Hasil untuk "Computer applications to medicine. Medical informatics"

Sarah N. Abdulkader, Ayman Atia, Mostafa-Sami M. Mostafa

Dagmar Niewold, Evy C E van Gestel, Latifa Abidi et al.

Abstract BackgroundCardiovascular disease (CVD) is the leading cause of death worldwide. Individuals with lower income or experiencing financial hardship face a significantly higher risk of developing CVD. However, there is a lack of in-depth insight into their experiences with CVD, and specific attention to women is essential. ObjectiveThe In a Heartbeat study aims to understand the relationship between CVD and financial hardship and enable earlier recognition and prevention of CVD among both women and men. In this study protocol, we describe our citizen science study, in which we unravel the mechanisms and contexts through which financial problems lead to the development and late recognition of CVD. MethodsThe main data for this study are collected by citizen scientists through qualitative lifeline interviews. All citizen scientists have experience with financial hardship, and some also have experience with CVD. We hold weekly project team meetings with citizen scientists and professional scientists in which we use methods such as photo elicitation, critical-creative hermeneutic analysis, design thinking, a dynamic learning agenda, and regular individual and group evaluations of the citizen science process. ResultsThe study was funded in October 2024 and started in January 2025. Data collection started in November 2025 and is expected to end halfway through 2026. Four qualitative lifeline interviews had been conducted as of December 6, 2025. Data analyses are planned for 2026. Manuscripts reporting findings on the central research question and the process evaluations will be submitted for publication in 2027. ConclusionsToward the end of the study in 2027, we will develop and disseminate concrete recommendations for various stakeholders to prevent CVD and recognize CVD earlier among people with financial hardship. In all our analyses and recommendations, we will consider sex and gender differences. Our study could contribute to a reduction in health inequalities.

Zhendong Ding, Linan Zhang, Yihan Zhang et al.

Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian et al.

Abstract Introduction Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or cardiovascular complications, while delays can strain hospital capacity. Machine learning algorithms offer a promising solution by leveraging large amounts of patient data to predict optimal discharge times. Unlike prior studies relying on statistical models or single-algorithm methods, this research assesses multiple ML models to predict discharge readiness, comparing them against staff evaluations and the Aldrete checklist. Methodology We conducted a cross-sectional study of 830 patients under general anesthesia from December 2023 to April 2024, collecting demographics, surgical details, and Aldrete scores. A power analysis ensured statistical robustness, targeting a 5% accuracy improvement (minimum clinically important difference, derived from Gabriel et al., 2017), with variance (SD ≈ 0.1) from pilot data, using a two-sample t-test (power = 0.8, alpha = 0.05), confirming the sample size’s adequacy. Two prediction approaches were tested: discharge timing in 15-minute intervals and binary classification (within 15 min or later). Models included Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and XGBoost, assessed via accuracy, precision, recall, F1 score, and AUC. Predictions were benchmarked against staff and Aldrete scores, with 99.5% confidence intervals (CIs) adjusting for multiple comparisons. Results he RF algorithm showed high performance in both prediction approaches. In the first approach, RF achieved an AUC of 0.75 (99.5% CI: 0.70–0.80) and accuracy of 0.87 (99.5% CI: 0.83–0.91) per staff evaluations, and an AUC of 0.87 (99.5% CI: 0.83–0.91) and accuracy of 0.71 (99.5% CI: 0.66–0.76) per Aldrete scores. In the second approach, RF recorded an AUC of 0.85 (99.5% CI: 0.81–0.89) and accuracy of 0.86 (99.5% CI: 0.82–0.90) per staff evaluations, with ANN also showing strong results (AUC = 0.88, 99.5% CI: 0.84–0.92; accuracy = 0.78, 99.5% CI: 0.74–0.82). Due to overlapping CIs, differences between models were not statistically significant (P >.005). According to the Aldrete checklist, RF, SVM, and ANN exhibited competitive predictive capability, with AUCs ranging from 0.80 to 0.86. Conclusion The strong performance of Random Forest (RF) and Artificial Neural Network (ANN) models in predicting PACU discharge timing upon admission highlights their potential as effective tools for evaluating discharge readiness, as compared to staff assessments and the Aldrete checklist. This study focused on assessing these models, showing their ability to produce consistent predictions, though differences between top models were not statistically significant due to overlapping confidence intervals. Practical application of these findings to improve patient outcomes or hospital efficiency requires further investigation.

S.G. Mueller, N. Garga, P. Garcia et al.

Background: Increased resting state functional connectivity between regions involved in emotion control with regions with other specializations, e.g. motor control (emotional hyperconnectivity) is one of the most consistent imaging findings in persons suffering from dissociative seizures (DS). The overall goal of this study was to better characterize DS-related emotional hyperconnectivity using dynamic resting state analysis combined with brainstem volumetry to investigate 1. If emotional hyperconnectivity is restricted to a single state. 2. How volume losses within the modulatory and emotional motor subnetworks of the neuromodulatory system influence the expression of the emotional hyperconnectivity. Methods: 13 persons with dissociative seizures (PDS) (f/m:10/3, mean age (SD) 44.6 (11.5)) and 15 controls (CON) (f/m:10/5, mean age (SD) 41.7 (13.0)) underwent a mental health test battery and structural and functional imaging at 3 T. Deformation based morphometry was used to assess brain volume loss by extracting the mean Jacobian determinants from 457 brain, forebrain and brainstem structures. The bold signals from 445 brainstem and brain rois were extracted with CONN and a dynamic fMRI analysis combined with graph and hierarchical analysis was used to identify and characterize 9 different brain states. Welch’s t tests and Kendall tau tests were used for group comparisons and correlation analyses. Results: The duration of Brain state 6 was longer in PDS than in CON (93.1(88.3) vs. 23.4(31.2), p = 0.01) and positively correlated with higher degrees of somatization, depression, PTSD severity and dissociation. Its global connectivity was higher in PDS than CON (90.4(3.2) vs 86.5(4.2) p = 0.01) which was caused by an increased connectivity between regions involved in emotion control and regions involved in sense of agency/body control. The brainstem and brainstem-forebrain modulatory and emotional motor subnetworks of the neuromodulatory system were atrophied in PDS. Atrophy severity within the brainstem-forebrain subnetworks was correlated with state 6 dwell time (modulatory: tau = -0.295, p = 0.03; emotional motor: tau = -0.343, p = 0.015) and atrophy severity within the brainstem subnetwork with somatization severity (modulatory: tau = -0.25, p = 0.036; emotional motor: tau = -0.256, p = 0.033). Conclusion: DS-related emotional hyperconnectivity was restricted to state 6 episodes. The remaining states were not different between PDS and CON. The modulatory subnetwork synchronizes brain activity across brain regions. Atrophy and dysfunction within that subnetwork could facilitate the abnormal interaction between regions involved in emotion control with those controlling sense of agency/body ownership during state 6 and contribute to the tendency for somatization in PDS. The emotional motor subnetwork controls the activity of spinal motoneurons. Atrophy and dysfunction within this subnetwork could impair that control resulting in motor symptoms during DS. Taken together, these findings indicate that DS have a neurophysiological underpinning.

Anil V. Parwani, Ankush Patel, Ming Zhou et al.

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Enrico Coiera, David Lyell, Thomas Campbell Bowden

Ming-Chung Chou, Jui-Hsing Lin, Ming-Ting Wu

Learning a skill has been demonstrated to relate to neural plasticity in both animal and human brains. Performing diabolo consists of different tricks and may cause brain structural changes associated with psychophysical functions. Therefore, the purpose of this study was to investigate gray matter (GM) and white matter (WM) changes associated with psychophysical functions induced by diabolo training in healthy subjects. Fourteen healthy right-handed male subjects were enrolled to receive the diabolo training. Whole brain T1-weighted images and diffusion tensor imaging (DTI) data were acquired from all subjects on a 3.0 T magnetic resonance scanner before and after the training. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and voxel-wise DTI analysis were carried out to detect the GM volume, cortical thickness, and WM diffusion changes using T1-weighted image and DTI data, respectively. In addition, two-arm coordination and mirror-drawing tests were performed to evaluate their psychophysical functions before and after 2, 4, 6 and 8 weeks of training. Analysis of variance was performed to understand whether the psychophysical functions changed over time after the training. The results showed that the psychophysical functions were significantly changed over time during the training. The VBM and SBM analyses revealed that the GM volume and cortical thickness were significantly increased in the brain areas associated with visual, motor, sensory, and spatial cognition functions. The voxel-wise DTI analysis further demonstrated that the mean diffusivity was significantly reduced in the genu of corpus callosum. Moreover, significant correlations were revealed between the increase rate of GM volume and the improvement rate of psychophysical functions in the left angular gyrus. The results suggest that the diabolo training may induce increased GM volume associated with improved psychophysical function in the brain region involved in spatial cognition and attention. Therefore, we conclude that the diabolo training may improve psychophysical function which might be reflected by the increased GM volume in the angular gyrus.

