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Objective: We aimed to evaluate epidemiology, seizure type, EEG, and etiology of neonatal seizures (NS) in a tertiary neonatal intensive care unit., Methods: Data on infants with a neurophysiological confirmation of NS were collected between 2009 and 2022. Seizure types and epileptic syndromes were classified by the ILAE classification and EEG by the Italian Neonatal Seizure Collaborative Network (INNESCO) score., Results: Out of 91,253 neonates, 145 presented with NS; 69.7 % were born at term and 30.3 % were preterm infants. The incidence of NS in neonates born at our center was 1.2 per 1,000 live newborns (96/80697 neonates) while in the entire neonatal population admitted to our center it was 1.6 per 1,000 live births, increasing with lower preterm age. Compared to previous studies, we found a lower proportion of hypoxic-ischemic encephalopathy (HIE) (23.4 %) and a higher rate of genetic contribution (26.2 %). The infection rate was higher in preterm (31.8 %) than in full term (9.9 %) infants. Electrographic seizures were associated with acute provoked seizures (35.9 %), preterm age (52.3 %), and HIE (52.9 %). Vascular etiology was associated with focal clonic seizures (56.8 %). Non-structural neonatal genetic epilepsy was associated with sequential seizures (68.2 %), particularly KCNQ2 and SCN2A epilepsy. Background EEG was abnormal in all HIE, infections (85.7 %) and metabolic NS (83.3 %). In genetic epilepsy, background EEG depended on the epileptic syndrome: normal in 80 % of self-limited neonatal epilepsy and abnormal in 77.8 % of developmental and epileptic encephalopathy. Electroclinical seizures were associated with focal onset, while electrographic seizures correlated with a multifocal onset., Conclusions: A low incidence of HIE and a high incidence of genetic etiology were observed in our cohort of NS. Seizure type and EEG features are fundamental to address etiology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: The bone strain index (BSI) is a marker of bone deformation based on a finite element analysis inferred from dual X-ray absorptiometry (DXA) scans, that has been proposed as a predictor of fractures in osteoporosis (i.e., higher BSI indicates a lower bone's resistance to loads with consequent higher risk of fractures). We aimed to investigate the association between lumbar BSI and vertebral fractures (VFs) in acromegaly., Methods: Twenty-three patients with acromegaly (13 males, mean age 58 years; three with active disease) were evaluated for morphometric VFs, trabecular bone score (TBS), bone mineral density (BMD) and BSI at lumbar spine, the latter being corrected for the kyphosis as measured by low-dose X-ray imaging system (EOS®-2D/3D)., Results: Lumbar BSI was significantly higher in patients with VFs as compared to those without fractures (2.90 ± 1.46 vs. 1.78 ± 0.33, p = 0.041). BSI was inversely associated with TBS (rho -0.44; p = 0.034), without significant associations with BMD (p = 0.151), age (p = 0.500), BMI (p = 0.957), serum IGF-I (p = 0.889), duration of active disease (p = 0.434) and sex (p = 0.563)., Conclusions: Lumbar BSI corrected for kyphosis could be proposed as integrated parameter of spine arthropathy and osteopathy in acromegaly helping the clinicians in identifying patients with skeletal fragility possibly predisposed to VFs., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Non-polio enteroviruses (EV) and human parechoviruses (HPeV) are known etiological agents of meningoencephalitis in neonates. However, reports of neuroradiological findings and neurodevelopmental outcomes in this population are scarce., Objectives: to describe clinical characteristics, neuroradiological findings and, in a subset of patients, neurodevelopmental outcomes in a cohort of infants with EV or HPeV meningoencephalitis within 60 days of life., Study Design: clinical/laboratory data, neuroradiological findings (cranial ultrasound, cUS, brain magnetic resonance imaging, MRI), and neurodevelopmental outcomes assessed by Ages and Stages Questionnaires - third edition were prospectively collected., Results: overall, 32 infants with EV (21, 67.8 %) or HPeV (11, 28.2 %) meningoencephalitis were enrolled. Infants