Prognostic Significance of Blood Glucose Levels and Alterations Among Patients with Aluminium Phosphide Poisoning

Objectives: This study aimed to assess the prognostic significance of blood glucose levels and blood glucose alterations (i.e. hyper- or hypoglycaemia) among patients with aluminium phosphide (AlP) poisoning. Methods: This prospective observational study was conducted at the Postgraduate Institute of Medical Education & Research, Chandigarh, India, between January 2010 and June 2011. All patients presenting to the emergency department with a definitive history of AlP ingestion or symptoms compatible with AlP poisoning were included in the study. Blood glucose levels were recorded at presentation and every six hours thereafter. Alterations in blood glucose levels and other clinical and laboratory variables were subsequently compared between survivors and non-survivors. Results: A total of 116 patients with AlP poisoning were identified. Of these, 57 patients (49%) survived and 59 patients (51%) died. At presentation, the mean blood glucose levels of survivors and non-survivors were 119.9 ± 35.7 mg/dL and 159.7 ± 92.5 mg/dL, respectively (P <0.001). In comparison to the survivors, non-survivors had significantly higher heart rates, total leukocyte counts, blood glucose level alterations and serum creatinine levels (P <0.050). In addition, systolic blood pressure, Glasgow coma scale scores, arterial blood gas pH and bicarbonate values and duration of hospital stay was significantly lower compared to survivors (P <0.001). However, neither blood glucose levels at admission nor blood glucose alterations correlated independently with mortality in a multivariate analysis. Conclusion: The role of blood glucose level alterations in predicting patient outcomes in AlP poisoning cases remains inconclusive. Further studies with larger sample sizes are required.Keywords: Aluminum Phosphide; Poisoning; Blood Glucose; Hyperglycemia; Hypoglycemia; Mortality; Prognostic Factors; India

Supply chain network design models for a circular economy: a review and a case study assessment

Global supply chains are getting increasingly dispersed, and hence, more complex. This has also made them more vulnerable to disruptions and risks. As a result, there is a constant need to reconfigure/redesign them to ensure competitiveness. However, the relevant aspects/facets for doing so are fragmented and scattered across the literature. This study reviews the literature to develop a holistic understanding of the key considerations (environment, cost, efficiency, and risks) in designing/redesigning global supply chains. This understanding is then applied to assess the global supply chain network of a leading multinational tire manufacturing firm; also to provide recommendations on redesigning it. The study has significant practical and research implications for global supply chain management

Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE’s healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI’s capacity to enhance healthcare’s operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges

Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges

AT2019wxt: An ultra-stripped supernova candidate discovered in electromagnetic follow-up of a gravitational wave trigger

We present optical, radio and X-ray observations of a rapidly-evolving transient AT2019wxt (PS19hgw), discovered during the search for an electromagnetic (EM) counterpart to the gravitational-wave (GW) trigger S191213g (LIGO Scientific Collaboration & Virgo Collaboration 2019a). Although S191213g was not confirmed as a significant GW event in the off-line analysis of LIGO-Virgo data, AT2019wxt remained an interesting transient due its peculiar nature. The optical/NIR light curve of AT2019wxt displayed a double-peaked structure evolving rapidly in a manner analogous to currently know ultra-stripped supernovae (USSNe) candidates. This double-peaked structure suggests presence of an extended envelope around the progenitor, best modelled with two-components: i) early-time shock-cooling emission and ii) late-time radioactive $^{56}$Ni decay. We constrain the ejecta mass of AT2019wxt at $M_{ej} \approx{0.20 M_{\odot}}$ which indicates a significantly stripped progenitor that was possibly in a binary system. We also followed-up AT2019wxt with long-term Chandra and Jansky Very Large Array observations spanning $\sim$260 days. We detected no definitive counterparts at the location of AT2019wxt in these long-term X-ray and radio observational campaigns. We establish the X-ray upper limit at $9.93\times10^{-17}$ erg cm$^{-2}$ s$^{-1}$ and detect an excess radio emission from the region of AT2019wxt. However, there is little evidence for SN1993J- or GW170817-like variability of the radio flux over the course of our observations. A substantial host galaxy contribution to the measured radio flux is likely. The discovery and early-time peak capture of AT2019wxt in optical/NIR observation during EMGW follow-up observations highlights the need of dedicated early, multi-band photometric observations to identify USSNe.Comment: 20 pages, 14 figures, Submitted to Ap

Boundary States for D-branes in AdS3AdS_3

We construct boundary states representing D-strings in $AdS_3$. These wrap twisted conjugacy classes of SL(2,R), and the boundary states are therefore based on continuous representations only. We check Cardy's condition and find a consistent open string spectrum. The open string spectrum on all the D-branes is the same.Comment: 23 pages, late

The Panchromatic Afterglow of GW170817: The full uniform dataset, modeling, comparison with previous results and implications

We present the full panchromatic afterglow light curve data of GW170817, including new radio data as well as archival optical and X-ray data, between 0.5 and 940 days post-merger. By compiling all archival data, and reprocessing a subset of it, we have ensured that the panchromatic dataset is uniform and therefore immune to the differences in data processing or flux determination methods used by different groups. Simple power-law fits to the uniform afterglow light curve indicate a t^(0.86±0.04) rise, a t^(−1.90±0.12) decline, and a peak occurring at 155±4 days. The afterglow is optically thin throughout its evolution, consistent with a single spectral index (−0.569±0.002) across all epochs. This gives a precise and updated estimate of the electron power-law index, p=2.138±0.004. By studying the diffuse X-ray emission from the host galaxy, we place a conservative upper limit on the hot ionized ISM density, <0.01 cm⁻³, consistent with previous afterglow studies. Using the late-time afterglow data we rule out any long-lived neutron star remnant having magnetic field strength between 10^(10.4) G and 10¹⁶ G. Our fits to the afterglow data using an analytical model that includes VLBI proper motion from Mooley et al (2018), and a structured jet model that ignores the proper motion, indicates that the proper motion measurement needs to be considered while seeking an accurate estimate of the viewing angle