bluuebunny/crossref_metadata_2025 huggingface dataset:
Early evaluation of a novel alert within cardiac procedural areas to facilitate genotype‐guided antiplatelet therapy
Authors: Van Heukelom Joel , Weaver Max , Max Carson , Baye Jordan F. , Peterson Ashley , Maryniak Andrii , Stys Tomsaz P. , Massmann Amanda
Published: May 2024

Introduction

Clopidogrel remains widely utilized in patients undergoing percutaneous coronary intervention despite compelling evidence that genetic variation impacts patient response to clopidogrel. Clinical decision support (CDS) is frequently used to aid in precision medicine; however, limitations within the electronic medical record can hinder reliable CDS. Procedural areas where standard order entry is not utilized create a barrier to CDS implementation.

Objectives

We aimed to evaluate the implementation of a novel alerting mechanism on genotype‐guided antiplatelet prescribing within the cardiac catheterization laboratory procedural setting.

Methods

A retrospective cohort study was conducted to assess the rate of antiplatelet ordering in patients with one or two loss‐of‐function cytochrome P450 2C19 (CYP2C19) alleles before and after alert implementation. Pharmacogenomic congruence was measured before and after the alert via chart abstraction that included the CYP2C19 genotype and antiplatelet medications ordered within that encounter.

Results

A total of 236 patients were included in analyses, 127 encounters within the cohort before alert implementation and 136 encounters in the cohort after alert implementation. Prior to alert implementation, 40.9% (n = 127) were prescribed clopidogrel compared with 25.7% (n = 136) post implementation. After implementing a genotype‐guided alert within the cardiac catheterization laboratory procedural setting, providers were 2.22 times more likely to prescribe an alternative antiplatelet (p = 0.024). Clopidogrel‐naïve patients were 9.75 times more likely to receive a genotype‐guided antiplatelet order following alert implementation (p < 0.05).

Conclusion

Providers were responsive to a novel alert within the cardiac catheterization laboratory procedural setting. Genotype‐guided antiplatelet prescribing significantly increased following the alert implementation.
Interpretation of molecular electron transport in ab initio many‐electron framework incorporating zero‐point nuclear motion effects
Authors: Jelenfi Dávid P. , Tajti Attila , Szalay Péter G.
Published: May 2024

A computational methodology, founded on chemical concepts, is presented for interpreting the role of nuclear motion in the electron transport through single‐molecule junctions (SMJ) using many‐electron ab initio quantum chemical calculations. Within this approach the many‐electron states of the system, computed at the SOS‐ADC(2) level, are followed along the individual normal modes of the encapsulated molecules. The inspection of the changes in the partial charge distribution of the many‐electron states allows the quantification of the electron transport and the estimation of transmission probabilities. This analysis improves the understanding of the relationship between internal motions and electron transport. Two SMJ model systems are studied for validation purposes, constructed from a conductor (BDA, benzene‐1,4‐diamine) and an insulator molecule (DABCO, 1,4‐diazabicyclo[2.2.2]octane). The trends of the resulting transmission probabilities are in agreement with the experimental observations, demonstrating the capability of the approach to distinguish between conductor and insulator type systems, thereby offering a straightforward and cost‐effective tool for such classifications via quantum chemical calculations.
Efficient Reductive N‐11C‐Methylation Using Arylamines or Alkylamines and In Situ–Generated [11C]Formaldehyde From [11C]Methyl Iodide
Authors: Kikuchi Tatsuya , Okamura Toshimitsu , Zhang Ming‐Rong
Published: May 2024

Reductive N‐11C‐methylation using [11C]formaldehyde and amines has been used to prepare N‐11C‐methylated compounds. However, the yields of the N‐11C‐methylated compounds are often insufficient. In this study, we developed an efficient method for base‐free reductive N‐11C‐methylation that is applicable to a wide variety of substrates, including arylamines bearing electron‐withdrawing and electron‐donating substituents. A 2‐picoline borane complex, which is a stable and mild reductant, was used. Dimethyl sulfoxide was used as the primary reaction solvent, and glacial acetic acid or aqueous acetic acid was used as a cosolvent. While reductive N‐11C‐methylation efficiently proceeded under anhydrous conditions in most cases, the addition of water to the reductive N‐11C‐methylation generally increased the yield of the N‐11C‐methylated compounds. Substrates with hydroxy, carboxyl, nitrile, nitro, ester, amide, and phenone moieties and amine salts were applicable to the reaction. This proposed method for reductive N‐11C‐methylation should be applicable to a wide variety of substrates, including thermo‐labile and base‐sensitive compounds because the reaction was performed under relatively mild conditions (70°C) without the need for a base.
Reproducibility of Quantitative Double‐Echo Steady‐State T2 Mapping of Knee Cartilage
Authors: Williams Ashley A. , Asay Jessica L. , Asare Daniella , Desai Arjun D. , Gold Garry E. , Hargreaves Brian A. , Chaudhari Akshay S. , Chu Constance R.
Published: May 2024

Background

Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double‐echo steady state (qDESS) sequence is attractive for knee cartilage T2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T2 is essential to profiling the technical performance of this biomarker.

