Current Research Research Articles

Consumer experience and outcomes of insertion, removal, and management practices of short and midline peripheral intravenous catheters in acute health care: a mixed methods systematic review protocol.

This review will synthesize and integrate current research on the practices relating to the insertion, removal, and management of short and midline peripheral intravenous catheters on consumer experiences and outcomes in acute health care. The aim is to provide consolidated evidence to support policy decision-makers and health care workers in overcoming peripheral intravenous catheter device management challenges. Implementing the evidence provided by this review could increase positive consumer outcomes and reduce the gap between research evidence and clinical practice. Peripheral intravenous catheterization is a process in which a device is inserted into a patient's peripheral vein to administer medication, blood products, and therapeutic fluids. Recent studies have demonstrated the positive and negative impacts of peripheral intravenous catheter site placement, pain relief for insertion, complications, consumer engagement, and routine versus clinically indicated removal of these devices on consumers and the health care system. Managing these impacts in relation to research-practice gaps and conflicting evidence remains challenging. Moreover, consumer experiences and outcomes concerning these challenges are unclear. This review will consider quantitative, qualitative, and mixed methods studies on consumer experiences and outcomes in acute health care. The quantitative component will consider outcomes of patients' pain, satisfaction, anxiety, distress, preferences, fear, and comfort. The qualitative component will consider consumer experiences, including perceptions, preferences, perspectives, attitudes, expectations, and satisfaction. This study will follow JBI's convergent segregated approach for mixed method systematic reviews. A search of 11 databases will be conducted for peer-reviewed published articles, theses, and dissertations. There will be no age, date, or language limitations. PROSPERO CRD42023410214.

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.

Nerolidol inhibited U-251 human glioblastoma cell proliferation and triggered apoptosis via the upregulation of the p38 MAPK signaling pathway.

Glioblastoma multiforme (GBM) is a lethal brain tumor with high mortality and morbidity. Nerolidol (NRD) is a sesquiterpene alcohol sequestered from the essential oils of aromatic florae with potent antioxidant, antiviral, anticancer, cardioprotective, and neuroprotective activity. The aim of the study was to investigate the underlying cell-cycle mechanisms of NRD-mediated antiproliferative and apoptosis activities in GBM using human U-251 cells. The current research investigated the antiproliferative and apoptotic activities of NRD on U-251 cells. The effects of NRD were measured using a Cell Counting Kit-8 (CCK-8) assay, 4',6-diamidino-2-phenylindole (DAPI) staining, messenger ribonucleic acid (mRNA) level assessment, and western blot assay. Nerolidol decreased U-251 viability in a dose-dependent manner, as well as induced apoptotic activity, reduced B-cell lymphoma-2 (BCL-2) levels, and increased mRNA expression of BCL-2-associated X (Bax), caspase-3 and caspase-9. The attenuation of the cyclin-D1, cyclin-dependent kinase 4 (CDK4) and CDK6 mRNA expression confirmed cell cycle regulation. Western blot analysis of CDK1 indicated reductions in cyclin-B1 and p21. Furthermore, NRD prompted apoptosis through p38 amelioration and increased phosphorylated extracellular signal-related kinase 1 (p-ERK1) and phosphorylated c-Jun N-terminal protein kinase 1 (p-JNK1) levels. Nerolidol inhibited GBM cell viability and induced apoptosis through the regulation of cell-cycle proteins via p38 mitogen-activated protein kinase (MAPK) signaling pathways. Thus, NRD could be developed as a potential natural therapeutic agent for GBM.

D-Optimal Mixture Design Enabled Development of Lyophilized Nanoemulsifying Drug Delivery System of Paliperidone

