Search results for: lung disease classification

Authors: Stephanie Newman

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Niemann-Pick Type C disease is a rare, usually fatal lysosomal storage disorder. Although clinically characterized by progressive neurodegeneration, there is also evidence of altered innate immune responses such as neuroinflammation that promote disease progression. We have initiated an investigation into whether phagocytosis, an important innate immune activity and the process by which particles are ingested is defective in NPC. Using an in vitro assay, we have shown that NPC macrophages have a deficiency in the phagocytosis of different particles. We plan to investigate the mechanistic basis for impaired phagocytosis, the contribution that this deficiency makes to disease pathology, and whether therapies that have shown in vivo benefit are able to restore phagocytic activity.

Keywords: Niemann Pick Disease C, phagocytosis, innate immunity, lysosomal storage disorder

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Faruk Emir, Simel Ayyıldız, Cem Şahin

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Behçet’s disease or Behçet’s syndrome is a chronic and multi-systemic inflammatory disease of unknown cause. This syndrome often presents with mucous membrane ulceration and ocular problems. As a systemic disease Behcet includes triple-symptom complex of recurrent oral aphthous ulcers, genital ulcers, and uveitis. Nearly all patients present with some form of painful oral mucocutaneous ulcerations in the form of aphthous ulcers. The aim of the treatment plan for Behçet’s Disease patients is to eliminate oral problems and increase the patient comfort.This clinical report represents the prosthodontic rehabilitation of Behcet’s disease patients mandibular total edentulism with the use of implant supported prosthesis that planned on custom abutments and bar retainers via CAD/CAM technology and patient satisfaction has been achieved in function and aesthetics.

Keywords: Behçet’s disease, CAD/CAM, custom-made manufacturing, titanium milled bar retainer

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

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The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Muhammad Asim Nazir

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The investigations reported in this manuscript were carried on the screening of one hundred and seventy-eight chickpea germplasm lines/cultivars against wilt disease, caused by Fusarium oxysporum f. sp. ciceris. The screening was conducted in vivo (field) conditions. The field screening was accompanied with the study of some epidemiological factors affecting the occurrence and severity of the disease. Among the epidemiological factors maximum temperature range (28-40°C), minimum temperature range (12-24°C), relative humidity (19-44%), soil temperature (26-41°C) and soil moisture range (19-34°C) was studied for affecting the disease incidence/severity. The results revealed that air temperature was positively correlated with diseases. Soil temperature data revealed that in all cultivars disease incidence was maximum as 39°C. Most of the plants show 40-50% disease incidence. Disease incidence decreased at 33.5°C. The result of correlation of relative humidity of air and wilt incidence revealed that all cultivars/lines were negatively correlated with relative humidity. With increasing relative humidity wilt incidence decreased and vice versa.

Keywords: chickpea, epidemiological, screening, disease

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Authors: Ahmed Abdallatif

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Background: Patients with malignant germ cell tumors have age distribution in two peaks, with the first one during infancy and the second after the onset of puberty. Gonadal germ cell tumors are the most common malignant ovarian tumor in females aged below twenty years. Sacrococcygeal and retroperitoneal abdominal tumors usually presents in a large size before the onset of symptoms. Methods: Patients with pediatric germ cell tumors presenting to Children’s Cancer Hospital Egypt and National Cancer Institute Egypt from January 2008 to June 2011 Patients underwent stratification according to risk into low, intermediate and high risk groups according to children oncology group classification. Objectives: Assessment of the clinicopathologic features of all cases of pediatric germ cell tumors and classification of malignant cases according to their stage, and the primary site to low, intermediate and high risk patients. Evaluation of surgical management in each group of patients focusing on surgical approach, the extent of surgical resection according to each site, ability to achieve complete surgical resection and perioperative complications. Finally, determination of the three years overall and disease-free survival in different groups and the relation to different prognostic factors including the extent of surgical resection. Results: Out of 131 cases surgically explored only 26 cases had re exploration with 8 cases explored for residual disease 9 cases for remote recurrence or metastatic disease and the other 9 cases for other complications. Patients with low risk kept under follow up after surgery, out of those of low risk group (48 patients) only 8 patients (16.5%) shifted to intermediate risk. There were 20 patients (14.6%) diagnosed as intermediate risk received 3 cycles of compressed (Cisplatin, Etoposide and Bleomycin) and all high risk group patients 69patients (50.4%) received chemotherapy. Stage of disease was strongly and significantly related to overall survival with a poorer survival in late stages (stage IV) as compared to earlier stages. Conclusion: Overall survival rate at 3 three years was (76.7% ± 5.4, 3) years EFS was (77.8 % ±4.0), however 3 years DFS was much better (89.8 ± 3.4) in whole study group with ovarian tumors had significantly higher Overall survival (90% ± 5.1). Event Free Survival analysis showed that Male gender was 3 times likely to have bad events than females. Patients who underwent incomplete resection were 4 times more than patients with complete resection to have bad events. Disease free survival analysis showed that Patients who underwent incomplete surgery were 18.8 times liable for recurrence compared to those who underwent complete surgery, and patients who were exposed to re-excision were 21 times more prone to recurrence compared to other patients.