Purpose

To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T2 using two different regional analysis schemes: 1) manual segmentation of subregions loaded during common activities and 2) automatic subregional segmentation.

Study Type

Prospective.

Subjects

11 uninjured participants (age: 28 ± 3 years; 8 (73%) female).

Field Strength/Sequence

3‐T, qDESS.

Assessment

Test–retest T2 maps were acquired twice on the same day and with a 1‐week interval between scans. For each acquisition, average cartilage T2 was calculated in four manually segmented regions encompassing tibiofemoral contact areas during common activities and 12 automatically segmented regions from the deep‐learning open‐source framework for musculoskeletal MRI analysis (DOSMA) encompassing medial and lateral anterior, central, and posterior tibiofemoral regions. Test–retest T2 values from matching regions were used to evaluate reproducibility.

Statistical Tests

Coefficients of variation (%CV), root‐mean‐square‐average‐CV (%RMSA‐CV), and intraclass correlation coefficients (ICCs) assessed test–retest T2 reproducibility. The median of test–retest standard deviations was used for T2 precision. Bland–Altman (BA) analyses examined test–retest biases. The smallest detectable difference (SDD) was defined as the BA limit of agreement of largest magnitude. Significance was accepted for P < 0.05.

Results

All cartilage regions across both segmentation schemes demonstrated intraday and interday qDESS T2 CVs and RMSA‐CVs of ≤5%. T2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test–retest T2 precision was <1.3 msec. The T2 SDD was 3.8 msec.

Data Conclusion

Excellent CV and RMSA‐CV reproducibility may suggest that qDESS T2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition.

Level of Evidence

2.

Technical Efficacy

Stage 2.
Regulation of soil organic carbon dynamics by microbial communities during reforestation of Chinese fir plantations after clear‐cutting
Authors: Wang Ying , Chen Liang , Xiang Wenhua , Zhang Xiulan , Ren Yan , Guo Zhiming , Gunina Anna
Published: May 2024

Reforestation after forest clear‐cutting is an effective measure to increase soil organic carbon (SOC) sequestration; still, the soil C balance under reforestation and the role of microbial communities in that process remain to be determined. Samples of organic (0–2 cm) and mineral (2–10 cm) horizons were collected from the 7‐, 15‐, 20‐, 29‐, and 36‐year‐old forest stands of Chinese fir (Cunninghamia lanceolata) after plantation clear‐cutting in subtropical zone under the condition of phosphorus limitation. Particulate organic carbon (POC), mineral‐associated organic carbon (MAOC), microbial phospholipid fatty acids (PLFAs), and enzymatic activities for C, nitrogen (N), and phosphorus (P) acquisition were analyzed. The lowest contents of POC (10%) and MAOC (13%) in the organic horizon were found in 7‐year‐old stands due to the slow tree regrowth and extensive decomposition of SOC in the first years of forest regrowth. POC (2.0×) and MAOC (0.8×) increases in the organic horizon with forest age were attributed to the stand development and accumulation of above‐ and belowground litter. The organic horizon had a higher POC:MAOC ratio than the mineral (0.7–1.1 vs. 0.2–0.5), indicating lower SOC stability in the first one. The ratio of POC:MAOC increased with the Gram‐positive to Gram‐negative bacteria (G+:G‐) ratio, pointing out that microbial communities developed a specific community structure and substrate utilization strategies of organic matter under plantation restoration. The increase of total PLFAs and the G+:G‐ ratio was closely linked with the microbial C and P limitations, indicating that microorganisms shifted community structure to slow‐growing species and increased their content to cope with the C and P restrictions. In the soils of young plantations, microorganisms were limited by C and P; however, the C limitation was alleviated in the 36‐year‐old plots in the organic horizon due to increased litter input, whereas the P limitation was not. This discrepancy between C and P limitation suppressed the decomposition of litter entering the soil, which was seen in decreased specific activity of C degrading enzymes and led to the accumulation of POC in the organic horizon. Thus, soil C sequestration under reforestation of Chinese fir can be controlled by the amount of litter entering the soil and by metabolic C, N, and P limitations that force microorganisms to shift community structure and change their activity.
Predicting river phytoplankton blooms and community succession using ecological niche modeling
Authors: Bowes Michael J. , Hutchins Michael G. , Nicholls David J. E. , Armstrong Linda K. , Scarlett Peter M. , Jürgens Monika D. , Bachiller‐Jareno Nuria , Fournier Isabelle , Read Daniel S.
Published: May 2024

