Estimating the air change rates in dwellings using a heat balance approach

Infiltration and ventilation rates in domestic buildings vary with construction type, weather conditions and the operation of openings in the fabric. Generating good estimates of ventilation is important for modelling, simulation and performance assessment as it has a significant impact on energy consumption. Physical tests can be applied to estimate leakage, but this is cumbersome and impractical to apply in most cases. This paper applies a heat balance approach to energy monitoring data to estimate a parameter that describes the combined ventilation and infiltration rates in real family homes. These estimates are compared with published values and a model is presented that describes the air change rate as a function of user behaviour (control of openings) and varying wind speed. The paper demonstrates that it is possible to estimate plausible air change rates from such data

Energy Modelling and Calibration of Building Simulations: A Case Study of a Domestic Building with Natural Ventilation

[EN] In this paper, the building energy performance modelling tools TRNSYS (TRaNsient SYstem Simulation program) and TRNFlow (TRaNsient Flow) have been used to obtain the energy demand of a domestic building that includes the air infiltration rate and the effect of natural ventilation by using window operation data. An initial model has been fitted to monitoring data from the case study, building over a period when there were no heat gains in the building in order to obtain the building infiltration air change rate. After this calibration, a constant air-change rate model was established alongside two further models developed in the calibration process. Air change rate has been explored in order to determine air infiltrations caused by natural ventilation due to windows being opened. These results were compared to estimates gained through a previously published method and were found to be in good agreement. The main conclusion from the work was that the modelling ventilation rate in naturally ventilated residential buildings using TRNSYS and TRNSFlow can improve the simulation-based energy assessment.Aparicio-Fernández, C.; Vivancos, J.; Cosar-Jorda, P.; Buswell, RA. (2019). Energy Modelling and Calibration of Building Simulations: A Case Study of a Domestic Building with Natural Ventilation. Energies. 12(17):1-13. https://doi.org/10.3390/en12173360S1131217Grygierek, K., & Ferdyn-Grygierek, J. (2018). Multi-Objective Optimization of the Envelope of Building with Natural Ventilation. Energies, 11(6), 1383. doi:10.3390/en11061383Moran, P., Goggins, J., & Hajdukiewicz, M. (2017). Super-insulate or use renewable technology? Life cycle cost, energy and global warming potential analysis of nearly zero energy buildings (NZEB) in a temperate oceanic climate. 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Energies, 12(9), 1677. doi:10.3390/en12091677Cheng, P. L., & Li, X. (2018). Air infiltration rates in the bedrooms of 202 residences and estimated parametric infiltration rate distribution in Guangzhou, China. Energy and Buildings, 164, 219-225. doi:10.1016/j.enbuild.2017.12.062Hou, J., Zhang, Y., Sun, Y., Wang, P., Zhang, Q., Kong, X., & Sundell, J. (2018). Air change rates at night in northeast Chinese homes. Building and Environment, 132, 273-281. doi:10.1016/j.buildenv.2018.01.030Zhai, Z. (John), Mankibi, M. E., & Zoubir, A. (2015). Review of Natural Ventilation Models. Energy Procedia, 78, 2700-2705. doi:10.1016/j.egypro.2015.11.355Han, G., Srebric, J., & Enache-Pommer, E. (2015). Different modeling strategies of infiltration rates for an office building to improve accuracy of building energy simulations. Energy and Buildings, 86, 288-295. doi:10.1016/j.enbuild.2014.10.028Laverge, J., & Janssens, A. (2013). Optimization of design flow rates and component sizing for residential ventilation. Building and Environment, 65, 81-89. doi:10.1016/j.buildenv.2013.03.019Bhandari, M., Hun, D., Shrestha, S., Pallin, S., & Lapsa, M. (2018). A Simplified Methodology to Estimate Energy Savings in Commercial Buildings from Improvements in Airtightness. Energies, 11(12), 3322. doi:10.3390/en11123322Pisello, A. L., Castaldo, V. L., Taylor, J. E., & Cotana, F. (2016). The impact of natural ventilation on building energy requirement at inter-building scale. Energy and Buildings, 127, 870-883. doi:10.1016/j.enbuild.2016.06.023Tronchin, L., Fabbri, K., & Bertolli, C. (2018). Controlled Mechanical Ventilation in Buildings: A Comparison between Energy Use and Primary Energy among Twenty Different Devices. Energies, 11(8), 2123. doi:10.3390/en11082123Ashdown, M. M. A., Crawley, J., Biddulph, P., Wingfield, J., Lowe, R., & Elwell, C. A. (2019). Characterising the airtightness of dwellings. International Journal of Building Pathology and Adaptation, 38(1), 89-106. doi:10.1108/ijbpa-02-2019-0024Crawley, J., Wingfield, J., & Elwell, C. (2018). The relationship between airtightness and ventilation in new UK dwellings. Building Services Engineering Research and Technology, 40(3), 274-289. doi:10.1177/0143624418822199Jones, B., Das, P., Chalabi, Z., Davies, M., Hamilton, I., Lowe, R., … Taylor, J. (2015). Assessing uncertainty in housing stock infiltration rates and associated heat loss: English and UK case studies. Building and Environment, 92, 644-656. doi:10.1016/j.buildenv.2015.05.033Schulze, T., & Eicker, U. (2013). Controlled natural ventilation for energy efficient buildings. Energy and Buildings, 56, 221-232. doi:10.1016/j.enbuild.2012.07.044Stavridou, A. D., & Prinos, P. E. (2017). Unsteady CFD Simulation in a Naturally Ventilated Room with a Localized Heat Source. 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Next-Generation Sequencing to Detect Deletion of RB1 and ERBB4 Genes in Chromophobe Renal Cell Carcinoma: A Potential Role in Distinguishing Chromophobe Renal Cell Carcinoma from Renal Oncocytoma

