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Scleroderma is an autoimmune rheumatic disorder accompanied by severe fibrosis in skin and other internal organs. During scleroderma progression, resident fibroblasts undergo activation and convert to α-smooth muscle actin (α-SMA) expressing myofibroblasts (MFBs) with increased capacity to synthesize collagens and fibrogenic components. Accordingly, MFBs are a major therapeutic target for fibrosis in scleroderma and treatment with blocking MFBs could produce anti-fibrotic effects. TLY012 is an engineered human TNF-related apoptosis-inducing ligand (TRAIL) which induces selective apoptosis in transformed cells expressing its cognate death receptors (DRs). Here we report that TLY012 selectively blocks activation of dermal fibroblasts and induces DR-mediated apoptosis in α-SMA +  MFBs through upregulated DR5 during its activation. In vivo, TLY012 reverses established skin fibrosis to near-normal skin architecture in mouse models of scleroderma. Thus, the TRAIL pathway plays a critical role in tissue remodeling and targeting upregulated DR5 in α-SMA + MFBs is a viable therapy for fibrosis in scleroderma. Dermal myofibroblasts are responsible for fibrosis development in scleroderma. Here the authors show that a bioengineered recombinant human TRAIL ligand reverses established fibrosis in mouse models of scleroderma by targeting the death receptor 5 and inducing apoptosis of myofibroblasts.

Toxicity to hepatocytes caused by various insults including drugs is a common cause of chronic liver failure requiring transplantation. Targeting therapeutics specifically to hepatocytes is often a challenge since they are relatively nonendocytosing unlike the highly phagocytic Kupffer cells in the liver. Approaches that enable targeted intracellular delivery of therapeutics to hepatocytes have significant promise in addressing liver disorders. We synthesized a galactose‐conjugated hydroxyl polyamidoamine dendrimer (D4‐Gal) that targets hepatocytes efficiently through the asialoglycoprotein receptors in healthy mice and in a mouse model of acetaminophen (APAP)‐induced liver failure. D4‐Gal localized specifically in hepatocytes and showed significantly better targeting when compared with the non‐Gal functionalized hydroxyl dendrimer. The therapeutic potential of D4‐Gal conjugated to N‐acetyl cysteine (NAC) was tested in a mouse model of APAP‐induced liver failure. A single intravenous dose of a conjugate of D4‐Gal and NAC (Gal‐D‐NAC) improved survival in APAP mice, decreased cellular oxidative injury and areas of necrosis in the liver, even when administered at the delayed time point of 8 h after APAP exposure. Overdose of APAP is the most common cause of acute hepatic injury and liver transplant need in the United States, and is treated with large doses of NAC administered rapidly within 8 h of overdose leading to systemic side effects and poor tolerance. NAC is not effective when treatment is delayed. Our results suggest that D4‐Gal is effective in targeting and delivering therapies to hepatocytes and Gal‐D‐NAC has the potential to salvage and treat liver injury with a broader therapeutic window.

The nuclear transcription factor estrogen receptor α (ERα) plays a critical role in breast cancer progression. ERα acts as an important growth stimulatory protein in breast cancer and the expression level of ERα is tightly related to the prognosis and treatment of patients. Small leucine zipper protein (sLZIP) functions as a transcriptional cofactor by binding to various nuclear receptors, including glucocorticoid receptor, androgen receptor, and peroxisome proliferator-activated receptor γ. However, the role of sLZIP in the regulation of ERα and its involvement in breast cancer progression is unknown. We found that sLZIP binds to ERα and represses the transcriptional activity of ERα in ERα-positive breast cancer cells. sLZIP also suppressed the expression of ERα target genes. sLZIP disrupted the binding of ERα to the estrogen response element of the target gene promoter, resulting in suppression of cell proliferation. sLZIP is a novel co-repressor of ERα, and plays a negative role in ERα-mediated cell proliferation in breast cancer.

Liver fibrosis, a common outcome of chronic liver disease characterized by excessive accumulation of extracellular matrix (ECM), is a leading cause of mortality worldwide. The tyrosine kinase inhibitor neratinib is a human epidermal growth factor receptor 2 (HER2) inhibitor approved by the FDA for HER2-positive breast cancer treatment; however, it has not yet been evaluated for liver fibrosis treatment. We elucidated the anti-fibrotic effects of neratinib in hepatic stellate cells (HSCs) and in vivo models of CCl 4 -induced liver fibrosis. HSC activation is a key step in liver fibrogenesis and has a crucial role in collagen deposition, as it is primarily responsible for excessive ECM production. The effect of neratinib on HSC was evaluated in transforming growth factor (TGF-β)-incubated LX-2 cells and culture-activated primary human HSCs. In vivo study results indicated that neratinib inhibited the inflammatory response, HSC differentiation, and collagen accumulation induced by CCl 4 . Moreover, the anti-fibrotic effects of neratinib were not associated with the HER2 signaling pathways. Neratinib inhibited FGF2 expression in activated HSCs and serum FGF2 level in the model, suggesting that neratinib possessed therapeutic potency against liver fibrosis and the potential for application against other fibrotic diseases.

