Knockdown of HSPA13 Inhibits TGFß1-Induced Epithelial-Mesenchymal Transition of RPE by Suppressing the PI3K/Akt Signaling Pathway.

Purpose: This study aimed to explore the impact of HSPA13 on epithelial-mesenchymal transition (EMT) in retinal pigment epithelial (RPE) cells and proliferative vitreoretinopathy (PVR) development, along with its associated molecular mechanisms. Methods: HSPA13 expression was evaluated in epiretinal membranes (ERMs) from patients with PVR using immunohistochemistry. The effects of HSPA13 knockdown on TGFß1-induced EMT in hESC-RPE cells were studied through quantitative PCR (qPCR), Western blot, and wound healing assays. Intracellular Ca2+ levels were measured using Fluo-8/AM incubation. A rat PVR model was induced by the intravitreal injection of RPE cells combined with platelet-rich plasma (PRP). RNA-seq was applied to study the molecular mechanism of HSPA13 knockdown-mediated EMT inhibition. Results: HSPA13 was found in human ERMs and its expression increased with TGFß1 treatment in hESC-RPE cells. Knockdown of HSPA13 inhibited TGFß1-induced EMT and migration. In the PVR rat model, HSPA13 was expressed in the ERMs and its knockdown in RPE cells reduced the development of PVR. Consistent with these observations, RNA-seq showed a global suppression of TGFß1-induced EMT and migration by shHSPA13 in RPE cells. Mechanistically, TGFß1 treatment increased intracellular Ca2+ levels, leading to an upregulation of HSPA13 expression. Downregulation of HSPA13 hindered the phosphorylation of PI3K/Akt in TGFß1-induced RPE cells. Conclusions: Our study revealed the involvement of HSPA13 in PVR development, as well as in TGFß1-induced EMT of RPE through the PI3K/Akt signaling pathway. Targeting HSPA13-related pathways involved in regulating EMT in RPE cells could serve as a novel therapeutic approach for patients with PVR.

Data-Driven Iterative Learning Model Predictive Control With Self-Modified Prior Knowledge.

Iterative learning model predictive control (ILMPC) has become an excellent data-driven intelligent control strategy for digitized batch manufacturing, featured by the progressive improvement of tracking performance along trials, and the persistent rejection of stochastic disturbance along time. The point-to-point learning mechanism of existing ILMPC generally relies on identical operating conditions along trials to guarantee the integrity and accuracy of historical data. However, the variations of production requirements usually lead to trial-varying operating references and durations, resulting in incomplete and inaccurate historical information for the iterative learning of subsequent trials. To promote the adaptability and flexibility of ILMPCs with unconformable prior information, a data-driven self-modification scheme is originally embedded into ILMPC in this article to transfer the prior knowledge contained in the historical operating data into the form consistent with the condition of each current trial. The control actions are imitated along trials by an adaptive deep neural network (DNN), which is then utilized to generate reference control signals for iterative learning in each trial. For attenuating the influence of the considerable DNN approximation error in early trials with limited data accumulation, the 2-D optimization of ILMPC is performed under a tube control frame to ensure the time-domain bounded stability. Based on the intrinsic recursive feasibility and the guaranteed time-domain stability, the iteration-domain bounded convergence of the developed ILMPC system is theoretically validated. Simulations on the nonlinear injection molding process verify the superiority of the proposed method in adapting to significant changes in operating reference and duration.

Fighting fire with fire: remodeling Aß aggregation with H-aggregates of a europium(III) complex.

We herein report a "Fight Aggregation with Aggregation" (FAA) approach for redirection of amyloid-ß peptide (Aß) aggregation using a europium(III) complex (EuL3) that can undergo H-aggregation in aqueous solution under physiological conditions. The H-aggregates of EuL3 may serve as scaffolds that can facilitate the accumulation of Aß to form non-fibrillar co-assemblies. As a result, the Aß aggregation-induced cytotoxicity was inhibited.

