCA2) protein to form a multiprotein complex that facilitates HR. The BRCA1-PALB2 interaction is mediated by association of predicted helical coiled-coil regions in both proteins. PALB2 can also homodimerize through the formation of a coiled coil by the self-association of helical elements at the N-terminus of the PALB2 protein, and this homodimerization has been proposed to regulate the efficiency of HR. We have produced a segment of PALB2, designated PALB2cc (PALB2 coiled coil segment) that forms α-helical structures, which assemble into stable homodimers. PALB2cc also forms heterodimers with a helical segment of BRCA1, called BRCA1cc (BRCA1 coiled coil segment). The three-dimensional structure of the homodimer formed by PALB2cc was determined by solution NMR spectroscopy. This PALB2cc homodimer is a classical antiparallel coiled-coil leucine zipper. NMR chemical-shift perturbation studies were used to study dimer formation for both the PALB2cc homodimer and the PALB2cc/BRCA1cc heterodimer. The mutation of residue Leu24 of PALB2cc  significantly reduces its homodimer stability, but has a more modest effect on the stability of the heterodimer formed between PALB2cc and BRCA1cc. We show that mutation of Leu24 leads to genomic instability and reduced cell viability after treatment with agents that induce DNA double-strand breaks. These studies may allow the identification of distinct mutations of PALB2cc that selectively disrupt homodimeric versus heterodimeric interactions, and reveal the specific role of PALB2cc homodimerization in HR.

Principles for designing ideal protein structures.

Unlike random heteropolymers, natural proteins fold into unique ordered structures. Understanding how these are encoded in amino-acid sequences is complicated by energetically unfavourable non-ideal features--for example kinked α-helices, bulged ß-strands, strained loops and buried polar groups--that arise in proteins from evolutionary selection for biological function or from neutral drift. Here we describe an approach to designing ideal protein structures stabilized by completely consistent local and non-local interactions. The approach is based on a set of rules relating secondary structure patterns to protein tertiary motifs, which make possible the design of funnel-shaped protein folding energy landscapes leading into the target folded state. Guided by these rules, we designed sequences predicted to fold into ideal protein structures consisting of α-helices, ß-strands and minimal loops. Designs for five different topologies were found to be monomeric and very stable and to adopt structures in solution nearly identical to the computational models. These results illuminate how the folding funnels of natural proteins arise and provide the foundation for engineering a new generation of functional proteins free from natural evolution.

Control over overall shape and size in de novo designed proteins.

We recently described general principles for designing ideal protein structures stabilized by completely consistent local and nonlocal interactions. The principles relate secondary structure patterns to tertiary packing motifs and enable design of different protein topologies. To achieve fine control over protein shape and size within a particular topology, we have extended the design rules by systematically analyzing the codependencies between the lengths and packing geometry of successive secondary structure elements and the backbone torsion angles of the loop linking them. We demonstrate the control afforded by the resulting extended rule set by designing a series of proteins with the same fold but considerable variation in secondary structure length, loop geometry, ß-strand registry, and overall shape. Solution NMR structures of four designed proteins for two different folds show that protein shape and size can be precisely controlled within a given protein fold. These extended design principles provide the foundation for custom design of protein structures performing desired functions.

A hybrid NMR/SAXS-based approach for discriminating oligomeric protein interfaces using Rosetta.

Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein-protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.

The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013.

The second round of the community-wide initiative Critical Assessment of automated Structure Determination of Proteins by NMR (CASD-NMR-2013) comprised ten blind target datasets, consisting of unprocessed spectral data, assigned chemical shift lists and unassigned NOESY peak and RDC lists, that were made available in both curated (i.e. manually refined) or un-curated (i.e. automatically generated) form. Ten structure calculation programs, using fully automated protocols only, generated a total of 164 three-dimensional structures (entries) for the ten targets, sometimes using both curated and un-curated lists to generate multiple entries for a single target. The accuracy of the entries could be established by comparing them to the corresponding manually solved structure of each target, which was not available at the time the data were provided. Across the entire data set, 71 % of all entries submitted achieved an accuracy relative to the reference NMR structure better than 1.5 Å. Methods based on NOESY peak lists achieved even better results with up to 100% of the entries within the 1.5 Å threshold for some programs. However, some methods did not converge for some targets using un-curated NOESY peak lists. Over 90% of the entries achieved an accuracy better than the more relaxed threshold of 2.5 Å that was used in the previous CASD-NMR-2010 round. Comparisons between entries generated with un-curated versus curated peaks show only marginal improvements for the latter in those cases where both calculations converged.

Quetiapine Ameliorates Schizophrenia-Like Behaviors and Protects Myelin Integrity in Cuprizone Intoxicated Mice: The Involvement of Notch Signaling Pathway.

BACKGROUND: White matter disturbances and myelin impairment are key features of schizophrenia. The antipsychotic drug quetiapine can promote the maturation of oligodendrocytes, but the molecular mechanisms remain largely unknown. METHODS: The schizophrenia-like behaviors, degrees of demyelination, and levels of Notch signaling molecules in forebrains of adult male C57BL/6 mice were examined after fed with cuprizone (0.2% wt/wt) in the presence or absence of 10mg/kg/d quetiapine for 6 weeks. These parameters were also observed after the transcranial injection of Notch signaling inhibitor MW167 (1mM) daily during the last week of the treatment period. RESULTS: Quetiapine ameliorated the schizophrenia-like behaviors and decreased expression of myelin basic protein and inhibition of Notch signaling molecules, such as Notch1, Hes1, and Hes5, in the forebrain that induced by cuprizone. These beneficial effects of quetiapine were abolished by MW167. CONCLUSIONS: The antipsychotic and myelin protective effects of quetiapine are mediated by Notch signaling in a mouse model of cuprizone-induced demyelination associated with schizophrenia-like behaviors. The Notch pathway might therefore be a novel target for the development of antipsychotic drugs.

