The mechanistic interaction, aggregation and neurotoxicity of α-synuclein after interaction with glycyrrhizic acid: Modulation of synucleinopathies.

This article reveals the binding mechanism between glycyrrhizic acid (GA) and α-synuclein to may provide further information for the modulation of synucleinopathies using bioactive compounds. Therefore, the inhibitory activities of GA against α-synuclein aggregation and induced neurotoxicity were evaluated using different assays. Results showed that α-synuclein-GA binding was mediated by intermolecular hydrogen bonds leading to the formation of a slightly folded complex. Theoretical studies revealed that GA binds to the N-terminal domain of α-synuclein and triggers a compact structure around a major part of the N-terminal and the NAC regions along with fluctuations in the C-terminal domain, which are prerequisites for the inhibition of α-synuclein aggregation. Then, the cellular assays showed that GA as a potential small molecule can inhibit the oligomerization of α-synuclein and relevant neurotoxicity through modulation of neural viability, membrane leakage, and ROS formation in a concentration-dependent manner. As a result, the primary mechanism of GA's anti-aggregation and neuroprotective activities is the reorganized α-synuclein structure and fluctuating C-terminal domain, which promotes long-range transient intramolecular contacts between the N-terminal and the C-terminal domain.

Short-Wave Infrared Colloidal QD Photodetector with Nanosecond Response Times Enabled by Ultrathin Absorber Layers.

Ultrafast short-wavelength infrared (SWIR) photodetection is of great interest for emerging automated vision and spatial mapping technologies. Colloidal quantum dots (QDs) stand out for SWIR photodetection compared to epitaxial (In,Ga)As or (Hg,Cd)Te semiconductors by their combining a size-tunable bandgap and a suitability for cost-effective, solution-based processing. However, achieving ultrafast, nanosecond-level response time has remained an outstanding challenge for QD-based SWIR photodiodes (QDPDs). Here, record 4 ns response time in PbS-based QDPDs that operate at SWIR wavelengths is reported, a result reaching the requirement of SWIR light detection and ranging based on colloidal QDs. These ultrafast QDPDs combine a thin active layer to reduce the carrier transport time and a small area to inhibit slow capacitive discharging. By implementing a concentration gradient ligand exchange method, high-quality p-n junctions are fabricated in these ultrathin QDPDs. Moreover, these ultrathin QDPDs attain an external quantum efficiency of 42% at 1330 nm, due to a 2.5-fold enhanced light absorption through the formation of a Fabry-Perot cavity within the QDPD and the highly efficient extraction (98%) of photogenerated charge carriers. Based on these results, it is estimated that a further increase of the charge-carrier mobility can lead to PbS QDPDs with sub-nanosecond response time.

Anaerobic membrane bioreactor coupled with polyaluminum chloride for high-strength phenolic wastewater treatment: Robust performance and potential mechanisms.

Anaerobic digestion of phenolic wastewater by anaerobic membrane bioreactor (AnMBR) has revealed increasing attractiveness, but the application of AnMBRs for treating high-strength phenolic wastewater faces challenges related to elevated phenol stress and membrane fouling. In this study, the coupling of AnMBR and polyaluminum chloride (PAC) was developed for efficient treatment of high-strength phenolic wastewater. The system achieved robust removal efficiencies of phenol (99%) and quinoline (98%) at a gradual increase of phenol concentration from 1000 to 5000 mg/L and a constant quinoline concentration of 100 mg/L. The dosing of PAC could effectively control the membrane fouling rate with the transmembrane pressure (TMP) increasing rate as low as 0.17 kPa/d. The robust performances were mainly attributed to the favorable retention of functional microbes through membrane interception, while pulse cross flow buffered against phenol stress and facilitated cake layer removal. Meanwhile, the enriched core functional microbes, such as Syntrophorhabdus, Syntrophus, Mesotoga and Methanolinea, played a crucial role in further reduction of phenol stress. Notably, the significant presence of biomacromolecule degrader, such as Levilinea, contributed to membrane fouling mitigation through extracellular polymer degradation. Moreover, the enlargement of particle size distribution (PSD) by PAC was expected to mitigate membrane fouling. This study provided a promising avenue for sustainable treatment of high-strength phenolic wastewater.

