Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were freque...Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Quantitative real-time reverse transcription-polymerase chain reaction （qRT-PCR） is widely used in studies of gene expression. In most of these studies, housekeeping genes are used as internal references without val...Quantitative real-time reverse transcription-polymerase chain reaction （qRT-PCR） is widely used in studies of gene expression. In most of these studies, housekeeping genes are used as internal references without validation. To identify appropriate reference genes for qRT-PCR in Pacific abalone Haliotis discus hannai, we examined the transcription stability of six housekeeping genes in abalone tissues in the presence and absence of bacterial infection. For this purpose, abalone were infected with the bacterial pathogen Fibrio anguillarum for 12 h and 48 h. The mRNA levels of the housekeeping genes in five tissues （digestive glands, foot muscle, gill, hemocyte, and mantle） were determined by qRT-PCR. The PCR data was subsequently analyzed with the geNorm and NormFinder algorithms. The results show that in the absence of bacterial infection, elongation factor-l-alpha and beta-actin were the most stably expressed genes in all tissues, and thus are suitable as cross-tissue type normalization factors. However, we did not identify any universal reference genes post infection because the most stable genes varied between tissue types. Furthermore, for most tissues, the optimal reference genes identified by both algorithms at 12 h and 48 h post-infection differed. These results indicate that bacterial infection induced significant changes in the expression of abalone housekeeping genes in a manner that is dependent on tissue type and duration of infection. As a result, different normalization factors must be used for different tissues at different infection points.展开更多
Under the National Innovation-Driven Development Strategy,establishing a scientifically sound evaluation system for normal university students’innovation and entrepreneurship capabilities serves as a crucial foundati...Under the National Innovation-Driven Development Strategy,establishing a scientifically sound evaluation system for normal university students’innovation and entrepreneurship capabilities serves as a crucial foundation for optimizing innovation education models and enhancing teacher candidates’comprehensive competencies.Based on existing indicator frameworks,we designed a questionnaire and applied exploratory factor analysis(EFA)to screen indicators,reduce dimensionality,and analyze weighting.This process identified key metrics for evaluating pedagogical students’innovation capacities,ultimately constructing a targeted assessment system for normal university students.The study provides theoretical support for cultivating teacher trainees’innovative capabilities while contributing to the national innovation strategy implementation.展开更多
Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in s...Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.展开更多
On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to f...On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.展开更多
Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic ...Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.展开更多
In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of ...In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
This systematic review synthesizes empirical research on external risk factors for adolescent smartphone addiction.Scopus and Web of Science were searched for English peer-reviewed empirical articles from 2008 onward;...This systematic review synthesizes empirical research on external risk factors for adolescent smartphone addiction.Scopus and Web of Science were searched for English peer-reviewed empirical articles from 2008 onward;28 met inclusion criteria(excluding non-adolescents,generic internet addiction,non-empirical work,or non-English).Thematic synthesis organized findings into three external risk domains—family,school,and peers—considering cultural/contextual mechanisms.Family dynamics(parental phubbing,harsh parenting,dysfunction),school stressors,and adverse peer relationships were identified as accumulating,direct and indirect contributors to smartphone addiction.These operate within a techno-ecological framework,where digital technologies amplify vulnerabilities and create new pathways for maladaptive use.Evidence favors an ecological,multi-level perspective.Future research should use longitudinal designs,standardize measures across cultures,and examine understudied regions—especially Africa—to guide culturally sensitive interventions.展开更多
BACKGROUND Ischemic stroke is one of the leading global causes of disability and death.Despite advances in modern medical technology that improve acute treatment and rehabilitation measures,post-stroke anxiety and dep...BACKGROUND Ischemic stroke is one of the leading global causes of disability and death.Despite advances in modern medical technology that improve acute treatment and rehabilitation measures,post-stroke anxiety and depression(PSD)do not receive sufficient attention.AIM To systematically evaluate risk factors and early identification markers for PSD for more precise screening and intervention strategies in clinical practice.METHODS This retrospective study analyzed clinical data from 112 patients with ischemic stroke admitted between January 2022 and December 2024.Based on assessments using the Hamilton Rating Scale for Anxiety(HAMA)and Hamilton Rating Scale for Depression(HAMD)at 2 weeks(±3 days)post-stroke,patients were classified into the PSD group(HAMA≥7 and/or HAMD≥7)and the non-PSD group(HAMA<7 and HAMD<7).Observation indicators included psychological assessment,demographic and clinical characteristics,stroke-related clinical indicators,neuroimaging assessments,and laboratory biomarkers.Multivariate logistic regression analysis was used to identify independent risk factors for PSD,and receiver operating characteristic curve analysis was used to evaluate the diagnostic value of potential biomarkers.RESULTS Of the 112 patients,46(41.1%)were diagnosed with PSD.Multivariate analysis identified five independent risk factors:Female