Keywords: extragonadal, germ cell tumors, gonadal, pediatric

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Authors: Vandana Mohan, Ashwani Koul

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Aim: To evaluate the potential of Aqueous Tinospora cordifolia stem extract (Aq.Tc) and Arabinogalactan (AG) on pulmonary carcinogenesis and associated tumor markers. Background: Lung cancer is one of the most frequent malignancy with high mortality rate due to limitation of early detection resulting in low cure rates. Current research effort focuses on identifying some blood-based biomarkers like CEA, ctDNA and LDH which may have potential to detect cancer at an early stage, evaluation of therapeutic response and its recurrence. Medicinal plants and their active components have been widely investigated for their anticancer potentials. Aqueous preparation of T. Cordifolia extract is enriched in the polysaccharide fraction i.e., AG when compared with other types of extract. Moreover, reports are available of polysaccharide fraction of T. Cordifolia in in vitro lung cancer models which showed profound anti-metastatic activity against these cell lines. However, not much has been explored about its effect in in vivo lung cancer models and the underlying mechanism involved. Experimental Design: Mice were randomly segregated into six groups. Group I animals served as control. Group II animals were administered with Aq. Tc extract (200 mg/kg b.w.) p.o.on the alternate days. Group III animals were fed with AG (7.5 mg/kg b.w.) p.o. on the alternate days (thrice a week). Group IV animals were installed with Benzo(a)pyrene (50 mg/kg b.w.), i.p. twice within an interval of two weeks. Group V animals received Aq. Tc extract as in group II along with it B(a)P was installed after two weeks of Aq. Tc administration following the same protocol as for group IV. Group VI animals received AG as in group III along with it B(a)P was installed after two weeks of AG administration. Results: Administration of B(a)P to mice resulted in increased tumor incidence, multiplicity and pulmonary somatic index with concomitant increase in serum/plasma markers like CEA, ctDNA, LDH and TNF-α.Aq.Tc and AG supplementation significantly attenuated these alterations at different stages of tumorigenesis thereby showing potent anti-cancer effect in lung cancer. A pronounced decrease in serum/plasma markers were observed in animals treated with Aq.Tc as compared to those fed with AG. Also, extensive hyperproliferation of alveolar epithelium was prominent in B(a)P induced lung tumors. However, treatment of Aq.Tc and AG to lung tumor bearing mice exhibited reduced alveolar damage evident from decreased number of hyperchromatic irregular nuclei. A direct correlation between the concentration of tumor markers and the intensity of lung cancer was observed in animals bearing cancer co-treated with Aq.Tc and AG. Conclusion: These findings substantiate the chemopreventive potential of Aq.Tc and AG against lung tumorigenesis. Interestingly, Aq.Tc was found to be more effective in modulating the cancer as reflected by various observations which may be attributed to the synergism offered by various components of Aq.Tc. Further studies are in progress to understand the underlined mechanism in inhibiting lung tumorigenesis by Aq.Tc and AG.