Suppression on the basis of template for rejection is reactive: Evidence from human electrophysiology.

According to most theories of attention, the selection of task-relevant visual information can be enhanced by holding them in visual working memory (VWM). However, there has been a long-standing debate concerning whether similar optimization can also be achieved for task-irrelevant information, known as a "template for rejection". The present study aimed to explore this issue by examining the consequence of cue distractors before visual search tasks. For this endeavor, we manipulated the display heterogeneity by using two distractor conditions, salient and non-salient, to explore the extent to which holding the distractor color in VWM might affect attentional selection. We measured the reaction times of participants while their EEG activity was recorded. The results showed that WM-matched distractors did not improve reaction times but rather slowed them down in both tasks. Event-related potential (ERP) results showed that the display heterogeneity had no modulatory effect on the degree of distractor suppression. Even in the salient distractor condition, the WM-matched distractor received no greater suppression. Furthermore, the WM-matched distractor but not the neutral distractor elicited an N2pc before the PD in salient distractor conditions. This suggests that the template for rejection operates reactively since suppression occurs after extra attentional processes to the distractor. Moreover, the presence of WM-matched distractors led to a reduction of P3b, indicating a competition between target processing and WM-matched distractor rejection. Our findings provide insights into the mechanisms underlying the optimization of attentional selection, and have implications for future studies aimed at understanding the role of VWM in cognition.

Adult-type granulosa cell tumor associated with elevated luteinizing hormone: Two rare case reports.

INTRODUCTION: Adult-type granulosa cell tumors (AGCTs), which account for 2% to 5% of all malignant ovarian tumors, are rare sex cord-stromal tumors that usually secrete excess estrogens, but they can also secrete androgens. PATIENT CONCERNS: We report 2 patients of childbearing age with AGCT who presented with the complaint of abnormal menstruation and elevated luteinizing hormone (LH), and mildly elevated testosterone. DIAGNOSIS: The ovarian tumors had hormonal activity. INTERVENTIONS: The 2 patients underwent laparoscopic left adnexectomy. The second patient underwent 4 cycles of chemotherapy with paclitaxel and carboplatin as adjuvant treatments. OUTCOMES: Their postoperative pathology confirmed AGCTs. Also, their menstrual cycle returned to normal, with normal serum LH and testosterone levels. There was no sign of recurrence. CONCLUSION: The cases suggest that elevated serum LH levels may be a sign of unknown tumors in cases of oligomenorrhea or secondary amenorrhea. It is useful to evaluate the serum levels of inhibin B and anti-Müllerian hormone to improve the early recognition of ovarian granulosa cell tumors.

An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction.

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum-maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.

Concurrent spinal meningioma and giant invasive schwannoma without neurofibromatosis in children: A case report and literature review.

BACKGROUND: Spinal meningiomas coexisting with schwannomas in patients without neurofibromatosis are extremely rare lesions. There were only 15 cases reported to date, which were concurrent intradural tumors of different pathological types. CASE PRESENTATION: Herein, we present a rare case of a 15-year-old child with concurrent spinal dorsal meningioma and ventral giant invasive schwannoma at C7-T3 and T10-S5 spinal levels. Preoperative magnetic resonance imaging and computed tomography indicated the schwannoma across the thoracic and lumbosacral transitional vertebra, with extensive bony erosion of the sacrum. The results of surgical resection were mostly satisfactory. CONCLUSIONS: The present case is the youngest patient diagnosed with concurrent intradural tumors at different spinal levels. The pathogenetic mechanism remains unclear. The clinical presentations are always atypical. Surgical resection of the tumors is the first choice. We use the non-fusion surgery to preserve the function of the lumbar spine.