Mohiuddin, Mohammed, Park, Rex, Wesselmann, Ursula et al.

BackgroundChronic pelvic pain with various etiologies and mechanisms affects men and women and is a major challenge. Monotherapy is often unsuccessful for chronic pelvic pain, and combinations of different classes of medications are frequently prescribed, with the expectation of improved outcomes. Although a number of combination trials for chronic pelvic pain have been reported, we are not aware of any systematic reviews of the available evidence on combination drug therapy for chronic pelvic pain. ObjectiveWe have developed a protocol for a systematic review to evaluate available evidence of the efficacy and safety of drug combinations for chronic pelvic pain. MethodsThis systematic review will involve a detailed search of randomized controlled trials investigating drug combinations to treat chronic pelvic pain in adults. The databases searched will include the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, and EMBASE from their inception until the date the searches are run to identify relevant studies. The primary outcome will be pain relief measured using validated scoring tools. Secondary outcomes, where reported, will include the following: adverse events, serious adverse events, sexual function, quality of life, and depression and anxiety. Methodological quality of each included study will be assessed using the Cochrane Risk of Bias Tool. ResultsThe systematic review defined by this protocol is expected to synthesize available good quality evidence on combination drug therapy in chronic pelvic pain, which may help guide future research and treatment choices for patients and their health care providers. ConclusionsThis review will provide a clearer understanding of the efficacy and safety of combination pharmacological therapy for chronic pelvic pain. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020192231; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=192231 International Registered Report Identifier (IRRID)PRR1-10.2196/21909

Paul Nii Tackie Ammah, Ebenezer Owusu

Qiang Ma, Shuailin Li, Zhiqiang Xu et al.

The data presented in this article are related to the research paper entitled “Changes in N supply pathways under different long-term fertilization regimes in Northeast China” [1]. Seasonal dynamics of soil NH4+–N, NO3−–N, soil microbial biomass nitrogen (N) and fixed NH4+ were provided on the basis of a 26-year long-term experiment, including six treatments: no fertilizer (CK), recycled manure (M), N and P fertilizers (NP), P and K fertilizers (PK), N, P and K fertilizers (NPK), and NPK fertilizers with recycled manure (NPKM). The presentation of potential N retention and supply through soil microbial biomass N and fixed NH4+ pools at different growth stages is helpful for comparing the effects of different N pools on soil N transformation and assessing synchronies between crop N demand and soil N supply through different N pools.

Muhammad Faisal, Andy Scally, Robin Howes et al.

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n  = 24,696) and compared the performance of these models in data from another hospital ( n  = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

Christian La, Patricia Linortner, Jeffrey D. Bernstein et al.

