This paper used small-scale analysis with single modality ECG signal to extract five critical features to train an effective 3 classification stress recognition model through the datasets of Stress Recognition in Automatic Drive. Using the local Hurst exponents and small-scale analysis, we could achieve 98.39% accuracy rate in detecting the three classes of stress.
Abstract Backgroud To evaluate the feasibility of deep learning (DL) models in identifying asymptomatic COVID-19 patients, based on chest CT images. Methods In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs)-or Transformer-architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou was randomly split into the training set (n = 2,140) and the internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Models’ performance was assessed by accuracy, recall and specificity and was compared with that of two radiologists. Results A total of 2,700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by EfficientNet model (0.954). The recall and precision of the Swin model were 0.989 and 1.000. In the test dataset, the Swin model still was the best that achieved the highest accuracy (0.980). All the DL models performed remarkable than two experts. Lastly, the time on the test set diagnosis spent by two experts 42min17s (Junior) and 29min43s (Senior), was significantly higher than that of those DL models (all below 2min). Conclusions This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found a Transformer model, the Swin model, performed best.
Periprosthetic osteolysis (PPO) triggered by wear particles is the most severe complication of total joint replacement (TJR) surgeries, representing the major cause of implant failure, which is public health concern worldwide. Previous studies have confirmed the specialized role of osteoclast-induced progressive bone destruction in the progression of PPO. Additionally, the reactive oxygen species (ROS) induced by wear particles can promote excessive osteoclastogenesis and bone resorption. Nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4), a cellular enzyme, is considered to be responsible for the production of ROS and the formation of mature osteoclasts. However, NOX4 involvement in PPO has not yet been elucidated. Therefore, we investigated the mechanism by which NOX4 regulates osteoclast differentiation and the therapeutic effects on titanium nanoparticle-induced bone destruction. We found that NOX4 blockade suppressed osteoclastogenesis and enhanced the scavenging of intracellular ROS. Our rescue experiment revealed that nuclear factor-erythroid 2-related factor 2 (Nrf2) silencing reversed the effects of NOX4 blockade on ROS production and osteoclast differentiation. In addition, we found increased expression levels of NOX4 in PPO tissues, while NOX4 inhibition in vivo exerted protective effects on titanium nanoparticle-induced osteolysis through antiosteoclastic and antioxidant effects. Collectively, these findings suggested that NOX4 blockade suppresses titanium nanoparticle-induced bone destruction via activation of the Nrf2 signaling pathway and that NOX4 blockade may be an attractive therapeutic approach for preventing PPO.
Schematic showing inhibition mechanism of PCG on wear-particle-induced inflammatory bone destruction by bi-directional regulation of osteoblastic formation and osteoclastic resorption.
Objective To investigate the clinicopathological features,immunohistochemical features,diagnosis,and relationship with sporadic prostate cancer in primary small cell neuroendocrine carcinoma of the bladder. Methods We retrospectively analyzed the clinical characteristics of 12 patients with primary small cell neuroendocrine carcinoma of the bladder diagnosed at Beijing Chao-Yang Hospital affiliated to Capital Medical University from January 2013 to September 2022.The histological features of primary small cell neuroendocrine carcinoma of the bladder were re-evaluated by two pathologists according to the 2022 revision of the World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs.Electronic medical records were retrieved,and telephone follow-up was conducted from the time of histopathological diagnosis to the death or the end of the last follow-up until January 31,2023. Results The 12 patients include 7 patients in pT3 stage and 1 patient in pT4 stage.Eight patients were complicated with other types of tumors,such as high-grade urothelial carcinoma of the bladder and squamous cell carcinoma.Five patients had sporadic prostate cancer.Immunohistochemical staining showed that 12 (100.0%),10 (83.3%),and 8 (66.7%) patients were tested positive for CD56,Syn,and CgA,respectively.The Ki67 proliferation index ranged from 80% to 90%.Five patients with urothelial carcinoma were tested positive for CK20,GATA3,and CK7.P504S was positive in all the 5 patients with prostate cancer,while P63 and 34βE12 were negative.The follow-up of the 12 patients lasted for 3-60 months.Eight of these patients died during follow-up,with the median survival of 15.5 months.Four patients survived. Conclusions Primary small cell neuroendocrine carcinoma of the bladder is a rare urological tumor with high aggressiveness and poor prognosis.In male patients with bladder prostatectomy,all prostate tissue should be sampled.If prostate cancer is detected,the prostate-specific antigen level should be monitored.目的 探讨原发性膀胱小细胞神经内分泌癌临床病理特征、免疫组织化学特点、诊断及与偶发前列腺癌的关系。方法 回顾性分析2013年1月至2022年9月在首都医科大学附属北京朝阳医院就诊的12例原发性膀胱小细胞神经内分泌癌患者的临床资料,经病理科2位副主任医师重新阅片后,按照2022版泌尿系统及男性生殖器官肿瘤世界卫生组织分类进行复核。通过检索电子病历及电话进行随访,随访自病理确诊开始至患者死亡或最后一次随访结束,截止日期2023年1月31日。结果 12例患者中,7例为pT3期,1例为pT4期;8例合并膀胱高级别尿路上皮癌、鳞状细胞癌等其他类型肿瘤,5例合并偶发前列腺癌。免疫组织化学染色结果显示,12例患者中,12例(100.0%)CD56阳性,10例(83.3%)Syn阳性,8例(66.7%)CgA阳性,Ki67增殖指数介于80%~90%;5例尿路上皮癌CK20、GATA3和CK7均为阳性;5例前列腺癌P504S 均为阳性,P63和34βE12均为阴性。随访时间3~60个月,8例患者在随访期间死亡,中位生存时间15.5个月;4例患者生存。结论 原发性膀胱小细胞神经内分泌癌是罕见的泌尿系统肿瘤,侵袭性强,预后差。男性膀胱前列腺切除患者,前列腺组织应全部取材。如发现具有临床意义的前列腺癌,术后应监测前列腺特异性抗原水平。.
