Complex Set Research Articles

Road map fusion imaging in PCI reduces contrast medium exposure irrespective of investigator's experience level

Abstract Background/Introduction Dynamic Coronary Roadmap (DCR) is a software tool that creates a real-time dynamic coronary artery overlay on the fluoroscopic image. Its efficacy in significantly reducing contrast medium use during percutaneous coronary interventions (PCI) has already been shown. Purpose The purpose of this study is to investigate whether the positive effects of DCR persist irrespective of the investigator’s experience level. Methods In this sub-analysis of a monocentric, open-label, randomized trial, 130 patients with hemodynamically relevant coronary type A and B lesions were randomized (1:1 ratio) and conducted with or without DCR software. PCI was randomly allocated and performed by an investigator with high (A) or medium (B) experience level. Results Overall, the primary endpoint contrast medium use was significantly reduced by both investigators in the +DCR group (Investigator A: –DCR 68.62 +/- 23.86ml vs. +DCR 43.39 +/- 21.91ml; Investigator B: –DCR 70.80 +/- 31.77ml vs. +DCR 29.46 +/- 12.26ml, p < 0.0001). With the software, Investigator B used significantly less contrast medium than Investigator A (p = 0.0468). The DCR software is not accompanied by increased radiation exposure for the patients or the teams. On the contrary, the dose area product was reduced by both investigators but was significantly reduced with DCR in the highly experienced investigator (Investigator A: –DCR 2415.25 +/- 1413.30 cGycm² vs. +DCR 1727.17 +/- 1148.69 cGycm², p = 0.0284). Fluoroscopy time was not different between investigators. Procedural success was 100%. Serious in-hospital adverse events were not observed. One patient of Investigator A suffered from post-procedural acute kidney injury in the –DCR group. Conclusion(s) DCR significantly reduces contrast medium use during PCI irrespective of the investigator’s experience level. Efficacy and safety do not differ between investigators’ experience levels. Whether these excellent results can also be translated into more complex lesion settings has to be evaluated in future trials.Operating principle of DCRPrimary and secondary endpoints

Predictors of periprocedural myocardial infarction after left main bifurcation percutaneous coronary intervention guided by optical coherence tomography imaging: the PREVENT study

Abstract Background PMI is one of the most common and relevant complications that occur after PCI, but it is still a matter of controversy. This clinical entity has been frequently correlated to peculiar plaque morphological features by several intravascular imaging studies. PCI of the LM coronary artery is a complex procedural setting, where the prognostic value of PMI is higher. Intravascular ultrasound (IVUS) has a traditional predominance in the guidance of LM-PCI, but optical coherence tomography (OCT) outperforms IVUS in spatial resolution and has affirmed itself as a strong support to PCI. Purpose The aim of this study is to identify preprocedural and postprocedural optical coherence tomography (OCT) findings potentially related to periprocedural myocardial infarction (PMI) in patients undergoing percutaneous coronary intervention (PCI) for non-ostial left main (LM) bifurcation disease. Methods We retrospectively analyzed 76 patients who underwent OCT before and after LM-PCI. Definitions of OCT findings were derived from expert consensus documents and major OCT studies. PMI was defined in accordance with the Academic Research Consortium-2 (ARC-2) definition. Results The presence of TCFA was found to be an independent predictor of PMI, and coherently a significantly higher lipid core arc was observed in the PMI group. Mean stent area was the only post-PCI feature to result significantly higher in the PMI group. Conclusions We found the presence of TCFA at pre-PCI OCT was an independent predictor of PMI. A higher mean stent area at post-PCI OCT in patients who developed PMI may suggest that aggressive stent expansion could not always be the right choice in such a context.

Neuro-HIV-New insights into pathogenesis and emerging therapeutic targets.