gender[Odds ratio(OR)=2.32,95%confidence interval(CI):1.56-3.45],history of mental disorders prior to stroke(OR=3.17,95%CI:1.89-5.32),infarct location in the frontal lobe or limbic system(OR=2.86,95%CI:1.73-4.71),stroke severity with National Institutes of Health Stroke Scale≥8 at admission(OR=2.54,95%CI:1.62-3.99),and low social support(Social Support Rating Scale<35,OR=2.18,95%CI:1.42-3.36).Subgroup analysis showed that depression patients more commonly had left hemisphere lesions(68.4%vs 45.2%),while anxiety patients more frequently presented with right hemisphere lesions(59.5%vs 39.5%).The PSD group exhibited larger infarct volumes(8.7 cm^(3) vs 5.3 cm^(3)),more severe white matter hyperintensities,and more pronounced frontal lobe atrophy.Analysis of inflammatory markers showed significantly elevated levels of interleukin-6(7.8 pg/mL vs 4.5 pg/mL)and tumor necrosis factor-alpha(15.6 pg/mL vs 9.8 pg/mL)in the PSD group,while hypothalamicpituitary-adrenal axis function assessment revealed higher cortisol levels(386.5±92.3 nmol/L vs 328.7±75.6 nmol/L)and flattened diurnal rhythm in the PSD group.CONCLUSION PSD is a complex neuropsychiatric consequence of stroke involving disruption of the frontal-limbic circuitry,neuroinflammatory responses,and dysfunction of the hypothalamic-pituitary-adrenal axis.展开更多
Strokes include both ischemic stroke,which is mediated by a blockade or reduction in the blood supply to the brain,and hemorrhagic stroke,which comprises intracerebral hemorrhage and subarachnoid hemorrhage and is cha...Strokes include both ischemic stroke,which is mediated by a blockade or reduction in the blood supply to the brain,and hemorrhagic stroke,which comprises intracerebral hemorrhage and subarachnoid hemorrhage and is characterized by bleeding within the brain.Stroke is a lifethreatening cerebrovascular condition characterized by intricate pathophysiological mechanisms,including oxidative stress,inflammation,mitochondrial dysfunction,and neuronal injury.Critical transcription factors,such as nuclear factor erythroid 2-related factor 2 and nuclear factor kappa B,play central roles in the progression of stroke.Nuclear factor erythroid 2-related factor 2 is sensitive to changes in the cellular redox status and is crucial in protecting cells against oxidative damage,inflammatory responses,and cytotoxic agents.It plays a significant role in post-stroke neuroprotection and repair by influencing mitochondrial function,endoplasmic reticulum stress,and lysosomal activity and regulating metabolic pathways and cytokine expression.Conversely,nuclear factor-kappa B is closely associated with mitochondrial dysfunction,the generation of reactive oxygen species,oxidative stress exacerbation,and inflammation.Nuclear factor-kappa B contributes to neuronal injury,apoptosis,and immune responses following stroke by modulating cell adhesion molecules and inflammatory mediators.The interplay between these pathways,potentially involving crosstalk among various organelles,significantly influences stroke pathophysiology.Advancements in single-cell sequencing and spatial transcriptomics have greatly improved our understanding of stroke pathogenesis and offer new opportunities for the development of targeted,individualized,cell typespecific treatments.In this review,we discuss the mechanisms underlying the involvement of nuclear factor erythroid 2-related factor 2 and nuclear factor-kappa B in both ischemic and hemorrhagic stroke,with an emphasis on their roles in oxidative stress,inflammation,and neuroprotection.展开更多
Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantit...Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantitative,descriptive correlational study sampled 368 student nurses from2 universities.This study used a biopsychosocial questionnaire,which included biological,physiological,and social dimensions.In this study,there was no significant demographic student nurse relationship with the biological,psychological,and social dimensions of health,at P-value 0.05(Age P=0.70,P=0.27,P=0.93)sex(P=1,P=0.919,P=0.5),as well as grade level(P=0.9,P=0.37,P=0.64).Student nurses were dynamic,such as process input,resulting in coping adaptation and the ability to care for themselves.There was a relationship between both universities with a psychological dimension and a P-value of 0.049.In terms of Generation Z technology,both universities played a role.Lifestyle influences can lead to intense feelings of isolation and loneliness in some teens,including self-negativity,fear of missing out on information,and shame about not meeting appropriate standards for social media.The influence of an unhealthy lifestyle impacts stress and anxiety.The student nurses assigned considered themselves to be“healthy”in terms of their biopsychosocial health status.Student nurses continued to develop in their biopsychosocial health by utilizing different coping strategies to adapt and adjust to their environment in their school of nursing.展开更多
Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divide...Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.展开更多
BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making ...BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making it a signifi cant public health concern.This study is to determine the prevalence and factors associated with acute pain among emergency trauma patients.METHODS:A multicenter cross-sectional study was conducted.Data were collected via interviewer-administered questionnaires and patient chart review.The data were analyzed via the statistical package for social science version 25.Bivariable and multivariable logistic regression analyses were used.Variables with a P-value<0.05 were considered statistically signifi cant.RESULTS:A total of 397 patients were included in the study,for a response rate of 96.8%.The prevalence of pain during admission was 91.9%(95%confi dence intervals[95%CIs]:88.8%-94.4%).Blunt trauma(adjusted odds ratio[aOR]=2.82;95%CI:1.23-6.45),analgesia before admission to the emergency department(aOR=2.71;95%CI:1.16-6.36),documentation of pain severity in the chart(aOR=2.71;95%CI:1.16-6.36),analgesia provided within two hours after admission(aOR=7.60;95%CI:2.79-20.68),use of non-pharmacological pain management methods(aOR=3.09;95%CI:1.35-7.08)and availability of analgesia(aOR=3.95;95%CI:1.36-11.43)were associated with acute pain experience.CONCLUSION:The prevalence of acute pain among emergency trauma patients was high in the study area.Analgesia should be administered prior to admission,and non-pharmacological pain management should be implemented.Moreover,training on pain assessment and management should be provided for healthcare providers in the emergency department.展开更多