Excessive phytoplankton concentrations in rivers can result in the loss of plant and invertebrate communities, and threaten drinking water supplies. Whilst the physicochemical controls on algal blooms have been identified previously, how these factors combine to control the initiation, size, and cessation of blooms in rivers is not well understood. We applied flow cytometry to quantify diatom, chlorophyte, and cyanobacterial group abundances in the River Thames (UK) at weekly intervals from 2011 to 2022, alongside physicochemical data. A niche modeling approach was used to identify thresholds in water temperature, flow, solar radiation, and soluble reactive phosphorus (SRP) concentrations required to produce periods of phytoplankton growth, with blooms only occurring when all thresholds were met. The thresholds derived from the 2011 to 2018 dataset were applied to a test data set (2019–2022), which predicted the timing and duration of blooms at accuracies of > 80%. Diatoms and nano‐chlorophyte blooms were initiated by flow and water temperature, and usually terminated due to temperature and flow going out of the threshold range, or SRP and Si becoming limiting. Cyanobacterial bloom dynamics were primarily controlled by water temperature and solar radiation. This simple methodology provides a key understanding of phytoplankton community succession and inter‐annual variation and can be applied to any river with similar water quality and phytoplankton data. It provides early warnings of algal and cyanobacterial bloom timings, which support future catchment management decisions to safeguard water resources, and provides a basis for modeling changing phytoplankton bloom risk due to future climate change.
Examining the contact problem of a functionally graded layer supported by an elastic half‐plane with the analytical and numerical methods
Authors: Yaylacı Murat , Yaylı Müjgen , Öztürk Şevval , Ay Sevil , Özdemir Mehmet Emin , Yaylacı Ecren Uzun , Birinci Ahmet
Published: May 2024

This study offers a comparative study of the analytical and numerical methods for investigating a contact problem. The contact problem comprises a functionally graded layer supported by a half‐plane and loaded with a distributed load from the top surface. First, the analytical and numerical solutions to the problem are acquired by utilizing a theory of elasticity and finite element method, respectively. The problem is transformed into a system of integral equations in which the contact stress is an unknown function. The solution of the integral equation was achieved with Gauss–Jacobi integration formulation. The finite element model of the problem is created using ANSYS software, and the two‐dimensional analysis of the problem is performed. Results were obtained from the samples for different material properties and loading conditions. The distributed load width and non‐homogeneity parameters significantly impact on contact mechanics. The results indicate that the contact area and the contact stress obtained from finite element method (FEM) are close to the analytical results. As a result, acceptable error rates were obtained. Finally, this study provides evidence of a good agreement between the two methods.
Improved quantification in CEST‐MRI by joint spatial total generalized variation
Authors: Huemer Markus , Stilianu Clemens , Maier Oliver , Fabian Moritz Simon , Schmidt Manuel , Doerfler Arnd , Bredies Kristian , Zaiss Moritz , Stollberger Rudolf
Published: May 2024

Purpose

In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool‐Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal‐to‐noise ratio (SNR) was investigated.

Theory and Methods

The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel‐wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss–Newton method combined with a Primal‐Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal‐means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values.

Results

The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities.

Conclusion

The proposed fitting method using TGV regularization leads to improved results over a range of different data‐sets and noise levels. Furthermore, it can be applied to all Z‐spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.
Multiparametric exchange protons using Z‐spectrum analysis proton (ZAP) and CEST on phantoms and human abdomen
Authors: Malis Vadim , Miyazaki Mitsue
Published: May 2024

Purpose

This study aims to investigate a multiparametric exchange proton approach using CEST and Z‐spectrum analysis protons (ZAP) in human abdominal organs, focusing on tissue differentiation for a potential early biomarker of abnormality. Prior to human studies, CEST and ZAP effects were studied in phantoms containing exchange protons.

Methods

Phantoms composed of iopamidol and iohexol solutions with varying pH levels, along with 12 human subjects, were scanned on a clinical 3T MR scanner. Subsequent ZAP analyses employed a two‐Lorentzian pool model to provide free and restricted apparent , and their fractions for data acquired across a wide range of offset frequencies (±100 kHz or ± 800 ppm), while a narrower range (±7 ppm or ± 900 Hz) was used for CEST analysis to estimate magnetization transfer ratio asymmetry (MTRAsym) for exchange protons like hydroxyl (—OH), amine (—NH2), and amide (—NH), resonating ˜1, 2, and 3.5 ppm, respectively. Differences in ZAP metrics across various organs were statistically analyzed using one‐way analysis of variance (ANOVA).

Results

The phantom study differentiated contrast agents based on resonance peaks detected from CEST analysis, while ZAP metrics showed sensitivity to pH variations. In human, ZAP metrics revealed significant differences in abdominal organs, with a subgroup study indicating changes in ZAP metrics due to the presence of gallstones.

