Recognition of occupational therapy exercises and detection of compensation mistakes for Cerebral Palsy

Depth camera-based virtual rehabilitation systems are gaining attention in occupational therapy for cerebral palsy patients. When developing such a system, domain-specific exercise recognition is vital. To design such a gesture recognition method, some obstacles need to be overcome: detection of gestures not related to the defined exercise set and recognition of incorrect exercises performed by the patients to compensate for their lack of ability. We propose a framework based on hidden Markov models for the recognition of upper extremity functional exercises. We determine critical compensation mistakes together with restrictions for classifying these mistakes with the help of occupational therapists. We first eliminate undefined gestures by evaluating two models that produce adaptive threshold values. Then we utilize specific negative models based on feature thresholding and train them for each exercise to detect compensation mistakes. We perform various tests using our method in a laboratory environment under the supervision of occupational therapists.     

Synthesis,characterization, toxicological and antibacterial activity evaluation of Cu@ZnO nanocomposites

Cu@ZnO is an important class of material with applications as catalysts, photocatalysts, optoelectronic devices and antimicrobial agents. Because of its potential for large-scale applications and its high redox activity, detailed examination of the properties and risk assessment of this class of materials should be performed. In this work, Cu@ZnO composites were synthesized using a two-step procedure. ZnO crystalline nanostructured materials were prepared within minutes by a solvothermal microwave-assisted method. Deposition of copper nanoparticles on the surface of ZnO was conducted by reduction of Cu²⁺ in ethylene glycol (EG). Copper nanoparticles with different morphologies (needle-like and spheres) were deposited on the surface of ZnO. The antibacterial activity of Cu@ZnO composites was evaluated using E. coli and S. aureus as model organisms. The Minimal Inhibitory Concentration (MIC) and Minimal Bactericidal Concentration (MBC) were evaluated for Cu@ZnO composites under visible light radiation (VLR) and in the dark (D). The composites exhibit antibacterial activity under VLR at low concentrations: 250 μg/mL and 750 μg/mL for E. coli, and 250 μg/mL and 500 μg/mL for S. aureus. Copper nanoparticles exert antibacterial activity and can be used to inhibit the growth of microorganisms in the absence of irradiation of Cu@ZnO material. Better antibacterial activity of Cu@ZnO material was achieved under radiation, demonstrating the synergic activity of Cu and ZnO materials for disinfection. Toxicity of the material was assessed towards Daphnia magna (D. magna) and Lecane papuana (L. papuana). Composites exert toxicity at lower concentrations than ZnO, observing LC50 values for L. papuana of 79.30 ± 6.70 μg/mL, and 5.59 ± 0.46 μg/mL for ZnO and Cu@ZnO, respectively. For D. magna, a LC50 of 9.66 ± 1.22 μg/mL (Cu@ZnO) was observed. Although Cu@ZnO can be considered as potential candidate for the development of efficient antibacterial agents, its antibacterial activity is achieved at doses that can be harmful to aquatic invertebrates. Thus, its application should avoid its entry to aquatic environments.     

Acute renal failure in a patient with mantle cell lymphoma

Mantle cell lymphoma is a rare form of B‐cell lymphoma. We present a 54‐year‐old gentleman with mantle cell lymphoma. It was diagnosed based on the demonstration of B‐cell antigens CD20 and CD5. It was further confirmed by demonstration of overexpression of cyclin D1 on these atypical lymphocytes in the immunohistochemical staining. He also had acute renal failure and proteinuria. Renal biopsy revealed crescents and lymphomatous infiltration of tubulointerstitium. The presence of infiltrating cells with similar markers in both the lymph node and the kidney confirmed the infiltration of kidney with lymphomatous cells. Our present patient, after a thorough literature search, is found to be the second one with a glomerular lesion and tubulointerstitial infiltration by malignant lymphoma cells.     

An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects

Mathematical modeling of cerebral tumor growth is of great importance in clinics. It can help in understanding the physiology of tumor growth, future prognosis of tumor shape and volume, quantify tumor aggressiveness, and the response to therapy. This can be achieved at macroscopic level using medical imaging techniques (particularly, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)) and complex mathematical models which are either diffusive or biomechanical. We propose an optimized generic modeling framework that couples tumor diffusivity and infiltration with the induced mass effect. Tumor cell diffusivity and infiltration are captured using a modified reaction-diffusion model with logistic proliferation term. On the other hand, tumor mass effect is modeled using continuum mechanics formulation. In addition, we consider the treatment effects of both radiotherapy and chemotherapy. The efficacy of chemotherapy is included via an adaptively modified log-kill method to consider tissue heterogeneity while the efficacy of radiotherapy is considered using the linear quadratic model. Moreover, our model efficiently utilizes the diffusion tensor of the diffusion tensor imaging. Furthermore, we optimize the tumor growth parameters to be patient-specific using bio-inspired particle swarm optimization (PSO) algorithm. Our model is tested on an atlas and real MRI scans of 8 low grade gliomas subjects. Experimental results show that our model efficiently incorporates both treatment effects in the growth modelingprocess. In addition, simulated growths of our model have high accuracy in terms of Dice coefficient (average 87.1%) and Jaccard index (77.14%) when compared with the follow up scans (ground truth) and other models as well.     