Nerve trauma commonly results in chronic neuropathic pain. This is by triggering the release of proinflammatory mediators from local and invading cells that induce inflammation and nociceptive neuron hyperexcitability...Nerve trauma commonly results in chronic neuropathic pain. This is by triggering the release of proinflammatory mediators from local and invading cells that induce inflammation and nociceptive neuron hyperexcitability. Even without apparent inflammation, injury sites are associated with increased inflammatory markers. This review focuses on how it might be possible to reduce neuropathic pain by reducing inflammation. Physiologically, pain is resolved by a combination of the out-migration of pro-inflammatory cells from the injury site, the down-regulation of the genes underlying the inflammation, up-regulating genes for anti-inflammatory mediators, and reducing nociceptive neuron hyperexcitability. While various techniques reduce chronic neuropathic pain, the best are effective on < 50% of patients, no technique reliably or permanently eliminates neuropathic pain. This is because most techniques are predominantly aimed at reducing pain, not inflammation. In addition, while single factors reduce pain, increasing evidence indicates significant and longer-lasting pain relief requires multiple factors acting simultaneously. Therefore, it is not surprising that extensive data indicate that the application of platelet-rich plasma provides more significant and longer-lasting pain suppression than other techniques, although its analgesia is neither complete nor permanent. However, several case reports indicate that platelet-rich plasma can induce permanent neuropathic pain elimination when the platelet concentration is significantly increased and is applied to longer nerve lengths. This review examines the primary triggers of the development and maintenance of neuropathic pain and techniques that reduce chronic neuropathic pain. The application of plateletrich plasma holds great promise for providing complete and permanent chronic neuropathic pain elimination.展开更多
BACKGROUND Post-transplant tertiary hyperparathyroidism(PT-tHPT)is a well-recognized complication following kidney transplantation,characterized by persistent excessive secretion of parathyroid hormone(PTH)despite imp...BACKGROUND Post-transplant tertiary hyperparathyroidism(PT-tHPT)is a well-recognized complication following kidney transplantation,characterized by persistent excessive secretion of parathyroid hormone(PTH)despite improved renal function.It is potentially associated with an increased risk of cardiovascular events,renal osteodystrophy,pathologic fractures,graft loss,and mortality.AIM To evaluate the incidence,risk factors,and outcomes of PT-tHPT amongst kidney transplant recipients.METHODS A total of 887 transplant recipients who underwent transplantation between 2000 and 2020 were evaluated.Univariable and multivariable logistic regression was performed to determine the predictors of tertiary hyperparathyroidism.Graft and recipient outcomes were assessed using multivariable Cox regression.A separate multivariable Cox regression was performed to determine the effect of treatment strategies on outcomes.RESULTS PT-tHPT,defined as elevated PTH(>65 ng/L)and persistent hypercalcemia(>2.60 mmol/L),was diagnosed in 14%of recipients.Risk factors for PT-tHPT included older age[odds ratio(OR)=1.36,P<0.001],Asian ethnicity(OR=0.33,P=0.006),total ischemia time(OR=1.03,P=0.048 per hour),pre-transplant serum calcium(OR=1.38,P<0.001)per decile increase,pre-transplant PTH level(OR=1.31,P<0.001)per decile increase,longer dialysis duration(OR=1.12,P=0.002)per year,history of acute rejection(OR=2.37,P=0.012),and slope of estimated glomerular filtration rate change(OR=0.91,P=0.001).There were a 3.4-fold higher risk of death-censored graft loss and a 1.9-fold greater risk of recipient death with PT-tHPT.The three treatment strategies of conservative management,calcimimetic and parathyroidectomy did not significantly change the graft or patient outcome.CONCLUSION Pretransplant elevated calcium and PTH levels,older age and dialysis duration are associated with PT-tHPT.While PT-tHPT significantly affects graft and recipient survival,the treatment strategies did not affect survival.展开更多
Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality...Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality of life,and mortality.Gaining an understanding of the trajectory of IC and the factors that influence it is of paramount importance for fostering healthy aging.This research is focused on exploring the trajectory of IC among older adults in China and examining the factors that influence it.Methods:This observational longitudinal cohort study leveraged data from the China Health and Retirement Longitudinal Study(CHARLS),which was conducted in the years 2011,2013,and 2015.For the purpose of this analysis,a total of 2,233 participants who were aged 60 and over were included.A Growth Mixture Model(GMM)was utilized to define trajectory categories for IC.Influential factors were ascertained based on the health ecology model,and binary logistic regression analysis was utilized to investigate the factors linked with the different trajectory categories.Results:Two distinct trajectory classes of IC were identified:Class 1,the normal-stable group,encompassed 90.4%of the elderly population,while Class 2,the declining group,made up 9.6%.Advanced age and a history of stroke were found to be significantly associated with Class 2.High scores in activities of daily living(ADL),employment status,receiving primary or junior high school education,and residence in the East or Central regions of China were significantly linked with Class 1.Conclusion:The trajectory of IC among older Chinese adults is marked by its heterogeneity.Advanced age and a history of stroke are significant risk factors for a declining IC trajectory,while higher ADL scores,being employed,receiving primary or junior high school education,and residing in the East or Central regions of China are protective factors associated with a stable IC trajectory.Healthcare institutions must closely monitor IC levels and understand these trajectory patterns to implement personalized and targeted interventions promptly to maintain IC at a healthy level and advocate for healthy aging.展开更多
