Publications by authors named "Nagendran M"

Effective governance is crucial for the success of conservation projects aimed at protecting wildlife populations and supporting human well-being. However, few large-scale, comprehensive syntheses have been conducted on the effects of different environmental governance types on conservation outcomes (i.e.

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  • The study looked at how doctors use AI tools for making important medical decisions, focusing on the importance of understanding AI suggestions.
  • They tested 19 ICU doctors to see how they reacted to safe and unsafe AI recommendations during practice simulations, using eye-tracking to see where the doctors looked.
  • The results showed that unsafe AI suggestions grabbed more attention, but the explanations provided by the AI didn’t help doctors make better choices, and how useful they thought the explanations were didn’t match how much they really paid attention to them.
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Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis.

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The influence of AI recommendations on physician behaviour remains poorly characterised. We assess how clinicians' decisions may be influenced by additional information more broadly, and how this influence can be modified by either the source of the information (human peers or AI) and the presence or absence of an AI explanation (XAI, here using simple feature importance). We used a modified between-subjects design where intensive care doctors (N = 86) were presented on a computer for each of 16 trials with a patient case and prompted to prescribe continuous values for two drugs.

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A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate.

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A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate.

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  • Clinical decision-making is significantly affected by how certain clinicians feel about their diagnoses and treatment choices.
  • A study analyzed 850 patient assessments, revealing that most single diagnoses were reported with certainty, while uncertainty was common in multiple diagnoses.
  • Patient feedback indicated strong support for researching clinician decision certainty, suggesting it could enhance diagnostic practices and lead to improved patient care.
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Understanding tissues in the context of development, maintenance and disease requires determining the molecular profiles of individual cells within their native spatial context. We developed a Proximity Ligation Hybridization technology (PLISH) that enables quantitative measurement of single cell gene expression in intact tissues, which we have now updated. By recording spatial information for every profiled cell, PLISH enables retrospective mapping of distinct cell classes and inference of their interactions.

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  • The distal lung comprises terminal bronchioles and alveoli that are essential for gas exchange, and developing 3D culture systems can aid in studying lung diseases like interstitial lung disease, cancer, and COVID-19 pneumonia.
  • Researchers have created a long-term, feeder-free culture system for organoids derived from human alveolar epithelial type II and KRT5 basal cells, enabling significant differentiation and the formation of specialized cell types within the lung architecture.
  • This organoid model allows for infection studies with SARS-CoV-2, identifying specific cell populations (like club cells) as potential targets for investigation, thus providing a valuable tool for understanding lung infections and their mechanisms.
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The novel COVID-19, a pandemic disease, is showing an alarming spread and severity throughout the world. Globally, the community transmission of this disease is affecting people in large clusters and so it is necessary to mitigate and control them in order to minimise the social and economic consequences. This review emphasize on the origin of the coronoviral epidemics, discussion on the structural and functional basis of SARS-CoV-2, epidemiology, pathognomonic symptoms, fatality, available rapid diagnostic methods and proposed possible treatment methods for the treatment of COVID-19.

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The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange and is affected by disorders including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. Investigations of these localized pathologies have been hindered by a lack of 3D in vitro human distal lung culture systems. Further, human distal lung stem cell identification has been impaired by quiescence, anatomic divergence from mouse and lack of lineage tracing and clonogenic culture.

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Objective: The present study tested whether ictal onset sites are regions of more severe interneuron loss in epileptic pilocarpine-treated rats, a model of human temporal lobe epilepsy.

Methods: Local field potential recordings were evaluated to identify ictal onset sites. Electrode sites were visualized in Nissl-stained sections.

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  • This text talks about a study that looks at how well deep learning algorithms (fancy computer programs) can do medical imaging tasks compared to expert doctors.
  • Researchers checked if the studies used good methods and if they were fair when comparing the algorithms to the doctors.
  • They found only 10 studies that really focused on how well these deep learning systems worked in real-life medical situations.
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The aim of this systematic review was to evaluate randomized clinical trials (RCTs) of cardiac catheter ablation (CCA) and to assess the prevalence, characteristics and reporting standards of clinically relevant patient-reported outcome measures (PROMs). Electronic database searches of Medline, Embase, CENTRAL, and the WHO Trial Registry were conducted in March 2019. The study protocol was registered on PROSPERO (CRD42019133086).

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BackgroundWe sought to establish to what extent decision certainty has been measured in real time and whether high or low levels of certainty correlate with clinical outcomes.MethodsOur pre-specified study protocol is published on PROSPERO, CRD42019128112. We identified prospective studies from Medline, Embase and PsycINFO up to February 2019 that measured real time self-rating of the certainty of a medical decision by a clinician.

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Purpose: We performed an individual patient data meta-analysis to investigate the possible benefits and harms of vasopressin therapy in adults with septic shock both overall and in pre-defined subgroups.

Methods: Our pre-specified study protocol is published on PROSPERO, CRD42017071698. We identified randomised clinical trials up to January 2019 investigating vasopressin therapy versus any other vasoactive comparator in adults with septic shock.

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To avoid liver insufficiency following major hepatic resection, portal vein embolisation (PVE) is used to induce liver hypertrophy pre-operatively. Associating liver partition with portal vein ligation for staged hepatectomy assisted with radiofrequency (RALPPS) was introduced as an alternative method. A randomized controlled trial comparing PVE with RALPPS for the pre-operative manipulation of liver volume in patients with a future liver remnant volume (FLRV) ≤25% (or ≤35% if receiving preoperative chemotherapy) was conducted.

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Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. Here, we use single cell transcriptomics and mass spectrometry-based proteomics to quantify changes in cellular activity states across 30 cell types and chart the lung proteome of young and old mice. We show that aging leads to increased transcriptional noise, indicating deregulated epigenetic control.

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Objectives: To examine the association between financial performance as measured by operating margin (surplus/deficit as a proportion of turnover) and clinical outcomes in English National Health Service (NHS) trusts.

Setting: Longitudinal, observational study in 149 acute NHS trusts in England between the financial years 2011 and 2016.

Participants: Our analysis focused on outcomes at individual NHS Trust-level (composed of one or more acute hospitals).

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A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue.

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Purpose: We performed an individual patient data meta-analysis to assess the possible benefits and harms of statin therapy in adults with acute respiratory distress syndrome (ARDS) and to investigate effects in specific ARDS subgroups.

Methods: We identified randomised clinical trials up to 31 October 2016 that had investigated statin therapy versus placebo in patients with ARDS. Individual patient data from each trial were compiled.

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Objective:  To examine whether a very large effect (VLE; defined as a relative risk of ≤0.2 or ≥5) in a randomised trial could be an empirical marker that subsequent trials are unnecessary.

Design:  Meta-epidemiological assessment of existing published data on randomised trials.

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