Keywords: Arabinogalactan, Benzo(a)pyrene B(a)P, carcinoembryonic antigen (CEA), circulating tumor DNA (ctDNA), lactate dehydrogenase (LDH), Tinospora cordifolia

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Cheng-Cao Sun, Shu-Jun Li, Cuili Yang, Yongyong Xi, Liang Wang, Feng Zhang, De-Jia Li

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Hsa-miRNA-326 (miR-326) has recently been discovered having anticancer efficacy in different organs. However, the role of miR-326 on non-small cell lung cancer (NSCLC) is still ambiguous. In this study, we investigated the role of miR-326 on the development of NSCLC. The results indicated that miR-326 was significantly down-regulated in primary tumor tissues and very low levels were found in NSCLC cell lines. Ectopic expression of miR-326 in NSCLC cell lines significantly suppressed cell growth as evidenced by cell viability assay, colony formation assay and BrdU staining, through inhibition of cyclin D1, cyclin D2, CDK4, and up-regulation of p57(Kip2) and p21(Waf1/Cip1). In addition, miR-326 induced apoptosis, as indicated by concomitantly with up-regulation of key apoptosis protein cleaved caspase-3, and down-regulation of anti-apoptosis protein Bcl2. Moreover, miR-326 inhibited cellular migration and invasiveness through inhibition of matrix metalloproteinases (MMP)-7 and MMP-9. Further, oncogene CCND1 was revealed to be a putative target of miR-326, which was inversely correlated with miR-326 expression in NSCLC. Taken together, our results demonstrated that miR-326 played a pivotal role on NSCLC through inhibiting cell proliferation, migration, invasion, and promoting apoptosis by targeting oncogenic CCND1.

Keywords: hsa-miRNA-326 (miR-326), cyclin D1, non-small cell lung cancer (NSCLC), proliferation, apoptosis

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Authors: Pintusorn Hansakul, Ruchilak Rattarom, Arunporn Itharat

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Background: Benjakul, a Thai traditional herbal formulation, comprises of five plants: Piper chaba, Piper sarmentosum, Piper interruptum, Plumbago indica, and Zingiber officinale. It has been widely used to treat cancer patients in the context of folk medicine in Thailand. This study aimed to investigate the cytotoxic effect of the ethanol extract of Benjakul against three non-small cell lung cancer (NSCLC) cell lines (NCI-H226, A549, COR-L23), small cell lung cancer (SCLC) cell line NCI-H1688 and normal lung fibroblast cell line MRC-5. The study further examined the molecular mechanisms underlying its cytotoxicity via induction of apoptosis in NCI-H226 cells. Methods: The cytotoxic effect of Benjakul was determined by SRB assay. The effect of Benjakul on cell cycle distribution was assessed by flow cytometric analysis. The apoptotic effects of Benjakul were determined by sub-G1 quantitation and Annexin V-FITC/PI flow cytometric analyses as well as by changes in caspase-3 activity. Results: Benjakul exerted potent cytotoxicity on NCI-H226 and A549 cells but lower cytotoxicity on COR-L23 and NCI-H1688 cells without any cytotoxic effect on normal cells. Molecular studies showed that Benjakul extract induced G2/M phase arrest in human NCI-H226 cells in a dose-dependent manner. The highest concentration of Benjakul (150 μg/ml) led to the highest increase in the G2/M population at 12 h, followed by the highest increase in the sub-G1 population (apoptotic cells) at 60 h. Benjakul extract also induced early apoptosis (AnnexinV +/PI−) in NCI-H226 cells in a dose- and time- dependent manner. Moreover, treatment with 150 μg/ml Benjakul extract for 36 h markedly increased caspase-3 activity by 3.5-fold, and pretreatment with the general caspase inhibitor z-VAD-fmk completely abolished such activity. Conclusions: This study reveals for the first time the regulation of apoptosis in human lung cancer NCI-H226 cells through caspase-dependent mechanism by Benjakul extract.

Keywords: apoptosis, Benjakul, caspase activation, cytotoxicity

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Yadav Shyamjeet Maniram

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Despite reports of more than seventy million population of India affected by rare diseases, it rarely figured on the agenda of the Indian scientist and policymakers. Hitherto ignored, a fresh initiative is being attempted to establish the first national registry for rare diseases. Though there are registries for rare diseases, established by the clinicians and patient advocacy groups, they are isolated, scattered and lacks information sharing mechanism. It is the first time that there is an effort from the government of India to make an initiative on the rare disease registries, which would be more formal and systemic in nature. Since there is lack of epidemiological evidence for the rare disease in India, it is interesting to note how rare disease policy is being attempted in the vacuum of evidence required for the policy process. The objective of this study is to analyse rare disease registry creation and implementation from the parameters of evolutionary policy perspective in the absence of evidence for the policy process. This study will be exploratory and qualitative in nature, primarily based on the interviews of stakeholders involved in the rare disease registry creation and implementation. Some secondary data will include various documents related to rare disease registry. The expected outcome of this study would be on the role of stakeholders in the generation of evidence for the rare disease registry creation and implementation. This study will also try to capture negotiations and deliberations on the ethical issues in terms of data collection, preservation, and protection.

