Hasil untuk "Computer applications to medicine. Medical informatics"

Hongqin Chen, Jian Meng, Qiao Li et al.

Abstract Background The heterogeneity of patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs) in published clinical studies on vaginal relaxation syndrome (VRS) hinders cross-study comparisons and integration of evidence-based findings, impeding the development of robust clinical evidence. Objective To comprehensively investigate the current use of PROs and PROMs in VRS research, compile a comprehensive catalog, and provide guidance for selecting outcome measures and tools VRS patients. Methods This study systematically searched clinical studies on VRS treatment published up to December 2024 in PUBMED, EMBASE, Web of Science, and Cochrane databases, focusing primarily on pelvic floor muscle training, physical energy therapies, and surgical interventions. PROs and PROMs were extracted, organized into a structured catalog, and categorized by thematic domains. The COSMIN checklist was applied to assess the measurement properties of commonly used PROMs. Results A total of 69 studies were included, comprising 14 randomized controlled trials (1193 patients) and 55 observational studies (3327 patients), totaling 4520 participants. These studies reported 68 PROs and 57 PROMs. The most commonly used PROMs were the Female Sexual Function Index (FSFI, 47.83%), Vaginal Laxity Questionnaire (VLQ), Visual Analog Scale (VAS), Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12), and Sexual Satisfaction Questionnaire (SSQ). Notably, 42 PROMs (73.68%) appeared only once. Conclusions PROs for surgical and non-surgical VRS treatments are similar, but non-surgical interventions include additional outcomes, such as overall efficacy and patient’s vaginal tightness satisfaction. The high proportion of unvalidated PROMs (81.09%) underscores the need for standardized, disease-specific measures. Future Delphi surveys and expert consensus are anticipated to facilitate the development of a comprehensive core outcome set (COS) and core outcome measurement set (COMS) for VRS.

Marcel Parciak, Noëlla Pierlet, Liesbet M Peeters

Health data integration platforms are vital to drive collaborative, interdisciplinary medical research projects. Developing such a platform requires input from different stakeholders. Managing these stakeholders and steering platform development is challenging, and misaligning the platform to the partners’ strategies might lead to a low acceptance of the final platform. We present the medEmotion project, a collaborative effort among 7 partners from health care, academia, and industry to develop a health data integration platform for the region of Limburg in Belgium. We focus on the development process and stakeholder engagement, aiming to give practical advice for similar future efforts based on our reflections on medEmotion. We introduce Personas to paraphrase different roles that stakeholders take and Demonstrators that summarize personas’ requirements with respect to the platform. Both the personas and the demonstrators serve 2 purposes. First, they are used to define technical requirements for the medEmotion platform. Second, they represent a communication vehicle that simplifies discussions among all stakeholders. Based on the personas and demonstrators, we present the medEmotion platform based on components from the Microsoft Azure cloud. The demonstrators are based on real-world use cases and showcase the utility of the platform. We reflect on the development process of medEmotion and distill takeaway messages that will be helpful for future projects. Investing in community building, stakeholder engagement, and education is vital to building an ecosystem for a health data integration platform. Instead of academic-led projects, the health care providers themselves ideally drive collaboration among health care providers. The providers are best positioned to address hospital-specific requirements, while academics take a neutral mediator role. This also includes the ideation phase, where it is vital to ensure the involvement of all stakeholders. Finally, balancing innovation with implementation is key to developing an innovative yet sustainable health data integration platform.

Austin M Stroud, Sarah A Minteer, Xuan Zhu et al.

As health systems incorporate artificial intelligence (AI) into various aspects of patient care, there is growing interest in understanding how to ensure transparent and trustworthy implementation. However, little attention has been given to what information patients need about these technologies to promote transparency of their use. We conducted three asynchronous online focus groups with 42 patients across the United States discussing perspectives on their information needs for trust and uptake of AI, focusing on its use in cardiovascular care. Data were analyzed using a rapid content analysis approach. Our results suggest that patients have a set of core information needs, including specific information factors pertaining to the AI tool, oversight, and healthcare experience, that are relevant to calibrating trust as well as perspectives concerning information delivery, disclosure, consent, and physician AI use. Identifying patient information needs is a critical starting point for calibrating trust in healthcare AI systems and designing strategies for information delivery. These findings highlight the importance of patient-centered engagement when developing AI model documentation and communicating and provisioning information about these technologies in clinical encounters.

Emily A. Prentice, Abby Moler, Amanda Sweeney et al.

