In pandemics, centralized healthcare leadership is a critical requirement. The objective of this study was to analyze the early development, operation, and effectiveness of a COVID-19 organizational leadership team and transformation of healthcare services at West Virginia University Hospitals and Health System (WVUHS). The analysis focused on how Kotter's Leading Change eight-stage paradigm could contribute to an understanding of the determinants of successful organizational change in response to the COVID-19 pandemic. Methods: The fifteen core leaders of WVUHS COVID-19 strategic system were interviewed. A qualitative thematic analysis of the interviews was used to evaluate key aspects of leadership dynamics and system-wide changes in healthcare policies and protocols to contain the pandemic. Outcome measures included the degree to which WVUHS could handle and contain COVID-19 cases as well as COVID-19 death and vaccination rates in West Virginia compared with other states. Results: The leadership team radically and rapidly revamped nearly all healthcare policies, procedures, and protocols for WVUHS hospitals and clinics, and launched a Hospital Incident Command System. As a result of this effective leadership team and strategic plan, WVUHS surge capacity was adequate for COVID-19 cases. In addition, West Virginia was an early frontrunner in COVID-19 vaccination rates as well as lower death rates. Conclusion: WVUHS's leadership response to the COVID-19 pandemic followed Kotter's eight-stage paradigm for Leading Change in organizations, including the establishment of a sense of urgency, formation of a powerful guiding coalition, creation of a vision, communication of the vision, empowerment of others to act on the vision, plan for and creation of short-term wins, consolidation of improvements and production of more changes, and institutionalization of new approaches. This approach was effective in limiting the spread and impact of COVID-19 within the hospital network and across the state, with many lessons learned along the way.

Tracheobronchial amyloidosis, manifested by amyloid deposits limited specifically to tracheal and bronchial tissue, is a rare manifestation with only a few hundred published cases. Patients classically present with symptoms related to fixed upper airway obstruction caused by tracheal stenosis. Clinical symptoms are non-specific and include hoarseness, dyspnea, cough, stridor, hemoptysis, and dysphagia, which are similar to those caused by more common airway disorders, often leading to incorrect, missed, and delayed diagnosis. The wide-spread use of computerized tomography (CT) imaging has the potential of dramatically advancing the early diagnosis of tracheobronchial amyloidosis. We present a case of a patient with chronic and progressive hoarseness, diagnosed with tracheobronchial amyloidosis, with a focus on unusually clear and precise CT soft tissue neck imaging. CT imaging demonstrated nodular circumferential raised mass-like thickening involving the long-segment posterior wall of the distal trachea. The wall thickening also extended into the proximal left main stem bronchi, but spared the distal bronchial tree. This resulted in moderate (approximately 50%) narrowing of the tracheal lumen, which explained the patient's hoarseness. Routine CT imaging of patients with chronic and progressive respiratory symptoms, including cough, hoarseness, and dyspnea, is recommended. Tracheobronchial amyloidosis is an uncommon disease, but it may become more commonly recognized with broader use of more effective CT imaging protocols.

Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice.

While the endogenous opioid system has generally been associated with regulation of pain, it also modulates the experience of distress and may play a central role in many psychiatric and neurodevelopmental disorders. Decades of preclinical research on the analgesic effects of endogenous opioids, i.e., endorphins, suggests that opioid receptors have plastic bimodal (inhibitory/excitatory) properties that may explain conflicting findings in clinical research. An exploratory study with 60 healthy volunteer participants, using a cold pressor-induced pain paradigm, found evidence that a combination of a nutraceutical agent that enhances endorphin release (Endorphin Enhancer) with one that switches opioid receptors from an excitatory to inhibitory mode (Opioid Receptor Switcher) not only increases pain tolerance but also reduces emotional and physical distress. This discovery led to clinical application of a critically formulated endorphinergic treatment in 203 case studies over a two-year period. Findings revealed the remarkable clinical efficacy and safety of this treatment in the relief of chronic emotional and physical distress, including anxiety, anger, depression, cravings, and hyperalgesia, as well as enhancement of well-being, productivity, mental clarity, relationships, and an adaptive response to life's stresses. These studies provide new insights into the role of endogenous opioid system imbalances in the development, treatment, and prevention of dysfunctional emotional and physical distress. We postulate that an Endorphinergic Distress Syndrome (EDS) consists of abnormal endorphin levels together with opioid receptors predominately in their excitatory mode. EDS may account for many core distress symptoms associated with chronic anxiety, addictions, pain, as well as affective personality, autism spectrum, attention-deficit, and distress-related medical problems. Our research has led to new endorphinergic formulations, combining Endorphin Enhancers, such as caffeine, with Opioid Receptor Switchers, such as n-acetylcysteine, for the relief of emotional and physical distress. Our studies also provide a novel method to reverse the anxiogenic effects of caffeine and related hyperexcitatory substances.

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Primary nodular chest wall amyloidoma, in which a solitary mass of amyloid is deposited in and around the lungs with no evidence of systemic amyloidosis, is extremely rare, most often asymptomatic, and may resemble primary bronchogenic carcinoma. As a result, there are fewer than 100 cases published in the literature and no controlled clinical trials. Primary nodular chest wall amyloidoma is typically diagnosed either as an incidental radiological finding or after very serious and destructive mass growth at which point late-stage respiratory and pain symptoms finally develop, most often in elderly patients. We present imaging studies of a 61-year-old male patient with an unusually massive and destructive chest wall mass, originating in the chest wall, diagnosed as chest wall amyloidoma by histopathology analysis. Our CT, MRI, and PET scan findings are consistent with and contribute to the developing pattern of imaging characteristics seen in other case studies, which can be used to identify amyloidoma before it becomes destructive using non-invasive imaging analyses.