Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Achievements, challenges, and perspectives in the design of polymer binders for advanced lithium-ion batteries.

Energy storage devices with high power and energy density are in demand owing to the rapidly growing population, and lithium-ion batteries (LIBs) are promising rechargeable energy storage devices. However, there are many issues associated with the development of electrode materials with a high theoretical capacity, which need to be addressed before their commercialization. Extensive research has focused on the modification and structural design of electrode materials, which are usually expensive and sophisticated. Besides, polymer binders are pivotal components for maintaining the structural integrity and stability of electrodes in LIBs. Polyvinylidene difluoride (PVDF) is a commercial binder with superior electrochemical stability, but its poor adhesion, insufficient mechanical properties, and low electronic and ionic conductivity hinder its wide application as a high-capacity electrode material. In this review, we highlight the recent progress in developing different polymeric materials (based on natural polymers and synthetic non-conductive and electronically conductive polymers) as binders for the anodes and cathodes in LIBs. The influence of the mechanical, adhesion, and self-healing properties as well as electronic and ionic conductivity of polymers on the capacity, capacity retention, rate performance and cycling life of batteries is discussed. Firstly, we analyze the failure mechanisms of binders based on the operation principle of lithium-ion batteries, introducing two models of "interface failure" and "degradation failure". More importantly, we propose several binder parameters applicable to most lithium-ion batteries and systematically consider and summarize the relationships between the chemical structure and properties of the binder at the molecular level. Subsequently, we select silicon and sulfur active electrode materials as examples to discuss the design principles of the binder from a molecular structure point of view. Finally, we present our perspectives on the development directions of binders for next-generation high-energy-density lithium-ion batteries. We hope that this review will guide researchers in the further design of novel efficient binders for lithium-ion batteries at the molecular level, especially for high energy density electrode materials.

Hypothalamic Corticotropin-Releasing Hormone Contributes to Hypertension in Spontaneously Hypertensive Rats.

Corticotropin-releasing hormone (CRH) is a neuropeptide regulating neuroendocrine and autonomic function. CRH mRNA and protein levels in the hypothalamic paraventricular nucleus (PVN) are increased in primary hypertension. However, the role of CRH in elevated sympathetic outflow in primary hypertension remains unclear. CRHR1 proteins were distributed in retrogradely labeled PVN presympathetic neurons with an increased level in the PVN tissue in adult spontaneously hypertensive rats (SHRs) compared with age-matched male Wistar-Kyoto (WKY) rats. CRH induced a more significant increase in the firing rate of PVN-rostral ventrolateral medulla (RVLM) neurons and sympathoexcitatory response in SHRs than in WKY rats, an effect that was blocked by preapplication of NMDA receptors (NMDARs) antagonist AP5 and PSD-95 inhibitor, Tat-N-dimer. Blocking CRHRs with astressin or CRHR1 with NBI35965 significantly decreased the firing rate of PVN-RVLM output neurons and reduced arterial blood pressure (ABP) and renal sympathetic nerve activity (RSNA) in SHRs but not in WKY, whereas blocking CRHR2 with antisauvagine-30 did not. Furthermore, Immunocytochemistry staining revealed that CRHR1 colocalized with NMDARs in PVN presympathetic neurons. Blocking CRHRs significantly decreased the NMDA currents in labeled PVN neurons. PSD-95-bound CRHR1 and PSD-95-bound GluN2A in the PVN were increased in SHRs. These data suggested that the upregulation of CRHR1 in the PVN is critically involved in the hyperactivity of PVN presympathetic neurons and elevated sympathetic outflow in primary hypertension.SIGNIFICANCE STATEMENT Our study found that corticotropin-releasing hormone receptor (CRHR)1 protein levels were increased in the paraventricular nucleus (PVN), and CRHR1 interacts with NMDA receptors (NMDARs) through postsynaptic density protein (PSD)-95 in the PVN neurons in primary hypertension. The increased CRHR1 and CRHR1-NMDAR-PSD-95 complex in the PVN contribute to the hyperactivity of the PVN presympathetic neurons and elevated sympathetic vasomotor tone in hypertension in SHRs. Thus, the antagonism of CRHR1 decreases sympathetic outflow and blood pressure in hypertension. These findings determine a novel role of CRHR1 in elevated sympathetic vasomotor tone in hypertension, which is useful for developing novel therapeutics targeting CRHR1 to treat elevated sympathetic outflow in primary hypertension. The CRHR1 receptor antagonists, which are used to treat health consequences resulting from chronic stress, are candidates to treat primary hypertension.