SUMOylation of GMFB regulates its stability and function in retinal pigment epithelial cells under hyperglycemia.

BACKGROUND: Glia maturation factor beta (GMFB) is a growth and differentiation factor that acts as an intracellular regulator of signal transduction pathways. The small ubiquitin-related modifier (SUMO) modification, SUMOylation, is a posttranslational modification (PTM) that plays a key role in protein subcellular localization, stability, transcription, and enzymatic activity. Recent studies have highlighted the importance of SUMOylation in the inflammation and progression of numerous diseases. However, the relationship between GMFB and SUMOylation is unclear. RESULTS: Here, we report for the first time that GMFB and SUMO1 are markedly increased in retinal pigment epithelial (RPE) cells at the early stage of diabetes mellitus (DM) under hyperglycemia. The GMFΒ protein could be mono-SUMOylated by SUMO1 at the K20, K35, K58 or K97 sites. SUMOylation of GMFB led to its increased protein stability and subcellular translocation. Furthermore, deSUMOylation of GMFΒ downregulates multiple signaling pathways, including the Jak-STAT signaling pathway, p38 pathway and NF-kappa B signaling pathway. CONCLUSIONS: This work provides novel insight into the role of SUMOylated GMFB in RPE cells and provides a novel therapeutic target for diabetic retinopathy (DR).

The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance.

Background: To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs. Methods: Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells. Results: Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells. Conclusion: This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.

A comprehensive stem cell laboratory module with blended learning for medical students at Tongji University.

The laboratory practice "Primary culture and directional differentiation of rat bone marrow mesenchymal stem cells (BMSCs)" is part of a required course for sophomore medical students at Tongji university, which has been conducted since 2012. Blended learning has been widely applied in medical courses. Based on a student-centered teaching philosophy, we reconstructed a comprehensive stem cell laboratory module with blended learning in 2021, aiming to facilitate students in enhancing their understanding of the multi-lineage differentiation potential of stem cells and improve their experimental skills, self-directed learning ability, and innovative thinking. First, we constructed in-depth online study resources, including videos demonstrating laboratory procedures, a PowerPoint slide deck, and published literature on student self-learning before class. In class, students performed a primary culture of BMSCs, freely chose among adipogenic, osteogenic, or chondrogenic differentiation, and used cytochemical or immunofluorescence staining for identification. After class, the extracurricular part involved performing quantitative polymerase chain reaction to examine the expression of multi-lineage differentiation marker genes, which was designed as an elective. After 2 years of practice, positive feedback was obtained from both students and faculty members who achieved, the learning goal as expected. The reconstructed stem cell laboratory module provides comprehensive practice opportunities for students. Students have a better understanding of BMSC at the molecular, cellular, and functional levels and have improved their experimental skills, which forms a basis for scientific research for medical students. Introducing blended learning into other medical laboratory practices thus seems valuable.

A terbium(III) complex-based time-resolved luminescent probe for selenocysteine as an inhibitor of selenoproteins.

A terbium(III) complex-based time-resolved luminescence probe for selenocysteine can inhibit selenoprotein activity via a selenolate-triggered cleavage reaction of sulfonamide bonds in living cells.

GMFB/AKT/TGF-ß3 in Müller cells mediated early retinal degeneration in a streptozotocin-induced rat diabetes model.