Climate change is one of the most critical issues in the business sector. This conceptual study proposes a corporate competitiveness evaluation model of climate change by adopting the Balanced Scorecard approach. This study provides a series of specific performance and competitiveness indicators of climate change in the four dimensions of learning and growth, internal process, external stakeholders, and finance and carbon performance. The indicators, which use both quantitative and qualitative methods, can be immediately applied in the field. This study presents practical guidelines to successfully adopt and implement the competitiveness evaluation model in an organization by considering prevalent innovation tools of business process management, process visualization, and knowledge socialization. Finally, it provides some implications for managers and policy-makers who wish to proactively address climate change in the business sector.

The first-line treatment for prostate cancer (PCa) is androgen ablation therapy. However, prostate tumors generally recur and progress to androgen-independent PCa (AIPC) within 2–3 years. α-Actinin-4 (ACTN4) is an actin-binding protein that belongs to the spectrin gene superfamily and acts as an oncogene in various cancer types. Although ACTN4 is involved in tumorigenesis and the epithelial–mesenchymal transition of cervical cancer, the role of ACTN4 in PCa remains unknown. We found that the ACTN4 expression level increased during the transition from androgen-dependent PCa to AIPC. ACTN4 overexpression resulted in enhanced proliferation and motility of PCa cells. Increased β-catenin due to ACTN4 promoted the transcription of genes involved in proliferation and metastasis such as CCND1 and ZEB1 . ACTN4-overexpressing androgen-sensitive PCa cells were able to grow in charcoal-stripped media. In contrast, ACTN4 knockdown using si-ACTN4 and ACTN4 nanobody suppressed the proliferation, migration, and invasion of AIPC cells. Results of the xenograft experiment revealed that the mice injected with LNCaP ACTN4 cells exhibited an increase in tumor mass compared with those injected with LNCaP Mock cells. These results indicate that ACTN4 is involved in AIPC transition and promotes the progression of PCa.

The nuclear transcription factor estrogen receptor [alpha] (ER[alpha]) plays a critical role in breast cancer progression. ER[alpha] acts as an important growth stimulatory protein in breast cancer and the expression level of ER[alpha] is tightly related to the prognosis and treatment of patients. Small leucine zipper protein (sLZIP) functions as a transcriptional cofactor by binding to various nuclear receptors, including glucocorticoid receptor, androgen receptor, and peroxisome proliferator-activated receptor [gamma]. However, the role of sLZIP in the regulation of ER[alpha] and its involvement in breast cancer progression is unknown. We found that sLZIP binds to ER[alpha] and represses the transcriptional activity of ER[alpha] in ER[alpha]-positive breast cancer cells. sLZIP also suppressed the expression of ER[alpha] target genes. sLZIP disrupted the binding of ER[alpha] to the estrogen response element of the target gene promoter, resulting in suppression of cell proliferation. sLZIP is a novel co-repressor of ER[alpha], and plays a negative role in ER[alpha]-mediated cell proliferation in breast cancer.

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques.

Robust visual place recognition (VPR) enables mobile robots to identify previously visited locations. For this purpose, the extracted visual information and place matching method plays a significant role. In this paper, we critically review the existing VPR methods and group them into three major categories based on visual information used, i.e., handcrafted features, deep features, and semantics. Focusing the benefits of convolutional neural networks (CNNs) and semantics, and limitations of existing research, we propose a robust appearance-based place recognition method, termed SVS-VPR, which is implemented as a hierarchical model consisting of two major components: global scene-based and local feature-based matching. The global scene semantics are extracted and compared with pre-visited images to filter the match candidates while reducing the search space and computational cost. The local feature-based matching involves the extraction of robust local features from CNN possessing invariant properties against environmental conditions and a place matching method utilizing semantic, visual, and spatial information. SVS-VPR is evaluated on publicly available benchmark datasets using true positive detection rate, recall at 100% precision, and area under the curve. Experimental findings demonstrate that SVS-VPR surpasses several state-of-the-art deep learning-based methods, boosting robustness against significant changes in viewpoint and appearance while maintaining efficient matching time performance.