Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed asse...Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed assessment is important to help identify highrisk infants,to help families,and to support appropriate interventions.A wide range of monitoring tools is available to assess changes over time,including urine and blood biomarkers,neurological examination,and electroencephalography.At present,magnetic resonance imaging is unique as although it is expensive and not suited to monitoring the early evolution of hypoxic-ischemic encephalopathy by a week of life it can provide direct insight into the anatomical changes in the brain after hypoxic-ischemic encephalopathy and so offers strong prognostic information on the long-term outcome after hypoxic-ischemic encephalopathy.This review investigated the temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries,with a particular emphasis on exploring the correlation between the prognostic implications of magnetic resonance imaging scans in the first week of life and their relationship to long-term outcome prediction,particularly for infants treated with therapeutic hypothermia.A comprehensive literature search,from 2016 to 2024,identified 20 pertinent articles.This review highlights that while the optimal timing of magnetic resonance imaging scans is not clear,overall,it suggests that magnetic resonance imaging within the first week of life provides strong prognostic accuracy.Many challenges limit the timing consistency,particularly the need for intensive care and clinical monitoring.Conversely,although most reports examined the prognostic value of scans taken between 4 and 10 days after birth,there is evidence from small numbers of cases that,at times,brain injury may continue to evolve for weeks after birth.This suggests that in the future it will be important to explore a wider range of times after hypoxic-ischemic encephalopathy to fully understand the optimal timing for predicting long-term outcomes.展开更多
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research comm...Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.展开更多
Alongside clinical achievements,experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ ischemic e...Alongside clinical achievements,experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ ischemic encephalopathy (HIE).Due to the reasonably fair degree of flexibility with experiments,most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury,following HI insult.This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents,piglets,sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.展开更多
Background:Many meta-analyses and systematic reviews have explored the impact of omega-3 supplementation on clinical outcomes in individuals with gastrointestinal(GI)cancers.Thus,this study aimed to capture the effect...Background:Many meta-analyses and systematic reviews have explored the impact of omega-3 supplementation on clinical outcomes in individuals with gastrointestinal(GI)cancers.Thus,this study aimed to capture the effects of omega-3 supplementation on GI cancers and associated complications.Methods:This umbrella study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses guidelines.A comprehensive advanced search was executed across Scopus,PubMed,and Web of Science until 25 January 2025.Data were pooled by using random-effects models based on heterogeneity.The entire statistical analysis was performed via RStudio and R.The statistical analysis results are presented as the mean difference(MD),standard mean difference(SMD),and relative risk(RR)in conjunction with their 95%confidence intervals(CIs).Results:Eight meta-analysis papers were included in our umbrella review.Omega-3 fatty acid supplementation improved the serum concentrations of tumor necrosis factor alpha(TNF-α)(SMD:−0.34;95%CI:−0.56,−0.11),interleukin-6(IL-6)(SMD:−0.30;95%CI:−0.49,−0.12;MD:−4.96;95%CI:−6.62,−3.30),and C-reactive protein(CRP)(MD:−5.46;95%CI:−10.06,−0.87).Omega-3 supplementation improved the CD4^(+)/CD8^(+)ratio(SMD:0.48;95%CI:0.26,0.71)and reduced the length of hospitalization(MD:−2.45 d;95%CI:−3.11,−1.80).Omega-3 supplementation was associated with a 24%significant reduction in the risk of overall complications(RR:0.76;95%CI:0.67,0.86).Conclusion:Omega-3 supplementation may reduce the risk of overall complications and length of hospitalization in individuals suffering from GI cancers.Additionally,supplementation with omega-3 may alleviate the levels of pro-inflammatory cytokines such as TNF-αand IL-6,and acute-phase proteins such as CRP.展开更多
Conventional fuzzy systems(type-1 and type 2)are universal approximators.The goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and ch...Conventional fuzzy systems(type-1 and type 2)are universal approximators.The goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and chaotic modelling.NCFS incorporates fuzzy reasoning of the fuzzy systems,self-adaptation of the neural networks,and chaotic signal generation in a unique structure.These features enable the structure to handle uncertainties by generating new information or by chaotic search among prior knowledge.The fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear problems.Based on the GFA and Stone-Weierstrass theorems,we show that the proposed model has the function approximation property.The NCFS perfor-mance is investigated by applying it to the problem of chaotic modelling.Simulation results are demonstrated to ilustrate the concept of function approximation.展开更多
Background:The accurate segmentation of meningiomas,the most common intracranial tumors in adults,in medical imaging data is an essential component of clinical workflows for diagnosis,treatment planning,and longitudin...Background:The accurate segmentation of meningiomas,the most common intracranial tumors in adults,in medical imaging data is an essential component of clinical workflows for diagnosis,treatment planning,and longitudinal monitoring.Manual segmentation is labor-intensive,subjective,and challenging for small,irregular,and atypical lesions.In recent years,deep learning has emerged as a transformative artificial intelligence(AI)tool that offers automated solutions for enhancing the efficiency,consistency,and scalability across diverse imaging settings.Methods:This review synthesizes findings from 34 peer-reviewed studies published between January 1,2020,and October 31,2025,identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,with a specific focus on AI-based methods for meningioma segmentation in magnetic resonance imaging(MRI)scans.We evaluate recent advances,including U-shaped convolutional neural network(U-Net)variants,attention-enhanced frameworks,and hybrid models.Additionally,we analyze the impact of dataset characteristics,imaging modalities,and pre-processing choices on performance.Results:The findings indicate that architectural innovation,rather than reliance on imaging protocols or preprocessing,is the primary driver of performance gains,with top models achieving Dice scores of up to 0.980 on large datasets.While numerous high-performing models rely on large public repositories,such as Figshare and brain tumor segmentation(BraTS)challenge,studies still employ custom datasets for targeted clinical use.Contrast-enhanced T1-weighted imaging is the most commonly used and effective imaging modality for meningioma segmentation.Nonetheless,challenges remain,including the segmentation of small tumors,generalizability across clinical sites,and real-time deployment of computationally demanding models.---Conclusions:These insights highlight the need for future research to develop optimized architectures that generalize well across multi-institutional datasets while aligning with the computational constraints of realworld clinical environments.展开更多
基金supported by a grant from the Health Research New Zealand(HRC)22/559(to AJG and LB)。
文摘Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed assessment is important to help identify highrisk infants,to help families,and to support appropriate interventions.A wide range of monitoring tools is available to assess changes over time,including urine and blood biomarkers,neurological examination,and electroencephalography.At present,magnetic resonance imaging is unique as although it is expensive and not suited to monitoring the early evolution of hypoxic-ischemic encephalopathy by a week of life it can provide direct insight into the anatomical changes in the brain after hypoxic-ischemic encephalopathy and so offers strong prognostic information on the long-term outcome after hypoxic-ischemic encephalopathy.This review investigated the temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries,with a particular emphasis on exploring the correlation between the prognostic implications of magnetic resonance imaging scans in the first week of life and their relationship to long-term outcome prediction,particularly for infants treated with therapeutic hypothermia.A comprehensive literature search,from 2016 to 2024,identified 20 pertinent articles.This review highlights that while the optimal timing of magnetic resonance imaging scans is not clear,overall,it suggests that magnetic resonance imaging within the first week of life provides strong prognostic accuracy.Many challenges limit the timing consistency,particularly the need for intensive care and clinical monitoring.Conversely,although most reports examined the prognostic value of scans taken between 4 and 10 days after birth,there is evidence from small numbers of cases that,at times,brain injury may continue to evolve for weeks after birth.This suggests that in the future it will be important to explore a wider range of times after hypoxic-ischemic encephalopathy to fully understand the optimal timing for predicting long-term outcomes.
基金supported by the Auckland Medical Research Foundation,No.1117017(to CPU)
文摘Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
基金supported by the Auckland Medical Research Foundation,No.1117017(to CPU)
文摘Alongside clinical achievements,experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ ischemic encephalopathy (HIE).Due to the reasonably fair degree of flexibility with experiments,most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury,following HI insult.This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents,piglets,sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.
基金the Ethics Committee of Shahid Beheshti University of Medical Sciences,Tehran,Iran(Ethics code:IR.SBMU.CRC.REC.1403.020)Shahid Beheshti University of Medical Sciences,Tehran,Iran,provided financial support for the investigation[Code 43011537].