Retinal degeneration, characterized by Müller cell gliosis and photoreceptor apoptosis, is considered an early event in diabetic retinopathy (DR). Our previous study proposed that GMFB may mediate diabetic retinal degeneration. This study identified GMFB as a sensitive and functional gliosis marker for DR. Compared to the wild type (WT) group, Gmfb knockout (KO) significantly improved visual function, attenuated gliosis, reduced the apoptosis of neurons, and decreased the mRNA levels of tumor necrosis factor α (Tnf-α) and interleukin-1ß (Il-1ß) in diabetic retinas. Tgf-ß3 was enriched by hub genes using RNA sequencing in primary WT and KO Müller cells. Gmfb KO significantly upregulated the transforming growth factor (TGF)-ß3 protein level via the AKT pathway. The protective effect of TGF-ß3 in the vitreous resulted in significantly improved visual function and decreased the number of apoptotic cells in the diabetic retina. The protection of Gmfb KO in primary Müller cells against high glucose (HG)-induced photoreceptor apoptosis was partially counteracted by TGF-ß3 antibody and administration of TGFBR1/2 inhibitors. Nuclear receptor subfamily 3 group C member 1 (NR3C1) binds to the promoter region of Gmfb and regulates Gmfb mRNA at the transcriptional level. NR3C1 was increased in the retinas of early diabetic rats but decreased in the retinas of late diabetic rats. N'-[(1E)-(3-Methoxyphenyl)Methylene]-3-Methyl-1H-Pyrazole-5-Carbohydrazide (DS-5) was identified as an inhibitor of GMFB, having a protective role in DR. We demonstrated that GMFB/AKT/TGF-ß3 mediated early diabetic retinal degeneration in diabetic rats. This study provides a novel therapeutic strategy for treating retinal degeneration in patients with DR.

Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Meta-Learning With Distributional Similarity Preference for Few-Shot Fault Diagnosis Under Varying Working Conditions.

Few-shot fault diagnosis is a challenging problem for complex engineering systems due to the shortage of enough annotated failure samples. This problem is increased by varying working conditions that are commonly encountered in real-world systems. Meta-learning is a promising strategy to solve this point, open issues remain unresolved in practical applications, such as domain adaptation, domain generalization, etc. This article attempts to improve domain adaptation and generalization by focusing on the distribution-shift robustness of meta-learning from the task generation perspective. In fact, few-shot fault diagnosis under varying working conditions allows to address the distribution shift problem in a natural way. An unsupervised across-tasks meta-learning strategy with distributional similarity preference is proposed, where the core is the distribution-distance-weighting mechanism. Differently from the naive random meta-train task generation strategy used in existing meta-learning methods, the source instances that present a more similar distribution with respect to the target instances gain larger weightings in the task generation. This strategy leads to a meta-task training set that is enough diverse, and at the same time can be easily learned due to the distribution similarity features of the source tasks. The proposed method introduces the concept of maximum mean discrepancy that is applied to derive the distribution distance of the measurements. Moreover, a model-agnostic meta-learning is applied to realize few-shot fault diagnosis under varying working conditions. The proposed solutions are verified and compared by considering two public datasets used for bearing fault diagnosis. The results show that the proposed strategy outperforms different related few-shot fault diagnosis methods under varying working conditions. Moreover, it is thus proved that, meta-learning with distribution similarity feature represents an effective approach for domain adaptation and generalization.

Recent progress of small-molecule-based theranostic agents in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia. As a multifactorial disease, AD involves several etiopathogenic mechanisms, in which multiple pathological factors are interconnected with each other. This complicated and unclear pathogenesis makes AD lack effective diagnosis and treatment. Theranostics, exerting the synergistic effect of diagnostic and therapeutic functions, would provide a promising strategy for exploring AD pathogenesis and developing drugs for combating AD. With the efforts in small drug-like molecules for both diagnosis and treatment of AD, small-molecule-based theranostic agents have attracted significant attention owing to their facile synthesis, high biocompatibility and reproducibility, and easy clearance from the body through the excretion systems. In this review, the small-molecule-based theranostic agents reported in the literature for anti-AD are classified into four groups according to their diagnostic modalities. Their design rationales, chemical structures, and working mechanisms for theranostics are summarized. Finally, the opportunities for small-molecule-based theranostic agents in AD are also proposed.

Subconjunctival injection of human umbilical cord mesenchymal stem cells alleviates experimental allergic conjunctivitis via regulating T cell response.