Conclusion

CEST and ZAP techniques demonstrated promise in specific CEST protons and wide range ZAP protons and identifying tissue‐specific characteristics. The preliminary findings underscore the necessity for more extensive study involving a broader subject pool to potentially establish biomarkers for diseased states.
High‐resolution anatomical imaging of the fetal brain with a reduced field of view using outer volume suppression
Authors: Jang MinJung , Gupta Ajay , Kovanlikaya Arzu , Scholl Jessica E. , Zun Zungho
Published: May 2024

Purpose

To achieve high‐resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full‐FOV fetal brain imaging.

Methods

Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single‐shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip‐angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in‐vivo experiments were performed to evaluate OVS performance. In‐vivo high‐resolution SSFSE images acquired using the proposed approach were compared with conventional and high‐resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics.

Results

Excellent signal suppression in the saturation bands was confirmed in phantom and in‐vivo experiments. High‐resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method.

Conclusion

High‐resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero.
Healthcare professionals' perceptions and management of obesity & knowledge of glucagon, GLP‐1, GIP receptor agonists, and dual agonists
Authors: Garvey W. Timothy , Mahle Cathy D. , Bell Trevor , Kushner Robert F.
Published: May 2024

Background

Anti‐obesity medications (AOMs) have historically had limited weight‐loss efficacy. However, newer glucagon‐like peptide‐1 receptor agonist (GLP‐1 RA)–based therapies seem to be more effective, including dual agonists of GLP‐1R and the glucagon receptor (GCGR) or glucose‐dependent insulinotropic polypeptide receptor.

Objective

To explore healthcare professionals' (HCPs) experience in obesity treatment and their understanding of agonists of GCGR, glucose‐dependent insulinotropic polypeptide (GIP) RA, and GLP‐1 RA.

Methods

This cross‐sectional online survey of HCPs prescribing AOMs was conducted in the United States in 2023 with a questionnaire designed to evaluate prescribing behavior and understanding of GCGR, GIP RA, and GLP‐1 RA.

Results

The 785 respondents (251 primary‐care physicians [PCPs], 263 endocrinologists, and 271 advanced practice providers [APPs]) reported 55% of their patients had obesity (body mass index ≥30 kg/m2 or ≥27 with weight‐related complications) and recommended AOMs to 49% overall, significantly more endocrinologists (57% of patients, p < 0.0005) than PCPs (43%) or APPs (46%). The greatest barriers to treatment were medication cost/lack of insurance (mean 4.2 on 1–5 scale [no barrier–extreme barrier]), low patient engagement/adherence (3.3), and inadequate time/staff (3.1). Metformin was the type 2 diabetes (T2D) medication most commonly prescribed to treat obesity in T2D patients (92.5% of respondents). Most HCPs (65%) were very/extremely familiar with GLP‐1 RA, but only 30% with GIP RA and 16% with GCGR. Most HCPs expected dual GCGR/GLP‐1 RA to benefit many obesity‐related conditions; however, only a minority of HCPs perceived that they would benefit non‐cardiometabolic complications of obesity.

Conclusions

Among HCPs prescribing AOMs, gaps exist in the management of people living with obesity as <50% are prescribed AOMs. Barriers to treatment indicate a need to improve access to AOMs. HCPs were less familiar with GCGR or GIP RA than GLP‐1 RA but expect dual GCGR/GLP‐1 RA may offer additional benefits, potentially addressing treatment barriers and access. Thus, there is a need for greater education among HCPs regarding the mechanism of action and therapeutic effects of GCGR agonists, and dual GCGR/GLP‐1 RA, so that the full range of obesity‐related complications can be effectively treated.
Immunization adherence among children with sickle cell disease and sickle cell trait: Results of a population‐based study
Authors: Shi Jiajing Scarlette , Sutaria Ankit , Lakshmanan Sangeetha , Attell Brandon , Zhou Mei , Tang Amy , Eckman James , Snyder Angela
Published: May 2024

Introduction

Despite the importance of timely vaccine completion for protection from infectious disease, there is limited knowledge of the immunization adherence rates of children with sickle cell disease (SCD).

Methods

This is a retrospective cohort study comparing the immunization rates of children with SCD to those with sickle cell trait between 2008 and 2019 in Georgia. Completion rates for each vaccine and the proportion of children with up‐to‐date status at 24 and 35 months were calculated and compared between the cohorts. Chi‐square tests with odds ratios (OR) for differences and 95% confidence intervals (CIs) were reported on the overall up‐to‐date rates and rates for individual vaccines at 24 and 35 months for the two cohorts.

Results

Children with SCD had higher up‐to‐date rates than children with sickle cell trait at 24 and 35 months. At 35 months, the overall up‐to‐date rates (OR = 1.17; 95% CI, 1.04‐1.31; p = .004) and the four‐dose pneumococcal conjugate vaccine series (OR = 1.36; 95% CI, 1.18‐1.57; p < .001) were significantly different between the groups. Both cohorts had the highest completion rates for the hepatitis B series and the lowest rates for the varicella vaccine. Doses of diphtheria, tetanus, and acellular pertussis vaccine; varicella; and pneumococcal conjugate vaccines were most commonly missed by children in both cohorts.

