Tumor necrosis factor-stimulated gene-6 ameliorates early brain injury after subarachnoid hemorrhage by suppressing NLRC4 inflammasome-mediated astrocyte pyroptosis

Subarachnoid hemorrhage is associated with high morbidity and mortality and lacks effective treatment.Pyroptosis is a crucial mechanism underlying early brain injury after subarachnoid hemorrhage.Previous studies have confirmed that tumor necrosis factor-stimulated gene-6(TSG-6)can exert a neuroprotective effect by suppressing oxidative stress and apoptosis.However,no study to date has explored whether TSG-6 can alleviate pyroptosis in early brain injury after subarachnoid hemorrhage.In this study,a C57BL/6J mouse model of subarachnoid hemorrhage was established using the endovascular perforation method.Our results indicated that TSG-6 expression was predominantly detected in astrocytes,along with NLRC4 and gasdermin-D(GSDMD).The expression of NLRC4,GSDMD and its N-terminal domain(GSDMD-N),and cleaved caspase-1 was significantly enhanced after subarachnoid hemorrhage and accompanied by brain edema and neurological impairment.To explore how TSG-6 affects pyroptosis during early brain injury after subarachnoid hemorrhage,recombinant human TSG-6 or a siRNA targeting TSG-6 was injected into the cerebral ventricles.Exogenous TSG-6 administration downregulated the expression of NLRC4 and pyroptosis-associated proteins and alleviated brain edema and neurological deficits.Moreover,TSG-6 knockdown further increased the expression of NLRC4,which was accompanied by more severe astrocyte pyroptosis.In summary,our study revealed that TSG-6 provides neuroprotection against early brain injury after subarachnoid hemorrhage by suppressing NLRC4 inflammasome activation-induced astrocyte pyroptosis.

Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

Targeting brain tumors with innovative nanocarriers:bridging the gap through the blood-brain barrier

Background:Glioblastoma multiforme(GBM)is recognized as the most lethal and most highly invasive tumor.The high likelihood of treatment failure arises fromthe presence of the blood-brain barrier(BBB)and stemcells around GBM,which avert the entry of chemotherapeutic drugs into the tumormass.Objective:Recently,several researchers have designed novel nanocarrier systems like liposomes,dendrimers,metallic nanoparticles,nanodiamonds,and nanorobot approaches,allowing drugs to infiltrate the BBB more efficiently,opening up innovative avenues to prevail over therapy problems and radiation therapy.Methods:Relevant literature for this manuscript has been collected from a comprehensive and systematic search of databases,for example,PubMed,Science Direct,Google Scholar,and others,using specific keyword combinations,including“glioblastoma,”“brain tumor,”“nanocarriers,”and several others.Conclusion:This review also provides deep insights into recent advancements in nanocarrier-based formulations and technologies for GBM management.Elucidation of various scientific advances in conjunction with encouraging findings concerning the future perspectives and challenges of nanocarriers for effective brain tumor management has also been discussed.

Multi-Level Parallel Network for Brain Tumor Segmentation

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

Wilm′s tumor gene1肽疫苗Galinpepimut-S在肿瘤免疫治疗中的应用

目的为Wilm′s tumor gene1(WT1)肽疫苗Galinpepimut-S(GPS)用于肿瘤免疫治疗的后续研究提供参考。方法采用计算机检索中国知网、PubMed等数据库自建库起至2022年12月的肿瘤免疫治疗相关文献,总结GPS在肿瘤免疫治疗中的应用现状。结果GPS能激发自身免疫系统,对WT1抗原产生强烈免疫反应而发挥抗肿瘤作用,在卵巢癌、恶性胸膜间皮瘤、急性髓系白血病、多发性骨髓瘤的治疗中均显示出较好的疗效。结论以GPS为代表的肿瘤疫苗是未来肿瘤治疗的重要方向,需进一步进行临床研究,以获取更多数据。

The action mechanism by which C1q/tumor necrosis factor-related protein-6 alleviates cerebral ischemia/reperfusion injury in diabetic mice

