18F-FDG PET/CT findings in a patient with blastic plasmacytoid dendritic cell neoplasm and post-transplant lymphoproliferative disorder after hematopoietic stem cell transplantation: a case report

BackgroundBlastic plasmacytoid dendritic cell neoplasm (BPDCN) is an extremely rare hematopoietic malignancy, which originating from precursors of plasmacytoid dendritic cells. Allogeneic hematopoietic stem cell transplantation (HSCT) is normally considered in the treatment of BPDCN patients to acquire sustained remission. Post-transplant lymphoproliferative disorder (PTLD) is a group of conditions involving abnormal lymphoid cells proliferation in the context of extrinsic immunosuppression after solid organ transplantation (SOT) or HSCT. Herein, we report a patient with BPDCN, who suffered from PTLD after allogeneic HSCT.Case presentationA 66-year-old man was diagnosed with BPDCN, confirmed by pathologic examination after splenectomy. The post-surgery 18F-fluoro-2-deoxy-D-glucose-positron emission tomography/computed tomography (18F-FDG PET/CT) showed multifocal 18F-FDG avidity in the left cheek, lymph nodes and bone marrow. The patient started chemotherapy, followed by allogeneic HSCT and immunosuppressive therapy. Four months after the HSCT, the patient developed intermittent fever and recurrent lymphadenopathy, accompanied with progressively elevated Epstein–Barr virus (EBV)-DNA both in serum and lymphocytes. 18F-FDG PET/CT was performed again and found multiple new enlarged 18F-FDG-avid lymph nodes, while the previous hypermetabolic lesions all disappeared. The pathology of mesenteric lymph node indicated a monomorphic PTLD (diffuse large B-cell lymphoma). Then the immunosuppressive medications were stopped and two cycles of Rituximab were given, and the follow-up CT scan indicated a complete response.ConclusionWhen patients with BPDCN recurred new enlarged lymph nodes after allogeneic HSCT and immunosuppressive therapy, PTLD should be taken into consideration. 18F-FDG PET/CT may provide additional evidence for supporting or refuting the suspicion of PTLD, and suggest lesions accessible for biopsy

Hydrogen Sulfide Protects against Chemical Hypoxia-Induced Injury by Inhibiting ROS-Activated ERK1/2 and p38MAPK Signaling Pathways in PC12 Cells

Hydrogen sulfide (H2S) has been proposed as a novel neuromodulator and neuroprotective agent. Cobalt chloride (CoCl2) is a well-known hypoxia mimetic agent. We have demonstrated that H2S protects against CoCl2-induced injuries in PC12 cells. However, whether the members of mitogen-activated protein kinases (MAPK), in particular, extracellular signal-regulated kinase1/2(ERK1/2) and p38MAPK are involved in the neuroprotection of H2S against chemical hypoxia-induced injuries of PC12 cells is not understood. We observed that CoCl2 induced expression of transcriptional factor hypoxia-inducible factor-1 alpha (HIF-1α), decreased cystathionine-β synthase (CBS, a synthase of H2S) expression, and increased generation of reactive oxygen species (ROS), leading to injuries of the cells, evidenced by decrease in cell viability, dissipation of mitochondrial membrane potential (MMP) , caspase-3 activation and apoptosis, which were attenuated by pretreatment with NaHS (a donor of H2S) or N-acetyl-L cystein (NAC), a ROS scavenger. CoCl2 rapidly activated ERK1/2, p38MAPK and C-Jun N-terminal kinase (JNK). Inhibition of ERK1/2 or p38MAPK or JNK with kinase inhibitors (U0126 or SB203580 or SP600125, respectively) or genetic silencing of ERK1/2 or p38MAPK by RNAi (Si-ERK1/2 or Si-p38MAPK) significantly prevented CoCl2-induced injuries. Pretreatment with NaHS or NAC inhibited not only CoCl2-induced ROS production, but also phosphorylation of ERK1/2 and p38MAPK. Thus, we demonstrated that a concurrent activation of ERK1/2, p38MAPK and JNK participates in CoCl2-induced injuries and that H2S protects PC12 cells against chemical hypoxia-induced injuries by inhibition of ROS-activated ERK1/2 and p38MAPK pathways. Our results suggest that inhibitors of ERK1/2, p38MAPK and JNK or antioxidants may be useful for preventing and treating hypoxia-induced neuronal injury

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment

Hydrogen Sulfide Protects against Chemical Hypoxia-Induced Cytotoxicity and Inflammation in HaCaT Cells through Inhibition of ROS/NF-κB/COX-2 Pathway

Hydrogen sulfide (H2S) has been shown to protect against oxidative stress injury and inflammation in various hypoxia-induced insult models. However, it remains unknown whether H2S protects human skin keratinocytes (HaCaT cells) against chemical hypoxia-induced damage. In the current study, HaCaT cells were treated with cobalt chloride (CoCl2), a well known hypoxia mimetic agent, to establish a chemical hypoxia-induced cell injury model. Our findings showed that pretreatment of HaCaT cells with NaHS (a donor of H2S) for 30 min before exposure to CoCl2 for 24 h significantly attenuated CoCl2-induced injuries and inflammatory responses, evidenced by increases in cell viability and GSH level and decreases in ROS generation and secretions of IL-1β, IL-6 and IL-8. In addition, pretreatment with NaHS markedly reduced CoCl2-induced COX-2 overexpression and PGE2 secretion as well as intranuclear NF-κB p65 subunit accumulation (the central step of NF-κB activation). Similar to the protective effect of H2S, both NS-398 (a selective COX-2 inhibitor) and PDTC (a selective NF-κB inhibitor) depressed not only CoCl2-induced cytotoxicity, but also the secretions of IL-1β, IL-6 and IL-8. Importantly, PDTC obviously attenuated overexpression of COX-2 induced by CoCl2. Notably, NAC, a ROS scavenger, conferred a similar protective effect of H2S against CoCl2-induced insults and inflammatory responses. Taken together, the findings of the present study have demonstrated for the first time that H2S protects HaCaT cells against CoCl2-induced injuries and inflammatory responses through inhibition of ROS-activated NF-κB/COX-2 pathway

