Search results for: wound classification

Authors: Dylan Shiting Lu, Samson Okello, Anita Chunyan Wei, Daniel Xiao Li

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Mammotome vacuum-assisted breast biopsy (MVB) introduced in 1995 can be used for the removal of benign breast lesions. Whether or not MVB is a better option compared to conventional open surgery is inconclusive. We aim to compare the clinical and patient-related outcomes between MVB and open surgery to remove benign breast tumors less than 5 cm in women. We searched English and Chinese electronic databases with the keywords of Mammotome, clinical trial (CT), vacuum-assisted breast biopsy for studies comparing MVB and open surgery until May 2021. We performed a systematic review and random-effects meta-analysis to compare incision size, operation time, intraoperative blood loss, healing time, scar length, patient satisfaction, postoperative hematoma rate, wound infection rate, postoperative ecchymosis, and postoperative sunken skin among those who have Mammotome and those who have surgery. Our analysis included nine randomized CTs with 1155 total patients (575 Mammotome, 580 surgery) and mean age 40.32 years (standard deviation 3.69). We found statistically significant favorable outcomes for Mammotome including blood loss (ml) [standardized mean difference SMD -5.03, 95%CI (-7.30, -2.76)], incision size (cm) [SMD -12.22, 95%CI (-17.40, -7.04)], operation time (min) [SMD -6.66, 95%CI (-9.01, -4.31)], scar length (cm) [SMD -7.06, 95%CI (-10.76, -3.36)], healing time (days) [SMD -6.57, 95%CI (-10.18, -2.95)], and patient satisfaction [relative risk RR 0.38, 95%CI (0.13, 1.08)]. In conclusion, Mammotome vacuum-assisted breast biopsy compared to open surgery shows better clinical and patient-related outcomes. Further studies should be done on whether or not MVB is a better option for benign breast tumors excision.

Keywords: clinical and patient outcomes, open surgery, Mammotome vacuum-assisted breast biopsy, meta-analysis

Procedia PDF Downloads 206

Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

Procedia PDF Downloads 217

Authors: Midhun Paul, T. Radha Ramanan

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The objective of this work is to present a state-of-the-art literature review highlighting the challenges in the research of the scheduling of assembly job shop problem and providing an insight on how the future directions of the research would be. The number of work has been substantial that it requires a review to enable one to understand the origin of the research and how it is getting evolved. This review paper presents a comprehensive review of the literature dealing with various studies carried on assembly job shop scheduling. The review details the evolution of the AJS from the perspective of other scheduling problems and also presents a classification scheme. The work also identifies the potential directions for future research, which we believe to be worthwhile considering.

Keywords: assembly job shop, future directions, manufacturing, scheduling

Procedia PDF Downloads 407

Authors: Yuliya Leonidovna Kiva-Khamzina

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The legal nature of the contract for educational services causes a lot of controversies. In particular, it raises the question about industry sector relationships, which require making a contract for educational services. The article describes the different types of contracts classifications for services provision from the perspective of civil law, deals with the specifics of the contract on rendering educational services; the author makes the conclusion that the contract for the provision of educational services is a complex institution that includes elements of the civil and administrative law. The following methods were used to conduct the study: dialectical method of cognition, the historical method, systemic analysis, classification.

Keywords: administrative aspect, civil aspect, educational service, industry, legal nature, services provision

Procedia PDF Downloads 322

Authors: Omar Nouri, Wafa Mnejja, Nejla Fourati, Fatma Dhouib, Wicem Siala, Ilhem Charfeddine, Afef Khanfir, Jamel Daoud

