bluuebunny/crossref_metadata_2025 huggingface dataset:
Early menopause and hormone therapy as determinants for lung health outcomes: a secondary analysis using the PLCO trial
Authors: Gai Xiaochun , Feng Yue , Flores Tessa M , Kang Huining , Yu Hui , Leslie Kimberly K , Zhu Yiliang , Doherty Jennifer A , Guo Yan , Belinsky Steven A , Cook Linda S , Leng Shuguang
Published: June 2024

Rationale

Early natural menopause (early-M; <45 years of age) increases the risk of lung morbidities and mortalities in smokers. However, it is largely unknown whether early-M due to surgery demonstrates similar effects and whether menopausal hormone therapy (MHT) is protective against lung diseases.

Objectives

To assess the associations of early-M and MHT with lung morbidities and mortalities using the prospective Prostate, Lung, Colorectal and Ovarian (PLCO) trial.

Methods

We estimated the risk among 69 706 postmenopausal women in the PLCO trial, stratified by menopausal types and smoking status.

Results

Early-M was associated with an increased risk of most lung disease and mortality outcomes in ever smokers with the highest risk seen for respiratory mortality (HR 1.98, 95% CI 1.34 to 2.92) in those with bilateral oophorectomy (BO). Early-M was positively associated with chronic bronchitis, and all-cause, non-cancer and respiratory mortality in never smokers with natural menopause or BO, with the highest risk seen for BO— respiratory mortality (HR 1.91, 95% CI 1.16 to 3.12). Ever MHT was associated with reduced all-cause, non-cancer and cardiovascular mortality across menopause types regardless of smoking status and was additionally associated with reduced risk of non-ovarian cancer, lung cancer (LC) and respiratory mortality in ever smokers. Among smokers, ever MHT use was associated with a reduction in HR for all-cause, non-cancer and cardiovascular mortality in a duration-dependent manner.

Conclusions

Smokers with early-M should be targeted for smoking cessation and LC screening regardless of menopause types. MHT users had a lower likelihood of dying from LC and respiratory diseases in ever smokers.
Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content
Authors: Choudhary Monika , Chouhan Satyendra Singh , Rathore Santosh Singh
Published: June 2024

User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question–answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, and images), multimodal representation of UGC has become increasingly popular. This article reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the article presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the article provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal UGC are presented.
Automated Backend Allocation for Multi-Model, On-Device AI Inference
Authors: Iyer Venkatraman , Lee Sungho , Lee Semun , Kim Juitem Joonwoo , Kim Hyunjun , Shin Youngjae
Published: June 2024

On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for execution on CPU and GPU computing backends. While GPU delegation is a common practice to achieve high speed computation, the approach suffers from degraded throughput and completion times under multi-model scenarios, i.e., concurrently executing services. This paper introduces a solution to sustain performance in multi-model, on-device AI contexts by dynamically allocating a combination of CPU and GPU backends per model. The allocation is feedback-driven, and guided by a knowledge of model-specific, multi-objective pareto fronts comprising inference latency and memory consumption. Our backend allocation algorithm that runs online per model, and achieves 25-100% improvement in throughput over static allocations as well as load-balancing scheduler solutions targeting multi-model scenarios.
Change Point Detection with Adaptive Measurement Schedules for Network Performance Verification
Authors: Lindståhl Simon , Proutiere Alexandre , Johnsson Andreas
Published: June 2024

When verifying that a communications network fulfills its specified performance, it is critical to note sudden shifts in network behavior as quickly as possible. Change point detection methods can be useful in this endeavor, but classical methods rely on measuring with a fixed measurement period, which can often be suboptimal in terms of measurement costs. In this paper, we extend the existing framework of change point detection with a notion of physical time. Instead of merely deciding when to stop, agents must now also decide at which future time to take the next measurement. Agents must now minimize the necessary number of measurements pre- and post-change, while maintaining a trade-off between post-change delay and false alarm rate. We establish, through this framework, the suboptimality of typical periodic measurements and propose a simple alternative, called crisis mode agents. We show analytically that crisis mode agents significantly outperform periodic measurements schemes. We further verify this in numerical evaluation, both on an array of synthetic change point detection problems as well as on the problem of detecting traffic load changes in a 5G test bed through end-to-end RTT measurements.
Distributed Speed Scaling in Large-Scale Service Systems
Authors: Rutten Daan , Zubeldia Martin , Mukherjee Debankur
Published: June 2024

