The effects of sodium alendronate on socket healing after tooth extraction: a systematic review of animal studies.

The aim of this systematic review was to answer the following question: "Does alendronate, a nitrogen-containing bisphosphonate, improve or impair alveolar socket healing after tooth extraction in animal models"? To this end, a systematic review of the literature was carried out in PubMed, Scopus, LILACS, Web of Science, as well as in the gray literature up to May 2023. Preclinical studies that evaluated alveolar healing after tooth extraction and the intake of sodium alendronate compared with placebo were included. Two investigators were responsible for screening the articles independently, extracting the data, and assessing their quality through the SYRCLE's RoB tool for randomized trials in animal studies. The study selection process, study characteristics, risk of bias in studies, impact of alendronate on bone healing, and certainty of evidence were described in text and table formats. Methodological differences among the studies were restricted to the synthesis methods. The synthesis of qualitative results followed the Synthesis Without Meta-analysis (SWiM) reporting guideline. From the 19 included studies, five were considered to have low risk, three were of unclear risk, and eleven presented a high risk of bias. The studies were considered heterogeneous regarding alendronate posology, including its dosage and route of administration. Furthermore, a variety of animal species, different age ranges, diverse teeth extracted, and exposure or not to ovariectomy contributed to the lack of parity of the selected studies. Our results indicated that alendronate monotherapy negatively affects the early phase of wound healing after tooth extraction in preclinical studies, suggesting that the bone resorption process after tooth extraction in animals treated with alendronate might impair the bone healing process of the extraction socket. In conclusion, alendronate administration restrains bone resorption, thereby delaying alveolar socket healing . Future studies should be conducted to validate these findings and to better understand the effects of alendronate therapy on oral tissues.

Rapid systematic review of readmissions costs after stroke.

BACKGROUND: Stroke readmissions are considered a marker of health quality and may pose a burden to healthcare systems. However, information on the costs of post-stroke readmissions has not been systematically reviewed. OBJECTIVES: To systematically review information about the costs of hospital readmissions of patients whose primary diagnosis in the index admission was a stroke. METHODS: A rapid systematic review was performed on studies reporting post-stroke readmission costs in EMBASE, MEDLINE, and Web of Science up to June 2021. Relevant data were extracted and presented by readmission and stroke type. The original study's currency values were converted to 2021 US dollars based on the purchasing power parity for gross domestic product. The reporting quality of each of the included studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. RESULTS: Forty-four studies were identified. Considerable variability in readmission costs was observed among countries, readmissions, stroke types, and durations of the follow-up period. The UK and the USA were the countries reporting the highest readmission costs. In the first year of follow-up, stroke readmission costs accounted for 2.1-23.4%, of direct costs and 3.3-21% of total costs. Among the included studies, only one identified predictors of readmission costs. CONCLUSION: Our review showed great variability in readmission costs, mainly due to differences in study design, countries and health services, follow-up duration, and reported readmission data. The results of this study can be used to inform policymakers and healthcare providers about the burden of stroke readmissions. Future studies should not solely focus on improving data standardization but should also prioritize the identification of stroke readmission cost predictors.

Anti-SSA Ro52 and anti-Ro60 autoantibodies: association with clinical phenotypes.

OBJECTIVES: Anti-SSA autoantibodies can be differentiated according to their antigenic target proteins as anti-Ro60 (60 kDa) or anti-Ro52 (52 kDa). Anti-SSA(Ro60) antibodies are clearly associated with connective tissue diseases (CTD), but the clinical significance of anti-SSA(Ro52) antibodies remains unclear. The aim of the present study was to analyse the disease phenotype of patients with anti-Ro52 and/or anti-Ro60 antibodies. METHODS: A multicentre, cross-sectional study was carried out of positive anti-Ro52 and/or Ro60 antibodies patients followed at 10 Rheumatology centres from January 2018 until December 2021. Patients were categorised into 3 groups: group 1 (Ro52+/Ro60-); group 2 (Ro52-/Ro60+); group 3 (Ro52+/Ro60+). Antinuclear antibodies were evaluated by indirect immunofluorescence assay and further screened for anti-extractable nuclear antigen (ENA) antibodies. Demographicsand clinical data were compared between the 3 groups, by patients' medical chart review. Univariate analysis was performed and subsequently logistic regression was used to identify intergroup differences and calculate the odds ratio with a 95% confidence interval (95% CI). RESULTS: We included 776 patients [female: 83.1%; median age: 59 (46-71) years]. Groups 1, 2, and 3 comprised 31.1%, 32.6%, and 36.3% of the patients, respectively. Anti-Ro52 antibody alone was more frequently associated with non-rheumatic diseases, older age, and men (p<0.05). Among patients with CTD, the diagnosis of systemic lupus erythematosus is 3 and 2 times more prevalent in groups 2 and 3, respectively, than in group 1 [OR 2.8 (95% CI 1.60, 4.97), p<0.001; OR 2.2 (95% CI 1.28, 3.86), p<0.01]. In group 2, the diagnosis of undifferentiated CTD is more frequent than in the other groups. Group 1 was more frequently associated with inflammatory myositis than group 2 [OR 0.09 (95% CI 0.01, 0.33), p<0.001] or group 3 [OR 0.08 (95% CI 0.01, 0.29), p<0.001]. Group 1 was also more frequently associated with arthritis (p<0.01), interstitial lung disease (p<0.01), and myositis (p<0.01). CONCLUSIONS: Anti-Ro52+ antibody alone is frequently found in patients with non-rheumatic diseases. In addition, anti-Ro52+ antibody is also prevalent in patients with CTD and associated with clinical phenotypes that are different from anti-Ro60+ antibody.

