Articles de revista
http://hdl.handle.net/2117/79687
2024-03-29T13:44:21ZMultisystem inflammatory syndrome in children and SARS-CoV-2 variants: a two-year ambispective multicentric cohort study in Catalonia, Spain
http://hdl.handle.net/2117/405167
Multisystem inflammatory syndrome in children and SARS-CoV-2 variants: a two-year ambispective multicentric cohort study in Catalonia, Spain
Pino Ramirez, Rosa Maria; Antoñanzas, Jesús M.; Paredes-Carmona, Fernando; Perramon Malavez, Aida; Rivière, Jacques G.; Martínez Mejías, Abel; Gatell Carbó, Anna; Soler-Palacín, Pere; Fina Avilés, Francesc; Prats Soler, Clara; Soriano-Arandes, Antoni
Multisystem inflammatory syndrome in children (MIS-C) is a rare but severe disease temporarily related to SARS-CoV-2. We aimed to describe the epidemiological, clinical, and laboratory findings of all MIS-C cases diagnosed in children < 18 years old in Catalonia (Spain) to study their trend throughout the pandemic. This was a multicenter ambispective observational cohort study (April 2020– April 2022). Data were obtained from the COVID-19 Catalan surveillance system and from all hospitals in Catalonia. We analyzed MIS-C cases regarding SARS-CoV-2 variants for demographics, symptoms, severity, monthly MIS-C incidence, ratio between MIS-C and accumulated COVID-19 cases, and associated rate ratios (RR). Among 555,848 SARS-CoV-2 infections, 152 children were diagnosed with MIS-C. The monthly MIS-C incidence was 4.1 (95% CI: 3.4–4.8) per 1,000,000 people, and 273 (95% CI: 230–316) per 1,000,000 SARS-CoV-2 infections (i.e., one case per 3,700 SARS-CoV-2 infections). During the Omicron period, the MIS-C RR was 8.2 (95% CI: 5.7–11.7) per 1,000,000 SARS-CoV-2 infections, which was significantly lower (p < 0.001) than that for previous variant periods in all age groups. The median [IQR] age of MIS-C was 8 [4–11] years, 62.5% male, and 80.2% without comorbidities. Common symptoms were gastrointestinal findings (88.2%) and fever > 39 °C (81.6%); nearly 40% had an abnormal echocardiography, and 7% had coronary aneurysm. Clinical manifestations and laboratory data were not different throughout the variant periods (p > 0.05). Conclusion: The RR between MIS-C cases and SARS-CoV-2 infections was significantly lower in the Omicron period for all age groups, including those not vaccinated, suggesting that the variant could be the main factor for this shift in the MISC trend. Regardless of variant type, the patients had similar phenotypes and severity throughout the pandemic.
2024-03-22T12:37:53ZPino Ramirez, Rosa MariaAntoñanzas, Jesús M.Paredes-Carmona, FernandoPerramon Malavez, AidaRivière, Jacques G.Martínez Mejías, AbelGatell Carbó, AnnaSoler-Palacín, PereFina Avilés, FrancescPrats Soler, ClaraSoriano-Arandes, AntoniMultisystem inflammatory syndrome in children (MIS-C) is a rare but severe disease temporarily related to SARS-CoV-2. We aimed to describe the epidemiological, clinical, and laboratory findings of all MIS-C cases diagnosed in children < 18 years old in Catalonia (Spain) to study their trend throughout the pandemic. This was a multicenter ambispective observational cohort study (April 2020– April 2022). Data were obtained from the COVID-19 Catalan surveillance system and from all hospitals in Catalonia. We analyzed MIS-C cases regarding SARS-CoV-2 variants for demographics, symptoms, severity, monthly MIS-C incidence, ratio between MIS-C and accumulated COVID-19 cases, and associated rate ratios (RR). Among 555,848 SARS-CoV-2 infections, 152 children were diagnosed with MIS-C. The monthly MIS-C incidence was 4.1 (95% CI: 3.4–4.8) per 1,000,000 people, and 273 (95% CI: 230–316) per 1,000,000 SARS-CoV-2 infections (i.e., one case per 3,700 SARS-CoV-2 infections). During the Omicron period, the MIS-C RR was 8.2 (95% CI: 5.7–11.7) per 1,000,000 SARS-CoV-2 infections, which was significantly lower (p < 0.001) than that for previous variant periods in all age groups. The median [IQR] age of MIS-C was 8 [4–11] years, 62.5% male, and 80.2% without comorbidities. Common symptoms were gastrointestinal findings (88.2%) and fever > 39 °C (81.6%); nearly 40% had an abnormal echocardiography, and 7% had coronary aneurysm. Clinical manifestations and laboratory data were not different throughout the variant periods (p > 0.05). Conclusion: The RR between MIS-C cases and SARS-CoV-2 infections was significantly lower in the Omicron period for all age groups, including those not vaccinated, suggesting that the variant could be the main factor for this shift in the MISC trend. Regardless of variant type, the patients had similar phenotypes and severity throughout the pandemic.The impact of COVID-19 certification mandates on the number of cases of and hospitalizations with COVID-19 in the UK: A difference-in-differences analysis
