Prevalent Studies Research Articles

Anti-cancer role of curcumin in prostate cancer cells via regulation of m6A-modified circ0030568-FMR1 signaling pathway.

Prostate cancer (PCa) is the most prevalent adult malignancies worldwide and studies have shown that circular RNAs (circRNAs) play critical roles in the development and progression of PCa. As the most abundant modification, N6-methyladenosine (m6A) modification functions in regulating circRNAs expression and has been shown to regulate PCa progression. However, the biological relevance of m6A modification of circRNAs in PCa remains unclear. In addition, curcumin is reported to inhibit a variety of cancer cells while the biological functions in PCa have not yet been fully elucidated. Thus, our study aims to investigate whether curcumin can suppress PCa progression through the m6A-modified circRNAs. By conducting m6A methylation immunoprecipitation combined with quantitative real-time polymerase chain reaction (MeRIP-qPCR) assay, cell counting kit-8 (CCK-8) assay and wound healing assay, increased m6A modification on circ0030568 was detected and upregulated circ0030568 was also observed in different PCa cells lines, which promotes proliferation and migration of PCa cells. More importantly, the results confirmed that curcumin could suppress the proliferation and migration of PCa cells lines by inhibiting METTL3-modified circ0030568. Mechanistically, m6A reader YTHDF2 elevated the stability of circ0030568 via m6A modification and curcumin could suppress PCa progression by inhibiting YTHDF2 mediated circ0030568 stability. Taken together, circ0030568 may act as a promising biomarker and an attractive target for PCa treatment and curcumin's inhibition of m6A-modified circ0030568 may be a potential mechanism of its anti-PCa.

Postinsertion Management of Cholecystostomy Tubes for Acute Cholecystitis: A Systematic Review

Background: Percutaneous gallbladder drainage (PGD) is indicated to treat high-risk patients with acute cholecystitis. Trends suggest increasing use of PGD over time as the population ages and lives longer with multiple comorbidities. There is no consensus on the management of cholecystostomies tube once inserted. This review aims to synthesize and describe the most common protocols in terms of the need and timing of follow-up imaging, management of a destination tube, timing of tube removal, and optimal interval time from tube positioning to delayed cholecystectomy. Methods: The study protocol has been registered on the International Prospective Register of Systematic Reviews–PROSPERO. Studies on adult patients diagnosed with acute cholecystitis who underwent a PGD from 2000 to November 2023 were included. The databases searched were MEDLINE, Embase, and Cochrane. The quality assessment tools provided by the NHLBI (National Heart, Lung, and Blood Institute) were applied and descriptive statistics were performed. Results: We included 22,349 patients from 94 studies with overall fair quality (6 prospective and 88 retrospective). In 92.7% of papers, the authors checked by imaging all patients with a PGD (41 studies included). Depending on protocol time, 30% of studies performed imaging within the first 2 weeks and 35% before tube removal (40 studies included). In the case of a destination tube, 56% of studies reported removing the tube (25 studies included). In the case of tube removal, the mean time after insertion was more than 4 weeks in 24 of the 33 included studies (73%). Interval cholecystectomies are more frequently performed after 5 weeks from PGD (32/38 included studies, 84%). Limitations included high clinical heterogeneity and prevalent retrospective studies. Conclusions: A standard management for percutaneous cholecystostomy after insertion is difficult to define based on existing evidence, and currently we can only rely on the most common existing protocols.

CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.

Prevalent studies on deep learning-based 3D medical image segmentation capture the continuous variation across 2D slices mainly via convolution, Transformer, inter-slice interaction, and time series models. In this work, via modeling this variation by an ordinary differential equation (ODE), we propose a cross instance query-guided Transformer architecture (CQformer) that leverages features from preceding slices to improve the segmentation performance of subsequent slices. Its key components include a cross-attention mechanism in an ODE formulation, which bridges the features of contiguous 2D slices of the 3D volumetric data. In addition, a regression head is employed to shorten the gap between the bottleneck and the prediction layer. Extensive experiments on 7 datasets with various modalities (CT, MRI) and tasks (organ, tissue, and lesion) demonstrate that CQformer outperforms previous state-of-the-art segmentation algorithms on 6 datasets by 0.44%-2.45%, and achieves the second highest performance of 88.30% on the BTCV dataset. The code will be publicly available after acceptance.

A Soul Laid Bare by Trauma

During treatment, most patients share their traumatic experiences, which can be a precipitating factor in the development of various psychiatric disorders. In numerous studies, traumatic experience has been highlighted as a possible etiological factor in the development of different personality disorders. Traumatic experiences in developmental stages of personality can result in disturbances in the ability to maintain feelings of security and individual identity in adulthood. Therefore, it is not difficult to imagine the dimension of trauma's impact on overall personality sensitivity and the inability to achieve individuation, separation, and overall personality cohesion. The most prevalent studies on the traumatic impact on personality development are related to borderline personality disorder (BPD). They point to the existence of a combination of genetic and environmental factors in the development of BPD, particularly the combination of biological vulnerability and exposure to traumatic experiences during childhood. Developing preventive programs and initiating psychotherapy in a timely manner, along with the use of contemporary pharmacotherapeutic treatment algorithms, can protect individuals at high risk of developing disorders or minimize disorder symptoms later in the lives of those affected.

Activated Migration of Hydroxide Ion in Anolyte for Enhancing Performance of Scaled-up Non-Noble Catalysts in Pure Water-Fed Anion Exchange Membrane Water Electrolysis

The continuous advancement in water electrolysis technologies has illuminated the potential of sustainable energy sources, particularly in the realm of green hydrogen production. Anion exchange membrane water electrolysis (AEMWE) stands out as a promising technology capable of theoretically yielding high-purity hydrogen. However, the commercialization of AEMWE faces challenges arising from the inherent discord between high performance and the elevated costs associated with noble metal catalysts, such as platinum and iridium. Moreover, prevalent studies often focus on a limited electrode area of less than 4 cm2, thereby restricting the exploitation of the high purity hydrogen production capabilities inherent in AEMWE. This research prioritizes the enhancement of performance through a meticulously crafted membrane-electrode assembly (MEA) employing a 25 cm2 Ni-frame caged NiMoO4 catalyst for cathode synthesis. The observed performance improvement is attributed to the augmented active site of the catalyst, consisting of NiMoO4 entrapped in the Ni substrate during the compression process of the electrode. Additionally, the increase in local pH at the interfacial between electrode and bulk electrolyte is facilitated by the capillary phenomenon resulting from the space between the Ni substrate and the NiMoO4 catalyst layer, leading to enhanced migration of the electrolyte containing hydroxide ions. Acknowledgements This research was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20213030040590).

Effect of cation species on pressure-driven electrokinetic energy conversion in charged conical nanochannels

Electrokinetic energy conversion (EEC) offers a promising avenue for transforming elusive natural energy into electric power, showing a wide application spectrum. Unlike prevalent studies that chiefly utilize symmetrical electrolyte solution (e.g., KCl) and uniformly structured negatively charged nanochannels for EEC, this paper delves into a thorough examination of EEC characteristics—streaming current, streaming potential, and output power—in both positively and negatively charged conical nanochannels with symmetrical and asymmetric (CaCl2 and LaCl3) electrolytes solutions. Operating under the assumption that the electrolyte solutions (KCl, CaCl2, and LaCl3) possess equivalent ionic strength and, thereby, a common Debye length, our findings reveal a significant dependency of EEC characteristics and their regulation parameters on the electrolyte type, nanochannel charge polarity, and ionic strength. As the ionic strength increases, both the streaming current and output power initially rise to a peak before subsequently declining, with the ionic strength at the peak being influenced by the cation valence: lower valence leads to lower ionic strength. In asymmetric electrolyte scenarios, optimal EEC characteristic is observed in positively charged conical nanochannels under a reverse pressure difference, attributed to the ion-selective and ionic concentration distribution in the charged conical nanochannels. Moreover, the complex behaviors of the regulation parameters of EEC characteristics are unveiled. Notably, a tri-valent electrolyte's regulation parameters exceed those of a bi-valent electrolyte, indicating that ionic valence asymmetry enhances the regulation effects on EEC in conical nanochannels.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis

BackgroundAscertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against “gold standard” (GS) methods to study the feasibility of using such methods in clinical trials.MethodsStudies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves.ResultsA total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5–98.3), but lacked sensitivity (63.5%, 95% CI 51.3–74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies.ConclusionsRCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

Artificial Intelligence and Productivity Improvement in the Digital Economy

The digital economy and AI development have significantly revolutionized diverse sectors, profoundly influencing productivity. Focusing on agriculture and the artificial fiber manufacturing industry, this paper scrutinizes the impact of AI on productivity in these domains. By methodically analyzing prevalent literature, research studies, industry reports, and case studies, this investigation aims to elucidate how AI has heightened productivity within these sectors. The results exhibit substantial enhancements in resource optimization, yield enhancement, quality control, and efficiency resulting from the implementation of AI. Furthermore, the subsequent sections of discussion and conclusion elucidate these findings, emphasizing the potential advantages and enduring challenges concerning AI’s assimilation to ameliorate productivity within the digital economy.

