A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Effects of delta-opioid receptor agonist pretreatment on the cardiotoxicity of bupivacaine in rats.

BACKGROUND: Delta-opioid receptor is widely expressed in human and rodent hearts, and has been proved to protect cardiomyocytes against ischemia/reperfusion and heart failure. The antagonist of delta-opioid receptor could block the rescue effect of lipid emulsion against local anesthetic cardiotoxicity. However, no evidence is available for the direct effect of delta-opioid-receptor agonists on the cardiotoxicity of local anesthetics. METHODS: Anesthetized Sprague Dawley rats were divided into five groups. Group NS received 2 ml·kg-1·min-1 normal saline, group LE received 2 ml·kg-1·min-1 30% lipid emulsion and group BW received 0.1, 1.0, or 5.0 mg/kg BW373U86, a delta-opioid-receptor agonist, for 5 min. Then 0.5% bupivacaine was infused intravenously at a rate of 3.0 mg·kg-1·min-1 until asystole. The time of arrhythmia, 50% mean arterial pressure-, 50% heart rate-reduction and asystole were recorded, and the dose of bupivacaine at each time point was calculated. RESULTS: All three different doses of BW373U86 did not affect the arrhythmia, 50% mean arterial pressure-reduction, 50% heart rate-reduction and asystole dose of bupivacaine compared with group NS. 30% LE significantly increased the bupivacaine threshold of 50% mean arterial pressure-reduction (17.9 [15.4-20.7] versus 7.2 [5.9-8.7], p = 0.018), 50% heart rate-reduction (18.7 ± 4.2 versus 8.8 ± 1.7, p < 0.001) and asystole (26.5 [21.0-29.1] versus 11.3 [10.7-13.4], p = 0.008) compared with group NS. There was no difference between group LE and group NS in the arrhythmia dose of bupivacaine (9.9 [8.9-11.7] versus 5.6 [4.5-7.0], p = 0.060). CONCLUSIONS: Our data show that BW373U86 does not affect the cardiotoxicity of bupivacaine compared with NS control in rats. 30% LE pretreatment protects the myocardium against bupivacaine-induced cardiotoxicity.

Perceptions of medical students towards and effectiveness of online surgical curriculum: a systematic review.

BACKGROUND: Online education has been used as an adjunct modality for teaching and it attracts attention in recent years as many medical students can not accomplish their clerkship in the hospital due to COVID-19 pandemic. This study aims to collect the articles related to online surgical education for medical students, and to analyze the effectiveness of online education and the perceptions of the medical students. METHOD: We performed a systemic literature search in PubMed, MEDLINE, EMBASE, ERIC and Cochrane library. Keywords used for searching included "medical student", "online education", "online teaching", "online learning", "distance learning", "electronic learning", "virtual learning" and "surgical". Medical education research study quality instrument (MERSQI) was used for the evaluation of the quality of the searched articles. RESULTS: From 1240 studies retrieved from the databases, 13 articles were included in this study after screening. The publication year was from 2007 to 2021. The mean MERSQI score of the 13 searched articles was 12.5 +/- 1.7 (range 10.0-14.5). There were totally 2023 medical students who attended online surgical curriculum. By online course, improvement of understanding and knowledge on the studied topics could be reached. The confidence in patient encounters could be improved by online curriculum with sharing experiences, discussing, and role playing. However, students felt concentration was poor during online course. Medical students studying through video platform could get better test scores than those studying with textbooks. Regarding basic surgical skills, online teaching of suturing and knot-tying could be possible and was appreciated by the students who could practice away from the hospital and get feedbacks by instructors through online environment. The scores for the clinical competence assessment for incision, suturing and knot-tying were found to be no significant difference between the online teaching group and face-to-face teaching group. CONCLUSION: Online surgical curriculum for medical students is not easy but inevitable in the era of COVID-19 pandemic. Although online course is not the same as physical course, there are some efforts which could be tried to increase the effectiveness. Basic surgical skills could also be taught effectively through online platform. Even if the COVID-19 pandemic is over in the future, online curriculum could still be a helpful adjunct for surgical education.