Accurate protein structure modeling using sparse NMR data and homologous structure information.

While information from homologous structures plays a central role in X-ray structure determination by molecular replacement, such information is rarely used in NMR structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein structures of up to 225 residues by combining (1)H(N), (13)C, and (15)N backbone and (13)Cß chemical shift data, distance restraints derived from homologous structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known structures and blind targets show that the method can accurately model protein structures, even with very remote homology information, to a backbone rmsd of 1.2-1.9 Å relative to the conventional determined NMR ensembles and of 0.9-1.6 Å relative to X-ray structures for well-defined regions of the protein structures. This approach facilitates the accurate modeling of protein structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.

Thymic Rosai-Dorfmann disease: a case report.

BACKGROUND: Rosai-Dorfman disease (RDD), known as sinus histiocytosis with massive lymphadenopathy, commonly involves lymph nodes in the neck or mediastinum, although extranodal involvement is observed in approximately 40% of RDD patients. RDD involving only the thymus has rarely been reported. Here, we report a case of RDD originating in the thymus. The lesion was surgically removed, and a cure was finally achieved. There was no recurrence after telephone follow-up for 3 years. CASE PRESENTATION: A 52-year-old male was accidentally found to have a 7 × 6 cm anterior mediastinum lump by chest computed tomography (CT). The mediastinal lesion was resected by surgery, and postoperative pathology revealed RDD originating from the thymus. Regular telephone follow-up after surgery lasted 3 years and showed that the patient remained in good condition without any relevant symptoms. CONCLUSIONS: RDD originating in the thymus cannot be characterized from CT images and is easily misdiagnosed as a traditional mediastinal tumor. This is mainly because there is so little disease in this area that physicians are not aware of it. We report this case with the hope that clinicians will have a better understanding of this disease. According to our follow-up results, surgery is an effective means of treatment.

Causal relationship between intervertebral disc degeneration and osteoporosis: a bidirectional two-sample Mendelian randomization study.

Introduction: The relationship between intervertebral disc degeneration (IVDD) and osteoporosis (OP), diagnosed primarily using bone mineral density (BMD), remains unclear so far. The present study, therefore, aimed to investigate the potential relationship between osteoporosis and intervertebral disc degeneration using Mendelian randomization and genome-wide association analyses. Specifically, the impact of bone mineral density on the development of intervertebral disc degeneration was evaluated. Materials and methods: The genome-wide association studies (GWAS) summary data of OP/BMDs and IVDD were collected from the FinnGen consortium, the GEFOS consortium, and MRC-IEU. The relationship between IVDD and OP was then explored using TSMR. The inverse-variance weighted (IVW) method was adopted as the primary effect estimate, and the reliability and stability of the results were validated using various methods, including MR-Egger, weighted median, simple mode, weighted mode, and MR-PRESSO. Results: No significant causal relationship was observed between OP and IVDD (IVW, P > 0.05) or between femoral neck BMD (FA-BMD) and IVDD when OP and FA-BMD were used as exposures. However, increased levels of total body BMD (TB-BMD) and lumbar spine BMD (LS-BMD) were revealed as significant risk factors for IVDD (TB-BMD: IVW, OR = 1.201, 95% CI: 1.123-1.284, P = 8.72 × 10-8; LS-BMD: IVW, OR = 1.179, 95% CI: 1.083-1.284, P = 1.43 × 10-4). Interestingly, both heel BMD (eBMD) and femur neck BMD (FN-BMD) exhibited potential causal relationships (eBMD: IVW, OR = 1.068, 95% CI: 1.008-1.131, P = 0.0248; FN-BMD, IVW, OR = 1.161, 95% CI: 1.041-1.295, P = 0.0074) with the risk of IVDD. The reverse MR analysis revealed no statistically causal impact of IVDD on OP and the level of BMD (P > 0.05). Conclusion: OP and the level of FA-BMD were revealed to have no causal relationship with IVDD. The increased levels of TB-BMD and LS-BMD could promote the occurrence of IVDD. Both eBMD and FN-BMD have potential causal relationships with the risk of IVDD. No significant relationship exists between IVDD and the risk of OP. Further research is warranted to comprehensively comprehend the molecular mechanisms underlying the impact of OP and BMD on IVDD and vice versa.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish.

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Riddle of the Sphinx: Emerging role of circular RNAs in cervical cancer.

Cervical cancer is a prominent cause of cancer-related mortality among women, with recent attention directed toward exploring the involvement of circular RNAs (circRNAs) in this particular cancer. CircRNAs, characterized by a covalently closed loop structure, belong to a class of single-stranded non-coding RNA (ncRNA) molecules that play crucial roles in cancer development and progression through diverse mechanisms. The abnormal expression of circRNAs in vivo is significantly associated with the development of cervical cancer. Notably, circRNAs actively interact with miRNAs in cervical cancer, leading to the regulation of diverse signaling pathways, and they can contribute to cancer hallmarks such as self-sufficiency in growth signals, insensitivity to antigrowth signals, limitless proliferation, evading apoptosis, tissue invasion and metastasis, and sustained angiogenesis. Moreover, the distinctive biomedical attributes exhibited by circRNAs, including their abundance, conservation, and stability in body fluids, position them as promising biomarkers for various cancers. In this review, we elucidate the tremendous potential of circRNAs as diagnostic markers or therapeutic targets in cervical cancer by expounding upon their biogenesis, characteristics, functions, and databases, highlighting the novel advances in the signaling pathways associated with circRNAs in cervical cancer.