Determining sample sizes for combined incident and prevalent cohort studies with and without follow-up

Determining sample sizes for combined incident and prevalent cohort studies with and without follow-up

Doubly robust estimation under covariate-induced dependent left truncation.

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of the time to event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time, possibly induced by measured covariates. Inverse probability of truncation weighting can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply semiparametric theory to find the efficient influence curve of the expectation of an arbitrarily transformed survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches were developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two datasets from practice, with different right-censoring patterns.

Exploring Promising Therapies for Non-Alcoholic Fatty Liver Disease: A ClinicalTrials.gov Analysis.

Non-alcoholic fatty liver disease (NAFLD) is a common disease and has been increasing in recent years. To date, no FDA-approved drug specifically targets NAFLD. The terms "Non-alcoholic Fatty Liver Disease" and "NAFLD" were used in a search of ClinicalTrials.gov on August 24, 2023. Two evaluators independently examined the trials using predetermined eligibility criteria. Studies had to be interventional, NAFLD focused, in Phase IV, and completed to be eligible for this review. The ClinicalTrials.gov database was searched for trials examining pharmacotherapeutics in NAFLD. The search revealed 1364 trials, with 31 meeting the inclusion criteria. Out of these, 19 were finalized for evaluation. The dominant intervention model was Parallel. The most prevalent studies were in Korea (26.3%) and China (21.1%). The most common intervention was metformin (12.1%), with others like Exenatide and Pioglitazone accounting for 9.1%. Therapeutics used to manage NAFLD are limited. However, various medications offer potential benefits. Further investigations are definitely warranted.

Low-Viscous, Dilute Phase Adhesive from Dense Polyphenolic Coacervates of Poly(vinyl alcohol) and Tannic acid.

This study explores a polyphenolic coacervate, named VATA, formed by poly(vinyl alcohol) (PVA) and tannic acid (TA). Distinct from conventional studies that have focused on the bottom, dense phase of coacervates, this research emphasizes the top, dilute phase, low-viscous coacervate liquid termed liquid-VATA (l-VATA). Due to TA's capability of intermolecular association as well as adhesiveness, phenomena not typically observed in the upper dilute phase of standard polyelectrolyte-based coacervates are revealed. At first glance, the dilute phase l-VATA coacervate resembles a water-like, low-viscous mixture solution of PVA, TA, and PVA/TA complexes. However, analysis shows that nearly all of the TA molecules associate with PVA chains, forming PVA/TA complexes. Furthermore, supraparticular association was observed between PVA/TA complex nanoparticles upon applying external shear force. A broad survey of shear rate and strain showed that the solution exhibited sequential shear-thickening, followed by shear-thinning behavior. The water-like, low viscosity of l-VATA unexpectedly reveals robust adhesiveness and thus able to lift an entire mouse using just a single human hair strand. Even in cases of failure, no interfacial failure was detected between mouse and human hair. In addition to enabling hair-to-hair bonding, our study also showcases the efficacy of l-VATA in facilitating hair-to-skin adhesion. The results illustrate how the lower viscosity of l-VATA can be exploited for a wide range of industrial and cosmetic applications, allowing the formulation of thin, uniform adhesive layers, something unachievable with the dense, viscous VATA glue. Thus, this study highlights the importance of investigating the top dilute phase of coacervates, shedding light on an area often underestimated compared to the bottom dense phase reported in prevalent coacervate studies.

Diameter explains transient allocation of non-photosynthetic organs in trees

A central issue in plant ecology is exploring universal rules and the mechanisms under which photosynthetic energies are allocated to different organ parts. Until recently, prevalent studies focused on testing either optimal allocation theory or allometric allocation theory in predicting plant biomass partitioning patterns. However, paying much attention to the stable state prevents the development of new biomass allocation theories in transient time scales. Here, based on theories in transients and the allometric relationships in plant traits, I develop general theoretical models to study the transient perturbations of plant biomass allocated to non-photosynthetic organ parts. With both simulation and empirical approaches, I investigate the effect of plant stem diameter at breast height (DBH) on the variation of biomass allocation patterns during plant ontogeny. Results show that increases in DBH can mitigate the magnitude of the perturbations of plant biomass and biomass fractions allocated to both plant stem and root parts. The findings are robust when either deterministic or stochastic models are conducted. Moreover, empirical analyses from a large forest database in Eurasia consistently support the predictions from the theoretical frameworks. In this paper, I draw attention to the transient allocation pattern of plant biomass for non-photosynthetic organs, and I find the significant role of DBH. This work has important implications in both theoretical breakthroughs and practical applications. It not only provides the foundation to test new biomass allocation hypotheses but also directs agricultural and forest management to achieve stabilized yields.