We consider a large-scale parallel-server loss system with an unknown arrival rate, where each server is able to adjust its processing speed. The objective is to minimize the system cost, which consists of a power cost to maintain the servers' processing speeds and a quality of service cost depending on the tasks' processing times, among others. We draw on ideas from stochastic approximation to design a novel speed scaling algorithm and prove that the servers' processing speeds converge to the globally asymptotically optimum value. Curiously, the algorithm is fully distributed and does not require any communication between servers. Apart from the algorithm design, a key contribution of our approach lies in demonstrating how concepts from the stochastic approximation literature can be leveraged to effectively tackle learning problems in large-scale, distributed systems. En route, we also analyze the performance of a fully heterogeneous parallel-server loss system, where each server has a distinct processing speed, which might be of independent interest.
FedQV: Leveraging Quadratic Voting in Federated Learning
Authors: Chu Tianyue , Laoutaris Nikolaos
Published: June 2024

Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregating local models to produce the global one, shares many similarities with public decision-making, and elections in particular. In that context, a major weakness of FL, namely its vulnerability to poisoning attacks, can be interpreted as a consequence of the one person one vote (henceforth 1p1v) principle that underpins most contemporary aggregation rules.

In this paper, we introduce FedQV, a novel aggregation algorithm built upon the quadratic voting, recently proposed as a better alternative to 1p1v-based elections. Our theoretical analysis establishes that FedQV is a truthful mechanism in which bidding according to one's true valuation is a dominant strategy that achieves a convergence rate matching that of state-of-the-art methods. Furthermore, our empirical analysis using multiple real-world datasets validates the superior performance of FedQV against poisoning attacks. It also shows that combining FedQV with unequal voting "budgets'' according to a reputation score increases its performance benefits even further. Finally, we show that FedQV can be easily combined with Byzantine-robust privacy-preserving mechanisms to enhance its robustness against both poisoning and privacy attacks. This extended abstract is an abridged version of [3].
A High-bandwidth High-capacity Hybrid 3D Memory for GPUs
Authors: Akbarzadeh Negar , Darabi Sina , Gheibi-Fetrat Atiyeh , Mirzaei Amir , Sadrosadati Mohammad , Sarbazi-Azad Hamid
Published: June 2024

GPUs execute thousands of active threads simultaneously, requiring high memory bandwidth to handle multiple memory requests efficiently. The memory bandwidth in GPUs has always been increasing, but it is still insufficient for the demands of fine-grained threads, necessitating a higher memory bandwidth. Important workloads like deep learning and data analytics demand terabytes of data processing, necessitating high memory capacity and bandwidth to avoid performance overheads. True-3D stacking of non-volatile memory layers on GPUs can provide the required higher bandwidth and capacity, enhancing performance and energy efficiency. We propose a high-bandwidth high-capacity hybrid 3D memory (H3DM) that doubles bandwidth through true-3D integration compared to the baseline GPU architecture and affords 272 GB of total memory capacity by stacking 8 PCM layers (each of 32 GB) and two DRAM layers (each of 8 GB).
HEAL: Performance Troubleshooting Deep inside Data Center Hosts
Authors: Pan Yicheng , Zhang Yang , Bi Tingzhu , Han Linlin , Zhang Yu , Ma Meng , Shen Xiangzhuang , Jiang Xinrui , Wang Feng , Liu Xian , Wang Ping
Published: June 2024

This study demonstrates the salient facts and challenges of host failure operations in hyperscale data centers. A host incident can involve hundreds of distinct host-level metrics. The faulting mechanism inside the host connects these heterogeneous metrics through direct and indirect correlation, making it extremely difficult to sort out the propagation procedures and the root cause from these intertwined indicators. To deeply understand the failure mechanism inside the host, we develop HEAL -- a novel host metrics analysis toolkit. HEAL discovers dynamic causality in sparse heterogeneous host metrics by combining the strengths of both time series and random variable analysis. It also extracts causal directional hints from causality's asymmetry and historical knowledge, which finally help HEAL produce accurate results given undesirable inputs. Evaluations in our production environment verify that HEAL provides significantly better result accuracy and full-process interpretability than the SOTA baselines. With these advantages, HEAL successfully serves our data center and worldwide product operations and impressively contributes to many other workflows.
Heavy-Traffic Optimal Size- and State-Aware Dispatching
Authors: Xie Runhan , Grosof Isaac , Scully Ziv
Published: June 2024

We study the problem of dispatching jobs to multiple FCFS (First-Come, First-Served) queues. We consider the case where the dispatcher is size-aware, meaning it learns the size (i.e. service time) of each job as it arrives; and state-aware, meaning it always knows the amount of work (i.e. total remaining service time) at each queue. While size- and state-aware dispatching to FCFS queues has been extensively studied, little is known about optimal dispatching for the objective of minimizing mean delay. In this work, we propose the first size- and state-aware dispatching policy, called CARD (Controlled Asymmetry Reduces Delay), that provably minimizes mean delay in heavy traffic. This work summarizes our full paper.
MetaVRadar: Measuring Metaverse Virtual Reality Network Activity
Authors: Lyu Minzhao , Tripathi Rahul Dev , Sivaraman Vijay
Published: June 2024

