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The kidney tubules constitute two-thirds of the cells of the kidney and account for the majority of the organ's metabolic energy expenditure. Acute tubular injury (ATI) is observed across various types of kidney diseases and may significantly contribute to progression to kidney failure. Non-invasive biomarkers of ATI may allow for early detection and drug development. Using the SomaScan proteomics platform on 434 patients with biopsy-confirmed kidney disease, we here identify plasma biomarkers associated with ATI severity. We employ regional transcriptomics and proteomics, single-cell RNA sequencing, and pathway analysis to explore biomarker protein and gene expression and enriched biological pathways. Additionally, we examine ATI biomarker associations with acute kidney injury (AKI) in the Kidney Precision Medicine Project (KPMP) (n = 44), the Atherosclerosis Risk in Communities (ARIC) study (n = 4610), and the COVID-19 Host Response and Clinical Outcomes (CHROME) study (n = 268). Our findings indicate 156 plasma proteins significantly linked to ATI with osteopontin, macrophage mannose receptor 1, and tenascin C showing the strongest associations. Pathway analysis highlight immune regulation and organelle stress responses in ATI pathogenesis., (© 2024. The Author(s).)

Objectives: To describe the extent and imaging findings of COVID-associated rhino-orbital-cerebral mucormycosis on magnetic resonance imaging and to evaluate the value of MRI severity score in grading the extent of involvement., Methods: Proven cases of ROCM with a history of concurrent or recently (<6 weeks) treated COVID-19 underwent MRI at the initial presentation. Findings were charted for each anatomical structure and the extent of involvement was scored for sinonasal, extra-sinus soft tissues, orbits, and brain. MR severity score was defined by summing up the individual scores of each compartment (sinonasal 20, orbital 20, soft tissue 10, and brain 10) and a total score out of 60 was assigned., Results: A total of 47 patients were included in our study with variable involvement of sinonasal compartment ( n = 43), extra-sinus soft tissue ( n = 25), orbits ( n = 23), and brain ( n = 17). In the sinonasal compartment, T2, DWI, and post-contrast T1 were the most useful sequences. A significantly higher mean sinonasal score was associated with mortality ( p = 0.007). In the orbits, a combination of STIR (orbital fat and extraconal muscles), DWI (optic nerves), and post-contrast images (superior ophthalmic vein) were the most accurate sequences. A higher mean orbital score was associated with vision loss ( p = 0.001). Patients with uncontrolled diabetes had greater extent of cranial involvement., Conclusion: A combination of magnetic resonance sequences is required to correctly evaluate the involvement of individual structures and thus to assign the correct MR scoring. The proposed MR severity score can effectively and objectively evaluate the severity of COVID-associated ROCM.

Background: There was an unprecedented increase in COVID-19-associated-Mucormycosis (CAM) cases during the second pandemic wave in India., Methods: This observational study was done to know the epidemiological profile of CAM cases andincluded all patients admitted with mucormycosis between May 2021 and July 2021., Results: Out of the enrolled 208 CAM cases (either SARS-CoV-2 RT-PCR or serology positive), 204, three and one had rhino-orbital-cerebral, pulmonary and gastrointestinal mucormycosis, respectively. 95.7 % of the patients had diabetes, out of which 42.3 % were recently diagnosed. Mean HbA1c was 10.16 ± 2.56 %. 82.5 % of the patients were unvaccinated. During their COVID-19 illness, 86.5 % were prescribed antibiotics, 84.6 % zinc preparations, 76.4 % ivermectin, and 64.9 % steroids, while only 39.5 % required oxygen therapy. The frequency of blood groups A, B, O and AB in our CAM patients was 29.5 %, 18.9 %, 38.9 % &12.6 %, respectively. At three months follow up, 60 (28.8 %) patients died, four (1.9 %) stopped antifungal treatment, and 144(69.23 %) were on antifungal treatment. 55 % (n = 33) of deaths occurred within 15 days of admission. Mortality was significantly associated with higher age, RT-PCR positive for SARS-CoV-2, raised serum creatinine and alkaline phosphatase during treatment. At 6 months follow-up, eight more patients died, three due to chronic kidney disease, four patients who had stopped treatment and one patient who was on a ventilator due to COVID-19 associated pneumonia and the rest 140(67.3 %) survived., Conclusion: Uncontrolled hyperglycemia, SARS-CoV-2 infection, rampant use of antibiotics, zinc supplementation and steroids were some of the risk factors for mucormycosis. Despite the overwhelming number of patients with an uncommon disease like mucormycosis, the six months mortality was much lower than expected., (Copyright © 2022. Published by Elsevier Inc.)