What constitutes a “Key Enabling Technology” for transition processes: Insights from the bioeconomy's technological landscape

In policy strategies, Key Enabling Technologies (KETs) are described as knowledge intensive and multidisciplinary technologies driving profound transformations for modernization of industries. However, within scientific literature the concept of KETs is rather unexplored and prevalent case studies lack any clear definition of KETs. The question therefore arises as to whether KETs are needed as a new concept and how they differ to other technology types. Therefore, in this study we apply and extend existing patent indicators targeting the identification of promising technologies for transition processes from an ex-ante as well as ex-post perspective. By relying on hierarchical clustering on principal components of patent data complemented by an expert-based survey, we delineate KETs from emerging technologies and General Purpose Technologies (GPTs). On the example of the transition towards a bio-based economy, our results highlight that KETs combine broad knowledge revealing complementarity within a specific field of transition, while emerging technologies are less knowledge intensive and GPTs accelerate further innovation. Hence, we provide guidance to managers and policymakers in developing, identifying, and promoting promising technologies as engines of transition processes and economic growth. We, thus, support designing funding schemes and directing research endeavors for development and application of KETs.

Triboinformatic Modeling of Wear in Total Knee Replacement Implants Using Machine Learning Algorithms

Pin-on-disk (PoD) tests, the most prevalent studies, are being carried out in order to evaluate tribological behaviour of different bearing materials. However, the comparison of results obtained from the PoD tests is very difficult. In this present study, several machine learning models were developed and trained and then these trained machine learning models were validated by quantifying forecasting error against the experimental data reported in literature. These machine learning based models can be utilized as alternative solution of PoD trials in order to minimize time consumption and experiment complexity.

The Mediating Effects of Anxiety and Happiness and the Moderating Effect of Social Network Services for Employee Silence and Psychological Withdrawal Behavior

Employees not being able to express their thoughts and opinions about work has been argued to result in detrimental organizational outcomes. Employee silence has recently become a prevalent organizational issue but studies that have explored proximal and distal outcomes of silence are scarce. Therefore, the study explored anxiety and happiness as mediating mechanisms for the relationship between silence and psychological withdrawal and the moderating effect of social network services usage for the relationship between silence and the mediators. The study conducted a two-wave self-reported questionnaire and sampled 257 full-time employees. Anxiety and happiness were found to mediate the relationship and social network services moderated the relationships between silence with the mediators. Moreover, supplementary analysis found mediated moderation for the study.

Neural Rendering-Based 3D Scene Style Transfer Method via Semantic Understanding Using a Single Style Image

In the rapidly emerging era of untact (“contact-free”) technologies, the requirement for three-dimensional (3D) virtual environments utilized in virtual reality (VR)/augmented reality (AR) and the metaverse has seen significant growth, owing to their extensive application across various domains. Current research focuses on the automatic transfer of the style of rendering images within a 3D virtual environment using artificial intelligence, which aims to minimize human intervention. However, the prevalent studies on rendering-based 3D environment-style transfers have certain inherent limitations. First, the training of a style transfer network dedicated to 3D virtual environments demands considerable style image data. These data must align with viewpoints that closely resemble those of the virtual environment. Second, there was noticeable inconsistency within the 3D structures. Predominant studies often neglect 3D scene geometry information instead of relying solely on 2D input image features. Finally, style adaptation fails to accommodate the unique characteristics inherent in each object. To address these issues, we propose a novel approach: a neural rendering-based 3D scene-style conversion technique. This methodology employs semantic nearest-neighbor feature matching, thereby facilitating the transfer of style within a 3D scene while considering the distinctive characteristics of each object, even when employing a single style image. The neural radiance field enables the network to comprehend the geometric information of a 3D scene in relation to its viewpoint. Subsequently, it transfers style features by employing the unique features of a single style image via semantic nearest-neighbor feature matching. In an empirical context, our proposed semantic 3D scene style transfer method was applied to 3D scene style transfers for both interior and exterior environments. This application utilizes the replica, 3DFront, and Tanks and Temples datasets for testing. The results illustrate that the proposed methodology surpasses existing style transfer techniques in terms of maintaining 3D viewpoint consistency, style uniformity, and semantic coherence.

