Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.Comment: Compilation of the contributions (148 pages) as presented in the `Hot QCD Matter 2022 conference', held from May 12 to 14, 2022, jointly organized by IIT Goa & Goa University, Goa, Indi

Blockchain Technology Applications in Healthcare Supply Chains—A Review

Understanding the prospective of blockchain technology and its uses in the healthcare sector is essential so that its considerable implementation can support the industry’s much-needed digitization. Furthermore, blockchain can provide answers to the issues in the healthcare industry today. Blockchain’s features like security, traceability, transparency, cost efficiency etc. can help bring supply chain transparency, health record management and prevent drug counterfeiting. Blockchain has emerged as a promising technology with great ability to bring changes to the healthcare sector. Therefore, this study aims to comprehend the current state of blockchain technology research in the healthcare supply chains. Further, it presents potential repercussions and the potential routes it may open for future research initiatives in this area. A systematic literature (SLR) process has been used and conducted in two stages. In the first stage, articles were identified through literature search and were subjected to keyword selection, database search and screening process. Finally, 124 papers were categorized through bibliographic coupling. A detailed investigation of these included papers was performed in the second stage with descriptive and content analysis. The results reveal that research related to blockchain applications or implementation is at a nascent stage. The publications in this area have been rising steadily over the past few years. When it comes to publishing in this field, India is the most productive nation while IEEE Access is the most productive journal. Applications for blockchain technology in healthcare include medical insurance, remote patient monitoring, medication supply chain management, electronic health records (EHRs), and more. The most popular use case is EHR management. The analysis further conveys that findings are less generalizable due to more theoretical or less empirically designed studies published in this domain. This study will help stakeholders, policymakers, researchers, and managers in taking strategic decisions regarding the adoption of the technology in the healthcare industry. This study is done concerning the blockchain’s use in the healthcare sector context, so other emerging technologies and sectors not taken must be considered while generalizing the results. This study is among the few up-to-date consolidated attempts to present a systematic literature review and bibliometric analysis for assessing blockchain technology’s potential in the healthcare sector. It provides an overview of the published work with implications and proposed cluster-wise future research directions

Childhood Vasculitis Syndrome Mimicking Guillain Barre Syndrome

Background: Childhood vasculitis is a heterogeneous disorder, characterized by inflammation of the blood vessel walls. Multiple organs and/or tissues are affected, either simultaneously or successively. Vascular neuropathy occurs when the vasa nervorum is affected. Presentation includes mononeuritis multiplex, acute motor/motor-sensory axonal neuropathy, pure sensory neuropathy, and chronic inflammatory demyelinating polyneuropathy. Making a diagnosis is a challenge when neurological manifestations appear for the first time or are isolated. Clinical Description: A 12-year-old girl presented with acute pain and progressive weakness of both lower limbs for 12 days, followed by diffuse abdominal pain and low-grade fever. Salient neurological findings were diminished power and hyporeflexia in all limbs. Guillain–Barre syndrome was suspected in view of symmetric ascending paralysis and a suggestive nerve conduction study. The child had neutrophilic leukocytosis but sterile cultures. The successive development of inflammatory demyelinating polyneuropathy, persistent fever, vasculitic phenomena (hypertension, severe myalgia, rashes, multiple infarcts, acute renal cortical necrosis, and gangrene of the digit), and elevated acute-phase reactants was suggestive of a multisystemic small-vessel and medium-sized vasculitis syndrome, such as polyarteritis nodosa (PAN). A diagnosis of PAN was established based on the satisfaction of clinical criteria. Management: The patient was administered pulse methylprednisolone and oral steroids, with which there was a dramatic recovery. Monthly cyclophosphamide was continued in view of major systemic involvement. Conclusion: Early recognition and management of childhood vasculitis syndrome is associated with good outcomes

Blockchain adoption challenges in the healthcare sector ::a waste management perspective

The proposed study aims to identify the major challenges for blockchain adoption to manage reverse logistics activities of recyclable hospital waste in the Indian healthcare sector, in the COVID era. Fifteen challenges are identified through literature review and experts’ views and are prioritized and analyzed for cause-and-effect relationships using a hybrid approach combining Best–Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). A sensitivity analysis is performed to evaluate the results’ robustness. The results reveal that the Technological and Regulatory challenges category plays the most influential role consisting of Lack of Government Support and Policies, Lack of Strategic Planning, Lack of Knowledge and Qualified Expertise, Lack of Standards and Regulations, High Cost Involved, and Lack of Top Management Support are the most significant challenges affecting blockchain adoption. This study can support healthcare stakeholders, policymakers, government, and researchers in planning the strategic removal of the challenges to blockchain adoption in the Indian healthcare sector. The identification of the mutual interaction among the challenges will help healthcare decision makers address strategic questions of waste management from a holistic point of view. Since the work is achieved in the Indian healthcare context, generalization of the results must be carefully considered. The present study contributes significantly to discussing blockchain’s potential in healthcare waste management. The study’s findings can aid decision making process of managers, policymakers, and benefit researchers in this field

Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility