Increased ß2-adrenergic signaling promotes fracture healing through callus neovascularization in mice.

Traumatic brain injury (TBI) leads to skeletal changes, including bone loss in the unfractured skeleton, and paradoxically accelerates healing of bone fractures; however, the mechanisms remain unclear. TBI is associated with a hyperadrenergic state characterized by increased norepinephrine release. Here, we identified the ß2-adrenergic receptor (ADRB2) as a mediator of skeletal changes in response to increased norepinephrine. In a murine model of femoral osteotomy combined with cortical impact brain injury, TBI was associated with ADRB2-dependent enhanced fracture healing compared with osteotomy alone. In the unfractured 12-week-old mouse skeleton, ADRB2 was required for TBI-induced decrease in bone formation and increased bone resorption. Adult 30-week-old mice had higher bone concentrations of norepinephrine, and ADRB2 expression was associated with decreased bone volume in the unfractured skeleton and better fracture healing in the injured skeleton. Norepinephrine stimulated expression of vascular endothelial growth factor A and calcitonin gene-related peptide-α (αCGRP) in periosteal cells through ADRB2, promoting formation of osteogenic type-H vessels in the fracture callus. Both ADRB2 and αCGRP were required for the beneficial effect of TBI on bone repair. Adult mice deficient in ADRB2 without TBI developed fracture nonunion despite high bone formation in uninjured bone. Blocking ADRB2 with propranolol impaired fracture healing in mice, whereas the ADRB2 agonist formoterol promoted fracture healing by regulating callus neovascularization. A retrospective cohort analysis of 72 patients with long bone fractures indicated improved callus formation in 36 patients treated with intravenous norepinephrine. These findings suggest that ADRB2 is a potential therapeutic target for promoting bone healing.

BERT-siRNA: siRNA target prediction based on BERT pre-trained interpretable model.

Silencing mRNA through siRNA is vital for RNA interference (RNAi), necessitating accurate computational methods for siRNA selection. Current approaches, relying on machine learning, often face challenges with large data requirements and intricate data preprocessing, leading to reduced accuracy. To address this challenge, we propose a BERT model-based siRNA target gene knockdown efficiency prediction method called BERT-siRNA, which consists of a pre-trained DNA-BERT module and Multilayer Perceptron module. It applies the concept of transfer learning to avoid the limitation of a small sample size and the need for extensive preprocessing processes. By fine-tuning on various siRNA datasets after pretraining on extensive genomic data using DNA-BERT to enhance predictive capabilities. Our model clearly outperforms all existing siRNA prediction models through testing on the independent public siRNA dataset. Furthermore, the model's consistent predictions of high-efficiency siRNA knockdown for SARS-CoV-2, as well as its alignment with experimental results for PDCD1, CD38, and IL6, demonstrate the reliability and stability of the model. In addition, the attention scores for all 19-nt positions in the dataset indicate that the model's attention is predominantly focused on the 5' end of the siRNA. The step-by-step visualization of the hidden layer's classification progressively clarified and explained the effective feature extraction of the MLP layer. The explainability of model by analysis the attention scores and hidden layers is also our main purpose in this work, making it more explainable and reliable for biological researchers.

Transcript-dependent effects of the CALCA gene on the progression of post-traumatic osteoarthritis in mice.

Osteoarthritis represents a chronic degenerative joint disease with exceptional clinical relevance. Polymorphisms of the CALCA gene, giving rise to either a procalcitonin/calcitonin (PCT/CT) or a calcitonin gene-related peptide alpha (αCGRP) transcript by alternative splicing, were reported to be associated with the development of osteoarthritis. The objective of this study was to investigate the role of both PCT/CT and αCGRP transcripts in a mouse model of post-traumatic osteoarthritis (ptOA). WT, αCGRP-/- and CALCA-/- mice were subjected to anterior cruciate ligament transection (ACLT) to induce ptOA of the knee. Mice were sacrificed 4 and 8 weeks post-surgery, followed by micro-CT and histological evaluation. Here we show that the expression of both PCT/CT and αCGRP transcripts is induced in ptOA knees. CALCA-/- mice show increased cartilage degeneration and subchondral bone loss with elevated osteoclast numbers compared to αCGRP-/- and WT mice. Osteophyte formation is reduced to the same extent in CALCA-/- and αCGRP-/- mice compared to WT controls, while a reduced synovitis score is noticed exclusively in mice lacking CALCA. Our data show that expression of the PCT/CT transcript protects from the progression of ptOA, while αCGRP promotes osteophyte formation, suggesting that CALCA-encoded peptides may represent novel targets for the treatment of ptOA.

