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In electric vehicle applications, early detection of demagnetization faults is crucial for ensuring the smooth operation of Permanent Magnet Synchronous Motors (PMSM). Indeed, an efficient maintenance operation can be carried out with the assistance of defect identification and classification. In this paper, Machine Learning Classifiers (MLC) based demagnetization fault binary classification for PMSM using motor current signal spectral analysis are presented. Threephase current signals are obtained by building a Finite Elements (FE) model with predefined demagnetization faults in order to obtain currents data. The Power Spectral Density (PSD) is used to extract the Amplitude of Sideband Components (ASBCs) from the frequency pattern. In order to classify the demagnetization fault state, different MLC are finally trained and evaluated by using the extracted feature set. The MLC exhibits really encouraging outcomes for accuracy and other recognized performance metrics. The suggested approach outperforms prior research studies with an accuracy that is marginally higher than 99%. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kim, Krichen, Ojo, Sampedro and Alamro.)

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB.

Misplacement of pericardiocentesis catheter in central veins is a rare complication that can be managed with several methods. In this case, we report a percutaneous image-guided plug-assisted management of a misplaced pericardiocentesis catheter into the inferior vena cava through a transhepatic tract successfully occluded. This minimally invasive technique was not previously described in this setting and had a favorable long-term outcome.

This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field. Android's dominant position in the mobile market justifies our focus on this platform. Additionally, we delve into the historical evolution of blockchain and its relevance to modern mobile app security in a dedicated section. Our examination of encryption techniques and the effectiveness of blockchain in securing mobile app data storage yields important insights. We discuss the advantages of blockchain over traditional encryption methods and their practical implications. The central contribution of this paper is the Blockchain-based Secure Android Data Storage (BSADS) framework, now consisting of six comprehensive layers. We address challenges related to data storage costs, scalability, performance, and mobile-specific constraints, proposing technical optimization strategies to overcome these obstacles effectively. To maintain transparency and provide a holistic perspective, we acknowledge the limitations of our study. Furthermore, we outline future directions, stressing the importance of leveraging lightweight nodes, tackling scalability issues, integrating emerging technologies, and enhancing user experiences while adhering to regulatory requirements.

The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects.

Objectives: To evaluate anatomical and volumetric predictability of a cone beam computed tomography (CBCT)-based virtual parenchymal perfusion (VPP) software for the single-photon-emission computed tomography (SPECT)/CT imaging results during the work-up for transarterial radioembolization (TARE) procedure in patients with hepatocellular carcinoma (HCC)., Methods: VPP was evaluated retrospectively on CBCT data of patients treated by TARE for HCC. 99m Tc macroaggregated albumin particles ( 99m Tc-MAA) uptake territories on work-up SPECT/CT was used as ground truth for the evaluation. Semi-quantitative evaluation consisted of the ranking of visual consistency of the parenchymal enhancement and portal vein tumoral involvement on VPP and 99m Tc-MAA SPECT/CT, using a three-rank scale and two-rank scale, respectively. Inter-reader agreement was evaluated using a kappa coefficient. Quantitative evaluation included absolute volume error calculation and Pearson correlation between volumes enhanced territories on VPP and 99m Tc-MAA SPECT/CT., Results: Fifty-two CBCTs were performed in 33 included patients. Semi-quantitative evaluation showed a good concordance between actual 99m Tc-MAA uptake and the virtual enhanced territories in 73% and 75% of cases; a mild concordance in 12% and 10% and a poor concordance in 15%, for the two readers. Kappa coefficient was 0.86. Portal vein involvement evaluation showed a good concordance in 58.3% and 66.7% for the two readers, respectively, with a kappa coefficient of 0.82. Quantitative evaluation showed a volume error of 0.46 ± 0.78 mL [0.01-3.55], and Pearson R 2 factor at 0.75 with a p value < 0.01., Conclusion: CBCT-based VPP software is accurate and reliable to predict 99m Tc-MAA SPECT/CT anatomical and volumetric results in HCC patients during TARE., Key Points: • Virtual parenchymal perfusion (VPP) software is accurate and reliable in the prediction of 99m Tc-MAA SPECT volumetric and targeting results in HCC patients during transarterial radioembolization (TARE). • VPP software may be used per-operatively to optimize the microcatheter position for 90 Y infusion allowing precise tumor targeting while preserving non-tumoral parenchyma. • Post-operatively, VPP software may allow an accurate estimation of the perfused volume by each arterial branch and, thus, a precise 90 Y dosimetry for TARE procedures., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages. This is largely owing to the difficulty obtaining pavement photographs and the tiny size of flaws (cracks). The existence of pavement cracks and potholes reduces the value of the infrastructure, thus the severity of the fracture must be estimated. Annually, operators in many nations must audit thousands of kilometers of road to locate this degradation. This procedure is costly, sluggish, and produces fairly subjective results. The goal of this work is to create an efficient automated system for crack identification, extraction, and 3D reconstruction. The creation of crack-free roads is critical to preventing traffic deaths and saving lives. The proposed method consists of five major stages: detection of flaws after processing the input picture with the Gaussian filter, contrast adjustment, and ultimately, threshold-based segmentation. We created a database of road cracks to assess the efficacy of our proposed method. The result obtained are commendable and outperform previous state-of-the-art studies.