Background: Schizophrenia is a chronic disease with acute psychotic symptoms, which is having frequent recurrence. Paliperidone palmitate (PP) is a second-generation antipsy-chotic drug to treat schizophrenia. Aims: The aim of the study was to prepare lyophilized nanoemulsifying drug delivery system (NEDDS) of paliperidone (PD). Objective: The primary objective of the current research work was to develop a lyophilized nanoemulsifying drug delivery system (NEDDS) of paliperidone (PD) to improve its oral bioa-vailability and stability. Methods: Optimization using D-Optimal Mixture Design DMD) was conducted, and optimized NEDDS was further lyophilized to improve stability. The lyophilized optimized NEDDS was fur-ther evaluated for biopharmaceutical evaluation. Result: A saturation solubility study revealed Peceol, Tween 80, and Plurol Olique CC497 as suitable candidates for oil, surfactant, and co-surfactant, respectively. Optimized NEDDS of PD showed mean globule size (MGS) of 185 nm, PDI of 0.27 and cumulative % drug release within 15 min Q15 of 86.6%. Lyophilized optimized NEDDS was found to have no significant change in quality attributes within the stability study period. A pharmacokinetic study revealed more than two-fold increases in bioavailability for lyophilized optimized NEDDS. Conclusion: Hence, lyophilized NEDDS of PD can be used as an effective approach for the im-provement of oral bioavailability and stability.

Emotion dysregulation and family functioning moderate family caregiving burden during the pandemic.

Since the onset of COVID-19 pandemic, additional risk factors affecting family caregivers' mental health have arisen. Therefore, personal stress coping strategies and family dynamics became important factors in reducing the impact of the pandemic on family caregivers' mental health. The present research aimed to estimate the association between COVID-19 stressors and family caregiving burden. Moreover, moderating effects of emotion dysregulation and family functioning on this association were investigated. This study analyzed data collected in April 2021 from 154 family caregivers (Mage=38.79, SDage=9.36, range=22-64) recruited through Amazon's Mechanical Turk (MTurk). The impact of COVID-19 stressors on family caregiving burden was tested, and moderating impacts of emotion dysregulation and family functioning were also investigated. Both COVID-19 stress exposure and stress appraisal were positively associated with family caregiving burden. Emotion dysregulation and problematic family functioning were also positively associated with family caregiving burden. A significant moderating effect of emotion dysregulation was found, such that family caregivers with higher emotion dysregulation were likely to feel more caregiving burden when they experienced more COVID-19 stressors. The current research highlighted the role of emotion regulation in reducing the negative impact of COVID-19 stressors on family caregiving burden. The research also emphasizes the need for intervention programs to improve emotion regulation strategies to decrease family caregiving burden during the pandemic.

English

In modern agriculture, soil salinity stress has been extensively studied by researchers owing to its disastrous impacts on soil and plant health. Zinc (Zn) is an essential micronutrient. Crucial role of Zn in regulating plant growth under salinity stress has been well documented. Current research consisted of two studies, first study was aimed at comparing three organic (i.e. sugarcane filter cake, farmyard manure and poultry manure) and inorganic sources (i.e. zinc oxide, zinc sulphate and zinc nitrate) of Zn for improving Zn uptake by salt tolerant wheat varieties in a pot trial. Whereas, in second experiment the effect of zinc sulfate (ZnSO4; inorganic) alone and in combination with sugarcane filter cake (SFC; organic) was evaluated in sustaining wheat growth under naturally salt affected soil. Results from pot trial suggested that among different organic sources, SFC application significantly enhanced the plant height (12.52%), spike length (9.07%) and number of fertile tillers (25%), whereas, application of ZnSO4 among the inorganic sources resulted in notable enhancements in plant height (7%), number of fertile tillers (24%), and spike length (8%) compared to their corresponding treatments. In addition, wheat variety Sehar-2006 proved to be the most efficient wheat variety under salinized conditions. Whereas the findings of the field trial revealed ZnSO4 application @ 24 kg ha-1 in combination with SFC 1 % w/w improved the plant height (2%), grain yield (81%), K+/Na+ ratio (1.4 fold), catalase (8%) and peroxidase (3%) activities.

Ecological influences and visual attention to infant phenotypes.