Tranexamic Acid Attenuates the Progression of Posttraumatic Osteoarthritis in Mice.

BACKGROUND: Posttraumatic osteoarthritis (OA) is a common disorder associated with a high socioeconomic burden, particularly in young, physically active, and working patients. Tranexamic acid (TXA) is commonly used in orthopaedic trauma surgery as an antifibrinolytic agent to control excessive bleeding. Previous studies have reported that TXA modulates inflammation and bone cell function, both of which are dysregulated during posttraumatic OA disease progression. PURPOSE: To evaluate the therapeutic effects of systemic and topical TXA treatment on the progression of posttraumatic OA in the knee of mice. STUDY DESIGN: Controlled laboratory study. METHODS: OA was induced via anterior cruciate ligament (ACL) transection on the right knee of female mice. Mice were treated with TXA or vehicle intraperitoneally daily or intra-articularly weekly for 4 weeks, starting on the day of surgery. Articular cartilage degeneration, synovitis, bone erosion, and osteophyte formation were scored histologically. Micro-computed tomography evaluation was conducted to measure the subchondral bone microstructure and osteophyte volume. Cartilage thickness and bone remodeling were assessed histomorphometrically. RESULTS: Both systemic and topical TXA treatment significantly reduced cartilage degeneration, synovitis, and bone erosion scores and increased the ratio of hyaline to calcified cartilage thickness in posttraumatic OA. Systemic TXA reversed ACL transection-induced subchondral bone loss and osteophyte formation, whereas topical treatment had no effect. Systemic TXA decreased the number and surface area of osteoclasts, whereas those of osteoblasts were not affected. No effect of topical TXA on osteoblast or osteoclast parameters was observed. CONCLUSION: Both systemic and topical TXA exerted protective effects on the progression of posttraumatic OA. Drug repurposing of TXA may, therefore, be useful for the prevention or treatment of posttraumatic OA, particularly after ACL surgery. CLINICAL RELEVANCE: TXA might be beneficial in patients with posttraumatic OA of the knee.

Calcitonin gene-related peptide attenuated discogenic low back pain in rats possibly via inhibiting microglia activation.

Discogenic low back pain (DLBP) is a multifactorial disease and associated with intervertebral disc degeneration. Calcitonin gene-related protein (CGRP) plays a critical role in pain processing, while the role in DLBP remains unclear. This study aims to investigate the anti-nociceptive role and related mechanisms of CGRP in DLBP. Here we established the DLBP rat and validated the model using histology and radiography. Minocycline, a microglial inhibitor, and CGRP were intrathecally injected and the behavioral test was performed to determine hyperalgesia. Further, BV2 microglial cells and microglial activation agent lipopolysaccharide (LPS) were employed for the in vitro experiment. We observed obvious lumbar intervertebral disc degeneration and hyperalgesia at 12 weeks postoperation in DLBP group, with significantly activated microglia in the spinal cord. CGRP treatment significantly inhibited the upregulation of proinflammatory cytokines and NLRP3/caspase-1 expression induced by LPS in BV2 cells, whereas treatment with CGRP alone had little effect on BV2 cells. The intrathecal injection of CGRP into DLBP rats relieved mechanical and thermal hyperalgesia, reverted the microglial activation and decreased the expression of NLRP3/caspase-1, similar to the effects produced by minocycline. Our results provide evidence that microglial activation in the spinal cord play a key role in hyperalgesia in DLBP rats. CGRP alleviates DLBP induced hyperalgesia and inhibits microglial activation in the spinal cord. Regulation of CGRP and microglial activation may provide a new strategy for ameliorating DLBP.