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Background and objective: Induction chemotherapy (IC) followed by concomitant chemo radiotherapy with intensity modulated radiation (IMRT) technique is actually the recommended treatment modality for locally advanced nasopharyngeal carcinomas (NPC). The aim of this study was to evaluate the prognostic factors predicting loco regional relapse with this new treatment protocol. Patients and methods: A retrospective study of 52 patients with NPC treated between June 2016 and July 2019. All patients received IC according to the protocol of the Head and Neck Radiotherapy Oncology Group (Gortec) NPC 2006 (3 TPF courses) followed by concomitant chemo radiotherapy with weekly cisplatin (40 mg / m2). Patients received IMRT with integrated simultaneous boost (SIB) of 33 daily fractions at a dose of 69.96 Gy for high-risk volume, 60 Gy for intermediate risk volume and 54 Gy for low-risk volume. Median age was 49 years (19-69) with a sex ratio of 3.3. Forty five tumors (86.5%) were classified as stages III - IV according to the 2017 UICC TNM classification. Loco regional relapse (LRR) was defined as a local and/or regional progression that occurs at least 6 months after the end of treatment. Survival analysis was performed according to Kaplan-Meier method and Log-rank test was used to compare anatomy clinical and therapeutic factors that may influence loco regional free survival (LRFS). Results: After a median follow up of 42 months, 6 patients (11.5%) experienced LRR. A metastatic relapse was also noted for 3 of these patients (50%). Target volumes coverage was optimal for all patient with LRR. Four relapses (66.6%) were in high-risk target volume and two (33.3%) were borderline. Three years LRFS was 85,9%. Four factors predicted loco regional relapses: histologic type other than undifferentiated (UCNT) (p=0.027), a macroscopic pre chemotherapy tumor volume exceeding 100 cm³ (p=0.005), a reduction in IC doses exceeding 20% (p=0.016) and a total cumulative cisplatin dose less than 380 mg/m² (p=0.0.34). TNM classification and response to IC did not impact loco regional relapses. Conclusion: For nasopharyngeal carcinoma, tumors with initial high volume and/or histologic type other than UCNT, have a higher risk of loco regional relapse. Therefore, they require a more aggressive therapeutic approaches and a suitable monitoring protocol.

Keywords: loco regional relapse, modulation intensity radiotherapy, nasopharyngeal carcinoma, prognostic factors

Procedia PDF Downloads 121

Authors: Abeer Heider

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The article considers the definitions of teacher’s competences and professionalism from different perspectives of Arab and foreign scientists. A special attention is paid to the definition, classification of the stages and components of University Arabic /foreign language teacher’s professionalism. The results of the survey are offered and recommendations are given. In this paper, only some of the problems of defining professional competence and professionalism of the university Arabic/ foreign language teacher have been mentioned. It needs much more analysis and discussion, because the quality of training today’s competitive and mobile students with a good knowledge of foreign languages depends directly on the teachers’ professional level.

Keywords: teacher’s professional competences, Arabic/ foreign language teacher’s professionalism, teacher evaluation, teacher quality

Procedia PDF Downloads 446

Authors: Ashvamedh, Chandra Bhushan Singh, Anil Kumar Sarda

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Introduction: Ileal perforation is a common cause for peritonitis in developing countries. Surgery is the ideal treatment as it eliminates soilage of peritoneal cavity in an effort to lessen the toxaemia and enhance the recovery of the patient. However, there is no uniformity of standardized operative procedure that is most effective for management. Material and method: The study was conducted on 66 patients of perforation peritonitis from November 2013 to February 2015 in Lok Nayak Hospital. Data of each patient were recorded on a pre-determined proforma. The methods used for repair were Primary repair, Resection anastomosis (RA) and Ileostomy. Result: Male preponderance was noticed among the patients with majority in their third decade. Of all perforations 40.9% were tubercular and 34.8% were typhoid. Amongst operated cases 27.3% underwent primary repair, RA was performed in 45.5%, Ileostomy in 27.3%patients. The average time taken for RA and ileostomy was more than primary repair. The type of repair bear no significance to size or no of perforation but was significant statistically for distance from I/C valve(P=.005) and edema of bowel wall(p=.002) when analysed for post op complications. Wound infection, dehiscence, intra-abdominal collections were complications observed bearing no significance to type of repair. Ileostomy per se has its own complications peristomal skin excoriation seen in 83.3%, electrolyte imbalance in 33.3%, duration for closure averaged 188 days (median 150 days, range 85-400 days). Conclusion: Primary closure is preferable in patients with single, small perforations. RA is advocated in patients with multiple or large perforation, perforation proximal to stricture. Ileostomy should not be considered as primary definitive procedure and reserved only for moribund patients as a lifesaving procedure. It has more morbidity and requires a second surgery for closure increasing the cost of treatment as well.