Objectives: To compare the high-resolution computed tomography (HRCT)-derived severity score in COVID-19 patients between those who had earlier received the vaccine against the SARS-CoV-2 and those who did not., Methods: A retrospective cross-sectional analysis of HRCT of the chest was done in correlation with the vaccination status of clinically diagnosed COVID-19 patients. The variable under evaluation was the CT severity score, whereby differential analysis of the variability on this parameter between incompletely (single dose) vaccinated, completely (both doses) vaccinated, and non-vaccinated individuals was the outcome., Results: The analysis included 826 patients of which 581 did not receive any vaccination whereas 196 patients received incomplete (single dose) vaccination and 49 received complete vaccination. Mean CT severity score was lower in completely vaccinated patients (3.5 ± 6.3) vis-à-vis incompletely vaccinated (10.1 ± 10.5) and non-vaccinated (10.1 ± 11.4) individuals. The mean CT score was significantly lower in completely vaccinated patients of lower ages (≤ 60 years) compared to patients above that age. The incidence of severe disease (CT score ≥ 20) was significantly higher in the incompletely vaccinated and non-vaccinated patients compared to that in the completely vaccinated group., Conclusions: CT severity scores in individuals receiving both doses of SARS-CoV-2 vaccination were less severe in comparison to those receiving a single dose of vaccine or no vaccine at all., Key Points: • Patients who received complete two doses of vaccination had significantly low mean CT scores compared to the partially vaccinated patients and non-vaccinated patients. • The mean CT scores were significantly lower in completely vaccinated patients of lower ages (< 60 years) while patients > 60 years did not show significantly different CT scores between the vaccinated and non-vaccinated groups. • Consolidations and ground-glass opacities were significantly lower in the group receiving complete vaccination as compared to the unvaccinated and incompletely vaccinated patients., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness., Methods: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs., Findings: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4 th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7 th - 9 th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity., Interpretation: We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials., Funding: C.V. received a Marie Skłodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript., Competing Interests: Declaration of interests LLM owns equity in Bayer AG and is a consultant for SimBiosys. R.K.J. received honorarium from Amgen; consultant fees from Chugai, Elpis, Merck, Ophthotech, Pfizer, SPARC, SynDevRx, XTuit; owns equity in Accurius, Enlight, Ophthotech, SynDevRx; and serves on the Boards of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors, Tekla Healthcare Opportunities Fund, Tekla World Healthcare Fund; and received a grant from Boehringer Ingelheim. Neither any reagent nor any funding from these organizations was used in this study. Other coauthors have no conflict of interests to declare., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Objectives: Although about 80% of coronavirus disease-2019 (COVID-19) cases are reported to be mild, the remaining 20% of cases often result in severe disease with the potential of crushing already overstrained health care services. There has been sustainable growth of COVID-19 cases worldwide since mid-May 2020. To keep tabs on community transmission of COVID-19 infection screening of the samples from a large population is needed which includes asymptomatic/symptomatic individuals along with the migrant population. This requires extra resources, man power, and time for detection of severe acute respiratory syndrome coronavirus 2 by real-time polymerase chain reaction (RT-PCR). In the current scenario, the pooled sample testing strategy advocated by the Indian Council of Medical Research, New Delhi is a new approach that is very promising in resource-limited settings. In this study, we have evaluated the pooled strategy in terms of accurate testing results, utilization of consumables, and identification of borderline positive cases., Materials and Methods: Between April and June 2020, we performed COVID-19 testing by RT-PCR from areas with varying prevalence of population referred to COVID laboratory, Dr Ram Manohar Lohia Institute of Medical Sciences, Lucknow. In the first step, the samples are collated into pools of 5 or 10. These pools are tested by RT-PCR. Negative pools were reported as negative whereas positive pools of 5 and 10 are then deconvoluted and each sample is tested individually., Results: In the present study, we tested 4620 samples in 462 pools of 10 and 14 940 samples in 2990 pools of 5. Among 10 samples pool, 61 (13%) pools flagged positive in the first step. In the second step, among 61 pools (610 samples) deconvoluted strategy was followed in which 72 individual samples came positive. The pooled-sample testing strategy helps saves substantial resources and time during surge testing and enhanced pandemic surveillance. This approach requires around 76% to 93% fewer tests done in low to moderate prevalence settings and group sizes up to 5-10 in a population, compared to individual testing., Conclusions: Pooled-sample PCR analysis strategies can save substantial resources and time for COVID-19 mass testing in comparison with individual testing without compromising the resulting outcome of the test. In particular, the pooled-sample approach can facilitate mass screening in the early coming stages of COVID-19 outbreaks, especially in low- and middle-income settings, and control the spread by meticulous testing of all risk groups., (© 2020 Wiley Periodicals LLC.)