Clinical Study of Mesenchymal Stem Cell Treatment for Acute Respiratory Distress Syndrome Induced by Epidemic Influenza A (H7N9) Infection: A Hint for COVID-19 Treatment

H7N9 viruses quickly spread between mammalian hosts and carry the risk of human-to-human transmission, as shown by the 2013 outbreak. Acute respiratory distress syndrome (ARDS), lung failure, and acute pneumonia are major lung diseases in H7N9 patients. Transplantation of mesenchymal stem cells (MSCs) is a promising choice for treating virus-induced pneumonia, and was used to treat H7N9-induced ARDS in 2013. The transplant of MSCs into patients with H7N9-induced ARDS was conducted at a single center through an open-label clinical trial. Based on the principles of voluntariness and informed consent, 44 patients with H7N9-induced ARDS were included as a control group, while 17 patients with H7N9-induced ARDS acted as an experimental group with allogeneic menstrual-blood-derived MSCs. It was notable that MSC transplantation significantly lowered the mortality of the experimental group, compared with the control group (17.6% died in the experimental group while 54.5% died in the control group). Furthermore, MSC transplantation did not result in harmful effects in the bodies of four of the patients who were part of the five-year follow-up period. Collectively, these results suggest that MSCs significantly improve the survival rate of H7N9-induced ARDS and provide a theoretical basis for the treatment of H7N9-induced ARDS in both preclinical research and clinical studies. Because H7N9 and the coronavirus disease 2019 (COVID-19) share similar complications (e.g., ARDS and lung failure) and corresponding multi-organ dysfunction, MSC-based therapy could be a possible alternative for treating COVID-19.     

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In this paper, the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning (DL) model, abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification, also known as FFEDL-ICH. The proposed FFEDL-ICH model has four stages namely, preprocessing, image segmentation, feature extraction, and classification. The input image is first preprocessed using the Gaussian Filtering (GF) technique to remove noise. Secondly, the Density-based Fuzzy C-Means (DFCM) algorithm is used to segment the images. Furthermore, the Fusion-based Feature Extraction model is implemented with handcrafted feature (Local Binary Patterns) and deep features (Residual Network-152) to extract useful features. Finally, Deep Neural Network (DNN) is implemented as a classification technique to differentiate multiple classes of ICH. The researchers, in the current study, used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance. The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance. For future researches, the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.     

基于代价敏感Faster R-CNN的脑CT影像出血诊断方法

脑出血是一种在患者颅内突然发生的重急症,常伴随有强烈的症状和较高致死率,基于脑CT影像对脑出血进行自动化快速诊断具有重要意义。其中,临床上有效应用的实现不仅要求诊断结果的准确性、诊断速度和结果解释能力,尤其要重视出血漏检情形。因此本文提出代价敏感的Faster R-CNN模型,通过自动调节模型中锚的训练样本比例以及在损失函数中引入衡量阳性样本重要性的超参数等方式,更多地关注阳性样本和漏检情形提升检测效果,最后通过定位的具体目标区域来诊断脑内出血情况。经多次实验选择性能最优的网络结构和合适的超参数,利用多项指标度量最终模型的检测和诊断效果。实验结果表明,代价敏感的Faster R-CNN方法能够从减少漏检的角度上更好地识别出血区域,进而提高不平衡代价下的脑出血诊断效果。     

SARS病疫的肆虐和对HVAC设计规范的思考

就新风量的设计标准 ,空气过滤要求与过滤器的设计选用 ,送风量与排风量的平衡 ,军团菌与喷淋式冷却塔 ,及制冷系统与制冷机房的安全设计与安全运行等 5个问题 ,对国家标准《采暖通风与空气调节设计规范》报批稿提出了应急修改建议 ,供有关主管部门研究考虑和同行讨论。     

Acute renal failure and chronic kidney disease following liver transplantation

Acute renal failure (ARF) and chronic kidney disease (CKD) are common complications after liver transplantation (LTx). The incidence of ARF post-LTx varies between 48% and 94%; 8% to 17% of patients require renal replacement therapy (RRT). The most common cause of ARF early after LTx is ischemic acute tubular necrosis, followed later by cyclosporine toxicity and sepsis. Preoperative serum creatinine >1.5 mg/dL and early hepatic allograft dysfunction are risk factors for the occurrence of postoperative ARF. Of patients with ARF due to the hepatorenal syndrome, approximately two-thirds will recover, although recovery may be delayed 3 months or longer after LTx. Mortality after LTx is affected modestly by the presence of ARF pretransplant (<2-fold increase), but increases markedly (up to 8-fold) in the face of ARF posttransplant. Mortality does not appear to be influenced by the mode of RRT used. The risk of CKD after LTx is approximately 18% at 5 years and increases to approximately 25% by 10 years after transplantation. Calcineurin inhibitor toxicity is the most common cause. Specific prognosticators for predicting CKD after LTx are presently lacking. The occurrence of CKD after LTx markedly impairs long-term survival.