The Burn Grafting Image Reclamation Redefined with the Peak-Valley Approach.

52 Objective: The diagnosis of ischemic stroke subtype is difficult in the emergency setting when based only upon a non-contrast CT scan and clinical findings. Accurate diagnosis may be important because prognosis depends upon the size and location of the infarct and emergency therapeutic decisions rest upon attempts to improve prognosis. Diffusion/perfusion weighted MR identifies acute ischemia and ischemic injury but is expensive, and not consistently accessible nationwide. We investigated the ability of a readily available CT contrast study (CT angiography, CTA; and whole brain CT perfusion, CTP) to enhance diagnostic accuracy of stroke subtype. Methods: All patients (1/97–12/98) who received a CTA within 24 hours of stroke onset (mean=4.6 hrs) and a follow-up CT or MRI within 2 weeks were analyzed (N=40). Stroke neurologists made stroke subtype diagnoses based upon: 1) non contrast CT and clinical vignette; 2) #1 and CTA; 2) #1, #2 and CTP. Diagnostic accuracy at each sequential step was measured against the gold standard based upon all available clinical, lab and follow-up imaging information. Results: The addition of the contrast CT study (CT+CTP)led to a statistically significant, relative improvement in accuracy of 1)infarct localization, 100%; 2) involved vascular territory, 69%; 3) occluded vessel, 94%; 4) TOAST stroke subtype, 44%; and 5) Oxfordshire stroke subtype, 81%. CTA led to significant improvement in diagnostic accuracy of vessel occlusion and TOAST subtype. CTP led to significant improvement in diagnostic accuracy of infarct localization, vascular territory and Oxfordshire classification. Conclusion: The addition of a contrast CT study to evaluate the intracranial vessels (CTA) and whole brain perfusion (CTP) enables highly accurate diagnosis of stroke subtype in the emergency setting. The ability of this widely accessible, emergency neuroimaging technique to predict functional outcome and guide therapeutic decisions can now be investigated.

52 Objective: The diagnosis of ischemic stroke subtype is difficult in the emergency setting when based only upon a non-contrast CT scan and clinical findings. Accurate diagnosis may be important because prognosis depends upon the size and location of the infarct and emergency therapeutic decisions rest upon attempts to improve prognosis. Diffusion/perfusion weighted MR identifies acute ischemia and ischemic injury but is expensive, and not consistently accessible nationwide. We investigated the ability of a readily available CT contrast study (CT angiography, CTA; and whole brain CT perfusion, CTP) to enhance diagnostic accuracy of stroke subtype. Methods: All patients (1/97–12/98) who received a CTA within 24 hours of stroke onset (mean=4.6 hrs) and a follow-up CT or MRI within 2 weeks were analyzed (N=40). Stroke neurologists made stroke subtype diagnoses based upon: 1) non contrast CT and clinical vignette; 2) #1 and CTA; 2) #1, #2 and CTP. Diagnostic accuracy at each sequential step was measured against the gold standard based upon all available clinical, lab and follow-up imaging information. Results: The addition of the contrast CT study (CT+CTP)led to a statistically significant, relative improvement in accuracy of 1)infarct localization, 100%; 2) involved vascular territory, 69%; 3) occluded vessel, 94%; 4) TOAST stroke subtype, 44%; and 5) Oxfordshire stroke subtype, 81%. CTA led to significant improvement in diagnostic accuracy of vessel occlusion and TOAST subtype. CTP led to significant improvement in diagnostic accuracy of infarct localization, vascular territory and Oxfordshire classification. Conclusion: The addition of a contrast CT study to evaluate the intracranial vessels (CTA) and whole brain perfusion (CTP) enables highly accurate diagnosis of stroke subtype in the emergency setting. The ability of this widely accessible, emergency neuroimaging technique to predict functional outcome and guide therapeutic decisions can now be investigated.

