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ABSTRACTEpidermolysis bullosa acquisita is a rare acquired autoimmune subepidermal blistering disease that clinically resembles other vesiculobullous lesions such as pemphigus vulgaris and cicatricial pemphigoid. Multiple myeloma is the most common plasma cell malignant disorder characterized by a single clonal expansion and increased level of a single immunoglobulin. Epidermolysis bullosa acquisita has been reported with other systemic diseases such as lymphoma. In this case report, we present a patient with paraneoplastic epidermolysis bullosa acquisita associated with multiple myeloma.

Processes as one of the valuable knowledge resources can create sustainable competitive advantages in organizations. There is a large number of processes in organizations. They generate a high volume of process data that leads to the high‐dimensionality problems, complex relationships, dynamic changes, and difficulties in the understanding of the process by human resources. Traditional process improvement methodologies have weaknesses in environment with the large number of processes. Data mining techniques can support process improvement in this environment. They can recommend the improvement suggestions through extracting valuable patterns from a high volume of the process dataset. Recently, knowledge‐intensive processes have been increasingly concentrated in the field of process improvement. These types of processes can induce a competitive behavior over the other processes. The main problem is the improvement of competitive and knowledge‐intensive processes in a high volume of process dataset.The main purpose of this paper is to present a model to identify the behavior of competitive and knowledge‐intensive processes and recommend improvement suggestions. For this purpose, data mining techniques are applied to extract valuable patterns hidden in a high volume of process dataset. In this regard, K‐means clustering and C5 classification algorithms are applied to extract valuable patterns. A real process dataset was used to evaluate the effectiveness and applicability of the model. The results confirmed that the proposed model can apply data mining techniques to support competitive and knowledge‐intensive process improvement in a high volume of process dataset.

Purpose: Industry 4.0 has profoundly impacted the structure of businesses and organizations worldwide, and the digital revolution has led to the transformation of the supply chain of various industries, including the supply chain of banking services. Therefore, it is necessary to redesign the supply chain in the new era.
Methodology: This study investigated the supply chain of banking services of Industry 4.0 and its simulation based on agent-based modeling. This research model simulates the profit flow and other important financial variables related to agents such as customers, banks, fintechs, and other players in the supply chain of digital banking services.
Findings: The simulation results in different time periods show the amount of changes in important parameters in the face of digital transformations in Industry 4.0. Factors such as the connection of organizations and fintechs to the supply chain of banking services, as well as the removal of physical branches and the use of digital tools, change the structure of the chain in terms of information evolution, service evolution, and financial evolution.
Originality/value: This paper is one of the first studies to analyze the effects of Industry 4.0 on the banking service supply chain and can be considered a starting point for digital banking supply chain studies.

Purpose of the study: The current paper seeks to evaluate the feasibility of good governance in the Ministry of Health (Case study at Tehran University of Medical Sciences). In this regard, good governance and providing its requirements including participation, rule of law, transparency, accountability, consensus, fairness, efficiency, and effectiveness. Methodology: The population of this study were managers at Tehran University of Medical Sciences. According to the characteristics of the population in which the number of employees and experts is unlimited and uncertain, 300 questionnaires were distributed and 267 questionnaires were collected. Main Findings: The result of the Friedman test for ranking the components of good governance showed that the highest average among good governance factors was related to the resource factor. Structure factors were placed in the second rank and process factors had the last rank among good governance factors. Applications of this study: The results of this study can be applied in the government's decision and as a result, effective management of policy-making, assistance in the implementation of general policies of the administrative system of the country, achieving the positive consequences of the effective implementation of the country's policy-making management system, helping the growth and development of the organization, managers, and employees of the organization. Novelty/Originality of this study: According to the studies and archival studies in the field of good governance, the model of good governance in the Ministry of Health and Medical Education in Iran has not been worked on before. This can lead to the expansion of knowledge and the production of science.

Mental health is a fundamental human right and is part of the well-being of society. The public health burden of mental health disorders affects people's social and economic status around the world. Coronavirus's (COVID-19) negative impact on the economy and mental health worldwide is concerning. This is a worldwide emergency, and there is an urgent need for research about this topic to prevent long-lasting adverse effects on the population. Unpreparedness and inconsistencies in guidelines, lockdowns, containment strategies, unemployment, financial losses, physical distancing, isolation, chaos, and uncertainty are among factors that lead to a rise in emotional distress, anxiety, and depression. Governments' decisions affect the socioeconomic status of a country and the psychological well-being of the people. COVID-19 pandemic exposed disparities in multiple mental health care systems by having adverse mental health effects in people with pre-existing mental health disorders and previously healthy individuals. Aggregation of concurrent or cumulative comorbid risk factors for COVID-19 disease and its psychosocial sequelae could provide invaluable information for the public health stakeholders. This review aims to address the burden and the psychosocial impact of the COVID-19 pandemic, the challenges and opportunities facing mental health systems, and proposes new strategies to improve the mental health outcomes in the post-COVID era.

Background:
Patients with cardiovascular diseases often experience fear of death, depression and anxiety, all of which are linked to a heightened risk of future cardiac events. Research indicates that improved empathy is associated with a reduced risk of such events, making the enhancement of empathy among cardiac nurses crucial. Knowledge brokering, a strategy that utilises various interventions to strengthen practice, is key to achieving this.
Purpose:
This study aims to examine the impact of knowledge brokering on nurses' empathy towards patients receiving cardiac care.
Methods:
This experimental study involved 100 cardiac nurses who were randomly assigned to control and intervention groups. The intervention group received knowledge brokering using Dobbin's seven-stage method. Empathy levels were measured using the Empathy Construct Rating Scale (ECRS), with scores ranging from +252 to -252, and analysed using SPSS version 21.
Results:
Findings showed a significant mean empathy change score (MECS) of 22.90 ± 50.93 in the intervention group (p=0.003) compared to 7.10 ± 60.20 in the control group (p=0.408). Notably, nurses with a baseline empathy score of ≥100 in the intervention group exhibited a significantly higher adjusted MECS than the control group (11.44 units versus -15.42 units).
Conclusion:
Knowledge brokering can enhance empathy in moderately empathic cardiac nurses, with its effectiveness influenced by the nurses' initial empathy levels. This study contributes to a deeper understanding of the knowledge brokering strategy in healthcare settings.