The selective norepinephrine reuptake inhibitor reboxetine promotes late-stage fracture healing in mice.

Impaired fracture healing is of high clinical relevance, as up to 15% of patients with long-bone fractures display non-unions. Fracture patients also include individuals treated with selective norepinephrine reuptake inhibitors (SNRI). As SNRI were previously shown to negatively affect bone homeostasis, it remained unclear whether patients with SNRI are at risk of impaired bone healing. Here, we show that daily treatment with the SNRI reboxetine reduces trabecular bone mass in the spine but increases cortical thickness and osteoblast numbers in the femoral midshaft. Most importantly, reboxetine does not impair bone regeneration in a standardized murine fracture model, and even improves callus bridging and biomechanical stability at late healing stages. In sum, reboxetine affects bone remodeling in a site-specific manner. Treatment does not interfere with the early and intermediate stages of bone regeneration and improves healing outcomes of the late-stage fracture callus in mice.

Increased beta2-adrenergic signaling is a targetable stimulus essential for bone healing by promoting callus neovascularization.

Traumatic brain injury (TBI) is associated with a hyperadrenergic state and paradoxically causes systemic bone loss while accelerating fracture healing. Here, we identify the beta2-adrenergic receptor (Adrb2) as a central mediator of these skeletal manifestations. While the negative effects of TBI on the unfractured skeleton can be explained by the established impact of Adrb2 signaling on bone formation, Adrb2 promotes neovascularization of the fracture callus under conditions of high sympathetic tone, including TBI and advanced age. Mechanistically, norepinephrine stimulates the expression of Vegfa and Cgrp primarily in periosteal cells via Adrb2, both of which synergistically promote the formation of osteogenic type-H vessels in the fracture callus. Accordingly, the beneficial effect of TBI on bone repair is abolished in mice lacking Adrb2 or Cgrp, and aged Adrb2-deficient mice without TBI develop fracture nonunions despite high bone formation in uninjured bone. Pharmacologically, the Adrb2 antagonist propranolol impairs, and the agonist formoterol promotes fracture healing in aged mice by regulating callus neovascularization. Clinically, intravenous beta-adrenergic sympathomimetics are associated with improved callus formation in trauma patients with long bone fractures. Thus, Adrb2 is a novel target for promoting bone healing, and widely used beta-blockers may cause fracture nonunion under conditions of increased sympathetic tone.

Fracture healing in a mouse model of Hajdu-Cheney-Syndrome with high turnover osteopenia results in decreased biomechanical stability.

Notch signaling regulates cell fate in multiple tissues including the skeleton. Hajdu-Cheney-Syndrome (HCS), caused by gain-of-function mutations in the Notch2 gene, is a rare inherited disease featuring early-onset osteoporosis and increased risk for fractures and non-union. As the impact of Notch2 overactivation on fracture healing is unknown, we studied bone regeneration in mice harboring a human HCS mutation. HCS mice, displaying high turnover osteopenia in the non-fractured skeleton, exhibited only minor morphologic alterations in the progression of bone regeneration, evidenced by static radiological and histological outcome measurements. Histomorphometry showed increased osteoclast parameters in the callus of HCS mice, which was accompanied by an increased expression of osteoclast and osteoblast markers. These observations were accompanied by inferior biomechanical stability of healed femora in HCS mice. Together, our data demonstrate that structural indices of bone regeneration are normal in HCS mice, which, however, exhibit signs of increased callus turnover and display impaired biomechanical stability of healed fractures.

Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.

BACKGROUND: Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper. RESULTS: PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively. CONCLUSIONS: By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.

Influence of Strengthening Elements and Heat Treatment on Microstructure and Fracture Toughness of NiAl-Cr(Mo)-Based Eutectic Alloy.

