Inflammatory breast cancer: An overview about the histo-pathological aspect and diagnosis.

Inflammatory Breast Cancer (IBC) is a rare and aggressive form of locally advanced breast cancer, classified as stage T4d according to the tumor-node-metastasis staging criteria. This subtype of breast cancer is known for its rapid progression and significantly lower survival rates compared to other forms of breast cancer. Despite its distinctive clinical features outlined by the World Health Organization, the histopathological characteristics of IBC remain not fully elucidated, presenting challenges in its diagnosis and treatment. Histologically, IBC tumors often exhibit a ductal phenotype, characterized by emboli composed of pleomorphic cells with a high nuclear grade. These emboli are predominantly found in the papillary and reticular dermis of the skin overlaying the breast, suggesting a primary involvement of the lymphatic vessels. The tumor microenvironment in IBC is a complex network involving various cells such as macrophages, monocytes, and predominantly T CD8+ lymphocytes, and elements including blood vessels and extracellular matrix molecules, which play a pivotal role in the aggressive nature of IBC. A significant aspect of IBC is the frequent loss of expression of hormone receptors like estrogen and progesterone receptors, a phenomenon that is still under active investigation. Moreover, the overexpression of ERBB2/HER2 and TP53 in IBC cases is a topic of ongoing debate, with studies indicating a higher prevalence in IBC compared to non-inflammatory breast cancer. This overview seeks to provide a comprehensive understanding of the histopathological features and diagnostic approaches to IBC, emphasizing the critical areas that require further research.

[Reversed polarity high-cell carcinoma of the breast: A case report]. / Carcinome à cellules hautes et à polarité inversée du sein : à propos d'un cas.

Reverse polarity high-cell carcinoma of the breast, formerly known as reverse polarity solid papillary carcinoma, is a rare entity recently introduced into the latest edition of the WHO classification of breast tumors. Its phenotype is triple-negative, and its diagnosis difficult. Although few cases have been reported in the literature, knowledge of this breast tumor is essential to distinguish it from other triple-negative carcinomas, which have a poorer prognosis. We report a case of high-cell, inverted-polarity carcinoma of the breast in a 43-year-old female patient with no history of breast neoplasia and no palpable mass on clinical examination. The tumour was discovered following a screening echomammogram, which revealed a lesion classified ACR 4b. A microbiopsy of this lesion concluded that it was a papillary proliferation that should be removed. A lumpectomy was performed. Histopathological and immunohistochemical studies of the surgical specimen confirmed the diagnosis of high-cell, reverse-polarity carcinoma expressing calretinin and IDH1. Given the rarity of this entity, there is no standard treatment. In our case, a mastectomy without lymph node curage was performed. The extension work-up was negative and the patient received no adjuvant treatment. After 12 months, the patient is in complete remission. In this case report, we describe the histopathological, immunohistochemical and molecular features of this rare entity.

Circulating levels of FoxP3, M2 (sCD163) and IGF-1 as potential biomarkers associated with Laryngeal Squamous Cell Carcinoma in Tunisian patients.

We aim to assess the clinical impact of circulating levels of sCD163, FoxP3, IGF-1 in LSCC patients (Laryngeal Squamous Cell Carcinoma). The concentrations of sCD163, FoxP3, and IGF-1 were measured using ELISA test in the serum samples collected from 70 pretreatment LSCC patients and 70 age and sex-matched healthy controls. Statistical analysis was performed using ANOVA to compare the two groups, and the correlation between markers and clinical parameters. Receiver-Operator Characteristic (ROC) curve analysis was conducted to determine the optimal cutoff values and evaluate the diagnostic impact of these markers. Significant differences in the levels of sCD163, FoxP3, and IGF-1 were observed between LSCC patients and the control group, with respective p-values of 0.01, 0.022, <0.0001. The determined cutoff values for sCD163, FoxP3, IGF-1 concentrations were 314.55 ng/mL, 1.69 ng/mL, and 1.69 ng/mL, respectively. The corresponding area under the curve (AUC) values were 0.67 (95% CI: 0.57-0.76), 0.70 (95% CI: 0.61-0.80), 0.84 (95% CI: 0.76-0.92), respectively. Furthermore, it was found that IGF-1 concentrations exceeding 125.20 ng/mL were positively correlated with lymph node metastasis. Elevated serum levels of sCD163, FoxP3 and IGF-1 are associated with the diagnosis of LSCC. IGF-1 appears to be the most promising indicator for the LSCC progression.

Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis.

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization's network security. This is because IDSs serve as the organization's first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs' performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model's performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF's capabilities in intrusion detection and network security solutions.

SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq.

In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has been a new trend. However, most popular deep learning models for peptide sequencing prediction suffer from poor interpretability and poor ability to capture long-range dependencies. To solve these issues, we propose a model named SeqNovo, which has the encoding-decoding structure of sequence to sequence (Seq2Seq), the highly nonlinear properties of multilayer perceptron (MLP), and the ability of the attention mechanism to capture long-range dependencies. SeqNovo use MLP to improve the feature extraction and utilize the attention mechanism to discover key information. A series of experiments have been conducted to show that the SeqNovo is superior to the Seq2Seq benchmark model, DeepNovo. SeqNovo improves both the accuracy and interpretability of the predictions, which will be expected to support more related research.

Solid-basaloid variant of adenoid cystic carcinoma of the breast unresponsive to neoadjuvant chemotherapy: Case report.

INTRODUCTION AND IMPORTANCE: Adenoid cystic carcinoma (ACC) is a rare triple-negative breast cancer, accounting for only 0.1 % of all primary breast carcinomas. At variance with the classic variant, the solid-basaloid variant of ACC (SB-ACC) is clinically more aggressive and has different molecular features. There is, currently, no consensus regarding the treatment of SB-ACC of the breast, especially the use of neoadjuvant chemotherapy. CASE PRESENTATION: Here, we present a rare case of SB-ACC in an elderly female patient, with no history of breast carcinoma, who presented with a 4.5 cm central round mass invading the nipple. Given the locally advanced triple negative breast cancer and the invasion of the nipple-areolar complex, the patient has received neoadjuvant chemotherapy followed by surgical treatment. On histopathological examination, the diagnosis of SB-ACC, non-responsive to neoadjuvant chemotherapy, with absence of rearrangement of the MYB gene was retained. The patient received adjuvant radiation therapy and was ambulatory followed without recurrence at the 12-month follow-up. DISCUSSION/CONCLUSION: This case provided direct evidence that SB-ACC of the breast wasn't responsive to neoadjuvant chemotherapy but cannot allow for definitive conclusions on chemotherapy recommendations. For this reason, more data must be published to investigate the real value of neoadjuvant chemotherapy in SB-ACC.

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments.

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.

[Sex cord tumors with annular tubules: About 4 cases and literature review]. / Les tumeurs des cordons sexuels à tubules annelés : à propos de 4 cas et revue de la littérature.

Sex cord tumor with annular tubules (SCTAT) is a rare ovarian tumor. It belongs to sex cord and stromal tumor of the ovary and represents less than 1% of cases. It includes two forms: the first one associated with Peuz-Jeghers syndrome and the second sporadic. We report 4 cases of SCTAT collected at the department of pathology of Salah Azaiez Institute of Tunis over the 12 last years. The age ranged from 10 to 32 years. Symptoms were non specific except for one case revealed by precocious puberty. One patient had Peutz-Jeghers syndrome associated. Tumors were unilateral. Gross findings showed often a solid tumor with yellow cut surface. Their size ranged from 0.5cm to 28cm. Their morphological features were characteristic. Immunohistochemistry showed that tumor cells expressed inhibin and claretinin. The treatment was surgical, often conservative. The diagnosis of malignancy wasn't focused on histological features, but on tumor extension, clinical course, and presence of metastases. Evolution was often favorable. We also performed a systematic review of the literature that identified 166 cases. Features of these cases were studied. We also compared these features between sporadic and syndromic forms and between benign and malignant forms. In conclusion, SCTAT is a rare tumor, usually benign. Its diagnosis is based on histological examination. There is a malignant potential especially in sporadic forms, estimated at 20%. Treatment is most often conservative, based on oophorectomy.

