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Hip augmentation cosmetic surgery is an increasingly popular procedure for patients seeking to enhance their body contour and improve their self-image. Despite its benefits, complications can arise, including the rare but serious foreign body reaction (FBR). We present a case of a 32-year-old patient with a history of hip augmentation cosmetic surgery who presented with persistent hip pain. A comprehensive clinical evaluation, followed by magnetic resonance imaging (MRI), revealed a foreign body reaction associated with the cosmetic augmentation procedure. This case report aims to describe the clinical presentation, diagnosis, and management of FBR in patients who have undergone hip augmentation cosmetic surgery. We will also discuss potential risk factors, prevention strategies, and the importance of early detection and intervention to avoid severe complications and improve patient outcomes. By sharing this case, we aim to raise awareness among healthcare professionals about this rare but significant complication of foreign body reaction and emphasize the need for close monitoring and timely intervention in patients who have undergone hip augmentation cosmetic surgery.

Hot corrosion is a critical problem in a wide range of high-temperature applications. Ni–20Cr alloy is frequently used in such applications owing to its good hot-corrosion resistance. In the current work, thick Ni–20Cr plates in a thickness range of 6–10 mm were fabricated using cold spray-based additive manufacturing (CSAM). High-pressure cold spraying was used with nitrogen as the propellant gas. Pre- (substrate heating, SH) and post-heat treatments (hot isostatic pressing, HIP) were also performed to comprehend the changes in the deposit properties. The deposits were subjected to microstructural and mechanical characterization to explore the potential of cold spraying for CSAM of Ni–20Cr standalone products. The cold-sprayed plates were successfully developed without any oxide formation. HIP treatment was found to be useful to reduce porosity. The cold-sprayed substrate-heated Ni–20Cr deposits exhibited excellent oxidation resistance at a high temperature of 900 °C.

Background The purpose of this study is to report the clinical features and outcomes of Black/African American (AA) and Latino Hispanic patients with Coronavirus disease 2019 (COVID-19) hospitalized in an inter-city hospital in the state of New Jersey. Methods This is a retrospective cohort study of AA and Latino Hispanic patients with COVID-19 admitted to a 665-bed quaternary care, teaching hospital located in Newark, New Jersey. The study included patients who had completed hospitalization between March 10, 2020, and April 10, 2020. We reviewed demographics, socioeconomic variables and incidence of in-hospital mortality and morbidity. Logistic regression was used to identify predictor of in-hospital death. Results Out of 416 patients, 251 (60%) had completed hospitalization as of April 10, 2020. The incidence of In-hospital mortality was 38.6% (n = 97). Most common symptoms at initial presentation were dyspnea 39% (n = 162) followed by cough 38%(n = 156) and fever 34% (n = 143). Patients were in the highest quartile for population’s density, number of housing units and disproportionately fell into the lowest median income quartile for the state of New Jersey. The incidence of septic shock, acute kidney injury (AKI) requiring hemodialysis and admission to an intensive care unit (ICU) was 24% (n = 59), 21% (n = 52), 33% (n = 82) respectively. Independent predictors of in-hospital mortality were older age, lower serum Hemoglobin < 10 mg/dl, elevated serum Ferritin and Creatinine phosphokinase levels > 1200 U/L and > 1000 U/L. Conclusions Findings from an inter-city hospital’s experience with COVID-19 among underserved minority populations showed that, more than one of every three patients were at risk for in-hospital death or morbidity. Older age and elevated inflammatory markers at presentation were associated with in-hospital death.

As artificial intelligence (AI) continues to evolve and mature, it is increasingly finding applications in the field of healthcare, particularly in specialties like radiology that are data-heavy and image-focused. Language learning models (LLMs) such as OpenAI's Generative Pre-trained Transformer-4 (GPT-4) are new in the field of medicine and there is a paucity of literature regarding the possible utilities of GPT-4 given its novelty. We aim to present an in-depth exploration of the role of GPT-4, an advanced language model, in radiology. Giving the GPT-4 model prompts for generating reports, template generation, enhancing clinical decision-making, and suggesting captivating titles for research articles, patient communication, and education, can occasionally be quite generic, and at times, it may present factually incorrect content, which could lead to errors. The responses were then analyzed in detail regarding their potential utility in day-to-day radiologist workflow, patient education, and research processes. Further research is required to evaluate LLMs' accuracy and safety in clinical practice and to develop comprehensive guidelines for their implementation.