Conclusions

Children with SCD have better immunization coverage than children with sickle cell trait, but there is an opportunity for improvement. Policymakers and healthcare professionals should focus on increasing access to care coordination services among children with SCD to ensure on‐time and preventive healthcare services.
Two Bayesian approaches of monitoring mean of Gaussian process using Bayes factor
Authors: Tan Yaxin , Mukherjee Amitava , Zhang Jiujun
Published: May 2024

This paper develops two novel process monitoring schemes for the mean of a Gaussian process: the Bayes factor (BF) and the improved Bayes factor (IBF) schemes. Conjugate priors are used to construct the plotting statistics. The performance of the proposed schemes is evaluated in terms of average run length (ARL), standard deviation of run length (SDRL), and several percentiles, and these performance metrics across different hyper‐parameters and various sample sizes are evaluated via Monte Carlo simulations. Both zero‐state and steady‐state out‐of‐control (OOC) performances are investigated comprehensively. The simulation results show that the IBF scheme outperforms the existing Bayesian exponentially weighted moving average (EWMA) schemes under different loss functions in zero‐state. In steady‐state conditions, the IBF scheme outperforms for small shifts. Finally, we present two examples to illustrate the practical application of the proposed schemes.
QSPR analysis of anti‐asthmatic drugs using some new distance‐based topological indices: A comparative study
Authors: Balasubramaniyan Deepa , Chidambaram Natarajan , Ravi Vignesh , Siddiqui Muhammad Kamran
Published: May 2024

Asthma is a widespread disease that has affected more than 300 million people worldwide. There is no efficient or preventive treatment for this disease yet. Drug scientists are striving hard to discover a variety of medicines that can treat asthma effectively. Quantitative structure‐property relationship (QSPR) analysis of drugs used in the treatment of a specific disease through topological descriptors is one of the best techniques adopted by drug scientists to cut down unnecessary laboratory expenses. In this article, we have done a QSPR analysis to identify the best predictor for the six properties namely, boiling point, enthalpy, flash point, log P, molar volume, and molar refractivity of 19 different anti‐asthmatic drugs using a set of 16 distance‐based topological indices. Also, we compare our results with QSPR analysis done by using certain degree‐based indices for the chosen drugs and obtain new directions.
Preparation and photocatalytic properties of Bi2WO6/Ag/ZnO composite nanomaterials
Authors: Tan Chong , Yin Yuhong , Zuo Jinlong , Li Junsheng
Published: May 2024

Photocatalysis leverages light energy for chemical transformations, presenting an environmentally friendly approach to mitigating pollution. We have developed Bi2WO6/Ag/ZnO composite nanomaterials that demonstrate enhanced photocatalytic efficiency. These nanomaterials have been characterized through advanced techniques such as x‐ray diffraction and x‐ray photoelectron spectroscopy. A key challenge in photocatalysis is the effective separation of electron–hole pairs, and our Bi2WO6/Ag/ZnO composites achieve an impressive degradation rate of 96.93% for methyl orange. This result notably exceeds previous performance standards and highlights the crucial role of Ag in broadening the light response spectrum of zinc oxide (ZnO), and the addition of Bi2WO6 in amplifying the electron–hole pair separation efficiency within these composite nanomaterials. These findings underscore the significant potential of Bi2WO6/Ag/ZnO composites in environmental detoxification and establish a new benchmark in photocatalytic material performance. The advancement of such nanomaterials could be transformative for environmental remediation strategies, offering efficient, sustainable, and cost‐effective solutions to ecological challenges. This study underscores the pivotal role of materials science in advancing environmental sustainability.
Insights into the DFT‐computed electronic and optical properties of binary and doped: Selenide for optoelectronic applications
Authors: Aamer Muhammad , Rafiq Qaiser , Hayat Sardar Sikandar , Khan Muhammad Tahir , Azam Sikander , Albaqami Munirah D. , Mohammad Saikh
Published: May 2024