Mucinous borderline ovarian tumors: pathological and prognostic study at Salah Azaiez Institute.

Introduction: ovarian Mucinous Borderline Tumors (MBT) are characterized by an epithelial proliferation similar to those of well differentiated adenocarcinomas but are distinguished by the absence of stromal invasion. They are often difficult to diagnose histologically. The aim of the work was to specify the pathological and clinical features and to highlight the prognostic of these tumors. Methods: study was retrospective including 49 cases of primary ovarian MBT, diagnosed at the Patholgy Department of Salah Azaiez Institute from 1992 to 2019. Results: median age was 48 years old. Histologically, the cases were divided into 34 cases of pure MBT, 13 cases with intraepithelial carcinoma and 2 cases associating an intraepithelial carcinoma with microinvasion. The majority of our cases were classified FIGO I and only one case FIGO III. Sixteen patients received conservative treatment and 30 received radical treatment. The treatment wasn't specified in three patients. The prognosis was good in the majority of cases. Only one patient had a contralateral recurrence after a follow-up period of three years. There were no significant differences regarding the risk of recurrence and risk factors such as age, gestation, hormonal status, FIGO stage and conservative treatment. We raised this part. Conclusion: the prognosis of the ovarian MBT is good. However, it is necessary to multiply the samples to avoid missing a carcinomatous focus with an anarchic invasion of the stroma which constitutes a poor prognosis factor. It was changed by these sentences below: the diagnosis of MBT is not easy. Indeed, the distinction of MBT from carcinomas remains the greatest challenge for pathologists. Once this diagnosis is made with certainty, the tumor can be considered to have a good prognosis, especially stage I tumors which are the most common.

A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks.

The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update procedures, and unsecured network connections. Traditional static IoT malware detection and analysis methods have been shown to be unsatisfactory solutions to understanding IoT malware behavior for mitigation and prevention. Deep learning models have made huge strides in the realm of cybersecurity in recent years, thanks to their tremendous data mining, learning, and expression capabilities, thus easing the burden on malware analysts. In this context, a novel detection and multi-classification vision-based approach for IoT-malware is proposed. This approach makes use of the benefits of deep transfer learning methodology and incorporates the fine-tuning method and various ensembling strategies to increase detection and classification performance without having to develop the training models from scratch. It adopts the fusion of 3 CNNs, ResNet18, MobileNetV2, and DenseNet161, by using the random forest voting strategy. Experiments are carried out using a publicly available dataset, MaleVis, to assess and validate the suggested approach. MaleVis contains 14,226 RGB converted images representing 25 malware classes and one benign class. The obtained findings show that our suggested approach outperforms the existing state-of-the-art solutions in terms of detection and classification performance; it achieves a precision of 98.74%, recall of 98.67%, a specificity of 98.79%, F1-score of 98.70%, MCC of 98.65%, an accuracy of 98.68%, and an average processing time per malware classification of 672 ms.

Association of ZEB1 and Vimentin with poor prognosis in metaplastic breast cancer.

Zinc finger E-box binding homeobox factor 1 (ZEB1) is a transcription factor involved in the epithelial to mesenchymal transition (EMT) process of metaplastic breast cancer (MBC). This study aimed to assess the expression of ZEB1 in MBC and explore its association with clinicopathological factors and prognosis. We analyzed the immunohistochemical expression of ZEB1 in 50 MBC tissue samples. ZEB1 was overexpressed in 36% (18/50) of cases. ZEB1 overexpression was significantly correlated to fibromatosis-like and spindle cell sarcoma subtypes (P < 0.001) and tended to be correlated to metastatic status (P = 0.069). Using the Kaplan-Meier method, ZEB1 expression was significantly associated with poor 5-years overall survival (OS) (P = 0.001) and relapse-free survival (RFS) (P = 0.0001). The multivariate Cox regression analysis showed that ZEB1 positive remained a significantly independent adverse prognostic factor for RFS and OS (HR = 4.9 [2.14-11.53]; P < 0.0001) and (HR = 4 [1.05-15.18]; P = 0.042), while Vimentin was an independent poor prognostic factor only for RFS (HR = 5.69 [1.79-18.11], P = 0.003). Our results indicated that ZEB1 and Vimentin overexpression might serve as adverse prognostic factors and potential therapeutic targets for MBC patients.

Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization.

Information fusion in automated vehicle for various datatypes emanating from many resources is the foundation for making choices in intelligent transportation autonomous cars. To facilitate data sharing, a variety of communication methods have been integrated to build a diverse V2X infrastructure. However, information fusion security frameworks are currently intended for specific application instances, that are insufficient to fulfill the overall requirements of Mutual Intelligent Transportation Systems (MITS). In this work, a data fusion security infrastructure has been developed with varying degrees of trust. Furthermore, in the V2X heterogeneous networks, this paper offers an efficient and effective information fusion security mechanism for multiple sources and multiple type data sharing. An area-based PKI architecture with speed provided by a Graphic Processing Unit (GPU) is given in especially for artificial neural synchronization-based quick group key exchange. A parametric test is performed to ensure that the proposed data fusion trust solution meets the stringent delay requirements of V2X systems. The efficiency of the suggested method is tested, and the results show that it surpasses similar strategies already in use.

Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images.

By the start of 2020, the novel coronavirus (COVID-19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID-19 rapidly and effectively is by analyzing chest X-ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND-CNN) architecture for the recognition of COVID-19. This network consists of a set of differently-sized hidden layers all created from scratch. The performance of this RND-CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID-19 datasets. Each of these datasets consists of medical images (X-rays) in one of three different classes: chests with COVID-19, with pneumonia, or in a normal state. The proposed RND-CNN model yields encouraging results for its accuracy in detecting COVID-19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID-19 dataset.

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity.

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Future Forecasting of COVID-19: A Supervised Learning Approach.

A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.

Primary Paratesticular Leiomyosarcoma: A Case Report and Literature Review.

Paratesticular soft tissue sarcomas are very rare malignant mesenchymal tumors. With only few cases reported in the literature, data regarding diagnostic and management of these tumors are limited. We reported a case of primary paratesticular leiomyosarcoma in a 72-year-old man complaining of a progressively growing painless right scrotal mass. The patient underwent radical inguinal right orchiectomy and adjuvant 3D conformal radiotherapy to the tumor bed including the surgical scar. The prescription dose was 54 Gy, and no pelvic irradiation was performed. He remained free of recurrence for the last 16 months.

Association between HER2 and IL-6 genes polymorphisms and clinicopathological characteristics of breast cancer: significant role of genetic variability in specific breast cancer subtype.

Clinical implications of single nucleotide polymorphisms (SNPs) in breast cancer have been explored to determine the impact of SNP in modulating the pathogenesis of breast cancer. This study aimed to evaluate the association between HER2 (rs2517956) and (IL-6) (rs1800795 and rs2069837) and clinicopathological characteristics in HER2-positive and HER2-negative breast cancer in Tunisian women. A retrospective cohort study included 273 patients. Genomic DNA was extracted from peripheral blood samples, and genotyping of selected SNP was performed by PCR-RFLP assays. Statistical analysis was then carried out to assess genotypic frequencies and genetic association in relation to breast cancer subtypes. SHEsis software was applied to IL-6 haplotypic structure analysis. The distribution of genotype frequencies of rs2517956, rs1800795 and rs2069837 showed no statistically difference between HER2-positive and HER2-negative breast cancer. HER2 (rs2517956) was associated with tumor size (p = 0.01) and age at diagnosis (p = 0.02) in HER2-negative breast cancers, but no significant association was observed in HER2-positive breast cancer. For IL-6 gene, none of the clinicopathological parameters were associated with rs1800795 and rs2069837 in both breast cancer subtypes (p > 0.05). SHEsis analysis revealed a high linkage disequilibrium between rs1800795 and rs2069837; differences in the distribution of IL-6 two loci haplotypes were statistically negative between HER2-positive and HER2-negative breast cancer (p = 0.20) which confirmed no association with HER2 overexpression. This study demonstrates that rs2517956 is associated with clinicopathological characteristics in HER2-negative breast cancer, which could have a differential prognostic role compared to HER2-positive breast cancer.