Current Research Research Articles

Alzheimer's Disease and Cancer: Common Targets.

There is growing epidemiologic evidence of an inverse association between cancer and AD. In addition, both cell survival and death are regulated by the same signaling pathways, and their abnormal regulation may be implicated in the occurrence and development of cancer and AD. Research shows that there may be a common molecular mechanism between cancer and AD. This review will discuss the role of GSK3, DAPK1, PP2A, P53 and CB2R in the pathogenesis of cancer and AD and describe the current research status of drug development based on these targets.

A Review of Current Research on Traditional Medicines for the Treatment of Gastrointestinal and Biliary Disorders

Abstract: Gastrointestinal (GI) and biliary disorders are the most commonly known health issues for people in the present time as a consequence of modern lifestyle, eating habits, stress, and many other conditions. The usage of traditional substances has been phased out over time, resulting in an increase in the occurrence of numerous diseases and a decrease in immunity. Therefore, in the current scenario, traditional medicine is increasingly being used to treat and manage diseases worldwide. Traditional medical systems of India, China, and Africa are the most frequently used today. Indian medicinal system includes the Ayurveda, Siddha, and Unani systems of medicine. Ayurveda is the most antique system of medicine, which relies on the usage of plant-based formulations. Herbal medicines are used nowadays by up to half of the world's population. Phytomedicines isolated from plants contain a wide variety of bioactive components that can have both negative and positive effects. Many herbal plants, such as Acacia, Aloe, Carum, Rose, Funnel, etc., are proven to be effective as phytomedicines that are effective for the cure of gastrointestinal and biliary problems. The available pharmacological therapeutic drugs often have efficacy but are associated with many adverse effects. To minimize the adverse effects, the use of phytomedicines can be the best possible alternative. In this study, a review of some of the most frequently used traditional herbal medicine isolates with their proven pharmacological activity and novel drug delivery systems for the proper delivery of isolates is presented. Moreover, currently available herbal-marketed formulations are also highlighted.

Recent Advances on PKM2 Inhibitors and Activators in Cancer Applications.

Metabolic reprogramming of cells, from the normal mode of glucose metabolism named glycolysis, is a pivotal characteristic of impending cancerous cells. Pyruvate kinase M2 (PKM2), an important enzyme that catalyzes the final rate-limiting stage during glycolysis, is highly expressed in numerous types of tumors and aids in development of favorable conditions for the survival of tumor cells. Increasing evidence has suggested that PKM2 is one of promising targets for innovative drug discovery, especially for the developments of antitumor therapeutics. Herein, we systematically summarize the recent advancement on PKM2 modulators including inhibitors and activators in cancer applications. We also discussed the classifications of pyruvate kinases in mammals and the biological functions of PKM2 in this review. We do hope that this review would provide a comprehensive understanding of the current research on PKM2 modulators, which may benefit the development of more potent PKM2-related drug candidates to treat PKM2-associated diseases including cancers in future.

Role of TBK1 Inhibition in Targeted Therapy of Cancer.

TANK-binding kinase 1 (TBK1) is a serine/threonine protein that plays a crucial role in various biological processes like immunity, autophagy, cell survival, and proliferation. The level and kinase activity of the TBK1 protein is regulated through post-translational modifications (PTMs). TBK1 mainly mediates the activation of IRF3/7 and NF-κB signaling pathways while also participating in the regulation of cellular activities such as autophagy, mitochondrial metabolism, and cell proliferation. TBK1 regulates immune, metabolic, inflammatory, and tumor occurrence and development within the body through these cellular activities. TBK1 kinase has emerged as a promising therapeutic target for tumor immunity. However, its molecular mechanism of action remains largely unknown. The identification of selective TBK1 small molecule inhibitors can serve as valuable tools for investigating the biological function of TBK1 protein and also as potential drug candidates for tumor immunotherapy. The current research progress indicates that some TBK1 inhibitors (compounds 15,16 and 21) exhibit certain antitumor effects in vitro culture systems. Here, we summarize the mechanism of action of TBK1 in tumors in recent years and the progress of small molecule inhibitors of TBK1.