FACS-Based Automated Pain Detection From Spontaneous Facial Expressions

Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurrence of pain-related AU combinations, whose automated detection would be highly beneficial for efficient and practical pain monitoring. Existing general Automated Facial Expression Recognition (AFER) systems prove inadequate when applied specifically for detecting pain as they either focus on detecting individual pain-related AUs but not on combinations or they seek to bypass AU detection by training a binary pain classifier directly on pain intensity data but are limited by lack of enough labeled data for satisfactory training. Our research is inspired by the clinical demand for automated pain evaluation in end-of-life patient care. The proposed system decouples pain detection into two consecutive tasks: the pain expression descriptor that detects AUs in a joint manner at the frame level, and sequence-level pain detection from low-dimensional frame-level AU predictions. As our major contribution of this research, the proposed system decouples the pain detection problem into two consecutive tasks: FACS based pain expression related AU predictions at the frame level, and pain detection at sequence-level from low-dimensional frame-level AU predictions. The two sub-tasks are handled by an AFER system and an APD system, which are trained independently. The architecture of two independent machine learning networks not only improves data utilization from existing different pain-oriented video datasets, but also improving data fusion newly acquired data in the future, which addresses the most challenge problem arising from the data insufficiency. The decoupled architecture is also features for its flexibility in customization. The AFER system is realized with three types of configurations, including one conventional CVML system and two types of deep learning architectures. The automated pain detection is modeled as a weakly supervised problem, and the APD system is realized by two multiple instance learning frameworks (MIL and MCIL). AU combination are encoded from single AU scores by two novel data structures (Compact and Clustered), and the multiple instance learning frameworks that are trained with low-dimensional features based on the pain-related AU combinations. In particular, We followed an end-to-front research strategy to develop the decoupled pain detection system in three research phases, and our ultimate goal is to establish a robust and generic automated pain analysis system for clinical applications. Experimental results on the UNBC-McMaster Shoulder Pain Expression dataset show that the deep learning-based multi-label AFER system outperforms state-of-the-art AFER system that are based on classical machine learning (ML) techniques. Further tests with the Wilkie video dataset of lung caner patients suggest the proposed decoupled framework has strong promise for effective pain monitoring in clinical settings, where segment-level patients’ self-report pain is the only available ground truth

Modeling soil detachment capacity by rill flow using hydraulic parameters

The relationship between soil detachment capacity (Dc) by rill flow and hydraulic parameters (e.g., flow velocity, shear stress, unit stream power, stream power, and unit energy) at low flow rates is investigated to establish an accurate experimental model. Experiments are conducted using a 4 0.1 m rill hydraulic flume with a constant artificial roughness on the flume bed. The flow rates range from 0.22 10 3 m2 s 1 to 0.67 10 3 m2 s 1, and the slope gradients vary from 15.8% to 38.4%. Regression analysis indicates that the Dc by rill flow can be predicted using the linear equations of flow velocity, stream power, unit stream power, and unit energy. Dc by rill flow that is fitted to shear stress can be predicted with a power function equation. Predictions based on flow velocity, unit energy, and stream power are powerful, but those based on shear stress, especially on unit stream power, are relatively poor. The prediction based on flow velocity provides the best estimates of Dc by rill flow because of the simplicity and availability of its measurements. Owing to error in measuring flow velocity at low flow rates, the predictive abilities of Dc by rill flow using all hydraulic parameters are relatively lower in this study compared with the results of previous research. The measuring accuracy of experiments for flow velocity should be improved in future research.<br style="orphans: 2; text-align: -webkit-auto; widows: 2;" /

Pressure sensitivity of dislocation density in copper single crystals at submicron scale

Abstract It is known that the mechanical responses of metallic samples are insensitive to confining pressure at macroscale. As a result, von Mises elastoplasticity has been commonly used to model metals in engineering practice. With the use of discrete dislocation dynamics in this study, we explore the dislocation behavior of finite-sized copper single crystals of different sizes under uniaxial compression and hydrostatic pressure, respectively. It is found that the dislocation density approaches a stable value with the increase of hydrostatic pressure while it still keeps increasing under uniaxial compression as the size-dependent yield stress is reached. This difference is also dependent on the loading rate. The yield stress under uniaxial compression exhibits the conventional loading rate effect, while the stable value of dislocation density under hydrostatic compression increases with the increase of loading rate. Moreover, a transition from being pressure-insensitive to pressure-sensitive on the evolution of dislocation density is observed under hydrostatic compression as the sample size becomes small. These findings provide useful insights into the elastoplastic responses of metallic samples at microscale