circGRHPR inhibits aberrant epithelial-mesenchymal transformation progression of lung epithelial cells associated with idiopathic pulmonary fibrosis.

Air pollution has greatly increased the risk of idiopathic pulmonary fibrosis (IPF). Circular RNAs (circRNAs) have been found to play a significant role in the advancement of IPF, but there is limited evidence of correlation between circRNAs and lung epithelial cells (LECs) in IPF. This research aimed to explore the influence of circRNAs on the regulation of EMT progression in LECs, with the objective of elucidating its mechanism and establishing its association with IPF. Our results suggested that the downregulation of circGRHPR in peripheral blood of clinical cases was associated with the diagnosis of IPF. Meanwhile, we found that circGRHPR was downregulated in transforming growth factor-beta1 (TGF-ß1)-induced A549 and Beas-2b cells. It is a valid model to study the abnormal EMT progression of IPF-associated LECs in vitro. The overexpression of circGRHPR inhibited the abnormal EMT progression of TGF-ß1-induced LECs. Furthermore, as the sponge of miR-665, circGRHPR released the expression of E3 ubiquitin-protein ligase NEDD4-like (NEDD4L), thus promoting its downstream transforming growth factor beta receptor 2 (TGFBR2) ubiquitination. It is helpful to reduce the response of LECs to TGF-ß1 signaling. In summary, circGRHPR/miR-665/NEDD4L axis inhibited the abnormal EMT progression of TGF-ß1-induced LECs by promoting TGFBR2 ubiquitination, which provides new ideas and potential targets for the treatment of IPF.

A Computational Framework for Atrioventricular Valve Modeling Using Open-Source Software.

Atrioventricular valve regurgitation is a significant cause of morbidity and mortality in patients with acquired and congenital cardiac valve disease. Image-derived computational modeling of atrioventricular valves has advanced substantially over the last decade and holds particular promise to inform valve repair in small and heterogeneous populations, which are less likely to be optimized through empiric clinical application. While an abundance of computational biomechanics studies has investigated mitral and tricuspid valve disease in adults, few studies have investigated its application to vulnerable pediatric and congenital heart populations. Further, to date, investigators have primarily relied upon a series of commercial applications that are neither designed for image-derived modeling of cardiac valves nor freely available to facilitate transparent and reproducible valve science. To address this deficiency, we aimed to build an open-source computational framework for the image-derived biomechanical analysis of atrioventricular valves. In the present work, we integrated an open-source valve modeling platform, SlicerHeart, and an open-source biomechanics finite element modeling software, FEBio, to facilitate image-derived atrioventricular valve model creation and finite element analysis. We present a detailed verification and sensitivity analysis to demonstrate the fidelity of this modeling in application to three-dimensional echocardiography-derived pediatric mitral and tricuspid valve models. Our analyses achieved an excellent agreement with those reported in the literature. As such, this evolving computational framework offers a promising initial foundation for future development and investigation of valve mechanics, in particular collaborative efforts targeting the development of improved repairs for children with congenital heart disease.

Neutrophil PPIF exacerbates lung ischemia-reperfusion injury after lung transplantation by promoting calcium overload-induced neutrophil extracellular traps formation.