The Therapeutic Effect of Natural Compounds on Osteoporosis through Ferroptosis.

Ferroptosis is a newly discovered non-apoptotic cell death whose key is lipid peroxidation. It has been reported that ferroptosis is involved in the occurrence and development of tumors and nervous system and musculoskeletal diseases. Cellular ferroptosis contributes to the imbalance of bone homeostasis and is involved in the development of osteoporosis; however, the detailed mechanism of which is still unclear though it may provide a new direction for anti-osteoporosis. The current drugs used in the treatment of osteoporosis, such as bisphosphonates and teriparatide, have many side effects, increasing people's search for natural compounds to treat osteoporosis. This review paper briefly summarizes the current research regarding the mechanisms of ferroptosis and natural anti-osteoporosis compounds targeting its pathway.

Earthquake, flood and resilience management through spatial planning, decision and information system

Assessment and planning of crisis management with the natural disasters approach include many components. In this regard, floods and earthquakes are some of the fundamental pillars in this field. With this view, paying attention to the current and future planning and research priorities of the world shows that crisis management in flood-prone and earthquake-prone areas and increasing resilience are the most important priorities for sustainable development studies and planning in the world. The western region of Iran (Lorestan province) has a special place due to the prominent features of flood and seismicity. This study aims to investigate the current situation regarding floods and earthquakes and increase the resilience of settlements with emphasis on the study area. After studying the current structure of flood and earthquake zoning according to multi-factor criteria, zoning was performed based on the integration of GIS and AHP information systems, and at the output of the work, high-risk settlements were determined separately for rural and urban areas with geographical coordinates, and then areas with low, very low, medium, high, and very high resilience were presented separately for rural and urban settlements. Also, after analyzing the SWOT model, strategies to increase the resilience of settlements to floods and earthquakes were presented. The study method of this research and the results, strategies, and operational options presented while being used in the study area are also applicable in other areas.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Interpersonal mattering and the experience of meaning in life

ABSTRACT Recent research in positive psychology has proposed that the experience of meaning in life (MIL) is multidimensional and consists of three components: mattering, purpose, and coherence. In this model, mattering has been operationalized as the extent to which people feel like they matter on the scale of the universe. The current research suggests this ‘cosmic mattering’ is only part of the picture and explored the relationships between the tripartite components of MIL and one well-established but previously unintegrated context of mattering: interpersonal mattering. Studies 1 and 2 found evidence that interpersonal mattering plays an important role in MIL even while accounting for cosmic mattering, purpose, and coherence. Study 3 experimentally demonstrated that increasing people’s feelings of interpersonal importance increases their sense of MIL. Together, these studies support the idea that interpersonal mattering is a context of existential mattering that contributes to MIL at least as much as cosmic mattering.

Synthesis, Characterization, Acute Dermal Toxicity, Anti-inflammatory, and Wound Healing Potential of Biogenic Silver Nanoparticles in Balb C Mice.