The ''metaverse'', wherein users can immerse in virtual worlds through their VR headsets to work, study, play, shop, socialize, and entertain, is fast becoming a reality. However, little is known about the network dynamics of metaverse VR applications, which are needed to make telecommunications network infrastructure ''metaverse ready'' to support superlative user experience. This work is an empirical measurement study of metaverse VR network behavior. By analyzing metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, characterizing network traffic per state, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Our second contribution develops a real-time method MetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implements MetaVRadar in a large university campus network to demonstrate its usability.
Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes
Authors: Hong Yige , Xie Qiaomin , Wang Weina
Published: June 2024

In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement varies over time as a Markov chain while in service. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in the steady state.

Under our problem formulation, we develop a job dispatch policy, named Join-Requesting-Server (JRS). We show that in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r, JRS achieves an additive optimality gap of O(√r) in the objective value, where the optimal objective value is Θ(r). Our approach highlights a novel policy conversion framework that utilizes the solution of a single-server problem.
Understanding Money Using Historical Evidence
Authors: Brzezinski Adam , Palma Nuno , Velde François R.
Published: June 2024

Debates about the nature and economic role of money are mostly informed by evidence from the twentieth century, but money has existed for millennia. We argue that there are many lessons to be learned from monetary history that are relevant for current topics of policy relevance. The past is a source of evidence on how money works across different situations, helping to tease out features of money that do not depend on one time and place. A close reading of history also offers testing grounds for models of economic behavior and can thereby guide theories on how money is transmitted to the real economy.
Some Reflections on a Career in Organizational Behavior
Authors: Oldham Greg R.
Published: June 2024

In this article, I reflect on my journey in the field of organizational behavior. It was an unplanned journey but one that has lasted more than 50 years and has been incredibly rewarding. I discuss some of the early decisions that were instrumental in my choosing this career and the people and experiences that shaped my research program. I also reflect on some of the changes in the field of organizational behavior that I've noticed over that past five decades and some of the opportunities for scholars to contribute in the years ahead to one of my research areas—the design of jobs. I conclude with a brief discussion of my journey's end.
Studying Retroviral Life Cycles Using Visible Viruses and Live Cell Imaging
Authors: Guerrero Jorge F. , Lesko Sydney L. , Evans Edward L. , Sherer Nathan M.
Published: June 2024

Viruses exploit key host cell factors to accomplish each individual stage of the viral replication cycle. To understand viral pathogenesis and speed the development of new antiviral strategies, high-resolution visualization of virus-host interactions is needed to define where and when these events occur within cells. Here, we review state-of-the-art live cell imaging techniques for tracking individual stages of viral life cycles, focusing predominantly on retroviruses and especially human immunodeficiency virus type 1, which is most extensively studied. We describe how visible viruses can be engineered for live cell imaging and how nonmodified viruses can, in some instances, be tracked and studied indirectly using cell biosensor systems. We summarize the ways in which live cell imaging has been used to dissect the retroviral life cycle. Finally, we discuss select challenges for the future including the need for better labeling strategies, increased resolution, and multivariate systems that will allow for the study of full viral replication cycles.
Simplified Universal Equations for Ionic Conductivity and Transference Number
Authors: Singh Meenesh R. , Singh Priyanka G. , Gande Vamsi V. , Chauhan Rohit , Minocha Nitin
Published: June 2024

Nernst-Einstein equation can provide a reasonable estimate of the ionic conductivity of dilute solutions. For concentrated solutions, alternate methods such as Green–Kubo relations and Einstein relations are more suitable to account for ion-ion interactions. Such computations can be expensive for multicomponent systems. Simplified mathematical expressions like the Nernst-Einstein equation do not exist for concentrated multicomponent mixtures. Newman’s treatment of multicomponent concentrated solutions yields a conductivity relation in terms of species concentration and Onsager phenomenological coefficients. However, the estimation of these phenomenological coefficients is not straightforward. Here, mathematical formulations that relate the phenomenological coefficients with the friction coefficients are developed, leading to simplified, ready-to-use expressions of conductivity and transference numbers that can be used for a wide range of ionic mixtures. This approach involves spectral decomposition of the matrix of Onsager phenomenological coefficients. The general analytical expressions for conductivity and transference number are simplified for binary electrolytes, and numerical solutions are provided for ternary and quaternary mixtures with ion dissociation.
The Use of Exercise Prescription in Australian Osteopathy Practice: Secondary Analysis of a Nationally Representative Sample of the Profession
Authors: Fleischmann Michael , Vaughan Brett , Fitzgerald Kylie
Published: June 2024