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task. It is estimated that people have been cultivating olive trees for 6000 years, making it one of the most useful and profitable fruit trees in history. Symptoms that appear on infected leaves can vary from one plant to another or even between individual leaves on the same plant. Because olive groves are susceptible to a variety of pathogens, including bacterial blight, olive knot, Aculus olearius , and olive peacock spot, it has been difficult to develop an effective olive disease detection algorithm. For this reason, we developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model. The goal of this method is to detect and classify diseases that can affect olive leaves. In addition, binary and multiclassification systems based on deep convolutional models were used to categorize olive leaf disease. The results are encouraging and show how effectively CNN and vision transformer models can be used together. Our model outperformed the other models with an accuracy of about 96% for multiclass classification and 97% for binary classification, as shown by the experimental results reported in this study., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Hamoud Alshammari et al.)

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs. To address this issue, in this paper, we propose a new topology management and connectivity maintenance scheme called a Tolerating Fault and Maintaining Network Connectivity using Array Antenna (ToMaCAA) for WSNs. ToMaCAA is a system designed to adapt to dynamic structures and maintain network connectivity while consuming fewer network resources. Thereafter, we incorporated a Phase Array Antenna into the existing topology management technologies, proving ToMaCAA to be a novel contribution. This new approach allows a node to connect to the farthest node in the network while conserving resources and energy. Moreover, data transmission is restricted to one route, reducing overheads and conserving energy in various other nodes' idle listening state. For the implementation of ToMaCAA, the MATLAB network simulation platform has been used to test and analyse its performance. The output results were compared with the benchmark schemes, i.e., Disjoint Path Vector (DPV), Adaptive Disjoint Path Vector (ADPV), and Pickup Non-Critical Node Based k-Connectivity (PINC). The performance of ToMaCAA was evaluated based on different performance metrics, i.e., the network lifetime, total number of transmitted messages, and node failure in WSNs. The output results revealed that the ToMaCAA outperformed the DPV, ADPV, and PINC schemes in terms of maintaining network connectivity during link failures and made the network more fault-tolerant and reliable.

Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Olfa Hrizi et al.)

Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient's critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient's classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.

Big graphs are part of the movement of "Not Only SQL" databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP., Competing Interests: Competing interestsThe authors declare that they have no competing interests, (© The Author(s) 2020.)

Autoimmune thyroid diseases (AITD), which include Hashimoto thyroiditis (HT), Graves' disease (GD) and primary idiopathic myxoedema (PIM), are recognized by their clinical and genetic heterogeneity. In this study, we have carried on a global approach gathering 20 year genetic and clinical data on a Tunisian multigenerational family (Akr). Our purpose was search for a combined genotype involved in AITD susceptibility using 33 gene polymorphisms. The Akr pedigree is composed of more than 400 members distributed on 10 generations. Clinical follow-up was performed by appreciation of the thyroid gland and measurement of both thyroid hormone and auto antibody levels. We used FBAT software to test for association between gene polymorphisms and AITDs. Clinical follow-up has showed that the number of AITD patients has increased from 25 to 78 subjects subdivided on 51 cases of GD, 22 PIM and 5 HT. Concerning genetic analysis, our study has revealed new gene association when compared with our previous analysis (considering single genes). Thus, PTPN22, TG and VDR gene polymorphisms have became associated with p-values ranging from 4.6  10(- 2) to 4  10(- 3) when considered with other genes on the same chromosome; giving evidence for gene interaction. The most significant association was found with the MHC region (p = 7.15 10(- 4)). Moreover, and among gene polymorphisms explored, our analysis has identified some of them as AITD biomarkers. Indeed, PDS gene polymorphisms were associated with either exophthalmia or goiter (p-values from 10(- 2) to 10(- 3)). In conclusion, our study gives evidence for gene interaction in AITD development confirming genetic complexity of these diseases.