Lung ischemia-reperfusion (I/R) injury is the main risk factor for primary graft dysfunction and patient death after lung transplantation (LTx). It is widely accepted that the main pathological mechanism of lung I/R injury are calcium overload, oxygen free radical explosion and neutrophil-mediated damage, which leading to the lack of effective treatment options. The aim of this study was to further explore the mechanisms of lung I/R injury after LTx and to provide potential therapeutic strategies. Our bioinformatics analysis revealed that the neutrophil extracellular traps (NETs) formation was closely involved in lung I/R injury after LTx, which was accompanied by up-regulation of peptidylprolyl isomerase F (PPIF) and peptidyl arginine deiminase 4 (PADI4). We further established an orthotopic LTx mouse model to simulate lung I/R injury in vivo, and found that PPIF and PADI4 inhibitors effectively reduced neutrophil infiltration, NETs formation, inflammatory response, and lung I/R injury. In the neutrophil model induced by HL-60 cell line in vitro, we found that PPIF inhibitor cyclosporin A (Cys A) better alleviated calcium overload induced inflammatory response, reactive oxygen species content and NETs formation. Further study demonstrated that interfering with neutrophil PPIF protected mitochondrial function by alleviating store-operated calcium entry (SOCE) during calcium overload and played the above positive role. On this basis, we found that the reduction of calcium content in neutrophils was accompanied by the inhibition of calcineurin (CN) and nuclear factor of activated T cells (NFAT). In conclusion, our findings suggested that neutrophil PPIF could serve as a novel biomarker and potential therapeutic target of lung I/R injury after LTx, which provided new clues for its treatment by inhibiting calcium overload-induced NETs formation.

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in using data-driven models to learn full-field mechanical responses such as displacement and strain from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, we propose a physics-informed machine learning approach to identify the elasticity map in nonlinear, large deformation hyperelastic materials. We evaluate the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) by inferring the heterogeneous elasticity maps across three materials with structural complexity that closely resemble real tissue patterns, such as brain tissue and tricuspid valve tissue. We further applied our improved architecture to three additional examples of breast cancer tissue and extended our analysis to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Our selected network architecture consistently produced highly accurate estimations of heterogeneous elasticity maps, even when there was up to 10% noise present in the training data.

Elevated Activated Partial Thromboplastin Time as a Predictor of 28-Day Mortality in Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Analysis.

Purpose: To address the critical mortality rates among sepsis-associated acute kidney injury (SA-AKI) patients, early prognosis is vital. This study investigates the relationship between coagulation indices and the 28-day mortality rate in patients with SA-AKI. Patients and Methods: This study was a retrospective cohort analysis including patients with SA-AKI admitted to the First Hospital of Fujian Medical University as a training cohort (n = 119) and patients admitted to the Third People's Hospital of Fujian University of Traditional Chinese Medicine as a validation cohort (n = 51). We examined the relationship between coagulation indices and 28-day mortality in SA-AKI, the cumulative mortality at different activated partial thromboplastin time (APTT) levels, and the nonlinear relationship between APTT and 28-day mortality. Receiver operating characteristic curves were plotted, and the area under the curve was calculated to assess the predictive power of APTT. Finally, subgroup analyses were performed to assess the robustness of the association. Results: Overall, 119 participants with a mean±standard deviation age of 70.47±15.20 years were included in the training cohort: 54 died, 65 survived. According to univariate and multivariate COX regression analyses, APACHE II score, CRP level, Lac level, and APTT level were independent risk factors for 28-day adverse prognosis. After controlling for some variables, an elevated baseline APTT (≥ 37.7 s) was associated with an elevated risk of 28-day mortality (HR, 1.017; 95% CI, 1.001-1.032), and Kaplan-Meier analyses further confirmed the increased mortality in the group with a higher APTT. The same results were shown when the validation cohort was analyzed (HR, 1.024; 95% CI, 0.958-1.096). Subgroup analyses showed the stability of the association between APTT and poor prognosis in SA-AKI. Conclusion: In essence, APTT elevation is synonymous with increased 28-day mortality rates, indicating a poor prognosis in SA-AKI scenarios.

The neuroprotective effect of near infrared light therapy in aged mice with postoperative neurocognitive disorder by upregulating IRF7.