An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals is difficult. This can be attributed to the fact that COVID-19 is a novel disease and its pathogenesis is still partially understood. However, machine learning algorithms have the capability to analyse a large number of parameters within a short period of time to identify the predictors of disease outcome. Implementing such an algorithm to predict high-risk individuals during the early stages of infection would be helpful in decision making for clinicians such that irreversible damage could be prevented. Here, we propose recommendations to develop prognostic machine learning models using electronic health records so that a real-time risk score can be developed for COVID-19., (© 2020 John Wiley & Sons Ltd.)

Background: Coronavirus disease 2019 (COVID-19) is a rapidly expanding global health crisis. A disintegrin and metalloproteinase 17 (ADAM17), an ectodomain sheddase, is a key component of ACE2 modulation and plays a complex role in inflammation and immunosurveillance., Areas of Uncertainty: Much remains unknown regarding the immunopathogenesis of COVID-19, including how the virus affects ADAM17 expression, activity, and regulation., Search Strategy: Three electronic databases (MEDLINE through PubMed, Embase through Ovid, and Google Scholar) were searched to identify articles relevant to ADAM17 and severe acute respiratory syndrome coronavirus 1 and 2. Relevant articles published from January 1, 2005, to April 30, 2020, were selected, and reference lists were screened and cross-referenced. We also searched preprint studies on medRxiv and bioRxiv given the rapidly evolving data on COVID-19 SARS-CoV-2., Therapeutic Opinion: Infection with SARS-CoV-2 may lead to an increase in ADAM17 sheddase activity contributing to an exuberant macrophage-predominant inflammatory response and diminished immunosurveillance capacity for viral clearance. Emerging data suggest severe lung injury in COVID-19 is associated with higher levels of TNF-α and IL-6, T-cell lymphopenia and exhaustion, hypercoagulability, and a macrophage-predominant immune response. This clinical picture is consistent with dysregulation of many of the molecular pathways in which ADAM17 participates., Conclusions: Elucidation of the role of ADAM17 in COVID-19 may identify novel molecular targets for drug development and therapeutic repurposement., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

The World Health Organization has declared "with great hope" an end to COVID-19 as a public health emergency. The vaccination drive that started in December 2020 played a crucial role in controlling the pandemic. However, the pace at which COVID-19 vaccines were developed and deployed for general population use led to vaccine hesitancy, largely owing to concerns regarding the safety and efficacy of the vaccines. Radiology has been instrumental in demonstrating the extent of pulmonary involvement and identification of the complications of COVID-19, and the same holds true for vaccine-related complications. This review summarizes the existing body of radiological literature regarding the efficacy, adverse events, and imaging pitfalls that accompany the global rollout of various COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

This paper delineates the mathematical modeling and dynamics of a novel coronavirus (COVID-19) an outbreak, and it is a control measurement; the effect of lockdown in terms of lakhs of cases and deaths. The lockdown effect is studied with a different lockdown success rate and also describes the multiple transmission route in the infection dynamics, and pushes the role of the environmental reservoir in the transmission and the spread of this disease. In this situation, mathematical models are an important tool to assign an impressive strategy in order to fight against this pandemic. We exhibit the boundedness of the system, the local stability analysis and global stability analysis of the equilibrium to examine its epidemiological relevance. We have also carried out numerical simulations to validate the analytical results. [ABSTRACT FROM AUTHOR]

