bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hepatic recompensation according to Baveno VII criteria is linked to a significant survival benefit in decompensated alcohol‐related cirrhosis
Authors: Hofer Benedikt Silvester , Simbrunner Benedikt , Hartl Lukas , Jachs Mathias , Balcar Lorenz , Paternostro Rafael , Schwabl Philipp , Semmler Georg , Scheiner Bernhard , Trauner Michael , Mandorfer Mattias , Reiberger Thomas 
Published: July 2023

AbstractBackground & AimsRemoving the primary aetiological factor in decompensated cirrhosis may lead to a restoration of hepatic function. In this study, we investigated the clinical implications of recompensation and the subsequent survival in patients with decompensated alcohol‐related cirrhosis.MethodsThe rate of recompensation was evaluated in patients with decompensated alcohol‐related cirrhosis and persistent alcohol abstinence undergoing a hepatic venous pressure gradient (HVPG) measurement. Recompensation was defined according to Baveno VII criteria as resolution of ascites and hepatic encephalopathy, absence of variceal bleeding and improvement in liver function.ResultsTwo hundred and four abstinent patients with decompensated alcohol‐related cirrhosis (age: 57.2 [IQR:50.1–63.7] years; 75.0% male; median MELD: 15 [IQR:11–19]) and a median HVPG of 20 (IQR:18–24) mmHg were included. During a median follow‐up of 24.4 (IQR:10.9–50.4) months, 37 patients (18.1%) achieved abstinence‐induced recompensation. Lower baseline HVPG, lower Child‐Pugh score, lower BMI, higher albumin and higher mean arterial pressure were linked to a higher probability of recompensation. After adjusting for age, disease severity, portal hypertension and systemic inflammation, achieving recompensation resulted in a significant and considerable reduction in liver‐related mortality (adjusted HR: 0.091 [95% CI: 0.012–0.677]; p = .019). Only 13 patients (6.4%) developed hepatocellular carcinoma, with a tendency towards a lower risk upon recompensation (HR: 0.398 [95% CI: 0.084–1.878]; p = .245), yet this finding did not reach statistical significance and requires further investigation.ConclusionsAlcohol abstinence led to recompensation in 18.1% of our cohort of HVPG‐characterised patients with decompensated alcohol‐related cirrhosis. Achieving hepatic recompensation resulted in a >90% risk reduction in liver‐related mortality.
The role of insider knowledge in the trajectories of highly accomplished scientists
Authors: Subotnik Rena F. , Olszewski‐Kubilius Paula , Worrell Frank C. 
Published: July 2023

AbstractInsider knowledge is critical information about how to achieve success that is not available to the general public but is relatively well‐known to individuals within the domain and to those who have access to those individuals. The goal of this study was to examine the perceived role of insider knowledge in a sample of highly accomplished American professionals in science, technology, engineering, and mathematics (STEM). We asked participants explicitly if insider knowledge had played a role in their talent development trajectory from K–12 education to their current creative work, with questions related to experiences at the undergraduate and graduate level and during their careers. The study was exploratory, so no formal hypotheses were put forward. Given the lack of research on the topic of insider knowledge as defined in this paper, we conducted semi‐structured interviews with a select group of individuals who had successful careers in STEM fields, both to see if insider knowledge was something that they considered important and to see if they felt that insider knowledge had played a role in their educational and career trajectories. Our hope is that the results of this paper will inform future in‐depth studies on the topic of insider knowledge.
Hybrid Substrates for Heterogeneous Integration
Authors: Lau John H. , Chen Gary Chang-Fu , Yang Channing Cheng-Lin , Teng Vincent , Peng Andy Yan-Jia , Huang Jones Yu-Cheng , Liu Hsing-Ning , Chen Y. H. , Tseng Tzyy-Jang , Li Ming 
Published: July 2023