Objective: Parkinson's disease (PD) episodic memory impairments are common; however, it is not known whether these impairments are due to hippocampal pathology. Hippocampal Lewy-bodies emerge by Braak stage 4, but are not uniformly distributed. For instance, hippocampal CA1 Lewy-body pathology has been specifically associated with pre-mortem episodic memory performance in demented patients. By contrast, the dentate gyrus (DG) is relatively free of Lewy-body pathology. In this study, we used ultra-high field 7-Tesla to measure hippocampal subfields in vivo and determine if these measures predict episodic memory impairment in PD during life. Methods: We studied 29 participants with PD (age 65.5 ± 7.8 years; disease duration 4.5 ± 3.0 years) and 8 matched-healthy controls (age 67.9 ± 6.8 years), who completed comprehensive neuropsychological testing and MRI. With 7-Tesla MRI, we used validated segmentation techniques to estimate CA1 stratum pyramidale (CA1-SP) and stratum radiatum lacunosum moleculare (CA1-SRLM) thickness, dentate gyrus/CA3 (DG/CA3) area, and whole hippocampus area. We used linear regression, which included imaging and clinical measures (age, duration, education, gender, and CSF), to determine the best predictors of episodic memory impairment in PD. Results: In our cohort, 62.1% of participants with PD had normal cognition, 27.6% had mild cognitive impairment, and 10.3% had dementia. Using 7-Tesla MRI, we found that smaller CA1-SP thickness was significantly associated with poorer immediate memory, delayed memory, and delayed cued memory; by contrast, whole hippocampus area, DG/CA3 area, and CA1-SRLM thickness did not significantly predict memory. Age-adjusted linear regression models revealed that CA1-SP predicted immediate memory (beta[standard error]10.895[4.215], p < .05), delayed memory (12.740[5.014], p < .05), and delayed cued memory (12.801[3.991], p < .05). In the fully-adjusted models, which included all five clinical measures as covariates, only CA1-SP remained a significant predictor of delayed cued memory (13.436[4.651], p < .05). Conclusions: In PD, we found hippocampal CA1-SP subfield thickness estimated on 7-Tesla MRI scans was the best predictor of episodic memory impairment, even when controlling for confounding clinical measures. Our results imply that ultra-high field imaging could be a sensitive measure to identify changes in hippocampal subfields and thus probe the neuroanatomical underpinnings of episodic memory impairments in patients with PD. Keywords: Parkinson's disease, Episodic memory, Cognitive impairment, MRI, Hippocampus, CA1, 7 Tesla

Vander Linden Catharine, Verhelst Helena, Deschepper Ellen et al.

Sustaining a traumatic brain injury (TBI) during adolescence has a profound effect on brain development and can result in persistent executive functioning deficits in daily life. Cognitive recovery from pediatric-TBI relies on the potential of neuroplasticity, which can be fostered by restorative training-programs. However the structural mechanisms underlying cognitive recovery in the immature brain are poorly understood. This study investigated gray matter plasticity following 2 months of cognitive training in young patients with TBI. Sixteen adolescents in the chronic stage of moderate-severe-TBI (9 male, mean age = 15y8m ± 1y7m) were enrolled in a cognitive computerized training program for 8 weeks (5 times/week, 40 min/session). Pre-and post-intervention, and 6 months after completion of the training, participants underwent a comprehensive neurocognitive test-battery and anatomical Magnetic Resonance Imaging scans. We selected 9 cortical-subcortical Regions-Of-Interest associated with Executive Functioning (EF-ROIs) and 3 control regions from the Desikan-Killiany atlas. Baseline analyses showed significant decreased gray matter density in the superior frontal gyri p = 0.033, superior parietal gyri p = 0.015 and thalamus p = 0.006 in adolescents with TBI compared to age and gender matched controls. Linear mixed model analyses of longitudinal volumetric data of the EF-ROI revealed no strong evidence of training-related changes in the group with TBI. However, compared to the change over time in the control regions between post-intervention and 6 months follow-up, the change in the EF-ROIs showed a significant difference. Exploratory analyses revealed a negative correlation between the change on the Digit Symbol Substitution test and the change in volume of the putamen (r = −0.596, p = 0.015). This preliminary study contributes to the insights of training-related plasticity mechanisms after pediatric-TBI. Keywords: Pediatric traumatic brain injury, Cognitive rehabilitation, Gray matter, Neural plasticity, Executive function, Linear mixed model analyses

Muhammad Hassan Khan, Julien Helsper, Muhammad Shahid Farid et al.

