With the use of medical image analysis, Artificial Neural Networks (ANNs) have shown effective results in the early detection and treatment of pancreatic cancer. A summary of current studies using ANNs for AI-driven pancreatitis cancer detection is given in this research study. Numerous studies have shown that ANNs could always identify and diagnose pancreatic cancer with high accuracy when utilizing CT scans, MRIs, and other types of medical scanning. The early detection of pancreatic cancer using ANNs is very useful for enhancing outcomes for patients. The design and implementation of ANNs for the detection of pancreatic cancer does not come without difficulties, though. These include the demand for extensive and varied datasets, the necessity for ongoing model training, and the necessity for point procedures to guarantee the precision and dependability of ANN-based diagnostic tools. In summary, using ANNs for AI-driven pancreatic detection of cancer has enormous potential to enhance outcomes for patients. These technologies need to be improved and validated, and the ethical and legal issues surrounding their usage in therapeutic contexts need to be addressed.
Bone marrow derived mesenchymal cells and stem cells are potential to differentiate into mature cells of various organs. Here we describe the initial results of study which suggests that these cells transformed to hepatocytes. In this prospective, single-center study during 18 months, liver biopsy were obtained from 9 patients who had undergone Co-Transplantation of Mesenchymal and Hematopoietic Stem Cell for beta thalassemia major class III. Five female patients and 4 male patients had received transplant from their sex mismatch donors. The biopsies were studied for the presence of donor-derived hepatocytes with the use of fluorescence in situ hybridization (FISH) and immunohistochemical staining (IHC) for CD45 (leukocyte common antigen), and a hepatocyte-specific antigen. According to sex mismatch transplantation, mixed donor-recipient XY-positive hepatocytes in liver specimen means that new chimer stem cells were originated from donor cells. These cells accounted for 8 to 70 percent of the cells on FISH slides and their hepatocyte properities were shown by IHC methods. Co-transplantation of Mesenchymal and Hematopoietic Stem Cell can enhance regeneration of mature hepatocytes in liver tissue.
Introduction Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results The “NDI Score (%)” consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.
Background: Breast cancer is one of the most common malignancies in the world and in Iran. Prevalence of this disease in Iran 21.4% was reported. One of the main alternatives for treating breast cancer is chemotherapy which causes complications such as acute and delayed nausea and vomiting. The aim of this study was to determine the effect of aromatherapy with peppermint oil on nausea and vomiting induced by chemotherapy among breast cancer patients.
Methods: This study was a randomized controlled trial on 100 women who suffering from Breast Cancer and receiving chemotherapy as outpatient's hospital care in Imam Khomeini. Before chemotherapy and after obtaining informed consent the patients with random sampling block were randomly allocated into intervention and control group. Intervention group received routine medications for controlling nausea and vomiting as well as aroma therapy with peppermint for five days. Meanwhile, the control group received only the routine medications. The data were gathered by using demographic and Rhodes Standard Questionnaire (about severity and number of nausea and vomiting). The results obtained from both groups were compared by using the SPSS version 11.5 software and descriptive and analytic statistics.
Results: The results showed there were no statistical differences between two groups in some variables such as age, duration of cancer, history of alcohol use, history of nausea and vomiting (p>0.05). Use of aromatherapy with peppermint in acute phase lead to decreased of nausea and vomiting without any complications (p 0.05). Indeed more than half of samples stated that they are satisfied with aromatherapy and recommend it to others.
Conclusions: Aromatherapy with peppermint in breast cancer patients could decrease nausea and vomiting in acute phase after chemotherapy. It is suggested that nurses use this aroma therapy as a complementary treatment, inexpensive and without complications for relieving the nausea and vomiting caused by chemotherapy.
