Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury.

PURPOSE: Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries. METHODS: This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. RESULTS: According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 - 25.42, p = 0.002), 2.85 (95% CI: 1.11 - 7.31, p = 0.030), 2.62 (95% CI: 1.12 - 6.13, p = 0.027), 2.44 (95% CI: 1.25 - 4.76, p = 0.009), and 1.5 (95% CI: 1.10 - 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. CONCLUSION: Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Investigating neuropathological changes and underlying neurobiological mechanisms in the early stages of primary blast-induced traumatic brain injury: Insights from a rat model.

The utilization of explosives and chemicals has resulted in a rise in blast-induced traumatic brain injury (bTBI) in recent times. However, there is a dearth of diagnostic biomarkers and therapeutic targets for bTBI due to a limited understanding of biological mechanisms, particularly in the early stages. The objective of this study was to examine the early neuropathological characteristics and underlying biological mechanisms of primary bTBI. A total of 83 Sprague Dawley rats were employed, with their heads subjected to a blast shockwave of peak overpressure ranging from 172 to 421 kPa in the GI, GII, and GIII groups within a closed shock tube, while the body was shielded. Neuromotor dysfunctions, morphological changes, and neuropathological alterations were detected through modified neurologic severity scores, brain water content analysis, MRI scans, histological, TUNEL, and caspase-3 immunohistochemical staining. In addition, label-free quantitative (LFQ)-proteomics was utilized to investigate the biological mechanisms associated with the observed neuropathology. Notably, no evident damage was discernible in the GII and GI groups, whereas mild brain injury was observed in the GIII group. Neuropathological features of bTBI were characterized by morphologic changes, including neuronal injury and apoptosis, cerebral edema, and cerebrovascular injury in the shockwave's path. Subsequently, 3153 proteins were identified and quantified in the GIII group, with subsequent enriched neurological responses consistent with pathological findings. Further analysis revealed that signaling pathways such as relaxin signaling, hippo signaling, gap junction, chemokine signaling, and sphingolipid signaling, as well as hub proteins including Prkacb, Adcy5, and various G-protein subunits (Gnai2, Gnai3, Gnao1, Gnb1, Gnb2, Gnb4, and Gnb5), were closely associated with the observed neuropathology. The expression of hub proteins was confirmed via Western blotting. Accordingly, this study proposes signaling pathways and key proteins that exhibit sensitivity to brain injury and are correlated with the early pathologies of bTBI. Furthermore, it highlights the significance of G-protein subunits in bTBI pathophysiology, thereby establishing a theoretical foundation for early diagnosis and treatment strategies for primary bTBI.

Blast injuries with contrasting outcomes treated by military surgery strategies: A case report.

The treatment strategy for blast injuries is closely linked to the clinical outcome of blast injury casualties. However, the application of military surgery experience to blast injuries caused by production safety accidents is relatively uncommon. In this study, the authors present 2 cases of blast injuries caused by one gas explosion, both cases involved individuals of the same age and gender and experienced similar degree of injury. The authors highlight the importance of using a military surgery treatment strategy, specifically emphasizing the need to understand the concept of damage control and disposal. It is recommended that relevant training in this area should be strengthened to improve the clinical treatment of such injuries. This study provides a valuable reference for healthcare professionals dealing with blast injuries.

MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation.

Medical image segmentation plays a crucial role in clinical assistance for diagnosis. The UNet-based network architecture has achieved tremendous success in the field of medical image segmentation. However, most methods commonly employ element-wise addition or channel merging to fuse features, resulting in smaller differentiation of feature information and excessive redundancy. Consequently, this leads to issues such as inaccurate lesion localization and blurred boundaries in segmentation. To alleviate these problems, the Multi-scale Subtraction and Multi-key Context Conversion Networks (MSMCNet) are proposed for medical image segmentation. Through the construction of differentiated contextual representations, MSMCNet emphasizes vital information and achieves precise medical image segmentation by accurately localizing lesions and enhancing boundary perception. Specifically, the construction of differentiated contextual representations is accomplished through the proposed Multi-scale Non-crossover Subtraction (MSNS) module and Multi-key Context Conversion Module (MCCM). The MSNS module utilizes the context of MCCM coding and redistribute the value of feature map pixels. Extensive experiments were conducted on widely used public datasets, including the ISIC-2018 dataset, COVID-19-CT-Seg dataset, Kvasir dataset, as well as a privately constructed traumatic brain injury dataset. The experimental results demonstrated that our proposed MSMCNet outperforms state-of-the-art medical image segmentation methods across different evaluation metrics.

Gd-EOB-DTPA-Enhanced Magnetic Resonance Imaging Combined with T1 Mapping Identifies Dysplastic Module and Hepatocellular Carcinoma: A Retrospective Study.

BACKGROUND: Hepatocellular carcinoma (HCC) is the leading type of liver cancer in adults, often resulting in fatal outcomes for those with cirrhosis. Dysplastic nodule (DN) is a liver nodule that is substantial in size, ranging from 1-2 cm. However, accurately distinguishing between DN and HCC on imaging has posed a challenge. OBJECTIVE: The aim of this study is to assess the usefulness of Gd-EOB-DTPA-enhanced MRI T1 mapping in distinguishing between DN and HCC. METHODS: This study analyzed 66 patients with confirmed HCC or DN who underwent Gd-EOB-DTPA-enhanced MRI T1 mapping before surgery or puncture at the Army Medical Center in China. The T1 values of each lesion were measured before and after Gd-EOB-DTPA administration, and various calculations were made, including absolute and percentage reduction in T1 value and coefficient of variation. The t-test was used to compare these values between the two groups, and the efficacy of T1 mapping values for differential diagnosis of HCC and DN was evaluated using the receiver operating characteristic curve (ROC). RESULTS: The study found that T1pre, T1hp, ΔT1, ΔT1%, and CV in the HCC group were significantly higher than in the DN group (p < 0.01). The accuracy of T1hp, ΔT1, and CVT1-hp in identifying HCC from DN was high, with AUCs of 0.955, 0.910, and 0.932, respectively. ΔT1% also had some accuracy, with an AUC of 0.818. CONCLUSION: Our results provide preliminary evidence that Gd-EOB-DTPA-enhanced MRI T1, CVT1-hp mapping, can be a valuable tool in diagnosing and differentiating between HCC and DN.