Studies have shown that C1q/tumor necrosis factor-related protein-6 (CTRP6) can alleviate renal ischemia/reperfusion injury in mice. However, its role in the brain remains poorly understood. To investigate the role of CTRP6 in cerebral ischemia/reperfusion injury associated with diabetes mellitus, a diabetes mellitus mouse model of cerebral ischemia/reperfusion injury was established by occlusion of the middle cerebral artery. To overexpress CTRP6 in the brain, an adeno-associated virus carrying CTRP6 was injected into the lateral ventricle. The result was that oxygen injury and inflammation in brain tissue were clearly attenuated, and the number of neurons was greatly reduced. In vitro experiments showed that CTRP6 knockout exacerbated oxidative damage, inflammatory reaction, and apoptosis in cerebral cortical neurons in high glucose hypoxia-simulated diabetic cerebral ischemia/reperfusion injury. CTRP6 overexpression enhanced the sirtuin-1 signaling pathway in diabetic brains after ischemia/reperfusion injury. To investigate the mechanism underlying these effects, we examined mice with depletion of brain tissue-specific sirtuin-1. CTRP6-like protection was achieved by activating the sirtuin-1 signaling pathway. Taken together, these results indicate that CTRP6 likely attenuates cerebral ischemia/reperfusion injury through activation of the sirtuin-1 signaling pathway.

The miR-9-5p/CXCL11 pathway is a key target of hydrogen sulfide-mediated inhibition of neuroinflammation in hypoxic ischemic brain injury

We previously showed that hydrogen sulfide(H2S)has a neuroprotective effect in the context of hypoxic ischemic brain injury in neonatal mice.However,the precise mechanism underlying the role of H2S in this situation remains unclear.In this study,we used a neonatal mouse model of hypoxic ischemic brain injury and a lipopolysaccharide-stimulated BV2 cell model and found that treatment with L-cysteine,a H2S precursor,attenuated the cerebral infarction and cerebral atrophy induced by hypoxia and ischemia and increased the expression of miR-9-5p and cystathionineβsynthase(a major H2S synthetase in the brain)in the prefrontal cortex.We also found that an miR-9-5p inhibitor blocked the expression of cystathionineβsynthase in the prefrontal cortex in mice with brain injury caused by hypoxia and ischemia.Furthermore,miR-9-5p overexpression increased cystathionine-β-synthase and H2S expression in the injured prefrontal cortex of mice with hypoxic ischemic brain injury.L-cysteine decreased the expression of CXCL11,an miR-9-5p target gene,in the prefrontal cortex of the mouse model and in lipopolysaccharide-stimulated BV-2 cells and increased the levels of proinflammatory cytokines BNIP3,FSTL1,SOCS2 and SOCS5,while treatment with an miR-9-5p inhibitor reversed these changes.These findings suggest that H2S can reduce neuroinflammation in a neonatal mouse model of hypoxic ischemic brain injury through regulating the miR-9-5p/CXCL11 axis and restoringβ-synthase expression,thereby playing a role in reducing neuroinflammation in hypoxic ischemic brain injury.

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

Solid pseudopapillary tumor of the pancreas:A systematic review of clinical,surgical and oncological characteristics of 1384 patients underwent pancreatic surgery

Background:Pancreatic solid pseudopapillary tumors(SPTs)are rare clinical entity,with low malignancy and still unclear pathogenesis.They account for less than 2%of exocrine pancreatic neoplasms.This study aimed to perform a systematic review of the main clinical,surgical and oncological characteristics of pancreatic SPTs.Data sources:MEDLINE/PubMed,Web of Science and Scopus databases were systematically searched for the main clinical,surgical and oncological characteristics of pancreatic SPTs up to April 2021,in accordance with the preferred reporting items for systematic reviews and meta-analyses(PRISMA)standards.Primary endpoints were to analyze treatments and oncological outcomes.Results:A total of 823 studies were recorded,86 studies underwent full-text reviews and 28 met inclusion criteria.Overall,1384 patients underwent pancreatic surgery.Mean age was 30 years and 1181 patients(85.3%)were female.The most common clinical presentation was non-specific abdominal pain(52.6%of cases).Mean overall survival was 98.1%.Mean recurrence rate was 2.8%.Mean follow-up was 4.2 years.Conclusions:Pancreatic SPTs are rare,and predominantly affect young women with unclear pathogenesis.Radical resection is the gold standard of treatment achieving good oncological impact and a favorable prognosis in a yearly life-long follow-up.