Abstract
The hybrid substrate (organic interposer + build-up package substrate + solder joints + underfill) of multiple systems and heterogeneous integration is investigated. The organic interposer of the hybrid substrate is fabricated by a fan-out chip-last on a temporary panel with two different kinds of dielectric materials, namely, the photo-imageable dielectric and the Ajinomoto Build-Up Film. These two hybrid substrates are supporting two different chips. The flatness of metal lines of the redistribution-layers, the quality of the hybrid substrate after chip-to-hybrid substrate bonding, and the reliability of the solder joints between these two hybrid substrates will be discussed.
Using collaborative critical autoethnography to decolonise through “seeing” and doing: Social work, community engagement, and ethical practice
Authors: Van Katwyk Trish , Guzik Catherine 
Published: July 2023

INTRODUCTION: This article reports on a collaborative critical autoethnographic study that we, two white settler social workers, conducted about our engagement with Inuit youth in Nunavut.
APPROACH: We facilitated three digital storytelling projects with youth living in three different Nunavut communities. By engaging in a collaborative critical autoethnography study, we were able to attend to the ways in which we were entering into communities, paying particular attention to the ways in which white supremist colonial thought has impacted our training and our locations within larger structures—shaped by colonising histories with consequences that mould day-to-day life and opportunity for the Inuit youth engaged in the digital storytelling.
FINDINGS: Through collaborative critical autoethnography, using individual research memos and guided dialogue, we considered the ways in which commodification was structured into our relationships, how these structures continue to be colonising, and consider the impact of the past and current colonisation. We also encountered the many strengths and resistances of the Inuit of Nunavut.
IMPLICATIONS: By bringing these considerations to light, we hope to enter into relationships with Inuit communities with fewer of the biases and assumptions that underlay and rationalise the structures that we have critically examined.
Hereditary Conditions Associated with Elevated Cancer Risk in Childhood
Authors: Suspitsin Evgeny N. , Imyanitov Evgeny N. 
Published: July 2023

Abstract
Widespread use of the next-generation sequencing (NGS) technologies revealed that a significant percentage of tumors in children develop as a part of monogenic hereditary diseases. Predisposition to the development of pediatric neoplasms is characteristic of a wide range of conditions including hereditary tumor syndromes, primary immunodeficiencies, RASopathies, and phakomatoses. The mechanisms of tumor molecular pathogenesis are diverse and include disturbances in signaling cascades, defects in DNA repair, chromatin remodeling, and microRNA processing. Timely diagnosis of tumor-associated syndromes is important for the proper choice of cancer treatment, genetic counseling of families, and development of the surveillance programs. The review describes the spectrum of neoplasms characteristic of the most common syndromes and molecular pathogenesis of these diseases.
Associations of Homeostatic Model Assessment for Insulin Resistance Trajectories With Cardiovascular Disease Incidence and Mortality
Authors: Lee Jun-Hyuk , Jeon Soyoung , Joung Boyoung , Lee Hye Sun , Kwon Yu-Jin 
Published: July 2023

Background:
Whether trends in insulin resistance changes are related to the risk of cardiovascular disease (CVD) incidence and mortality remains unclear. We aimed to examine the association of homeostatic model assessment for insulin resistance (HOMA-IR) trajectories with CVD incidence and mortality.

Methods:
Data from 6755 adults aged 40 to 69 years in the Korea Epidemiology and Genome Study were analyzed. During the exposure period (2001–2006), participants were classified into the increasing HOMA-IR trajectory group and the stable HOMA-IR trajectory group using a latent class mixture model. During the event accrual period (2007–2018), information about CVD and mortality were collected.

Results:
During the median 9.83-year event accrual period, there were 379 (5.6%) new-onset CVD, 535 (7.9%) all-cause mortality, 102 (1.5%) CVD mortality, and 47 (0.7%) major adverse cardiovascular event mortality cases. Compared with the stable HOMA-IR trajectory group, the fully adjusted hazard ratios (95% CIs) for the increasing HOMA-IR trajectory group were 1.59 (1.04–2.44) for incident CVD, 1.87 (1.30–2.69) for all-cause mortality, 2.33 (1.11–4.89) for CVD mortality, and 3.67 (1.38–9.76) for major adverse cardiovascular event mortality.