Hidradenitis Suppurativa and Risk of Coronary Artery Disease: A Systematic Review and Meta-Analysis.

Patients with hidradenitis suppurativa (HS) may have a higher risk of coronary artery disease (CAD) due to the excessive inflammatory burden. However, data on this association is still relatively limited. To investigate the association between HS and risk of prevalent and incident CAD by combining result from all available studies using systematic review and meta-analysis technique. Potentially eligible studies were identified from Medline and EMBASE databases from inception to November 2021 using search strategy that comprised of terms for 'hidradenitis suppurativa' (HS) and 'coronary artery disease' (CAD). Eligible study must be cohort study that consisted of one cohort of patients with HS and another cohort of individuals without HS. The study must report incidence or prevalence of CAD in both groups. The retrieved point estimates with standard errors from each study were summarized into pooled result using random-effect model and generic inverse variance method. Meta-analyses of the prevalent and incident CAD were conducted separately. A total of 876 articles were identified. After two rounds of independent review by three investigators, seven cohort studies (four incident studies and three prevalent studies) met the eligibility criteria and were analysed in the meta-analyses. The meta-analysis found a significantly elevated risk of both incident and prevalent CAD in patients with HS compared to individuals without psoriasis with the pooled risk ratio of 1.38 (95% CI, 1.21-1.58; I2 83%) and 1.70 (95% CI, 1.13-2.57; I2 89%), respectively. Limited accuracy of diagnosis of HS and CSD as most included studies relied on diagnostic codes and high between-study statistical heterogeneity. The current systematic review and meta-analysis found a significantly increased risk of both prevalent and incident CAD among patients with HS.

Anxiety disorders are associated with reduced bone mineral density in men: Findings from the Geelong Osteoporosis Study.

Certain psychiatric disorders, including depression, appear to impact adversely on bone health. Anxiety disorders are highly prevalent but few studies have examined their effects on bone tissue. This study investigated the effect of anxiety disorders on bone mineral density (BMD). This prospective cohort study used data from the Geelong Osteoporosis Study. Participants were women and men aged ≥20 years randomly selected from the electoral roll and followed up for a mean of 14.7 and 11.0 years, respectively. Participants were assessed for a lifetime history of an anxiety disorder using the Structured Clinical Interview for DSM-IV-TR. BMD in the lumbar spine and femoral neck was measured using dual-energy x-ray absorptiometry. Eight hundred and ninety women and 785 men participated in the study. Adjusting for sociodemographic, biometric and lifestyle factors, medical comorbidities and medication use, anxiety disorders were associated with reduced BMD at the lumbar spine (partial η2 = 0.006; p = 0.018) and femoral neck (partial η2 = 0.006; p = 0.003) in men. These associations became non-significant when men with a history of comorbid mood disorders were excluded from the analysis. There was no significant association between anxiety disorders and BMD in women (p ≥ 0.168). Anxiety disorders are associated with reduced BMD in men. This effect may be mediated by comorbid depression.

Point-of-Care Ultrasound in Children With Medical Complexity

Children with medical complexity (CMC) have significant chronic health conditions that involve congenital or acquired multisystem disease associated with medical fragility, functional limitations, dependence on technology, and high health care utilization. The objective of this study was to describe the indications, applications, and point-of-care ultrasound (POCUS) findings in this population. A descriptive study of POCUS scans performed for clinical purposes in CMC admitted to a single pediatric postacute care hospital. All children for whom a POCUS was requested by a medical team provider were eligible for inclusion. One hundred and four POCUS evaluations were performed for 33 patients. Diagnostic groups for the 33 patients included multiple congenital anomalies (41%), neurologic or neuromuscular (31%), prematurity (25%) and cardiac (3%). Lung, cardiac, and diaphragmatic ultrasound accounted for 57% of POCUS requested. POCUS was abnormal in 82% of diaphragmatic, 73% of lung, and 11% of cardiac ultrasounds. Twenty-three percent of POCUS studies were requested to answer a specific clinical question, 56% for follow-up information, and 21% for baseline evaluation. Lung, diaphragmatic, and cardiac ultrasound were the most prevalent POCUS studies requested in a postacute care hospital. POCUS may offer an expanded role in such patients and settings by answering clinical questions and by providing baseline and follow-up information.