Keywords: evolutionary policy perspective, evidence for policy, rare disease policy, rare disease in India

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Diana Islam, Juan Fang, Vito Fanelli, Bing Han, Julie Khang, Jianfeng Wu, Arthur S. Slutsky, Haibo Zhang

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Introduction: MSC delivery in preclinical models of ARDS has demonstrated significant improvements in lung function and recovery from acute injury. However, the role of MSC delivery in ARDS associated pulmonary fibrosis is not well understood. Some animal studies using bleomycin, asbestos, and silica-induced pulmonary fibrosis show that MSC delivery can suppress fibrosis. While other animal studies using radiation induced pulmonary fibrosis, liver, and kidney fibrosis models show that MSC delivery can contribute to fibrosis. Hypothesis: The beneficial and deleterious effects of MSC in ARDS are modulated by the lung microenvironment at the time of MSC delivery. Methods: To induce ARDS a two-hit mouse model of Hydrochloric acid (HCl) aspiration (day 0) and mechanical ventilation (MV) (day 2) was used. HCl and injurious MV generated fibrosis within 14-28 days. 0.5x106 mouse MSCs were delivered (via both intratracheal and intravenous routes) either in the active inflammatory phase (day 2) or during the remodeling phase (day 14) of ARDS (mouse fibroblasts or PBS used as a control). Lung injury accessed using inflammation score and elastance measurement. Pulmonary fibrosis was accessed using histological score, tissue collagen level, and collagen expression. In addition alveolar epithelial (E) and mesenchymal (M) marker expression profile was also measured. All measurements were taken at day 2, 14, and 28. Results: MSC delivery 2 days after HCl exacerbated lung injury and fibrosis compared to HCl alone, while the day 14 delivery showed protective effects. However in the absence of HCl, MSC significantly reduced the injurious MV-induced fibrosis. HCl injury suppressed E markers and up-regulated M markers. MSC delivery 2 days after HCl further amplified M marker expression, indicating their role in myofibroblast proliferation/activation. While with 14-day delivery E marker up-regulation was observed indicating their role in epithelial restoration. Conclusions: Early MSC delivery can be protective of injurious MV. Late MSC delivery during repair phase may also aid in recovery. However, early MSC delivery during the exudative inflammatory phase of HCl-induced ARDS can result in pro-fibrotic profiles. It is critical to understand the interaction between MSC and the lung microenvironment before MSC-based therapies are utilized for ARDS.

Keywords: acute respiratory distress syndrome (ARDS), mesenchymal stem cells (MSC), hydrochloric acid (HCl), mechanical ventilation (MV)

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Authors: Laura Gleason, Sahithi Talasila, Lauren Banner, Ladan Afifi, Neda Nikbakht