Keywords: ileal perforation, ileostomy, perforation peritonitis, typhoid perforation management

Procedia PDF Downloads 247

Authors: Riddhi Jangid, Gajendra Pratap Singh

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Petri nets, a formalized modeling language that was introduced back around 50-60 years, have been widely used for modeling discrete event dynamic systems and simulating their behavior. Reachability analysis of Petri nets gives many insights into a modeled system. This idea leads us to study the reachability technique and use it in the reachability problem in the state space of reachable markings. With the same concept, Crisp Boolean Petri nets were defined in which the marking vectors that are boolean are distinct in the reachability analysis of the nets. We generalize the concept and define ‘Crisp’ Petri nets that generate the marking vectors exactly once in their reachability-based analysis, not necessarily Boolean.

Keywords: marking vector, n-vector, Petri nets, reachability

Procedia PDF Downloads 77

Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: text detection, CNN, PZM, deep learning

Procedia PDF Downloads 78

Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

Procedia PDF Downloads 523

Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

Procedia PDF Downloads 533

Authors: Siriporn Meenanan

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The objective of this study is to study about the uses and gratification of the sample who use the website that named secret-thai.com which provides moral contents, inspires, and builds up the spirit. The study found that the samples mainly use this website to follow up on the dharma activities. They also use the space as the web board to discuss about dharma issues. Moreover, the contents help readers to relax and also provides the guidelines to deal with stress and uncomfortable situations properly. The samples found to be most satisfied. In other words, the samples found the contents of the website are complete, and can cover their needs. Moreover, they found that contents useful in their ways of living. In addition, they are satisfied with the beautiful and interesting design of the website and well classification of the contents that readers can easily find the information that they want.

Keywords: uses and gratification, website, Secret-Thai.com, moral contents

Procedia PDF Downloads 227

Authors: Mary Ann L. Halliday, Zoengpari Gohain

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The stigma associated with HIV-AIDS negatively affect mental health and ability to effectively manage the disease. While the number of People living with HIV/AIDS (PLHIV) has been increasing day by day in Mizoram (a small north-eastern state in India), research on HIV/AIDS stigma has so far been limited. Despite the potential significance of Internalized HIV Stigma (IHS) in the lives of PLHIV, there has been very limited research in this area. It was therefore, felt necessary to explore the internalized HIV stigma, mental health, coping and perceived social support of PLHIV in Aizawl District, Mizoram. The present study was designed with the objectives to determine the degree of IHS, to study the relationship between the socio-demographic characteristics and level of IHS, to highlight the mental health status, coping strategies and perceived social support of PLHIV and to elucidate the relationship between these psychosocial variables. In order to achieve the objectives of the study, six hypotheses were formulated and statistical analyses conducted accordingly. The sample consisted of 300 PLWHA from Aizawl District, 150 males and 150 females, of the age group 20 to 70 years. Two- way classification of “Gender” (male and female) and three-way classification of “Level of IHS” (High IHS, Moderate IHS, Low IHS) on the dependent variables was employed, to elucidate the relationship between Internalized HIV Stigma, mental health, coping and perceived social support of PLHIV. The overall analysis revealed moderate level of IHS (67.3%) among PLHIV in Aizawl District, with a small proportion of subjects reporting high level of IHS. IHS was found to be significantly different on the basis of disclosure status, with the disclosure status of PLHIV accounting for 9% variability in IHS.  Results also revealed more or less good mental health among the participants, which was assessed by minimal depression (50.3%) and minimal anxiety (45%), with females with high IHS scoring significantly higher in both depression and anxiety (p<.01). Examination of the coping strategies of PLHIV found that the most frequently used coping styles were Acceptance (91%), Religion (84.3%), Planning (74.7%), Active Coping (66%) and Emotional Support (52.7%). High perception of perceived social support (48%) was found in the present study. Correlation analysis revealed significant positive relationships between IHS and depression as well as anxiety (p<.01), thus revealing that IHS negatively affects the mental health of PLHIV. Results however revealed that this effect may be lessened by the use of various coping strategies by PLHIV as well as their perception of social support.

