Lymphocyte Subpopulations in Sjögren’s Syndrome Are Distinct in Anti-SSA-Positive Patients and Related to Disease Activity

Objectives: Sjögren's syndrome (SjS) patients exhibit great phenotypical heterogeneity, reinforced by the positiveness of anti-SSA antibody. We aimed to evaluate lymphocyte subpopulations in SSA-positive (SSA+SjS) and SSA-negative (SSA-SjS) SjS patients, Sicca patients, and healthy controls (HC), and to investigate associations between lymphocyte subpopulations and disease activity in SjS. Methods: According to the fulfilment of the ACR/EULAR 2016 classification criteria, patients were included as SjS or as Sicca. HC were selected from the Ophthalmology outpatient clinic. Lymphocyte subpopulations were characterized by flow cytometry. Statistical analysis was performed with GraphPad PrismTM, with statistical significance concluded if p < 0.05. Results: We included 53 SjS patients (38 SSA+ and 15 SSA-), 72 Sicca, and 24 HC. SSA+SjS patients presented increased IL-21+CD4+ and CD8+ T cells compared to Sicca and HC, whereas compared to SSA-SjS patients, only IL-21+CD4+ T cell percentages were increased and Tfh17 percentages and numbers were decreased. Compared to Sicca and HC, SSA+SjS patients had higher levels of CD24HiCD38Hi B cells, naïve B cells, and IgM-/+CD38++ plasmablasts, and lower levels of memory B cells, including CD24HiCD27+ B cells. SSA+SjS patients with clinically active disease had positive correlations between ESSDAI and IL-21+CD4+ (p = 0.038, r = 0.456) and IL-21+CD8+ T cells (p = 0.046, r = 0.451). Conclusions: In SjS, a distinct lymphocyte subset distribution profile seems to be associated with positive anti-SSA. Moreover, the association between ESSDAI and IL-21+CD4+ and IL-21+CD8+ (follicular) T cells in SSA+SjS patients suggests the involvement of these cells in disease pathogenesis and activity, and possibly their utility for the prognosis and assessment of response to therapy. Key Points • SSA+SjS patients have a pronounced naïve/memory B cell imbalance. • SSA+SjS patients have more active disease associated with IL-21+CD4+ and IL-21+CD8+ follicular T cell expansion. • IL-21+CD4+ and IL-21+CD8+ T cell quantification may be useful for the prognosis and assessment of response to therapy.info:eu-repo/semantics/publishedVersio

Electro-Hydraulic Transient Regimes in Isolated Pumps Working as Turbines with Self-Excited Induction Generators

