Fabrication of Zinc Doped Titanium Dioxide Nanoparticles to Inhibit Escherichia coli Growth and Proliferation of Liver Cancer Cells (HepG2).

The current research is related to the synthesis of different concentrations (0, 3, and 7 wt %) Zn doped TiO2-NPs by using the coprecipitation method. The rutile, anatase crystal structure appeared on different diffracted peaks in TiO2-NPs, and the crystallite size (12 to 24 nm) was calculated by using XRD analysis. The spherical, irregular, porous grain-like surface morphology was observed by SEM analysis, and the identification of different functional modes such as hydroxyl, -C-O, -C-O-C, and Ti-O-Ti attached on the surface of the spectrum was examined via FTIR analysis. After that, the increased absorbance of TiO2-NPs by increasing the Zn concentration in TiO2-NPs was observed by UV-visible analysis. After that, the well diffusion method was performed to measure antibacterial activity, and the MTT assay was used to investigate anticancer activity against the HepG2 cell line. It was observed that the inhibition zone of S. aureus and E. coli increased by increasing the concentration of Zn-doped TiO2-NPs from 2 to 32 mm. The 7 wt % Zn-doped TiO2-NPs provided significant anticancer activity against the liver cancer cell line and antibacterial activity. In the future, Zn doped TiO2-NPs can be used for in vitro analysis against different microbial and animal models for the treatment of cancer.

Investigation of antibacterial and anticancer activities of copper, aluminum and nickel doped zinc sulfide nanoparticles.

First time compared the different metals doped ZnS nanoparticles for antibacterial and liver cancer cell line. In this study, copper, aluminum and nickel doped ZnS NPs were synthesized via co-precipitation method. The XRD analysis was confirmed the presence of cubic crystal structure and crystallite size decreased from 6 to 3 nm with doping elements. While as SEM micro-grains were revealed slightly irregular and agglomerated morphology with the presence of dopant elements. The presence of different dopant elements such as Cu, Al and Ni in ZnS NPs was identified via EDX analysis. The FTIR results demonstrate various vibrational stretching and bending modes attached to the surface of ZnS nanomaterials. After that the well diffusion method was used to conduct in-vitro bioassays for evaluation of antibacterial and anticancer activities against E.coli and B.cereus, as well as HepG2 liver cancer cell line. Our findings unveil exceptional results with maximum inhibition zone of approximately 9 to 23 mm observed against E.coli and 12 to 27 mm against B.cereus, respectively. In addition, the significant reduction in cell viability was achieved against the HepG2 liver cancer cell line. These favorable results highlight the potential of Ni doped ZnS NPs for various biomedical applications. In future, the doped ZnS nanomaterials will be suitable for hyperthermia therapy and wound healing process.

Enhancing thermodynamic performance with an advanced combined power and refrigeration cycle with dual LNG cold energy utilization.

Utilizing waste heat to drive thermodynamic systems is imperative for improving energy efficiency, thereby improving sustainability. A combined cooling and power systems (CCP) utilizes heat from a temperature source to deliver both power and cooling. However, CCP systems utilizing LNG cold energy suffers from low second law efficiency due to significant temperature differences. To address this, an "Advanced Power and Cooling with LNG Utilization (ACPLU)" system is proposed, integrating a cascaded transcritical carbon dioxide (TCO2)-LNG cycle with an Organic Rankine cycle (ORC) for improved power generation and an absorption refrigeration system (ARS) for simultaneous cooling. This study evaluates the second law efficiency, net work output, and exergy destruction performance through a sensitivity analysis, optimizing variables such as heat source temperature, superheater temperature difference, ORC and CO2 turbine inlet and condenser pressures, evaporator temperature, and pinch point temperatures of heat exchangers and generator. Compared to previous studies on CCP systems, the ACPLU shows a superior performance, with a second law efficiency of 27.3 % and a net work output of 11.76 MW. Cyclopentane as an ORC working fluid resulted in the highest second law efficiency of 29.06 % and net work output of 12.27 MW. Parametric analysis suggested that heat source temperature significantly impacts net power output. The exergy analysis concluded that a high-pressure ratio and good thermal match between the heat exchangers enhance overall performance. Utilizing artificial neural network (ANN) to produce a multiple-input-multiple-output (MIMO) objective function and performing multi-objective optimization (MOO) using genetic algorithm (GA), an improved second law efficiency and net power output by 28.11 % and 14.16 MW respectively, with pentane as the working fluid, is demonstrated. An average cost rate of 9.121 $/GJ was observed through a thermo-economic analysis. The ACPLU system is promising for medium temperature waste heat recovery, such as, pharmaceutical manufacturing plants.

