The Management of Chronic Graft-Versus-Host Disease Skin Ulcers after Hematopoietic Stem Cell Transplantation: A Case Report.

ABSTRACT: The comprehensive management of a patient with chronic graft-versus-host disease skin ulcers after hematopoietic stem cell transplantation is challenging. This report describes the case of a 53-year-old woman who presented with ulcers on her right leg 140 weeks after a bone marrow transplant. The patient received wound assessment and management based on the Triangle of Wound Assessment and Wound Bed Preparation 2021, respectively. Hydrogel and antibacterial protease dressings were applied along with systemic oral administration of moxifloxacin hydrochloride (two capsules, two times daily) and JiXueGanPian tablets (classic Chinese herbal formula; two capsules, two times daily), hospital-community-home continuous care, and patient-centered education. Finally, after 133 days of nursing, the patient's wound was completely healed without complications or other skin issues. The use of hydrogel combined with the antibacterial protease dressing was a promising technique for handling this type of wound, enhanced by multidisciplinary collaboration. Of course, providing patients with education that focuses on prevention is necessary.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Perceptions and Life Experiences of Patients With Wound Healing by Secondary Intention After the Removal of Thoracic Drainage Tube: A Qualitative Study.

PURPOSE: The purpose of this study was to explore the perceptions and experiences of patients with wound healing by secondary intention after the removal of a thoracic drainage tube. DESIGN: A qualitative phenomenological study. SUBJECTS AND SETTING: After removal of the tube, patients who were attending a nursing clinic that provides WOC care to a population of around 1 million people in Suzhou, China, were invited to participate. METHODS: Semistructured interviews were digitally audio-recorded and transcribed verbatim. Analysis of data was performed using Colaizzi's 7-step thematic analysis. RESULTS: Three major themes emerged from the interviews, namely, emotional stress response, impaired social function, and increased disease burden. CONCLUSION: Patients with wound healing by secondary intention after the removal of the drainage tube perceived they experienced an emotional stress reaction accompanied by increased psychological and economic burden. They also experienced impaired social function. There is a critical need to develop health education plans for use during the pre- and postoperative periods to reduce emotional, social, and economic consequences associated with delayed wound healing.

Multiscale Mask R-CNN-Based Lung Tumor Detection Using PET Imaging.

Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of "computer-aided diagnosis" is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region-Based Convolutional Neural Network (Mask R-CNN)-based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.

Comparative transcriptome analysis of tobacco (Nicotiana tabacum) leaves to identify aroma compound-related genes expressed in different cultivated regions.

To identify genes that are differentially expressed in tobacco in response to environmental changes and to decipher the mechanisms by which aromatic carotenoids are formed in tobacco, an Agilent Tobacco Gene Expression microarray was adapted for transcriptome comparison of tobacco leaves derived from three cultivated regions of China, Kaiyang (KY), Weining (WN) and Tianzhu (TZ). A total of 1,005 genes were differentially expressed between leaves derived from KY and TZ, 733 between KY and WN, and 517 between TZ and WN. Genes that were upregulated in leaves from WN and TZ tended to be involved in secondary metabolism pathways, and included several carotenoid pathway genes, e.g., NtPYS, NtPDS, and NtLCYE, whereas those that were down-regulated tended to be involved in the response to temperature and light. The expression of 10 differentially expressed genes (DEGs) was evaluated by real-time quantitative polymerase chain reaction (qRT-PCR) and found to be consistent with the microarray data. Gene Ontology and MapMan analyses indicate that the genes that were differentially expressed among the three cultivated regions were associated with the light reaction of photosystem II, response to stimuli, and secondary metabolism. High-performance liquid chromatography (HPLC) analysis showed that leaves derived from KY had the lowest levels of lutein, ß-carotene, and neoxanthin, whereas the total carotenoid content in leaves from TZ was greatest, a finding that could well be explained by the expression patterns of DEGs in the carotenoid pathway. These results may help elucidate the molecular mechanisms underlying environmental adaptation and accumulation of aroma compounds in tobacco.

Anti-sine-cosine atom search optimization (ASCASO): a novel approach for parameter estimation of PV models.

Nowadays, solar power generation has gradually become a part of electric energy sharing. How to effectively enhance the energy conversion efficiency of solar cells and components has gradually emerged as a focal point of research. This paper presents a boosted atomic search optimization (ASO) with a new anti-sine-cosine mechanism (ASCASO) to realize the parameter estimation of photovoltaic (PV) models. The anti-sine-cosine mechanism is inspired by the update principle of sine cosine algorithm (SCA) and the mutation strategy of linear population size reduction adaptive differential evolution (LSHADE). The working principle of anti-sine-cosine mechanism is to utilize two mutation formulas containing arcsine and arccosine functions to further update the position of atoms. The introduction of anti-sine-cosine mechanism achieves the populations' random handover and promotes the neighbors' information communication. For better evaluation, the proposed ASCASO is devoted to estimate parameters of three PV models of R.T.C France, one Photowat-PWP201 PV module model, and two commercial polycrystalline PV panels including STM6-40/36 and STM6-120/36 with monocrystalline cells. The proposed ASCASO is compared with nine reported comparative algorithms to assess the performance. The results of parameter estimation for different PV models of various methods demonstrate that ASCASO performs more accurately and reliably than other reported comparative methods. Thus, ASCASO can be considered a highly effective approach for accurately estimating the parameters of PV models.

Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.

The widespread of highly infectious disease, i.e., COVID-19, raises serious concerns regarding public health, and poses significant threats to the economy and society. In this study, an efficient method based on deep learning, deep feature fusion classification network (DFFCNet), is proposed to improve the overall diagnosis accuracy of the disease. The method is divided into two modules, deep feature fusion module (DFFM) and multi-disease classification module (MDCM). DFFM combines the advantages of different networks for feature fusion and MDCM uses support vector machine (SVM) as a classifier to improve the classification performance. Meanwhile, the spatial attention (SA) module and the channel attention (CA) module are introduced into the network to improve the feature extraction capability of the network. In addition, the multiple-way data augmentation (MDA) is performed on the images of chest X-ray images (CXRs), to improve the diversity of samples. Similarly, the utilized Grad-CAM++ is to make the features more intuitive, and the deep learning model more interpretable. On testing of a collection of publicly available datasets, results from experimentation reveal that the proposed method achieves 99.89% accuracy in a triple classification of COVID-19, pneumonia, and health X-ray images, there by outperforming the eight state-of-the-art classification techniques.

Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution.

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.

Efficiently deep learning for monitoring Ipomoea cairica (L.) sweets in the wild.

Ipomoea cairica (L.) sweets are an invasive weed which has caused serious harm to the biodiversity and stability of the ecosystem. It is very important to accurately and rapidly identifying and monitoring Ipomoea cairica (L.) sweets in the wild for managements taking the necessary strategies to control the Ipomoea cairica (L.) sweets to rapidly grow in the wild. However, current approaches mainly depend on manual identification, which result in high cost and low efficiency. Satellite and manned aircraft are feasible assisting approaches, but the quality of the images collected by them is not well since the ground sampling resolution is low and cloud exists. In this study, we present a novel identifying and monitoring framework and method for Ipomoea cairica (L.) sweets based on unmanned aerial vehicle (UAV) and artificial intelligence (AI). In the proposed framework, we low-costly collected the images with 8256 × 5504 pixels of the monitoring area by the UAV and the collected images are split into more small sub-images with 224 × 224 pixels for identifying model. For identifying Ipomoea cairica (L.) sweets, we also proposed a novel deep convolutional neural network which includes 12 layers. Finally, the Ipomoea cairica (L.) sweets can be efficiently monitored by painting the area containing Ipomoea cairica (L.) sweets. In our experiments, we collected 100 raw images and generated 288000 samples, and made comparison with LeNet, AlexNet, GoogleNet, VGG and ResNet for validating our framework and model. The experimental results show the proposed method is excellent, the accuracy is 93.00% and the time cost is 7.439 s. The proposed method can achieve to an efficient balance between high accuracy and low complexity. Our method is more suitable for the identification of Ipomoea cairica (L.) sweets in the wild than other methods.

Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients.

Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC-MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC-MS provides the original data that efficiently identified the paraquat-poisoned patients. According to the high dimensionality of the original data, in the second stage, the chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature sets to improve the accuracy of identification. Finally, the extreme learning machine (ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed method, four measures were used in our experiments and comparisons were made with six other methods. The PQ-poisoned patients and robust volunteers can be well identified by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%, 94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients.

Developing a new intelligent system for the diagnosis of tuberculous pleural effusion.

BACKGROUND AND OBJECTIVE: In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely. METHODS: For this study, data of 140 patients whose clinical signs, routine blood test results, blood biochemistry markers, pleural fluid cell type and count, and pleural fluid biochemical tests' results were prospectively collected into a database. An Artificial intelligence based diagnostic model, which employs moth flame optimization based support vector machine with feature selection (FS-MFO-SVM), is constructed to predict the diagnosis of TPE. RESULTS: The optimal model results in an average of 95% accuracy (ACC), 0.9564 the area under the receiver operating characteristic curve (AUC), 93.35% sensitivity, and 97.57% specificity for FS-MFO-SVM. CONCLUSIONS: The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples. Therefore, the proposed model can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies.

Statistical inference for community detection in signed networks.

The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community. In this study, we present a statistical inference method for community detection in signed networks, in which a probabilistic model is proposed to model signed networks and the expectation-maximization-based parameter estimation method is deduced to find communities in signed networks. In addition, to efficiently analyze signed networks without any a priori information, a model selection criterion is also proposed to automatically determine the number of communities. In our experiments, the proposed method is tested in the synthetic and real-word signed networks and compared with current methods. The experimental results show the proposed method can more efficiently and accurately find the communities in signed networks than current methods. Notably, the proposed method is a mathematically principled method.

An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach.

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

Co-circulation of multiple hemorrhagic fever diseases with distinct clinical characteristics in Dandong, China.