Allogeneic hematopoietic stem cell transplantation (HSCT) is a potentially cure for acute myeloid leukemia (AML). Patients who undergone HSCT are at increased risk of infection due to impaired immunity.To evaluate the rate of bacterial, viral and fungal infection and its relationship with 2-year overall survival of AML patients who had undergone HSCT.This was a retrospective cross-sectional study of 49 patients who underwent allogenic bone marrow transplantation (BMT) from full-matched donors at BMT Center, Imam Khomeini Hospital Complex, Tehran, Iran, from 2006 to 2013. All autologous transplantations and promyelocytic leukemia (PML) transplantations were excluded.All patients, except for one, had fever for a mean of 7 days post-transplantation and received broad-spectrum antibiotic. The rate of severe sepsis was 6.1%. None of the patients developed fungal infection during admission. The rate of admission due to sepsis after discharge was 27% in the alive group (mean onset of 54 days), and 73% in the deceased group (mean onset of 52 days) (p<0.05). The most common site of infection was lung (70%). The rate of cytomegalovirus (CMV) antigenemia (positive PP65) was 20% during the 2-year period after HSCT.The rate of infection was a negative prognostic factor for 2-year overall survival. The rate of CMV antigenemia is less than similar studies (51%), which could be due to full-matched donor-recipients requiring less immunosuppression.
Disruptions to sleep have a substantial influence on people's overall health and quality of life. The conventional techniques for diagnosing and managing sleep disorders usually rely on subjective assessments and qualitative evaluations, that may have some accuracy and efficacy limitations. Nevertheless, recent developments in the field of artificial intelligence (AI) have presented new opportunities for better diagnosing and treating problems with insomnia. The paper reviews in depth the uses of AI in the domain of medical sleep medicine. We look at the use of algorithmic techniques for deep learning and machine learning for identifying indicators of sleep-related issues, the assessment of sleep quality, sleep tracking, and the establishment of individualized sleep therapeutics. We also discuss how AI is being used to construct forecasting models that may be used to identify individuals who are at risk of experiencing sleep issues and improve treatment strategies. In addition, we talk about the challenges and potential outcomes of incorporating AI-based techniques into clinical practice. Overall, our research highlights how AI has the potential to transform the field of sleeping medicine and improve outcomes for people with sleep-related conditions.
It is possible to improve patient care, assessment, and medicine with the integration of computational intelligence innovation into the health care system. However, there are significant social, legal, and ethical implications to the employment of AI in healthcare. They include worries about the impact on medical professionals and the healthcare system as a whole, as well as issues like privacy for patients, bias, and prejudice, as well as issues with transparency and responsibility. It is essential to carefully consider and manage these repercussions if artificial intelligence is to be used in medical treatment in a way that is morally right, legal, and socially responsible. The considerably expanded usage of machine cognitive ability (AI) systems for medical applications has brought about a number of significant benefits, including enhanced diagnosis, individualized treatment regimens, and significantly more efficient delivery of medical services. However, utilizing all these technologies also raises questions regarding sociological, legal, and ethical matters that must be considered. These implications include issues with fairness and discrimination, information privacy and security, comprehension and transparency, responsibility and accountability, and social inequity. Health practitioners, politicians, and researchers must concentrate on these issues to ensure the moral and completely accountable use of Ml in medicine. In order to promote the responsible implementation of these innovations, this paper highlights the need for ongoing discussions and collaboration and offers an overview of the ethical, legal, and sociological ramifications of using AI in the field of health care.
Industry 4.0 relies heavily on artificial-intelligence(AI) and cloud-computing(CC), both of which have been greatly aided by the 5th-generation mobile-network (5G). The arrival of 5G, however, is seen as a watershed moment that will radically alter the current global trends in wireless communication practices and the lives of the masses. 5G envisions a future where the digital and physical worlds merge. The 6th-generation (6G) wireless communication network will likely unite terrestrial, aerial, and maritime communications into a single, unified system that is both more stable and faster, and can accommodate a far larger number of devices with ultra-low latency needs. This research hopes to foresee a scenario in which 6G supersedes 5G as the dominant standard for wireless communication in the years to come. Several advances have been made, but the utopian period of instantaneous global communication, instantaneous global computation, and no latency has not yet arrived. This essay investigates the most significant obstacles and difficulties that the transition from 5G to 6G may encounter on the way to realizing these loftier goals. The vital goal of "technology for humanity" is to improve service to the world's most disadvantaged people, and this article lays out a plan for 6G that includes the enabling technology infrastructures, obstacles, and research directions that will get us there.
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