Conclusions:
An increasing HOMA-IR appears to be independently and positively related to incident CVD, all-cause mortality, CVD mortality, and major adverse cardiovascular event mortality. Early lifestyle interventions for individuals with increasing HOMA-IR trend could be a practical strategy to prevent CVD and CVD mortality.
Real-World Evidence of the Top 100 Prescribed Drugs in the USA and Their Potential for Drug Interactions with Nirmatrelvir; Ritonavir
Authors: Gerhart Jacqueline , Draica Florin , Benigno Michael , Atkinson Jo , Reimbaeva Maya , Francis Domenick , Baillon-Plot Nathalie , Sidhu Gurinder Singh , Damle Bharat D. 
Published: July 2023

AbstractNirmatrelvir (coadministered with ritonavir as PAXLOVIDTM) reduces the risk of COVID-19-related hospitalizations and all-cause death in individuals with mild-to-moderate COVID-19 at high risk of progression to severe disease. Ritonavir is coadministered as a pharmacokinetic enhancer. However, ritonavir may cause drug-drug interactions (DDIs) due to its interactions with various drug-metabolizing enzymes and transporters, including cytochrome P450 (CYP) 3A, CYP2D6, and P-glycoprotein transporters. To better understand the extent of DDIs (or lack thereof) of nirmatrelvir; ritonavir in a clinical setting, this study used real-world evidence (RWE) from the Optum Clinformatics Data Mart database to identify the top 100 drugs most commonly prescribed to US patients at high risk of progression to severe COVID-19 disease. The top 100 drugs were identified based on total counts associated with drugs prescribed to high-risk patients (i.e.,  ≥ 1 medical condition associated with an increased risk of severe COVID-19) who were continuously enrolled in the database throughout 2019 and had  ≥ 1 prescription claim. Each of the 100 drugs was then assessed for DDI risk based on their metabolism, excretion, and transport pathways identified from available US prescribing and medical literature sources. Seventy drugs identified were not expected to have DDIs with nirmatrelvir; ritonavir, including many cardiovascular agents, anti-infectives, antidiabetic agents, and antidepressants. Conversely, 30 drugs, including corticosteroids, narcotic analgesics, anticoagulants, statins, and sedatives/hypnotics, were expected to cause DDIs with nirmatrelvir; ritonavir. This RWE analysis is complementary to the prescribing information and other DDI management tools for guiding healthcare providers in managing DDIs.
Graphical Abstract
The Consequences of Revealing First-Generational Status
Authors: Belmi Peter , Raz Kelly , Neale Margaret , Thomas-Hunt Melissa 
Published: July 2023

College is regarded as the great equalizer. People with four-year degrees expect to reap the rewards of their education. This paper examines the pivotal transition from college to the labor market. How do candidates fare when they reveal to prospective employers that they are “first-gen”? Based on the literature, one may advance two competing predictions. One perspective predicts the possibility of a first-gen advantage. This view predicts that revealing one’s first-gen status can help applicants, by making them seem motivated, committed, responsible, and hardworking. It also makes for a compelling narrative; many Americans love stories of “bootstrapped” success. In contrast, a competing perspective predicts the possibility of a first-generation disadvantage. According to this view, there are forces that block decision makers from recognizing the strengths of first-gen students. We tested these two perspectives with an audit study (n = 1,783) and four follow-up studies (n = 4,920). The results supported the first-gen disadvantage hypothesis. Even in the mainstream labor market, first-gen students were evaluated less favorably. We traced this bias to the impact of one possible mechanism: deficit thinking. Despite overcoming hardships, first-gen students were often viewed through the lens of deficits. As a consequence, they were often denied opportunities to gain entry into organizations. Importantly, we found that a mindset shift can help ameliorate the problem. When we nudged decision makers to adopt a strengths-based lens, they became more receptive to hiring first-gen applicants. This work extends knowledge on the mechanisms that drive social class gaps in hiring. It also invites a reassessment of how to study social class in organizations. Deficit models dominate the study of social class. However, as we demonstrated, focusing on deficits can exacerbate inequality. It is important to consider people’s experiences and humanity holistically. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2023.1682 .
Tindak Pidana Pemerkosaan Anak (Telaah Putusan Pengadilan Negeri Bandung Nomor: 989/ Pid.Sus/2022/PN.Bdg)
Authors: Akbar Muhammad Ilham , Ratnadilah Nike Ayu , D.S Andrian Eka 
Published: July 2023