The global pandemic Corona Virus Disease-2019 (COVID-19) is being transmitted aggressively in forms of progressive waves even after the 1.5 years of its occurrence. Development of vaccine has nearly concluded the prevention of mortality in suffered patients. Along with vaccination, the prevention of further transmission is aimed by several countries with the help of social distancing, bearing a mask and appropriate sanitization. In absence of any specific treatment, this study was needed to compile the available research in relation to transmission, etiology, pathophysiology, and the available options to clinically manage the patients suffering from COVID-19. Newly identified β-coronavirus has genomic similarities with SARS-CoV and MERS-CoV. Such similarity made basis for new virus to be known as SARS-Corona virus-2 (SARS-CoV-2). A total of 40,12,863 people globally amongst 18,32,34,647 infected population have lost their life till 20th June 2021. The prime mechanism of transmission was alleged to be animal to human transmission. Thereafter, consequent cases were not related with such transmission concluding human to human exposure. CDC (Disease Control and Prevention) investigated the first case of Wuhan and interpreted that incubation period could range generally from three to seven days and lasts until 2 weeks and the lengthiest time from contamination to symptoms was found to be 12.5 days. The virus bears crown like glycoprotein's spike on its envelop and hence it is termed corona from coronam (Latin) meaning crown in English. There is no treatment available currently for COVID-19 either in form of vaccine or in form of antiviral drug. The symptomatic relief is only the option to deal with the disease. Oxygen therapy remains the major intervention for severe patients. Convalescent plasma or immunoglobulins were used to enhance the rate of survival in SARS patients. Chloroquine, Ruxolitinib, Baricitinib and fedratinib claimed to lower the symptoms in associate pneumonia. A very few patients have been enrolled in clinical trials. No scientific evidence so far exists to support the chance of vertical transmission of COVID-19 contamination from the pregnant mother to the baby. Sustaining staff mental well-being is crucial to better control infections, even though the best approach to combat with this through the epidemic time remains blurred. Vaccines seem to be most promising solution to attenuate newer strains of corona virus. The recognition of genomic sequence as well study of protein structure of corona virus-2019, were studied in very lesser time. This allowed the development of inactivated or live attenuated vaccine for the prevention of COVID-19. [ABSTRACT FROM AUTHOR]

• A dynamic user-based redistribution framework for bike-sharing systems (BSS). • A novel Distance-Willingness-Reward (DWR) matrix to determine incentives. • An improved station suggestion strategy measure, the Station Impact Index (SII). • Development of a BSS simulator to implement and validate the proposed framework. • Improved service levels of BSS even at smaller user willingness levels. The increased demand for a safe, reliable, cost-effective, clean, and social distancing-friendly mode of commuting in the post-COVID world has motivated the transportation research community to closely analyse and optimise the operations of the existing Bike-Sharing Systems (BSS). One of the key elements in BSS operation is the redistribution of bicycles to cope with the spatiotemporal asymmetry in the demands across the docking stations. Operators usually employ trucks to do the rebalancing at regular intervals. Alternatively, strategies encouraging user-based rebalancing that allow the continuous redistribution of bicycles can be implemented. Dynamic user-based rebalancing strategies are often considered a feasible approach to improve the reliance and performance of the BSS and have been gaining prominence. This paper presents a simulation-driven dynamic user-based redistribution framework using the novel Distance-Willingness-Reward (DWR) matrix method. The framework shows the capability to generate dynamic incentives for the users, motivating them to participate in the rebalancing process, leading to the continuous redistribution of bicycles at each station. The Distance-Willingness-Reward (DWR) matrix method generates the required dynamic incentives for each user, and the bike requirements at each docking station are evaluated using an improved suggestion measure, the Station Impact Index (SII). Simulation experiments are designed and run for 30 days of operation considering 30 stations. Results show that the proposed approach gives better performance compared to the existing operator-based rebalancing and no rebalancing approaches. [ABSTRACT FROM AUTHOR]

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment--or combination of treatments--depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19. [ABSTRACT FROM AUTHOR]

This Viewpoint reviews the pathophysiological and observational basis for speculating that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) might worsen clinical outcomes for patients with COVID-19, and summarizes guidance from specialty societies to continue the drugs in patients who need them pending more definitive evidence of harm. [ABSTRACT FROM AUTHOR]