The current study aimed to develop an economic plant-based therapeutic agent to improve the treatment strategies for diseases at the nano-scale. In the current research, silver nanoparticles were synthesized using Trillium govanianum aqueous extract. Characterizations were done using UV-Visible spectrophotometer, X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. In vivo biological activities such as acute dermal toxicity, wound healing, and anti-inflammatory were done on Balb C mice. Absorbance at 295 nm corresponds to the out-of-plane quadrupole Plasmon-resonance while at 350 nm corresponds to in-plane dipole resonance. SEM images showed the morphology of TGAgNPs is not exactly spherical while XRD analysis shows that highly crystalline TGAgNPs with an average size of 27.94 nm. The FTIR spectrum represents sharp peaks of aldehyde, amide I, aromatic rings, and polysaccharides. The microscopic assessment did not find any epidermal and dermal layer abnormalities in Blab C mice when exposed to TGAgNPs during acute dermal toxicity. Results revealed that 1000 mg/kg is not a lethal dose. In the wound healing activity, no mortality and no abnormal signs were observed when petroleum jelly, Nitrofuranose, TGaqu, and TGAgNPs-based ointments were applied. Enhanced epithelization was recorded in TGaqu and TGAgNPs treated mice (p≤0.001). The wound contraction percentage was higher in nitrofuranose-treated mice (74%) followed by TGAgNPs (71%), and TGaqu (69%) compared to vehicle-treated and open-wounded mice. The paw edema model proved the potential use of TGAgNPs and TGaqu as anti-inflammatory agents. Hence, the results proved that both TGaqu and TGAgNPs are not toxic and possessed strong anti-inflammatory and wound-healing effects due to the presence of phytochemical constituents and could be used in various drug production as a therapeutic agent.

Multimodal Physiological Analysis of Impact of Emotion on Cognitive Control in VR.

Cognitive control is often perplexing to elucidate and can be easily influenced by emotions. Understanding the individual cognitive control level is crucial for enhancing VR interaction and designing adaptive and self-correcting VR/AR applications. Emotions can reallocate processing resources and influence cognitive control performance. However, current research has primarily emphasized the impact of emotional valence on cognitive control tasks, neglecting emotional arousal. In this study, we comprehensively investigate the influence of emotions on cognitive control based on the arousal-valence model. A total of 26 participants are recruited, inducing emotions through VR videos with high ecological validity and then performing related cognitive control tasks. Leveraging physiological data including EEG, HRV, and EDA, we employ classification techniques such as SVM, KNN, and deep learning to categorize cognitive control levels. The experiment results demonstrate that high-arousal emotions significantly enhance users' cognitive control abilities. Utilizing complementary information among multi-modal physiological signal features, we achieve an accuracy of 84.52% in distinguishing between high and low cognitive control. Additionally, time-frequency analysis results confirm the existence of neural patterns related to cognitive control, contributing to a better understanding of the neural mechanisms underlying cognitive control in VR. Our research indicates that physiological signals measured from both the central and autonomic nervous systems can be employed for cognitive control classification, paving the way for novel approaches to improve VR/AR interactions.

Narrative Testimony and Characteristics of Rottlerin for the Treatment of Various Kinds of Cancer

Abstract: From ancient times until now, scientists have focused on herbal medicaments for treating various diseases. Rottlerin, a potent PKCδ inhibitor, is one of the greatest herbal medications. Over the years, it was identified that rottlerin has several cellular and molecular targets that could be involved in the fight against cancer. The past and present research has clearly shown that rottlerin inhibits the transcription factors, enzymes, and signaling molecules that regulate the death and life of cancer cells. Although the anticancer property of rottlerin has primarily been attributed to the induction of autophagy and apoptosis, current research has revealed the existence of different damage pathways. The major part described in this mini-review is the antitumor/anticancer effects of rottlerin in various organs of the human body affected by breast cancer, pancreatic cell cancer, bladder cancer, NSCLC, prostate cancer, ovarian cancer, nasopharyngeal cancer, etc.

A Survey on Deep Learning Event Extraction: Approaches and Applications.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

Evolving Advances in the Cosmetic Use of Probiotics and Postbiotics: Health, Regulatory and Marketing Aspects.