Background: The Links to Care Community Grants Project was developed to improve breast cancer outcomes by increasing access to appropriate follow-up care, improving processes for care transitions, and enhancing care coordination between community health centers (CHCs) and hospital partners. Methods: This 24-month multi-pronged project encompasses quality improvement (QI) coaching, technical assistance support, and evaluation. QI coaching follows the Model for Improvement to test and adapt to changes. Local and centralized technical assistance supports the individual needs of the health system. A data collection tool was developed to evaluate implemented interventions and assess changes in breast cancer screening and diagnostic testing completion rates, time between care transitions, and process improvements made throughout the project period. Results: Seven CHCs comprised of 27 clinic sites with 26 255 patients eligible for breast cancer screening agreed to participate. Baseline findings demonstrate an average screening rate of 51.1%. Conclusion: The Links to Care Community Grants Project will evaluate the effectiveness of implemented patient, provider, and/or system-level interventions and care coordination process improvements on reducing delays along the breast cancer care continuum.

Muhammad Attique Khan, Usama Shafiq, Ameer Hamza et al.

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computational inefficiencies due to numerous hyperparameters persist. This study aims to address these challenges by presenting a novel deep-learning framework for classifying and localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed framework begins with dataset augmentation to enhance training robustness. Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. These features are classified using a Shallow Wide Neural Network (SWNN) and traditional classifiers. Experimental evaluations on the Kvasir-V1 and Kvasir-V2 datasets demonstrate superior performance, achieving accuracies of 99.60% and 95.10%, respectively. The proposed framework offers improved accuracy, precision, and computational efficiency compared to state-of-the-art models. The proposed framework addresses key challenges in GI disease diagnosis, demonstrating its potential for accurate and efficient clinical applications. Future work will explore its adaptability to additional datasets and optimize its computational complexity for broader deployment.

Tia G. Sawhney, Angela Dobes, Sirimon O'Charoen

The quality-adjusted life-year (QALY) is a metric widely used when assessing the cost-effectiveness of drugs and other health interventions. The assessments are used in the development of recommendations for pricing, formulary placement decisions, and health policy decisions. A new bill, H.R. 485, the Protecting Health Care for All Patients Act of 2023, was approved by the US House Energy and Commerce Health Subcommittee that will, if passed, end the practice of using QALYs in all federal programs.^1,2^ Proponents of the ban say that QALYs undervalue the positive effects of therapeutics on people with disabilities.^3^ We share their concerns. Furthermore, our review of the mathematical properties of QALYs, including an analysis of quality-of-life utility (QOL utility) data recently collected from patients with inflammatory bowel disease (IBD), has led us to conclude that QALYs are an inappropriate metric of drug and treatment cost-effectiveness for all people, both disabled and nondisabled, and should not be the basis for US healthcare policy decisions.

Jingmin An, Xiaogang Weng

Abstract Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language models by jointly learning protein properties from strongly-correlated protein tasks. Here we elaborately designed a multi-task learning (MTL) architecture, aiming to decipher implicit structural and evolutionary information from three sequence-level classification tasks for protein family, superfamily and fold. Considering the co-existing contextual relevance between human words and protein language, we employed BERT, pre-trained on a large natural language corpus, as our backbone to handle protein sequences. More importantly, the encoded knowledge obtained in the MTL stage can be well transferred to more fine-grained downstream tasks of TAPE. Experiments on structure- or evolution-related applications demonstrate that our approach outperforms many state-of-the-art Transformer-based protein models, especially in remote homology detection.

Shuangbin Xu, Lin Li, Xiao Luo et al.

Abstract While phylogenetic trees and associated data have been getting easier to generate, it has been difficult to reuse, combine, and synthesize the information they provided, because published trees are often only available as image files and associated data are often stored in incompatible formats. To increase the reproducibility and reusability of phylogenetic data, the ggtree object was designed for storing phylogenetic tree and associated data, as well as visualization directives. The ggtree object itself is a graphic object and can be rendered as a static image. More importantly, the input tree and associated data that are used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree, and data in one single object) and thus enhancing data reuse and analytical reproducibility, as well as facilitating integrative and comparative studies. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree.

Adolfo Jara, Pooja Lepcha, Sankyun Kim et al.