Glial cell line-derived neurotrophic factor alters lipid composition and protein distribution in MPP+-injured differentiated SH-SY5Y cells.

Parkinson's disease (PD) is a neurodegenerative disease characterized by progressive loss of dopaminergic neurons in the substantia nigra and striatum. Glial cell line-derived neurotrophic factor (GDNF) can effectively promote the differentiation and survival of many types of neurons, especially dopaminergic neurons, suggesting it could be a treatment for PD. Lipid rafts are highly dynamic cell membrane domains that contain numerous signal protein receptors, providing an important platform for signal transduction. Compelling evidence indicates that alterations in lipid rafts are associated with PD, and some studies have reported that GDNF can regulate the expression of caveolin-1, a lipid raft-marker protein. However, the precise effects of GDNF on lipid rafts remain unknown. We developed a cellular PD model, purified detergent-resistant membranes (membrane rafts), and performed proteomic and lipid metabolomics analyses to examine changes in lipid rafts after GDNF treatment. The results showed considerable protein and lipid alterations in response to GDNF, especially altered levels of dopamine-ß-hydroxylase, heat shock 70 kDa protein, neural cell adhesion molecule, cytoskeletal proteins, and long-chain polysaturated/unsaturated fatty acids. These findings reveal a new avenue to explore the relationships between GDNF, lipid rafts, and PD and support the hypothesis that GDNF may be a useful treatment for PD.

Effect of Hydroxyethyl Starch Loading on Glycocalyx Shedding and Cerebral Metabolism During Surgery.

BACKGROUND: Fluid therapy influences glycocalyx shedding; however, the effect of this intervention on glycocalyx shedding in patients with glioma remains unclear. In this study, we have investigated glycocalyx shedding and cerebral metabolism during colloid loading in patients with and without glioma. METHODS: Forty patients undergoing general anesthesia were assigned to the glioma brain group (n = 20) or the normal brain group (n = 20); patients in the normal brain group were undergoing partial hepatectomy to treat liver cancer. All patients were subjected to 15 mL/kg hydroxyethyl starch (HES) loading after the induction of anesthesia. Glycocalyx shedding, reflected by syndecan-1 and heparan sulfate levels at the jugular venous bulb, was measured in both groups. We also evaluated cerebral metabolism parameters, including jugular venous oxygen saturation (SjvO2), arterial-jugular venous differences in oxygen (CajvO2), glucose (A-JvGD), lactate (A-JvLD), the cerebral extraction ratio for oxygen (CERO2), and the oxygen-glucose index. RESULTS: Our results showed that patients in the glioma brain group had lower preoperative basal syndecan-1 shedding in plasma than patients in the normal brain group. The hematocrit (Hct)-corrected syndecan-1 level was significantly increased after 15 mL/kg HES fluid administration (19.78 ± 3.83 ng/mL) compared with the Hct-correct baseline syndecan-1 level (15.67 ± 2.35 ng/mL) in patients in the glioma brain group. Similarly, for patients in the normal brain group, Hct-corrected syndecan-1 level was significantly increased after HES loading (34.71 ± 12.83 ng/mL) compared with the baseline syndecan-1 level (26.07 ± 12.52 ng/mL). However, there were no intergroup or intragroup differences in Hct-corrected heparan sulfate levels at any time point. Our study also showed that the SjvO2 was lower and CajvO2 and CERO2 were higher in the glioma brain group at 30 min after HES loading. Intragroup analysis showed that CERO2 and CajvO2 increased after general anesthesia compared with the baseline values in the glioma brain group. In contrast, cerebral metabolism in the normal brain group was unchanged during perioperative period. There were no significant differences in oxygen-glucose index between the two groups throughout the study period. CONCLUSIONS: Preoperative 15 mL/kg HES loading had similar effects on systemic glycocalyx shedding in both the glioma brain and normal brain groups, although patients in the normal brain group had higher levels of plasma syndecan-1. Furthermore, the intraoperative anesthetic management may substantially influence cerebral metabolism in patients with glioma.