Microbial removal of uranyl from aqueous solution by Leifsonia sp. in the presence of different forms of iron oxides.

Immobilization of uranyl by indigenous microorganisms has been proposed as an economic and clean in-situ approach for removal of uranium, but the potential mechanisms of the process and the stability of precipitated uranium in the presence of widespread Fe(III) (hydr)oxides remain elusive. The potential of iron to serve as a reductant and/or an oxidant of uranium indicates that bioemediation strategies which mainly rely on the reduction of highly soluble U(VI) to poorly soluble U(IV) minerals to retard uranium transport in groundwater may be enhanced or hindered under different environmental conditions. This study purposes to determine the effect of ubiquitous Fe(III) (hydr)oxides (two-line ferrihydrite, hematite and goethite) on the removal of U(VI) by Leifsonia sp. isolated from an acidic tailings pond in China. The removal mechanism was elucidated via SEM-EDS, XPS and Mössbauer. The results show that the removal of U(VI) was retarded by Fe(III) (hydr)oxides when the initial concentration of U(VI) was 10 mg/L, pH was 6, temperature was 25 °C. Particularly, the retardatory effect of hematite on U(VI) removal was blindingly obvious. Also, it is worth noting that the U(VI) in the precipitate slow-released in the Fe(III) (hydrodr) oxide treatment groups, accompanied by an increase in Fe(II) concentration. SEM-EDS results demonstrated that the ferrihydrite converted to goethite may be the reason for U(VI) release in the process of 15 days culture. Mössbauer spectra fitting results further imply that the metastable iron oxides were transformed into stable Fe3O4 state. XPS measurements results showed that uranium product is most likely a mixture of Iron-U(IV) and Iron-U(VI), which indicated that the hexavalent uranium was converted into tetravalent uranium. These observations imply that the stability of the uranium in groundwater may be impacted on the prevailing environmental conditions, especially the solid-phase Fe(III) (hydr)oxide in groundwater or sediment.

Deep Generative Models in De Novo Drug Molecule Generation.

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in de novo generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.

Integrated PbS Colloidal Quantum Dot Photodiodes on Silicon Nitride Waveguides.

Colloidal quantum dots (QDs) have become a versatile optoelectronic material for emitting and detecting light that can overcome the limitations of a range of electronic and photonic technology platforms. Photonic integrated circuits (PICs), for example, face the persistent challenge of combining active materials with passive circuitry ideally suited for guiding light. Here, we demonstrate the integration of photodiodes (PDs) based on PbS QDs on silicon nitride waveguides (WG). Analyzing planar QDPDs first, we argue that the main limitation WG-coupled QDPDs face is detector saturation induced by the high optical power density of the guided light. Using the cladding thickness and waveguide width as design parameters, we mitigate this issue, and we demonstrate WG-QDPDs with an external quantum efficiency of 67.5% at 1275 nm that exhibit a linear photoresponse for input powers up to 400 nW. In the next step, we demonstrate a compact infrared spectrometer by integrating these WG-QDPDs on the output channels of an arrayed waveguide grating demultiplexer. This work provides a path toward a low-cost PD solution for PICs, which are attractive for large-scale production.

Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks.

Automating retrosynthesis with artificial intelligence expedites organic chemistry research in digital laboratories. However, most existing deep-learning approaches are hard to explain, like a "black box" with few insights. Here, we propose RetroExplainer, formulizing the retrosynthesis task into a molecular assembly process, containing several retrosynthetic actions guided by deep learning. To guarantee a robust performance of our model, we propose three units: a multi-sense and multi-scale Graph Transformer, structure-aware contrastive learning, and dynamic adaptive multi-task learning. The results on 12 large-scale benchmark datasets demonstrate the effectiveness of RetroExplainer, which outperforms the state-of-the-art single-step retrosynthesis approaches. In addition, the molecular assembly process renders our model with good interpretability, allowing for transparent decision-making and quantitative attribution. When extended to multi-step retrosynthesis planning, RetroExplainer has identified 101 pathways, in which 86.9% of the single reactions correspond to those already reported in the literature. As a result, RetroExplainer is expected to offer valuable insights for reliable, high-throughput, and high-quality organic synthesis in drug development.