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Primary cutaneous anaplastic large cell lymphoma (pcALCL) accounts for 9% of all cutaneous T-cell lymphomas. pcALCL is classically characterized as a solitary papulonodule that often enlarges, ulcerates, and can be locally destructive, but overall exhibits an indolent course with overall 5-year survival estimated to be 90%. Distinguishing pcALCL from systemic ALCL (sALCL) is essential as sALCL confers a poorer prognosis with average 5-year survival being 40-50%. Although extremely rare, there have been several cases of ALK-positive ALCL diagnosed on skin biopsy without evidence of systemic involvement, which poses several challenges in the classification, prognostication, treatment, and follow-up of these patients. Objectives: We present a case of cutaneous ALK-positive ALCL without evidence of systemic involvement, and a narrative review of the literature to further characterize that ALK-positive ALCL limited to the skin is a distinct variant with a unique presentation, history, and prognosis. A 30-year-old woman presented for evaluation of an erythematous-violaceous papule present on her right chest for two months. With the development of multifocal disease and persistent lymphadenopathy, a bone marrow biopsy and lymph node excisional biopsy were performed to assess for systemic disease. Both biopsies were unrevealing. The patient was counseled on pursuing systemic therapy consisting of Brentuximab, Cyclophosphamide, Doxorubicin, and Prednisone given the concern for sALCL. Apropos of the patient we searched for clinically evident, cutaneous ALK-positive ALCL cases, with and without systemic involvement, in the English literature. Risk factors, such as tumor location, number, size, ALK localization, ALK translocations, and recurrence, were evaluated in cases of cutaneous ALK-positive ALCL. The majority of patients with cutaneous ALK-positive ALCL did not progress to systemic disease. The majority of cases that progressed to systemic disease in adults had recurring skin lesions and cytoplasmic localization of ALK. ALK translocations did not influence disease progression. Mean time to disease progression was 16.7 months, and significant mortality (50%) was observed in those cases that progressed to systemic disease. Pediatric cases did not exhibit a trend similar to adult cases. In both the adult and pediatric cases, a subset of cutaneous-limited ALK-positive ALCL were treated with chemotherapy. All cases treated with chemotherapy did not progress to systemic disease. Apropos of an ALK-positive ALCL patient with clinical cutaneous limited disease in the histologic presence of systemic markers, we discussed the literature data, highlighting the crucial issues related to developing a clinical strategy to approach this rare subtype of ALCL. Physicians need to be aware of the overall spectrum of ALCL, including cutaneous limited disease, systemic disease, disease with NPM-ALK translocation, disease with ALK and EMA positivity, and disease with skin recurrence.

Keywords: anaplastic large cell lymphoma, systemic, cutaneous, anaplastic lymphoma kinase, ALK, ALCL, sALCL, pcALCL, cALCL

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Chi V. Phan, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le

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Wilson’s disease is an autosomal recessive disorder of copper metabolism, which is caused by mutation in copper- transporting P-type ATPase (ATP7B). The mechanism of this disease is a failure of hepatic excretion of copper to the bile, and it leads to copper deposits in the liver and other organs. Most clinical symptoms of Wilson’s disease can present as liver disease and/or neurologic disease. Objective: The goal of the study is prenatal diagnosis for pregnant women at high risk of Wilson’s disease in Northern Vietnam. Material and method: Three probands with clinically diagnosed liver disease were detected in the mutations of 21 exons and exon-intron boundaries of the ATP7B gene by direct Sanger-sequencing. Prenatal diagnoses were performed by amniotic fluid sampling from pregnant women in the 16th-18th weeks of pregnancy after the genotypes of parents with the probands were identified. Result: A total of three different mutations of the probands, including of S105*, P1052L, P1273G, were detected. Among three fetuses which underwent prenatal genetic testing, one fetus was homozygote; two fetuses were carriers. Conclusion: Genetic testing provided a useful method for prenatal diagnosis, and is a basis for genetic counseling.

Keywords: ATP7B gene, genetic testing, prenatal diagnosis, pedigree, Wilson disease

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Authors: Abdelkhalek Bakkari

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Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

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Authors: Elif Erdem, Nalan Kaya, Gonca Ozan, Durrin Ozlem Dabak, Enver Ozan

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This study was carried out to determine the histological and biochemical changes in the lungs of the rat pups exposed to tobacco smoke during pregnancy period and to investigate the protective effects of alpha lipoic acid, which is administered during pregnancy, on these changes. In our study, 24 six-week old Spraque-Dawley female rats weighing 160 ± 10 g were used (n:7). Rats were randomly divided into four equal groups: group I (control), group II (tobacco smoke), group III (tobacco smoke + alpha lipoic acid) and group IV (alpha lipoic acid). Rats in the group II, group III were exposed to tobacco smoke twice a day for one hour starting from eight weeks before mating and during pregnancy. In addition to tobacco smoke, 20 mg/kg of alpha lipoic acid was administered via oral gavage to the rats in the group III. Only alpha lipoic acid was administered to the rats in the group IV. Once after the delivery, all administrations were stopped. On the 7 and 21th days, the seven pups of all groups were decapitated. A portion of the lung was taken and stained with HE, PAS and Masson. In addition to immunohistochemical staining of surfactant protein A, vascular endothelial growth factor, caspase-3, TUNEL method was also used to determine apoptosis. Biochemical analyzes were performed with some part of the lung tissue specimens. In the histological evaluations performed under light microscopy, inflammatory cell increase, hemorrhagic areas, edema, interalveolar septal thickening, alveolar numbers decrease, degeneration of some bronchi and bronchial epithelium, epithelial cells that were fallen into the lumen and hyaline membrane formation were observed in tobacco smoke group. These findings were ameliorated in tobacco smoke + ALA group. Hyaline membrane formation was not detected in this group. The TUNEL positive cell numbers a significant increase was detected in the tobacco smoke group, whereas a significant decrease was detected in the tobacco smoke + ALA group. In terms of the immunoreactivity of both SP-A and VEGF, a significant decrease was observed in the tobacco smoke group, and a significant increase was observed in the tobacco smoke + ALA group. Regarding the immunoreactivity of caspase-3, there was a significant increase in the group of tobacco smoke and a significant decrease in the group of tobacco smoke + ALA. The malondialdehyde levels were determined to be significantly increased in the tobacco smoke group, and a significant decreased in the tobacco smoke + ALA. Glutathione and superoxide dismutase enzyme activities showed a significant decrease in the group of tobacco smoke and a significant increase in the tobacco smoke + ALA group. In conclusion, we suggest that the exposure to tobacco smoke during pregnancy leads to morphological, histopathological and functional changes on lung development by causing oxidative damage in lung tissues of neonatal rats and the maternal use of alpha lipoic acid can provide a protective effect on the neonatal lung development against this oxidative stress originating from tobacco smoke.