Microwave-Assisted Catalytic Deconstruction of Plastics Waste into Nanostructured Carbon and Hydrogen Fuel Using Composite Magnetic Ferrite Catalysts.

Finding new catalysts and pyrolysis technologies for efficiently recycling wasted plastics into fuels and structured solid materials of high selectivity is the need of time. Catalytic pyrolysis is a thermochemical process that cracks the feedstock in an inert gas environment into gaseous and liquid fuels and a residue. This study is conducted on microwave-assisted catalytic recycling of wasted plastics into nanostructured carbon and hydrogen fuel using composite magnetic ferrite catalysts. The composite ferrite catalysts, namely, NiZnFe2O4, NiMgFe2O4, and MgZnFe2O4 were produced through the coprecipitation method and characterized for onward use in the microwave-assisted valorization of wasted plastics. The ferrite nanoparticles worked as a catalyst and heat susceptor for uniformly distributed energy transfer from microwaves to the feedstock at a moderate temperature of 450°C. The type of catalyst and the working parameters significantly impacted the process efficiency, gas yield, and structural properties of the carbonaceous residue. The tested process took 2-8 minutes to pulverize feedstock into gas and carbon nanotubes (CNTs), depending on the catalyst type. The NiZnFe2O4-catalyzed process produced CNTs with good structural properties and fewer impurities compared to other catalysts. The NiMgFe2O4 catalyst performed better in terms of hydrogen evolution by showing 87.5% hydrogen (H2) composition in the evolved gases. Almost 90% of extractable hydrogen from the feedstock evolved during the first 2 minutes of the reaction.

Multi-objective optimization and 4E (energy, exergy, economy, environmental impact) analysis of a triple cascade refrigeration system.

The post-pandemic energy crisis and ever-increasing environmental degradation necessitate researchers to scrutinize refrigeration systems, major contributors to these issues, for minimal environmental impact and maximum performance. Thus, this study aims to comprehensively examine a triple cascade refrigeration system (TCRS) equipped with hydrocarbon refrigerants (1-butene/Heptane/m-Xylene). This system is specifically designed for ultra-low temperature applications, including vaccine storage, quick-freezing, frozen food preservation, cryogenic processes, and gas liquefaction. The investigation integrates conventional thermodynamic analysis with economic and environmental impact assessments, and finally multi-objective optimization (MOO) to ascertain optimal operating conditions for the system. The effect of (1) evaporator temperature, Tevap (2) condenser temperature, Tcond (3) Lower Temperature Circuit (LTC) condenser temperature, TLTC (4) Mid Temperature Circuit (MTC) condenser temperature, TMTC and (5) Cascade Condenser temperature difference, Δ T on three objective functions (COP, exergy efficiency, and overall plant cost) have been investigated employing a parametric analysis. Subsequently, quadratic equations for these objective functions are generated using the Box-Behnken method, and MOO utilizing the Genetic algorithm has been performed to maximize COP and exergy efficiency while minimizing the overall cost rate. The decision-making techniques TOPSIS and LINMAP are used to retrieve a unique solution from the Pareto Front, and the system performance has been assessed at the optimal point. The optimization result demonstrates that for the 10-kW capacity TCRS, COP, exergy efficiency, and total plant cost are 0.71, 0.51, and 38262.05 $/year respectively, at optimum condition (Tevap = -101.023 °C , Tcond = 36.545 °C , TLTC = - 69.047 °C and TMTC = - 34.651 °C ). Exergy analysis identifies HTC compressor (19.3 %) and throttle valve (15.5 %) as key contributors to total exergy destruction, while economic analysis underscores capital and maintenance costs (72 %) as the primary contributors to the overall cost, with evaporator (43 %) and condenser (20 %) accounting for 63 % of this cost.