Introduction. Exercise is beneficial for improving general health, wellbeing, and specific medical conditions. In musculoskeletal conditions such as chronic low back and neck pain, prescribed exercise has been found to be moderately effective in decreasing pain and improving function. Osteopaths are primary contact health professionals who manage predominantly musculoskeletal complaints. This work presents a secondary data analysis of the Australian osteopathy practice‐based research network and profiles the characteristics of osteopaths who often use exercise prescription in patient care. Methodology. Secondary analysis of a cross‐sectional survey of 992 osteopaths was registered with the Osteopathy Research and Innovation Network, an Australian practice‐based research network. Demographics, practice, and treatment characteristics of Australian osteopaths who “often” use exercise prescription in patient care were examined. Results. Seven‐hundred and thirty‐three Australian osteopaths (74%) indicated that they use exercise prescription “often” in patient care. Australian osteopaths who often use exercise prescription are more likely to be colocated with another osteopath (ORa 1.54) and send referrals to an exercise physiologist (ORa 1.94) and a specialist medical practitioner (ORa 1.72). Those osteopaths who often used exercise prescription were also more likely to discuss physical activity (ORa 5.61) and nutrition (ORa 1.90). Australian osteopaths who use exercise prescription often were more likely to treat patients with sports injuries (ORa 2.43) and use soft tissue techniques (ORa 1.92), trigger point techniques (ORa 2.72), and sports taping (ORa 1.78). Conclusion. Osteopaths who utilise exercise prescription were more likely to discuss physical activity, diet, and nutrition and utilise referral networks with specialist medical practitioners and exercise physiologists. Australian osteopaths who often use exercise prescriptions were also more likely to treat patients with sport injury. The results suggest that most Australian osteopaths use exercise prescription and have referral networks with other health professionals for patient management. Further work is required to explore the type of exercise prescription used and for what conditions.
Famine Algorithm and Pseudo‐Kinetic Energy for Structural Damage Detection
Authors: Mojtahedi Seyederfan , Ghorbani-Tanha Amir K. , Rahami Hossein
Published: June 2024

In this study, a novel damage detection framework for skeletal structures is presented. The introduced scheme is based on the optimization‐based model updating method. A new multipopulation framework, namely, the Famine Algorithm, is introduced that hopes to reduce the number of objective function evaluations needed. Furthermore, using static displacement patterns, a damage‐sensitive feature named pseudo‐kinetic energy is presented. By exploiting the new feature, an efficient cost function is developed. Two mathematical benchmark problems and a two‐membered truss for damage detection problem are depicted in 2D space to track the search behavior of the Famine Algorithm and show the changes in the search space when using the new feature. Four numerical examples, including three trusses and a frame structure, are used to evaluate the overall performance of the proposed damage detection methods. Moreover, an experimental shear frame is studied to test the performance of the suggested method in real‐life problems. The obtained results of the examples reveal that the proposed method can identify and quantify the damaged elements accurately by only utilizing the first five vibrating modes, even in noise‐contaminated conditions.
Bayesian Spectral Decomposition for Efficient Modal Identification Using Ambient Vibration
Authors: Feng Zhouquan , Zhang Jiren , Katafygiotis Lambros , Hua Xugang , Chen Zhengqing
Published: June 2024

Modal parameter identification via ambient vibration is popular but faces challenges from uncertainties due to unknown inputs and low signal‐to‐noise ratio. Bayesian methods are gaining increasing attention for operational modal identification due to their ability to quantify uncertainties. However, improvements in computational efficiency are needed, particularly when addressing numerous modes and degrees of freedom. To address this challenge, this study proposes an innovative approach, termed the “Bayesian spectral decomposition” method (BSD), employing the decompose‐and‐conquer strategy. This novel method, operating within the frequency domain, identifies each mode individually by exploiting their inherent separated modal characteristics. For each mode, the response spectrum matrix undergoes an eigenvalue decomposition, yielding crucial eigenvalues (incorporating frequency and damping information) and eigenvectors (containing mode shape information). Subsequently, statistical properties of the eigenvalues and eigenvectors are utilized to establish likelihood functions for Bayesian parameter identification. By combining prior information, the posterior probability distribution functions of modal parameters are derived. The optimal solution is then obtained by resolving the maximum posterior probability distribution function problem. To further quantify the uncertainty of modal parameters, Gaussian distributions are employed to approximate the posterior probability distribution functions. The adoption of the decomposition approach circumvents the joint identification of all modal parameters, substantially reducing the parameter dimensions for optimization. Consequently, this strategy leads to decreased computational complexity and significantly improved computational stability. The effectiveness of the BSD is confirmed through simulated data generated from an 8‐story shear building as well as measured data collected from both an experimental shear frame and the Canton Tower. The results demonstrate that the proposed method achieves high accuracy in identifying modal parameters, greatly improves computational efficiency, and effectively quantifies the uncertainties in modal parameters.
A Cost‐Effective Epidemiological Exposition of Diphtheria Outbreak by the Optimal Control Model: A Case Study of Rohingya Refugee Camp in Bangladesh
Authors: Islam Zahurul , Kabir K. M. Ariful , Rahman M. M. , Rahman Md. Atikur
Published: June 2024