Keywords: alpha lipoic acid, lung, neonate, tobacco smoke, pregnancy

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Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

Procedia PDF Downloads 51

Authors: M. Safa, A. Sabet, S. MacGillivray, M. Davidson, K. Kaczmarczyk, C. T. Haas, G. E. Gibson, D. Rayside

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To address construction project requirements and specifications, scholars and practitioners need to establish a taxonomy according to a scheme that best fits their need. While existing characterization methods are continuously being improved, new ones are devised to cover project properties which have not been previously addressed. One such method, the Project Definition Rating Index (PDRI), has received limited consideration strictly as a classification scheme. Developed by the Construction Industry Institute (CII) in 1996, the PDRI has been refined over the last two decades as a method for evaluating a project's scope definition completeness during front-end planning (FEP). The main contribution of this study is a review of practical project classification methods, and a discussion of how PDRI can be used to classify projects based on their readiness in the FEP phase. The proposed model has been applied to 59 construction projects in Ontario, and the results are discussed.

Keywords: project classification, project definition rating index (PDRI), risk, project goals alignment

Procedia PDF Downloads 653

Authors: Ouafae Baida, Najma Hamzaoui, Maha Akbib, Abdelfettah Sedqui, Abdelouahid Lyhyaoui

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Numerous scientific works present various methods to analyze the data for several domains, specially the comparison of classifications. In our recent work, we presented a new approach to help the user choose the best classification method from the results obtained by every method, by basing itself on the distances between the trees of classification. The result of our approach was in the form of a dendrogram contains methods as a succession of connections. This approach is much needed in phylogeny analysis. This discipline is intended to analyze the sequences of biological macro molecules for information on the evolutionary history of living beings, including their relationship. The product of phylogeny analysis is a phylogenetic tree. In this paper, we recommend the use of a new method of construction the phylogenetic tree based on comparison of different classifications obtained by different molecular genes.

Keywords: hierarchical classification, classification methods, structure of tree, genes, phylogenetic analysis

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: power spectral density, 3D EEG model, brain balancing, kNN

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Authors: Liudmila Garzanova, Lidia Ananyeva, Olga Koneva, Olga Ovsyannikova, Oxana Desinova, Mayya Starovoytova, Rushana Shayahmetova, Anna Khelkovskaya-Sergeeva