HIV-associated neurocognitive disorders (HAND) is a term describing a complex set of cognitive impairments accompanying HIV infection. Successful antiretroviral therapy (ART) reduces the most severe forms of HAND, but milder forms affect over 50% of people living with HIV (PLWH). Pathogenesis of HAND in the ART era remains unknown. A variety of pathogenic factors, such as persistent HIV replication in the brain reservoir, HIV proteins released from infected brain cells, HIV-induced neuroinflammation, and some components of ART, have been implicated in driving HAND pathogenesis in ART-treated individuals. Here, we propose another factor-impairment of cholesterol homeostasis and lipid rafts by HIV-1 protein Nef-as a possible contributor to HAND pathogenesis. These effects of Nef on cholesterol may also underlie the effects of other pathogenic factors that constitute the multifactorial nature of HAND pathogenesis. The proposed Nef- and cholesterol-focused mechanism may provide a long-sought unified explanation of HAND pathogenesis that takes into account all contributing factors. Evidence for the impairment by Nef of cellular cholesterol balance, potential effects of this impairment on brain cells, and opportunities to therapeutically target this element of HAND pathogenesis are discussed.

The influence of internship training experience on Kenyan and Ugandan doctors’ career intentions and decisions: a qualitative study

ABSTRACT Background Medical internship is a key period for doctors’ individual career planning and also a transition period for the broader labour market. Objectives We aimed to understand the complex set of factors influencing the career intentions and decisions of junior doctors, post-internship in Kenya and Uganda. Methods We conducted semi-structured interviews with 54 junior medical officers and 14 consultants to understand doctors’ internship experiences and subsequent employment experiences. We analysed the data using a mix of a direct content approach, informed by an internship experience and career intentions framework developed primarily from high-income country literature, alongside a more inductive thematic analysis. Results Echoing the internship experience and career intentions framework, we found that clinical exposure during internship, work–life balance, aspects of workplace culture such as relationships with consultants and other team members, and concerns over future job security and professional development all influenced Kenyan and Ugandan doctors’ career preferences. Additionally, we added a new category to the framework to reflect our finding that interns might want to ‘fill a health system gap’ when they choose their future careers, based on what they witness as interns. However, often career intentions did not match career and employment decisions due to specific contextual factors, most importantly a shortage of job opportunities. Conclusion We have shown how internship experiences shape medical doctors’ career intentions in Kenya and Uganda and highlighted the importance of job availability and context in influencing doctors’ career choices.

Re/assembling Antiracisms – or How to Produce “Para-Sites” of Co-Articulation. A Field Attempt

Abstract Against the background of the ongoing collaborative research and archiving project “Re/assembling antiracisms in Germany,” I discuss what anthropologists have to offer in highly complex settings of “activist expert cultures,” and thus how such projects can best be designed. The paper argues that rather than thinking of such projects in terms of collaboration (or “co-laboration”), it is more about co-articulating the differently situated knowledges and experiences on offer, with anthropologists more and more taking on the role of a curator. By drawing on the concept of “para-sites,” as proposed by Douglas Holmes and Georg Marcus, I further argue that it is no longer about helping others to find their voice but rather about creating spaces and infrastructures of listening.

Balancing the Objectives of Statistical Efficiency and Allocation Randomness in Randomized Controlled Trials

Various restricted randomization procedures are available to achieve equal (1:1) allocation in a randomized clinical trial. However, for some procedures, there is a nonnegligible probability of imbalance in the final numbers which may result in an underpowered study. It is important to assess such probability at the study planning stage and make adjustments in the design if needed. In this article, we perform a quantitative assessment of the tradeoff between randomness, balance, and power of restricted randomization designs targeting equal allocation. First, we study the small-sample performance of biased coin designs with known asymptotic properties and identify a design with an excellent balance–randomness tradeoff. Second, we investigate the issue of randomization-induced treatment imbalance and the corresponding risk of an underpowered study. We propose two risk mitigation strategies: increasing the total sample size or fine-tuning the biased coin parameter to obtain the least restrictive randomization procedure that attains the target power with a high, user-defined probability for the given sample size. Additionally, we investigate an approach for finding the most balanced design that satisfies a constraint on the chosen measure of randomness. Our proposed methodology is simple and yet generalizable to more complex settings, such as trials with stratified randomization and multi-arm trials with possibly unequal randomization ratios.

