Inflammatory dilated cardiomyopathy associated with psoriasis: a case report.

BACKGROUND: Psoriasis is a chronic inflammatory skin disease with a genetic basis. Psoriasis is accepted as a systemic, immune-mediated disease. Hypertension, obesity, metabolic disorders including diabetes mellitus and hyperlipidemia, and psychiatric disorders are more prevalent among children with psoriasis compared to children without psoriasis. In this study, we report a case of dramatic response of inflammatory cardiomyopathy to anti-inflammatory treatment of psoriasis; which might reveal similar pathogenesis basis of these two diseases. CASE PRESENTATION: A 9-year-old Caucasian boy presenting with signs and symptoms of heart failure refractory to conventional therapies was admitted to our pediatric cardiology service. As the patient also had psoriasis, and considering the fact that there might be an association between the two conditions, immunosuppressive drugs were administered, which led to a dramatic improvement in heart function. CONCLUSIONS: The results of this study add to evidence linking psoriasis with inflammatory dilated cardiomyopathy. Clinicians, particularly cardiologists, must pay special attention to the cardiac complications of systemic diseases.

Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning.

Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white-red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.

Risk and Protective Factors Affecting Drug Craving among Patients with Substance Use Disorders Undergoing Opioid Agonists Maintenance Therapy.

Background: In this study, the purpose was to investigate the risk and protective factors affecting craving among patients with substance use disorders (SUDs) on buprenorphine, methadone, or opium maintenance treatment in Isfahan, Iran. Methods: In the current cross-section path analysis model, the statistical population was all SUD patients in Isfahan who were under treatment with Methadone or Buprenorphine in 2018. The study sample included 482 people who were on maintenance treatment in Isfahan who were selected by random sampling in two stages. The Franken, Hendriks, and Brink Opiate Craving Questionnaire (OCQ), Substance Related Beliefs Questionnaire (SRBQ), Cognitive Emotion Regulation Questionnaire (CERQ), Patient Health Questionnaire, Sixbey Family Resilience Assessment Scale (FRAS) and the Self-Resiliency Scale (SRS) were used to collect data. A path analysis method and PLS software were used to analyze the data. Results: The results showed that the direct impacts of self-resilience (ß=-0.147, p = 0.009) and uncompromising strategies (ß = 0.249, p = 0.0001) on depression are significant. Also, the direct effects of belief in drugs (ß = 0.518, p = 0.0001) and depression (ß = 0.219, p = 0.0001) on craving are significant. Conclusion: Substance-related beliefs play an essential role in craving both directly and indirectly. The results of the present study can be used to carry out educational and therapeutic interventions for drug SUD patients.

Metaverse and Medical Diagnosis: A Blockchain-Based Digital Twinning Approach Based on MobileNetV2 Algorithm for Cervical Vertebral Maturation.

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer.

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment.

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Evaluation of autoantibodies against vimentin and α-enolase in rheumatoid arthritis patients.

INTRODUCTION: Rheumatoid arthritis (RA) is categorized as an autoimmune disease with a frequency of 0.2-1% worldwide. It is reported that various autoantibodies are produced in the RA population, particularly against citrullinated peptides. Among various candidate markers for RA diagnosis, the citrullinated proteins have the highest specificity and sensitivity for both diagnosis and prognosis of RA. Anti-mutated citrullinated vimentin and α-enolase constitute a new class of autoantibodies for early detection of RA. MATERIAL AND METHODS: 45 serum samples and 19 synovial fluid (SF) specimens collected from RA patients were considered for American College of Rheumatology criteria and 20 serum samples and 10 SF specimens were provided from healthy subjects as a control group. To assess the quantity of anti-citrullinated protein antibodies (ACPA), anti-mutated citrullinated vimentin (MCV) and anti-α-enolase in the serum and SF of RA patients were determined by the enzyme-linked immunosorbent assay (ELISA) method. For the evaluation of disease activity and joint destruction, we used the Disease Activity Score of 28 joints based on erythrocyte sedimentation rate (ESR) Disease Activity Score 28 (DAS28). Furthermore, to measure the molecular weight of vimentin and α-enolase, electrophoresis on 10% SDS-PAGE was performed as described before. RESULTS: The anti-α-enolase level among serum samples from RA patients was significantly higher than in healthy subjects (4.49 ±0.20 ng/ml vs. 0.76 ±0.12 ng/ml) (p < 0.001). There was a direct relation between α-enolase quantity and (rheumatoid factor) RF and C-reactive protein (CRP) levels. The mean ESR value in positive and negative ACPA patients was 38.2 ±22.6 mm/h and 9.2 ±5.8 mm/h respectively (p < 0.0001). The mean DAS28-ESR was 3.3. The level of anti-MCV in the serum of RA patients (244.6 ±53.3 U/ml) was higher than in serum of the healthy group (148.73 ±71.8) (p < 0.0001). The level of anti-MCV in the SF of patients was 687.5 ±148.4 U/ml. CONCLUSIONS: In conclusion, both autoantibodies against MCV and α-enolase are two important markers that increase in serum and SF of RA patients and are specific for diagnosis of RA disease.

Shiga toxin-producing Escherichia coli isolated from chicken meat in Iran: serogroups, virulence factors, and antimicrobial resistance properties.

The aim of the current study was to determine the virulence factors, serogroups, and antibiotic resistance properties of Shiga toxin-producing Escherichia coli isolated from chicken meat samples. A total of 422 chicken meat samples were collected from 5 townships of Iran. Specimens were immediately transferred to the laboratory in a cooler with an ice pack. Samples were cultured, and the positive culture samples were analyzed by PCR assays. Finally, the antimicrobial susceptibility test was performed using the disk diffusion method in Mueller-Hinton agar. According to the results, out of 422 samples, 146 (34.59%) were confirmed to be E. coli positive and among E. coli-positive samples, 51 (34.93%) and 31 (21.23%) were from attaching and effacing E. coli (AEEC) and enterohemorrhagic E. coli (EHEC) subgroups, respectively. All of the EHEC-positive samples had all stx1, eaeA, and ehly virulence genes, whereas only 5 (9.80%) of AEEC subgroup had all stx1, stx2, and eaeA genes. As the data revealed, O157 was the most prevalent and O111 was the least prevalent strains in the Shiga toxin-producing E. coli (STEC) population. Among STEC strains, sulI and blaSHV had the highest and lowest incidence rate, respectively. There was a high resistance to tetracycline (76.82%), followed by chloramphenicol (73.17%) and nitrofurantoin (63.41%), but there was low resistance to cephalotine (7.31%) antibiotics in isolated strains. Results shows that the PCR technique has a high performance for detection of serogroups, virulence genes, and antibiotic resistance genes in STEC strains. This study is the first prevalence report of detection of virulence genes, serogroups, and antibiotic resistance properties of STEC strains isolated from chicken meat samples in Iran. Based on the results, chicken meat is one of the main sources of STEC strains and its virulence factors in Iran, so an accurate meat inspection would reduce disease outbreaks.