Hemorrhagic fevers (HF) caused by viruses and bacteria are a major public health problem in China and characterized by variable clinical manifestations, such that it is often difficult to achieve accurate diagnosis and treatment. The causes of HF in 85 patients admitted to Dandong hospital, China, between 2011-2012 were determined by serological and PCR tests. Of these, 34 patients were diagnosed with Huaiyangshan hemorrhagic fever (HYSHF), 34 with Hemorrhagic Fever with Renal Syndrome (HFRS), one with murine typhus, and one with scrub typhus. Etiologic agents could not be determined in the 15 remaining patients. Phylogenetic analyses of recovered bacterial and viral sequences revealed that the causative infectious agents were closely related to those described in other geographical regions. As these diseases have no distinctive clinical features in their early stage, only 13 patients were initially accurately diagnosed. The distinctive clinical features of HFRS and HYSHF developed during disease progression. Enlarged lymph nodes, cough, sputum, and diarrhea were more common in HYSHF patients, while more HFRS cases presented with headache, sore throat, oliguria, percussion pain kidney area, and petechiae. Additionally, HYSHF patients displayed significantly lower levels of white blood cells (WBC), higher levels of creations kinase (CK) and alanine aminotransferase (ALT), while HFRS patients presented with an elevation of blood urea nitrogen (BUN) and creatinine (CREA). These clinical features will assist in the accurate diagnosis of both HYSHF and HFRS. Overall, our data reveal the complexity of pathogens causing HFs in a single Chinese hospital, and highlight the need for accurate early diagnosis and a better understanding of their distinctive clinical features.

[Quercetin affects leptin and its receptor in human gastric cancer MGC-803 cells and JAK-STAT pathway].

AIM: Quercetin affects the expressions of leptin and its receptor in human gastric cancer MGC-803 cells and JAK-STAT pathway. METHODS: The cultured MGC-803 cells were divided into three groups: CONTROL GROUP: the cultured cells without quercetin, and Quercetin group: the cultured cells with quercetin(40 µmol/L), and AG490group: the cultured cells with AG490(40 µmol/L)The expressions of Leptin, Leptin receptor and P-STAT3 were detected in protein level by immunocytochemical and Western bloting method respectively. The expressions of Leptin, Leptin receptor were detected in mRNA level by RT-PCR method. MGC-803 cell cycle was arrest by flow cytometry (FCM); MGC-803 cell apoptosis ratio by apoptotic marker An-necxinV. RESULTS: The protein expression of Leptin, Leptin receptor, P-STAT3 and the the mRNA expression of Leptin and Leptin receptor were significantly increased (P<0.05), compared with the control group.There was the rectilinear correlation relationship not only between Leptin and P-STAT3 protein(r=0.741, P<0.05) but also between Leptin receptor and P-STAT3 protein(r=0.693, P<0.05). FCM analysis showed that quercetin arrested MGC-803 cells at the G2/M phase, The ratio of apoptotic and necrosic cells increased with added quercetin concentration. CONCLUSION: Quercetin could inhibit the Proliferation of MGC-803 cells. It is probably relevant to the down-regulation the expressions of Leptin and Leptin receptor protein, Leptin mRNA and Leptin receptor mRNA by JAK-STAT pathway.

[Effects of quercetin on the expression of VEGF-C and VEGFR-3 in human cancer MGC-803 cells].

AIM: To investigate the mechanism of quercetin on the inhibition of the lymphatic metastasis in human gastric cancer cells MGC-803. METHODS: Cells were divided into the control group and the quercetin (Que)-treated group. Immunohistochemistry and RT-PCR were used to detect the expression of vascular endothelial growth factor C (VEGF-C) and VEGFR-3 of human gastric cancer cells MGC-803 in response to Que. RESULTS: Que significantly decreased the expression of VEGF-C and VEGFR-3 at 40 mumol/L compared with the control group after 48 h (P<0.01). CONCLUSION: Que can down-regulate the expression of VEGF-C and VEGFR-3 in human gastric cancer cells MGC-803.

[Quercetin inhibits growth and induces apoptosis of human gastric carcinoma cells].

AIM: To study the effect of quercetin on the growth and apoptosis of human gastric carcinoma cell line MGC-803. METHODS: The measurement of inhibitory rate and apoptotic index(AI) of quercetin were done by MTT assay and TUNEL assay. The positive expression rate of P53, C-myc and P16 were detected by immunocytochemical staining. RESULTS: Quercetin at concentrations ranging from 40 mumol/L to 100 mumol/L significantly inhibited the proliferation of MGC-803 cells in a dose- and time-dependent manner (P<0.01). TUNEL assay indicated that the number of apoptotic cells in quercetin-treated group was greater than that in the control group (P<0.01). Expression of P53 and C-myc protein decreased following quercetin induction in a dose-dependent manner, whereas P16 expression increased significantly compared with that of the control group (P<0.01). CONCLUSION: Quercetin can inhibit the growth and induce apoptosis of MGC-803 cells in a dose- and time-dependent manner. Its mechanisms may be relevant to the down-regulation of P53 and C-myc protein expression as well as up-regulation of P16 expression.