with HPeV (73 %: type 3 HPeV) presented more frequently with seizures (18.2 % vs. 0, p value=0.03), lymphopenia (1120 vs. 2170 cells/mm 3 , p = 0.02), focal anomalies at electroencephalography (EEG) (63.6 vs. 23.8 %, p = 0.03), and pathological findings at MRI (72.7 % vs. 15.8 %, p value=0.004) compared to those affected by EV. cUS was not significantly altered in any of the enrolled infants. All infants with EV meningoencephalitis evaluated at 12-24 months and at 30-48 months were normal. Two out of the 7 infants with HPeV meningoencephalitis showed some concerns in gross motor (1/7, 14.3 %) or in problem solving (1/7, 14.3 %) function at 30-48 months of age., Conclusions: In our cohort, neonates infected by HPeV had more severe clinical manifestations, more alterations at brain MRI, and some signs of long-term neurodevelopmental delay. Our data highlight the heterogeneity of manifestations in infants with EV or HPeV meningoencephalitis, and the need for long-term follow-up of those infected by HPeV in the neonatal period., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To investigate the frequency and factors associated with disease flare following vaccination against SARS-CoV-2 in people with inflammatory/autoimmune rheumatic and musculoskeletal diseases (I-RMDs)., Methods: Data from the European Alliance of Associations for Rheumatology Coronavirus Vaccine physician-reported registry were used. Factors associated with flare in patients with I-RMDs were investigated using multivariable logistic regression adjusted for demographic and clinical factors., Results: The study included 7336 patients with I-RMD, with 272 of 7336 (3.7%) experiencing flares and 121 of 7336 (1.6%) experiencing flares requiring starting a new medication or increasing the dosage of an existing medication. Factors independently associated with increased odds of flare were: female sex (OR=1.40, 95% CI=1.05 to 1.87), active disease at the time of vaccination (low disease activity (LDA), OR=1.45, 95% CI=1.08 to 1.94; moderate/high disease activity (M/HDA), OR=1.37, 95% CI=0.97 to 1.95; vs remission), and cessation/reduction of antirheumatic medication before or after vaccination (OR=4.76, 95% CI=3.44 to 6.58); factors associated with decreased odds of flare were: higher age (OR=0.90, 95% CI=0.83 to 0.98), non-Pfizer/AstraZeneca/Moderna vaccines (OR=0.10, 95% CI=0.01 to 0.74; vs Pfizer), and exposure to methotrexate (OR=0.57, 95% CI=0.37 to 0.90), tumour necrosis factor inhibitors (OR=0.55, 95% CI=0.36 to 0.85) or rituximab (OR=0.27, 95% CI=0.11 to 0.66), versus no antirheumatic treatment. In a multivariable model using new medication or dosage increase due to flare as the dependent variable, only the following independent associations were observed: active disease (LDA, OR=1.47, 95% CI=0.94 to 2.29; M/HDA, OR=3.08, 95% CI=1.91 to 4.97; vs remission), cessation/reduction of antirheumatic medication before or after vaccination (OR=2.24, 95% CI=1.33 to 3.78), and exposure to methotrexate (OR=0.48, 95% CI=0.26 to 0.89) or rituximab (OR=0.10, 95% CI=0.01 to 0.77), versus no antirheumatic treatment., Conclusion: I-RMD flares following SARS-CoV-2 vaccination were uncommon. Factors associated with flares were identified, namely higher disease activity and cessation/reduction of antirheumatic medications before or after vaccination., Competing Interests: Competing interests: BF does not report conflicts of interest. SL-T does not report conflicts of interest. KLH reports non-personal speaker's fees from AbbVie and grant income from BMS and Pfizer, all unrelated to this manuscript; and her institute was supported by EULAR for COVAX database management. LG reports research grants from Amgen, Galapagos, Lilly, Pfizer and Sandoz; and consulting fees from AbbVie, Amgen, BMS, Celltrion, Galapagos, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis and UCB, all unrelated to this manuscript. LC has not received fees or personal grants from any laboratory, but her institute works by contract for laboratories among other institutions, such as Galapagos, Pfizer, Lilly, MSD, Novartis, Roche, Sanofi Aventis, BMS and Sandoz; she also reports safety board participation, not paid personally. AS reports personal fees from lectures for