Decoding molecular mechanisms:brain aging and Alzheimer's disease

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

Long-term outcome of stem cell transplantation with and without anti-tumor necrotic factor therapy in perianal fistula with Crohn’s disease

BACKGROUND Stem cell transplantation is a promising therapeutic option for curing perianal fistula in Crohn’s disease(CD).Anti-tumor necrotic factor(TNF)therapy combined with drainage procedure is effective as well.However,previous studies are limited to proving whether the combination treatment of biologics and stem cell transplantation improves the effect of fistula closure.AIM This study aimed to evaluate the long-term outcomes of stem cell transplantation and compare Crohn’s perianal fistula(CPF)closure rates after stem cell transplantation with and without anti-TNF therapy,and to identify the factors affecting CPF closure and recurrence.METHODS The patients with CD who underwent stem cell transplantation for treating perianal fistula in our institution between Jun 2014 and December 2022 were enrolled.Clinical data were compared according to anti-TNF therapy and CPF closure.RESULTS A total of 65 patients were included.The median age of females was 26 years(range:21-31)and that of males was 29(44.6%).The mean follow-up duration was 65.88±32.65 months,and complete closure was observed in 50(76.9%)patients.The closure rates were similar after stem cell transplantation with and without anti-TNF therapy(66.7%vs 81.6%at 3 year,P=0.098).The patients with fistula closure had short fistulous tract and infrequent proctitis and anorectal stricture(P=0.027,0.002,and 0.008,respectively).Clinical factors such as complexity,number of fistulas,presence of concurrent abscess,and medication were not significant for closure.The cumulative 1-,2-,and 3-year closure rates were 66.2%,73.8%,and 75.4%,respectively.CONCLUSION Anti-TNF therapy does not increase CPF closure rates in patients with stem cell transplantation.However,both refractory and non-refractory CPF have similar closure rates after additional anti-TNF therapy.Fistulous tract length,proctitis,and anal stricture are risk factors for non-closure in patients with CPF after stem cell transplantation.

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain.It can be divided into benign and malignant.The main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so on.However,these artificial diagnosis methods are easily affected by external factors.Scholars have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in CNN.In this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain tumors.We use fine-tuned ResNet18 to extract the features of brain tumor images.The RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the classifier.Meanwhile,our team selects the bat algorithm(BA)to opti7mize the parameters of RNN.We use fivefold cross-validation to verify the superiority of the RBEBT.The accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

New method of local adjuvant therapy with bicarbonate Ringer’s solution for tumoral calcinosis: A case report

BACKGROUND Tumoral calcinosis is a condition characterized by deposits of calcium phosphate crystals in extra-articular soft tissues,occurring in hemodialysis patients.Calcium phosphate crystals are mainly composed of hydroxyapatite,which is highly infilt-rative to tissues,thus making complete resection difficult.An adjuvant method to remove or resolve the residual crystals during the operation is necessary.CASE SUMMARY A bicarbonate Ringer’s solution with bicarbonate ions(28 mEq/L)was used as the adjuvant.After resecting calcium phosphate deposits of tumoral calcinosis as much as possible,while filling with the solution,residual calcium phosphate deposits at the pseudocyst wall can be gently scraped by fingers or gauze in the operative field.A 49-year-old female undergoing hemodialysis for 15 years had swelling with calcium deposition for 2 years in the shoulders,bilateral hip joints,and the right foot.A shoulder lesion was resected,but the calcification remained and early re-deposition was observed.Considering the difficulty of a complete rection,we devised a bicarbonate dissolution method and excised the foot lesion.After resection of the calcified material,the residual calcified material was washed away with bicarbonate Ringer’s solution.CONCLUSION The bicarbonate dissolution method is a new,simple,and effective treatment for tumoral calcinosis in hemodialysis patients.