[EN] The use of pumps working as turbines (PATs) is a sustainable technical measure that contributes to the improvement of energy efficiency in water systems. However, its performance analysis in off-grid recovery systems is a complex task that must consider both hydraulic (PAT) and electrical machines (typically a self-excited induction generator-SEIG). Aside from several kinds of research that analyze the PAT-SEIG behavior under steady-state constant hydraulic and electrical conditions, this research focuses on the analysis of PAT-SEIG transient regimes, by analyzing their variation when a sudden change occurs in the hydraulic or electrical components. Analytical models were developed to represent the operation of SEIG, PAT, and the PAT-SEIG coupled system. Hydraulic and electromechanical experimental tests validated these models. An excellent fit was obtained when analytical and experimental values were compared. With these models, the impact on the operation of the PAT-SEIG system was examined when sudden change occurred in the excitation capacitances, resistive loads, or recovered head. With a sudden increase of resistive load, the hydraulic power and SEIG stator current remain almost constant. However, there is an increase of SEIG reactive power, decreasing the PAT-SEIG efficiency. Also, with a sudden increase of SEIG capacitors or PAT hydraulic head, the SEIG stator current increases once and not again, while PAT-SEIG efficiency decreases, but the induction generator can be overloaded. The development of this research is key to the advancement of future models which can analyze the coupling of micro-hydropower solutions.This research received some support from the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA.Madeira, FC.; Fernandes, JFP.; Pérez-Sánchez, M.; López Jiménez, PA.; Ramos, HM.; Costa Branco, PJ. (2020). Electro-Hydraulic Transient Regimes in Isolated Pumps Working as Turbines with Self-Excited Induction Generators. Energies. 13(17):1-23. https://doi.org/10.3390/en13174521S1231317Postacchini, M., Darvini, G., Finizio, F., Pelagalli, L., Soldini, L., & Di Giuseppe, E. (2020). Hydropower Generation Through Pump as Turbine: Experimental Study and Potential Application to Small-Scale WDN. Water, 12(4), 958. doi:10.3390/w12040958Capelo, B., Pérez-Sánchez, M., Fernandes, J. F. P., Ramos, H. M., López-Jiménez, P. A., & Branco, P. J. C. (2017). Electrical behaviour of the pump working as turbine in off grid operation. Applied Energy, 208, 302-311. doi:10.1016/j.apenergy.2017.10.039Fecarotta, O., Aricò, C., Carravetta, A., Martino, R., & Ramos, H. M. (2014). Hydropower Potential in Water Distribution Networks: Pressure Control by PATs. Water Resources Management, 29(3), 699-714. doi:10.1007/s11269-014-0836-3Ramos, H., & Borga, A. (1999). Pumps as turbines: an unconventional solution to energy production. Urban Water, 1(3), 261-263. doi:10.1016/s1462-0758(00)00016-9Fernandes, J. F. P., Pérez-Sánchez, M., da Silva, F. F., López-Jiménez, P. A., Ramos, H. M., & Branco, P. J. C. (2019). Optimal energy efficiency of isolated PAT systems by SEIG excitation tuning. Energy Conversion and Management, 183, 391-405. doi:10.1016/j.enconman.2019.01.016Yi, Y., Zhang, Z., Chen, D., Zhou, R., Patelli, E., & Tolo, S. (2017). State feedback predictive control for nonlinear hydro-turbine governing system. Journal of Vibration and Control, 107754631774001. doi:10.1177/1077546317740013Khan, M. F., & Khan, M. R. (2016). Analysis of voltage build-up and speed disturbance ride through capability of a self-excited induction generator for renewable energy application. International Journal of Power and Energy Conversion, 7(2), 157. doi:10.1504/ijpec.2016.076521Han, Y., & Tan, L. (2020). Dynamic mode decomposition and reconstruction of tip leakage vortex in a mixed flow pump as turbine at pump mode. Renewable Energy, 155, 725-734. doi:10.1016/j.renene.2020.03.142Pérez-Sánchez, M., Sánchez-Romero, F. J., López-Jiménez, P. A., & Ramos, H. M. (2018). PATs selection towards sustainability in irrigation networks: Simulated annealing as a water management tool. Renewable Energy, 116, 234-249. doi:10.1016/j.renene.2017.09.060Pérez-Sánchez, M., López-Jiménez, P., & Ramos, H. (2018). PATs Operating in Water Networks under Unsteady Flow Conditions: Control Valve Manoeuvre and Overspeed Effect. Water, 10(4), 529. doi:10.3390/w1004052

Added value of lymphocyte subpopulations in the classification of Sjögren's syndrome

Sjögren's Syndrome (SjS) is a chronic systemic immune-mediated inflammatory disease characterized by lymphocytic infiltration and consequent lesion of exocrine glands. SjS diagnosis and classification remains a challenge, especially at SjS onset, when patients may have milder phenotypes of the disease or uncommon presentations. New biomarkers are needed for the classification of SjS, thus, we aimed to evaluate the added-value of lymphocyte subpopulations in discriminating SjS and non-Sjögren Sicca patients. Lymphocyte subsets from 62 SjS and 63 Sicca patients were characterized by flow cytometry. The 2002 AECG and the 2016 ACR/EULAR SjS classification criteria were compared with clinical diagnosis. The added discriminative ability of joining lymphocytic populations to classification criteria was assessed by the area under the Receiver-Operating-Characteristic Curve (AUC). Considering clinical diagnosis as the gold-standard, we obtained an AUC = 0.952 (95% CI: 0.916-0.989) for AECG and an AUC = 0.921 (95% CI: 0.875-0.966) for ACR/EULAR criteria. Adding Tfh and Bm1 subsets to AECG criteria, performance increased, attaining an AUC = 0.985 (95% CI: 0.968-1.000) (p = 0.021). Th1/Breg-like CD24hiCD27+ and switched-memory B-cells maximized the AUC of ACR/EULAR criteria to 0.953 (95% CI: 0.916-0.990) (p = 0.043). Our exploratory study supports the potential use of lymphocyte subpopulations, such as unswitched memory B cells, to improve the performance of classification criteria, since their discriminative ability increases when specific subsets are added to the criteria.info:eu-repo/semantics/publishedVersio

