Caspase-11 signaling promotes damage to hippocampal CA3 to enhance cognitive dysfunction in infection.

BACKGROUND: Cognitive dysfunction caused by infection frequently emerges as a complication in sepsis survivor patients. However, a comprehensive understanding of its pathogenesis remains elusive. METHODS: In our in vivo experiments, an animal model of endotoxemia was employed, utilizing the Novel Object Recognition Test and Morris Water Maze Test to assess cognitive function. Various techniques, including immunofluorescent staining, Western blotting, blood‒brain barrier permeability assessment, Limulus Amebocyte Lysate (LAL) assay, and Proximity-ligation assay, were employed to identify brain pathological injury and neuroinflammation. To discern the role of Caspase-11 (Casp11) in hematopoietic or non-hematopoietic cells in endotoxemia-induced cognitive decline, bone marrow chimeras were generated through bone marrow transplantation (BMT) using wild-type (WT) and Casp11-deficient mice. In vitro studies involved treating BV2 cells with E. coli-derived outer membrane vesicles to mimic in vivo conditions. RESULTS: Our findings indicate that the deficiency of Casp11-GSDMD signaling pathways reverses infection-induced cognitive dysfunction. Moreover, cognitive dysfunction can be ameliorated by blocking the IL-1 effect. Mechanistically, the absence of Casp11 signaling significantly mitigated blood‒brain barrier leakage, microglial activation, and synaptic damage in the hippocampal CA3 region, ultimately leading to improved cognitive function. CONCLUSION: This study unveils the crucial contribution of Casp11 and GSDMD to cognitive impairments and spatial memory loss in a murine sepsis model. Targeting Casp11 signaling emerges as a promising strategy for preventing or treating cognitive dysfunction in patients with severe infections.

The mediating role of perceived social support between work-family conflict and presenteeism among ICU nurses working shift work in Chinese public hospitals: A cross-sectional investigation.

OBJECTIVE: Relative to explicit absenteeism, nurses' presenteeism has a more lasting impact and is more harmful and costly. This study aimed to explore the relationship between work-family conflict, perceived social support, and presenteeism and whether perceived social support mediates the relationship between work-family conflict and presenteeism among ICU nurses working on shifts in Chinese public hospitals. MATERIALS AND METHODS: A cross-sectional research design was conducted from January to April 2023 in Sichuan Province, China. A total of 609 valid questionnaires were collected. The questionnaires contained information on demographic characteristics, the Work-Family Conflict (WFC) scale, the Perceived Social Support Scale (PSSS), and Stanford Presenteeism Scale-6 (SPS-6). Multiple stratified regression was used to explore the mediating role of perceived social support between work-family conflict and presenteeism. The mediating effect of perceived social support in work-family conflict and presenteeism was tested by Model 4 in the PROCESS 4.1 macro program in SPSS. RESULTS: A total of 609 nurses were included in this study, and the mean presenteeism score for ICU nurses working on shifts was 16.01 ± 4.293 (Mean ± SD), with high presenteeism accounting for 58.46%. After controlling for sociodemographic characteristic variables, work-family conflict was positively associated with presenteeism, explaining 7.7% of the variance. High perceived social support was related to low presenteeism, explaining 11.5% of the variance. Perceived social support mediated the association between work-family conflict and presenteeism among ICU nurses working on shifts. CONCLUSIONS: Chinese shift-work ICU nurses' high presenteeism scores deserve managers' attention. Work-family conflict is a significant predictor of nurses' presenteeism. Perceived social support is essential in improving nurses' work-family conflict and mediates the relationship between work-family conflict and presenteeism. Improving social support can reduce the impact of work-family conflict on presenteeism among nurses working shifts.

Risk Factors of Sarcopenia in COPD Patients: A Meta-Analysis.