Keywords: Aizawl, anxiety, depression, internalized HIV stigma, HIV/AIDS, mental health, mizoram, perceived social support

Procedia PDF Downloads 255

Authors: I. Baddari, A. Riahla, M. Mezghich

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Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.

Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

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Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

Procedia PDF Downloads 51

Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

Procedia PDF Downloads 417

Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

Procedia PDF Downloads 76

Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

Procedia PDF Downloads 127

Authors: Tamphasana Wairokpam

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Inflammatory myopathies (IMs) have long been recognised as a heterogenous family of myopathies with acute, subacute, and sometimes chronic presentation and are potentially treatable. Necrotizing autoimmune myopathies (NAM) are a relatively new subset of myopathies. Patients generally present with subacute onset of proximal myopathy and significantly elevated creatinine kinase (CK) levels. It is being increasingly recognised that there are limitations to the independent diagnostic utility of muscle biopsy. Immunohistochemistry tests may reveal important information in these cases. The traditional classification of IMs failed to recognise NAM as a separate entity and did not adequately emphasize the diversity of IMs. This review and case report on NAM aims to highlight the heterogeneity of this entity and focus on the distinct clinical presentation, biopsy findings, specific auto-antibodies implicated, and available treatment options with prognosis. This article is a meta-analysis of literatures on NAM and a case report illustrating the clinical course, investigation and biopsy findings, antibodies implicated, and management of a patient with NAM. The main databases used for the search were Pubmed, Google Scholar, and Cochrane Library. Altogether, 67 publications have been taken as references. Two biomarkers, anti-signal recognition protein (SRP) and anti- hydroxyl methylglutaryl-coenzyme A reductase (HMGCR) Abs, have been found to have an association with NAM in about 2/3rd of cases. Interestingly, anti-SRP associated NAM appears to be more aggressive in its clinical course when compared to its anti-HMGCR associated counterpart. Biopsy shows muscle fibre necrosis without inflammation. There are reports of statin-induced NAM where progression of myopathy has been seen even after discontinuation of statins, pointing towards an underlying immune mechanism. Diagnosisng NAM is essential as it requires more aggressive immunotherapy than other types of IMs. Most cases are refractory to corticosteroid monotherapy. Immunosuppressive therapy with other immunotherapeutic agents such as IVIg, rituximab, mycophenolate mofetil, azathioprine has been explored and found to have a role in the treatment of NAM. In conclusion,given the heterogeneity of NAM, it appears that NAM is not just a single entity but consists of many different forms, despite the similarities in presentation and its classification remains an evolving field. A thorough understanding of underlying mechanism and the clinical correlation with antibodies associated with NAM is essential for efficacious management and disease prognostication.

Keywords: inflammatory myopathies, necrotising autoimmune myopathies, anti-SRP antibody, anti-HMGCR antibody, statin induced myopathy

Procedia PDF Downloads 99

Authors: Darshana Rathod, Kirtikumar Rathod, Kamlesh Kumari, Abhilasha Motghare

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Background and Aims: Hypospadias is one of the common congenital anomalies in males. Various modalities are used for pain management, including caudal, penile, pudendal, ring blocks, and systemic analgesics. There has yet to be a consensus regarding the most effective and safe analgesic method for controlling pain in these children. We planned this study to determine our institute's pain management practices for hypospadias surgeries. Material and Methods: This retrospective cohort study reviewed 150 children with hypospadias undergoing surgery from January 2020 to December 2023. Data regarding the mode of pain management, postoperative opioid requirement, PACU discharge, and complications was collected from the records. Results: For postoperative pain, 33 (22%) children received caudal block, 60 (40%) penile block, and 57 (38%) were managed by intravenous analgesics. A significant difference was found in the three groups, with the IV analgesic group requiring significantly higher opioid boluses in PACU [43 (75.4%) required two boluses (p < 0.05)]. The difference in PACU discharge time among the three groups was statistically significant (p< 0.05), with IV analgesics groups having the highest (55 mins [47, 60]), the Caudal group at 35mins (30, 40), and the dorsal penile block group at 35mins (25, 40). There was no significant difference in complications like edema, meatal stenosis, urethra-cutaneous fistula, or wound dehiscence among all three groups. Conclusion: Intravenous analgesics and regional blocks like caudal and penile blocks are the common pain management modalities in our institute. The regional blocks are effective in managing pain in the postoperative period and are not significantly associated with complications.