BACKGROUND: T helper 2 (Th2) cells are thought to play critical roles in allergic conjunctivitis (AC). They release inflammatory cytokines to promote an allergic response in AC. Due to individual heterogeneity and long-term chronic management, current therapies do not always effectively control AC. Mesenchymal stem cells (MSCs) have been shown to be effective in treating allergy-related disorders, but it is unclear how exactly the Th2-mediated allergic response is attenuated. This study aims to elucidate the therapeutic effect and mechanism of the human umbilical cord MSCs (hUCMSCs) in a mouse model of experimental AC (EAC). METHODS: A mouse EAC model was established by inoculating short ragweed (SRW) pollen. After the SRW pollen challenge, the mice received a single subconjunctival or tail vein injection of 2 × 106 hUCMSCs, or subconjunctival injection of hUCMSCs conditioned medium (hUCMSC-CM), and dexamethasone eye drops was used as positive control; subsequent scratching behavior and clinical symptoms were assessed. Immunostaining and flow cytometry were carried out to show allergic reactions and the activation of CD4 + T cell subsets in the conjunctiva and cervical lymph nodes (CLNs). Gene expression was determined by RNA-seq and further verified by qRT-PCR and Western blot. Co-culture assays were performed to explore the regulatory role of hUCMSCs in the differentiation of CD4 + naive T cells (Th0) into Th2 cells. RESULTS: Subconjunctival administration of hUCMSCs resulted in fewer instances of scratching and lower inflammation scores in EAC mice compared to the tail vein delivery, hUCMSC-CM and control groups. Subconjunctival administration of hUCMSCs reduced the number of activated mast cells and infiltrated eosinophils in the conjunctiva, as well as decreased the number of Th2 cells in CLNs. After pretreatment with EAC mouse serum in vitro to mimic the in vivo milieu, hUCMSCs were able to inhibit the differentiation of Th0 into Th2 cells. Further evidence demonstrated that repression of Th2 cell differentiation by hUCMSCs is mediated by CRISPLD2 through downregulation of STAT6 phosphorylation. Additionally, hUMCSCs were able to promote the differentiation of Th0 cells into regulatory T cells in CLNs of EAC mice. CONCLUSIONS: Subconjunctival injection of hUCMSCs suppressed the Th2-allergic response and alleviated clinical symptoms. This study provides not only a potential therapeutic target for the treatment of AC but also other T cell-mediated diseases.

Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate.

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.

Recent developments in the chemical biology of amyloid-ß oligomer targeting.

Aggregation of amyloid-ß (Aß) peptides is characteristic of Alzheimer's disease (AD), which is the most common neurodegenerative disorder. Increasing evidence shows that Aß oligomers, the intermediates during aggregation, rather than the fully mature fibrils are the most toxic species of Aß and the key contributors to neurodegeneration. Aß oligomers have been considered as both biomarkers and drug targets for the diagnosis and treatment of AD. However, the high heterogeneity and metastability of oligomers make it difficult to determine their exact pathogenic mechanisms. Recent developments in Aß oligomer-targeting agents and techniques have provided great opportunities for overcoming the existing limitations. This review introduces the formation, structure, and toxicity of Aß oligomers and categorizes the Aß oligomer-targeting agents based on their chemical biological applications, including recognition and detection of Aß oligomers for diagnosis, intervention of Aß oligomerization for treatment, and stabilization of Aß oligomers for pathogenic studies. The design strategies and working mechanisms of the representative examples published in the past five years are highlighted. Finally, future development directions and challenges of Aß oligomer targeting are tentatively proposed.

Event-Based Switching Iterative Learning Model Predictive Control for Batch Processes With Randomly Varying Trial Lengths.