Major cities in Southeast Asia (SEA) are faced with severe air quality problems including dust, smog and haze pollution, which are mainly caused by atmospheric aerosols (smoke) from biomass burning. Technological advances in monitoring atmospheric aerosol and biomass burning have been fostered by a series of new space based satellite instruments and data products. In this study, a variety of satellite product maps of aerosol optical depth (AOD), precipitation, wind, city light, burned area (BA) and active fire were collected and processed to evaluate the spatial and temporal variations among atmospheric aerosol, climate factors, human activities and biomass burning in SEA during 2002-2011. Satellite data applied in this study includes: 1) the Moderate Resolution Imaging Spectroradiometer (MODIS) derived AOD; 2) three MODIS BA products, including the BA derived from vegetation change and land-cover classification (MCD45A1), the BA derived from active-fire (GFED4.0), and the combination of GFED4.0 and BA caused by small-scale fires (GFED4.0s); 3) the MODIS active fire data (MCD14ML); 4) the National Oceanic and Atmospheric Administration (NOAA) surface wind data; 5) the MODIS International Geosphere-Biosphere Programme (IGBP) classes land cover dataset (MCD12Q1); 6) the Global Precipitation Climatology Project (GPCP) monthly precipitation dataset; and 7) the DMSP-OLS nighttime light representing the strength of human activities. All satellite data was converted, visualized, summarized and analyzed using the spatial analyst tool within ESRI ArcGIS® 10.2. To better understand the cause and effect relationships between various causative factors and atmospheric aerosols, the results were organized into five sections. First, the spatial and temporal variations of aerosol optical depth in SEA during 2002 to 2011 were examined. High aerosol areas (HAA) located in the northern and southern intertropical zone are identified, respectively, from the monthly AOD distribution maps. The northern HAA consists of Myanmar, Vietnam, Laos, Thailand, and Cambodia, with the peak AOD months are from November to March. The southern HAA includes Malaysia, Sumatra, Java, and Kalimantan, with the peak AOD months are from May to October. Generally, the peak AOD months are consistent with the dry season in each region, which provides evidence that the temporal AOD distribution in SEA is partly related to biomass burning. Second, the recently released BA product (GFED4.0s) shows that Myanmar has the largest annual BA in north intertropical zone, followed by Cambodia, and Thailand. Burned areas in south intertropical zone are mainly distributed in Indonesia. The peak burning months are also consistent with the dry months in each region. Noted that the burning area in the northern intertropical zone is ten times higher than that found in southern intertropical zone. However, the level of annual average AOD in the southern HAA is very similar with that in the northern HAA. It is evidence that biomass burning in peatlands results in a higher emission factor of particulate matter. Third, the correlations between AOD and climate factors were assessed. The level of AOD is generally inversely proportional to precipitation, which is partly related to less biomass burning occurring during the wet seasons. The monthly average wind climatology can partly explain the large scale movement of aerosol plumes in the northern HAA during the burning months (November to next April). For the southern HAA, there is no significant correlation between wind and the spatial distribution of AOD. Fourth, the level of AOD is generally high in urban and metropolitan areas, however, there is no significant temporal correlation between AOD and the strength of human activity. Finally, to seek a quantifiable linkage between AOD and biomass burning, the study area focuses on HAAs only, and different products representing biomass burning are applied. Among the three BA products applied (MCD45A1, GFED4.0, and GFED4.0s), GFED4.0s considers both the BA identified by GFED4.0 and BA caused by small-scale fires, and can better explain the temporal and spatial distributions of AOD in HAAs (R=0.5 and 0.85 for northern and southern HAA, respectively). The correlation between commonly used MCD45A1 BA and AOD is not significant (R=0.25 and 0.58 for north and south HAA, respectively). Compared to other BA or active fire products, it was found that the MCD45A1 BA has the lowest correlation to AOD, and it is suspected that the BA derived from vegetation-change may seriously underestimate the area of burning in SEA. To better quantify the relationship between AOD and biomass burning, this study develops two simple regression models for the estimation of monthly AOD from remotely sensed burning products in HAAs. The regression model developed for northern HAA uses MCD14ML active fire data as the independent variable and obtained a R2 value of 0.57. The model developed for southern HAA uses GFED4.0s BA data as the independent variable and obtained a R2 value of 0.76. Generally, the empirical models can explain well the temporal trends of AOD in HAAs.