Keywords: caudal block, hypospadias, pain management, penile block

Procedia PDF Downloads 37

Authors: Enjie Xu, Zhen Huang, Kunpeng Zhu, Jianping Hu, Xiaolong Ma, Yongjie Wang, Jiazhuang Zhu, Chunlin Zhang

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Abstract: Hypoxia and dedifferentiation of osteosarcoma (OS) cells leads to poor prognosis. We plan to identify the role of hypoxia on dedifferentiation and the associated signaling pathways. We performed a sphere formation assay and determined spheroid cells as dedifferentiated cells by detecting stem cell-like markers. RNAi assay was used to explore the expression relationship between hypoxia inducible factor 1 subunit alpha (HIF1A) and platelet derived growth factor receptor beta (PDGFRB). We obtained PDGFRB knockdown and overexpression cells through lentiviral infection experiments and the effects of PDGFRB on cytoskeleton rearrangement and cell adhesion were explored by immunocytochemistry. Wound-healing experiments, transwell assays, and animal trials were employed to investigate the effect of PDGFRB on OS metastasis. Dedifferentiated OS cells were found to exhibit high expression of HIF1A and PDGFRB, and HIF1A promoted the expression of PDGFRB, subsequently activated ras homolog family member A (RhoA), and increased the phosphorylation of myosin light chain (MLC). PDGFRB also enhanced the phosphorylation of focal adhesion kinase (FAK). The OS cell morphology and vinculin distribution were altered by PDGFRB. PDGFRB also promoted cell dedifferentiation and had a significant impact on the metastasis of OS cells both in vitro and in vivo. Our results demonstrated that HIF1A up-regulated PDGFRB under hypoxic conditions, and PDGFRB regulated the actin cytoskeleton by activating RhoA and subsequently phosphorylating MLC, thereby promoting OS dedifferentiation and pulmonary metastasis.

Keywords: osteosarcoma, dedifferentiation, metastasis, cytoskeleton rearrangement, PDGFRB, hypoxia

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Authors: G. Wojcik, J. Gindlhuber, A. Syarif, K. Hoetzenecker, P. Bohm, P. Vesely, V. Biasin, G. Kwapiszewska

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Pulmonary fibrosis is a rare disease where uncontrolled wound healing processes damage the lung structure. Intensive changes within the extracellular matrix (ECM) and its interaction with fibroblasts have a major role in pulmonary fibrosis development. Among others, collagen is one of the main components of the ECM, and it is important for lung structure. In IPF, constant production of collagen by fibroblast, through TGFβ1-SMAD2/3 pathways, leads to an uncontrolled deposition of matrix and hence lung remodeling. Abnormal changes in lipid production, alterations in fatty acids (FAs) metabolism, enhanced oxidative stress, and lipid peroxidation in fibrotic lung and fibrotic fibroblasts have been reported; however, the interplay between the collagen and lipids is not yet established. One of the FAs influx regulators is Angiopoietin-like 4 (ANGPTL4), which inhibits lipoprotein lipase work, decreasing the availability of FAs. We hypothesized that altered lipid composition or availability could have the capability to influence the phenotype of different fibroblast populations in the lung and hence influence lung fibrosis. To prove our hypothesis, we aim to investigate lipids and their influence on human, animal, and in vitro levels. In the bleomycin model, treatment with the well-known metabolic drugs Rosiglitazone or Metformin significantly lower collagen production. Similar results were noticed in ANGPTL4 KO animals, where the KO of ANGPTL4 leads to an increase of FAs availability and lower collagen deposition after the bleomycin challenge. Currently, we study the treatment of different FAs on human lung para fibroblasts (hPF) isolated from donors. To understand the lipid composition, we are collecting human lung tissue from donors and pulmonary fibrosis patients for Liquid chromatography-mass spectrometry. In conclusion, our results suggest the lipid influence on collagen deposition during lung fibrosis, but further research needs to be conducted to understand the matter of this relationship.