Diabetes mellitus is a chronic metabolic disease with a proinflammatory microenvironment, causing poor vascularization and bone regeneration. Due to the lack of effective therapy and one-sided focus on the direct angiogenic properties of biomaterials and osteogenesis stimulation, the treatment of diabetic bone defect remains challenging and complex. In this study, using gelatin methacryloyl (GelMA) as a template, a lithium (Li) -modified bioglass-hydrogel for diabetic bone regeneration is developed. It exhibits a sustained ion release for better bone regeneration under diabetic microenvironment. The hydrogel is shown to be mechanically adaptable to the complex shape of the defect. In vitro, Li-modified bioglass-hydrogel promoted cell proliferation, direct osteogenesis, and regulated macrophages in high glucose (HG) microenvironment, with the secretion of bone morphogenetic protein-2 and vascular endothelial growth factor to stimulate osteogenesis and neovascularization indirectly. In vivo, composite hydrogels containing GelMA and Li-MBG (GM/M-Li) release Li ions to relieve inflammation, providing an anti-inflammatory microenvironment for osteogenesis and angiogenesis. Applying Li-modified bioglass-hydrogel, significantly enhances bone regeneration in a diabetic rat bone defect. Together, both remarkable in vitro and in vivo outcomes in this study present an opportunity for diabetic bone regeneration on the basis of HG microenvironment.
Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.
Abstract Background Three-dimensional (3D) multimodality fusion imaging has been proved to be a promising neurosurgical tool for presurgical evaluation of tumor removal. We aim to develop a scoring system based on this new tool to predict the resection grade of medial sphenoid wing meningiomas (mSWM) intuitively. Methods We included 46 patients treated for mSWM from 2014 to 2019 to evaluate their tumors’ location, volume, cavernous sinus involvement, vascular encasement and bone invasion by 3D multimodality fusion imaging. A scoring system based on the significant parameters detected by statistical analysis was created and evaluated. Results The tumor volumes ranged from 0.8 cm 3 to 171.9 cm 3 . A total of 39 (84.8%) patients had arterial involvement. Cavernous sinus (CS) involvement was observed in 23 patients (50.0%) and bone invasion was noted in 10 patients (21.7%). Simpson I resection was achieved in 10 patients (21.7%) and Simpson II resection was achieved in 17 patients (37.0%). Fifteen patients (32.6%) underwent Simpson III resection and 4 patients (8.7%) underwent Simpson IV resections. A scoring system was created. The score ranged from 1 to 10 and the mean score of our patients was 5.3 ± 2.8. Strong positive monotonic correlation existed between the score and resection grade (R s = .772, P < .001). The scoring system had good predictive capacity with an accuracy of 69.60%. Conclusions We described a scoring system that enabled neurosurgeons to predict extent of resection and outcomes for mSWM preoperatively with 3D multimodality fusion imaging. Trial registration: Retrospectively registered.
Glucocorticoids (GCs) are used in treating viral infections, acute spinal cord injury, autoimmune diseases, and shock. Several patients develop GC-induced osteonecrosis of the femoral head (ONFH). However, the pathogenic mechanisms underlying GC-induced ONFH remain poorly understood. GC-directed bone marrow mesenchymal stem cells (BMSCs) fate is an important factor that determines GC-induced ONFH. At high concentrations, GCs induce BMSC apoptosis by promoting oxidative stress. In the present study, we aimed to elucidate the molecular mechanisms that relieve GC-induced oxidative stress in BMSCs, which would be vital for treating ONFH. The endocannabinoid system regulates oxidative stress in multiple organs. Here, we found that monoacylglycerol lipase (MAGL), a key molecule in the endocannabinoid system, was significantly upregulated during GC treatment in osteoblasts both in vitro and in vivo. MAGL expression was positively correlated with expression of the NADPH oxidase family and apoptosis-related proteins. Functional analysis showed that MAGL inhibition markedly reduced oxidative stress and partially rescued BMSC apoptosis. Additionally, in vivo studies indicated that MAGL inhibition effectively attenuated GC-induced ONFH. Pathway analysis showed that MAGL inhibition regulated oxidative stress in BMSCs via the Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. The expression of Nrf2, a major regulator of intracellular antioxidants, was upregulated by inhibiting MAGL. Nrf2 activation can mimic the effect of MAGL inhibition and significantly reduce GC-induced oxidative damage in BMSCs. The beneficial effects of MAGL inhibition were attenuated after the blockade of the Keap1/Nrf2 antioxidant signaling pathway. Notably, pharmacological blockade of MAGL conferred femoral head protection in GC-induced ONFH, even after oxidative stress responses were initiated. Therefore, MAGL may represent a novel target for the prevention and treatment of GC-induced ONFH.
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