Objective: Sarcopenia is a common complication of COPD associated with an age-related reduction in skeletal muscle mass associated with decreased muscle strength and / or reduced mobility. The incidence of sarcopenia in patients with COPD is twice that of non-COPD patients and is associated with poor prognosis, this study aimed to investigate the influencing factors of sarcopenia in COPD patients. Methods: Selected studies from PubMed, Embase, Web of Science, Cochrane Library, Wanfang, Wanfang, CNKI, CBM, and Wanfang databases as of November 12023. Patients aged 18 were selected; data were then independently extracted by two reviewers using a standard data collection form. Results: In total, 17 articles reporting on 5408 patients were included. Age (OR = 1.083; 95% CI, 1.024-1.145), ALB (OR = 0.752; 95% CI, 0.724-0.780), BMI(OR = 0.701; 95% CI, 0.586-0.838), smoking (OR = 1.859; 95% CI, 1.037-3.334), diabetes (OR = 1.361; 95% CI, 1.095-1.692), qi deficiency (OR = 9.883; 95% CI, 2.052, 47.593), GOLD C (OR =2.232; 95% CI, 1.866, 2.670) and GOLD D (OR = 2.195; 95% CI, 1.826-2.637) were factors affecting muscle loss in COPD patients. Conclusion: Sarcopenia is more prevalent in patients with COPD. Age, body mass index, smoking, diabetes mellitus, qi deficiency, ALB, and GOLD grade were the contributing factors for sarcopenia in patients with chronic obstructive pulmonary disease. In the future, medical staff should not only pay attention to the early screening of sarcopenia in high-risk groups, but also provide relevant prevention information.

Partners' centrality in scientific collaboration and exploratory innovation: Roles of patent stock and structural holes.

Studies adopting a first-order social-capital perspective have found that firms' innovation is influenced by network embeddedness in scientific collaboration. The present study adopts a second-order social-capital perspective to examine the effect of partners' centrality on exploratory innovation. We further investigate the boundaries of this effect via patent stock and structural holes (SHs), individually and jointly. Our theoretical framework is validated using negative binomial regression models and a sample of patents and joint publications by 194 Chinese pharmaceutical firms between 2007 and 2021. The results indicate that a focal firm's exploratory innovation is boosted by partners' centrality in scientific collaboration, but this effect is weakened by patent stock and SHs. Moreover, patent stock and SHs jointly influence this relationship-that is, partners' centrality has a strong positive effect on exploratory innovation when the firm holds low patent stock and occupies fewer SHs. This study offers insights into how businesses might profit from the innovation advantages of influential partners by exploring the contingencies of patent stock and SHs. Extending the framework to second-order aspects, we further illuminate the scope of the scientific collaboration-innovation issue.

Traditional Chinese medicine Pien-Tze-Huang ameliorates LPS-induced sepsis through bile acid-mediated activation of TGR5-STAT3-A20 signalling.

Pien Tze Huang (PZH), a class I nationally protected traditional Chinese medicine (TCM), has been used to treat liver diseases such as hepatitis; however, the effect of PZH on the progression of sepsis is unknown. Here, we reported that PZH attenuated lipopolysaccharide (LPS)-induced sepsis in mice and reduced LPS-induced production of proinflammatory cytokines in macrophages by inhibiting the activation of mitogen-activated protein kinase (MAPK) and nuclear factor-kappa B (NF-κB) signalling. Mechanistically, PZH stimulated signal transducer and activator of transcription 3 (STAT3) phosphorylation to induce the expression of A20, which could inhibit the activation of NF-κB and MAPK signalling. Knockdown of the bile acid (BA) receptor G protein-coupled bile acid receptor 1 (TGR5) in macrophages abolished the effects of PZH on STAT3 phosphorylation and A20 induction, as well as the LPS-induced inflammatory response, suggesting that BAs in PZH may mediate its anti-inflammatory effects by activating TGR5. Consistently, deprivation of BAs in PZH by cholestyramine resin reduced the effects of PZH on the expression of phosphorylated-STAT3 and A20, the activation of NF-κB and MAPK signalling, and the production of proinflammatory cytokines, whereas the addition of BAs to cholestyramine resin-treated PZH partially restored the inhibitory effects on the production of proinflammatory cytokines. Overall, our study identifies BAs as the effective components in PZH that activate TGR5-STAT3-A20 signalling to ameliorate LPS-induced sepsis.