This study investigates the alterations in structural, electronic, and optical characteristics of ZnSe1−x Inx (x = 0%, 12.5%, 25%) employing the framework of density functional theory (DFT), utilizing generalized gradient approximation (GGA) in conjunction with the full‐potential linearized augmented plane wave (FP‐LAPW) approach. The findings reveal that pure ZnSe exhibits a direct bandgap of 1.79 eV at the Γ point, which reduces to 0 eV upon doping with Indium, indicating a transition to metallic characteristics. This change is attributed to the predominant involvement of the d‐orbital of Zn, s‐orbital of Se, and p‐orbitals of Indium in the valence band of ZnSe1−x Inx materials. Conversely, the s‐orbital of Zn predominantly contributes to the conduction band of undoped ZnSe, whereas the d‐orbitals of Indium play a significant role in the conduction band of ZnSe1−x Inx (x = 0%, 12.5%, 25%). Moreover, the study computes other optical parameters, including reflectivity, electron energy loss spectrum, extinction coefficient, refractive index, and absorption coefficient, as functions of photon energy. A notable observation is the considerable rise in the zero‐frequency dielectric constant, ε1(0), which increases from 5.0 in pure ZnSe to 40.0 and 50.0 in Indium‐doped ZnSe, highlighting a significant alteration in electronic properties. Furthermore, optical analyses demonstrate an expansion in the absorption coefficient spectrum, which extends from photon energies of 2.0 eV in pristine ZnSe to begin at 0 eV in Indium‐doped variants, indicating a broadening of the material's capacity to absorb light across a wider spectrum of photon energies. This expansion infers improved light absorption potential, which could be particularly beneficial for the fabrication of light‐emitting modules and solar energy converters. These insights underline the considerable influence that Indium incorporation exerts on the band architecture and optical responses of ZnSe, offering critical directions for the advancement of optoelectronic devices.
Investigation of the X‐ray fluorescence technique in the characterization of Ni coatings applied to historical heritage studies
Authors: Silveira Paula , Veit Hugo Marcelo , Dias Johnny , Falcade Tiago
Published: May 2024

Historical heritage pieces allow a better comprehension of the characteristics of different civilizations over time. The characterization of such pieces is of paramount importance for their preservation. Energy‐dispersive X‐ray fluorescence (EDXRF) is recommended for this application, mostly because it is non‐destructive and the equipment is portable. However, one of its limitations is the difficulty in separating the signal from the substrate and the coating of multilayered samples, as it does not provide depth resolution. This project investigates the application of EDXRF in the characterization of metallic coatings, in the context of historical heritage. The elemental concentration of electrodeposited Ni coatings onto carbon steel substrates was obtained from EDXRF analyses. The relationship between the layer thickness and the variation of the Kα/Kβ intensity ratio of nickel peaks was exploited and the thicknesses of the coatings were estimated through EDXRF measurements. For comparison, particle‐induced x‐ray emission spectroscopy and scanning electron microscopy of the cross‐section were employed, providing a depth profile of the coatings. The estimated thicknesses from EDXRF analysis were comparable to those observed in the microscopy images for thinner films (up to 8 μm). On the other hand, for thicker films, the thicknesses were underestimated, due to the technique's depth limit and matrix effects, as secondary absorption. Despite these limitations, EDXRF remains valuable for evaluating cultural heritage pieces, often providing sufficient information to address authenticity concerns or to guide restoration processes. A case study was also performed to apply the methodology discussed on historical metallic pieces.
Digital Empathic Healthcare: Designing Virtual Interactions for Human-Centered Experiences
Authors: Grech Amy , Wodehouse Andrew , Brisco Ross
Published: May 2024

The evolution of the relationship between healthcare professionals and patients towards patient-centered care has emphasized the importance of understanding patients’ perspectives, values, and needs. This shift has transformed decision-making from a technical standpoint to a more holistic approach integrating moral influences, driven by empathy. This research explores the transformative role of empathy, facilitated by Virtual Reality (VR) technology, in healthcare practitioners’ interactions with patients. Inspired by VR's immersive capabilities, the novel specification entitled the Digital Empathic Design Voyage is presented as a foundation for operational virtual environments that empower humans to experience empathy. Through outcomes from literature and a qualitative study, this paper determines appropriate digital environment interactions relevant to a healthcare scenario. The research envisions a deeper understanding of patients, fostering human-oriented healthcare practices and solutions.
Developing a Digital Gaming Intervention with Yetitablet® to Improve Older People’s Functioning and Activity in Long-Term-Care – a Feasibility Study
Authors: Kukkohovi Saara , Siira Heidi , Elo Satu
Published: May 2024

Long-term care (LTC) residents often have many health problems and functional limitations, and their sedentary behavior is common. Playing digital games is one way to improve the well-being, functioning, and activity of older people. The purpose of this study was to test a digital gaming intervention with a new device in an LTC environment before the larger effectiveness study. The aim was to produce information on the benefits of the digital gaming intervention for residents, the success of the implementation of the intervention and the factors affecting it. One LTC facility for older people participated in the study. The data was collected with a semi-structured thematic interview after an eight-week intervention. The interview data was analyzed using inductive content analysis. Staff experiences of the benefits of the intervention were classified into three main categories: The intervention enabled a new kind of physical activity, the intervention increased the social activity and brought residents together and the intervention brought joy and variety to the residents. Success of the intervention implementation was classified into three main categories: active participation of the residents in the gaming sessions, low involvement of staff in the implementation of the intervention and variable success of implementing the intervention protocol in the everyday life of LTC facility. Factors affecting the implementation formed nine main categories. This feasibility study highlighted important factors related to the implementation of the intervention, which must be considered in the future for the success of the effectiveness study.
Orchestrating Customer-Oriented Public-Private Ecosystem
Authors: Nätti Satu , Komulainen Hanna , Saraniemi Saila , Ulkuniemi Pauliina
Published: May 2024