Pro-inflammatory cytokines such as IL-1β and TNFα are known to affect thyroid function. They stimulate IL-6 secretion and modify epithelium integrity by altering junction proteins. To study the role of cytokines on thyroid epithelia tightness in autoimmune thyroid diseases (AITD), we analyzed the expression profiles of junction proteins (ZO-1, Claudin, JAM-A) and cytokines in human thyroid slices and also investigated the effect of IL-1β on the epithelium integrity in primary cultures of human thyrocytes. Junction proteins expression (ZO-1, Claudin, JAM-A) has been analyzed by immunohistochemistry on thyroid slices and by Western blot on membrane proteins extracted from thyrocytes of patients suffering from Graves and Hashimoto diseases. The high expression of junction proteins we found on Graves' disease thyroid slices as well as in cell membrane extracts acknowledges the tightness of thyroid follicular cells in this AITD. In contrast, the reduced expression of JAM and ZO-1 in thyroid cells from patients suffering from Hashimoto thyroiditis is in agreement with the loss of thyroid follicular cell integrity that occurs in this pathology. Concerning the effects on epithelium integrity of TSH and of the pro-inflammatory cytokine IL-1β in primary cultures of human thyroid cells, TSH appeared able to modify JAM-A localization but without any change in the expression levels of JAM-A, Claudin and ZO-1. Inversely, IL-1β provoked a decrease in the expression of- and a redistribution of both, Claudin and ZO-1 without modifying the expression and sub-cellular distribution patterns of JAM-A in thyroid cells. These results demonstrate (i) that Hashimoto's- and Graves' diseases display different junction proteins expression patterns with a loss of epithelium integrity in the former and (ii) that IL-1β modifies thyroid epithelial tightness of human thyrocytes by altering the expression and localization of junction proteins. Therefore, IL-1β could play a role in the pathogenesis of thyroid autoimmunity., (Copyright © 2012 Elsevier GmbH. All rights reserved.)

Interactions between cytokines and others soluble factors (hormones, antibodies...) can play an important role in the development of thyroid pathogenesis. The purpose of the present study was to examine the possible correlation between serum cytokine concentrations, thyroid hormones (FT4 and TSH) and auto-antibodies (Tg and TPO), and their usefulness in discriminating between different thyroid conditions. In this study, we investigated serum from 115 patients affected with a variety of thyroid conditions (44 Graves' disease, 17 Hashimoto's thyroiditis, 11 atrophic thyroiditis, 28 thyroid nodular goitre and 15 papillary thyroid cancer), and 30 controls. Levels of 17 cytokines in serum samples were measured simultaneously using a multiplexed human cytokine assay. Thyroid hormones and auto-antibodies were measured using ELISA. Our study showed that IL-1β serum concentrations allow the discrimination between atrophic thyroiditis and papillary thyroid cancer groups (p = 0.027).

Purpose: To evaluate the treatment results of localised aggressive gastric lymphomas with favourable prognosis using chemotherapy and radiotherapy., Patients and Methods: Between February 1993 and December 2004, nine patients with primary gastric high-grade lymphoma have been treated by the Lymphoma Committee of Sfax (Tunisia). The sex ratio was 1.5. The mean age was 44.9 years. Histological type was the large cell B lymphoma in 100% of the cases. Disease was stage I in eight cases and stage II in one case (Ann Arbor Classification). The treatment consisted in primary chemotherapy followed by radiotherapy of the stomach with or without regional nodes (40 Gy)., Results: After treatment, all patients had complete remission. With a mean follow-up of 55.7 months, one patient was lost to follow-up; the other patients were alive with complete remission. No major acute toxicities or late effects were found. Overall survival was 100% at five years., Conclusion: Chemotherapy associated with radiotherapy can be proposed to patients having localised and aggressive primary gastric lymphoma with favourable prognosis, since this treatment is well tolerated and provides satisfactory control of the disease.

Autoimmune thyroid diseases (AITDs), including Graves' disease (GD) and Hashimoto thyroiditis, are associated with human MHC polymorphisms. The present study analysed two polymorphisms within tumour necrosis factor (TNF) genes (TNF-308 A/G SNP and TNFb (CT)n microsatellite) in a sample of 106 GD patients and 199 controls from the Tunisian population. The present study was designed to investigate genetic association of these polymorphisms (taken separately or considered as a haplotype) with GD development. Statistical analysis confirmed the association between the TNF-308 A allele and GD (p = 0.002), previously reported in a Tunisian familial study. The data from the present study suggest that the TNF-308 A allele plays a role in GD pathogenesis in the Tunisian population. This association was further confirmed by a meta-analysis on eight published studies (p < 0.0001). Haplotype analysis with GD revealed an associated haplotype (TNFb3-TNF-308 G haplotype: chi2 = 13.16; p = 0.0003).