BACKGROUND: Postoperative neurocognitive disorder (PND) is a common central nervous system complication after undergoing surgery and anesthesia especially in elderly patients, while the therapeutic options are very limited. This study was carried out to investigate the beneficial effects of transcranial near infrared light (NIRL) which was employed to the treatment of PND and propose the involved mechanisms. METHODS: The PND mice were established through left carotid artery exposure under isoflurane anesthesia and received transcranial NIRL treatment. Behavioral testing was performed to evaluate the cognitive function of PND mice after transcranial NIRL therapy. Changes in the transcriptomic profiles of prefrontal cortex (PFC) and hippocampus (HP) were identified by next generation sequencing (NGS), and the molecular mechanisms involved were examined by both in vivo mouse model and in vitro cell culture studies. RESULTS: We found that transcranial NIRL therapy effectively ameliorated learning and memory deficit induced by anesthesia and surgery in aged mice. Specifically, we identified down-regulation of interferon regulatory factor 7 (IRF7) in the brains of PND mice that was mechanistically associated with increased pro-inflammatory M1 phenotype of microglia and elevated neuroinflammatory. NIRL treatment produced protective effects through the upregulation of IRF7 expression and reversing microglial phenotypes from pro-inflammatory to neuroprotective, resulting in reduced brain damage and improved cognitive function in PND mice. CONCLUSION: Our results indicate that transcranial NIRL is an effective and safe therapy for PND via alleviating neuroinflammation, and IRF7 plays a key transcription factor in regulating the M1-to-M2 switch of microglia.

The Effects of leaflet material properties on the simulated function of regurgitant mitral valves.

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated 1) the influence of tissue extensibility and 2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

The effects of leaflet material properties on the simulated function of regurgitant mitral valves.

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated (1) the influence of tissue extensibility and (2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

Transcranial Photobiomodulation Therapy Ameliorates Perioperative Neurocognitive Disorder Through Modulation of Mitochondrial Function in Aged Mice.

Perioperative neurocognitive disorder (PND) is a serious nervous system complication characterized by progressive cognitive impairment, especially in geriatric population. However, the neuropathogenesis of PND is complex, and there are no approved disease-modifying therapeutic options. Mitochondrial dysfunction has been demonstrated to contribute to the occurrence and development of PND. Transcranial near-infrared (tNIR) light treatment helps to improve mitochondrial dysfunction and enhance cognition, but its effect on PND remains unclear. Here, we evaluated the effect of tNIR light treatment on PND caused by anesthesia and surgery in aged mice. We built the PND models with 18-month C57BL/6 male mice by exploratory laparotomy under isoflurane inhalation anesthesia, and treated by tNIR light with wavelength 810 nm for 2 weeks. The short-term and long-term changes in cognitive function were analyzed by behavioral tests. We further explored the effects of tNIR light on mitochondria, synapses, neurons, and signaling pathways through different experimental methods. The results demonstrated that the cognitive impairment and mitochondrial dysfunction in PND mice were ameliorated after tNIR light treatment. Further experiments demonstrated that photobiomodulation therapy (PBMT) increased synapse-related protein expression, neuronal survival, and protected synapse from depletion. Moreover, downregulated sirtuin 1 (SIRT1) and peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) were increased after tNIR light treatment. Our results suggested that tNIR light was an effective treatment of PND through PBMT effect, accompanied by synaptic and neuronal improvement. The improvement of mitochondrial dysfunction mediated by SIRT1/PGC-1α signaling pathway might participate in this process. Those findings might provide a novel and noninvasive therapeutic target for PND.

Transcranial near-infrared laser improves postoperative neurocognitive disorder in aged mice via SIRT3/AMPK/Nrf2 pathway.