Overlapping morphologic, immunohistochemical, and ultrastructural features make it difficult to diagnose chromophobe renal cell carcinoma (ChRCC) and renal oncocytoma (RO). Because ChRCC is a malignant tumor, whereas RO is a tumor with benign behavior, it is important to distinguish these two entities. We aimed to identify genetic markers that distinguish ChRCC from RO by using next-generation sequencing (NGS). NGS for hotspot mutations or gene copy number changes was performed on 12 renal neoplasms, including seven ChRCC and five RO cases. Matched normal tissues from the same patients were used to exclude germline variants. Rare hotspot mutations were found in cancer-critical genes (TP53 and PIK3CA) in ChRCC but not RO. The NGS gene copy number analysis revealed multiple abnormalities. The two most common deletions were tumor-suppressor genes RB1 and ERBB4 in ChRCC but not RO. Fluorescence in situ hybridization was performed on 65 cases (ChRCC, n = 33; RO, n = 32) to verify hemizygous deletion of RB1 (17/33, 52%) or ERBB4 (11/33, 33%) in ChRCC, but not in RO (0/32, 0%). In total, ChRCCs (23/33, 70%) carry either a hemizygous deletion of RB1 or ERBB4. The combined use of RB1 and ERBB4 fluorescence in situ hybridization to detect deletion of these genes may offer a highly sensitive and specific assay to distinguish ChRCC from RO

Somatic molecular analysis augments cytologic evaluation of pancreatic cyst fluids as a diagnostic tool

Objective: Better tools are needed for early diagnosis and classification of pancreatic cystic lesions (PCL) to trigger intervention before neoplastic precursor lesions progress to adenocarcinoma. We evaluated the capacity of molecular analysis to improve the accuracy of cytologic diagnosis for PCL with an emphasis on non-diagnostic/negative specimens. Design: In a span of 7 years, at a tertiary care hospital, 318 PCL endoscopic ultrasound-guided fine needle aspirations (EUS-FNA) were evaluated by cytologic examination and molecular analysis. Mucinous PCL were identified based on a clinical algorithm and 46 surgical resections were used to verify this approach. The mutation allele frequency (MAF) of commonly altered genes (BRAF, CDKN2A, CTNNB1, GNAS, RAS, PIK3CA, PTEN, SMAD4, TP53 and VHL) was evaluated for their ability to identify and grade mucinous PCL. Results: Cytology showed a diagnostic sensitivity of 43.5% for mucinous PCL due in part to the impact of non-diagnostic (28.8%) and negative (50.5%) specimens. Incorporating an algorithmic approach or molecular analysis markedly increased the accuracy of cytologic evaluation. Detection of mucinous PCL by molecular analysis was 93.3% based on the detection of KRAS and/or GNAS gene mutations (p = 0.0001). Additional genes provided a marginal improvement in sensitivity but were associated with cyst type (e.g. VHL) and grade (e.g. SMAD4). In the surgical cohort, molecular analysis and the proposed algorithm showed comparable sensitivity (88.9% vs. 100%). Conclusions: Incorporating somatic molecular analysis in the cytologic evaluation of EUS-FNA increases diagnostic accuracy for detection, classification and grading of PCL. This approach has the potential to improve patient management

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors. Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality