Evaluation of automated pediatric sleep stage classification using U-Sleep: a convolutional neural network.

STUDY OBJECTIVES: U-Sleep is a publicly available automated sleep stager, but has not been independently validated using pediatric data. We aimed to a) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and b) identify clinical and demographic characteristics that impact U-Sleep accuracy, using a clinical dataset of 3114 polysomnograms from a tertiary center. METHODS: Agreement between U-Sleep and 'gold' 30-second epoch sleep staging was determined across both datasets. Utilizing the concordance dataset, the hypothesis of equivalence between human scorers and U-Sleep was tested using a Wilcoxon two one-sided test (TOST). Multivariable regression and generalized additive modelling were used on the clinical dataset to estimate the effects of age, comorbidities and polysomnographic findings on U-Sleep performance. RESULTS: The median (interquartile range) Cohen's kappa agreement of U-Sleep and individual trained humans relative to "gold" scoring for 5-stage sleep staging in the concordance dataset were similar, kappa=0.79 (0.19) vs 0.78 (0.13) respectively, and satisfied statistical equivalence (TOST p < 0.01). Median (interquartile range) kappa agreement between U-Sleep 2.0 and clinical sleep-staging was kappa=0.69 (0.22). Modelling indicated lower performance for children < 2 years, those with medical comorbidities possibly altering sleep electroencephalography (kappa reduction=0.07-0.15) and those with decreased sleep efficiency or sleep-disordered breathing (kappa reduction=0.1). CONCLUSIONS: While U-Sleep algorithms showed statistically equivalent performance to trained scorers, accuracy was lower in children < 2 years and those with sleep-disordered breathing or comorbidities affecting electroencephalography. U-Sleep is suitable for pediatric clinical utilization provided automated staging is followed by expert clinician review.

A growth chart of brain function from infancy to adolescence based on EEG.

BACKGROUND: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS: The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION: A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING: This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

Real-world utility of overnight oximetry for the screening of obstructive sleep apnea in children.

BACKGROUND: Obstructive sleep apnea (OSA) is a common problem in children and can result in developmental and cognitive complications if untreated. The gold-standard tool for diagnosis is polysomnography (PSG); however, it is an expensive and time-consuming test to undertake. Overnight oximetry has been suggested as a faster and cheaper initial test in comparison to PSG as it can be performed at home using limited, reusable equipment. AIM: This retrospective case control study aims to evaluate the effectiveness of a home oximetry service (implemented in response to extended waiting times for routine PSG) in reducing the time between patient referral and treatment. METHODS: Patients undergoing diagnostic sleep evaluation for suspected OSA who utilized the Queensland Children's Hospital screening home oximetry service in the first year since its inception in 2021 (n = 163) were compared to a historical group of patients who underwent PSG in 2018 (n = 311). Parameters compared between the two groups included time from sleep physician review to sleep test, ENT review, and definitive treatment in the form of adenotonsillectomy surgery (or CPAP initiation for those who had already undergone surgery). RESULTS: The time from sleep physician review and request of the sleep-related study to ENT surgical treatment was significantly reduced (187 days for the HITH oximetry group vs 359 days for the comparable PSG group; p-value <0.05), and time from sleep study request to the report of results was significantly lower for patients in the oximetry group compared to those in the PSG group (11 days vs 105 days; p-value <0.05). CONCLUSION: These results suggest that for children referred to a tertiary sleep center for possible obstructive sleep disordered breathing, a home oximetry service can be effective in assisting sleep evaluation and reducing the time to OSA treatment.

S.O.S. - Save our supplies: Understanding reasons to tackle the challenge of wasted returned blood products.

OBJECTIVES: To evaluate the return of blood components across different hospital areas, reasons for the same and suggest preventive strategies which might reduce out of controlled temperature storage (CTS) blood logistics and wastage. MATERIAL AND METHODS: A retrospective audit was carried out in the department of Transfusion Medicine from January 2019 to December 2022. Data related to returned blood components was compiled using departmental records and blood centre software entries. RESULTS: A total of 218 instances of returned components were noted and the total number of components returned were 442 (0.4% of all issued components) (38.4% (170) packed red blood cells, 16.2% (72) single donor cryoprecipitate concentrate, 19.6% (87) platelet concentrate and 25.5% (113) fresh frozen plasma). Components were returned back within 30 mins in only 27% (59/218) of all instances . Wards followed by high dependency units/intensive care units were noted to have the highest number of instances (86 (39.4%) and 69 (31.6%) respectively) with emergency department having the least,comprising 19 instances (8.7%). 77.9% (170/218) instances were observed for routine transfusion requests and 44.5% (97/218) of all instances could have been prevented by an appropriate clinical status assessment of the patient. CONCLUSION: Stakeholders such as clinicians, transfusion laboratory professional and nursing staff must take consolidated efforts to eliminate wastage of blood components. Instances of returned blood components can be targeted by the hospital quality team as a quality improvement project.

Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.

PURPOSE: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer. METHODS: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R2 score). RESULTS: The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2  = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense. CONCLUSION: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.

Attention Please! Unravelling the Link Between Brain Network Connectivity and Cognitive Attention Following Acquired Brain Injury: A Systematic Review of Structural and Functional Measures.

Traumatic brain injury (TBI) and stroke are the most common causes of acquired brain injury (ABI), annually affecting 69 million and 15 million people, respectively. Following ABI, the relationship between brain network disruption and common cognitive issues including attention dysfunction is heterogenous. Using PRISMA guidelines, we systematically reviewed 43 studies published by February 2023 that reported correlations between attention and connectivity. Across all ages and stages of recovery, following TBI, greater attention was associated with greater structural efficiency within/between executive control network (ECN), salience network (SN), and default mode network (DMN) and greater functional connectivity (fc) within/between ECN and DMN, indicating DMN interference. Following stroke, greater attention was associated with greater structural connectivity (sc) within ECN; or greater fc within the dorsal attention network (DAN). In childhood ABI populations, decreases in structural network segregation were associated with greater attention. Longitudinal recovery from TBI was associated with normalization of DMN activity, and in stroke, normalization of DMN and DAN activity. Results improve clinical understanding of attention-related connectivity changes after ABI. Recommendations for future research include increased use of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to measure connectivity at the point of care, standardized attention and connectivity outcome measures and analysis pipelines, detailed reporting of patient symptomatology, and casual analysis of attention-related connectivity using brain stimulation.

Optimization of time series features to estimate brain age in children from electroencephalography.

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

Analysing heart rate variability in preterm infants: the effect of temporal adjustment of NN peaks and missing data.

The measurement of heart rate variability (HRV) in preterm infants provides important information on function to clinicians. Measuring the underlying electrocardiogram (ECG) in the neonatal intensive care unit is a challenge and there is a trade off between extracting accurate measurements of the HRV and the amount of ECG processed due to contamination. Knowledge on the effects of 1) quantization in the time domain and 2) missing data on the calculation of HRV features will inform clinical implementation. In this paper, we studied multiple 5 minute epochs from 148 ECG recordings on 56 extremely preterm infants. We found that temporal adjustment of NN peaks improves the estimate of the NN interval resulting in HRV features (m = 9) that are better correlated with age (median percentage increase in correlation of individual features: 0.2%, IQR: 0.0 to 5.6%; correlation with age predictor and age from 0.721 to 0.787). Improved (sub-sample) quantization of the NN intervals (via interpolation) reduced the overall value of HRV features (median percentage reduction in feature value: -1.3%, IQR: -18.8 to 0.0; m = 9), primarily through a reduction in the energy of high-frequency oscillations. HRV features were also robust to missing data, with measures such as mean NN, fractal dimension and the smoothed nonlinear energy operator (SNEO) less susceptible to missing data than features such as VLF, LF, and HF. Furthermore, age predictions derived from a combination of HRV measures were more robust to missing data than individual HRV measures.Clinical Relevance-Poor quantization in time when estimating the NN peak and the presence of missing data confound HRV measures, particularly spectral measures.

Mechanisms underlying pathological cortical bursts during metabolic depletion.

Cortical activity depends upon a continuous supply of oxygen and other metabolic resources. Perinatal disruption of oxygen availability is a common clinical scenario in neonatal intensive care units, and a leading cause of lifelong disability. Pathological patterns of brain activity including burst suppression and seizures are a hallmark of the recovery period, yet the mechanisms by which these patterns arise remain poorly understood. Here, we use computational modeling of coupled metabolic-neuronal activity to explore the mechanisms by which oxygen depletion generates pathological brain activity. We find that restricting oxygen supply drives transitions from normal activity to several pathological activity patterns (isoelectric, burst suppression, and seizures), depending on the potassium supply. Trajectories through parameter space track key features of clinical electrophysiology recordings and reveal how infants with good recovery outcomes track toward normal parameter values, whereas the parameter values for infants with poor outcomes dwell around the pathological values. These findings open avenues for studying and monitoring the metabolically challenged infant brain, and deepen our understanding of the link between neuronal and metabolic activity.

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer.

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

BACKGROUND: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Modulations of right hemisphere connectivity in young children relates to the perception of spoken words.