[Shared functional modules for nasopharyngeal and oral squamous cell carcinoma identified by network analysis of transcriptomes].

Although nasopharyngeal carcinoma (NPC) and oral squamous cell carcinoma (OSCC) are highly correlated clinical diseases, the underling molecular mechanisms to link the two diseases remain largely unknown. The aim of this study is to identify the shared functional modules for NPC and OSCC by using large-scale transcriptomic data. Gene expression profile datasets of NPC and OSCC were obtained from the GEO database. A total of 1279 differentially expressed genes (DEGs) of NPC and 1293 DEGs of OSCC were identified by fold change and empirical Bayes method, and 278 DEGs were common to these two diseases. These overlapped genes were translated into a primary network consisting of 1290 nodes (genes) and 1766 edges. The primary network was then decomposed into 15 compacted modules (subnets) with high modularity by Newman's algorithm. Topological analysis of these modules identified a total of 58 hub genes, most of which (e.g., PCNA, CDK1, STAT1, CCL5, and MMP1) have been proved to be associated with NPC and/or OSCC, while the rest (e.g., MELK, NME1, RACGAP1, INHBA, and NID1) might be novel risk genes for the two diseases. Further bioinformatics analysis of KEGG databases revealed that these modules are involved in multiple pathogenic biological pathways for either NPC or OSCC (e.g., p53 signaling pathway, ECM-receptor interaction, focal adhesion, and cell cycle). This study demonstrates that NPC and OSCC have similar molecular bases, and the identified pleiotropic modules may shape the complicated molecular interplays underlying the two clinically correlated diseases.

The association of body mass index with difficult tracheal intubation management by direct laryngoscopy: a meta-analysis.

BACKGROUND: Obesity is a serious disorder and may bring about many difficulties of perioperative management. A systematic review was conducted to assess the association between obesity and difficult intubation. METHODS: We searched electronic databases for related reviews and references of meta-analyses on August 14, 2017. The databases of PubMed, Embase, and the Cochrane controlled trials register were searched compared obese with non-obese patients in which difficult intubation rate of the adult population were retrieved. Patients with a BMI ≥ 30 kg·m- 2 were considered obese. The primary outcome was difficult tracheal intubation; secondary outcomes were the rates of difficult laryngoscopy and Mallampati score ≥ 3. This review included papers published from 1998 to 2015. RESULTS: This review included 204,303 participants in 16 studies. There was a statistically significant association between obesity and risk of difficult tracheal intubation (pooled RR = 2.04, 95% CI: 1.16-3.59, p = 0.01; I2 = 71%, p = 0.008, Power = 1.0). It also showed significantly association between obesity and risk of difficult laryngoscopy (pooled RR = 1.54, 95% CI: 1.25-1.89, p < 0.0001; I2 = 45%, p = 0.07, Power = 1.0), obesity and risk of Mallampati score ≥ 3 (pooled RR = 1.83, 95% CI: 1.24-2.69, p = 0.002; I2 = 81%, p < 0.00001, Power = 0.93). However, there were no association of obesity and risks of difficult intubation compared with non-obesity in the cohort studies (pooled RR = 3.41, 95% CI: 0.88-13.23, p = 0.08; I2 = 50%, p = 0.14) and the elective tracheal intubation (pooled RR = 2.31, 95% CI: 0.76-6.99, p = 0.14; I2 = 73%, p = 0.01), no associated with an increased risk of difficult laryngoscopy in the sniffing position (pooled RR = 2.00, 95% CI: 0.97-4.15, p = 0.06; I2 = 67%, p = 0.03). CONCLUSION: Obesity was associated with an increased risk of difficult intubation, difficult laryngoscopy and Mallampati score ≥ 3 in adults patients undergoing general surgical procedures. However, there were no association of obesity and risks of difficult intubation compared with non-obesity in the cohort studies and the elective tracheal intubation, no associated with an increased risk of difficult laryngoscopy in the sniffing position. Future analyses should explore the association of BMI and difficult airway.