Integrated Microfluidic Chip for Rapid Antimicrobial Susceptibility Testing Directly from Positive Blood Cultures.

Rapid and accurate antimicrobial prescriptions are critical for bloodstream infection (BSI) patients, as they can guide drug use and decrease mortality significantly. The traditional antimicrobial susceptibility testing (AST) for BSI is time-consuming and tedious, taking 2-3 days. Avoiding lengthy monoclonal cultures and shortening the drug sensitivity incubation time are keys to accelerating the AST. Here, we introduced a bacteria separation integrated AST (BSI-AST) chip, which could extract bacteria directly from positive blood cultures (PBCs) within 10 min and quickly give susceptibility information within 3 h. The integrated chip includes a bacteria separation chamber, multiple AST chambers, and connection channels. The separator gel was first preloaded into the bacteria separation chamber, enabling the swift separation of bacteria cells from PBCs through on-chip centrifugation. Then, the bacteria suspension was distributed in the AST chambers with preloaded antibiotics through a quick vacuum-assisted aliquoting strategy. Through centrifuge-assisted on-chip enrichment, detectable growth of the phenotype under different antibiotics could be easily observed in the taper tips of AST chambers within a few hours. As a proof of concept, direct AST from artificial PBCs with Escherichia coli against 18 antibiotics was performed on the BSI-AST chip, and the whole process from bacteria extraction to AST result output was less than 3.5 h. Moreover, the integrated chip was successfully applied to the diagnosis of clinical PBCs, showing 93.3% categorical agreement with clinical standard methods. The reliable and fast pathogen characterization of the integrated chip suggested its great potential application in clinical diagnosis.

CircDUS2L (circ_0039908) promotes lung adenocarcinoma progression by upregulating PGAM1 by acting as a miR-590-5p molecular sponge.

Lung adenocarcinoma (LUAD) is usually found at the metastatic stage. Circular RNA dihydrouridine synthase 2-like (DUS2L) (circDUS2L) has been discovered to be upregulated in LUAD. Nevertheless, the function of circDUS2L in LUAD has not been verified. Levels of circDUS2L, microRNA-590-5p (miR-590-5p), and phosphoglycerate mutase 1 (PGAM1) mRNA were analyzed using quantitative real-time polymerase chain reaction (RT-qPCR). Cell proliferation, apoptosis, metastasis, and invasion were assessed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT), colony formation, 5-ethynyl-2'-deoxyuridine (Edu), flow cytometry, and transwell assays. Protein levels were detected by western blotting. Cell glycolysis was analyzed by measuring cell glucose consumption, lactate production, and extracellular acidification rate (ECAR). The regulatory mechanism of circDUS2L in LUAD cells was analyzed by bioinformatics analysis, dual-luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. Xenograft assay was conducted to confirm the function of circDUS2L in vivo. CircDUS2L was highly expressed in LUAD tissues and cells. CircDUS2L silencing constrained xenograft tumor growth in vivo. CircDUS2L knockdown induced apoptosis, repressed viability, colony formation, proliferation, metastasis, invasion, and glycolysis of LUAD cells in vitro by releasing miR-590-5p via functioning as a miR-590-5p sponge. MiR-590-5p was lowly expressed in LUAD tissues and cells, and miR-590-5p mimic curbed malignant behaviors and glycolysis of LUAD cells by targeting PGAM1. PGAM1 was overexpressed in LUAD tissues and cells, and circDUS2L sponged miR-590-5p to regulate PGAM1 expression. CircDUS2L elevated PGAM1 expression through functioning as a miR-590-5p sponge, thus driving malignant behaviors and glycolysis of LUAD cells.