How do residents perceive local food system activity? Perspectives of system performance and awareness using the local food vitality survey

ABSTRACT Local food systems are complex sets of relationships between farmers, residents, market venues, intermediating institutions, social values, and agricultural landscapes. They are also, by definition, locally unique. How might advocates of local food systems (LFSs) evaluate the complicated expectations, hopes, and perceptions of those who reside within these systems? In this manuscript, we demonstrate a generalizable survey approach that involves collecting residential perspectives to understand food system performance. We use survey responses from 13 diversely sized and structured communities in the Southeastern United States to understand residents’ 1) awareness of and 2) performance evaluations for 21 different aspects of their local food systems. We use probit and ordered probit models, respectively, to conceptualize how different place-based demographic and geographic characteristics impact these perceptions. By bringing consumer perspectives into view, LFS development personnel and other stakeholders may better understand where their residents place value within their unique systems.

A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning

The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.

IST1 regulates select recycling pathways.

ESCRTs (Endosomal Sorting Complex Required for Transports) are a modular set of protein complexes with membrane remodeling activities that include the formation and release of intraluminal vesicles (ILVs) to generate multivesicular endosomes. While most of the 12 ESCRT-III proteins are known to play roles in ILV formation, IST1 has been associated with a wider range of endosomal remodeling events. Here, we extend previous studies of IST1 function in endosomal trafficking and confirm that IST1, along with its binding partner CHMP1B, contributes to scission of early endosomal carriers. Functionally, depleting IST1 impaired delivery of transferrin receptor from early/sorting endosomes to the endocytic recycling compartment and instead increased its rapid recycling to the plasma membrane via peripheral endosomes enriched in the clathrin adaptor AP-1. IST1 is also important for export of mannose 6-phosphate receptor from early/sorting endosomes. Examination of IST1 binding partners on endosomes revealed that IST1 interacts with the MIT domain-containing sorting nexin SNX15, a protein previously reported to regulate endosomal recycling. Our kinetic and spatial analyses establish that SNX15 and IST1 occupy a clathrin-containing subdomain on the endosomal perimeter distinct from those previously implicated in cargo retrieval or degradation. Using live-cell microscopy, we see that SNX15 and CHMP1B alternately recruit IST1 to this subdomain or the base of endosomal tubules. These findings indicate that IST1 contributes to a subset of recycling pathways from the early/sorting endosome.

A Critical Review of the Effectiveness of Machine Learning & Deep Learning Approaches in Forecasting Stock Market Trends

For a long time, traders as well as researchers have been actively interested in financial market forecasting. A financial market prediction has an interest for investors & researchers since long time. An existing method for predicting stock prices often rely on technical and fundamental analysis, which have limitations in handling the complexity and volume of financial data. To handle complex data sets for forecasting the value of stock using new emerging learning techniques. The most commonly used approach such as artificial neural networks (ANN), neural network techniques, SVM, decision trees algorithm, and random forests. These techniques can capture complex nonlinear relationships between variables and adjust to changing market conditions. The machine learning & deep learning analyze the trend for the future values of stock prediction and provides observation for making decision. The importance of feature selection and data preprocessing are used for improving the prediction accuracy of stock value. The genetic method feature selection concept can reduce the dimensionality of the data and remove irrelevant features, while data preprocessing techniques such as normalization and scaling can improve the stability and convergence of the algorithms. Sentiment analysis of social media data can capture market sentiment and investor behavior, while news articles can provide insights into company performance and industry trends. The accuracy of predictions tends to decrease as the prediction horizon increases and the prediction accuracy varies across different stock markets. A lot of significant work, the most recent stock market-related prediction system includes multiple limitations. Now the conclusion, the projection of stock markets is a hybrid approach & certain parameters for stock market forecasting should be as accurate as possible.