Effects of Epidural Labor Analgesia With Low Concentrations of Local Anesthetics on Obstetric Outcomes: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Low concentrations of local anesthetics (LCLAs) are increasingly popular for epidural labor analgesia. The effects of epidural analgesia with low concentrations of anesthetics on the duration of the second stage of labor and the instrumental birth rate, however, remain controversial. A systematic review was conducted to compare the effects of epidural analgesia with LCLAs with those of nonepidural analgesia on obstetric outcomes. METHODS: The databases of PubMed, Embase, and the Cochrane controlled trials register were independently searched by 2 researchers, and randomized controlled trials that compared epidural labor analgesia utilizing LCLAs with nonepidural analgesia were retrieved. The primary outcomes were the duration of the second stage of labor and the instrumental birth rate; secondary outcomes included the cesarean delivery rate, the spontaneous vaginal delivery rate, and the duration of the first stage of labor. RESULTS: Ten studies (1809 women) were included. There was no significant difference between groups in the duration of the second stage of labor (mean difference = 5.71 minutes, 95% confidence interval [CI], -6.14 to 17.83; P = .36) or the instrumental birth rate (risk ratio [RR] = 1.52, 95% CI, 0.97-2.4; P = .07). There was no significant difference between groups in the cesarean delivery rate (RR = 0.8, 95% CI, 0.6-1.05; P = .11), the spontaneous vaginal delivery rate (RR = 0.98, 95% CI, 0.91-1.06; P = .62), or the duration of the first stage of labor (mean difference = 17.34 minutes, 95% CI, -5.89 to 40.56; P = .14). CONCLUSIONS: Compared with nonepidural analgesia, epidural analgesia with LCLAs is not associated with a prolonged duration of the second stage of labor or an increased instrumental birth rate. The results of this meta-analysis are based on small trials of low quality. These conclusions require confirmation by large-sample and high-quality trials in the future.

The Actin Cytoskeleton Is Involved in Glial Cell Line-Derived Neurotrophic Factor (GDNF)-Induced Ret Translocation into Lipid Rafts in Dopaminergic Neuronal Cells.

Glial cell line-derived neurotrophic factor (GDNF), a potential therapeutic factor for Parkinson's disease (PD), exerts its biological effects through the Ret receptor tyrosine kinase. The redistribution of Ret into lipid rafts substantially influences Ret signaling, but the mechanisms underlying Ret translocation remain unclear. The purpose of our study was to further explore the signaling mechanisms of GDNF and to determine whether the actin cytoskeleton is involved in the GDNF-induced Ret translocation into lipid rafts. In MN9D dopaminergic neuronal cells, we used density gradient centrifugation and immunofluorescence confocal microscopy to separate and visualize lipid rafts, co-immunoprecipitation to analyze protein-protein interactions, and latrunculin B (Lat B) and jasplakinolide (Jas) to disrupt and enhance the polymerization of the actin cytoskeleton, respectively. The results showed that Ret translocated into lipid rafts and coimmunoprecipitated with actin in response to GDNF treatment. After Lat B or Jas treatment, the Ret-F-actin association induced by GDNF was impaired or enhanced respectively and then the levels of Ret translocated into lipid rafts were correspondingly inhibited or promoted. These data indicate that actin polymerization and cytoskeletal remodeling are integral to GDNF-induced cell signaling in dopaminergic cells and define a new role of the actin cytoskeleton in promoting Ret redistribution into lipid rafts.

