iTRAQ proteomics analysis of placental tissue with gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes. METHODS: Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot. RESULTS: Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway. DISCUSSION: This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.

The Role of the Complement System in Synaptic Pruning after Stroke.

Stroke is a serious disease that can lead to local neurological dysfunction and cause great harm to the patient's health due to blood cerebral circulation disorder. Synaptic pruning is critical for the normal development of the human brain, which makes the synaptic circuit completer and more efficient by removing redundant synapses. The complement system is considered a key player in synaptic loss and cognitive impairment in neurodegenerative disease. After stroke, the complement system is over-activated, and complement proteins can be labeled on synapses. Microglia and astrocytes can recognize and engulf synapses through corresponding complement receptors. Complement-mediated excessive synaptic pruning can cause post-stroke cognitive impairment (PSCI) and secondary brain damage. This review summarizes the latest progress of complement-mediated synaptic pruning after stroke and the potential mechanisms. Targeting complement-mediated synaptic pruning may be essential for exploring therapeutic strategies for secondary brain injury (SBI) and neurological dysfunction after stroke.

The neuroprotective effects of normobaric oxygen therapy after stroke.

BACKGROUND: Stroke, including ischemic and hemorrhagic stroke, is a severe and prevalent acute cerebrovascular disease. The development of hypoxia following stroke can trigger a cascade of pathological events, including mitochondrial dysfunction, energy deficiency, oxidative stress, neuroinflammation, and excitotoxicity, all of which are often associated with unfavorable prognosis. Nonetheless, a noninvasive intervention, referred to as normobaric hyperoxia (NBO), is known to have neuroprotective effects against stroke. RESULTS: NBO can exert neuroprotective effects through various mechanisms, such as the rescue of hypoxic tissues, preservation of the blood-brain barrier, reduction of brain edema, alleviation of neuroinflammation, improvement of mitochondrial function, mitigation of oxidative stress, reduction of excitotoxicity, and inhibition of apoptosis. These mechanisms may help improve the prognosis of stroke patients. CONCLUSIONS: This review summarizes the mechanism by which hypoxia causes brain injury and how NBO can act as a neuroprotective therapy to treat stroke. We conclude that NBO has significant potential for treating stroke and may represent a novel therapeutic strategy.

Optimizing the image quality of peripancreatic blood vessels through the clinical application of single-energy spectral computed tomography (CT) imaging.

Background: With the increase of pancreatic tumor patients in recent years, there is an urgent need to find a way to treat pancreatic tumors. Surgery is one of the best methods for the treatment of pancreatic tumors, the success of which depends on the evaluation of peripancreatic vessels before surgery. Computed tomography (CT), as a non-invasive, fast, and economical auxiliary examination method, is undoubtedly one of the best means of clinical auxiliary examination. In this study, we investigated the impact of single-energy spectral CT imaging on the image quality of peripancreatic blood vessels and the clinical value of low-keV imaging in enhancing the image quality of peripancreatic arteriovenous vessels. Methods: We prospectively enrolled 103 patients who underwent abdominal vascular-enhanced CT examinations at the Affiliated Hospital of Hebei University between December 2022 and May 2023 and who were all scanned with the dual-energy feature on the United Imaging ATLAS scanner. The images were reconstructed at 70 keV, mixed energy, and optimized single energy in the post-processing station of United Imaging Healthcare Technology Co., Ltd. The CT value and contrast-to-noise ratio (CNR) of the superior mesenteric artery (SMA), gastroduodenal artery (GDA), inferior pancreaticoduodenal artery (IPDA), and superior mesenteric vein (SMV) were compared across energy levels, and then the image quality was subjectively evaluated. One-way analysis of variance and rank-sum tests were utilized for the statistical analysis. Results: The CT values of SMA, GDA, IPDA, and SMV in the optimal single energy group were 358.37±70.24, 323.36±88.23, 300.76±76.27, and 257.74±20.56 Hounsfield unit (HU), respectively, which were superior to those in the mixed energy (241.66±47.69, 235.17±53.71, 207.36±45.17, and 187.39±23.21 HU) and 70 keV groups (260.89±54.27, 252.41±58.87, 223.17±43.65, and 203.18±18.17 HU) (P<0.05). The diagnostic efficacy was greater in the optimal single energy group than in the other 2 groups (4.63±0.50, 3.91±0.57, and 4.23±0.83) (P<0.05). Conclusions: The optimal single energy for showing peripancreatic blood vessels is 62±7 keV when utilizing single-energy spectral CT imaging.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.

Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

OBJECTIVE: To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer.

Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model.

Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value.

Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Current evidence of synaptic dysfunction after stroke: Cellular and molecular mechanisms.