AbbVie, Galapagos, Lilly, Pfizer and Takeda. EFM does not report conflicts of interest. MS does not report conflicts of interest. AR does not report conflicts of interest. EH does not report conflicts of interest. JAG-P does not report conflicts of interest. MM reports having received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, all unrelated to this manuscript. PD reports speaker's fees from AbbVie, Lilly, Galapagos, Janssen and Biogen, all unrelated to this manuscript. LT does not report conflicts of interest. TG does not report conflicts of interest. MC does not report conflicts of interest. ES reports receiving grants from AbbVie; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, AbbVie, Pfizer, Eli Lilly, UCB, Sobi, Roche, Sanofi, Boehringer-Ingelheim, Viatris, Sandoz, Mylan, MSD, Amgen and Janssen-Johnson & Johnson; support for attending meetings and/or travel from AbbVie and Janssen-Johnson & Johnson; participation in a data safety monitoring board or advisory board from AbbVie, Eli Lilly, UCB, Sobi, Boehringer-Ingelheim and Janssen-Johnson & Johnson; and is a committee member of Slovak Society of Rheumatology, all unrelated to this manuscript. IB reports receiving speaker's fees from AbbVie, Pfizer, Boehringer Ingelheim and Janssen; and has received support for attending meetings and/or travel from AbbVie, all unrelated to this manuscript. ES does not report conflicts of interest. JZ reports consulting fees from AbbVie, Janssen, Novartis and Boehringer-Ingelheim; speaker's fees from AbbVie, Janssen, Novartis, Boehringer Ingelheim Latvia and UAB Viasana; receiving support for attending meetings and/or travel from AbbVie, UAB Viasana and Johnson & Johnson; and is a board member of Latvian Society of Adult Rheumatology; all unrelated to this manuscript. NR has nothing to disclose. OB has nothing to disclose. EV has nothing to disclose. BR has nothing to disclose. GRB has nothing to disclose. XM reports personal consultant fees from BMS, Galapagos, GSK, Novartis and Pfizer; and having received support for attending meetings and/or travel from Novartis, all unrelated to this manuscript. PMM has received consulting/speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript., (© European Alliance of Associations for Rheumatology, EULAR 2024. Re-use permitted under CC BY-NC-ND. No commercial re-use. No derivatives. See rights and permissions. Published by BMJ on behalf of EULAR.)

Background: Exercise is recommended for patients with chronic heart failure (CHF) and its intensity is usually set as a percentage of the maximal work rate (MWR) during cardiopulmonary exercise testing (CPX) or a symptom-limited incremental test (SLIT). As these tests are not always available in cardiac rehabilitation due to logistic/cost constraints, we aimed to develop a predictive model to estimate MWR at CPX (estMWR@CPX) in CHF patients using anthropometric and clinical measures and the 6-min walk test (6 MWT), the most widely used exercise field test., Methods: This is a multicentre cross-sectional retrospective study in a cardiac rehabilitation setting. Six hundred patients with HF in New York Heart Association (NYHA) functional class I-III underwent both CPX and 6 MWT and, t hrough multivariable linear regression analysis, we defined several predictive models to define estMWR@CPX., Results: The best model included 6 MWT, sex, age, weight, NYHA class, left ventricular ejection fraction (LVEF), smoking status and chronic obstructive pulmonary disease COPD (adjusted R 2  = 0.55; 95% LoA -39 to 33 W). When LVEF was excluded as a predictor, the resulting model performed only slightly worse (adjusted R 2  = 0.54; 95% LoA -42 to 34 W). Only in 34% of cases was the percentage difference between estMWR@CPX and real MWR@CPX <10% in absolute value. EstMWR@CPX tended to overestimate low values and underestimate high values of true MWR@CPX., Conclusions: Our results showed a lack of accuracy in the predictive model evaluated; therefore, for an accurate prescription of cycle-ergometer exercise training, it is necessary to assess MWR by CPX or SLIT., Competing Interests: All Authors declare no conflict of interest relevant to the research, analysis, or interpretation presented in the manuscript., (© 2024 The Authors.)