Keywords: collagen, fibroblasts, lipidomics, lung, pulmonary fibrosis

Procedia PDF Downloads 77

Authors: Younmin Shin, Jin Kyu Kim, Mirim Jin, Jeong June Choi

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Demilitarized Zone (DMZ) is a peace region located between South and North Korea border to avoid accidental armed conflict. Because human accessing to the area was forced to be prohibited for more than 60 years, DMZ is one of the cleanest land keeping wild lives as nature itself in South Korea. In this study, we evaluated the biological efficacies of plants (SS, PC, and AR) inhabiting in DMZ for the development of functional cosmetics. First, we tested the cytotoxicity of plant extracts in keratinocyte and melanocyte, which are the major cell components of skin. By 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay with the cell lines, we determined the safety concentrations of the extracts for the efficacy tests. Next, we assessed the anti-wrinkle cosmetic function of SS by demonstrating that SS treatment decreased the expression of Matrix metalloproteinase-1 (MMP-1) in UV-irradiated keratinocytes via real-time PCR. The suppressive effect of SS was greatly potentiated by combination with other DMZ-inhabiting plants, PC and AR. The expression of tyrosinase, which is one the main enzyme that producing melanin in melanocyte, was also down-regulated by the DMZ-inhabiting SS extract. Wound healing activity was also investigated by in vitro test with HaCat cell line, a human fibroblast cell line. All the natural materials extracted form DMZ habiting plants accelerated the recovery of the cells. These results suggested that DMZ is a treasure island of functional plants and DMZ-inhabiting natural products are warranted to develop functional cosmetic materials. This study was carried out with the support of R&D Program for Forest Science Technology (Project No. 2017027A00-1819-BA01) provided by Korea Forest Service (Korea Forestry Promotion Institute).

Keywords: anti-wrinkle, Demilitarized Zone, functional cosmetics, whitening

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Authors: Krisala Joshi, Sayanta Ghosh, Renu Lata, Jagdish C. Kuniyal

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Throughout the world, monitoring and estimating the changing pattern of forests across diverse landscapes through remote sensing is instrumental in understanding the interactions of human activities and the ecological environment with the changing climate. Forest change detection using satellite imageries has emerged as an important means to gather information on a regional scale. Kullu valley in Himachal Pradesh, India is situated in a transitional zone between the lesser and the greater Himalayas. Thus, it presents a typical rugged mountainous terrain with moderate to high altitude which varies from 1200 meters to over 6000 meters. Due to changes in agricultural cropping patterns, urbanization, industrialization, hydropower generation, climate change, tourism, and anthropogenic forest fire, it has undergone a tremendous transformation in forest cover in the past three decades. The loss and degradation of forest cover results in soil erosion, loss of biodiversity including damage to wildlife habitats, and degradation of watershed areas, and deterioration of the overall quality of nature and life. The supervised classification of LANDSAT satellite data was performed to assess the changes in forest cover in Kullu valley over the years 2000 to 2020. Normalized Burn Ratio (NBR) was calculated to discriminate between burned and unburned areas of the forest. Our study reveals that in Kullu valley, the increasing number of forest fire incidents specifically, those due to anthropogenic activities has been on a rise, each subsequent year. The main objective of the present study is, therefore, to estimate the change in the forest cover of Kullu valley and to address the various social aspects responsible for the anthropogenic forest fires. Also, to assess its impact on the significant changes in the regional climatic factors, specifically, temperature, humidity, and precipitation over three decades, with the help of satellite imageries and ground data. The main outcome of the paper, we believe, will be helpful for the administration for making a quantitative assessment of the forest cover area changes due to anthropogenic activities and devising long-term measures for creating awareness among the local people of the area.