Environment serves as the pivotal medium to produce fermented food,with fluctuations in environmental factors exerting a profound impact on the modulation of fermentation microbial communities.Such shifts are crucial ...Environment serves as the pivotal medium to produce fermented food,with fluctuations in environmental factors exerting a profound impact on the modulation of fermentation microbial communities.Such shifts are crucial for the distinctiveness of fermented food flavor and the variability in quality.Chinese liquor(Baijiu)is one of the typical representatives of spontaneous fermented food.In this review,the multifaceted relationship between regional environmental attributes and the fermentation dynamics of Baijiu was examined,with a spotlight on the strong-flavor,sauce-flavor,and light-flavor varieties.It reveals the influence of regional environmental factors and brewing environmental factors on microbial function and metabolism,which results in the formation of unique flavor characteristics of Baijiu.The 9 main factors affecting the microecology of Baijiu fermentation were further explored,including environmental sensitivity,microbial interactions,biogeographic patterns,and key abiotic factors such as temperature and humidity.Environmental factor management is crucial for controlling microbial community in fermentation.Intelligent detection of the fermentation system is combined with artificial intelligence to realize the digitalization of Baijiu fermentation,with a view to further studying the environmental mechanism or quantitative control relationship of natural fermentation,improving the environmental stability of natural fermentation,and promoting the mechanization and intelligence of fermentation production.展开更多
Understanding water chemistry in karst regions is crucial for improving global water resource management and deepening our knowledge of the biogeochemical cycles shaping these sensitive environments.Despite advance-me...Understanding water chemistry in karst regions is crucial for improving global water resource management and deepening our knowledge of the biogeochemical cycles shaping these sensitive environments.Despite advance-ments in karst hydrology,significant gaps remain in long-term trends,underlying processes,and quantitative effects of environmental changes.This is especially true in areas like the Wujiang River(WJ)in China,where human activities such as reservoir construction and land use/cover changes have accelerated hydrochemical changes.We combined recent and historical monitoring data to provide a detailed analysis of the spatial and temporal characteristics,evolution,and controlling factors of major ions in WJ.These findings are important for local water management and contribute to global efforts to manage similar karst systems facing human-induced pressures.Our research shows clear seasonal differences in solute concentrations,with higher levels during the dry season.WJ’s water is rich in calcium,with Ca-HCO_(3) ion pairs being the most common.Reservoir monitor-ing stations show much higher levels of NO_(3)^(−)and SO_(4)^(2−)compared to river-type stations,likely due to longer hydraulic retention time and increased acid deposition.The study confirms the significant role of pH and water temperature in rock weathering processes.Land use/cover changes were identified as the primary drivers of solute variations(46.37%),followed by lithology(13.92%)and temperature(8.35%).Over the past two decades,in-tense carbonate weathering has been observed,especially during wet seasons.Among karstic provinces,Guizhou Province stands out with the highest ion concentrations,indicative of its extensive karst coverage and heightened weathering processes.展开更多
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(No.NIER-2019-04-02-039)supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金Supported by the Knowledge Innovation Program of Chinese Academy of Sciences(No.KSCX2-EW-G-12B)the Knowledge Innovation Program of the Chinese Academy of Sciences(No.KZCX2-EW-Q213)the National High Technology Research and Development Program of China (863 Program)(No.2012AA10A412)
文摘Quantitative real-time reverse transcription-polymerase chain reaction （qRT-PCR） is widely used in studies of gene expression. In most of these studies, housekeeping genes are used as internal references without validation. To identify appropriate reference genes for qRT-PCR in Pacific abalone Haliotis discus hannai, we examined the transcription stability of six housekeeping genes in abalone tissues in the presence and absence of bacterial infection. For this purpose, abalone were infected with the bacterial pathogen Fibrio anguillarum for 12 h and 48 h. The mRNA levels of the housekeeping genes in five tissues （digestive glands, foot muscle, gill, hemocyte, and mantle） were determined by qRT-PCR. The PCR data was subsequently analyzed with the geNorm and NormFinder algorithms. The results show that in the absence of bacterial infection, elongation factor-l-alpha and beta-actin were the most stably expressed genes in all tissues, and thus are suitable as cross-tissue type normalization factors. However, we did not identify any universal reference genes post infection because the most stable genes varied between tissue types. Furthermore, for most tissues, the optimal reference genes identified by both algorithms at 12 h and 48 h post-infection differed. These results indicate that bacterial infection induced significant changes in the expression of abalone housekeeping genes in a manner that is dependent on tissue type and duration of infection. As a result, different normalization factors must be used for different tissues at different infection points.