Constraints on crustal recycling from boron isotopes in Italian melt inclusions

Boron represents an important tracer of crustal recycling processes in subduction zones, because it is readily mobilised from the subducted lithosphere and different components in the slab are isotopically distinct. Profiles of boron content and isotope ratio across magmatic arcs generally show that B concentrations decrease with increasing slab depth, which implies decreasing amount of slab-derived fluids. To date, however, data on continental-collision zones and post-collisional subduction settings are scarce.This study examines Plio-Quaternary Italian magmatism to quantify crustal recycling in a complex subduction setting. Magmatic products vary from (ultra)potassic along the Tyrrhenian side in the north, to calcalkaline and Na-alkaline in the south.Combined major and trace element and [B] content and δ11B values are reported in 99 Melt Inclusions (MIs), analyses from a wide range of Italian lavas. [B] vary from 4 to 298 µg/g and δ11B from -29.2 to -3.9‰. The B isotopic values are considerably lower than previously reported in arcs and other post-collisional setting magmatism. We infer a role for phengite in the source of all studied Italian magmas (with the exception of Mt. Etna lavas). This white mica is stable to high pressures in subducted sediments of altered oceanic crust and records dehydration and 11B depletion due to dehydration processes.MIs hosted in highly fosteritic olivines (Fo >74; median of 89) from across Italy reveal that primary melts tap heterogeneous mantle including subducted oceanic and continental components that were introduced during the Alpine, and Adriatic and Ionian subduction phases.The combined geochemical data record the involvement of sediments that variably metasomatized the mantle wedge. We propose that slab detachment and consequent heat input from the inflow of hot asthenosphere was responsible for phengite breakdown in subducted sediments and locally produced metasomatism of the mantle wedge, imposing a characteristic B isotope signature to the overlying mantle. Continued heating due to asthenosphere inflow led to melting of the metasomatized mantle wedge and generation of the Italian magmatism. Mt. Etna represents an exception being dominated by asthenosphere upwelling through a slab window with minimal influence from active subduction.

Ontogenetic dynamics of the nudibranch epithelium in Onchidoris muricata (O.F. Müller, 1776)

The integumentary system is the set of organs forming the outermost layer of an animal's body. It comprises the epithelium, muscles, and elements of connective and nerve tissue. The integument acts as a physical barrier between the external environment and the internal environment that serves to protect and maintain the body of the animal. The body of nudibranch mollusks undergo significant changes during ontogenesis, with the subepidermal space changing as the mollusk grows. As the extracellular subepidermal matrix is modified, the number of collagen fibers increases, muscles and nerves develop, and calcite spicules appear and grow. Yet, specific knowledge pertaining to the transformation of the epithelium is absent. In the present work, the ontogenetic dynamics of the surface epithelium of nudibranch mollusks are traced for the first time using Onchidoris muricata (O. F. Müller, 1776) during the postlarval stages of development. Ontogenetic changes in the epithelium of O. muricata were studied using a complex set of morphological methods. According to our data, the degree of modification to the epithelium in ontogenesis depends on individual body parts and is not consistent throughout. First x-cells were recognized as the probable precursors to sclerocytes.

Designing and Implementing the Adaptive Learning Meeting Cycle: The (re)solve Project Experience in Burkina Faso.

Through the collection and utilization of timely data, program implementers can review feedback and make rapid adjustments and adaptations to interventions while they are in progress.Responsive feedback mechanisms (RFMs), which emphasize flexibility and iterative adaptation, provide opportunities for improving the fit and feasibility of a program, as well as its effectiveness. The (re)solve project-a 5-year, multicountry project testing new, context-specific solutions to address unmet need for family planning-used responsive feedback to ensure products and services designed and implemented were responsive to the context and preferences of health care workers, women, and girls. The adaptive learning meeting (ALM) cycle was designed as an RFM during implementation. This frequent series of rapid, actionable, cross-team meetings builds on several existing frameworks and practices. The ALM cycle used rapid, close to real-time data and observations from a wide range of stakeholders; routine monitoring data; and a structured, facilitated process to examine and act on feedback. Each cycle was repeated every 2 weeks for 3 months in Burkina Faso. During each cycle, the team interpreted data and feedback from multiple sources; assessed pragmatic, actionable options to address the feedback; and identified and agreed upon short- and long-loop adaptations that could improve the implementation process, coordination, efficiency, and outputs of the project. The ALM cycle proved helpful in engendering practices of surfacing and checking assumptions, careful interpretation of data, options analysis, and decision-making. The emphasis was on actionable feedback for improvement of the intervention rather than rigor of results and findings. The (re)solve project's experience with designing, structuring, and implementing the ALM cycle to address challenges and gaps in implementation can be informative for similar programs seeking to implement RFMs in complex and dynamic settings, especially where technology-based RFMs are not an option.