Keywords: Anthropogenic Activities, Forest Change Detection, Normalized Burn Ratio (NBR), Supervised Classification

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Devinder Kaur Sugga, Ekamdeep Kaur, Jaspreet Kaur, C. Rajesh, Preeti Rajesh, Harsimran Kaur

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Withanolides are steroidal lactones and are highly oxygenated phytoconstituents that can be developed as potential anti-carcinogenic agents. The two main withanolides, namely Withaferin A and Withanolides D, have been extensively studied for their pharmacological activities. Both these withanolides are present in the Withania somnifera (WS) leaves belonging to the family Solanaceae, also known as “Indian ginseng .”In this study effects of WS leaf extract on the MCF7 breast cancer cell line were investigated by performing a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay to evaluate the cytotoxic effects and in vitro wound-healing assay to study the effect on cancer cell migration. Our data suggest WS extracts have cytotoxic effects and are effective anti-migrating agents and thus can be a source of potential candidates for the development of potential agents against metastasis. Thus, it can be a source of potential candidates for the development of potential agents against metastasis. Insight into these results, the in-silico approach to identify the possible protein targets interacting with withanolides was taken. Protein kinase C alpha (PKCα) was among the selected 5 top-ranked target proteins identified by the Swiss Target Prediction tool. PKCα is known to promote the growth and invasion of cancer cells and is being evaluated as a prognostic biomarker and therapeutic target in clinically aggressive tumors. Molecular docking of Withaferin A and Withanolides D was performed using AutoDock Vina. Both the bioactive compounds interacted with PKCα. The targets predicted using this approach will serve as leads for the possible therapeutic potential of withanolides, the bioactive ingredients of WS extracts, as anti-cancer drugs.

Keywords: withania somnifera, withaferin A, withanolides D, PKCα

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Yating Mu

Abstract:

Young children acquire native language with amazing rapidity. After noticing this interesting phenomenon, lots of linguistics, as well as psychologists, devote themselves to exploring the best explanations. Thus researches on first language acquisition emerged. Early lexical development is an important branch of children’s FLA (first language acquisition). Verb, the most significant class of lexicon, the most grammatically complex syntactic category or word type, is not only the core of exploring syntactic structures of language but also plays a key role in analyzing semantic features. Obviously, early verb development must have great impacts on children’s early lexical acquisition. Most scholars conclude that verbs, in general, are very difficult to learn because the problem in verb learning might be more about mapping a specific verb onto an action or event than about learning the underlying relational concepts that the verb or relational term encodes. However, the previous researches on early verb development mainly focus on the argument about whether there is a noun-bias or verb-bias in children’s early productive vocabulary. There are few researches on general characteristics of children’s early verbs concerning both semantic and syntactic aspects, not mentioning a general survey on Chinese-speaking children’s verb acquisition. Therefore, the author attempts to examine the general conditions and characteristics of Chinese-speaking children’s early productive verbs, based on data from a longitudinal study on three Chinese-speaking children. In order to present an overall picture of Chinese verb development, both semantic and syntactic aspects will be focused in the present study. As for semantic analysis, a classification method is adopted first. Verb category is a sophisticated class in Mandarin, so it is quite necessary to divide it into small sub-types, thus making the research much easier. By making a reasonable classification of eight verb classes on basis of semantic features, the research aims at finding out whether there exist any universal rules in Chinese-speaking children’s verb development. With regard to the syntactic aspect of verb category, a debate between nativist account and usage-based approach has lasted for quite a long time. By analyzing the longitudinal Mandarin data, the author attempts to find out whether the usage-based theory can fully explain characteristics in Chinese verb development. To sum up, this thesis attempts to apply the descriptive research method to investigate the acquisition and the usage of Chinese-speaking children’s early verbs, on purpose of providing a new perspective in investigating semantic and syntactic features of early verb acquisition.

Keywords: Chinese-speaking children, early verb acquisition, verb classes, verb grammatical structures

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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