Iterative learning model predictive control (ILMPC) has been recognized as an excellent batch process control strategy for progressively improving tracking performance along trials. However, as a typical learning-based control method, ILMPC generally requires the strict identity of trial lengths to implement 2-D receding horizon optimization. The randomly varying trial lengths extensively existing in practice can result in the insufficiency of learning prior information, and even the suspension of control update. Regarding this issue, this article embeds a novel prediction-based modification mechanism into ILMPC, to adjust the process data of each trial into the same length by compensating the data of absent running periods with the predictive sequences at the end point. Under this modification scheme, it is proved that the convergence of the classical ILMPC is guaranteed by an inequality condition relative with the probability distribution of trial lengths. Considering the practical batch process with complex nonlinearity, a 2-D neural-network predictive model with parameter adaptability along trials is established to generate highly matched compensation data for the prediction-based modification. To best utilize the real process information of multiple past trials while guaranteeing the learning priority of the latest trials, an event-based switching learning structure is proposed in ILMPC to determine different learning orders according to the probability event with respect to the trial length variation direction. The convergence of the nonlinear event-based switching ILMPC system is analyzed theoretically under two situations divided by the switching condition. The simulations on a numerical example and the injection molding process verify the superiority of the proposed control methods.

Glia maturation factor beta deficiency protects against diabetic osteoporosis by suppressing osteoclast hyperactivity.

Excessive osteoclast activation, which depends on dramatic changes in actin dynamics, causes osteoporosis (OP). The molecular mechanism of osteoclast activation in OP related to type 1 diabetes (T1D) remains unclear. Glia maturation factor beta (GMFB) is considered a growth and differentiation factor for both glia and neurons. Here, we demonstrated that Gmfb deficiency effectively ameliorated the phenotype of T1D-OP in rats by inhibiting osteoclast hyperactivity. In vitro assays showed that GMFB participated in osteoclast activation rather than proliferation. Gmfb deficiency did not affect osteoclast sealing zone (SZ) formation but effectively decreased the SZ area by decreasing actin depolymerization. When GMFB was overexpressed in Gmfb-deficient osteoclasts, the size of the SZ area was enlarged in a dose-dependent manner. Moreover, decreased actin depolymerization led to a decrease in nuclear G-actin, which activated MKL1/SRF-dependent gene transcription. We found that pro-osteoclastogenic factors (Mmp9 and Mmp14) were downregulated, while anti-osteoclastogenic factors (Cftr and Fhl2) were upregulated in Gmfb KO osteoclasts. A GMFB inhibitor, DS-30, targeting the binding site of GMFB and Arp2/3, was obtained. Biocore analysis revealed a high affinity between DS-30 and GMFB in a dose-dependent manner. As expected, DS-30 strongly suppressed osteoclast hyperactivity in vivo and in vitro. In conclusion, our work identified a new therapeutic strategy for T1D-OP treatment. The discovery of GMFB inhibitors will contribute to translational research on T1D-OP.

Conic Input Mapping Design of Constrained Optimal Iterative Learning Controller for Uncertain Systems.

In this article, we study the optimal iterative learning control (ILC) for constrained systems with bounded uncertainties via a novel conic input mapping (CIM) design methodology. Due to the limited understanding of the process of interest, modeling uncertainties are generally inevitable, significantly reducing the convergence rate of the control systems. However, huge amounts of measured process data interacting with model uncertainties can easily be collected. Incorporating these data into the optimal controller design could unlock new opportunities to reduce the error of the current trail optimization. Based on several existing optimal ILC methods, we incorporate the online process data into the optimal and robust optimal ILC design, respectively. Our methodology, called CIM, utilizes the process data for the first time by applying the convex cone theory and maps the data into the design of control inputs. CIM-based optimal ILC and robust optimal ILC methods are developed for uncertain systems to achieve better control performance and a faster convergence rate. Next, rigorous theoretical analyses for the two methods have been presented, respectively. Finally, two illustrative numerical examples are provided to validate our methods with improved performance.

Model Fusion and Multiscale Feature Learning for Fault Diagnosis of Industrial Processes.