Study of Cation Distribution and Photocatalytic Activity of Nonthermal Plasma-Modified NiZnFe2O4 Magnetic Nanocomposites.

In this study, NiZnFe2O4 composite was synthesized using a sol-gel route and subjected to nonthermal plasma treatment for tailoring their cations' distribution and physicochemical, magnetic, and photocatalytic properties. Microwave plasma treatment was given to the composites for 60 min in support of postsynthesis sintering at 700 °C for 5 h. X-ray diffraction (XRD) analysis was conducted on pre- and postplasma-modified ferrite composites to identify phase-pure cubic spinel structure and cations' distribution. The cation distributions were measured from the ratio of XRD intensity peaks corresponding to (220), (311), (422) and (440) planes. The intensity ratio of plasma-treated ferrite composites decreased compared to that of pristine composites. The crystallite size and lattice constant were increased on plasma treatment of the composite. The morphological analysis showed nanoflower-like structures of the particles with an increased surface area in the plasma-treated composites. The plasma oxidation and sputtering effects caused a reduction in the nanoflower size. The energy bandgap increased with a decrease in particle size due to plasma treatment. The rhodamine B dye solution was then irradiated with a light source in the presence of the nanocomposites. The dye degradation efficiency of the composite photocatalyst increased from 80 to 96% after plasma treatment.

Molecular and physiological characterization of early semi-dwarf mutants of rice and localization of SNP variants in Sd1 locus generated through gamma radiation.

PURPOSE: The 'Improved White Ponni' (IWP) rice variety, which is susceptible to lodging, leading to yield losses. Our primary goal is to develop new rice lines with non-lodging traits, enhancing stem strength and resistance to adverse conditions. Additionally, we aim to improve yield-contributing agronomic traits, benefiting farmers, food security, and the environment. Our work contributes to scientific knowledge and addresses a significant issue in Southern Indian rice cultivation. MATERIALS AND METHODS: In the present study, early and semi-dwarf early mutants of IWP were developed without altering the native grain quality traits using gamma ray-mediated mutagenesis. The seeds (500) were irradiated with γ-rays after fixing the Lethal Dose 50 (LD50), and selection for semi-dwarfism and earliness was imposed on a large M2 population. The selected traits were confirmed by evaluating the M3 lines at morpho-physiological, biochemical, and molecular levels. RESULTS: The response of mutants to gibberellic acid has been studied, which identified responsive mutants as well as slow-responding mutant lines including IWP-11-2, IWP-48-2, IWP-50-11, and IWP-33-2. Agar plate assay indicated low α- amylase content in IWP-50-11, IWP-33-2, IWP-43-1, IWP-47-2, and IWP-18-1. The scanning electron microscopy demonstrated that the mutants displayed an increased cellular dimension in comparison to the wild type. In dwarf mutants, null alleles were observed for the SD1 gene-specific primers which depicts gene undergone mutation. Further sequencing revealed the presence of single nucleotide polymorphisms in the SD1 gene resulting in semi-dwarfism in the mutant IWP-D-1. CONCLUSIONS: The impact of a defective gibberellic acid-mediated signaling pathway in mutants to produce a novel high-yielding and early maturing semi-dwarf rice variety.

The Relationship Between Metabolic Syndrome and Mortality Among Patients With Acute Respiratory Distress Syndrome in Acute Respiratory Distress Syndrome Network and Prevention and Early Treatment of Acute Lung Injury Network Trials.