In humans, the allocation of resources, such as parental care and attention, is vital to offspring survival. Life history strategies are influenced by cues in the environment, particularly those that signal the availability of resources. What has yet to be determined is how individuals allocate resources to infants as a function of perceived ecological harshness and life history strategy. In the current research we hypothesized that perceived ecology would influence infant ratings (Study 1), and that visual attention to infant phenotypes would be associated with life history strategies (Study 2). Study 1 investigated the effect of ecological conditions (control vs. harsh) on preferences to infant phenotypes (i.e., underweight, average weight, overweight). Participants (N = 246) were less likely to rate infants favorably under a harsh ecological condition. Study 2 investigated visual perception in processing infant images. Using an eye-tracking task, participants (N = 239) viewed images of infants while their eye movements were recorded. Participants displayed an early attentional bias (i.e., first fixation duration) to the head of the infant and focused most of their visual attention to the torso of infants (i.e., total visit duration). The results of the both studies indicate that ecological factors play an important role in rating infants, and data from eye-tracking demonstrates that phenotypes influence the amount of attention given to infants.

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 Plasmonresonance 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.

Associations between day-level affect profiles and same-day substance use among young adults.

Emotions play a critical role in health risk behaviors, including substance use. However, current research often focuses exclusively on average levels of positive and negative affect, neglecting the complexity of daily emotional patterns. By capturing multiple dimensions of affect, including arousal and discrete states, we can improve our understanding of proximal predictors of substance use. The present study demonstrates the utility of a novel methodological approach for assessing affect patterns in daily life in relation to alcohol and cannabis use. Daily diary data from N = 154 young adults who reported recent heavy episodic drinking and simultaneous use of alcohol and cannabis were analyzed using a mixed-indicator latent profile analysis to identify and describe day-level affective patterns and outcomes. Results revealed five distinct day-level profiles of affect: undifferentiated negative affect days, undifferentiated positive affect days, high-arousal positive affect days, mixed affect days, and low reactivity days. Undifferentiated positive affect days, high-arousal positive affect days, and low reactivity days were associated with significantly greater odds of same-day alcohol use compared to days characterized by undifferentiated negative affect (χ² = 10.55, p = .032). Findings suggest that daily affect patterns differentially impact alcohol use and can inform the development of interventions for problematic substance use. Additionally, the innovative methodological approach employed herein could be applicable for investigating the role of emotion in other health behaviors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

A new measure of feeling safe: Developing psychometric properties of the Neuroception of Psychological Safety Scale (NPSS).

The study comprised of 3 stages: (a) item generation, (b) item reduction, and (c) assessment of factor structure and internal consistency. Exploratory and confirmatory factor analysis was conducted from 2 samples who completed a survey online (exploratory n = 342, confirmatory n = 455). Initially, 107 items were generated. Item reduction and exploratory factor analysis resulted in a 29-item NPSS with subscales of compassion, social engagement and body sensations. The NPSS was found to have a consistent factor structure and internal consistency. The NPSS is a novel measure of psychological safety which can be used across a range of health and social care settings. This research provides a platform for further work to support and enhance understandings of the science of safety through the measurement of psychological, relational and physiological components of safety. The NPSS will help shape new approaches to evaluating trauma treatments, relational issues and mental health concerns. Research to establish the convergent, discriminant and concurrent validity of the NPSS and to explore its use with diverse community and clinical populations is underway. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Advancements and current trends in tumor treating fields: a scientometric analysis.

Tumor treating fields (TTFields) therapy is a novel and effective noninvasive cancer therapy, and it has been approved by FDA in the treatment of recurrent and newly diagnosed glioblastoma, and malignant pleural mesothelioma. Moreover, TTFields therapy has been widely studied in both clinical trials and preclinical studies in recent years. Based on its high efficacy, research on TTFields therapy has been a hot topic. Thus, the authors made this scientometric analysis of TTfields to reveal the scientometric distributions such as annual publications and citations, countries and institutions, authors, journals, references, and more importantly, research status and hot topics of the field. In recent years, publication numbers have been stable at high values, and citation numbers have been increasing greatly. The United States and Israel were the top two countries with the highest publication numbers, followed by Germany and Switzerland. Scientometric analyses of keywords indicated that clinical applications and antitumor mechanisms are probably the two main parts of current research on TTfields. Most clinical trials of TTfields focus on the treatment of glioblastoma. And a variety of other cancers such as lung cancer especially nonsmall cell lung cancer, hepatic cancer, other brain tumors, etc. have also been studied in both clinical trials and preclinical studies.

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.