基金Mid-term Results of the 2024 Langfang Normal University Special Teaching Reform Project on Innovation and Entrepreneurship Education Reform,“Research on the Evaluation System of Innovation and Entrepreneurship Ability for Normal University Students Based on Big Data Application-A Case Study of Langfang Normal University”(Project No.:CXJG2024-06)。
文摘Under the National Innovation-Driven Development Strategy,establishing a scientifically sound evaluation system for normal university students’innovation and entrepreneurship capabilities serves as a crucial foundation for optimizing innovation education models and enhancing teacher candidates’comprehensive competencies.Based on existing indicator frameworks,we designed a questionnaire and applied exploratory factor analysis(EFA)to screen indicators,reduce dimensionality,and analyze weighting.This process identified key metrics for evaluating pedagogical students’innovation capacities,ultimately constructing a targeted assessment system for normal university students.The study provides theoretical support for cultivating teacher trainees’innovative capabilities while contributing to the national innovation strategy implementation.
基金supported by the National Natural Science Foundation of China,Nos.82072165 and 82272256(both to XM)the Key Project of Xiangyang Central Hospital,No.2023YZ03(to RM)。
文摘Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.
基金supported by the National Research Foundation of Korea(NRF)grant for RLRC funded by the Korea government(MSIT)(No.2022R1A5A8026986,RLRC)supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-01304,Development of Self-Learnable Mobile Recursive Neural Network Processor Technology)+3 种基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Grand Information Technology Research Center support program(IITP-2024-2020-0-01462,Grand-ICT)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)supported by the Korea Technology and Information Promotion Agency for SMEs(TIPA)supported by the Korean government(Ministry of SMEs and Startups)’s Smart Manufacturing Innovation R&D(RS-2024-00434259).
文摘On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.
文摘Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2022R1A2C2012243).
文摘In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
基金supported by the 2025 Fujian Provincial Social Science Foundation Project(FJ2025C074).
文摘This systematic review synthesizes empirical research on external risk factors for adolescent smartphone addiction.Scopus and Web of Science were searched for English peer-reviewed empirical articles from 2008 onward;28 met inclusion criteria(excluding non-adolescents,generic internet addiction,non-empirical work,or non-English).Thematic synthesis organized findings into three external risk domains—family,school,and peers—considering cultural/contextual mechanisms.Family dynamics(parental phubbing,harsh parenting,dysfunction),school stressors,and adverse peer relationships were identified as accumulating,direct and indirect contributors to smartphone addiction.These operate within a techno-ecological framework,where digital technologies amplify vulnerabilities and create new pathways for maladaptive use.Evidence favors an ecological,multi-level perspective.Future research should use longitudinal designs,standardize measures across cultures,and examine understudied regions—especially Africa—to guide culturally sensitive interventions.