Role of cerebral oxygenation for prediction of hypotension after spinal anesthesia for caesarean section.

To investigate the role of cerebral oxygen saturation (ScO2) for prediction of hypotension after spinal anesthesia for caesarean section. Forty-five parturients undergoing elective caesarean section under spinal anesthesia were selected. Blood pressure, heart rate and pulse oxygen saturation before and after anesthesia were recorded, and the association between changes in ScO2 before and after anesthesia with hypotension after spinal anesthesia was explored. Hypotension occurred in 32 parturients after spinal anesthesia. The decrease in ScO2 after spinal anesthesia in parturients with hypotension was larger than in parturients without hypotension (P < 0.05). The duration from the intrathecal injection to 5 % decrease in ScO2 was shorter than that from the intrathecal injection to the occurrence of hypotension (P < 0.05). The mean time from 5 % decrease in ScO2 to hypotension was 38 s. The area under the receiver operation characteristic curve was 0.83 for decrease in ScO2 for prediction of hypotension (P < 0.05), and the optimal threshold value was 4.5 %. The sensitivity, specificity, positive predictive value and negative predictive value of 4.5 % decrease in ScO2 for prediction of hypotension were 0.75, 0.78, 0.92 and 0.47, respectively. The decrease in ScO2 after spinal anesthesia is associated with hypotension after spinal anesthesia for cesarean section, and may be a clinically useful predictor.

A Distinguishing Arterial Pulse Waves Approach by Using Image Processing and Feature Extraction Technique.

Traditional Chinese Medicine (TCM) is based on five main types of diagnoses methods consisting of inspection, auscultation, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by doctor's fingers for detecting patient's health state. In this paper, it is carried out by using a specialized pulse measuring instrument to classify one's pulse type. The measured pulse waves (MPWs) were segmented into the arterial pulse wave curve (APWC) by image proposing method. The slopes and periods among four specific points on the APWC were taken to be the pulse features. Three algorithms are proposed in this paper, which could extract these features from the APWCs and compared their differences between each of them to the average feature matrix, individually. These results show that the method proposed in this study is superior and more accurate than the previous studies. The proposed method could significantly save doctors a large amount of time, increase accuracy and decrease data volume.

Multi-exposure laser speckle contrast imaging using a high frame rate CMOS sensor with a field programmable gate array.

A system has been developed in which multi-exposure laser speckle contrast imaging (LSCI) is implemented using a high frame rate CMOS imaging sensor chip. Processing is performed using a field programmable gate array (FPGA). The system allows different exposure times to be simulated by accumulating a number of short exposures. This has the advantage that the image acquisition time is limited by the maximum exposure time and that regulation of the illuminating light level is not required. This high frame rate camera has also been deployed to implement laser Doppler blood flow processing, enabling a direct comparison of multi-exposure laser speckle imaging and laser Doppler imaging (LDI) to be carried out using the same experimental data. Results from a rotating diffuser indicate that both multi-exposure LSCI and LDI provide a linear response to changes in velocity. This cannot be obtained using single-exposure LSCI, unless an appropriate model is used for correcting the response.

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset.

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data.

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

Pilot study on optimizing pressure for standardized capillary refill time measurement.