Autoimmune thyroid diseases (AITDs) including Graves disease (GD) and autoimmune hypothyroidism (AH) are associated with TNF genes polymorphisms. TNF molecules bind to TNFRI and TNFRII. No genetic association was reported between TNFR and AITDs. In this study, we have analysed two polymorphisms in TNFRI gene (TNFRI+36A/G SNP and a microsatellite (GT)(17) (GA)(n)) and one polymorphism in TNFRII gene (TNFRII +676 T/G). All these polymorphisms were studied in a large Tunisian family with high prevalence of AITDs, and on a case-control sample of 91 GD patients and 165 controls. The present study was undertaken to investigate the genetic association of these polymorphisms with AITDs development. We reported the implication of TNFRIA3 allele in AITDs pathogenesis in familial and case control studies, respectively (chi(2)=4.13, p=0.042; chi(2)=9.26, p(c)=0.005). In addition, Case-control study has revealed for the first time that TNFRII+676G allele was associated with GD (chi(2)=11.53; p=0.0007). Two TNFRI haplotypes were found to be associated with GD: TNFRI+36G-A8, TNFRI+36A-A3 (chi(2)=88.07; p=6.32x10(-21), chi(2)=16.78; p=4.2x10(-5), respectively). Our data showed that TNFRI polymorphisms have an important role in AITDs pathogenesis in both familial and case-control samples and that TNFRII was rather implicated in GD development in the Tunisian population.

Autoimmune thyroid diseases (AITDs), including Graves' disease (GD) and autoimmune hypothyroidism (AH), are inherited as complex traits. Among the genes contributing to AITDs susceptibility are genes of the IL-1 family. IL-1 regulates T and B lymphocyte maturation, including the induction of several cytokines and cytokine receptors. Therefore, disturbances of this balance may not only play a role in inflammation but also in the pathogenesis of autoimmunity. In order to investigate genetic association of IL-1 gene polymorphisms with AITDs, we performed both a familial study in a large Tunisian pedigree with high prevalence of AITDs (64 patients and 176 controls), and a case-control study (131 GD unrelated patients and 225 healthy controls). PCR and PCR-RFLP methods were used to analyse respectively a VNTR in the IL-1RN gene and three SNPs in both IL-1B genes (-511 C/T and +3954 C/T) and IL-1A (-889 C/T). The family-based association study showed an association of the IL-1B+3954 C/T polymorphism (p=0.02) and two haplotypes IL-1RN*3/C/T/T and IL-1RN*1/C/T/T (p=0.009 and p=0.047 respectively) with AITDs. The case-control study is the first study revealing a significant association of the IL-1A-889 C/T polymorphism (chi2=10.23; p=0.0014) with susceptibility to GD. Our data suggest that the IL-1 gene cluster may harbour susceptibility genes for AITDs and GD pathogenesis in the Tunisian population.

Introduction: Involvement of the skin in Crohn's disease is rare. We report the case of a young woman presenting cutaneous nodules revealing a Crohn's disease., Case Report: R. K. a 19 year old woman was admitted the impatient clinic for a 2 week history of nodular lesions of the extremities. Simultaneously, she had watery diarrhoea and abdominal pain associated with a bad general condition and fever. Skin examination showed ulcerated lesions with a non infiltrated inflammatory bader of the left food and the right calf associated with an abscess of the left forearm. Skin biopsy showed an inflammatory infiltrate of the deep dermis with tuberculoid noncaseating granulomas. Colonoscopy demonstrated multiple ulcers, a "globlestone appearance" of the glow and segmental glitis. Colonoscopy was followed by a peritonitis which made steroid therapy questionable. Meanwhile, the patient underwent a colonostomy and was fed intravenously, the skin lesions resolved. A follow up colonoscopy demonstrated colon polyps, and the biopsy of the colon showed features of colitis and "follicular" duodenitis which was in concordance with the diagnosis of Crohn's disease., Commentaries: Usually, cutaneous lesions in Crohn's disease are consistent with nodules and granulomatous fissures of the grain. Involvement of the mucosa is rare. We report the case of "metastatic" localisation of nodular abscesses in Corhn disease. Improvement of cutaneous lesions might be explained by the nonuse of the digestive track allowed by the parenteral supplementation.