文摘BACKGROUND Ischemic stroke is one of the leading global causes of disability and death.Despite advances in modern medical technology that improve acute treatment and rehabilitation measures,post-stroke anxiety and depression(PSD)do not receive sufficient attention.AIM To systematically evaluate risk factors and early identification markers for PSD for more precise screening and intervention strategies in clinical practice.METHODS This retrospective study analyzed clinical data from 112 patients with ischemic stroke admitted between January 2022 and December 2024.Based on assessments using the Hamilton Rating Scale for Anxiety(HAMA)and Hamilton Rating Scale for Depression(HAMD)at 2 weeks(±3 days)post-stroke,patients were classified into the PSD group(HAMA≥7 and/or HAMD≥7)and the non-PSD group(HAMA<7 and HAMD<7).Observation indicators included psychological assessment,demographic and clinical characteristics,stroke-related clinical indicators,neuroimaging assessments,and laboratory biomarkers.Multivariate logistic regression analysis was used to identify independent risk factors for PSD,and receiver operating characteristic curve analysis was used to evaluate the diagnostic value of potential biomarkers.RESULTS Of the 112 patients,46(41.1%)were diagnosed with PSD.Multivariate analysis identified five independent risk factors:Female gender[Odds ratio(OR)=2.32,95%confidence interval(CI):1.56-3.45],history of mental disorders prior to stroke(OR=3.17,95%CI:1.89-5.32),infarct location in the frontal lobe or limbic system(OR=2.86,95%CI:1.73-4.71),stroke severity with National Institutes of Health Stroke Scale≥8 at admission(OR=2.54,95%CI:1.62-3.99),and low social support(Social Support Rating Scale<35,OR=2.18,95%CI:1.42-3.36).Subgroup analysis showed that depression patients more commonly had left hemisphere lesions(68.4%vs 45.2%),while anxiety patients more frequently presented with right hemisphere lesions(59.5%vs 39.5%).The PSD group exhibited larger infarct volumes(8.7 cm^(3) vs 5.3 cm^(3)),more severe white matter hyperintensities,and more pronounced frontal lobe atrophy.Analysis of inflammatory markers showed significantly elevated levels of interleukin-6(7.8 pg/mL vs 4.5 pg/mL)and tumor necrosis factor-alpha(15.6 pg/mL vs 9.8 pg/mL)in the PSD group,while hypothalamicpituitary-adrenal axis function assessment revealed higher cortisol levels(386.5±92.3 nmol/L vs 328.7±75.6 nmol/L)and flattened diurnal rhythm in the PSD group.CONCLUSION PSD is a complex neuropsychiatric consequence of stroke involving disruption of the frontal-limbic circuitry,neuroinflammatory responses,and dysfunction of the hypothalamic-pituitary-adrenal axis.
基金supported by grants from the Zhejiang Provincial TCM Science and Technology Plan Project,No.2023ZL156(to YH)Ningbo Top Medical and Health Research Program,No.2022020304(to XG)+1 种基金the Natural Science Foundation of Ningbo,No.2023J019(to YH)Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang Province,No.2022E10026(to YH)。
文摘Strokes include both ischemic stroke,which is mediated by a blockade or reduction in the blood supply to the brain,and hemorrhagic stroke,which comprises intracerebral hemorrhage and subarachnoid hemorrhage and is characterized by bleeding within the brain.Stroke is a lifethreatening cerebrovascular condition characterized by intricate pathophysiological mechanisms,including oxidative stress,inflammation,mitochondrial dysfunction,and neuronal injury.Critical transcription factors,such as nuclear factor erythroid 2-related factor 2 and nuclear factor kappa B,play central roles in the progression of stroke.Nuclear factor erythroid 2-related factor 2 is sensitive to changes in the cellular redox status and is crucial in protecting cells against oxidative damage,inflammatory responses,and cytotoxic agents.It plays a significant role in post-stroke neuroprotection and repair by influencing mitochondrial function,endoplasmic reticulum stress,and lysosomal activity and regulating metabolic pathways and cytokine expression.Conversely,nuclear factor-kappa B is closely associated with mitochondrial dysfunction,the generation of reactive oxygen species,oxidative stress exacerbation,and inflammation.Nuclear factor-kappa B contributes to neuronal injury,apoptosis,and immune responses following stroke by modulating cell adhesion molecules and inflammatory mediators.The interplay between these pathways,potentially involving crosstalk among various organelles,significantly influences stroke pathophysiology.Advancements in single-cell sequencing and spatial transcriptomics have greatly improved our understanding of stroke pathogenesis and offer new opportunities for the development of targeted,individualized,cell typespecific treatments.In this review,we discuss the mechanisms underlying the involvement of nuclear factor erythroid 2-related factor 2 and nuclear factor-kappa B in both ischemic and hemorrhagic stroke,with an emphasis on their roles in oxidative stress,inflammation,and neuroprotection.
文摘Nursing education in Indonesia experienced a number of changes during the new normal.The biopsychosocial health status reveals how students can complete their studies well at nursing school in the new normal.A quantitative,descriptive correlational study sampled 368 student nurses from2 universities.This study used a biopsychosocial questionnaire,which included biological,physiological,and social dimensions.In this study,there was no significant demographic student nurse relationship with the biological,psychological,and social dimensions of health,at P-value 0.05(Age P=0.70,P=0.27,P=0.93)sex(P=1,P=0.919,P=0.5),as well as grade level(P=0.9,P=0.37,P=0.64).Student nurses were dynamic,such as process input,resulting in coping adaptation and the ability to care for themselves.There was a relationship between both universities with a psychological dimension and a P-value of 0.049.In terms of Generation Z technology,both universities played a role.Lifestyle influences can lead to intense feelings of isolation and loneliness in some teens,including self-negativity,fear of missing out on information,and shame about not meeting appropriate standards for social media.The influence of an unhealthy lifestyle impacts stress and anxiety.The student nurses assigned considered themselves to be“healthy”in terms of their biopsychosocial health status.Student nurses continued to develop in their biopsychosocial health by utilizing different coping strategies to adapt and adjust to their environment in their school of nursing.