http://hdl.handle.net/2117/405162
The impact of COVID-19 certification mandates on the number of cases of and hospitalizations with COVID-19 in the UK: A difference-in-differences analysis
López-Güell, Kim; Prats-Uribe, Albert; Català Sabaté, Martí; Prats Soler, Clara; Jotun, Hein; Prieto-Alhambra, Daniel
Background: Mandatory COVID-19 certification, showing proof of vaccination, negative test, or recent infection to access to public venues, was introduced at different times in the four countries of the UK. We aim to study its effects on the incidence of cases and hospital admissions.
Methods: We performed Negative binomial segmented regression and ARIMA analyses for four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences models to compare the latter three to England, as a negative control group, since it was the last country where COVID-19 certification was introduced. The main outcome was the weekly averaged incidence of COVID-19 cases and hospital admissions.
Results: COVID-19 certification led to a decrease in the incidence of cases and hospital admissions in Northern Ireland, as well as in Wales during the second half of November. The same was seen for hospital admissions in Wales and Scotland during October. In Wales the incidence rate of cases in October already had a decreasing tendency, as well as in England, hence a particular impact of COVID-19 certification was less obvious. Method assumptions for the Difference-in-Differences analysis did not hold for Scotland. Additional NBSR and ARIMA models suggest similar results, while also accounting for correlation in the latter. The assessment of the effect in England itself leads one to believe that this intervention might not be strong enough for the Omicron variant, which was prevalent at the time of introduction of COVID-19 certification in the country.
Conclusions: Mandatory COVID-19 certification reduced COVID-19 transmission and hospitalizations when Delta predominated in the UK, but lost efficacy when Omicron became the most common variant.
2024-03-22T12:17:40ZLópez-Güell, KimPrats-Uribe, AlbertCatalà Sabaté, MartíPrats Soler, ClaraJotun, HeinPrieto-Alhambra, DanielBackground: Mandatory COVID-19 certification, showing proof of vaccination, negative test, or recent infection to access to public venues, was introduced at different times in the four countries of the UK. We aim to study its effects on the incidence of cases and hospital admissions.
Methods: We performed Negative binomial segmented regression and ARIMA analyses for four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences models to compare the latter three to England, as a negative control group, since it was the last country where COVID-19 certification was introduced. The main outcome was the weekly averaged incidence of COVID-19 cases and hospital admissions.
Results: COVID-19 certification led to a decrease in the incidence of cases and hospital admissions in Northern Ireland, as well as in Wales during the second half of November. The same was seen for hospital admissions in Wales and Scotland during October. In Wales the incidence rate of cases in October already had a decreasing tendency, as well as in England, hence a particular impact of COVID-19 certification was less obvious. Method assumptions for the Difference-in-Differences analysis did not hold for Scotland. Additional NBSR and ARIMA models suggest similar results, while also accounting for correlation in the latter. The assessment of the effect in England itself leads one to believe that this intervention might not be strong enough for the Omicron variant, which was prevalent at the time of introduction of COVID-19 certification in the country.
