Hospital Preparedness for the Covid-19 Crisis; an Overview

Aim: The situation, structure, and facilities of hospitals change in a crisis, which negatively affects the provision of care quality of health services. One of the current world crises is the Covid-19 pandemic. This study aimed to investigate the preparedness of hospitals to deal with the Covid-19 crisis. Materials and Methods: This narrative review searched the SID, PubMed, Scopus, Google Scholar databases/search engines in published articles between 2019-2022. A search strategy was defined for PubMed and it was translated into other selected databases. Also, the reference list of the included articles was searched. The databases/search engines were searched by two authors independently, and any disagreement was resolved through discussions. To find related articles, Iranian and International databases were searched using Persian keywords and their English equivalents (Covid-19, Hospital, Preparedness, epidemic, and Pandemic). Results: A total of 311 articles were found, of which 15 were reviewed. Inclusion criteria included being an original paper, in Persian or English, and compliance with the purpose of the study. The exclusion criteria included not having access to the full text of the article. The study showed that hospital preparedness against the Covid-19 pandemic in most countries and different regions in Iran is not optimal. Hospitals should be prepared in terms of personal protective equipment, staffing, and beds. Rapid response management and hospital equipment should be strengthened

Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services Dispatching; a Letter to Editor

The emergency medical dispatcher (EMD) serves as a crucial link between individuals in need of emergency medical assistance and the emergency medical services (EMS) resource delivery system. Through their expertise and training, EMDs are able to accurately assess emergency situations, provide appropriate guidance over the phone, and dispatch the necessary EMS personnel to the scene. With adequate training, program management, supervision, and medical guidance, the EMD can accurately assess the caller's needs, choose an appropriate response approach, furnish relevant information to responders, and offer suitable assistance and guidance to patients through the caller. By diligently adhering to a written and medically approved EMD protocol, informed decisions regarding EMS responses can be made in a reliable, replicable, and fair manner (1, 2)

Artificial intelligence and Nursing: Dawn of a new era?

Artificial Intelligence (AI) has emerged as a transformative force across various industries and healthcare. With the ever-expanding capabilities of AI, there is growing interest in the exploration of its potential applications in nursing practice to enhance patient care, improve workflow efficiency, and possibly revolutionize healthcare systems

Metabolomics in systems medicine: an overview of methods and applications

Patient-derived metabolomics offers valuable insights into the metabolic phenotype underlying diseases with a strong metabolic component. Thus, these data sets will be pivotal to the implementation of personalized medicine strategies in health and disease. However, to take full advantage of such data sets, they must be integrated with other omics within a coherent pathophysiological framework to enable improved diagnostics, to identify therapeutic interventions, and to accurately stratify patients. Herein, we provide an overview of the state-of-the-art data analysis and modeling approaches applicable to metabolomics data and of their potential for systems medicine

Clinical Significance and Different Expression of Dipeptidyl Peptidase IV and Procalcitonin in Mild and Severe COVID-19

Background: Coronavirus has become a global concern in 2019-20. The virus belongs to the coronavirus family, which has been able to infect many patients and victims around the world. The virus originated in the Chinese city of Wuhan, which eventually spread around the world and became a pandemic. Materials and Methods: A total of 60 Patients with severe (n=30) and mild (n=30) symptoms of COIVD-19 were included in this study. Peripheral blood samples were collected from the patients. Real-time PCR was used to compare the relative expression levels of Procalcitonin and dipeptidyl peptidase IV (DPPIV) in a patient with severe and mild Covid-19 infection. Results: Procalcitonin and dipeptidyl peptidase IV markers in the peripheral blood of patients with severe symptoms, were positive in 29 (96.60%) and 26 (86.60%), respectively (n=30); however, positive rates in the mild symptoms patients group were 27 (90%) and 25 (83.30%), respectively. There was a statistically significant difference between these two groups in terms of DDPIV and Procalcitonin (p<0.001). Conclusion: Procalcitonin and DPPIV increase in patients with COVID-19 infection, significantly higher in the patients with more severe clinical symptoms than those with milder ones. More studies will be needed to verify the reliability of the current findings. Keywords: Procalcitonin, DPPIV, Severe symptoms, Mild symptoms, COVID-1