OBJECTIVES: Metabolic syndrome is known to predict outcomes in COVID-19 acute respiratory distress syndrome (ARDS) but has never been studied in non-COVID-19 ARDS. We therefore aimed to determine the association of metabolic syndrome with mortality among ARDS trial subjects. DESIGN: Retrospective cohort study of ARDS trials' data. SETTING: An ancillary analysis was conducted using data from seven ARDS Network and Prevention and Early Treatment of Acute Lung Injury Network randomized trials within the Biologic Specimen and Data Repository Information Coordinating Center database. PATIENTS: Hospitalized patients with ARDS and metabolic syndrome (defined by obesity, diabetes, and hypertension) were compared with similar patients without metabolic syndrome (those with less than three criteria). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was 28-day mortality. Among 4288 ARDS trial participants, 454 (10.6%) with metabolic syndrome were compared with 3834 controls (89.4%). In adjusted analyses, the metabolic syndrome group was associated with lower 28-day and 90-day mortality when compared with control (adjusted odds ratio [aOR], 0.70 [95% CI, 0.55-0.89] and 0.75 [95% CI, 0.60-0.95], respectively). With each additional metabolic criterion from 0 to 3, adjusted 28-day mortality was reduced by 18%, 22%, and 40%, respectively. In subgroup analyses stratifying by ARDS etiology, mortality was lower for metabolic syndrome vs. control in ARDS caused by sepsis or pneumonia (at 28 d, aOR 0.64 [95% CI, 0.48-0.84] and 90 d, aOR 0.69 [95% CI, 0.53-0.89]), but not in ARDS from noninfectious causes (at 28 d, aOR 1.18 [95% CI, 0.70-1.99] and 90 d, aOR 1.26 [95% CI, 0.77-2.06]). Interaction p = 0.04 and p = 0.02 for 28- and 90-day comparisons, respectively. CONCLUSIONS: Metabolic syndrome in ARDS was associated with a lower risk of mortality in non-COVID-19 ARDS. The relationship between metabolic inflammation and ARDS may provide a novel biological pathway to be explored in precision medicine-based trials.

Precision Medicine and Heterogeneity of Treatment Effect in Therapies for ARDS.

ARDS is a clinically heterogeneous syndrome, rather than a distinct disease. This heterogeneity at least partially explains the difficulty in studying treatments for these patients and contributes to the numerous trials of therapies for the syndrome that have not shown benefit. Recent studies have identified different subphenotypes within the heterogeneous patient population. These different subphenotypes likely have variable clinical responses to specific therapies, a concept known as heterogeneity of treatment effect. Recognizing different subphenotypes and heterogeneity of treatment effect has important implications for the clinical management of patients with ARDS. This review presents studies that have identified different subphenotypes and discusses how they can modify the effects of therapies evaluated in trials that are commonly considered to have shown no overall benefit in patients with ARDS.

HVD-LSTM based recognition of epileptic seizures and normal human activity.

In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal, we obtain multiple monocomponents varying in terms of amplitude and frequency. After decomposition, we extract features from the monocomponent matrix of the EEG signals. The instantaneous amplitude of the HVD monocomponents varies because of the motion artifacts present in EEG signals. Hence, the acquired statistical features from the instantaneous amplitude help in identifying the epileptic seizures and the normal human activities. The features selected by correlation-based Q-score are classified using an LSTM (Long Short Term Memory) based deep learning model in which the feature-based weight update maximizes the classification accuracy. For epilepsy diagnosis using the Bonn dataset and activity recognition leveraging our Sensor Networks Research Lab (SNRL) data, we achieve testing classification accuracies of 96.00% and 83.30% respectively through our proposed method.

Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada.