From July 1992 to December 2000, 288 cases of monoclonal gammapathy (MG) were collected at the university hospital of Sfax. The middle age of the patients at the time of the diagnosis was 62 years and 7 months with extremes to 18 months and 99 years and median to 64 years. One hundred and eighty-two patients were men and 106 women. Among the 270 observations for which aetiology has been established, 73 were classified MG of undetermined significance (MGUS), 160 myeloma (or plasmocytoma) and 37 other malignant MG (Waldenstrom's macroglobulinemia: 13, lymphoma: 9, alpha heavy chains disease: 6, primary amyloidosis: 5, chronic lymphocytic leukaemia: 4). Rheumatological affections (19.2%), infections and renal failure (10% each), haematological and autoimmune diseases (9.6% each) were pathologies most often associated with MGUS. Agarose gel electrophoresis did not show a monoclonal peak in 16% of the cases. In the 242 patients with a peak on electrophoresis, the peak was in the beta zone in 22% of cases and in the gamma zone in 78% of cases. The IgG isotype represents more than the half of the cases of our set (51.7%). IgG is even more predominant in the MGUS group (65.8%). The IgA isotype counts for 20.8% of the cases in our set and the free light chains (kappa or lambda) for 13.6% of the cases whereas the IgM represents 8.7% only of the 288 cases of our set which involves three cases of IgD myeloma and six cases of biclonal gammapathy.

In order to determine the sensitivity of CEA in the diagnosis of colo-rectal carcinoma, we studied a series of 48 patients with colo-rectal carcinoma (1992-1996). The sensitivity was at 52% with a reference value of 5 ng/ml and 68.7% for a reference value of 2.5 ng/ml. With a reference value of 5 ng/ml, the sensitivity of CEA was at 37% only for patients with colo-rectal carcinoma at Dukes B stage, 66.6% for patients at stage C and 75% for patients at stage D. The dosage of CEA was carried out with a sandwich immunoenzymatic technique in tube. There is no statistic significant correlation between the pre-operative rate of CEA and the localisation of the tumor and its histologic type; in contrast, it was significantly correlated with the ganglionnary metastasis. A significant relationship between the pre-operative rate of CEA and the Dukes stage was found for a reference value of 10 ng/ml but not for a reference value of 5 ng/ml. We calculated the specificity of the CEA for the cancers of colon and rectum which was at 76.98% with a reference value of 5 ng/ml and 86% with a reference value of 10 ng/ml.

Abnormal liver function in thyroid disorders may be secondary to thyrotoxicosis or to autoimmune injury to the liver. We report the case of a 36-year-old female who developed jaundice and pruritus with mild cholestasis and moderately elevated transaminase levels. The diagnosis of Graves' disease was made shortly thereafter. Laboratory findings were: alanine and aspartate aminotransferase 219 (IU/I (N: 9-50) and 102 IU/I (N: 10-15) respectively, alkaline phosphatase 336 IU/I (N: 40-135), bilirubin 24 micromol/I (N: 2-23), and gamma-glutamyl transpeptidase 232 IU/I (N: 9-43). Abdominal ultrasonography showed normal bile ducts; echocardiography ruled out heart failure; viral and autoimmune markers for hepatitis and cirrhosis were negative. Percutaneous liver biopsy showed moderate intrahepatic steatosis, anisokaryosis, lymphocyte infiltration in the portal areas, and Kupffer cell hyperplasia. Outcome was favorable after seven months of iodine therapy, confirming the diagnosis of thyrotoxicosis hepatitis.

Background: Cutaneous metastases from gastric carcinoma are uncommon and are exceptionally the first sign of disease., Case Report: A 33-year-old man presented with nodular lesions that had developed three months earlier on the trunk, scalp and limbs. A skin biopsy suggested a metastatic origin. The upper digestive fibroscopy with biopsy demonstrated gastric linitis. Due to the presence of bone metastasis, treatment was not initiated, the patient died four months later., Discussion: Metastatic dissemination of gastric carcinoma to the skin usually occurs in advanced stage disease. Generally, the skin lesions are painless hard nodules on the anterior abdominal wall. Our patient developed unusual inaugural skin metastases with an exceptional localization.

Introduction: Esophageal involvement in Hodgkin's disease, commonly known as a belated localization of the advanced forms, has been seldom reported (3 to 5% in post-mortem series and 0.7% in clinical series)., Exegesis: We report the case of a 61-year-old man who had an esophagus localization revealing Hodgkin's disease stage IV EBb of Ann Arbor classification. The originality of this case was represented by: the revelation mode of the esophageal involvement such as dysphagia and upper gastrointestinal bleeding; the localization at the distal third of the esophagus with contiguous involvement of the gastric fundus; the absence of mediastinal nodes showing the primitive character of the esophageal injury., Conclusion: This observation incites us to consider Hodgkin's disease in the list of differential diagnoses of tumoral dysphagia, even if there was no ganglionic and/or visceral localization of the disease.