We consider a deterministic optimal control approach with cost‐effectiveness analysis for the diphtheria outbreak in the Rohingya refugee camp in Bangladesh. A deterministic epidemic dynamical model for diphtheria outbreaks has been developed with three optimal controls: vaccination, latent, and infectious treatment. Here, the qualitative study of the model has been interpreted. An objective function has been regarded as a cost function introduced by the optimal controls and the diseases themselves. Then, the existence and uniqueness of the optimal system have also been shown with the help of Pontryagin’s minimum principle. A numerical investigation has been carried out to solve the state and adjoint systems of optimality conditions. Furthermore, it has been investigated using several levels of availability of the optimal controls (vaccination, latent, and infectious treatment) to identify which level gives the best outcomes. Moreover, the cost‐effective analysis has been inquired with the numerical value of the cost function and the total cured population in each control strategy to clarify the strategy with the least cost but maximum remediation.
A Two‐Step Matrix‐Splitting Iterative Method for Solving the Generalized Absolute Value Equation
Authors: Zheng Lin , Tang Yangxin
Published: June 2024

In this paper, we present a two‐step Newton‐based matrix‐splitting iteration method for solving the generalize absolute value equation. This method can produce a number of two‐step Newton‐based relaxation iteration algorithms with the right matrix‐splitting options. In particular, some specific sufficient conditions are presented, when A is an H+‐matrix. Finally, numerical results indicate that the two‐step Newton‐based relaxation iteration techniques are effective for solving the generalized absolute value equation.
Psychological Profile of Nurse Managers in the Post‐COVID‐19 Era: Implications for Nursing Leadership
Authors: Mowanes Alruwaili Majed
Published: June 2024

Aim. This study examined the mental health of nurse managers in the Kingdom of Saudi Arabia. Background. The COVID‐19 pandemic has affected the physical and mental health of senior nurses, including effects of shortages of staff and medical supplies. However, no study has examined this topic among nurse managers in the Kingdom of Saudi Arabia despite their exposure to mental stress during the pandemic. Methods. A cross‐sectional design was used to investigate the levels of depression, anxiety, stress, and general psychological distress among nurse managers in Saudi Arabia. Data were collected August 2023 to February 2024. The main tool was the reliable and validated Arabic translation of the Depression, Anxiety, and Stress Scale (DASS‐21). Data collection was performed using an online platform. IBM SPSS software was used for data analysis. The data was analysed using multiple regression to examine the relationship between the dependent (outcome) and independent (predictor) variables. A significant p‐value was set at 0.05. Results. Stress and general psychological distress were the most common problems among nurse managers in the post‐COVID‐19 era. Individual educational attainment was the only significant predictor of anxiety, stress, and general psychological distress. Moreover, the nationalities of nurse managers were correlated with stress outcomes. Conclusions. Nursing managers are very likely to suffer from stress and general mental health problems in cases of exposure to crises. They may find the results of this study useful in understanding the factors that may play a role in the development of mental health problems during clinical work. Different strategies can be considered to alleviate depression, anxiety, and stress among managers, including the proper delegation of tasks. Top‐level management and healthcare stakeholders should give special considerations to the nationality and education level upon selecting nurse mangers at different levels. Implications in Nursing Management. Policy makers involved in planning care for healthcare professionals may find this study valuable in planning for future pandemics by developing a strategy that could reduce stress and psychological distress.
Nivolumab with or without Ipilimumab Combined with Stereotactic Body Radiotherapy in Patients with Metastatic Biliary Tract Cancer: A Randomized Phase 2 Study
Authors: Markussen Alice , Johansen Julia S. , Larsen Finn O. , Theile Susann , Hasselby Jane P. , Willemoe Gro L. , Lorentzen Torben , Madsen Kasper , Høgdall Estrid , Poulsen Tim S. , Wilken Eva E. , Geertsen Poul , Behrens Claus P. , Svane Inge M. , Nielsen Dorte , Chen Inna M.
Published: June 2024

Purpose:

The purpose of this study was to evaluate the clinical benefits of nivolumab with/without ipilimumab combined with stereotactic body radiotherapy (SBRT) in patients with pretreated metastatic biliary tract cancer (mBTC).