Astrocyte-TREM2 alleviates brain injury by regulating reactive astrocyte states following ischemic stroke.

Triggering receptor expressed on myeloid cells 2 (TREM2) has been shown to confer strong neuroprotective effects in acute ischemic stroke (AIS). However, as the vast majority of research findings to date are based on its functions in microglia, the precise role of TREM2 in astrocytes after AIS is unknown. Here, both loss- and gain-of-function experiments were employed to investigate how astrocytic TREM2 influences the pathogenesis of AIS in vivo and in vitro. Our results demonstrated that cerebral ischemia triggered induction of TREM2 expression on reactive astrocytes following AIS. In addition, astrocyte-specific TREM2 knockout mice exhibited much greater brain injury than TREM2 flox/flox controls following AIS, as evidenced by increased cerebral infarct volume, neuronal apoptosis and neurological deficit, which was associated with an increased expression of pro-inflammatory molecule complement component 3 (C3) on reactive astrocytes and activation of microglia/macrophages but decreased expression of S100 calcium binding protein A10 (S100A10) and arginase1 (Arg1) on reactive astrocytes. Mechanistic analyses revealed that astrocytic TREM2 alleviated brain injury by inhibiting detrimental actions of reactive astrocytes but promoting their neuro- and glioprotective actions via the kruppel-like transcription factor-4-nuclear factor-κB axis. Together, this study provides novel evidence for a critical protective role of astrocyte-derived TREM2 in AIS and highlights a potential therapeutic target for the treatment of AIS.

Queen bee gut microbiota extends honeybee lifespan by inhibiting insulin signaling.

Queen and worker bees are natural models for aging research, as their lifespans vary considerably independent of genetic variation. Investigating the reasons why queens live longer than workers is of great significance for research on the universal processes of aging in animals. The gut microbiome has received attention as a vital regulator of host health, while its precise role in honeybee aging needs further investigation. The effects and mechanisms behind the relationship between gut microbiota and worker lifespan were measured by transplanting queen bee gut bacteria (QG) and worker bee gut bacteria (WG) into microbiota-free (MF) workers. The transplantation of QG to MF bees significantly extended the workers' lifespans compared with MF and WG bees. Untargeted metabolomics identified 49 lifespan-related differential metabolites, and Kyoto Encyclopedia of Genes and Genomes analysis of these revealed three lifespan-related metabolic pathways: insulin/insulin-like growth factor signaling, immune, and ketone body metabolism pathways. Further verification showed that QG inhibited the expression of insulin-like peptides (ILPs), and the expression of ILPs was lower in natural queens than in natural workers. QG transplantation also stimulated the expression of antioxidant genes and lowered oxidative damage products in natural queen bees. However, gut microbiota transplantation failed to mimic the immune properties and ketone body metabolism profiles of natural queens and workers. Concisely, QG could increase the antioxidant capacity to extend lifespan by inhibiting insulin signaling. These findings may help determine the mechanisms behind queen longevity and provide further insights into the role of gut symbionts. IMPORTANCE: Queen and worker bees share the same genetic background but have vastly different lifespans. The gut microbiome regulates host health, suggesting that differences in lifespan between queen and worker bees could be related to gut bacteria. Herein, we used an innovative method to transplant gut microbiota from adult queen or worker bees to microbiota-free bees. The transplantation of queen gut microbiota to microbiota-free bees extended their lifespan. Insulin/insulin-like growth factor signaling, a highly conserved metabolic pathway related to lifespan, displayed identical expression profiles in natural queen bees and microbiota-free bees transplanted with queen microbiota. This finding significantly expands our understanding of the relationships between intestinal bacteria, host health, and the biology of aging.

4 mC site recognition algorithm based on pruned pre-trained DNABert-Pruning model and fused artificial feature encoding.

DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.

Repair of Limb Ischemia Is Dependent on Hematopoietic Stem Cell Specific-SHP-1 Regulation of TGF-ß1.