Conclusions: Mandatory COVID-19 certification reduced COVID-19 transmission and hospitalizations when Delta predominated in the UK, but lost efficacy when Omicron became the most common variant.Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
http://hdl.handle.net/2117/405157
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Sherratt, Katharine; Gruson, Hugo; Johnson, Helen; Grah, Rok; Niehus, Rene; Villanueva, Inmaculada; Alonso Muñoz, Sergio; Álvarez Lacalle, Enrique; López Codina, Daniel; Prats Soler, Clara; Català Sabaté, Martí; Funk, Sebastian
Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
2024-03-22T11:55:32ZSherratt, KatharineGruson, HugoJohnson, HelenGrah, RokNiehus, ReneVillanueva, InmaculadaAlonso Muñoz, SergioÁlvarez Lacalle, EnriqueLópez Codina, DanielPrats Soler, ClaraCatalà Sabaté, MartíFunk, SebastianBackground: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.Association between ethnic background and COVID-19 morbidity, mortality and vaccination in England: a multistate cohort analysis using the UK Biobank
http://hdl.handle.net/2117/405151
Association between ethnic background and COVID-19 morbidity, mortality and vaccination in England: a multistate cohort analysis using the UK Biobank
Urdiales, Tomás; Dernie, Francesco; Català Sabaté, Martí; Prats-Uribe, Albert; Prats Soler, Clara; Prieto-Alhambra, Daniel
Objectives Despite growing evidence suggesting increased COVID-19 mortality among people from ethnic minorities, little is known about milder forms of SARS- CoV- 2 infection. We sought to explore the association between ethnic background and the probability of testing, testing positive, hospitalisation, COVID-19 mortality and vaccination uptake. Design A multistate cohort analysis. Participants were followed between 8 April 2020 and 30 September 2021. Setting The UK Biobank, which stores medical data on around half a million people who were recruited between 2006 and 2010. Participants 405 541 subjects were eligible for analysis, limited to UK Biobank participants living in England. 23 891 (6%) of participants were non-white. Primary and secondary outcome measures The associations between ethnic background and testing, testing positive, hospitalisation and COVID-19 mortality were studied using multistate survival analyses. The association with single and double-dose vaccination was also modelled. Multistate models adjusted for age, sex and socioeconomic deprivation were fitted to estimate adjusted HRs (aHR) for each of the multistate transitions. Results 18 172 (4.5%) individuals tested positive, 3285 (0.8%) tested negative and then positive, 1490 (6.9% of those tested positive) were hospitalised, and 129 (0.6%) tested positive at the moment of hospital admission (ie, direct hospitalisation). Finally, 662 (17.4%) died after admission. Compared with white participants, Asian participants had an increased risk of negative to positive transition (aHR 1.24 (95% CI 1.02 to 1.52)), testing positive (95% CI 1.44 (1.33 to 1.55)) and direct hospitalisation (1.61 (95% CI 1.28 to 2.03)). Black participants had an increased risk of hospitalisation following a positive test (1.71 (95% CI 1.29 to 2.27)) and direct hospitalisation (1.90 (95% CI 1.51 to 2.39)). Although not the case for Asians (aHR 1.00 (95% CI 0.98 to 1.02)), black participants had a reduced vaccination probability (0.63 (95% CI 0.62 to 0.65)). In contrast, Chinese participants had a reduced risk of testing negative (aHR 0.64 (95% CI 0.57 to 0.73)), of testing positive (0.40 (95% CI 0.28 to 0.57)) and of vaccination (0.78 (95% CI 0.74 to 0.83)). Conclusions We identified inequities in testing, vaccination and COVID-19 outcomes according to ethnicity in England. Compared with whites, Asian participants had increased risks of infection and admission, and black participants had almost double hospitalisation risk, and a 40% lower vaccine uptake.