Patients and Methods:

The study was a phase 2 randomized trial with Simon’s optimal two-stage design requiring 36 evaluable patients per group after second stage. Sixty-one patients were included from September 2018 to January 2022 and randomized (1:1) to receive SBRT (15 Gy × 1 on day 1 to a primary or metastatic lesion) and nivolumab (3 mg/kg intravenously on day 1 and every 2 weeks) with/without ipilimumab (1 mg/kg intravenously on day 1 and every 6 weeks). Primary endpoint was clinical benefit rate (CBR), defined as the percentage of patients with complete response, partial response, or stable disease. Decision to continue accrual into the second stage depended on the CBR from the first stage.

Results:

Forty-two patients received SBRT/nivolumab/ipilimumab with a CBR of 31.0% [95% confidence interval (CI), 17.6–47.1]. Five patients (11.9%) achieved partial response with median duration of 4.4 months (range, 1.1–21.5). Nineteen patients received SBRT/nivolumab. This group was closed after the initial stage based on a CBR of 10.5% (95% CI, 1.3–33.1). Adverse events were graded with National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0. Grade ≥3 treatment-related adverse events occurred in 13 (31%) and 3 (16%) patients in the SBRT/nivolumab/ipilimumab and SBRT/nivolumab groups, respectively. One patient died from immune-related hepatitis in the SBRT/nivolumab/ipilimumab group.

Conclusions:

Combining SBRT, nivolumab, and ipilimumab is well tolerated, feasible, and shows response in a subgroup of patients with mBTC.
Investigation of Genetic Changes in Three Families with Bipolar Disease
Authors: Çolak-Geniş Esra , Özdemir Erdoğan Müjdan , Çam Fethi Sırrı , Aydemir Ömer , Akin Funda , Gerik-Celebi Hamide Betül , Solak Mustafa
Published: June 2024

Introduction: Bipolar disorder (BD) is a serious psychiatric disorder characterized by mood swings (depressive and manic phases) that can strongly affect the quality of life of patients and their families. The lifetime prevalence of BD in the general population is 1%. The pathogenesis of BD is unknown; however, comprehensive epidemiological studies have shown that both genetic and environmental factors play a role. Within the scope of the current project, we aim to determine the genetic change responsible for the emergence of the disease and to make a genotype-phenotype correlation. Methods: In this study, we evaluated single nucleotide gene variants in three families (n = 6 patients) with bipolar disorder using whole-exome sequencing. Results: Seven genes (TMTC1, DGKH, STARD9, ITIH1, MARCKS, CSMD1, and ADRA2B) were identified as possibly associated with BPD. In addition, two novel variants were presented in the TMTC1 (c.1214T>G) and STARD9 (c.8288C>G) genes. Conclusion: Prospective studies in larger patient groups are required to determine the role of these genes in the etiology of the disease and their potential in diagnosis and treatment. To the best of our knowledge, this is the first methodically comprehensive study conducted in our country and can contribute to the identification of genes that may be associated with BD and the etiopathogenesis of the disease.
Dementia among Minority Populations: A Scoping Review of Meaning, Language, and Translation
Authors: Kiadarbandsari Atefeh , Lemalu Miraneta Tafue , Wilson Sharyn , Fa’alau Fuafiva
Published: June 2024

Background: Dementia as a neurocognitive disorder is becoming increasingly common worldwide, and minority groups are more vulnerable than the general population. Many factors may contribute to their vulnerability such as misconceptions, language barriers, cultural factors, invalid assessment tools, lack of knowledge, or assigning spiritual beliefs to dementia symptoms. Therefore, this scoping literature review aimed to clarify how empirical studies reflect the meaning of dementia, language, and translation among minority ethnic groups. Summary: The PRISMA extension for the scoping review protocol was used. Thirty-eight studies published in English were reviewed and analysed. The findings revealed that lack of knowledge about dementia and attributing the disease to the normal ageing process were frequent among minority groups. Furthermore, their cultural-specific perspectives and worldviews of wellness and well-being can impact the way dementia is perceived, consequent help-seeking behaviours, or caregiving. Facilitating educational programs to enhance the knowledge and experiences of ethnic communities might be beneficial. Moreover, language is shown to be an important aspect in dementia assessment and participants’ educational level could significantly impact their functional capacity when responding to cognitive measures. Even though there are some useful screening tests, diagnosis barriers might be eased by assessment tool development, modifications, and accurate translations for ethnic communities. Key Messages: A promising pathway to support ethnically diverse communities regarding dementia can be raising awareness, providing ethnic-specific services, developing cultural-specific tools to assess dementia or any cognitive impairment by considering perceptions, language, and culture among ethnic groups. Cultural and spiritual considerations could also encourage engagement during assessment.
Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease
Authors: Ma Yuan , Ji Jing , Liu Xintong , Zheng Xizi , Xu Lingyi , Zhou Qingqing , Li Zehua , Yang Li
Published: June 2024

Introduction: The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited. Methods: This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients. Results: Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD-CD27-B cell Absolute Count (IVW: OR, 1.102 [1.023–1.189], p = 0.011) and IgD-CD24-B cell Absolute Count (IVW: OR, 1.106 [1.030–1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24+CD27+B cell %B cell (IVW: OR, 0.943 [0.898–0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20−CD38−B cell (IVW: OR, 0.946 [0.904–0.989], p = 0.015) and BAFF-R on IgD-CD38+B cell (IVW: OR, 0.902 [0.834–0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD. Conclusion: Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.
Prognostic Efficacy of the Albumin-Bilirubin Score and Treatment Outcomes in Hepatocellular Carcinoma: A Large-Scale, Multi-Center Real-World Database Study
Authors: Kim Kyu-Pyo , Kim Kang Mo , Ryoo Baek-Yeol , Choi Won-Mook , Cha Won Chul , Kang Mira , Sinn Dong Hyun , Goh Myung Ji , Kim Do Young , Lee Min Ji , Lim Subin , Kim DongKyu , Baek Kyoungdae , Kim Joohyun , Choi Eui Jun , Lee Doik , Kim Jung-Ae , Kim Ki-Hun
Published: June 2024

Introduction: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, with treatment outcomes closely tied to liver function. This study evaluates the prognostic utility of the albumin-bilirubin (ALBI) score compared to the traditional Child-Pugh (CP) grading, leveraging real-world evidence from a large-scale, multi-center database. Methods: The Liver Cancer IN Korea (LINK) research network, a multi-center initiative, retrospectively collected electronic health records from three academic hospitals in South Korea, encompassing HCC patients diagnosed between 2015 and 2020. Inclusion criteria mandated at least one HCC treatment and excluded patients with other primary cancer diagnoses. The study followed patients until death, the last visit, or June 2021, employing standardized data processing and rule-based algorithms for data consistency. The prognostic efficacy of ALBI scores and CP scores was compared through time-dependent receiver operating characteristic (ROC) curves and the inverse probability censoring weighting method. Results: From 25,248 newly diagnosed patients, 10,297 were included, with 65.82% having hepatitis B etiology and a mean follow-up of 27.49 months. Patients’ classification by modified ALBI (mALBI) grade at diagnosis revealed: grade 1 (48.87%), 2a (20.50%), 2b (24.54%), and 3 (5.17%), with a minimal percentage missing (0.92%). Transarterial therapy (54.07%) and tyrosine kinase inhibitors (84.14% as the first-line systemic therapy) were predominant treatments. The ALBI score demonstrated greater prognostic efficacy than the CP score in long-term outcomes, with time-dependent area under the ROC curve analysis showing a score of 0.71 for ALBI versus 0.67 for CP at 60 months. Furthermore, higher mALBI grades were significantly associated with poorer survival outcomes, as indicated by both univariate and multivariate Cox proportional regression model analyses (p < 0.001). Conclusions: The study confirmed the ALBI score’s superior prognostic ability over the CP score, especially evident in long-term outcomes, suggesting a shift toward more nuanced liver function assessment tools in real-world clinical practice.
Utility of Thrombectomy in Nonagenarians: A Scoping Review
Authors: Sugar Benjamin P. , Drasler Nathan E. , Lee Jonathan , Beutler Bryce D. , Moody Alastair E. , Cadavona John Jay P. , Leung Lisa , Tabaac Burton J.
Published: June 2024

Background: Mechanical thrombectomy represents a mainstay of management for acute ischemic stroke in the setting of large vessel occlusion. However, there are no clinical practice guidelines defining the role of thrombectomy at the extremes of age. In this scoping review, we aimed to summarize the existing medical and neurosurgical literature pertaining to mechanical thrombectomy in nonagenarians. The PubMed database was queried using the following terms and relevant citations assessed: “thrombectomy nonagenarian,” “thrombectomy age 90,” “stroke nonagenarian,” and “ischemic stroke thrombectomy.” Common measurable outcomes, including mortality, modified Rankin scale (mRS) score, and thrombolysis in cerebral infarction (TICI) scale score, were utilized to compare results. Summary: Thrombectomy was shown to improve functional outcomes in all eight of the studies included in the analysis. Mortality was assessed in only two reported studies, and thrombectomy was shown to provide a mortality benefit in 1 study among patients for whom first-pass reperfusion was achieved. Other outcomes of reported interest included greater early neurologic recovery at discharge and improved functional outcomes at 90 days among nonagenarians who underwent thrombectomy as compared to those who received thrombolytic therapy alone. Nonagenarians with good functional status at baseline were the most likely to have favorable outcomes. Key Messages: Mechanical thrombectomy improves outcomes among nonagenarians presenting with acute ischemic stroke due to large vessel occlusion. Further large-scale prospective studies are warranted to optimize patient selection and develop clinical practice guidelines specific to this important patient demographic.
The Effects of Early-Phase, Low- or Standard-Intensity Continuous Renal Replacement Therapy on Acid-Base Control and Clinical Outcomes: An Observational Study
Authors: Yagi Kosuke , Fujii Tomoko , Kageyama Akira , Takagi Toshishige , Ikeda Junpei , Uezono Shoichi
Published: June 2024