Kasus pemerkosaan yang dilakukan oleh Ust. HW menjadi sorotan media saat ini. Pemerkosaan yang dilakukan kepada 12 santri didikannya hingga melahirkan seorang anak dari hasil pemerkosaan tersebut. Dalam Hukum Positif di Indonesia, pada UU No. 35 tahun 2014 tentang perlindungan Anak, pasal 81 berisi tentang sanksi pemerkosaan anak mendapatkan hukuman penjara minimal 5 tahun dan maksimal 15 tahun. Namun dalam kasus tersebut, terdapat pertimbangan yang dilakukan oleh hakim sehingga Ust. HW dihukum mati, setelah sebelumnya sempat mendapatkan hukuman penjara seumur hidup. Sedangkan dalam hukum Islam, pemerkosaan termasuk dalam hukuman ta’zir, yang dapat berupa hukuman mati, salib, potong tangan, atau diasingkan.
Application of Artificial Intelligence in Predicting Machining Surface Quality
Authors: Pandey Rohit 
Published: July 2023

Achieving high-quality machining surface finishes is crucial in numerous manufacturing industries, as it directly impacts the performance and reliability of machined components. Traditionally, predicting machining surface quality involves extensive trial-and-error experiments, which are time-consuming, costly, and often impractical for complex machining processes. In recent years, the application of artificial intelligence (AI) techniques, particularly machine learning algorithms, has emerged as a promising approach for predicting machining surface quality accurately and efficiently. This abstract provides an overview of the application of AI in predicting machining surface quality, highlighting its benefits, challenges, and future prospects. The adoption of AI in predicting machining surface quality involves various stages. Firstly, a comprehensive dataset is collected, comprising machining parameters, tooling characteristics, and corresponding surface quality measurements. Next, pre-processing techniques are applied to clean and normalize the dataset, ensuring its suitability for training AI models. Subsequently, machine learning algorithms, such as support vector machines, neural networks, and random forests, are trained using the pre-processed data to develop predictive models. These models can capture complex relationships between machining parameters and surface quality, enabling accurate predictions.
The performance of the trained models is assessed using appropriate evaluation metrics, such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). Comparative analyses are conducted to identify the most effective AI model for predicting machining surface quality. Additionally, sensitivity analyses and feature selection techniques can be applied to identify the critical machining parameters that significantly impact surface quality. The application of AI in predicting machining surface quality offers several practical implications. It enables manufacturers to optimize machining processes, reduce scrap rates, and enhance product quality. By accurately predicting surface quality, manufacturers can make informed decisions regarding machining parameters, tooling selection, and process optimization, resulting in improved efficiency and cost-effectiveness. Furthermore, AI-based prediction models can be integrated into real-time monitoring systems, enabling continuous quality control and immediate adjustments to machining processes.
Despite the benefits, challenges exist in the application of AI for predicting machining surface quality. These include the availability and quality of training data, selection of appropriate features, and the interpretability of AI models. Overcoming these challenges requires continuous research and development efforts, such as the collection of large and diverse datasets, advanced feature engineering techniques, and the exploration of explainable AI methodologies.
Multiscale Modelling and Characterization of Coupled Damage-Healing For Materials in Concurrent Computational Homogenization Approach Using Machine Learning
Authors: Verma Deepak 
Published: July 2023