The integration of artificial intelligence (AI) into healthcare has introduced tools that improve medical education and clinical practice. OpenEvidence is an example, providing real-time synthesis and access to medical literature, particularly for medical students during clinical rotations. By enabling efficient searches for clinical guidelines, diagnostic criteria, and therapeutic approaches, it streamlines decision-making and study preparation. Its ability to present recent publications and highlight less commonly discussed treatments supports evidence-based learning. Despite these strengths, OpenEvidence has limitations. It struggles with targeted searches for specific articles, authors, or journals and operates through an opaque curation process. Compared to ChatGPT, which offers conversational interactivity, and UpToDate, known for its comprehensive, CME-accredited content, OpenEvidence lacks certain advanced features. However, its user-friendly design and focus on clinical evidence make it a valuable, accessible alternative. This editorial critically examines OpenEvidence's capabilities and limitations, comparing it with established tools. It emphasizes the need for greater transparency, broader evidence integration, and enhanced functionality to maximize its impact. Addressing these challenges could improve OpenEvidence's utility, supporting a more effective, evidence-based approach to medical education and clinical practice.

Introduction: The ongoing pandemic has transformed the entire structural and functional framework of the world including the medical and dental healthcare services. The current study intends to examine the pattern of dynamically changing working conditions and orthodontic treatment delivery during the various phases of pandemic. Method: An online survey was conducted using Google form for orthodontic specialists practicing in India. Information regarding the impact of the pandemic on various aspects like patient turnover, treatment demand, clinical management, and new challenges faced were analysed through a self-designed close-ended questionnaire for two phases. Phase I (March 2020 to September 2020) corresponded to the onset of COVID 19 pandemic and lockdown, whereas the Phase II (October 2020 to March 2021) coincided with the time of Unlock and resumption of activities thereafter. Results: The parameters showing similar trend in Phases I and II included the willingness of ongoing orthodontic patients to report for appointments, choice of treatment modality, number and type of emergencies, cost of materials, guidelines for work, and duration of non-delivery of orthodontic procedures. The new patients reporting, complex orthodontic therapy, tele-consultation, and financial wellbeing showed an improvement while the usage of personal protective equipment kit, fear amongst orthodontists reduced in Phase II. Conclusions: Challenging situations warrant prudent measures to combat and continue the essential services, especially those related to the healthcare. A detailed analysis of the various phases of the ongoing pandemic will enable us to devise suitable measures to ensure uninterrupted orthodontic treatment even in such critical times.

In the study, four machine learning algorithms, Quadratic Discriminant Analysis (QDA), logistic regression (LR), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (K-NN) were used with GBS, Rock, AIM65, and others. [...]examining “Clinical application of artificial intelligence in emergency and critical care medicine,” Volume IV, has provided a nuanced insight into the transformative potential of artificial intelligence within the critical domains of emergency and critical care medicine. (2023)57:848–53.10.1097/MCG.000000000000174635960536 5.BaekHChoMKimSHwangHSongMYooS.Analysis of length of hospital stay using electronic health records: a statistical and data mining approach.PLoS ONE.

There has been tremendous interest in recent years in a new class of multi-component metallic alloys that are referred to as high entropy alloys, or more generally, as complex concentrated alloys. These multi-principal element alloys represent a new paradigm in structural material design, where numerous desirable attributes are achieved simultaneously from multiple elements in equimolar (or near equimolar) proportions. While there are several review articles on alloy development, microstructure, mechanical behavior, and other bulk properties of these alloys, then there is a pressing need for an overview that is focused on their surface properties and surface degradation mechanisms. In this paper, we present a comprehensive view on corrosion, erosion and wear behavior of complex concentrated alloys. The effect of alloying elements, microstructure, and processing methods on the surface degradation behavior are analyzed and discussed in detail. We identify critical knowledge gaps in individual reports and highlight the underlying mechanisms and synergy between the different degradation routes.

This editorial provides an in-depth review of the Ayushman Bharat initiative, India's universal healthcare scheme, designed to address significant disparities in healthcare access and quality across the country. Following the structure of the healthcare system and socioeconomic trends, the manuscript assesses the reasons for the initiative's creation, its coverage, implementation strategies, role during the COVID-19 pandemic, auxiliary pilot programs, and challenges for future progress. It focuses on how the initiative has increased healthcare accessibility, financial protection, transformed the healthcare infrastructure, and provided relief during the COVID-19 crisis. Critical issues such as gaps between supply and demand, the need for increased government spending, and the challenges of access and quality in rural health centers are also discussed. We aim to raise awareness about the program's benefits among potential beneficiaries, which is a key to the initiative's success and a potential role model for equitable global healthcare.

Teleradiology, an established telemedicine practice globally, has significantly enhanced the accessibility of high-quality radiological interpretations in remote areas worldwide, especially in India, thereby improving healthcare outcomes. The utilization of teleradiology services has seen a marked increase, expanding its reach into more distant regions of the country. However, this burgeoning field faces numerous regulatory, financial, and technical challenges. The current scenario regarding teleradiology in India is a double-edged sword. There is an increasing demand for it due to the expanding population, resulting in larger imaging volumes needing radiologist services. This editorial aims to examine the prevailing challenges in teleradiology in India, with an emphasis on the compensation model for teleradiologists, which has not kept pace with the growing demand for services and often remains inequitable.