2024-03-22T11:34:10ZUrdiales, TomásDernie, FrancescoCatalà Sabaté, MartíPrats-Uribe, AlbertPrats Soler, ClaraPrieto-Alhambra, DanielObjectives Despite growing evidence suggesting increased COVID-19 mortality among people from ethnic minorities, little is known about milder forms of SARS- CoV- 2 infection. We sought to explore the association between ethnic background and the probability of testing, testing positive, hospitalisation, COVID-19 mortality and vaccination uptake. Design A multistate cohort analysis. Participants were followed between 8 April 2020 and 30 September 2021. Setting The UK Biobank, which stores medical data on around half a million people who were recruited between 2006 and 2010. Participants 405 541 subjects were eligible for analysis, limited to UK Biobank participants living in England. 23 891 (6%) of participants were non-white. Primary and secondary outcome measures The associations between ethnic background and testing, testing positive, hospitalisation and COVID-19 mortality were studied using multistate survival analyses. The association with single and double-dose vaccination was also modelled. Multistate models adjusted for age, sex and socioeconomic deprivation were fitted to estimate adjusted HRs (aHR) for each of the multistate transitions. Results 18 172 (4.5%) individuals tested positive, 3285 (0.8%) tested negative and then positive, 1490 (6.9% of those tested positive) were hospitalised, and 129 (0.6%) tested positive at the moment of hospital admission (ie, direct hospitalisation). Finally, 662 (17.4%) died after admission. Compared with white participants, Asian participants had an increased risk of negative to positive transition (aHR 1.24 (95% CI 1.02 to 1.52)), testing positive (95% CI 1.44 (1.33 to 1.55)) and direct hospitalisation (1.61 (95% CI 1.28 to 2.03)). Black participants had an increased risk of hospitalisation following a positive test (1.71 (95% CI 1.29 to 2.27)) and direct hospitalisation (1.90 (95% CI 1.51 to 2.39)). Although not the case for Asians (aHR 1.00 (95% CI 0.98 to 1.02)), black participants had a reduced vaccination probability (0.63 (95% CI 0.62 to 0.65)). In contrast, Chinese participants had a reduced risk of testing negative (aHR 0.64 (95% CI 0.57 to 0.73)), of testing positive (0.40 (95% CI 0.28 to 0.57)) and of vaccination (0.78 (95% CI 0.74 to 0.83)). Conclusions We identified inequities in testing, vaccination and COVID-19 outcomes according to ethnicity in England. Compared with whites, Asian participants had increased risks of infection and admission, and black participants had almost double hospitalisation risk, and a 40% lower vaccine uptake.iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
http://hdl.handle.net/2117/404932
iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
Rubio Maturana, Carles; Oliveira, Allisson Dantas de; Nadal Francesch, Sergi; Zarzuela Serrat, Francesc; Sulleiro Igual, Elena; Ruiz Marti, Edurne; Bilalli, Besim; Veiga Lluch, Anna; Espasa Soley, Mateu; Abelló Gamazo, Alberto; Pumarola Sunyer, Tomas; Segu Estruch, Marta; López Codina, Daniel; Sayrol Clos, Elisa; Joseph Munné, Joan
Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.
The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.
2024-03-19T12:42:51ZRubio Maturana, CarlesOliveira, Allisson Dantas deNadal Francesch, SergiZarzuela Serrat, FrancescSulleiro Igual, ElenaRuiz Marti, EdurneBilalli, BesimVeiga Lluch, AnnaEspasa Soley, MateuAbelló Gamazo, AlbertoPumarola Sunyer, TomasSegu Estruch, MartaLópez Codina, DanielSayrol Clos, ElisaJoseph Munné, JoanMalaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.
The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.Spatial distribution of calcium sparks determines their ability to induce afterdepolarizations in human atrial myocytes
http://hdl.handle.net/2117/401668
Spatial distribution of calcium sparks determines their ability to induce afterdepolarizations in human atrial myocytes
Tarifa Lora, Carmen; Vallmitjana Lees, Alexander; Jiménez Sábado, Verónica; Marchena Angos, Miquel; Llach, Anna; Herraiz Martínez, Adela; Nolla Colomer, Carme; Ginel Iglesias, Antonino; Montiel Dacosta, José Antonio; Ciruela, Francisco; Echebarría Domínguez, Blas; Benítez Iglesias, Raúl; Cinca Cuscullola, Juan Maria; Hove-Madsen, Leif
Analysis of the spatio-temporal distribution of calcium sparks showed a preferential increase in sparks near the sarcolemma in atrial myocytes from patients with atrial fibrillation (AF), linked to higher ryanodine receptor (RyR2) phosphorylation at s2808 and lower calsequestrin-2 levels. Mathematical modeling, incorporating modulation of RyR2 gating, showed that only the observed combinations of RyR2 phosphorylation and calsequestrin-2 levels can account for the spatio-temporal distribution of sparks in patients with and without AF. Furthermore, we demonstrate that preferential calcium release near the sarcolemma is key to a higher incidence and amplitude of afterdepolarizations in atrial myocytes from patients with AF.