Provision of Healthcare Services for Children in Iran: Common Ethical Principles and Obstacles to Successful Implementation

Ethics is an essential element in the provision of healthcare services. Fundamental ethical values determine the manner in which the professional behavior is implemented in the healthcare area. These ethical principles find meaning in time and place and in the social context of ethical values and among children as vulnerable groups. So, this study examined the ethical principles of providing health care services for children and barriers to their application in Iran from key informants’ perspective. Therefore, qualitative content analysis method was used by means of semi-structured questionnaire to theoretical saturation scale with the participation of 20 key informants. Each interview underwent the process of implementation, evaluation, coding, and analysis, and then its findings were presented in two dimensions: desirable principles and barriers for its application, including 15 classes. Desirable principles include autonomy, beneficence, non-maleficence, justice, confidentiality, accent, consent, and participation. Obstacles to their compliance also included weakness of the policy landscape, weakness of the judicial system, cultural conservatism, socio-economic inequality, services commodification with unequal distribution, resource mismanagement (limitation), weakness of the professional education system, and the emergence of complex situations. From the key informants’ point of view, codes of ethics do not differ significantly from international principles, but their application is faced with difficulties, and they are likely to be improved through evidence-based policies according to the results of scientific studies

Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain

Alzheimer’s disease (AD) is a neurodegenerative disease and the major cause of dementia, affecting more than 50 million people worldwide. Chronic pain is long-lasting, persistent pain that affects more than 1.5 billion of the world population. Overlapping and heterogenous symptoms of AD and chronic pain conditions complicate their diagnosis, emphasizing the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms. To characterize disease pathology of AD, we measured the protein changes in the temporal neocortex region of the brain of AD subjects using mass spectrometry (MS). We found proteins involved in exo-endocytic and extracellular vesicle functions displaying altered levels in the AD brain, potentially resulting in neuronal dysfunction and cell death in AD. To detect novel biomarkers for AD, we used MS to analyze cerebrospinal fluid (CSF) of AD patients and found decreased levels of eight proteins compared to controls, potentially indicating abnormal activity of complement system in AD. By integrating new proteomics markers with absolute levels of Aβ42, total tau (t-tau) and p-tau in CSF, we improved the prediction accuracy from 83% to 92% of early diagnosis of AD. We found increased levels of chitinase-3-like protein 1 (CH3L1) and decreased levels of neurosecretory protein VGF (VGF) in AD compared to controls. By exploring the CSF proteome of neuropathic pain patients before and after successful spinal cord stimulation (SCS) treatment, we found altered levels of twelve proteins, involved in neuroprotection, synaptic plasticity, nociceptive signaling and immune regulation. To detect biomarkers for diagnosing a chronic pain state known as fibromyalgia (FM), we analyzed the CSF of FM patients using MS. We found altered levels of four proteins, representing novel biomarkers for diagnosing FM. These proteins are involved in inflammatory mechanisms, energy metabolism and neuropeptide signaling. Finally, to facilitate fast and robust large-scale omics data handling, we developed an e-infrastructure. We demonstrated that the e-infrastructure provides high scalability, flexibility and it can be applied in virtually any fields including proteomics. This thesis demonstrates that proteomics is a promising approach for gaining deeper insight into mechanisms of nervous system disorders and find biomarkers for diagnosis of such diseases