Background: Postoperative neurocognitive disorder (PND) is a common central nervous system (CNS) complication that might increase the morbidity and mortality of elderly patients after anesthesia/surgery. Neuroinflammation, oxidative stress, and synaptic dysfunction are closely related to cognitive dysfunction, an important clinical feature of PND. Transcranial near-infrared laser (TNIL) is regarded as an effective treatment for cognitive-related diseases by improving mitochondrial function and alleviating neuroinflammation and oxidative stress damage. Materials and methods: Aged male C57BL/6 mice underwent a carotid artery exposure procedure under isoflurane anesthesia. We treated PND-aged mice for three consecutive days (4 h post-operation, 1-laser) with 810 nm continuous wave (CW) laser 18 J/cm2 at 120 mW/cm2. The post-treatment evaluation included behavioral tests, RTq-PCR, immunofluorescence, and Western blot. Results: The results demonstrated that TNIL improved PND and the levels of synaptic function-associated proteins such as post-synaptic density protein 95 (PSD95), synaptophysin (SYP), and brain-derived neurotrophic factor (BDNF). Besides, neuroinflammatory cytokine levels of tumor necrosis factor (TNF)-α and interleukin (IL)-1ß as well as microglia activation and oxidative stress damage were attenuated after TNIL treatment in aged mice with PND. Further investigation suggested that TNIL relieved oxidative stress response by activating the SIRT3/AMPK/Nrf2 pathway. Conclusion: Transcranial near-infrared laser improved cognitive impairment in aged mice with PND, which may be a promising therapeutic for PND.

Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.

To identify the molecular mechanisms and novel therapeutic agents of late-onset Alzheimer's disease (AD), we performed integrative network analysis using multiple transcriptomic profiles of human brains. With the hypothesis that AD pathology involves the whole cerebrum, we first identified co-expressed modules across multiple cerebral regions of the aging human brain. Among them, two modules (M3 and M8) consisting of 1,429 protein-coding genes were significantly enriched with AD-correlated genes. Differential expression analysis of microarray, bulk RNA-sequencing (RNA-seq) data revealed the dysregulation of M3 and M8 across different cerebral regions in both normal aging and AD. The cell-type enrichment analysis and differential expression analysis at the single-cell resolution indicated the extensive neuronal vulnerability in AD pathogenesis. Transcriptomic-based drug screening from Connectivity Map proposed Gly-His-Lys acetate salt (GHK) as a potential drug candidate that could probably restore the dysregulated genes of the M3 and M8 network. Pretreatment with GHK showed a neuroprotective effect against amyloid-beta-induced injury in differentiated human neuron-like SH-SY5Y cells. Taken together, our findings uncover a dysregulated network disrupted across multiple cerebral regions in AD and propose pretreatment with GHK as a novel neuroprotective strategy against AD.

Mast cell stabilizer disodium cromoglycate improves long-term cognitive impairment after general anesthesia exposure in neonatal mice.

Background: Prolonged exposure to general anesthesia (GA) results in long-lasting cognitive impairment, especially during critical stages of brain development. An exaggerated neuroinflammation induced by anesthesia is generally considered to be a key cause of cognitive impairment. Materials and methods: Postnatal day 7 (PND 7) mice were exposed to GA by isoflurane inhalation for 6 h or mock anesthesia. Disodium cromoglycate (DSCG) was intraperitoneally injected daily for 2 weeks, beginning from 30 min before anesthesia. The post-anesthesia evaluation included behavioral tests, toluidine blue staining, immunofluorescence and western blot. Results: Our results demonstrated the long-term cognition were impaired after 6 h GA exposure in neonatal mice. DSCG treatment ameliorated early mast cells (MCs) degranulation and mast cell tryptase (MCT) expression, which helps to attenuate subsequent neuroinflammation, activation of microglia and astrocytes, and damage to oligodendrocytes and synapses to improve cognitive impairment. Conclusion: Disodium cromoglycate could effectively improve long-term cognitive impairment after GA exposure in neonatal mice.

Identification of the Potential Gene Regulatory Networks and Therapeutics in Aged Mice With Postoperative Neurocognitive Disorder.