基金Supported by the National Natural Science Foundation of China(Grant No.12071092)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515012072)+1 种基金the Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH051298)the Scientific Research Foundation of Bozhou University(Grant No.BYKQ202419).
文摘Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.
文摘BACKGROUND:Acute pain is a sudden experience secondary to injuries and varies in perception among individuals.In trauma patients,it can negatively aff ect respiratory function,immune response,and wound healing,making it a signifi cant public health concern.This study is to determine the prevalence and factors associated with acute pain among emergency trauma patients.METHODS:A multicenter cross-sectional study was conducted.Data were collected via interviewer-administered questionnaires and patient chart review.The data were analyzed via the statistical package for social science version 25.Bivariable and multivariable logistic regression analyses were used.Variables with a P-value<0.05 were considered statistically signifi cant.RESULTS:A total of 397 patients were included in the study,for a response rate of 96.8%.The prevalence of pain during admission was 91.9%(95%confi dence intervals[95%CIs]:88.8%-94.4%).Blunt trauma(adjusted odds ratio[aOR]=2.82;95%CI:1.23-6.45),analgesia before admission to the emergency department(aOR=2.71;95%CI:1.16-6.36),documentation of pain severity in the chart(aOR=2.71;95%CI:1.16-6.36),analgesia provided within two hours after admission(aOR=7.60;95%CI:2.79-20.68),use of non-pharmacological pain management methods(aOR=3.09;95%CI:1.35-7.08)and availability of analgesia(aOR=3.95;95%CI:1.36-11.43)were associated with acute pain experience.CONCLUSION:The prevalence of acute pain among emergency trauma patients was high in the study area.Analgesia should be administered prior to admission,and non-pharmacological pain management should be implemented.Moreover,training on pain assessment and management should be provided for healthcare providers in the emergency department.
文摘Nerve trauma commonly results in chronic neuropathic pain. This is by triggering the release of proinflammatory mediators from local and invading cells that induce inflammation and nociceptive neuron hyperexcitability. Even without apparent inflammation, injury sites are associated with increased inflammatory markers. This review focuses on how it might be possible to reduce neuropathic pain by reducing inflammation. Physiologically, pain is resolved by a combination of the out-migration of pro-inflammatory cells from the injury site, the down-regulation of the genes underlying the inflammation, up-regulating genes for anti-inflammatory mediators, and reducing nociceptive neuron hyperexcitability. While various techniques reduce chronic neuropathic pain, the best are effective on < 50% of patients, no technique reliably or permanently eliminates neuropathic pain. This is because most techniques are predominantly aimed at reducing pain, not inflammation. In addition, while single factors reduce pain, increasing evidence indicates significant and longer-lasting pain relief requires multiple factors acting simultaneously. Therefore, it is not surprising that extensive data indicate that the application of platelet-rich plasma provides more significant and longer-lasting pain suppression than other techniques, although its analgesia is neither complete nor permanent. However, several case reports indicate that platelet-rich plasma can induce permanent neuropathic pain elimination when the platelet concentration is significantly increased and is applied to longer nerve lengths. This review examines the primary triggers of the development and maintenance of neuropathic pain and techniques that reduce chronic neuropathic pain. The application of plateletrich plasma holds great promise for providing complete and permanent chronic neuropathic pain elimination.
文摘BACKGROUND Post-transplant tertiary hyperparathyroidism(PT-tHPT)is a well-recognized complication following kidney transplantation,characterized by persistent excessive secretion of parathyroid hormone(PTH)despite improved renal function.It is potentially associated with an increased risk of cardiovascular events,renal osteodystrophy,pathologic fractures,graft loss,and mortality.AIM To evaluate the incidence,risk factors,and outcomes of PT-tHPT amongst kidney transplant recipients.METHODS A total of 887 transplant recipients who underwent transplantation between 2000 and 2020 were evaluated.Univariable and multivariable logistic regression was performed to determine the predictors of tertiary hyperparathyroidism.Graft and recipient outcomes were assessed using multivariable Cox regression.A separate multivariable Cox regression was performed to determine the effect of treatment strategies on outcomes.RESULTS PT-tHPT,defined as elevated PTH(>65 ng/L)and persistent hypercalcemia(>2.60 mmol/L),was diagnosed in 14%of recipients.Risk factors for PT-tHPT included older age[odds ratio(OR)=1.36,P<0.001],Asian ethnicity(OR=0.33,P=0.006),total ischemia time(OR=1.03,P=0.048 per hour),pre-transplant serum calcium(OR=1.38,P<0.001)per decile increase,pre-transplant PTH level(OR=1.31,P<0.001)per decile increase,longer dialysis duration(OR=1.12,P=0.002)per year,history of acute rejection(OR=2.37,P=0.012),and slope of estimated glomerular filtration rate change(OR=0.91,P=0.001).There were a 3.4-fold higher risk of death-censored graft loss and a 1.9-fold greater risk of recipient death with PT-tHPT.The three treatment strategies of conservative management,calcimimetic and parathyroidectomy did not significantly change the graft or patient outcome.CONCLUSION Pretransplant elevated calcium and PTH levels,older age and dialysis duration are associated with PT-tHPT.While PT-tHPT significantly affects graft and recipient survival,the treatment strategies did not affect survival.