The way public procurers interact with the supply market is developing from purely transactional towards more resource focused and collaborative exchanges. Still, what seems to be missing in the public procurement culture is knowledge of how to connect to a wider network of resource providers. We do not have adequate understanding about customer orientation in public sector, nor about customer-centric ecosystem. This is especially true in public health services; systems that are under huge transformation. In this paper, we have followed that development to understand: How can public health care transformation towards customer-oriented ecosystems be orchestrated? We had a unique opportunity to follow the renewal of the healthcare system in Finland, having access to interview those involved in planning the system. In total 17 in-depth interviews enabled us to create understanding of this challenge.
An LSTM Framework for the Effective Screening of Dementia for Deployment on Edge Devices
Authors: Wilkie Bernard , Muñoz Esquivel Karla , Roche Jamie
Published: May 2024

Dementia is a series of neurodegenerative disorders that affect 1 in 4 people over the age of 80 and can greatly reduce the quality of life of those afflicted. Alzheimer’s disease (AD) is the most common variation, accounting for roughly 60% of cases. The current financial cost of these diseases is an estimated $1.3 trillion per year. While treatments are available to help patients maintain their mental function and slow disease progression, many of those with AD are asymptomatic in the early stages, resulting in late diagnosis. The addition of the routine testing needed for an effective level of early diagnosis would put a costly burden on both patients and healthcare systems. This research proposes a novel framework for the modelling of dementia, designed for deployment in edge hardware. This work extracts a wide variety of thoroughly researched Electroencephalogram (EEG) features, and through extensive feature selection, model testing, tuning, and edge optimization, we propose two novel Long Short-Term Memory (LSTM) neural networks. The first, uses 4 EEG sensors and can classify AD and Frontotemporal Dementia from cognitively normal (CN) subjects. The second, requires 3 EEG sensors and can classify AD from CN subjects. This is achieved with optimisation that reduces the model size by 83×, latency by 3.7×, and performs with an accuracy of 98%. Comparative analysis with existing research shows this performance exceeds current less portable techniques. The deployment of this model in edge hardware could aid in routine testing, providing earlier diagnosis of dementia, reducing the strain on healthcare systems, and increasing the quality of life for those afflicted with the disease.
Intelligent Mental Workload Mobile Application in Personalized Digital Care Pathway for Lifestyle Chronic Disease
Authors: Keikhosrokiani Pantea , Isomursu Minna , Korhonen Olli , Sean Tan Teik
Published: May 2024

In the new healthcare paradigm, personalized digital care pathway enables the provision of tailored information and empowers patients. In healthcare, it is crucial to attend to patients’ physical and emotional requirements. Stress and heavy mental workload can be detrimental to managing chronic lifestyle disorders. However, a reliable, standardized, and widely used paradigm for incorporating mental workload into the digital care pathway for providing long-term personalized care is missing from the current care pathway. Therefore, this study aims to investigate the use of mental workload tools and mobile applications in personalized digital care pathways for managing lifestyle chronic diseases. The study was focused on determining and characterizing the variables that determine mental workload; and then, investigating the ways in which these variables might function as supplementary data sources to enhance the personalization of care pathway. Based on the proposed mental workload tool, data was collected from 304 employees in the manufacturing industry, software development department. An intelligent mobile application was developed to manage and classify mental workload. Ensemble learning algorithms were used for mental workload classification, among which Hard Voting Ensemble Model outperforms the other techniques with 0.97 accuracy. Based on the findings, the most variable factor of mental workload is psychological factors with a median of 3.25, suggesting that individual differences or specific psychological conditions can significantly affect mental workload. Regarding personalization for managing chronic diseases, the mental workload variables may be utilized to individually adjust digital treatments to the specific requirements of every patient in a person-centered care.
Dynamics in Entry and Exit Registrations in a 14-Year Follow-Up of Nationwide Electronic Prescription and Patient Data Repository Services in Finland
Authors: Jormanainen Vesa
Published: May 2024