Postoperative neurocognitive disorder (PND) is one of the most common postoperative neurological complications in aged patients, characterized by mental disorder, anxiety, personality changes, and impaired memory. At present, the molecular mechanism of PND remains largely unclear, and the ideal biomarker for clinical diagnosis and prognosis are lacking. Circular RNA (circRNA) and microRNA (miRNA), as unique non-coding RNAs, affecting the regulation of miRNAs on genes and further intervening in the progression of diseases through the sponge action between the two. Besides, it could be served as novel biomarkers in various diseases. In order to detect the differential expression profiles of genes caused by PND, a total of 26 18-month-old male C57BL/6 mice were randomly assigned to control group and PND group. Behavioral tests showed that mice in the PND group had impaired cognitive function compared with the control group. Three mice in each group were randomly selected to harvest the brain for analysis the expressions of circRNAs, miRNAs, and mRNAs in the prefrontal cortex by next-generation sequencing (NGS) technology. Differentially expressed genes, including 1192 circRNAs, 27 miRNAs, and 266 mRNAs were identified, and its accuracy was further confirmed by qRT-PCR. Bioinformatics analysis results suggested that neuroinflammation was the main pathological mechanism of PND. The construction of competitive endogenous RNA (ceRNA) networks and the identification of hub genes provided possible therapeutic targets for PND. Cinnarizine and Clemastine were predicted to have the potential therapeutic effects on PND. This is the first study to explore the differential expression profiles of genes and their regulation mechanisms in PND, our results provided new clues and targets for the treatment of this refractory disease.

Clemastine Ameliorates Perioperative Neurocognitive Disorder in Aged Mice Caused by Anesthesia and Surgery.

Perioperative neurocognitive disorder (PND) leads to progressive deterioration of cognitive function, especially in aged patients. Demyelination is closely associated with cognitive dysfunction. However, the relationship between PND and demyelination remains unclear. Here we showed that demyelination was related to the pathogenesis of PND. Clemastine, an antihistamine with potency in remyelination, was predicted to have a potential therapeutic effect on PND by next-generation sequencing and bioinformatics in our previous study. In the present study, it was given at 10 mg/kg per day for 2 weeks to evaluate the effects on PND in aged mice. We found that clemastine ameliorated PND and reduced the expression levels of inflammatory factors such as tumor necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1ß). Further investigation suggested clemastine increased the expression of oligodendrocyte transcription factor 2 (OLIG2) and myelin basic protein (MBP) to enhance remyelination by inhibiting the overactivation of the WNT/ß-catenin pathway. At the same time, the expression of post-synaptic density protein 95 (PSD95, or DLG4), brain-derived neurotrophic factor (BDNF), synaptosomal-associated protein 25 (SNAP25) and neuronal nuclei (NEUN) were also improved. Our results suggested that clemastine might be a therapy for PND caused by anesthetic and surgical factors in aged patients.

Focal adhesion kinase is overexpressed in thymic epithelial tumors and may serve as an independent prognostic biomarker.

Focal adhesion kinase (FAK) has long been considered to be a key regulator of growth factor receptor- and integrin-mediated signals, with pivotal roles in tumor cells through its kinase activity and scaffolding function. Increased FAK expression and activity has been observed in tumors of various origins and is often associated with a poor prognosis. However, there have been no studies on the aberrant expression of FAK in thymic epithelial tumors to date. The aim of the present study was to evaluate FAK expression in thymic epithelial tumors and to explore the prognostic significance of FAK. FAK expression was investigated in 100 formalin-fixed, paraffin-embedded human thymic epithelial tumor (TET) specimens using immunohistochemical analysis with FAK-specific monoclonal antibody 4.47, and the associations between FAK expression and clinicopathological parameters (including sex, age, tumor size, myasthenia gravis, World Health Organization classification and Masaoka-Koga stage) were analyzed. FAK was significantly overexpressed in TETs compared with in normal thymus tissues (P<0.001). Additionally, FAK overexpression was significantly associated with advanced tumor stages (stages III or IV; P<0.001) and highly aggressive TET subtypes (type B2 and B3 thymomas and thymic carcinomas; P<0.001). Furthermore, FAK overexpression was significantly associated with a worse 10-year overall survival, as determined by univariate analysis (P<0.001). Multivariate analysis revealed that FAK overexpression was an independent prognostic factor for patients with TETs (P=0.034). The results of the present study suggest that FAK serves an important role in the tumorigenesis and progression of TETs. Therefore, FAK may serve as a prognostic biomarker and is a potential therapeutic target for the treatment of TETs.