Importance: Resurgent COVID-19 cases have resulted in the reinstitution of nonpharmaceutical interventions, including school closures, which can have adverse effects on families. Understanding the associations of school closures with the number of incident and cumulative COVID-19 cases is critical for decision-making. Objective: To estimate the association of schools being open or closed with the number of COVID-19 cases compared with community-based nonpharmaceutical interventions. Design, Setting, and Participants: This decision analytical modelling study developed an agent-based transmission model using a synthetic population of 1 000 000 individuals based on the characteristics of the population of Ontario, Canada. Members of the synthetic population were clustered into households, neighborhoods, or rural districts, cities or rural regions, day care facilities, classrooms (ie, primary, elementary, or high school), colleges or universities, and workplaces. Data were analyzed between May 5, 2020, and October 20, 2020. Exposures: School reopening on September 15, 2020, vs schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcomes and Measures: Incident and cumulative COVID-19 cases between September 1, 2020, and October 31, 2020. Results: Among 1 000 000 simulated individuals, the percentage of infections among students and teachers acquired within schools was less than 5% across modeled scenarios. Incident COVID-19 case numbers on October 31, 2020, were 4414 (95% credible interval [CrI], 3491-5382) cases in the scenario with schools remaining closed and 4740 (95% CrI, 3863-5691) cases in the scenario for schools reopening, with no other community-based nonpharmaceutical intervention. In scenarios with community-based nonpharmaceutical interventions implemented, the incident case numbers on October 31 were 714 (95% CrI, 568-908) cases for schools remaining closed and 780 (95% CrI, 580-993) cases for schools reopening. When scenarios applied the case numbers observed in early October in Ontario, the cumulative case numbers were 777 (95% CrI, 621-993) cases for schools remaining closed and 803 (95% CrI, 617-990) cases for schools reopening. In scenarios with implementation of community-based interventions vs no community-based interventions, there was a mean difference of 39 355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed vs reopening them yielded a mean difference of 2040 cases. Conclusions and Relevance: This decision analytical modeling study of a synthetic population of individuals in Ontario, Canada, found that most COVID-19 cases in schools were due to acquisition in the community rather than transmission within schools and that the changes in COVID-19 case numbers associated with school reopenings were relatively small compared with the changes associated with community-based nonpharmaceutical interventions.

Demographic characteristics, acute care resource use and mortality by age and sex in patients with COVID-19 in Ontario, Canada: a descriptive analysis.

BACKGROUND: Understanding resource use for coronavirus disease 2019 (COVID-19) is critical. We conducted a descriptive analysis using public health data to describe age- and sex-specific acute care use, length of stay (LOS) and mortality associated with COVID-19. METHODS: We conducted a descriptive analysis using Ontario's Case and Contact Management Plus database of individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ontario from Mar. 1 to Sept. 30, 2020, to determine age- and sex-specific hospital admissions, intensive care unit (ICU) admissions, use of invasive mechanical ventilation, LOS and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. RESULTS: During the observation period, 56 476 individuals testing positive for SARS-CoV-2 were reported; 41 049 (72.7%) of these were younger than 60 years, and 29 196 (51.7%) were female. Proportion of cases shifted from older populations (> 60 yr) to younger populations (10-39 yr) over time. Overall, 5383 (9.5%) of individuals were admitted to hospital; of these, 1183 (22.0%) were admitted to the ICU, and 712 (60.2%) of these received invasive mechanical ventilation. Mean LOS for individuals in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation was 12.8 (standard deviation [SD] 15.4), 8.5 (SD 7.8) and 20.5 (SD 18.1) days, respectively. Among patients receiving care in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation, 911/3834 (23.8%), 124/418 (29.7%) and 287/635 (45.2%) died, respectively. All outcomes varied by age and decreased over time, overall and within age groups. INTERPRETATION: This descriptive study shows use of acute care and mortality varying by age and decreasing between March and September 2020 in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes.

Estimation de l'épuisement des ressources hospitalières attribuable à la COVID-19 en Ontario, au Canada.