2024-02-09T16:45:54ZTarifa Lora, CarmenVallmitjana Lees, AlexanderJiménez Sábado, VerónicaMarchena Angos, MiquelLlach, AnnaHerraiz Martínez, AdelaNolla Colomer, CarmeGinel Iglesias, AntoninoMontiel Dacosta, José AntonioCiruela, FranciscoEchebarría Domínguez, BlasBenítez Iglesias, RaúlCinca Cuscullola, Juan MariaHove-Madsen, LeifAnalysis of the spatio-temporal distribution of calcium sparks showed a preferential increase in sparks near the sarcolemma in atrial myocytes from patients with atrial fibrillation (AF), linked to higher ryanodine receptor (RyR2) phosphorylation at s2808 and lower calsequestrin-2 levels. Mathematical modeling, incorporating modulation of RyR2 gating, showed that only the observed combinations of RyR2 phosphorylation and calsequestrin-2 levels can account for the spatio-temporal distribution of sparks in patients with and without AF. Furthermore, we demonstrate that preferential calcium release near the sarcolemma is key to a higher incidence and amplitude of afterdepolarizations in atrial myocytes from patients with AF.Increased Ca2+ transient underlies RyR2-related left ventricular noncompaction
http://hdl.handle.net/2117/398769
Increased Ca2+ transient underlies RyR2-related left ventricular noncompaction
Ni, Mingke; Li, Yanhui; Wei, Jinhong; Song, Zhenpeng; Wang, Hui; Yao, Jinjing; Chen, Yong-Xiang; Belke, Darrell; Estillore, John Paul; Wang, Ruiwu; Vallmitjana Lees, Alexander; Benítez Iglesias, Raúl; Hove Madsen, Leif; Feng, Wei; Chen, Ju
Background:
A loss-of-function cardiac ryanodine receptor (RyR2) mutation, I4855M+/–, has recently been linked to a new cardiac disorder termed RyR2 Ca2+ release deficiency syndrome (CRDS) as well as left ventricular noncompaction (LVNC). The mechanism by which RyR2 loss-of-function causes CRDS has been extensively studied, but the mechanism underlying RyR2 loss-of-function-associated LVNC is unknown. Here, we determined the impact of a CRDS-LVNC-associated RyR2-I4855M+/– loss-of-function mutation on cardiac structure and function.
Methods:
We generated a mouse model expressing the CRDS-LVNC-associated RyR2-I4855M+/– mutation. Histological analysis, echocardiography, ECG recording, and intact heart Ca2+ imaging were performed to characterize the structural and functional consequences of the RyR2-I4855M+/– mutation.
Results:
As in humans, RyR2-I4855M+/– mice displayed LVNC characterized by cardiac hypertrabeculation and noncompaction. RyR2-I4855M+/– mice were highly susceptible to electrical stimulation–induced ventricular arrhythmias but protected from stress-induced ventricular arrhythmias. Unexpectedly, the RyR2-I4855M+/– mutation increased the peak Ca2+ transient but did not alter the L-type Ca2+ current, suggesting an increase in Ca2+-induced Ca2+ release gain. The RyR2-I4855M+/– mutation abolished sarcoplasmic reticulum store overload–induced Ca2+ release or Ca2+ leak, elevated sarcoplasmic reticulum Ca2+ load, prolonged Ca2+ transient decay, and elevated end-diastolic Ca2+ level upon rapid pacing. Immunoblotting revealed increased level of phosphorylated CaMKII (Ca2+-calmodulin dependent protein kinases II) but unchanged levels of CaMKII, calcineurin, and other Ca2+ handling proteins in the RyR2-I4855M+/– mutant compared with wild type.
Conclusions:
The RyR2-I4855M+/– mutant mice represent the first RyR2-associated LVNC animal model that recapitulates the CRDS-LVNC overlapping phenotype in humans. The RyR2-I4855M+/– mutation increases the peak Ca2+ transient by increasing the Ca2+-induced Ca2+ release gain and the end-diastolic Ca2+ level by prolonging Ca2+ transient decay. Our data suggest that the increased peak-systolic and end-diastolic Ca2+ levels may underlie RyR2-associated LVNC.