A highly selective probe engineered to detect polarity and distinguish normal cells and tumor cells in tissue sections.

Early diagnostics and therapies for diseases such as cancer are limited by the fact that the inducing factors for the development of cytopathies are not clear. The stable polarity of lipid droplets is a potential biomarker for tumor cells; however, the complex intracellular biological environment poses great difficulties for specific detection of the polarity. Therefore, to meet this pressing challenge, we designed a highly selective fluorescent probe, DCI-Cou-polar, which used the ICT mechanism to differentiate normal cells and tumor cells in tissue sections by detecting changes in the polarities of intracellular lipid droplets. The introduction of a cyclic amine at the 7-position of coumarin (benzoquinolizine coumarin) reduced its ability to donate electrons compared with the diethylamino group, which increased the probe selectivity while retaining the sensitivity to polarity. With NIR emission and large Stokes shifts, DCI-Cou-polar has high sensitivity to polarity, excellent photostability, and biocompatibility, and it tracks lipid droplets with high fidelity. Therefore, we believe that this polarity-sensitive probe provides information on the connection between the polarity of lipid droplets and tumors while improving the development of highly selective polarity probes.

The effect of occupational coping self-efficacy on presenteeism among ICU nurses in Chinese public hospitals: a cross-sectional study.

Background: Nurses are the largest occupational group in the health field, with inestimable value in realizing universal health coverage, and nurses' physical and mental health has become an ordinary global reality. Compared with explicit absence, nurses' presenteeism has a more lasting impact and significant harm and loss. It has become an essential factor affecting nurses' physical and mental health, declining quality of healthcare services, and elevated healthcare-related risks. There is a lack of research exploring whether occupational coping self-efficacy influences nurses' presenteeism behavior, especially in less-developed regions of China. Objective: This study aimed to investigate the current status of ICU nurses' occupational coping self-efficacy and presenteeism in public hospitals in western China and to explore the impact of ICU nurses' occupational coping self-efficacy on presenteeism. Methods: A cross-sectional research design selected 722 ICU nurses in western China from January to February 2023 as survey respondents. A general information questionnaire, Occupational Coping Self-Efficacy Scale (OCSE-N), and Stanford Presenteeism Scale (SPS-6) were used. SPSS 21.0 software was used for statistical analysis. Pearson correlation analysis and multivariate hierarchical regression were used to explore the influence of ICU nurses' occupational coping self-efficacy on presenteeism. Results: A total of 722 ICU nurses completed the questionnaire. The OCSE-N score of ICU nurses was (22.24 ± 6.15), and the SPS-6 score was (16.83 ± 4.24). The high presenteeism was 67.23%. Correlation analysis showed that in ICU nurses, OCSE-N total score was negatively correlated with SPS-6 total score (r = -0.421, p < 0.05), indicating that the higher the level of occupational coping self-efficacy, the lower the presenteeism. Multiple hierarchical regression analysis showed that occupational coping self-efficacy strongly predicted presenteeism, accounting for approximately 18.35% of the total variance. Conclusion: There is a correlation between ICU nurses' occupational coping self-efficacy and presenteeism, and nurses' occupational coping self-efficacy affects presenteeism differently. Managers should pay attention to nurses' occupational coping self-efficacy to promote nurses' presenteeism reduction.

Altered Callosal Morphology and Connectivity in Asymptomatic Carotid Stenosis.