CONTEXTE: La propagation à l'échelle planétaire de la maladie à coronavirus 2019 (COVID-2019) se poursuit dans plusieurs pays, mettant à rude épreuve les systèmes de santé. Cette étude avait pour but de prédire les répercussions de la pandémie sur les issues des patients et l'utilisation des ressources hospitalières en Ontario (Canada). MÉTHODES: Nous avons conçu un modèle de simulation axé sur les personnes illustrant le flux de patients atteints de la COVID-19 dans les hôpitaux ontariens. Nous avons simulé diverses combinaisons de trajectoires épidémiques et de capacités de soins hospitaliers. Les paramètres à l'étude étaient le nombre de patients devant être admis au service d'hospitalisation ou à l'unité des soins intensifs (USI) ­avec ou sans respirateur mécanique ­, le nombre de jours jusqu'à l'épuisement des ressources, le nombre de patients en attente de ressources et le nombre de décès. RÉSULTATS: Nous avons constaté que la mise en place rapide de mesures de santé publique efficaces éviterait l'épuisement des ressources hospitalières. Les simulations dans lesquelles les mesures d'éloignement sanitaire étaient inefficaces ou adoptées tardivement ont montré que l'épuisement des ressources prendrait de 14 à 26 jours et qu'il y aurait, dans le pire des cas, 13 321 décès de personnes en attente de ressources. Cet épuisement pourrait être évité ou retardé par la mise en place de mesures rigoureuses visant à améliorer la capacité des hôpitaux en matière de soins intensifs, de respirateurs mécaniques et de soins hospitaliers. INTERPRÉTATION: Sans l'adoption de mesures d'éloignement sanitaire rigoureuses, le système de santé ontarien n'aurait pas eu les ressources nécessaires pour prendre en charge le nombre attendu de patients atteints de la COVID-19, même en cas d'augmentation rapide de sa capacité hospitalière. Les pénuries auraient fait augmenter le taux de mortalité. En ralentissant la transmission de la maladie par la mise en place de mesures de santé publique et l'augmentation de la capacité des hôpitaux, l'Ontario a probablement évité que ces derniers subissent une pression catastrophique.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia / COVID-19: Predicción de las necesidades de recursos sanitarios en el Valle del Cauca, Colombia

Abstract Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

Resumen Introducción: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. Métodos: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. Resultados: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada.

BACKGROUND: The global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing substantial strain to health care systems. The purpose of our study was to predict the effect of the COVID-19 pandemic on patient outcomes and use of hospital resources in Ontario, Canada. METHODS: We developed an individual-level simulation to model the flow of patients with COVID-19 through the hospital system in Ontario. We simulated different combined scenarios of epidemic trajectory and hospital health care capacity. Our outcomes included the number of patients who needed admission to the ward or to the intensive care unit (ICU) with or without the need for mechanical ventilation, number of days to resource depletion, number of patients awaiting resources and number of deaths. RESULTS: We found that with effective early public health measures, hospital system resources would not be depleted. For scenarios with late or ineffective implementation of physical distancing, hospital resources would be depleted within 14-26 days, and in the worst case scenario, 13 321 patients would die while waiting for needed resources. Resource depletion would be avoided or delayed with aggressive measures to increase ICU, ventilator and acute care hospital capacities. INTERPRETATION: We found that without aggressive physical distancing measures, the Ontario hospital system would have been inadequately equipped to manage the expected number of patients with COVID-19 despite a rapid increase in capacity. This lack of hospital resources would have led to an increase in mortality. By slowing the spread of the disease using public health measures and by increasing hospital capacity, Ontario may have avoided catastrophic stresses to its hospitals.

The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic.

In Ontario on March 16, 2020, a directive was issued to all acute care hospitals to halt nonessential procedures in anticipation of a potential surge in COVID-19 patients. This included scheduled outpatient cardiac surgical and interventional procedures that required the use of intensive care units, ventilators, and skilled critical care personnel, given that these procedures would draw from the same pool of resources required for critically ill COVID-19 patients. We adapted the COVID-19 Resource Estimator (CORE) decision analytic model by adding a cardiac component to determine the impact of various policy decisions on the incremental waitlist growth and estimated waitlist mortality for 3 key groups of cardiovascular disease patients: coronary artery disease, valvular heart disease, and arrhythmias. We provided predictions based on COVID-19 epidemiology available in real-time, in 3 phases. First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local epidemiology data, we predicted that across 5 regions of Ontario, there may be insufficient resources to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the estimated incremental growth in waitlist for all cardiac procedures is likely substantial. These outputs informed timely data-driven decisions during the COVID-19 pandemic regarding the provision of cardiovascular care.

Estimating healthcare resource needs for COVID-19 patients in Nigeria.

INTRODUCTION: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. METHODS: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. RESULTS: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources. CONCLUSION: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia.

BACKGROUND: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. METHODS: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. RESULTS: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. CONCLUSION: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.

INTRODUCCIÓN: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. MÉTODOS: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. RESULTADOS: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Estimating healthcare resource needs for COVID-19 patients in Nigeria

Introduction: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria.Methods: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon.Results: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources.Conclusion: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further