Asset pricing with neural networks: Significance tests

This study proposes a novel hypothesis test for evaluating the statistical significance of input variables in multi-layer perceptron (MLP) regression models. Theoretical foundations are established through consistency results and estimation rate analysis using the sieves method. To validate the test’s performance in complex and realistic settings, an extensive Monte Carlo simulation is conducted. Results of the simulation reveal that the test has a high power and low rate of false positives, making it a powerful tool for detecting true effects in data. The test is further applied to identify the most influential predictors of equity risk premiums, with results indicating that only a small number of characteristics have statistical significance and all macroeconomic predictors are insignificant at the 1% level. These findings are consistent across a variety of neural network architectures.

A qualitative study on healthcare professional and patient perspectives on nurse-led virtual prostate cancer survivorship care

BackgroundVirtual nurse-led care models designed with health care professionals (HCPs) and patients may support addressing unmet prostate cancer (PCa) survivor needs. Within this context, we aimed to better understand the optimal design of a service model for a proposed nurse-led PCa follow-up care platform (Ned Nurse).MethodsA qualitative descriptive study exploring follow-up and virtual care experiences to inform a nurse-led virtual clinic (Ned Nurse) with an a priori convenience sample of 10 HCPs and 10 patients. We provide a health ecosystem readiness checklist mapping facilitators onto CFIR and Proctor’s implementation outcomes.ResultsWe show that barriers within the current standard of care include: fragmented follow-up, patient uncertainty, and long, persisting wait times despite telemedicine modalities. Participants indicate that a nurse-led clinic should be scoped to coordinate care and support patient self-management, with digital literacy considerations.ConclusionA nurse-led follow-up care model for PCa is seen by HCPs as acceptable, feasible, and appropriate for care delivery. Patients value its potential to provide role clarity, reinforce continuity of care, enhance mental health support, and increase access to timely and targeted care. These findings inform design, development, and implementation strategies for digital health interventions within complex settings, revealing opportunities to optimally situate these interventions to improve care.

Designing human-AI systems for complex settings: ideas from distributed, joint, and self-organising perspectives of sociotechnical systems and cognitive work analysis

Real-world events like the COVID-19 pandemic and wildfires in Australia, Europe, and America remind us that the demands of complex operational settings are met by multiple, distributed teams interwoven with a large array of artefacts and networked technologies, including automation. Yet, current models of human-automation interaction, including those intended for human-machine teaming or collaboration, tend to be dyadic in nature, assuming individual humans interacting with individual machines. Given the opportunities and challenges of emerging artificial intelligence (AI) technologies, and the growing interest of many organisations in utilising these technologies in complex operations, we suggest turning to contemporary perspectives of sociotechnical systems for a way forward. We show how ideas of distributed cognition, joint cognitive systems, and self-organisation lead to specific concepts for designing human-AI systems, and propose that design frameworks informed by contemporary views of complex work performance are needed. We discuss cognitive work analysis as an example.

Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms.

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10-2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed.

Exploring disruption through the lens of an adapted Five Senses Framework

This quasi-experimental research design surveyed 688 students through a self-administered online survey to specifically explore relations between student self-assessed capabilities (Lizzio Five Senses, 2006), overall program satisfaction, withdrawal behaviours, demographics and year of study in their university courses during an emergency COVID-19 lockdown experience. Importantly, this research offers a more nuanced view of the Five Senses and confirms their importance as a university strategy for student success. These findings offer further granularity into the complex set of relations that impact decisions around satisfaction, persistence, and capability in higher education and support previous research by Lizzio and Wilson (2008) indicating students’ perceptions of purpose is the strongest predictor of satisfaction, lower anxiety and lower course withdrawal. Ultimately, the paper suggests as higher education looks towards future possible disruptions due to climate, health or political realities, equipping and fostering a strong sense of purpose, connectedness, and resourcefulness as well as sense of capability and academic culture will buffer and support students to persevere. In addition, this research suggests that those students who may have weak associations with these senses merit additional attention.

Evaluation of seasonal variation of geoelectrical parameters in a part of the basement complex setting, southwestern Nigeria: implication for groundwater

Evaluation of seasonal variation of geoelectrical parameters in a part of the basement complex setting, southwestern Nigeria: implication for groundwater

Connectedness, Empathy, Relationships, and Leadership

Physician leadership is a complex set of aptitudes, attributes, knowledge, and experience, and the leadership required to ensure true patient-centered care is complicated. Critical to every situation is the sense of connectedness and the variety of relationships with patients, peers, friends, and family.