The multiscale modelling and characterization of coupled damage-healing phenomena in materials play a crucial role in understanding and predicting the behaviour of complex material systems. In this study, we propose a concurrent computational homogenization approach combined with machine learning techniques to model and characterize the coupled damage-healing process. The objective of this research is to develop an efficient and accurate methodology that can capture the intricate interactions between damage evolution and healing mechanisms at multiple scales. By integrating machine learning algorithms into the computational homogenization framework, we aim to enhance the predictive capabilities and computational efficiency of the modelling approach. The significance of this lies in its potential to provide valuable insights into the damage-healing behaviour of materials, which can aid in the development of advanced materials with enhanced durability and longevity. Furthermore, the proposed methodology has the potential to accelerate the design and optimization processes for engineering structures by providing accurate predictions of material response under varying loading conditions.
To achieve these objectives, we will review the existing literature on multiscale modelling, damage mechanics, healing mechanisms, and machine learning techniques. This literature review will serve as the foundation for developing the methodology. We will also investigate previous studies that have utilized machine learning in the context of material damage and healing to gain insights into the potential advantages and limitations of incorporating machine learning into the concurrent computational homogenization approach. The evaluation of the proposed methodology will be conducted through extensive numerical simulations and comparison with experimental results. Various metrics, such as damage evolution accuracy, healing efficiency, and computational efficiency, will be employed to assess the performance of the approach.
The outcomes of this research will provide a deeper understanding of the coupled damage-healing process in materials and establish a foundation for further advancements in multiscale modelling and characterization. The application of machine learning techniques in concurrent computational homogenization has the potential to revolutionize the field of materials science and engineering by enabling more accurate predictions and efficient design of materials with enhanced damage tolerance and self-healing capabilities.
Computational Modelling and Analysis of Heat Transfer in Microchannel Heat Exchangers Using Machine Learning
Authors: Joshi Gaurav 
Published: July 2023

Microchannel heat exchangers (MHEs) are becoming increasingly popular due to their compact size, high heat transfer efficiency, and potential for integration in various applications. However, accurately predicting and analyzing heat transfer in MHEs remains a challenging task due to the complex fluid dynamics and thermal behavior within the microchannels. In this study, we propose a computational modeling and analysis approach for heat transfer in MHEs using machine learning techniques. A numerical model is developed based on the conservation equations for mass, momentum, and energy. The model takes into account the effects of fluid flow, convection, and conduction within the microchannels. The solving these equations directly can be computationally expensive and time-consuming, especially for large-scale systems or complex geometries. We leverage the power of machine learning algorithms to build an efficient surrogate model. We employ a supervised learning approach and train the model using a dataset generated from the numerical simulations. Several machine learning algorithms, including random forest, support vector regression, and neural networks, are evaluated and compared for their predictive performance. The models are trained using a subset of the dataset and validated against the remaining data to ensure their generalizability. The surrogate model is trained, it can be used for rapid and efficient prediction of heat transfer performance in MHEs. By inputting the relevant parameters, such as fluid properties and channel dimensions, the model provides accurate predictions of heat transfer coefficients and pressure drops. This enables engineers and researchers to optimize the design and operation of MHEs without the need for extensive numerical simulations. The proposed approach not only reduces computational costs but also provides valuable insights into the underlying physics of heat transfer in MHEs. By analyzing the feature importance derived from the machine learning models .This study demonstrates the effectiveness of machine learning in computational modeling and analysis of heat transfer in microchannel heat exchangers. The developed surrogate model offers a promising tool for efficient design optimization and performance prediction of MHEs, paving the way for advancements in the field of microscale heat transfer.