Coupled pyrite and sulfur autotrophic denitrification for simultaneous removal of nitrogen and phosphorus from secondary effluent: feasibility, performance and mechanisms.

The discharge standards of nitrogen (N) and phosphorus (P) in wastewater treatment plants (WWTPs) have become increasingly strict to reduce water eutrophication. Further reducing N and P in effluent from municipal WWTPs need to be achieved effectively and eco-friendly. In this study, a carbon independent pyrite and sulfur autotrophic denitrification (PSAD) system using pyrite and sulfur as electron donor was developed and compared with pyrite autotrophic denitrification (PAD) and sulfur autotrophic denitrification (SAD) systems through batch and continuous flow biofilter experiments. Compare to PAD and SAD, PSAD was more effective in simultaneous removal in N and P. At hydraulic retention time (HRT) 3 h, average effluent concentrations of total nitrogen (TN) and total phosphate (TP) of 1.40 ± 0.03 and 0.19 ± 0.02 mg/L were achieved when treating real secondary effluent with 20.65 ± 0.24 mg/L TN and 1.00 ± 0.24 mg/L TP. The improvement in simultaneous removal of N and P was attributed to the coupling of PAD and SAD in enhancing the transformation of sulfur and iron and enlarging the reaction zone in the pyrite and sulfur autotrophic denitrification biofilter (PSADB) system. Therefore, more biomass was accumulated and the microbial denitrification functional stability, including electrons transfer and consumption was enhanced on the surface of pyrite and sulfur particles in the PSADB system. Moreover, autotrophic denitrifiers (Thiobacillus and Ferritrophicum), sulfate-reducing bacteria (Desulfocapsa) and iron reducing bacteria (Geothrix), acting as contributors to microbial nitrogen, sulfur and iron cycle, were specially enriched. In addition, the leaching of iron ions was promoted, which facilitated the removal of phosphate in the form of Fe3(PO4)2·8H2O and Fe3PO4. PSADB has proven to be an efficient technology for simultaneous removal of N and P, which could meet increasingly stringent discharge standards effectively and eco-friendly.

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting the predictive performance. To address the problem, we propose a novel neural network architecture named DrugormerDTI, which uses Graph Transformer to learn both sequential and topological information through the input molecule graph and Resudual2vec to learn the underlying relation between residues from proteins. By conducting ablation experiments, we verify the importance of each part of the DrugormerDTI. We also demonstrate the good feature extraction and expression capabilities of our model via comparing the mapping results of the attention layer and molecular docking results. Experimental results show that our proposed model performs better than baseline methods on four benchmarks. We demonstrate that the introduction of Graph Transformer and the design of residue are appropriate for drug-target prediction.

A metadata framework for computational phenotypes.

With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.

The characteristics of serum lipid spectrum in PanNENs and its correlation with clinicopathological features and prognosis.

Background: The role of dyslipidemia in pancreatic neuroendocrine tumors (PanNENs) is unclear. The aim of this study is to analyze the characteristics of serum lipid spectrum in PanNENs, and the effect of the variation in lipid profile on the development of PanNENs clinicopathological features and prognosis. Methods: All PanNENs patients between November 2012 and September 2020 in the authors' research center were identified from patient medical records and databases. A total of 185 with PanNENs patients were ultimately included in this study, including 100 nonfunctional PanNENs and 85 insulinomas. Clinicopathologic features, serum lipid level and overall survival results were retrospectively analyzed using statistical methods. Results: In 185 PanNENs, 95 (51.4%) patients appear to have dyslipidemia. Patients with insulinoma had a lower proportion of abnormal HDL than those with nonfunctional PanNENs (10.6% vs 23%, P=0.026). The mean serum HDL levels of insulinomas were 0.131 mmol/L higher than the NF-PanNENs (1.306 ± 0.324 vs 1.175 ± 0.315, P=0.006). In multivariate logistic analysis, high levels of HDL are negatively correlated to tumor size (OR 0.233, 95% CI: 0.069-0.790, P=0.019), but HDL was not associated with pathological grade or metastasis. And a correlation has been found between hypercholesterolemia and the original location of the tumor (OR:0.224, 95%CI: 0.066-0.753, P =0.016). In addition, the outcome of the survival analysis revealed that dyslipidemia did not influence the prognosis of PanNENs patients (P>0.05). Conclusions: HDL was negatively correlated with the tumor size of PanNENs. The serum HDL level of insulinoma patients is higher than nonfunctional PanNENs.