This article presents a database containing on-orbit data samples of the Electrical Power System (EPS) from 4 different 1U CubeSats belonging to the BIRDS constellation. The EPS is responsible for providing uninterrupted power to overall satellite both during sunlight and eclipse. The satellites are based on the BIRDS open-source standardized bus designed by Kyutech for research and education. BIRDS bus was used for six satellites that were delivered to ISS on board the Cygnus re-supply spacecraft launched by Antares rocket and released from International Space Station (ISS) into ISS orbit (altitude 400 km, inclination: 51.6°, duration: 92.6 min). The dataset contains the data of voltage (mV), current (mA) and temperature (Celsius) of the battery and solar panels attached to 5 sides of the satellite. This data is collected by the on-board computer every 90 seconds in nominal operation or every 10 seconds in fast sampling mode. The data is downloaded from the satellite memory by the ground station operators. Next, space engineering experts from Kyushu Institute of Technology have analysed the dataset to classify each data sample into normal or anomaly classes. This paper provides one datafile per satellite, that includes data from solar panels and battery since their deployment into orbit until the end of its life for the UGUISU, RAAVANA, and NEPALISAT satellites, first two showing a failure in one of their panels during more than two years of operation on-orbit. The TSURU satellite dataset includes data since its deployment into orbit and will continue to be collected until the end of its life. The dataset generated will be useful for 1U CubeSat, such as BIRDS platform, users, and satellite developers by using it as a reference to compare the behaviour of their Electric Power System under different operating scenarios and align their missions according to the available power on-orbit. At the same time, the dataset can help computer science researchers to build and validate new models for fault diagnosis and outlier detection.

Zella King, Joseph Farrington, Martin Utley et al.

Abstract Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

Joshua C. Denny, Jessica D. Tenenbaum, Matt Might

Ya Chai, José R. Chimelis-Santiago, Kristy A. Bixler et al.

Background: Sex-specific neurobiological underpinnings of impulsivity in youth with externalizing disorders have not been well studied. The only report of functional connectivity (FC) findings in this area demonstrated sex differences in fronto-subcortical connectivity in youth with attention-deficit/hyperactivity disorder (ADHD). Methods: The current study used functional magnetic resonance imaging (fMRI) to examine sex differences in resting-state seed-based FC, self-rated impulsivity, and their interactions in 11-12-year-old boys (n = 43) and girls (n = 43) with externalizing disorders. Generalized linear models controlling for pubertal development were used. Seeds were chosen in the ventral striatum, medial prefrontal cortex, middle frontal gyrus and amygdala. Results: Impulsivity scores were greater in boys than girls (p < 0.05). Boys showed greater positive connectivity within a ventromedial prefrontal-ventral striatal network. In addition, boys demonstrated weaker connectivity than girls within two medial–lateral prefrontal cortical networks. However, only boys showed greater medial–lateral prefrontal connectivity correlated with greater impulsivity. Conclusions: The findings provide evidence supporting sex differences in both ventral striatal-ventromedial prefrontal and medial–lateral prefrontal functional networks in youth with externalizing disorders. These important networks are thought to be implicated in impulse control. Medial-lateral prefrontal connectivity may represent a male-specific biomarker of impulsivity.

Shivi Garg, Niyati Baliyan

The data in this article have been collaborated from mainly four sources- Google Playstore,11 Google Playstore https://play.google.com/store/apps. Wandoujia22 Wandoujia apps http://www.wandoujia.com/apps. (third party app store market), AMD33 AMD http://amd.arguslab.org/sharing. and Androzoo.44 Androzoo https://androzoo.uni.lu/access. These data include ~85,000 APKs (Android Package Kit), both malicious and benign from these data sources. Static and dynamic features are extracted from these APK files, and then supervised machines learning algorithms are employed for malware detection in Android. This data article also provides the Python code for data analysis. For feature extraction, a generic algorithm has also been incorporated, thereby, selecting important and relevant feature subset. Conclusive results obtained from this data set are further comprehended and interpreted in our latest research study “A Novel Parallel Classifier Scheme for Vulnerability Detection in Android” (Garg et al., 2018). This proved to be precious contribution for ensembling classifiers in machine learning to detect malware in Android.

Cynthia M. Ortinau, Kathryn Mangin-Heimos, Joseph Moen et al.