The early school years shape a young brain's capability to comprehend and contextualize words within milliseconds of exposure. Parsing word sounds (phonological interpretation) and word recognition (enabling semantic interpretation) are integral to this process. Yet little is known about the causal mechanisms of cortical activity during these early developmental stages. In this study, we aimed to explore these causal mechanisms via dynamic causal modelling of event-related potentials (ERPs) acquired from 30 typically developing children (ages 6-8 years) as they completed a spoken word-picture matching task. Source reconstruction of high-density electroencephalography (128 channels) was used to ascertain differences in whole-brain cortical activity during semantically "congruent" and "incongruent" conditions. Source activations analyzed during the N400 ERP window identified significant regions-of-interest (pFWE<.05) localized primarily in the right hemisphere when contrasting congruent and incongruent word-picture stimuli. Dynamic causal models (DCMs) were tested on source activations in the fusiform gyrus (rFusi), inferior parietal lobule (rIPL), inferior temporal gyrus (rITG) and superior frontal gyrus (rSFG). DCM results indicated that a fully connected bidirectional model with self-(inhibiting) connections over rFusi, rIPL and rSFG provided the highest model evidence, based on exceedance probabilities derived from Bayesian statistical inferences. Connectivity parameters of rITG and rSFG regions from the winning DCM were negatively correlated with behavioural measures of receptive vocabulary and phonological memory (pFDR<.05), such that lower scores on these assessments corresponded with increased connectivity between temporal pole and anterior frontal regions. The findings suggest that children with lower language processing skills required increased recruitment of right hemisphere frontal/temporal areas during task performance.

Bedside tracking of functional autonomic age in preterm infants.

BACKGROUND: Preterm birth predisposes infants to adverse outcomes that, without early intervention, impacts their long-term health. To assist bedside monitoring, we developed a tool to track the autonomic maturation of the preterm by assessing heart rate variability (HRV) changes during intensive care. METHODS: Electrocardiogram (ECG) recordings were longitudinally recorded in 67 infants (26-38 weeks postmenstrual age (PMA)). Supervised machine learning was used to generate a functional autonomic age (FAA), by combining 50 computed HRV features from successive 5-minute ECG epochs (median of 23 epochs per infant). Performance of the FAA was assessed by correlation to PMA, clinical outcomes and the infant's functional brain age (FBA), an index of maturation derived from the electroencephalogram. RESULTS: The FAA was strongly correlated to PMA (r = 0.86, 95% CI: 0.83-0.93) with a mean absolute error (MAE) of 1.66 weeks and also accurately estimated FBA (MAE = 1.58 weeks, n = 54 infants). The relationship between PMA and FAA was not confounded by neurodevelopmental outcome (p = 0.18, n = 45), sex (p = 0.88, n = 56), patent ductus arteriosus (p = 0.08, n = 56), IVH (p = 0.63, n = 56) or body weight at birth (p = 0.95, n = 56). CONCLUSIONS: The FAA, an index derived from the ubiquitous ECG signal, offers direct avenues towards estimating autonomic maturation at the bedside during intensive care monitoring. IMPACT: The development of a tool to track functional autonomic age in preterm infants based on heart rate variability features in the electrocardiogram provides a rapid and specialized view of autonomic maturation at the bedside. Functional autonomic age is linked closely to postmenstrual age and central nervous system function response, as determined by its relationship to functional brain age from the electroencephalogram. Tracking functional autonomic age during neonatal intensive care unit monitoring offers a unique insight into cardiovascular health in infants born extremely preterm and their maturational trajectories to term age.

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).

OBJECTIVE: To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification. METHODS: The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models. RESULTS: The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models. CONCLUSION: The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.

Debridement of diabetic foot ulcers: public health and clinical implications - a systematic review, meta-analysis, and meta-regression.

Background: Diabetic foot ulceration (DFU) has devastating complications and a lifetime occurrence of 15%-34%. Debridement of DFU is regarded as an intervention that accelerates ulcer healing and may reduce complications including amputations, infections, and poor quality of life (QoL), which have serious public health and clinical implications. A systematic review (SR) of SRs and of randomized controlled trials (RCTs) with meta-analyses (MAs) on debridement of DFU that synthesizes all human experimental evidence is warranted. Objectives: Are debridement methods in DFU beneficial over other forms and standard gauze dressings (control condition) in these outcomes? Study eligibility criteria: All SRs/MAs/RCTs comparing debridement methods for DFU with alternative methods of debridement and with control. Data sources: Cochrane Wounds Group Specialized Register, Cochrane Central Register of Controlled Trials (Cochrane Library), Ovid MEDLINE, PubMed, EMBASE, EBSCO, CINAHL, and Web of Science. Participants and interventions: Adults with type 1/2 diabetes with DFU and any debridement method compared with alternative debridement methods or control. Main Outcomes: Amputation rates, wound infections, QoL, proportion of ulcers healed, time to complete healing, ulcer recurrence, and treatment cost. Study selection and analysis: Data extraction/synthesis by two independent reviewers pooled using a random-effects model with sensitivity analysis. Results: 10 SRs were retrieved and reported qualitatively. Six SRs included MAs. This SR included 30 studies, with 2654 participants, using 19 debridement combinations. The debridement methods were compared with findings pooled into MAs. Meta-regression (MR) did not identify significant predictors/moderators of outcomes. Limitations: The studies may have been under-powered. The inclusion/exclusion criteria varied and the increased risk of bias contributed to low-quality evidence. Discussion/Conclusion: Weak evidence exists that debridement methods are superior to other forms of debridement or control in DFU. Implications: Researchers should follow standardized reporting guidelines (Consolidated Standards of Reporting Trials). Clinicians/investigators could use the findings from this SR/MA/MR in guiding patient-individualized decision making and designing future RCTs.

Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance.

The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain's low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

Microbiota links to neural dynamics supporting threat processing.

There is growing recognition that the composition of the gut microbiota influences behaviour, including responses to threat. The cognitive-interoceptive appraisal of threat-related stimuli relies on dynamic neural computations between the anterior insular (AIC) and the dorsal anterior cingulate (dACC) cortices. If, to what extent, and how microbial consortia influence the activity of this cortical threat processing circuitry is unclear. We addressed this question by combining a threat processing task, neuroimaging, 16S rRNA profiling and computational modelling in healthy participants. Results showed interactions between high-level ecological indices with threat-related AIC-dACC neural dynamics. At finer taxonomic resolutions, the abundance of Ruminococcus was differentially linked to connectivity between, and activity within the AIC and dACC during threat updating. Functional inference analysis provides a strong rationale to motivate future investigations of microbiota-derived metabolites in the observed relationship with threat-related brain processes.

Dorsal and Ventral Cortical Connectivity Is Mediated by the Inferior Frontal Gyrus During Facilitated Naming of Pictures.

Background: It is well recognized that semantic processing and auditory repetition facilitate subsequent naming of pictures. However, the neurocognitive mechanisms that underpin these facilitation effects remain unclear. Materials and Methods: The current study utilized a dynamic causal modeling (DCM) approach to examine high-density electroencephalographic (128-channel EEG) recordings and investigate connectivity modulations during facilitated naming of pictures in 18 healthy older adults (mean age 61.50 years). Source reconstruction of event-related potentials was performed in two specific time windows, (1) 150-250 msec and (2) 300-500 msec, to establish the timescale of significant cortical activations present during participation of semantic and phonological tasks. Hypothesis-driven DCM of source-activated regions was tested to ascertain which model most likely explained the semantic and phonological conditions, respectively. Results: DCM results indicated that a common cortical network comprising dorsal and ventral cortical connections best explained EEG task data derived from repetition and semantic tasks. For repetition (phonological) tasks, this model featured long feedback, bidirectional connections from inferior frontal gyrus (IFG) to occipitotemporal areas. Semantic tasks were most plausibly explained by a model that featured a self-inhibiting connection over the IFG only. Conclusions: Findings from this study reveal that a common cortical model comprising pathways that include dorsal and ventral regions is appropriate for characterizing EEG naming facilitation data, and that distinct cortical connections explain differences between semantic and auditory repetition processes. These models could be repurposed for naming facilitation paradigms in patients with language difficulties to optimize prediction and responsiveness to such paradigms. Impact statement The combination of semantic (word-level) and phonological (sound-level) processing in the cortex facilitates one of the most robust responses-the naming of pictures. Here, dynamic causal modeling of high-density electroencephalography during facilitated naming tasks revealed a model consisting of common dorsal and ventral connections that best explained response to semantic and phonological stimuli. Within this cortical network, phonological facilitation involved a long-range connection from inferior frontal gyrus (IFG) to occipitotemporal regions, whereas semantic facilitation contributed to self-inhibition of the IFG. The IFG is therefore a key region mediating cortical activity when switching between phonological and semantic conditions.

Debridement of Diabetic Foot Ulcers.

Diabetic foot ulcerations have devastating complications, including amputations, poor quality of life, and life-threatening infections. Diabetic wounds can be protracted, take significant time to heal, and can recur after healing. They are costly consuming health care resources. These consequences have serious public health and clinical implications. Debridement is often used as a standard of care. Debridement consists of both nonmechanical (autolytic, enzymatic) and mechanical methods (sharp/surgical, wet to dry debridement, aqueous high-pressure lavage, ultrasound, and biosurgery/maggot debridement therapy). It is used to remove nonviable tissue, to facilitate wound healing, and help prevent these serious outcomes. What are the various forms and rationale behind debridement? This article comprehensively reviews cutting-edge methods and the science behind debridement and diabetic foot ulcers.