Purpose: Capillary Refill Time (CRT) measurement has gained increasing attention in the field of sepsis and septic shock. Recognizing pressure as a fundamental determinant in CRT measurement is crucial for establishing a standardized CRT measurement procedure. In this preliminary study, we elucidated the optimal pressing strength for CRT measurement by analyzing the CRTs measured under varying pressures. Method: Seventeen healthy individuals were enlisted to undergo CRT tests on their fingertips at various pressure levels. The applied force was initiated at 0.5N and incrementally increased by 0.5N until it reached 10.5N. An integrated Photoplethysmography (PPG) device was employed to capture fluctuations in light intensity. The CRT was automatically derived from the PPG signals via a specialized algorithm. The study included correlation assessment and reliability evaluation. Box plot and Bland-Altman plot were used to visualize the impact of pressure levels on CRTs. Results: A dataset of 1414 CRTs across 21 pressures showed significant differences (Kruskal-Wallis test, p < 0.0001), highlighting the impact of pressure on CRT. CRT values between 4.5N and 10.5N pressures varied less, with an Intraclass Correlation Coefficient (ICC) of 0.499 indicating moderate consistency. Notably, CRTs at 10N and 10.5N pressures revealed a high ICC of 0.790, suggesting strong agreement. Conclusion: A pressure range of 4.5N-10.5N is recommended for stable CRT measurements, with 10.0N-10.5N providing optimal consistency and reliability.

Higher complication risk of totally implantable venous access port systems in patients with advanced cancer - a single institution retrospective analysis.

BACKGROUND: Totally implantable port systems are generally recommended for prolonged central venous access in diverse settings, but their risk of complications remains unclear for patients with advanced cancer. AIM: The aim of this study was to assess the risk of port system failure in patients with advanced cancer. DESIGN: We conducted a retrospective cohort study in a comprehensive cancer centre. SETTING/PARTICIPANTS: A detailed chart review was conducted among 566 patients with 573 ports inserted during January-June, 2009 (average 345.3 catheter-days). Cox regression analysis was applied to evaluate factors during insertion and early maintenance that could lead to premature removal of the port systems due to infection or occlusion. RESULTS: Port system-related infection was significantly associated with receiving palliative care immediately after implantation (hazard ratio, HR = 7.3, 95% confidence interval, 95% CI = 1.2-46.0), after adjusting for probable confounders. Primary cancer site also impacted the occurrence of device-related infection. Receiving oncologic/palliative care (HR = 3.0, P = 0.064), advanced cancer stage (HR = 6.5, P = 0.077) and body surface area above 1.71 m(2) (HR = 3.4, P = 0.029) increased the risk of port system occlusion. CONCLUSIONS: Our study indicates that totally implantable port systems yield a higher risk of complications in terminally ill patients. Further investigation should be carefully conducted to compare outcomes of various central venous access devices in patients with advanced cancer and to develop preventive strategies against catheter failure.

Effects of pretreatment with a small dose of dexmedetomidine on sufentanil-induced cough during anesthetic induction.

PURPOSE: We aimed to investigate the effects of pretreatment with a small dose of dexmedetomidine on the cough caused by sufentanil during anesthetic induction. METHODS: Two hundred and forty patients undergoing elective gynecological surgery under general anesthesia were randomly allocated to 4 groups (n = 60, each group). Dexmedetomidine 0, 0.1, 0.25, and 0.5 µg/kg was administered in 5 min to groups I, II, III, and IV, respectively, followed by the induction of general anesthesia with intravenous propofol, at a target concentration of 5 µg/ml, and sufentanil 0.5 µg/kg. The incidences and severity of cough that occurred within 1 min after the injection of sufentanil were recorded, and the incidences of cardiovascular adverse events that occurred between the administration of the dexmedetomidine infusion and 1 min after tracheal intubation were recorded. RESULTS: The incidences of cough in group II, group III, and group IV were lower than that in group I (6.7, 5.0, and 6.7 vs. 26.7 %, P < 0.01), while there were no significant differences between group II, group III, and group IV. The incidences of severe sinus bradycardia in group III and group IV were higher than that in group I (18.3 and 23.3 vs. 0.0 %, P < 0.01), while there was no significant difference between group I and group II. There was no significant difference in the incidence of low blood pressure among the four groups. CONCLUSION: Dexmedetomidine at 0.10, 0.25, and 0.50 µg/kg significantly reduced the incidence of sufentanil-induced cough during anesthetic induction, with the effect being most marked for 0.10 µg/kg dexmedetomidine.

A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging.

Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.