BACKGROUND: Stroke is an acute cerebrovascular disease in which brain tissue is damaged due to sudden obstruction of blood flow to the brain or the rupture of blood vessels in the brain, which can prompt ischemic or hemorrhagic stroke. After stroke onset, ischemia, hypoxia, infiltration of blood components into the brain parenchyma, and lysed cell fragments, among other factors, invariably increase blood-brain barrier (BBB) permeability, the inflammatory response, and brain edema. These changes lead to neuronal cell death and synaptic dysfunction, the latter of which poses a significant challenge to stroke treatment. RESULTS: Synaptic dysfunction occurs in various ways after stroke and includes the following: damage to neuronal structures, accumulation of pathologic proteins in the cell body, decreased fluidity and release of synaptic vesicles, disruption of mitochondrial transport in synapses, activation of synaptic phagocytosis by microglia/macrophages and astrocytes, and a reduction in synapse formation. CONCLUSIONS: This review summarizes the cellular and molecular mechanisms related to synapses and the protective effects of drugs or compounds and rehabilitation therapy on synapses in stroke according to recent research. Such an exploration will help to elucidate the relationship between stroke and synaptic damage and provide new insights into protecting synapses and restoring neurologic function.

High-resolution magnetic resonance vessel wall imaging provides new insights into Moyamoya disease.

Moyamoya disease (MMD) is a rare condition that affects the blood vessels of the central nervous system. This cerebrovascular disease is characterized by progressive narrowing and blockage of the internal carotid, middle cerebral, and anterior cerebral arteries, which results in the formation of a compensatory fragile vascular network. Currently, digital subtraction angiography (DSA) is considered the gold standard in diagnosing MMD. However, this diagnostic technique is invasive and may not be suitable for all patients. Hence, non-invasive imaging methods such as computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are often used. However, these methods may have less reliable diagnostic results. Therefore, High-Resolution Magnetic Resonance Vessel Wall Imaging (HR-VWI) has emerged as the most accurate method for observing and analyzing arterial wall structure. It enhances the resolution of arterial walls and enables quantitative and qualitative analysis of plaque, facilitating the identification of atherosclerotic lesions, vascular entrapment, myofibrillar dysplasia, moyamoya vasculopathy, and other related conditions. Consequently, HR-VWI provides a new and more reliable evaluation criterion for diagnosing vascular lesions in patients with Moyamoya disease.

Targeting dysregulated lipid metabolism for the treatment of Alzheimer's disease and Parkinson's disease: Current advancements and future prospects.

Alzheimer's and Parkinson's diseases are two of the most frequent neurological diseases. The clinical features of AD are memory decline and cognitive dysfunction, while PD mainly manifests as motor dysfunction such as limb tremors, muscle rigidity abnormalities, and slow gait. Abnormalities in cholesterol, sphingolipid, and glycerophospholipid metabolism have been demonstrated to directly exacerbate the progression of AD by stimulating Aß deposition and tau protein tangles. Indirectly, abnormal lipids can increase the burden on brain vasculature, induce insulin resistance, and affect the structure of neuronal cell membranes. Abnormal lipid metabolism leads to PD through inducing accumulation of α-syn, dysfunction of mitochondria and endoplasmic reticulum, and ferroptosis. Great progress has been made in targeting lipid metabolism abnormalities for the treatment of AD and PD in recent years, like metformin, insulin, peroxisome proliferator-activated receptors (PPARs) agonists, and monoclonal antibodies targeting apolipoprotein E (ApoE). This review comprehensively summarizes the involvement of dysregulated lipid metabolism in the pathogenesis of AD and PD, the application of Lipid Monitoring, and emerging lipid regulatory drug targets. A better understanding of the lipidological bases of AD and PD may pave the way for developing effective prevention and treatment methods for neurodegenerative disorders.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

Moyamoya syndrome may result from psoriasis: Four case reports.

BACKGROUND: Moyamoya syndrome (MMS) is a group of diseases that involves more than one underlying disease and is accompanied by moyamoya vascular phenomena. Psoriasis is a chronic immune skin disease closely linked to high blood pressure and heart disease. However, psoriasis-related MMS has not been reported. CASE SUMMARY: We collected data on patients with stroke due to MMS between January 2017 and December 2019 and identified four cases of psoriasis. Case histories, imaging, and hematological data were collected. The average age of the initial stroke onset was 58.25 ± 11.52 years; three cases of hemorrhagic and one case of ischemic stroke were included. The average duration from psoriasis confirmation to the initial MMS-mediated stroke onset was 17 ± 3.56 years. All MMS-related stenoses involved the bilateral cerebral arteries: Suzuki grade III in one case, grade IV in two cases, and grade V in one case. Abnormally elevated plasma interleukin-6 levels were observed in four patients. Two patients had abnormally elevated immunoglobulin E levels, and two had thrombocytosis. All four patients received medication instead of surgery. With an average follow-up time of 2 years, two causing transient ischemic attacks occurred in two patients, and no hemorrhagic events occurred. CONCLUSION: Psoriasis may be a potential risk factor for MMS. Patients with psoriasis should be screened for MMS when they present with neurological symptoms.