文摘Background:Many meta-analyses and systematic reviews have explored the impact of omega-3 supplementation on clinical outcomes in individuals with gastrointestinal(GI)cancers.Thus,this study aimed to capture the effects of omega-3 supplementation on GI cancers and associated complications.Methods:This umbrella study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and MetaAnalyses guidelines.A comprehensive advanced search was executed across Scopus,PubMed,and Web of Science until 25 January 2025.Data were pooled by using random-effects models based on heterogeneity.The entire statistical analysis was performed via RStudio and R.The statistical analysis results are presented as the mean difference(MD),standard mean difference(SMD),and relative risk(RR)in conjunction with their 95%confidence intervals(CIs).Results:Eight meta-analysis papers were included in our umbrella review.Omega-3 fatty acid supplementation improved the serum concentrations of tumor necrosis factor alpha(TNF-α)(SMD:−0.34;95%CI:−0.56,−0.11),interleukin-6(IL-6)(SMD:−0.30;95%CI:−0.49,−0.12;MD:−4.96;95%CI:−6.62,−3.30),and C-reactive protein(CRP)(MD:−5.46;95%CI:−10.06,−0.87).Omega-3 supplementation improved the CD4^(+)/CD8^(+)ratio(SMD:0.48;95%CI:0.26,0.71)and reduced the length of hospitalization(MD:−2.45 d;95%CI:−3.11,−1.80).Omega-3 supplementation was associated with a 24%significant reduction in the risk of overall complications(RR:0.76;95%CI:0.67,0.86).Conclusion:Omega-3 supplementation may reduce the risk of overall complications and length of hospitalization in individuals suffering from GI cancers.Additionally,supplementation with omega-3 may alleviate the levels of pro-inflammatory cytokines such as TNF-αand IL-6,and acute-phase proteins such as CRP.
文摘Conventional fuzzy systems(type-1 and type 2)are universal approximators.The goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and chaotic modelling.NCFS incorporates fuzzy reasoning of the fuzzy systems,self-adaptation of the neural networks,and chaotic signal generation in a unique structure.These features enable the structure to handle uncertainties by generating new information or by chaotic search among prior knowledge.The fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear problems.Based on the GFA and Stone-Weierstrass theorems,we show that the proposed model has the function approximation property.The NCFS perfor-mance is investigated by applying it to the problem of chaotic modelling.Simulation results are demonstrated to ilustrate the concept of function approximation.
基金supported by the Centre for Brain Research’s Freemasons Neurosurgery Research Unit at the University of Auckland(No.3718016)the Health Research Council of New Zealand(No.HRC 25/220)the University of Auckland’s Doctoral Scholarship.
文摘Background:The accurate segmentation of meningiomas,the most common intracranial tumors in adults,in medical imaging data is an essential component of clinical workflows for diagnosis,treatment planning,and longitudinal monitoring.Manual segmentation is labor-intensive,subjective,and challenging for small,irregular,and atypical lesions.In recent years,deep learning has emerged as a transformative artificial intelligence(AI)tool that offers automated solutions for enhancing the efficiency,consistency,and scalability across diverse imaging settings.Methods:This review synthesizes findings from 34 peer-reviewed studies published between January 1,2020,and October 31,2025,identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,with a specific focus on AI-based methods for meningioma segmentation in magnetic resonance imaging(MRI)scans.We evaluate recent advances,including U-shaped convolutional neural network(U-Net)variants,attention-enhanced frameworks,and hybrid models.Additionally,we analyze the impact of dataset characteristics,imaging modalities,and pre-processing choices on performance.Results:The findings indicate that architectural innovation,rather than reliance on imaging protocols or preprocessing,is the primary driver of performance gains,with top models achieving Dice scores of up to 0.980 on large datasets.While numerous high-performing models rely on large public repositories,such as Figshare and brain tumor segmentation(BraTS)challenge,studies still employ custom datasets for targeted clinical use.Contrast-enhanced T1-weighted imaging is the most commonly used and effective imaging modality for meningioma segmentation.Nonetheless,challenges remain,including the segmentation of small tumors,generalizability across clinical sites,and real-time deployment of computationally demanding models.---Conclusions:These insights highlight the need for future research to develop optimized architectures that generalize well across multi-institutional datasets while aligning with the computational constraints of realworld clinical environments.