BACKGROUND: Carotid stenosis, even in the clinically asymptomatic stage, causes cognitive impairment, silent lesions, and hemispheric changes. The corpus callosum (CC) is crucial for hemispheric cortical integration and specialization. PURPOSE: To examine if CC morphology and connectivity relate to cognitive decline and lesion burden in asymptomatic carotid stenosis (ACS). STUDY TYPE: Retrospective, cross-sectional. POPULATION: 33 patients with unilaterally severe (70%) ACS and 28 demographically and comorbidity-matched controls. A publicly available healthy adult lifespan (ages between 18 and 80; n = 483) MRI dataset was also included. FIELD STRENGTH/SEQUENCE: A 3.0 T; T1 MPRAGE and diffusion weighted gradient echo-planar imaging sequences. ASSESSMENT: Structural MRI and multidomain cognitive data were obtained. Midsagittal CC area, circularity, thickness, integrity, and probabilistic tractography were calculated and correlated with cognitive tests and white matter hyperintensity. Fractional anisotropy, mean diffusivity (MD), and radial diffusivity were determined from DTI. STATISTICAL TESTS: Independent two-sample t-tests, χ2 tests, Mann-Whitney U, locally weighted scatterplot smoothing (LOWESS) curve fit, and Pearson correlation. A P value < 0.05 was considered statistically significant. RESULTS: Patients with ACS demonstrated significant reductions in callosal area, circularity, and thickness compared to controls. The callosal atrophy was significantly correlated with white matter hyperintensity size (r = -0.629, P < 0.001). Voxel-wise analysis of diffusion measures in the volumetric CC showed that ACS patients exhibited significantly lower fractional anisotropy and higher MD and radial diffusivity in the genu and splenium of the CC than controls. Further lifespan trajectory analysis showed that although the midsagittal callosal area, circularity, and thickness exhibited age-related decreases, the values in the ACS patients were significantly lower in all age groups. DATA CONCLUSION: Midsagittal callosal atrophy and connectivity reflect the load of silent lesions and the severity of cognitive decline, respectively, suggesting that CC degeneration has potential to serve as an early marker in ACS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Hemispheric coupling between structural and functional asymmetries in clinically asymptomatic carotid stenosis with cognitive impairment.

Advanced carotid stenosis is a known risk factor for ischemic stroke and vascular dementia, and it is associated with multidomain cognitive impairment as well as asymmetric alterations in hemispheric structure and function. Here we introduced a novel measure-the asymmetry index of amplitude of low-frequency fluctuations (ALFF_AI)-derived from resting-state functional magnetic resonance imaging. This measure captures the hemispheric asymmetry of intrinsic brain activity using high-dimensional registration. We aimed to investigate functional brain asymmetric alterations in patients with severe asymptomatic carotid stenosis (SACS). Furthermore, we extended the analyses of ALFF_AI to different frequencies to detect frequency-specific alterations. Finally, we examined the coupling between hemispheric asymmetric structure and function and the relationship between these results and cognitive tests, as well as the white matter hyperintensity burden. SACS patients presented significantly decreased ALFF_AI in several clusters, including the visual, auditory, parahippocampal, Rolandic, and superior parietal regions. At low frequencies (0.01-0.25 Hz), the ALFF_AI exhibited prominent group differences as frequency increased. Further structure-function coupling analysis indicated that SACS patients had lower coupling in the lateral prefrontal, superior medial frontal, middle temporal, superior parietal, and striatum regions but higher coupling in the lateral occipital regions. These findings suggest that, under potential hemodynamic burden, SACS patients demonstrate asymmetric hemispheric configurations of intrinsic activity patterns and a decoupling between structural and functional asymmetries.

Latent profile analysis and influence factors study of presenteeism among ICU nurses in China.