Burn injuries are a significant challenge in clinical and military settings, requiring accurate and timely assessment to guide treatment. Traditional methods for determining burn depth, a key factor in severity, rely heavily on subjective evaluation, leading to variability and delays in decision-making. Advances in Artificial Intelligence (AI) offer solutions to improve diagnostic accuracy and standardization. This study aims to evaluate the diagnostic performance of an AI model for burn depth assessment by comparing its outputs against a gold standard-focusing on image-based diagnosis of burn type and depth. This study analyzed 29 burn patients, under an Institutional Review Board-approved protocol (IRB# 12,689) at the Eskenazi Burn Center, Indianapolis. Digital images of burns were collected and classified into 3 burn depth categories: first-degree, second-degree, and third-degree. The AI model was fine-tuned on 131 annotated digital images, augmented to 1,200 using techniques such as rotation, flipping, and brightness adjustment. Style transfer using a machine learning models (called GAN) was used to further enhance the dataset by simulating burn variations. Zero-shot (meaning no previous training) segmentation, employing pretrained foundation models, was used to localize burn regions without task-specific training. The proposed AI prediction model achieved 79% accuracy in classifying 3 burn depth categories. Data augmentation improved performance, while segmentation demonstrated strong utility, particularly in identifying burn regions effectively in diverse scenarios. Style transfer augmented the dataset by simulating realistic burn appearances, further enhancing model robustness. Zero-shot segmentation, meaning it identified burn areas without any prior training on similar images, successfully localized burn regions, aligning with clinical expectations. This study highlights the potential of AI in improving burn depth classification and segmentation. The results demonstrate that integrating AI-driven models into clinical care can enhance diagnostic accuracy, efficiency, and scalability, offering transformative tools for clinical and military applications in burn care. These methods provide a foundation for automated and standardized burn assessment, improving outcomes across diverse settings.

Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144.

Precise determination of burn depth during the immediate postburn period remains an unresolved clinical problem. In an attempt to provide a new clinical option to aid in diagnosis of burn depth, an immunohistochemical marker (antivimentin) was used to examine excisional tissues or serial punch biopsies, or both, in partial-thickness human burn injuries. To test the hypothesis that burn injury continues to progress beyond the first 24 hours, burn depth was assessed by quantitative morphometric analysis in both a partial-thickness porcine burn model and in sequential samples from human patients. Vimentin immunostaining of ubiquitous mesenchymal populations resulted in a precise demarcation between burn eschar and the viable underlying dermis at 1 to 5 days after burn trauma. Porcine wounds showed continuous and significant progression in burn depth during days 1 through 3, but wounds were no deeper on the fourth postburn day. Similarly, 13 of 14 patients showed significant progression in burn depth between 1 to 5 days after burn injury. In conclusion, immunohistochemical staining with an antisera targeted toward a widely dispersed cell population in the dermis can be utilized as an effective tool to confirm the depth of tissue injury during the acute postburn period. Data from our randomly selected patients with partial-thickness burn suggest that burn wounds continue to demarcate for several days.

Major burns: Part 1. Epidemiology, pathophysiology and initial management

Introduction: Burns, depending on the degree of severity, induce a significant pathophysiological response in the body. The complement system participates in the body>s defenses as well as in immune responses after burn-induced trauma. Objectives: The main objective of the study was to examine how burn severity affects serum C3 and serum C4 complement values; whether burn severity correlates with serum C3 and C4 complement, and establish the predictive value of the serum C3 complement and serum C4 complement for assessing the severity of the burn. Patients and methods: According to the degree of TBSA, patients were classified into three groups: group with %TBSA < 15% (30 patients), group with %TBSA > 15%-25% (30 patients), and group with %TBSA > 25% to 40% (30 patients). According to the depth of burns, patients were classified into two groups partial-thickness burns (39 patients) and full-thickness burns (51 patients). We followed laboratory parameters: value serum C3 complement and serum C4 complement on the first and seventh day after burn trauma. Results: Serum C3 complement was significantly lower in patients with %TBSA > 25%-40% and in the group with %TBSA > 15%-25% compared to patients with %TBSA < 15% on the first and seventh day after burn trauma. Serum C3 complement was significantly lower in patients with %TBSA > 15%-25% compared to patients with %TBSA < 15% on day one and day seven after burn trauma. Serum complement C4 was not significantly different between burn groups on the first and seventh day. Full-thickness burns have significantly lower levels of serum complement C3, compared to partial-thickness burns, on the 1st and 7th day. Full-thickness burns result in a decrease in serum C4 complement compared to partial-thickness burns on the 7th day after burn trauma, but this decrease is not significant. On the 1st day after burn trauma, we found a negative correlation between %TBSA with serum C3 complement. Serum C4 complement was not correlated with %TBSA on the day 1st. Conclusions: %TBSA and depth of burn result in a significant decrease in serum C3 complement but not serum C4 complement. There is a negative correlation of %TBSA and C3 complement but not serum C4 complement on the 1st day after burn trauma. Serum C3 complement is a significant predictor of burn severity. The predictory significance of the C4 complement is not statistically significant.