2023-12-22T13:14:20ZNi, MingkeLi, YanhuiWei, JinhongSong, ZhenpengWang, HuiYao, JinjingChen, Yong-XiangBelke, DarrellEstillore, John PaulWang, RuiwuVallmitjana Lees, AlexanderBenítez Iglesias, RaúlHove Madsen, LeifFeng, WeiChen, JuBackground:
A loss-of-function cardiac ryanodine receptor (RyR2) mutation, I4855M+/–, has recently been linked to a new cardiac disorder termed RyR2 Ca2+ release deficiency syndrome (CRDS) as well as left ventricular noncompaction (LVNC). The mechanism by which RyR2 loss-of-function causes CRDS has been extensively studied, but the mechanism underlying RyR2 loss-of-function-associated LVNC is unknown. Here, we determined the impact of a CRDS-LVNC-associated RyR2-I4855M+/– loss-of-function mutation on cardiac structure and function.
Methods:
We generated a mouse model expressing the CRDS-LVNC-associated RyR2-I4855M+/– mutation. Histological analysis, echocardiography, ECG recording, and intact heart Ca2+ imaging were performed to characterize the structural and functional consequences of the RyR2-I4855M+/– mutation.
Results:
As in humans, RyR2-I4855M+/– mice displayed LVNC characterized by cardiac hypertrabeculation and noncompaction. RyR2-I4855M+/– mice were highly susceptible to electrical stimulation–induced ventricular arrhythmias but protected from stress-induced ventricular arrhythmias. Unexpectedly, the RyR2-I4855M+/– mutation increased the peak Ca2+ transient but did not alter the L-type Ca2+ current, suggesting an increase in Ca2+-induced Ca2+ release gain. The RyR2-I4855M+/– mutation abolished sarcoplasmic reticulum store overload–induced Ca2+ release or Ca2+ leak, elevated sarcoplasmic reticulum Ca2+ load, prolonged Ca2+ transient decay, and elevated end-diastolic Ca2+ level upon rapid pacing. Immunoblotting revealed increased level of phosphorylated CaMKII (Ca2+-calmodulin dependent protein kinases II) but unchanged levels of CaMKII, calcineurin, and other Ca2+ handling proteins in the RyR2-I4855M+/– mutant compared with wild type.
Conclusions:
The RyR2-I4855M+/– mutant mice represent the first RyR2-associated LVNC animal model that recapitulates the CRDS-LVNC overlapping phenotype in humans. The RyR2-I4855M+/– mutation increases the peak Ca2+ transient by increasing the Ca2+-induced Ca2+ release gain and the end-diastolic Ca2+ level by prolonging Ca2+ transient decay. Our data suggest that the increased peak-systolic and end-diastolic Ca2+ levels may underlie RyR2-associated LVNC.Single-cell Bayesian deconvolution
http://hdl.handle.net/2117/398434
Single-cell Bayesian deconvolution
Torregrosa Cortés, Gabriel; Oriola Santandreu, David; Trivedi, Vikas; García Ojalvo, Jordi
Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here, we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, providing unbiased estimates of the resulting confidence intervals. We use this approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.