Ongoing development in cosmetics is increasingly making use of probiotics, which are defined as "live microorganisms with health-enhancing properties mediated through ingestion or topical application to the host". The observation that several bacterial strains augment normal processes of healthy tissue maintenance, particularly for the skin, has opened up new avenues for the use of bacterial strains in cosmetics. A principal feature of such "cosmeceuticals" is an application of increasing insight into the biochemical nature of the skin's normal microbial flora, also called its microbiome. The opportunity of manipulating the skin microbiome to address various skin disorders has revealed novel routes for treatment. The skin microbiome manipulation approaches to address various skin disorders include skin microbiome transplantation, skin bacteriotherapy, and prebiotic stimulation. Research in this field has revealed that medical outcome-targeted manipulation of skin microbiome bacterial strain makeup may significantly increase skin health and appearance. Commercial availability of probiotic skincare products is rapidly expanding worldwide due to satisfactory laboratory results and public perception of probiotics as being intrinsically more wholesome than other bioactive substances, such as synthetics. Major outcomes of probiotic use include a significant reduction in skin wrinkling, acne and other conditions adversely affecting skin appearance and healthy function. Moreover, probiotics may additionally promote normal skin hydration, resulting in a vibrant and lustrous appearance. Nevertheless, significant technical challenges remain for the full optimization of probiotics in cosmetic products. This article summarizes the evolving nature of this field and explores current probiotic research initiatives, along with regulatory aspects and significant challenges in the manufacturing of cosmetics in the context of market expansion for these products.

Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation.

Medical image segmentation is crucial in clinical diagnosis, helping physicians identify and analyze medical conditions. However, this task is often accompanied by challenges like sensitive data, privacy concerns, and expensive annotations. Current research focuses on personalized collaborative training of medical segmentation systems, ignoring that obtaining segmentation annotations is time-consuming and laborious. Achieving a perfect balance between annotation cost and segmentation performance while ensuring local model personalization has become a valuable direction. Therefore, this study introduces a novel Model-Heterogeneous Semi-Supervised Federated (HSSF) Learning framework. It proposes Regularity Condensation and Regularity Fusion to transfer autonomously selective knowledge to ensure the personalization between sites. In addition, to efficiently utilize unlabeled data and reduce the annotation burden, it proposes a Self-Assessment (SA) module and a Reliable Pseudo-Label Generation (RPG) module. The SA module generates self-assessment confidence in real-time based on model performance, and the RPG module generates reliable pseudo-label based on SA confidence. We evaluate our model separately on the Skin Lesion and Polyp Lesion datasets. The results show that our model performs better than other methods characterized by heterogeneity. Moreover, it exhibits highly commendable performance even in homogeneous designs, most notably in region-based metrics. The full range of resources can be readily accessed through the designated repository located at HSSF(github.com) on the platform of GitHub.

Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions

Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions

Risk of Subsequent Primary Cancers in Thyroid Cancer Survivors according to the Dose of Levothyroxine: A Nationwide Cohort Study.

Current research has not investigated the effect of thyroid-stimulating hormone suppression therapy with levothyroxine on the risk for developing subsequent primary cancers (SPCs). This study aimed to investigate the association between levothyroxine dosage and the risk for SPCs in thyroid cancer patients. We conducted a nationwide population-based retrospective cohort study form Korean National Health Insurance database. This cohort included 342,920 thyroid cancer patients between 2004 and 2018. Patients were divided into the non-levothyroxine and the levothyroxine groups, the latter consisting of four dosage subgroups according to quartiles. Cox proportional hazard models were performed to evaluate the risk for SPCs by adjusting for variables including cumulative doses of radioactive iodine (RAI) therapy. A total of 17,410 SPC cases were observed over a median 7.3 years of follow-up. The high-dose levothyroxine subgroups (Q3 and Q4) had a higher risk for SPC (adjusted hazard ratio [HR], 1.14 and 1.27; 95% confidence interval [CI], 1.05-1.24 and 1.17- 1.37; respectively) compared to the non-levothyroxine group. In particular, the adjusted HR of stomach (1.31), colorectal (1.60), liver and biliary tract (1.95), and pancreatic (2.48) cancers were increased in the Q4 subgroup. We consistently observed a positive association between high levothyroxine dosage per body weight and risk of SPCs, even after adjusting for various confounding variables. Moreover, similar results were identified in the stratified analyses according to thyroidectomy type and RAI therapy, as well as in a subgroup analysis of patients with good adherence. High-dose levothyroxine use was associated with increased risk of SPCs among thyroid cancer patients regardless of RAI therapy.