It has been suggested that intramyocellular lipids (IMCLs) may serve as biomarkers of insulin resistance and mitochondrial dysfunction. Using a hind-limb mouse model of burn trauma, we tested the hypothesis that severe localized burn trauma involving 5% of the total body surface area causes a local increase in IMCLs in the leg skeletal muscle. We quantified IMCLs from ex vivo intact tissue specimens using High-Resolution Magic Angle Spinning (HRMAS) 1H NMR and characterized the accompanying gene expression patterns in burned versus control skeletal muscle specimens. We also quantified plasma-free fatty acids (FFAs) in burn versus control mice. Our results from HRMAS 1H NMR measurements indicated that IMCL levels were significantly increased in mice exposed to burn trauma. Furthermore, plasma FFA levels were also significantly increased, and gene expression of Glut4, insulin receptor substrate 1 (IRS1), glycolytic genes, and PGC-1beta was downregulated in these mice. Backward stepwise multiple linear regression analysis demonstrated that IMCL levels correlated significantly with FFA levels, which were a significant predictor of IRS1 and PGC-1beta gene expression. We conclude from these findings that IMCLs can serve as metabolic biomarkers in burn trauma and that FFAs and IMCLs may signal altered metabolic gene expression. This signaling may result in the observed burn-induced insulin resistance and skeletal muscle mitochondrial dysfunction. We believe that IMCLs may therefore be useful biomarkers in predicting the therapeutic effectiveness of hypolipidemic agents for patients with severe burns.

As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers.

Previous studies have demonstrated potent inhibition of burn oedema and progressive ischaemia by local anaesthetics. Since eicosanoids have been suggested to play an important role in the pathophysiology of burns, we compared in the present ex vivo study the effects of topical lidocaine/prilocaine cream (EMLA, ASTRA, Sweden) and intravenous lidocaine with that of saline on eicosanoid formation by normal and burned rat skin. A full-thickness burn trauma was induced in the abdominal skin. All the agents were given 5 min postburn until 2 h after the trauma. The experimental skin was subsequently removed and incubated in Krebs solution for 1 h. Eicosanoid concentrations in the solution were analysed by radioimmunoassay. EMLA cream induced a significant inhibition of TXB2 (P<0.05) and 6-Keto-PGF1alpha (P<0.01) but not of PGE release from burned skin as compared to saline treatment. Intravenous lidocaine infusions did not significantly influence the release of any of the measured eicosanoids versus saline. In conclusion, the lack of effect of intravenous lidocaine could relate to the severe burn trauma inducing rapid ischaemia which may have interfered with the delivery of the agent to the burned tissues or to insufficient concentrations achieved in the burn area. Topical treatment of burned skin with a local anaesthetic cream significantly reduced the release of TXB2 and 6-Keto-PGF1alpha, suggesting a possible mechanism of action in progressive burn ischaemia.

Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches

Burn is one of the serious public health problems. Usually, burn diagnoses are based on expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have a burn where there is no specialized facility available, and in such a case a computerized automatic burn assessment tool may aid diagnosis. Burn area, depth, and location are the critical factors in determining the severity of burns. In this paper, a classification model to diagnose burns is presented using automated machine learning. The objective of the research is to develop the feature extraction model to classify the burn. The proposed method based on support vector machine (SVM) is evaluated on a standard data set of burns—BIP_US database. Training is performed by classifying images into two classes, i.e., those that need grafts and those that are non-graft. The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82.43% was achieved for the SVM based model, which was higher than the 79.73% achieved in past work using the multidimensional scaling analysis (MDS) approach.

Deep learning-based fault diagnosis of high-power PEMFCs with ammonia-based hydrogen sources

Accurate determination of burn size and depth forms an integral part of the initial assessment of any burn injury. Errors might lead to inaccurate fluid resuscitation and inappropriate transfer of patients to specialized burns units (BUs). Although recent data suggest some improvement in the estimation of burn injury in adults, this has not been shown in children. A retrospective review of children with burn injuries referred to the BU of our institution was performed. Data were collected from all patients presenting to the BU during the calendar year 2009. The total body surface area burned (TBSA-B) estimated by the referring centre was compared with the actual TBSA determined measured on arrival at the BU. Of the 71 paediatric patients referred during the study period, 10 did not have any TBSA-B estimation documented by the referring hospital. Inaccurate estimation of burn area was noted in 48 out of 61 patients (79%). Burn size was more likely to be overestimated than underestimated by a ratio of 2.2 to 1, especially in burns >10% TBSA-B (P= 0.002). Inaccurate estimation of burn size remains a problem in children. The persistent miscalculation of burn size might be a result of the various methods employed in assessing burn area, the inclusion of simple erythema and inadequate training or exposure of first responders. Accurate assessment of TBSA-B and burn depth in children remains elusive and would appear to require additional training and education.