Background: Presenteeism is a significant global public health problem, and nurses are a high-prevalence group of presenteeism, affecting not only nurses' physical and mental health, work efficiency, and quality of work but even poses a serious threat to patient safety. Objective: The categorization of presenteeism among ICU nurses is unclear. Our research aims to explore the subtypes of presenteeism among ICU nurses based on latent profile analysis, analyze the influencing factors of different subtypes, and provide a reference basis for developing targeted interventions to reduce the presenteeism rate. Methods: From January to February 2023, 509 ICU nurses in Sichuan Province, China, were selected as respondents and surveyed using the general information questionnaire, the presenteeism scale, and the perceived social support scale. Potential categories of presenteeism among ICU nurses were explored using potential profile analysis, and factors influencing the potential types of presenteeism among ICU nurses were investigated using the chi-square test and multivariate logistic regression analysis. Results: The best model was suggested to consist of three profiles: low presenteeism-normal coping group (18.3%), moderate presenteeism group (47.9%), and high presenteeism-work limitation group (33.8%). Multiple logistic regression results showed that secondary hospitals (OR = 0.116, p = 0.015), good physical health (OR = 0.084, p = 0.023), general physical health (OR = 0.016, p = 0.037), ICU human resource allocation = 1:2.5 to 3 (OR = 0.315, p = 0.007), and higher social support scores (OR = 0.975, p = 0.047) were more likely to be grouped into low presenteeism-normal coping group; married with no children (OR = 24.554, p = 0.005) were more likely to be grouped into moderate presenteeism group; and having experienced workplace violence in the past year (OR = 1.182, p = 0.049) were more likely to be grouped into high presenteeism-work limitation group. Conclusion: There is group heterogeneity in the presenteeism of ICU nurses, and nursing managers should develop targeted interventions to reduce the presenteeism rate of ICU nurses according to the characteristics and influencing factors of each type of presenteeism of ICU nurses.

Postpolypectomy syndrome without abdominal pain led to sepsis/septic shock and gastrointestinal bleeding: A case report.

BACKGROUND: Postpolypectomy syndrome (PPS) is a rare postoperative complication of colonic polypectomy. It presents with abdominal pain and fever accompanied by coagulopathy and elevated inflammatory markers. Its prognosis is usually good, and it only requires outpatient treatment or observation in a general ward. However, it can be life-threatening. CASE SUMMARY: The patient was a 58-year-old man who underwent two colonic polypectomies, each resulting in life-threatening sepsis, septic shock, and coagulopathy. Each of the notable manifestations was a rapid drop in blood pressure, an increase in heart rate, loss of consciousness, and heavy sweating, accompanied by shortness of breath and decreased oxygen in the finger pulse. Based on the criteria of organ dysfunction due to infection, we diagnosed him with sepsis. The patient also experienced severe gastrointestinal bleeding after the second operation. Curiously, he did not complain of any abdominal pain throughout the course of the illness. He had significantly elevated concentrations of inflammatory markers and coagulopathy. Except for the absence of abdominal pain, his fever, significant coagulopathy, and elevated inflammatory marker concentrations were all consistent with PPS. Abdominal computed tomography and superior mesenteric artery computed tomography angiography showed no free air or vascular damage. Thus, the diagnosis of colon perforation was not considered. The final blood culture results indicated Moraxella osloensis. The patient was transferred to the intensive care unit and quickly improved after fluid resuscitation, antibiotic treatment, oxygen therapy, and blood transfusion. CONCLUSION: PPS may induce dysregulation of the systemic inflammatory response, which can lead to sepsis or septic shock, even in the absence of abdominal pain.

Neuroanatomical correlates of cognitive impairment following basal ganglia-thalamic post-hemorrhagic stroke: Uncovering network-wide alterations in hemispheric gray matter asymmetry.