Due to their potential improvement of high-temperature properties, the refractory metal hafnium (Hf) and the rare earth holmium (Ho) have attracted much attention. In the present research, NiAl-Cr(Mo) eutectic alloys with different Ho and Hf additions were fabricated by conventional smelting method and heat-treated to study the synergetic influence of strengthening elements and heat treatment. The samples were characterized using XRD, SEM, and TEM, and the three-point bending test was performed to obtain fracture toughness. The results demonstrate that Hf addition leads to the formation of Ni2AlHf Heusler phase and that Ho promoted the formation of Ni2Al3Ho phase. The microstructure of the alloy is affected by thermal treatment, with the coarsening of eutectic lamellae after heat treatment. The mechanical properties are improved by Hf and Ho additions, with increased fracture toughness. Overall, this study provides insights into the microstructure and properties of NiAl-Cr(Mo) eutectic alloys and highlights the potential of Hf and Ho addition to improve room-temperature properties. Specifically, the as-cast NiAl-Cr(Mo)-Hf eutectic alloy contains a relatively fine NiAl/Cr(Mo) eutectic lamella but coarse eutectic cell with Ni2AlHf phase embellished along the cell boundary. Minor Ho addition induces the formation of Ni2Al3Ho phase, which leads to the coarsening of the intercellular region but contributes to the refining of eutectic cell. In addition, the synergetic effect of Ho and Hf promotes the precipitation of Ni2Al3Ho and Ni2AlHf phases in the intercellular zone and increases the interface dislocations. Heat treatment benefits the solid solution of Ni2Al3Ho and Ni2AlHf phases, which improves their size and distribution by secondary precipitation. The Ni2AlHf phase in the NiAl-Cr(Mo)-Hf eutectic alloy becomes fine and uniformly distributed, but the NiAl/Cr(Mo) eutectic lamella in the eutectic cell becomes coarse. In comparison, heat treatment mainly optimizes the size and distribution of the Ni2Al3Ho and Ni2AlHf phases in the NiAl-Cr(Mo)-Hf-Ho eutectic alloy. Furthermore, heat treatment helps to eliminate the interface dislocations in the large NiAl precipitates and the NiAl/Cr(Mo) phase interfaces, which also contributes to fracture toughness by decreasing stress concentration. Minor Ho addition decreases the fracture toughness of as-cast NiAl-Cr(Mo)-Hf eutectic alloy from 6.7 to 6.1 MPa·m1/2, which should be ascribed to the coarsened intercellular region including aggregated Ni2Al3Ho and Ni2AlHf phases. However, minor Ho-doped NiAl-Cr(Mo)-Hf eutectic alloy obtained the highest fracture toughness of 8.2 MPa·m1/2 after heat treatment. This improved fracture toughness should be mainly attributed to the refined and well-distributed Ni2Al3Ho and Ni2AlHf phases in the heat-treated NiAl-Cr(Mo)-Hf-Ho eutectic alloy.

Research Progress of Laser Cladding on the Surface of Titanium and Its Alloys.

Titanium (Ti) and its alloys have been widely employed in aeronautical, petrochemical, and medical fields owing to their fascinating advantages in terms of their mechanical properties, corrosion resistance, biocompatibility, and so on. However, Ti and its alloys face many challenges, if they work in severe or more complex environments. The surface is always the origin of failure for Ti and its alloys in workpieces, which influences performance degradation and service life. To improve the properties and function, surface modification becomes the common process for Ti and its alloys. The present article reviews the technology and development of laser cladding on Ti and its alloys, according to the cladding technology, cladding materials, and coating function. Generally, the laser cladding parameters and auxiliary technology could influence the temperature distribution and elements diffusion in the molten pool, which basically determines the microstructure and properties. The matrix and reinforced phases play an important role in laser cladding coating, which can increase the hardness, strength, wear resistance, oxidation resistance, corrosion resistance, biocompatibility, and so on. However, the excessive addition of reinforced phases or particles can deteriorate the ductility, and thus the balance between functional properties and basic properties should be considered during the design of the chemical composition of laser cladding coatings. In addition, the interface including the phase interface, layer interface, and substrate interface plays an important role in microstructure stability, thermal stability, chemical stability, and mechanical reliability. Therefore, the substrate state, the chemical composition of the laser cladding coating and substrate, the processing parameters, and the interface comprise the critical factors which influence the microstructure and properties of the laser cladding coating prepared. How to systematically optimize the influencing factors and obtain well-balanced performance are long-term research issues.

In vivo deep-brain 2-photon fluorescent microscopy labeled with near-infrared dyes excited at the 1700 nm window.