There exist a need to carry out further research in order to describe implementation and adoption of nationwide healthcare information systems. This research aimed to follow-up in a 14-year period (2010–2023) of public and private healthcare service organizations’ entries to and exits from the centralized electronic Prescription and Patient Data Repository Services in Finland. Our material comes from the official Social Welfare and Healthcare Organization Registry (SOTE-organisaatiorekisteri), which is part of the national Code Server and the Kanta Services. Registry data were extracted in an excel file format in 3 January 2024. Outcomes were continuous registration of services or registered exist from the services. We found profound dynamics in the registry data. In the nationwide Prescription Services, the registered organizations provided altogether 8,884 follow-up years, during which in 2010–2023 there were in total 1,530 healthcare service organization entries and 553 exits from the national services, whereas 977 organizations had the national services in production in 2023. In Patient Data Repository Services, the registered organizations provided altogether 7,692 follow-up years, during which in 2011–2023 there were totally 1,980 healthcare service organization entries and 494 exits from the national services, whereas 1,486 organizations had the national services in production in 2023. No effects of Covid-19 epidemic were observed. Permanent legislation may explain many of the peak numbers observed in this research. Effects of the structural reform to reorganize healthcare, social welfare and rescue services to wellbeing services counties starting January 2023 were observed in this registry research on public healthcare service organizations.
Fatigue Assessment with Visualizations of Patient-Generated Data: An Evaluation with Informatics-Savvy Healthcare Professionals
Authors: Guardado Sharon , Holappa Terhi , Isomursu Minna
Published: May 2024

Severe and chronic fatigue is a prevalent symptom in multiple chronic conditions. Its complexity, its multifaceted nature and its varied manifestations across different conditions require a nuanced approach for accurate assessment by healthcare professionals. In our research, informatics-savvy public health nurses from a Digital Health Services and Health Promotion Master’s program evaluated various visualizations of patient-generated health data which could potentially be collected through a mobile app designed for people with Multiple Sclerosis. The data visualization prototypes could be a tool to support fatigue assessment and effective communication during consultations and their design was based on prior suggestions from healthcare professionals with experience in fatigue assessment. The patient-generated health data represented in the prototypes comprised a combination of fatigue-related factors and physical activity tracked by Google Fit. This study presents the recommendations of the participants regarding various aspects linked to the visualizations of patient-generated health data, including their utility in the clinical setting, the most suitable types of data summaries, usability aspects and the possibility of meaningful interrelations between distinct types of data. The results of our study emphasize the importance of well-designed data visualizations to support healthcare professionals in decision-making and to improve patient participation in the chronic care process. The iterative design process of the prototypes ensures that the final visualizations have proper usability and the potential to become clinically relevant, and instrumental in the effective assessment of fatigue in chronic management.
Wearable Motion Sensors in the Detection of ADHD: A Critical Review
Authors: Basic Jakov , Uusimaa Johanna , Salmi Juha
Published: May 2024

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with inattention, hyperactivity, and impulsivity as core symptoms. Current diagnostic methods of ADHD consisting of interviews and self-ratings come with a risk of subjective bias and are dependent on the limited availability of healthcare professionals. However, recent technological advances have opened new opportunities to develop objective and scalable methods for precision diagnostics. The present critical review covers the current literature concerning one of the promising technologies, the use of motion sensors or accelometers for detecting ADHD, particularly evaluating the related clinical potential. Several studies in this field, especially recent studies with advanced computational methods, have demonstrated excellent accuracy in detecting individual participants with ADHD. Machine learning methods provide several benefits in the analysis of rich sensor data, but the existing studies still have critical limitations in explaining the underlying cognitive functions and demonstrating the capacity for differential diagnostics is still underway. Clinical utility of sensor-based diagnostic methods could be improved by conducting rigorous cross-validation against other methods in representative samples and employing multi-sensor solutions with sophisticated analysis methods to improve interpretation of the symptom manifestation. We conclude that motion sensors provide cost-effective and easy-to-use solutions with strong potential to increase the precision and availability of ADHD diagnostics. Nevertheless, these methods should be employed with caution, as only a fraction of ADHD symptoms relate to hyperactivity captured by motion sensors. At best, this technique could complement the existing assessment methods or be used along with other digital tools such as virtual reality.
Influence of Arterial Vessel Diameter and Blood Viscosity on PTT in Pulsatile Flow Model
Authors: Zienkiewicz Aleksandra , Vihriälä Erkki , Myllylä Teemu
Published: May 2024

Modelling relation between Pulse Transit Time (PTT) and blood pressure (BP) is a critical step in BP estimation for wearable technology. Recognizing the limitation of assuming constant vessel and blood conditions, we developed a simplified pulsatile flow model to analyze how various factors affect PTT values. Our research focuses on the impact of mechanical characteristics, such as vessel diameter, wall thickness, blood viscosity, and pressure, on PTT measurements and subsequent BP estimation. Measurements were conducted using accelerometer sensors within a custom-designed mock circulatory loop. This setup allowed for the testing of a wide range of pressure values and pulsation rates, as well as the modification of viscosity in blood-mimicking liquids across different vessel models. We employed the Moens-Korteweg conversion model for pressure estimation, initially trained on PTT data from a specific setup parameter combination, and subsequently tested with data from varied setup parameters. We observed high correlation levels (r = 0.93 ± 0.09) paired with high error (RMSE = 163 ± 100 mHg), suggesting potential inaccuracies in pressure estimation. We present the recorded signals and discuss how alterations in physical conditions influence PTT values and the precision of BP estimation.