Altered brain development is a common feature of the neurological sequelae of complex congenital heart disease (CHD). These alterations include abnormalities in brain size and growth that begin prenatally and persist postnatally. However, the longitudinal trajectory of changes in brain volume from the prenatal to postnatal environment have not been investigated. We aimed to evaluate the trajectory of brain growth in a cohort of patients with complex CHD (n = 16) and healthy controls (n = 15) to test the hypothesis that patients with complex CHD would have smaller total brain volume (TBV) prenatally, which would become increasingly prominent by three months of age. Participants underwent fetal magnetic resonance imaging (MRI) at a mean of 32 weeks gestation, a preoperative/neonatal MRI shortly after birth, a postoperative MRI (CHD only), and a 3-month MRI to evaluate the trajectory of brain growth. Three-dimensional volumetric analysis was applied to the MRI data to measure TBV, as well as tissue-specific volumes of the cortical gray matter (CGM), white matter (WM), subcortical (deep nuclear) gray matter (SCGM), cerebellum, and cerebrospinal fluid (CSF). A random coefficients model was used to investigate longitudinal changes in TBV and demonstrated an altered trajectory of brain growth in the CHD population. The estimated slope for TBV from fetal to 3-month MRI was 11.5 cm3 per week for CHD infants compared to 16.7 cm3 per week for controls (p = 0.0002). Brain growth followed a similar trajectory for the CGM (p < 0.0001), SCGM (p = 0.002), and cerebellum (p = 0.005). There was no difference in growth of the WM (p = 0.30) or CSF (p = 0.085). Brain injury was associated with reduced TBV at 3-month MRI (p = 0.02). After removing infants with brain injury from the model, an altered trajectory of brain growth persisted in CHD infants (p = 0.006). These findings extend the existing literature by demonstrating longitudinal impairments in brain development in the CHD population and emphasize the global nature of disrupted brain growth from the prenatal environment through early infancy. Keywords: Brain volume, Magnetic resonance imaging, Congenital heart disease, Fetal

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis et al.

Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. Results Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. Conclusions The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

Yomtov Almozlino, Nir Atias, Dana Silverbush et al.

Abstract Background ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. Results Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets. Conclusions ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.

Lukhanyo Mekuto, Seteno K.O. Ntwampe, John B.N. Mudumbi et al.

The data presented in this article contains the bacterial community structure of the free cyanide (CN-) and thiocyanate (SCN-) degrading organisms that were isolated from electroplating wastewater and synthetic SCN- containing wastewater. PCR amplification of the 16S rRNA V1-V3 regions was undertaken using the 27F and 518R oligonucleotide primers following the metacommunity DNA extraction procedure. The PCR amplicons were processed using the illumina® reaction kits as per manufacturer׳s instruction and sequenced using the illumina® MiSeq-2000, using the MiSeq V3 kit. The data was processed using bioinformatics tools such as QIIME and the raw sequence files are available via NCBI׳s Sequence Read Archive (SRA) database. Keywords: Cyanide degrading organisms, Thiocyanate degrading organisms, Metagenomics, 16S rRNA gene

G. Olivito, M. Cercignani, M. Lupo et al.

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease characterized by a progressive cerebellar syndrome, which can be isolated or associated with extracerebellar signs. It has been shown that patients affected by SCA2 present also cognitive impairments and psychiatric symptoms. The cerebellum is known to modulate cortical activity and to contribute to distinct functional networks related to higher-level functions beyond motor control. It is therefore conceivable that one or more networks, rather than isolated regions, may be dysfunctional in cerebellar degenerative diseases and that an abnormal connectivity within specific cerebello-cortical regions might explain the widespread deficits typically observed in patients. In the present study, the network-based statistics (NBS) approach was used to assess differences in functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes.

Eline Desmet, Stefanie Bracke, Katrien Forier et al.

This article contains original data, figures and methods used in the characterization of the liposomal carrier ‘DDC642’ for topical applications, described in “An elastic liposomal formulation for RNAi-based topical treatment of skin disorders: proof-of-concept in the treatment of psoriasis” (Desmet et al., 2016) [1]. Several elastic liposomal formulations have been evaluated for their ability to encapsulate and deliver RNA interference (RNAi) molecules to cultured primary skin cells. The efficiency and effectiveness of these liposomes were compared to that of our previously characterized liposomes, the ‘SECosomes’ (SEC) (Geusens et al., 2010) [2]. After selection of a potential superior carrier, based on encapsulation and transfection efficiency data (Desmet et al., 2016) [1], the selected DDC642 liposomes were characterized more in-depth. Herein, a detailed characterization of the DDC642 liposome and RNAi-loaded lipoplexes is given, including the matching protocols. Keywords: Gene therapy, Lipid-based nanoparticle, Liposome, RNA interference, Topical drug delivery

Hang Xiong, Puqing Wang, Yueji Zhu

Empirical studies on social diffusions are often restricted by the access to data of diffusion and social relations on the same objects. We present a set of first-hand data that we collected in ten rural villages in central China through household surveys. The dataset contains detailed and comprehensive data of the diffusion of an innovation, the major social relationships and the household level demographic characteristics in these villages. The data have been used to study peer effects in social diffusion using simulation models, “Peer Effects and Social Network: The Case of Rural Diffusion in Central China” [1]. They can also be used to estimate spatial econometric models. Data are supplied with this article. Keywords: High-value crop, Diffusion, Social networks, Rural China, Household survey