Optimized Transition Metal Phosphides for Direct Seawater Electrolysis: Current Trends.

Seawater electrolysis presents a viable route for sustainable large-scale hydrogen production, yet its practical application is hindered by several technical challenges. These include the sluggish kinetics of hydrogen evolution, poor stability, cation deposition at the cathode, electrode corrosion, and competing chloride oxidation at the anode. To overcome these obstacles, the development of innovative electrocatalysts is crucial. Transition metal phosphides (TMPs) have emerged as promising candidates owing to their superior catalytic performance and tunable structural properties. This review provides a comprehensive analysis of recent progress in the structural engineering of TMPs tailored for efficient seawater electrolysis. We delve into the catalytic mechanisms underpinning hydrogen and oxygen evolution reactions in different pH conditions, along with the detrimental side reactions that impede hydrogen production efficiency. Several methods to prepare TMPs are then introduced. Additionally, detailed discussions on structural modifications and interface engineering tactics are presented, showcasing strategies to enhance the activity and durability of TMP electrocatalysts. By analyzing current research findings, our review aims to inform ongoing research endeavors and foster advancements in seawater electrolysis for practical and ecologically sound hydrogen generation.

Deconstructing Fears of Terrorism: A Comparison between Fear from Domestic and International Terrorist Groups

ABSTRACT Although research on fears of terrorism has been growing, previous studies approached the topic of the fear of terrorism as a monolithic concept without considering the various political aspects of terrorism as a crime. The current research employed a novel approach to studying the fear of terrorism as a polylithic phenomenon by comparing three different types of fears of terrorism among Americans: general fears of terrorism, fears of domestic terrorist groups, and fears of international terrorist groups. Drawing on a nationally representative sample from the Chapman Survey of American Fears, Wave 5 (2018), the results of the analysis indicated that the predictors were differentially associated with the three types of fear with respect to their statistical significance and correlation directions. Thus, this study provides a significant contribution to the literature on the fear of terrorism topic by illustrating the important differences among general fears of terrorism, fears of domestic terrorist groups, and fears of international terrorist groups.

Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.

Clinical Application of MRI in Coronavirus Disease 2019: A Bibliometric Analysis.

Currently, coronavirus disease 2019 (COVID-19) continues to remain in the pandemic stage, leading to severe challenges in the global public healthcare system. Magnetic resonance imaging (MRI) methods have played an important role in the diagnosis of COVID-19 and the structural evaluation of the affected organs. Reviewing and summarizing the application of MRI has significant clinical implications for COVID-19. The study aimed to analyze literature related to the application of MRI in COVID-19 using bibliometric tools, to explore the research status, hotspots, and developmental trends in this field, and to provide a reference for the application of MRI in the clinical diagnosis and evaluation of COVID-19. We used the Web of Science Core Collection database to search and collect relevant literature on the use of MRI in COVID-19. The authors, institutes, countries, journals, and keyword modules of the bibliometric analysis software CiteSpace and VOSviewer were used to analyze and plot the network map. A total of 1506 relevant articles were shortlisted through the search; the earliest study was published in 2019, showing an overall upward trend every year. The research was mainly presented as published articles. Clinical neurology was found to be the primary discipline. The United States had the highest publication volume and influence in this field. Countries around the world cooperated more closely. The Cureus Journal of Medical Science was the main periodical to publish articles. Institutes, such as Harvard Medical School, Mayo Clinic, and Massachusetts General Hospital, have published a large number of papers. Some of the high-frequency keywords were "COVID-19", "SARS-CoV-2", "magnetic resonance", "myocarditis", and "cardiac magnetic resonance imaging". The keyword clustering study showed that the current research mainly focuses on five "hot" directions. There is a need to strengthen cross-teamwork and multidisciplinary collaboration in the future to completely explore the positive role of MRI in COVID-19 and to discover breakthroughs for the challenges in the clinical diagnosis and treatment of COVID-19.