Cognitive impairment and recovery are central issues in hemorrhagic stroke. This study aimed to investigate whether post-hemorrhagic stroke cognitive impairment (PhSCI) is associated with cortical gray matter (GM) loss and hemispheric asymmetry changes and whether these changes could predict improvements in cognitive function during the recovery. Nineteen patients with PhSCI, comprising 10 with basal ganglia hemorrhage and 9 with thalamic hemorrhage, were recruited. Among them, 9 completed a course of repetitive transcranial magnetic stimulation (rTMS). Additionally, 19 demographically and comorbidity-matched healthy controls were also included. Structural brain MRI and cognitive assessments were performed. Voxel-wise GM volume and hemispheric asymmetry were analyzed. The PhSCI patients exhibited bilateral, yet asymmetric, GM losses in the hippocampus, fusiform, lateral temporal, prefrontal, somatomotor, and inferior parietal regions. The analysis of GM asymmetry revealed that patients showed rightward GM in the lateral temporal, somatomotor, and inferior parietal regions. Among the 9 PhSCI patients who completed rTMS, there was a marginal trend of regional GM increase and leftward GM, and these changes were in parallel with the improvements in cognitive tests. Further lesion connectivity and metanalytic mapping identified two interconnected systems linked to the lesions, which were anchored in the default mode, somatomotor, and salience/cognitive control networks and in the cognitive domains of memory, language, decision-making, and executive function. In conclusion, PhSCI patients exhibited network-wide cortical GM losses, distal to subcortical hemorrhagic lesions, and hemisphere asymmetry changes. These changes appear to predict rTMS-related cognitive improvements, suggesting that even subcortical focal lesions can lead to alterations in distal cortical neuroanatomical architecture. Our preliminary findings provide new insights into the neuroanatomical basis of PhSCI.

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI.

Introduction: This study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. Methods: A total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison. Results: The optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model's predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics. Discussion: The constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.

Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction.

Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC. Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance. Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42). Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach.

PURPOSE: Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C0/D) in Chinese children with refractory NS, and then develop and validate the TAC C0/D prediction models. PATIENTS AND METHODS: The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C0/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients. RESULTS: GBDT algorithm performed best in the whole group (R2=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R2=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R2=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs (ACTN4 rs3745859, ACTN4 rs56113315, ACTN4 rs62121818, CTLA4 rs4553808, CYP3A5 rs776746, IL2RA rs12722489, INF2 rs1128880, MAP3K11 rs7946115, MYH9 rs2239781, and MYH9 rs4821478). CONCLUSION: The association between the clinical and genetic variables and TAC C0/D was described, and three TAC C0/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.

Asymptomatic carotid stenosis is associated with both edge and network reconfigurations identified by single-subject cortical thickness networks.

Background and purpose: Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS). Methods: Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms. Results: In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests. Conclusion: We conclude that SACS exhibits abnormal eSC and a less-optimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals.

The effect of the humanistic care teaching model on nurse patient conflict and nurse turnover intention in a pediatric outpatient department: results of a randomized trial.

BACKGROUND: This study was carried out based on the background that sharp nurse-patient conflicts in the pediatric outpatient department lead to a high turnover rate of nurses. METHODS: A total of 68 nurses working in the pediatric outpatient department of Xiangyang No. 1 People's Hospital were selected as the study subjects, and randomly divided into an experimental group (n=34) and a control group (n=34). Nurses in the control group received a traditional pediatric nursing teaching model, while those in the experimental group received a traditional pediatric nursing teaching model combined with the humanistic care teaching model. The effect of these two nursing teaching models on nurse-patient conflicts in the pediatric outpatient department and the turnover intention of nurses was then compared and analyzed. RESULTS: There were no significant differences in personal information between the two groups (P>0.05). The strain capacity, operational capacity, nurse-patient communication skills, autonomous learning ability, and teamwork ability of the nurses in the experimental group after training were significantly higher than those in the control group (P<0.05). Both groups after training had significantly higher scores on a professional identity scale than before training, and nurses in the experimental group had significantly higher scores of professional identity than those in the control group (P<0.001). The turnover intention of the nurses in the experimental group were significantly lower than those in the control group (P<0.001). The problem solving ability of nurses in the experimental group was significantly better than that in the control group (P<0.001). Scores in the domains of waiting to see the doctor, the health knowledge education, the ward environment, and nursing quality of nurses in `the experimental group were significantly higher than those in the control group (P<0.001). CONCLUSIONS: The humanistic care teaching model can significantly improve the professional identity and problem solving ability of nurses in facing different nurse-patient conflicts with significant effect and is worthy of application and popularization in clinical nursing teaching. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100048751.

Capsule Network for ERP Detection in Brain-Computer Interface.

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.