Block copolymer self-assembly to pattern gold nanodots for site-specific placement of DNA origami and attachment of nanomaterials.

Directed placement of DNA origami could play a key role in future integrated nanoelectronic devices. Here we demonstrated the site-selective attachment of DNA origami on gold dots formed using a pattern transfer method through block copolymer self-assembly. First, a random copolymer brush layer is grafted on the Si surface and then poly (styrene-b-methylmethacrylate) block copolymer is spin-coated to give a hexagonal nanoarray after annealing. UV irradiation followed by acetic acid etching is used to remove the PMMA, creating cylindrical holes and then oxygen plasma etching removes the random copolymer layer inside those holes. Next, metal evaporation, followed by lift-off creates a gold dot array. We evaluated different ligand functionalization of Au dots, as well as DNA hybridization to attach DNA origami to the nanodots. DNA-coated Au nanorods are assembled on the DNA origami as a step towards creating nanowires and to facilitate electron microscopy characterization of the attachment of DNA origami on these Au nanodots. The DNA hybridization approach showed better DNA attachment to Au nanodots than localization by electrostatic interaction. This work contributes to the understanding of DNA-templated assembly, nanomaterials, and block copolymer nanolithography. Furthermore, the work shows potential for creating DNA-templated nanodevices and their placement in ordered arrays in future nanoelectronics.

TBK1-medicated DRP1 phosphorylation orchestrates mitochondrial dynamics and autophagy activation in osteoarthritis.

Mitochondrial dynamics, including mitochondrial fission and fusion, are critical for maintaining mitochondrial functions. Evidence shows that TANK-binding kinase 1 (TBK1) regulates mitochondrial fusion and fission and then mitophagy. Since a previous study demonstrates a strong correlation between mitophagy and osteoarthritis (OA), we herein investigated the potential role of TBK1 in OA process and mitochondrial functions. We demonstrated a strong correlation between TBK1 and OA, evidenced by significantly downregulated expression of TBK1 in cartilage tissue samples of OA patients and in the chondrocytes of aged mice, as well as TNF-α-stimulated phosphorylation of TBK1 in primary mouse chondrocytes. TBK1 overexpression significantly attenuated TNF-α-induced apoptosis and abnormal mitochondrial function in primary mouse chondrocytes. Furthermore, TBK1 overexpression induced remodeling of mitochondrial morphology by directly phosphorylating dynamin-related protein 1 (DRP1) at Ser637, abolishing the fission of DRP1 and preventing its fragmentation function. Moreover, TBK1 recruitment and DRP1 phosphorylation at Ser637 was necessary for engulfing damaged mitochondria by autophagosomal membranes during mitophagy. Moreover, we demonstrated that APMK/ULK1 signaling contributed to TBK1 activation. In OA mouse models established by surgical destabilization of the medial meniscus, intraarticular injection of lentivirus-TBK1 significantly ameliorated cartilage degradation via regulation of autophagy and alleviation of cell apoptosis. In conclusion, our results suggest that the TBK1/DRP1 pathway is involved in OA and pharmacological targeting of the TBK1-DRP1 cascade provides prospective therapeutic benefits for the treatment of OA.

iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.