Introduction: The Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline recommends administering an effluent volume of 20–25 mL/kg/h during continuous renal replacement therapy (CRRT) for acute kidney injury. Recent evidence on CRRT initiation showed that less intervention might be beneficial for renal recovery. This study aimed to explore the association between early-phase low CRRT intensity and acid-base balance corrections and clinical outcomes. Methods: This was a single-centre, retrospective, observational study at a tertiary intensive care unit (ICU) in Japan. All adult patients requiring CRRT in the ICU were included. Eligible patients were classified into the Low group (dialysate flow rate [QD] 10.0–19.9 mL/kg/h) and the Standard group (QD ≥20 mL/kg/h) by the intensity of CRRT at the beginning. The primary outcomes were acid-base parameters 6 h after CRRT initiation. We used an inverse probability of treatment weighting analysis to estimate the association between the intensity group and the outcomes. Results: Overall, 194 patients were classified into the Low group (n = 144) and the Standard group (n = 50). The Standard group presented with more severe acid-base disturbances, including lower pH and base excess (BE) at baseline. At 6 h after CRRT initiation, pH, BE, and strong ion difference values were comparable, even after adjusting for baseline severity. Despite the efficient correction, no evident differences were observed in clinical outcomes between the two groups. Conclusions: The initial standard intensity appeared to be efficient in correcting acid-base imbalance at the early phase of CRRT; however, further studies are needed to assess the impact on clinical outcomes.
Socio-science spirituality learning model for cultivating student spirituality and science process skills at Islamic schools
Authors: Rahmatika Resty , Amin Mohamad , Muhdhar Mimien Henie Irawati Al , Suwono Hadi
Published: June 2024

Complementing science subjects with spirituality components is almost universal and is a significant requirement in religion-based schools. However, integrating spiritual components into science classes is challenging, particularly in inquiry-based problem-solving tasks. In this experiment, we modified inquiry learning and contextual teaching. We tested the efficacy of a combined model called the socio-science spirituality (3S) model on students’ spirituality and science process skills (SPS). Through a quasi-experiment, this learning model was applied in classes addressing the digestive system, additives and addictive chemicals, and the circulatory system. Following the 3S classroom implementation, we compared the mean pre- and post-test scores of the experimental and control groups using the analysis of covariance (ANCOVA) test and then assessed the relationship between spirituality and SPS in the classrooms using the Pearson-correlation test. The research found that spirituality and SPS were much higher and more prevalent in the experimental group compared to the control group. In the experimental class, there was also a link between spirituality and SPS. Theoretically, this research finding contributes to spiritual education and the development of SPS skills in science classes simultaneously. This research provides a unique way to prioritize the integration of spirituality in science learning. Practically, the research explain the details of the implementation of spirituality that can be applied to inquiry activities in science classes through the 3S model.
The effect of Islam and science integration implementing on science learning in Indonesia: a meta-analysis
Authors: Ardi Ardi , Lufri Lufri , Amran Ali , Kosasih Ahmad , Hervi Fauziah
Published: June 2024

National education aims to produce dedicated, religious, moral individuals and cognitive intelligence. Applying students’ Islamic values through integrating Islam and science in science learning is one of the strategies that can be used to achieve these goals. Integrating Islam and science in science learning can direct the formation of knowledge about science concepts and improve students’ attitudes and spiritual skills. This study aims to analyze the effect of integrating Islam and science in science learning. This study explores the effect of integrating Islam and science in science learning with meta-analysis research by calculating effect size (ES). Research data were obtained from 38 studies from national and international journals. The results showed that integrating Islam and science in science learning has a significant impact, as seen from the ES of 0.658. It is explained that science learning integrating Islam and science can be an alternative learning method. Integrating Islam and science into science learning is an alternative educational method to achieve national education goals that can increase students’ intellectual and spiritual values. The results of this study can be utilized as a reference in the learning process and the development of teaching materials in science learning in the future.