Identification of genes involved in T-cell differentiation

Background: T-cells are involved in many immune functions. Each function is carried out by specific sub-set of T-cells. All T-cell sub-types originate from one stem cell and the fate of each cell is dictated by its pattern of gene expression. The pattern of gene expression is the direct outcome of genetic regulatory network which can be visualized as a network containing nodes (genes) with edges (interaction) between them.  Simulation of the dynamics of gene regulatory networks reveals several attributes of not only the network itself but also the pattern of gene expression of different developmental or differentiation processes. Since gene regulatory networks often include thousands of genes, the network has to shrink or contain only a small subset of possible states of the large networks can be explored through the simulation. Therefore, different methods are needed to extract information from gene regulatory network. Methods: Central T-cell Network (the main gene regulatory network in T-cells) is used to start the simulation with customized random Boolean networks. Because of large scale of the network an initiative approach was used to reduce the number of possible states which were needed to be explored. Graph theory was used to find the attractors. GO analysis was used to find information in attractors. Clustering methods were applied on attractors in order to find groups of interesting gene states. Finally, data mining and microarray data analysis were utilized to verify the simulation system. Results: Forty experiments resulted in 833 attractors (with period 2 or 4). GO analysis (performed on most frequent attractors) resulted in no significance in T-cell differentiation processes. Clustering methods classified each type of attractors to exactly two different clusters. The simulated gene expression divided the genes into 3 groups, and GO analysis did not show significance in any differentiation process. The result of gene expression ratio of CD4+ and CD8+ cells showed a significant difference between the microarray data experiments and simulated gene expression ratios. Finally, the result of data mining suggested that CD4+ cells were located in one of the clustered attractors. Conclusion: A new environment was developed to simulate the dynamics of the gene regulatory network in T-cells. A novel approach was used to reduce the state space and in finding attractors. The resulting attractors were analyzed by several experiments. Although the genes involved in differentiation processes were distributed sporadically on the attractor clusters, CD4+ related genes were clustered in one group. This indicates the usability of the system for distinguishing different cell types. The result also indicates that the system can be used not only for T-cells but also for any biological network. A conclusion can be drawn that this new system is applicable for different networks but more experiments with different parameters are needed to verify the simulation system. Keywords: T-cells differentiation, immunology, random Boolean networks, genes involved in T-cell differentiatio

Identification of genes involved in T-cell differentiation

Background: T-cells are involved in many immune functions. Each function is carried out by specific sub-set of T-cells. All T-cell sub-types originate from one stem cell and the fate of each cell is dictated by its pattern of gene expression. The pattern of gene expression is the direct outcome of genetic regulatory network which can be visualized as a network containing nodes (genes) with edges (interaction) between them.  Simulation of the dynamics of gene regulatory networks reveals several attributes of not only the network itself but also the pattern of gene expression of different developmental or differentiation processes. Since gene regulatory networks often include thousands of genes, the network has to shrink or contain only a small subset of possible states of the large networks can be explored through the simulation. Therefore, different methods are needed to extract information from gene regulatory network. Methods: Central T-cell Network (the main gene regulatory network in T-cells) is used to start the simulation with customized random Boolean networks. Because of large scale of the network an initiative approach was used to reduce the number of possible states which were needed to be explored. Graph theory was used to find the attractors. GO analysis was used to find information in attractors. Clustering methods were applied on attractors in order to find groups of interesting gene states. Finally, data mining and microarray data analysis were utilized to verify the simulation system. Results: Forty experiments resulted in 833 attractors (with period 2 or 4). GO analysis (performed on most frequent attractors) resulted in no significance in T-cell differentiation processes. Clustering methods classified each type of attractors to exactly two different clusters. The simulated gene expression divided the genes into 3 groups, and GO analysis did not show significance in any differentiation process. The result of gene expression ratio of CD4+ and CD8+ cells showed a significant difference between the microarray data experiments and simulated gene expression ratios. Finally, the result of data mining suggested that CD4+ cells were located in one of the clustered attractors. Conclusion: A new environment was developed to simulate the dynamics of the gene regulatory network in T-cells. A novel approach was used to reduce the state space and in finding attractors. The resulting attractors were analyzed by several experiments. Although the genes involved in differentiation processes were distributed sporadically on the attractor clusters, CD4+ related genes were clustered in one group. This indicates the usability of the system for distinguishing different cell types. The result also indicates that the system can be used not only for T-cells but also for any biological network. A conclusion can be drawn that this new system is applicable for different networks but more experiments with different parameters are needed to verify the simulation system. Keywords: T-cells differentiation, immunology, random Boolean networks, genes involved in T-cell differentiatio