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/).Background: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. Objective: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. Methods: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. Results: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. Conclusion: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.info:eu-repo/semantics/publishedVersio

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

Background: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. Objective: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. Methods: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. Results: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. Conclusion: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors

Spectrum of ankylosing spondylitis in Portugal. Development of BASDAI, BASFI, BASMI and mSASSS reference centile charts

The availability of population-specific normative data regarding disease severity measures is essential for patient assessment. The goals of the current study were to characterize the pattern of ankylosing spondylitis (AS) in Portuguese patients and to develop reference centile charts for BASDAI, BASFI, BASMI and mSASSS, the most widely used assessment tools in AS. AS cases were recruited from hospital outpatient clinics, with AS defined according to the modified New York criteria. Demographic and clinical data were recorded. All radiographs were evaluated by two independent experienced readers. Centile charts for BASDAI, BASFI, BASMI and mSASSS were constructed for both genders, using generalized linear models and regression models with duration of disease as independent variable. A total of 369 patients (62.3% male, mean ± (SD) age 45.4 ± 13.2 years, mean ± (SD) disease duration 11.4 ± 10.5 years, 70.7% B27-positive) were included. Family history of AS in a first-degree relative was reported in 17.6% of the cases. Regarding clinical disease pattern, at the time of assessment 42.3% had axial disease, 2.4% peripheral disease, 40.9% mixed disease and 7.1% isolated enthesopatic disease. Anterior uveitis (33.6%) was the most common extra-articular manifestation. The centile charts suggest that females reported greater disease activity and more functional impairment than males but had lower BASMI and mSASSS scores. Data collected through this study provided a demographic and clinical profile of patients with AS in Portugal. The development of centile charts constitutes a useful tool to assess the change of disease pattern over time and in response to therapeutic interventions

ANKH and susceptibility to and severity of ankylosing spondylitis

Objective. Unconfirmed reports describe association of ankylosing spondylitis (AS) with several candidate genes including ANKH. Cellular export of inorganic pyrophosphate is regulated by the ANK protein, and mutant mice (ank/ank), which have a premature stop codon in the 3′ end of the ank gene, develop severe ankylosis. We tested the association between single-nucleotide polymorphisms (SNP) in these genes and susceptibility to AS in a population of patients with AS. We investigated the role of these genes in terms of functional (BASFI) and metrological (BASMI) measures, and the association with radiological severity (mSASSS). Methods. Our study was conducted on 355 patients with AS and 95 ethnically matched healthy controls. AS was defined according to the modified New York criteria. Four SNP in ANKH (rs27356, rs26307, rs25957, and rs28006) were genotyped. Association analysis was performed using Cochrane-Armitage and linear regression tests for dichotomous and quantitative variables. Analyses of Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), BASFI, and mSASSS were controlled for sex and disease duration. Results. None of the 4 markers showed significant single-locus disease associations (p > 0.05), suggesting that ANKH was not a major determinant of AS susceptibility in our population. No association was observed between these SNP and age at symptom onset, BASDAI, BASFI, BASMI, or mSASSS. Conclusion. These results confirm data in white Europeans that ANKH is probably not a major determinant of susceptibility to AS. ANKH polymorphisms do not markedly influence AS disease severity, as measured by BASMI and mSASSS. The Journal of Rheumatology Copyrigh