2023-12-20T19:01:39ZTorregrosa Cortés, GabrielOriola Santandreu, DavidTrivedi, VikasGarcía Ojalvo, JordiIndividual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here, we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, providing unbiased estimates of the resulting confidence intervals. We use this approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.TS-CNN: a three-tier self-interpretable CNN for multi-region medical image classification
http://hdl.handle.net/2117/397999
TS-CNN: a three-tier self-interpretable CNN for multi-region medical image classification
Ashwath, V. A.; Okkath Krishnanunni, Sikha; Benítez Iglesias, Raúl
Medical image classification is critical, where reliability and transparency are crucial for the safe and accurate diagnosis of diseases. Deep Convolutional Neural Networks (DCNNs) are widely used in medical image classification due to their high performance. However, they are often considered black-boxes because they offer little insight into decision-making. Therefore, improving the interpretability of DCNNs is crucial for their adoption in medical diagnoses. This paper proposes a novel three-tier self-interpretable DCNN (TS-CNN) architecture for multi-region medical image classification, which improves classification performance while being inherently interpretable. The proposed TS-CNN architecture is well-suited for medical images with multiple regions, such as images with scattered and randomly shaped lesions. The proposed architecture has three branches: a global branch that learns the relevant patterns from the raw input image; an attention branch that selects the important regions and discards the irrelevant parts for the local branch to learn; and a fusion branch that distills knowledge from both the global and local branches for classification. The proposed architecture is flexible in terms of the backbone CNNs used for classification and post-hoc interpretability methods used for attention capture. We demonstrate the flexibility and generalization of the architecture through a series of experiments involving multiple state-of-the-art CNN architectures such as DenseNet-121, Inception, Xception, and ResNet-50 as the global/local branches, each paired with GradCAM and Saliency maps as attention modules. The proposed architecture outperformed the backbone model in classification tasks on two datasets: a custom-made blob dataset and a publicly available skin lesion PAD-UFES-20 dataset, demonstrating its potential for improving accuracy in medical image classification tasks.
2023-12-14T12:26:08ZAshwath, V. A.Okkath Krishnanunni, SikhaBenítez Iglesias, RaúlMedical image classification is critical, where reliability and transparency are crucial for the safe and accurate diagnosis of diseases. Deep Convolutional Neural Networks (DCNNs) are widely used in medical image classification due to their high performance. However, they are often considered black-boxes because they offer little insight into decision-making. Therefore, improving the interpretability of DCNNs is crucial for their adoption in medical diagnoses. This paper proposes a novel three-tier self-interpretable DCNN (TS-CNN) architecture for multi-region medical image classification, which improves classification performance while being inherently interpretable. The proposed TS-CNN architecture is well-suited for medical images with multiple regions, such as images with scattered and randomly shaped lesions. The proposed architecture has three branches: a global branch that learns the relevant patterns from the raw input image; an attention branch that selects the important regions and discards the irrelevant parts for the local branch to learn; and a fusion branch that distills knowledge from both the global and local branches for classification. The proposed architecture is flexible in terms of the backbone CNNs used for classification and post-hoc interpretability methods used for attention capture. We demonstrate the flexibility and generalization of the architecture through a series of experiments involving multiple state-of-the-art CNN architectures such as DenseNet-121, Inception, Xception, and ResNet-50 as the global/local branches, each paired with GradCAM and Saliency maps as attention modules. The proposed architecture outperformed the backbone model in classification tasks on two datasets: a custom-made blob dataset and a publicly available skin lesion PAD-UFES-20 dataset, demonstrating its potential for improving accuracy in medical image classification tasks.Light distribution in tanks with the green seaweed Ulva ohnoi: effect of stocking density, incident irradiance and chlorophyll content
http://hdl.handle.net/2117/393267
Light distribution in tanks with the green seaweed Ulva ohnoi: effect of stocking density, incident irradiance and chlorophyll content
Ginovart Gisbert, Marta; Pintado Valverde, José; Del Olmo Berenguer, Gonzalo; Cremades Ugarte, Javier; Jiménez de Ridder, Patrícia; Masaló Llorà, Ingrid
eaweed farming is an interesting technique to meet the future global food demand. However, it is essential to increase productivity in order to reduce the land surface required and make tank-based production more cost competitive with other food systems. Seaweed productivity is strongly dependent on irradiance, stocking density and nutrients. When nutrients are non-limiting, culture management to increase the productivity has to include the handling of the stocking densities and irradiance at the tanks surface.
2023-09-08T09:19:15ZGinovart Gisbert, MartaPintado Valverde, JoséDel Olmo Berenguer, GonzaloCremades Ugarte, JavierJiménez de Ridder, PatríciaMasaló Llorà, Ingrideaweed farming is an interesting technique to meet the future global food demand. However, it is essential to increase productivity in order to reduce the land surface required and make tank-based production more cost competitive with other food systems. Seaweed productivity is strongly dependent on irradiance, stocking density and nutrients. When nutrients are non-limiting, culture management to increase the productivity has to include the handling of the stocking densities and irradiance at the tanks surface.