The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order to solve the above problems, this article proposes an end-to-end multiscale feature learning method based on model fusion, which can simultaneously extract multiscale spatial features and temporal features of data, effectively reducing the loss of feature information. First, this article combines the convolutional neural network (CNN) with residual learning and designs a multiscale residual network (MRCNN) to extract high-dimensional nonlinear spatial features of different scales in the data. Then, the extracted features are input into the long and short-term memory (LSTM) network to further extract the temporal features of the data. After the fully connected layer, it is input into the classifier for final fault classification. The residual learning in MRCNN can effectively avoid the problem of model degradation and improve the training efficiency of the model. Through the fusion of MRCNN and LSTM, we can significantly improve the feature extraction ability of the model, thereby greatly improving the diagnosis effect. In the final case experiment, the method improved the comprehensive diagnostic accuracy of the Tennessee-Eastman (TE) process and industrial coking furnace datasets to 94.43% and 97.80%, respectively, which was significantly better than the existing deep learning model and proves the effectiveness and superiority of this method.

Rapamycin ameliorates brain damage and maintains mitochondrial dynamic balance in diabetic rats subjected to middle cerebral artery occlusion.

To investigate the effect of rapamycin on mitochondrial dynamic balance in diabetic rats subjected to cerebral ischemia-reperfusion injury. Male Sprague Dawley (SD) rats (n = 78) were treated with high fat diet combined with streptozotocin injection to construct diabetic model in rats. Transient middle cerebral artery occlusion (MCAO) of 2 hours was induced and the brains were harvested after 1 and 3 days of reperfusion. Rapamycin was injected intraperitoneally for 3 days prior to and immediately after operation, once a day. The neurological function was assessed, infarct volumes were measured and HE staining as well as immunohistochemistry were performed. The protein of hippocampus was extracted and Western blotting were performed to detect the levels of mTOR, mitochondrial dynamin related proteins (DRP1, p-DRP1, OPA1), SIRT3, and Nix/BNIP3L. Diabetic hyperglycemia worsened the neurological function performance (p < 0.01), enlarged infarct size (p < 0.01) and increased ischemic neuronal cell death (p < 0.01). The increased damage was associated with elevations of p-mTOR, p-S6, and p-DRP1; and suppressions of SIRT3 and Nix/BNIP3L. Rapamycin ameliorated diabetes-enhanced ischemic brain damage and reversed the biomarker alterations caused by diabetes. High glucose activated mTOR pathway and caused mitochondrial dynamics toward fission. The protective effect of rapamycin against diabetes-enhanced ischemic brain damage was associated with inhibiting mTOR pathway, redressing mitochondrial dynamic imbalance, and elevating SIRT3 and Nix/BNIP3L expression.

Induced retinal pigment epithelial cells with anti-epithelial-to-mesenchymal transition ability delay retinal degeneration.

The hostile microenvironment of the retina in patients with age-related macular degeneration (AMD) may trigger epithelial-to-mesenchymal transition (EMT) of grafted retinal pigment epithelial (RPE) cells, thus attenuating the therapeutic outcome. Here, we transformed human dedifferentiated induced pluripotent stem cell-derived RPE (iPSC-RPE) cells into induced RPE (iRPE) cells using a cocktail of four transcription factors (TFs)-CRX, MITF-A, NR2E1, and C-MYC. These critical TFs maintained the epithelial property of iRPE cells by regulating the expression of bmp7, forkhead box f2, lin7a, and pard6b, and conferred resistance to TGF-ß-induced EMT in iRPE cells by targeting ppm1a. The iRPE cells with Tet-on system-regulated c-myc expression exhibited EMT resistance and better therapeutic function compared with iPSC-RPE cells in rat AMD model. Our study demonstrates that endowing RPE cells with anti-EMT property avoids the risk of EMT after cells are grafted into the subretinal space, and it may provide a suitable candidate for AMD treatment.