Therapeutic Effects of Insulin-Producing Human Umbilical Cord-Derived Mesenchymal Stem Cells in a Type 1 Diabetes Mouse Model

In patients with type 1 diabetes (T1D), compromised pancreatic β-cell functions are compensated through daily insulin injections or the transplantation of pancreatic tissue or islet cells. However, both approaches are associated with specific challenges. The transplantation of mesenchymal stem cells (MSCs) represents a potential alternative, as MSCs have tissue-forming capacity and can be isolated from various tissues. The human umbilical cord (hUC) is a good source of freely available MSCs, which can be collected through pain-free, non-invasive methods subject to minimal ethical concerns. We sought to develop a method for the in vitro generation of insulin-producing cells (IPCs) using MSCs. We examined the potential therapeutic uses and efficacy of IPCs generated from hUC-derived MSCs (hUC-IPCs) and human adipose tissue (hAD)-derived MSCs (hAD-IPCs) through in vitro experiments and streptozotocin (STZ)-induced C57BL/6 T1D mouse models. We discovered that compared to hAD-IPCs, hUC-IPCs exhibited a superior insulin secretion capacity. Therefore, hUC-IPCs were selected as candidates for T1D cell therapy in mice. Fasting glucose and intraperitoneal glucose tolerance test levels were lower in hUC-IPC-transplanted mice than in T1D control mice and hAD-IPC-transplanted mice. Our findings support the potential use of MSCs for the treatment of T1D.     

Relationship between Brain Metabolic Disorders and Cognitive Impairment: LDL Receptor Defect

The low-density-lipoprotein receptor (LDLr) removes low-density lipoprotein (LDL), an endovascular transporter that carries cholesterol from the bloodstream to peripheral tissues. The maintenance of cholesterol content in the brain, which is important to protect brain function, is affected by LDLr. LDLr co-localizes with the insulin receptor and complements the internalization of LDL. In LDLr deficiency, LDL blood levels and insulin resistance increase, leading to abnormal cholesterol control and cognitive deficits in atherosclerosis. Defects in brain cholesterol metabolism lead to neuroinflammation and blood–brain-barrier (BBB) degradation. Moreover, interactions between endoplasmic reticulum stress (ER stress) and mitochondria are induced by ox-LDL accumulation, apolipoprotein E (ApoE) regulates the levels of amyloid beta (Aβ) in the brain, and hypoxia is induced by apoptosis induced by the LDLr defect. This review summarizes the association between neurodegenerative brain disease and typical cognitive deficits.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

A study on the factors that affect the adoption of Smart Water Grid

Due to recent climate changes that accompany drastic changes in water recycle process, extreme floods and droughts are occurring frequently throughout the world. In response to these events, Smart Water Management that refers to implement intelligent water information systems by IT convergence is considered as a new paradigm. In this study, we seek to explore the Smart Water Management, especially the adoption of Smart Water Grid Technology. We aim to review previous studies to identify factors that influence the adoption of the Smart Water Grid and to analyze the importance and priority of the factors affecting Smart Water Grid adoption, thereby presenting the successful adoption measures for Smart Water Grid in Korea. This study set a research model with the influencing factors in relation to the adoption of new IT technologies that were identified through the literature reviews of previous studies based on the TOE framework. This study also conducted empirical analysis of the findings and found out that the issue of privacy protection and security due to hacker’s attack would be emerged as a significant risk factor.     

Multiple Decision Expert Systems for Performance Analysis of a Boiler System

Artificial neural networks (ANN)-based multiple decision expert systems (MDES) were developed for assessing the performance of a boiler system. Different configurations of ANN were used with different decision combination methods, including a neural combiner, to propose the model. The model was developed using the plant data collected over a period of five months to predict steam temperature, pressure, and mass flow rate, using feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature as the input parameters. The predictive capability of the model is evaluated in terms of both correlation coefficient (R) and mean absolute percentage error (MAPE). The results observed from this work demonstrate that neural combiner and ANN-based MDES can efficiently predict the data on steam properties consistently, and that the model can serve as an efficient tool for monitoring boiler behavior under real-time conditions. Superiority of the proposed model over others under various scenarios is also demonstrated.     

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.     

Dual-wavelength cascaded Raman fibre laser

Dual-wavelength lasing at 1480 and 1500 nm has been demonstrated from a cascaded Raman fibre laser with a WDM coupler and two pairs of Bragg gratings. Intensity-adjustable, wavelength-tunable laser operation was achieved by tensile stress wavelength tuning of the gratings     

A formation of cobalt silicide on silicon field emitter arrays byelectrical stress

A novel process utilizing electrical stress is proposed for the formation of Co silicide on single crystal silicon (c-Si) FEAs to improve the field emission characteristics. Co silicide FEAs formed by electrical stress (ES) exhibited a significant improvement in turn-on voltage and emission current compared with c-Si FEAs. The improvement mainly comes from the lower effective work function of Co silicide and less blunting of tips during silicidation by electrical stress in an ultra high vacuum (UHV) environment less than 10^-8 torr     

Generalized Approach towards Secretion-Based Protein Production via Neutralization of Secretion-Preventing Cationic Substrate Residues

Many heterologous proteins can be secreted by bacterial ATP-binding cassette (ABC) transporters, provided that they are fused with the C-terminal signal sequence, but some proteins are not secretable even though they carry the right signal sequence. The invention of a method to secrete these non-secretable proteins would be valuable both for understanding the secretory physiology of ABC transporters and for industrial applications. Herein, we postulate that cationic “supercharged” regions within the target substrate protein block the secretion by ABC transporters. We also suggest that the secretion of such substrate proteins can be rescued by neutralizing those cationic supercharged regions via structure-preserving point mutageneses. Surface-protruding, non-structural cationic amino acids within the cationic supercharged regions were replaced by anionic or neutral hydrophilic amino acids, reducing the cationic charge density. The examples of rescued secretions we provide include the spike protein of SARS-CoV-2, glutathione-S-transferase, streptavidin, lipase, tyrosinase, cutinase, growth factors, etc. In summary, our study provides a method to predict the secretability and a tool to rescue the secretion by correcting the secretion-blocking regions, making a significant step in understanding the physiological properties of ABC transporter-dependent protein secretion and laying the foundation for the development of a secretion-based protein-producing platform.