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Objectives. There is very few data on changes of the musculoskeletal manifestations (artritis, arthralgia, muscle weakness, etc.) in systemic sclerosis (SSc) on rituximab (RTX) therapy. The aim of our study was to assess the severity of the musculoskeletal involvement in SSc patients (pts) and its changes during RTX therapy. Methods. Our study included 103 pts with SSc. The mean followup period was 12.6±10.7 months. The mean age was 47±12.9 years, female-87 pts (84%), the diffuse cutaneous subset of the disease had 55 pts (53%). The mean disease duration was 6.2±5.5 years. All pts had interstitial lung disease (ILD) and were positive for ANA, 67% of them were positive for antitopoisomerase-1. All patients received prednisolone at a dose of 11.3±4.5 mg/day, immunosuppressants at inclusion received 47% of them. Pts received RTX due to the ineffectiveness of previous therapy for ILD. The cumulative mean dose of RTX was 1.7±0.6 grams. Arthritis was observed in 22 pts (21%), arthralgias in 47 pts (46%). Muscle weakness was observed in 17 pts (17%). Tendon friction rubs was established in 7 pts (7%). The results at baseline and at the end of the follow up are presented in the form of mean values. Results. There was an improvement of all outcome parameters and musculoskeletal manifestations on RTX therapy. There was a decrease in the number of pts with arthritis from 22 (21%) to 10 (9%), a decrease in the number of pts with arthralgias from 47 (46%) to 31 (30%). The number of pts with muscle weakness decreased from 17 (17%) to 7 (7%). The number of pts with tendon friction rubs decreased from 7 (7%) to 3 (3%). The creatine phosphokinase decreased from 365.5±186 to 70.8±50.4 (p=0.00006). The C-reactive protein (CRP) decreased from 23.2±31.3 to 8.62±7.4 (p=0.001). The dose of prednisolone was reduced from 11.3±4.5 to 9.8±3.5 mg/day (p=0.0004). Conclusion. In our study, musculoskeletal involvement was detected in almost half of the patients with SSc-ILD. There was an improvement of musculoskeletal manifestations despite a small cumulative dose of RTX. We also managed to reduce the dose of glucocorticosteroids. The improvement of musculoskeletal manifestations was accompanied by a decrease in laboratory parameters - creatine phosphokinase and CRP. RTX is effective option for treatment of musculoskeletal manifestations in SSc.

Keywords: arthritis, musculoskeletal involvement, systemic sclerosis, rituximab

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Authors: M. J. Falade

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Field experiment was conducted to compare the efficacy of hot water extracts of three plants (Ricinus communis, Jatropha gossypifolia and Datura stramonium) with benomyl in the control of cowpea anthracnose disease. Three concentrations of the extracts (65, 50 and 30%) were used in the study. Result from the experiment shows that all the extracts at the tested concentration reduced the incidence and severity of the disease. D. stramonium at 65% concentration compares favourably with that of benomyl fungicide in reducing incidence and severity of infection. At 65% concentration of D. stramonium, incidence of the disease was 22% on pooled mean basis, and this was not significantly different from that of benomyl (21%). Similarly, the percentage of normal seeds recorded at this same concentration of the extract was 85% and was not significantly different from that of benomyl (86%). In terms of disease severity trace infections were observed on the cowpea plants at this concentration of the extract and that of benomyl. However, at lower concentrations of all the extracts, significant variations were observed on incidence of disease and percentage of normal seeds such that values obtained from use of benomyl were higher than those obtained from the use of the extracts. The study, therefore, shows that extracts of these indigenous plants can be used as a substitute for the benomyl fungicide in the management of anthracnose disease.

Keywords: benomyl, C. lindemuthianum, disease incidence, disease severity

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Authors: Shalini Tripathi, Pardeep Kumar

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The method of translating observed symptoms into disease names is known as disease diagnosis. The ability to solve clinical problems in a complex manner is critical to a doctor's effectiveness in providing health care. The accuracy of his or her expertise is crucial to the survival and well-being of his or her patients. Artificial Intelligence (AI) has a huge economic influence depending on how well it is applied. In the medical sector, human brain-simulated intellect can help not only with classification accuracy, but also with reducing diagnostic time, cost and pain associated with pathologies tests. In light of AI's present and prospective applications in the biomedical, we will identify them in the paper based on potential benefits and risks, social and ethical consequences and issues that might be contentious but have not been thoroughly discussed in publications and literature. Current apps, personal tracking tools, genetic tests and editing programmes, customizable models, web environments, virtual reality (VR) technologies and surgical robotics will all be investigated in this study. While AI holds a lot of potential in medical diagnostics, it is still a very new method, and many clinicians are uncertain about its reliability, specificity and how it can be integrated into clinical practice without jeopardising clinical expertise. To validate their effectiveness, more systemic refinement of these implementations, as well as training of physicians and healthcare facilities on how to effectively incorporate these strategies into clinical practice, will be needed.

Keywords: Artificial Intelligence, medical diagnosis, virtual reality, healthcare ethical implications 

Procedia PDF Downloads 110