2-Photon fluorescence microscopy (2PFM) is an indispensable imaging technology for neuroscience. However, the imaging depth is usually limited to the cortical layer in mouse brain in vivo. Here, we demonstrate deep brain 2PFM in vivo excited at the 1700 nm window, using IR780 and aza-IR780 as fluorescent labels. Our detailed characterization of the multiphoton excitation and emission properties of IR780 and aza-IR780 show that: (1) IR780 or aza-IR780 generate 2-photon fluorescence excited at the 1700 nm window and are promising for 2PFM; (2) aza-IR780 exhibits a larger ησ2 with better anti-photobleaching property compared to IR780; The 2-photon action cross-sections of IR780 and aza-IR780 in plasma are an order-of-magnitude larger than those in PBS; (3) In vivo 2-photon emission spectra for both dyes show a notable red shift compared to those in vitro. Based on these characterization results, we demonstrate deep brain 2PFM labeled by them. A maximum imaging depth of 1585 µm (labeled by IR780) and 1800 µm (labeled by aza-IR780) into the mouse brain in vivo readily penetrates the subcortical region of hippocampus. Besides, a maximum of 1528 µm hemodynamic imaging depth is realized via 2PFM with aza-IR780 labeling, enabling us to measure blood flow speed in the hippocampus.

In Vivo Deep-Brain 3- and 4-Photon Fluorescence Imaging of Subcortical Structures Labeled by Quantum Dots Excited at the 2200 nm Window.

Multiphoton microscopy (MPM) is an enabling technology for visualizing deep-brain structures at high spatial resolution in vivo. Within the low tissue absorption window, shifting to longer excitation wavelengths reduces tissue scattering and boosts penetration depth. Recently, the 2200 nm excitation window has emerged as the last and longest window suitable for deep-brain MPM. However, multiphoton fluorescence imaging at this window has not been demonstrated, due to the lack of characterization of multiphoton properties of fluorescent labels. Here we demonstrate technologies for measuring both the multiphoton excitation and emission properties of fluorescent labels at the 2200 nm window, using (1) 3-photon (ησ3) and 4-photon action cross sections (ησ4) and (2) 3-photon and 4-photon emission spectra both ex vivo and in vivo of quantum dots. Our results show that quantum dots have exceptionally large ησ3 and ησ4 for efficient generation of multiphoton fluorescence. Besides, the 3-photon and 4-photon emission spectra of quantum dots are essentially identical to those of one-photon emission, which change negligibly subject to the local environment of circulating blood. Based on these characterization results, we further demonstrate deep-brain vasculature imaging in vivo. Due to the superb multiphoton properties of quantum dots, 3-photon and 4-photon fluorescence imaging reaches a maximum brain imaging depth of 1060 and 940 µm below the surface of a mouse brain, respectively, which enables the imaging of subcortical structures. We thus fill the last gap in multiphoton fluorescence imaging in terms of wavelength selection.

KDM1A drives hepatoblastoma progression by activating the Wnt/ß-catenin pathway through inhibition of DKK3 transcription.

This study is to explore the mechanism of KDM1A-regulated hepatoblastoma (HB) development. Cancerous and paracancer tissues of 30 HB patients were collected for detection of KDM1A and DKK3 expression. HuH-6 and HepG2 cells were subjected to assays of cellular activities after treatment with sh-KDM1A, sh-DKK3, and/or XAV-939 (an inhibitor of the Wnt/ß-catenin pathway). Chromatin immunoprecipitation was used to determine the interaction of KDM1A with DKK3. Nude mice were injected with HuH-6 cells in which KDM1A was knocked down. KDM1A was highly expressed and DKK3 was lowly expressed in HB patients. Knockdown of KDM1A reduced the proliferative and invasive capabilities of HepG2 and HuH-6 cells and accelerated the cell apoptosis; these influences were nullified by knockdown of DKK3. KDM1A inhibited DKK3 transcription by reducing H3 methylation. XAV-939 treatment inhibited the development of HepG2 and HuH-6 cells in which KDM1A and DKK3 were both knocked down. Knockdown of KDM1A reduced the tumor mass, inactivated the Wnt/ß-catenin signaling, and increased the expression of DKK3 in nude mice. KDM1A stimulates HB development by activating the Wnt/ß-catenin pathway through inhibition of DKK3 transcription.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.