基金China Health and Retirement Longitudinal Study(CHARLS)the 2022-2023 Nursing Research Project of Chinese Medical Association Publishing House(Grant No.CMAPH-NRD2022024)。
文摘Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality of life,and mortality.Gaining an understanding of the trajectory of IC and the factors that influence it is of paramount importance for fostering healthy aging.This research is focused on exploring the trajectory of IC among older adults in China and examining the factors that influence it.Methods:This observational longitudinal cohort study leveraged data from the China Health and Retirement Longitudinal Study(CHARLS),which was conducted in the years 2011,2013,and 2015.For the purpose of this analysis,a total of 2,233 participants who were aged 60 and over were included.A Growth Mixture Model(GMM)was utilized to define trajectory categories for IC.Influential factors were ascertained based on the health ecology model,and binary logistic regression analysis was utilized to investigate the factors linked with the different trajectory categories.Results:Two distinct trajectory classes of IC were identified:Class 1,the normal-stable group,encompassed 90.4%of the elderly population,while Class 2,the declining group,made up 9.6%.Advanced age and a history of stroke were found to be significantly associated with Class 2.High scores in activities of daily living(ADL),employment status,receiving primary or junior high school education,and residence in the East or Central regions of China were significantly linked with Class 1.Conclusion:The trajectory of IC among older Chinese adults is marked by its heterogeneity.Advanced age and a history of stroke are significant risk factors for a declining IC trajectory,while higher ADL scores,being employed,receiving primary or junior high school education,and residing in the East or Central regions of China are protective factors associated with a stable IC trajectory.Healthcare institutions must closely monitor IC levels and understand these trajectory patterns to implement personalized and targeted interventions promptly to maintain IC at a healthy level and advocate for healthy aging.
基金financially supported by the National Natural Science Foundation of China(22138004)National Treasure Ecological Research Synergetic Innovation Center.
文摘Environment serves as the pivotal medium to produce fermented food,with fluctuations in environmental factors exerting a profound impact on the modulation of fermentation microbial communities.Such shifts are crucial for the distinctiveness of fermented food flavor and the variability in quality.Chinese liquor(Baijiu)is one of the typical representatives of spontaneous fermented food.In this review,the multifaceted relationship between regional environmental attributes and the fermentation dynamics of Baijiu was examined,with a spotlight on the strong-flavor,sauce-flavor,and light-flavor varieties.It reveals the influence of regional environmental factors and brewing environmental factors on microbial function and metabolism,which results in the formation of unique flavor characteristics of Baijiu.The 9 main factors affecting the microecology of Baijiu fermentation were further explored,including environmental sensitivity,microbial interactions,biogeographic patterns,and key abiotic factors such as temperature and humidity.Environmental factor management is crucial for controlling microbial community in fermentation.Intelligent detection of the fermentation system is combined with artificial intelligence to realize the digitalization of Baijiu fermentation,with a view to further studying the environmental mechanism or quantitative control relationship of natural fermentation,improving the environmental stability of natural fermentation,and promoting the mechanization and intelligence of fermentation production.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Nos.2023A1515110824 and 2025A1515011839)Shenzhen Science and Technology Program(No.RCBS20231211090638066).
文摘Understanding water chemistry in karst regions is crucial for improving global water resource management and deepening our knowledge of the biogeochemical cycles shaping these sensitive environments.Despite advance-ments in karst hydrology,significant gaps remain in long-term trends,underlying processes,and quantitative effects of environmental changes.This is especially true in areas like the Wujiang River(WJ)in China,where human activities such as reservoir construction and land use/cover changes have accelerated hydrochemical changes.We combined recent and historical monitoring data to provide a detailed analysis of the spatial and temporal characteristics,evolution,and controlling factors of major ions in WJ.These findings are important for local water management and contribute to global efforts to manage similar karst systems facing human-induced pressures.Our research shows clear seasonal differences in solute concentrations,with higher levels during the dry season.WJ’s water is rich in calcium,with Ca-HCO_(3) ion pairs being the most common.Reservoir monitor-ing stations show much higher levels of NO_(3)^(−)and SO_(4)^(2−)compared to river-type stations,likely due to longer hydraulic retention time and increased acid deposition.The study confirms the significant role of pH and water temperature in rock weathering processes.Land use/cover changes were identified as the primary drivers of solute variations(46.37%),followed by lithology(13.92%)and temperature(8.35%).Over the past two decades,in-tense carbonate weathering has been observed,especially during wet seasons.Among karstic provinces,Guizhou Province stands out with the highest ion concentrations,indicative of its extensive karst coverage and heightened weathering processes.