Daniel Wehner, Thomas Becker, Catherina G. Becker

This data article contains descriptive and experimental data on spinal cord regeneration in larval zebrafish and its dependence on Wnt/β-catenin signaling. Analyzing spread of intraspinally injected fluorescent dextran showed that anatomical continuity is rapidly restored after complete spinal cord transection. Pharmacological interference with Wnt/β-catenin signaling (IWR-1) impaired restoration of spinal continuity. For further details and experimental findings please refer to the research article by Wehner et al. Wnt signaling controls pro-regenerative Collagen XII in functional spinal cord regeneration in zebrafish (Wehner et al., 2017) [1]. Keywords: Wnt, Beta-catenin, Regeneration, Spinal cord, Zebrafish

Amrita Kumari Panda, Satpal Singh Bisht, Bodh Raj Kaushal et al.

Abstract Background Hot springs harbor rich bacterial diversity that could be the source of commercially important enzymes, antibiotics and many more products. Most of the hot springs present in Northeast of India are unexplored and their microbial diversity analysis could be of great interest to facilitate various industrial, agricultural and medicinal applications. The present study is an attempt to analyze the comprehensive bacterial diversity of Yumthang hot spring, Sikkim located at an altitude of 11, 800 ft. with a close proximity of Tibet 27° 47′ 30″ N 88° 42′ E using culture independent approach i.e. 16S rRNA gene amplicon metagenomic sequencing. Results The temperature and pH of the hot spring was recorded as 390–410 C and 8 respectively. Metagenome comprised of 1, 381,343 raw sequences with a sequence length of 151 bp and 55.62% G + C content. Metagenome sequence information is submitted at NCBI, SRA database under accession no. SRP057072. A total of 9, 95, 955 pre-processed reads were clustered into 1, 999 representative OTUs (operational taxonomical units) phylogenetically comprising of 17 bacterial phyla including unknown phylum indicating 99 families. Hot spring bacterial community is dominated by Proteobacteria (54.33%), Actinobacteria (32.19%), Firmicutes (6.03%), Bacteroidetes (2.87%) and unclassified bacteria (2.91%) respectively out of the total reads. Conclusions Several bacterial and archaeal sequences remained taxonomically unclassified, indicating potentially novel microorganisms in this hot spring ecosystem. Metagenomics of this habitat will facilitate identification of microorganisms possessing industrially relevant traits.

Simeon J.A. Visscher, Henk F. van Stel

This article provides both qualitative and quantitative data on practice variation amongst preventive child healthcare professionals in the prevention of child maltreatment in the Netherlands. Qualitative data consist of topics identified during interviews with 11 experts (with quotes), resulting in an online survey. The quantitative data are survey responses from 1104 doctors and nurses working in 29 preventive child healthcare organizations. Additionally, the interview topic list, the qualitative data analysis methodology, the survey (in English and Dutch) and anonymized raw survey data (http://hdl.handle.net/10411/5LJOGH) are provided as well. This data-in-brief article accompanies the paper “Variation in prevention of child maltreatment by Dutch child healthcare professionals” by Simeon Visscher and Henk van Stel [1].

Santosh K. Yadav, Rakesh K. Gupta, Ravindra K. Garg et al.

Pediatric HIV patients often suffer with neurodevelopmental delay and subsequently cognitive impairment. While tissue injury in cortical and subcortical regions in the brain of adult HIV patients has been well reported there is sparse knowledge about these changes in perinatally HIV infected pediatric patients. We analyzed cortical thickness, subcortical volume, structural connectivity, and neurocognitive functions in pediatric HIV patients and compared with those of pediatric healthy controls. With informed consent, 34 perinatally infected pediatric HIV patients and 32 age and gender matched pediatric healthy controls underwent neurocognitive assessment and brain magnetic resonance imaging (MRI) on a 3 T clinical scanner. Altered cortical thickness, subcortical volumes, and abnormal neuropsychological test scores were observed in pediatric HIV patients. The structural network connectivity analysis depicted lower connection strengths, lower clustering coefficients, and higher path length in pediatric HIV patients than healthy controls. The network betweenness and network hubs in cortico-limbic regions were distorted in pediatric HIV patients. The findings suggest that altered cortical and subcortical structures and regional brain connectivity in pediatric HIV patients may contribute to deficits in their neurocognitive functions. Further, longitudinal studies are required for better understanding of the effect of HIV pathogenesis on brain structural changes throughout the brain development process under standard ART treatment.