BACKGROUND: Hematopoietic stem cell (HSC) therapy has shown promise for tissue regeneration after ischemia. Therefore, there is a need to understand mechanisms underlying endogenous HSCs activation in response to ischemic stress and coordination of angiogenesis and repair. SHP-1 plays important roles in HSC quiescence and differentiation by regulation of TGF-ß1 signaling. TGF-ß1 promotes angiogenesis by stimulating stem cells to secrete growth factors to initiate the formation of blood vessels and later aid in their maturation. We propose that SHP-1 responds to ischemia stress in HSC and progenitor cells (HSPC) via regulation of TGF-ß1. METHODS: A mouse hind limb ischemia model was used. Local blood perfusion in the limbs was determined using laser doppler perfusion imaging. The number of positive blood vessels per square millimeter, as well as blood vessel diameter (µm) and area (µm2), were calculated. Hematopoietic cells were analyzed using flow cytometry. The bone marrow transplantation assay was performed to measure HSC reconstitution. RESULTS: After femoral artery ligation, TGF-ß1 was initially decreased in the bone marrow by day 3 of ischemia, followed by an increase on day 7. This pattern was opposite to that in the peripheral blood, which is concordant with the response of HSC to ischemic stress. In contrast, SHP-1 deficiency in HSC is associated with irreversible activation of HSPCs in the bone marrow and increased circulating HSPCs in peripheral blood following limb ischemia. In addition, there was augmented auto-induction of TGF-ß1 and sustained inactivation of SHP-1-Smad2 signaling, which impacted TGF-ß1 expression in HSPCs in circulation. Importantly, restoration of normal T GF-ß1 oscillations helped in the recovery of limb repair and function. CONCLUSIONS: HSPC-SHP-1-mediated regulation of TGF-ß1 in both bone marrow and peripheral blood is required for a normal response to ischemic stress.

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

Association of dietary selenium intake and all-cause mortality of Parkinson's disease and its interaction with blood cadmium level: a retrospective cohort study.

BACKGROUND: Parkinson's disease (PD) is a slowly progressive neurodegenerating disease that may eventually lead to disabling condition and pose a threat to the health of aging populations. This study aimed to explore the association of two potential risk factors, selenium and cadmium, with the prognosis of Parkinson's disease as well as their interaction effect. METHODS: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2006 to 2015-2016 and National Death Index (NDI). Participants were classified as Parkinson's patients by self-reported anti-Parkinson medications usage. Cox regression models and restricted cubic spline models were applied to evaluate the association between PD mortality and selenium intake level as well as blood cadmium level. Subgroup analysis was also conducted to explore the interaction between them. RESULTS: A total of 184 individuals were included. In full adjusted cox regression model (adjusted for age, gender, race, hypertension, pesticide exposure, smoking status and caffeine intake), compared with participants with low selenium intake, those with normal selenium intake level were significantly associated with less risk of death (95%CI: 0.18-0.76, P = 0.005) while no significant association was found between low selenium intake group and high selenium group (95%CI: 0.16-1.20, P = 0.112). Restricted cubic spline model indicated a nonlinear relationship between selenium intake and PD mortality (P for nonlinearity = 0.050). The association between PD mortality and blood cadmium level was not significant (95%CI: 0.19-5.57, P = 0.112). However, the interaction term of selenium intake and blood cadmium showed significance in the cox model (P for interaction = 0.048). Subgroup analysis showed that the significant protective effect of selenium intake existed in populations with high blood cadmium but not in populations with low blood cadmium. CONCLUSION: Moderate increase of selenium intake had a protective effect on PD mortality especially in high blood cadmium populations.

Boosting the Activation of Molecular Oxygen and the Degradation of Rhodamine B in Polar-Functional-Group-Modified g-C3N4.

Molecular oxygen activation often suffers from high energy consumption and low efficiency. Developing eco-friendly and effective photocatalysts remains a key challenge for advancing green molecular oxygen activation. Herein, graphitic carbon nitride (g-C3N4) with abundant hydroxyl groups (HCN) was synthesized to investigate the relationship between these polar groups and molecular oxygen activation. The advantage of the hydroxyl group modification of g-C3N4 included narrower interlayer distances, a larger specific surface area and improved hydrophilicity. Various photoelectronic measurements revealed that the introduced hydroxyl groups reduced the charge transfer resistance of HCN, resulting in accelerated charge separation and migration kinetics. Therefore, the optimal HCN-90 showed the highest activity for Rhodamine B photodegradation with a reaction time of 30 min and an apparent rate constant of 0.125 min-1, surpassing most other g-C3N4 composites. This enhanced activity was attributed to the adjusted band structure achieved through polar functional group modification. The modification of polar functional groups could alter the energy band structure of photocatalysts, narrow band gap, enhance visible-light absorption, and improve photogenerated carrier separation efficiency. This work highlights the significant potential of polar functional groups in tuning the structure of g-C3N4 to enhance efficient molecular oxygen activation.