Unraveling the mystery: congenital absence of the portal vein and its cardiac implications.

A 25-year-old male with tachycardia and right heart enlargement was diagnosed with atrial-ventricular nodal reentrant tachycardia (AVNRT) and successfully treated with radiofrequency ablation. Cardiac MRI confirmed right heart enlargement but ruled out cardiomyopathy. Further imaging revealed a rare congenital absence of the portal vein (CAPV) with an associated portosystemic shunt. This case highlights the importance of considering CAPV in unexplained cardiac symptoms and structural heart changes.

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions.

PURPOSE: This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation. METHODS: This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy. RESULTS: The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89). CONCLUSION: Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

Normobaric hyperoxia alleviates complement C3-mediated synaptic pruning and brain injury after intracerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is a common cerebrovascular disease, and the complement cascade exacerbates brain injury after ICH. As the most abundant component of the complement system, complement component 3 (C3) plays essential roles in all three complement pathways. However, the effects of C3 on neurological impairment and brain injury in ICH patients and the related mechanism have not been fully elucidated. Normobaric hyperoxia (NBO) is regarded as a treatment for ICH patients, and recent clinical studies also have confirmed the neuroprotective role of NBO against acute ICH-mediated brain damage, but the underlying mechanism still remains elusive. AIMS: In the present study, we investigated the effects of complement C3 on NBO-treated ICH patients and model mice, and the underlying mechanism of NBO therapy in ICH-mediated brain injury. RESULTS: Hemorrhagic injury resulted in the high plasma C3 levels in ICH patients, and the plasma C3 levels were closely related to hemorrhagic severity and clinical outcomes after ICH. BO treatment alleviated neurologic impairments and rescued the hemorrhagic-induced increase in plasma C3 levels in ICH patients and model mice. Moreover, the results indicated that NBO exerted its protective effects of on brain injury after ICH by downregulating the expression of C3 in microglia and alleviating microglia-mediated synaptic pruning. CONCLUSIONS: Our results revealed that NBO exerts its neuroprotective effects by reducing C3-mediated synaptic pruning, which suggested that NBO therapy could be used for the clinical treatment of ICH.

Delayed cardiotoxicity following osimertinib therapy in non-small cell lung cancer: a unique case report.

This case report features a 62-year-old male with stage IB lung adenocarcinoma harboring an epidermal growth factor receptor exon 19 deletion, who underwent treatment with osimertinib following a left upper lobectomy and lymph node dissection. Despite a history of smoking and well-managed type 2 diabetes, the patient developed heart failure 18 months post-initiation of osimertinib therapy, marking one of the latest occurrences of heart failure following osimertinib treatment documented in limited literature. Cardiac MRI revealed significant left ventricular enlargement, lateral wall myocardial thinning, and localized myocardial fibrosis without perfusion defects, a finding not previously reported in literature. This case underscores the severe and unusual cardiac effects of osimertinib in patients with latent risk factors, highlighting the importance of vigilant cardiac monitoring and a multidisciplinary management approach.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model.

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases.

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.

Mechanisms and therapeutic targets of mitophagy after intracerebral hemorrhage.

Mitochondria are dynamic organelles responsible for cellular energy production. In addition to regulating energy homeostasis, mitochondria are responsible for calcium homeostasis, clearance of damaged organelles, signaling, and cell survival in the context of injury and pathology. In stroke, the mechanisms underlying brain injury secondary to intracerebral hemorrhage are complex and involve cellular hypoxia, oxidative stress, inflammatory responses, and apoptosis. Recent studies have shown that mitochondrial damage and autophagy are essential for neuronal metabolism and functional recovery after intracerebral hemorrhage, and are closely related to inflammatory responses, oxidative stress, apoptosis, and other pathological processes. Because hypoxia and inflammatory responses can cause secondary damage after intracerebral hemorrhage, the restoration of mitochondrial function and timely clearance of damaged mitochondria have neuroprotective effects. Based on studies on mitochondrial autophagy (mitophagy), cellular inflammation, apoptosis, ferroptosis, the BNIP3 autophagy gene, pharmacological and other regulatory approaches, and normobaric oxygen (NBO) therapy, this article further explores the neuroprotective role of mitophagy after intracerebral hemorrhage.