MOTIVATION: Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS: In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY: The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

Bibliometric and visualized analysis of exercise and osteoporosis from 2002 to 2021.

Background: Bibliometric analysis was designed to investigate a systematic understanding of developments in exercise and osteoporosis research over the past 20 years. Methods: Relevant publications from the Web of Science Core Collection were downloaded on April 26, 2022. CiteSpace, VOSviewer, and the online bibliometric analysis platform were used to conduct this scientometric study. Results: A total of 5518 publications were in 1202 academic journals with 137405 co-cited references in by 5637 institutions from 98 countries/regions. The country leading the research was the USA. The University of Melbourne was the most active institution. Osteoporosis International was the most productive journal concerning exercise and osteoporosis research. According to the burst references, "low-level vibration," "high-frequency" and "resistance exercise" have been recognized as the hotspots research in the domain. The keywords co-occurrence analysis identified "skeletal muscle," "sarcopenia" and "mesenchymal stem cell" as the important future research directions. Conclusion: This study was the first comprehensive metrological and statistical analysis of exercise and osteoporosis research over the past 20 years. Our findings would provide guidance to understand the research frontiers and hot directions in the near future.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.

Objectives: To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM). Methods: 175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test. Results: The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration. Conclusion: The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

Role of hepatic stellate cells in liver ischemia-reperfusion injury.

Liver ischemia-reperfusion injury (IRI) is a major complication of liver trauma, resection, and transplantation. IRI may lead to liver dysfunction and failure, but effective approach to address it is still lacking. To better understand the cellular and molecular mechanisms of liver IRI, functional roles of numerous cell types, including hepatocytes, Kupffer cells, neutrophils, and sinusoidal endothelial cells, have been intensively studied. In contrast, hepatic stellate cells (HSCs), which are well recognized by their essential functions in facilitating liver protection and repair, have gained less attention in their role in IRI. This review provides a comprehensive summary of the effects of HSCs on the injury stage of liver IRI and their associated molecular mechanisms. In addition, we discuss the regulation of liver repair and regeneration after IRI by HSCs. Finally, we highlight unanswered questions and future avenues of research regarding contributions of HSCs to IRI in the liver.

Neuropeptide Y and receptors are associated with the pyroptosis of nucleus pulposus in aging and degenerative intervertebral discs of rats.

The neuropeptide Y(NPY) mediates bone metabolism and the degradation of cartilage in the peripheral nervous system. However, its role in the intervertebral disc degeneration (IDD) is less clear and warrant further study. The process of IDD has always been accompanied by inflammatory response and pyroptosis of nucleus pulposus cells (NPCs). The aim of this study was to investigate the relationship between NPY, Y1R, Y2R and pyroptosis in aging and degenerative discs and the direct effect of NPY on NPCs. First, we have assessed NPY, Y1R, Y2R and the expression of pyroptosis related protein in the immature (6 weeks), mature (16 weeks), aged (54 weeks), and degenerated discs. As part of our studies, we also have evaluated pyroptotic changes in the NPCs, induced by exposure to NPY. Our results suggested that compared with natural aging discs, the degenerative discs showed the high expression of NPY, Y1R and Y2R. Correlation analysis showed that the level of NPY and Y1R in degenerative discs were positively correlated with GSDMD, whereas there was no significant correlation between Y2R and GSDMD. In vitro, NPY treatment stimulated the activation of caspase-1-dependent pyroptosis of NPCs. However, Y1R antagonist inhibited NPY-induced pyroptosis of NPCs. Western blot confirmed that Y1R antagonist decreased the level of cleaved.GSDMD and caspase-1 in NPCs. In conclusion, our results indicated that compared with natural aging discs, the degenerated discs showed the high expression of NPY, Y1R and Y2R. NPY-Y1R involve the IDD development by the regulation of pyroptosis in the NPCs. Regulating the function of NPY may be a promising strategy for IDD treatment.