Impacts of ambient air quality on acute asthma hospital admissions during the COVID-19 pandemic in Oxford City, UK: a time-series study.

OBJECTIVES: The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO2), particulate matter pollution-particles with diameter<2.5 µm (PM2.5) and PM10) and incidence of asthma hospital admissions among adults, in Oxford, UK. DESIGN: Retrospective time-series study. SETTING: Oxford City (postcode areas OX1-OX4), UK. PARTICIPANTS: Adult population living within the postcode areas OX1-OX4 in Oxford, UK from 1 January 2015 to 31 December 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: Hourly NO2, PM2.5 and PM10 concentrations and meteorological data for the period 1 January 2015 to 31 December 2020 were analysed and used as exposures. We used Poisson linear regression analysis to identify independent associations between air pollutant concentrations and asthma admissions rate among the adult study population, using both single (NO2, PM2.5, PM10) and multipollutant (NO2 and PM2.5, NO2 and PM10) models, where they adjustment for temperature and relative humidity. RESULTS: The overall 5-year average asthma admissions rate was 78 per 100 000 population during the study period. The annual average rate decreased to 46 per 100 000 population during 2020 (incidence rate ratio 0.58, 95% CI 0.42 to 0.81, p<0.001) compared to the prepandemic years (2015-2019). In single-pollutant analysis, we observed a significantly increased risk of asthma admission associated with each 1 µg/m3 increase in monthly concentrations of NO2 4% (95% CI 1.009% to 1.072%), PM2.5 3% (95% CI 1.006% to 1.052%) and PM10 1.8% (95% CI 0.999% to 1.038%). However, in the multipollutant regression model, the effect of each individual pollutant was attenuated. CONCLUSIONS: Ambient NO2 and PM2.5 air pollution exposure increased the risk of asthma admissions in this urban setting. Improvements in air quality during COVID-19 lockdown periods may have contributed to a substantially reduced acute asthma disease burden. Large-scale measures to improve air quality have potential to protect vulnerable people living with chronic asthma in urban areas.

Predictors of Interstitial Lung Disease in Mixed Connective Tissue Disease.

Interstitial lung disease (ILD) frequently complicates mixed connective tissue disease (MCTD) and contributes to increased mortality. We aimed to identify predictors of ILD in MCTD patients. This is a nationwide, multicentre, retrospective study including patients with an adult-onset MCTD clinical diagnosis who met Sharp's, Kasukawa, Alarcón-Segovia, or Kahn's diagnostic criteria and had available chest high-resolution computed tomography (HRCT) data. Univariate and multivariate analyses were conducted. We included 57 MCTD patients, with 27 (47.4%) having ILD. Among ILD patients, 48.1% were asymptomatic, 80.0% exhibited a restrictive pattern on pulmonary function tests, and 81.5% had nonspecific interstitial pneumonia on chest HRCT. Gastroesophageal involvement (40.7% vs. 16.7%, p = 0.043) and lymphadenopathy at disease onset (22.2% vs. 3.3%, p = 0.045) were associated with ILD. Binary logistic regression identified lymphadenopathy at disease onset (OR 19.65, 95% CI: 1.91-201.75, p = 0.012) and older age at diagnosis (OR 1.06/year, 95% CI: 1.00-1.12, p = 0.046) as independent ILD predictors, regardless of gender and gastroesophageal involvement. This study is the first to assess a Portuguese MCTD cohort. As previously reported, it confirmed the link between gastroesophageal involvement and ILD in MCTD patients. Additionally, it established that lymphadenopathy at disease onset and older age at diagnosis independently predict ILD in MCTD patients.

Teriflunomide treatment outcomes in multiple sclerosis: A Portuguese real-life experience.

Teriflunomide is an oral disease-modifying therapy for relapsing-remitting multiple sclerosis patients. A decline in physical and cognitive functions, which negatively impacts their quality of life (QoL), is observed in relapsing-remitting multiple sclerosis patients. The aim of this study was to characterise adult Portuguese relapsing-remitting multiple sclerosis patients treated with teriflunomide in routine clinical practice concerning their quality of life, comorbidities, treatment effectiveness, satisfaction, compliance and safety. TeriLIVE-QoL was a multicentre, non-interventional, prospective cohort study that collected demographic and clinical characteristics, patient-reported outcomes and adverse events from patients treated with teriflunomide of 14 mg over 2 years. Notably, around 18 months of this period occurred during the COVID-19 pandemic. Of the 99 participants, 25% were treatment-naïve. Annualised relapse rate and the score for the Hospital Anxiety and Depression Scale decreased after 1 (p = 0.01) and 2 years of treatment (p < 0.001), respectively. Convenience (p = 0.001), effectiveness (p = 0.002) and global satisfaction scores (p < 0.001) presented high values (up to 95.6) and continued to improve along the study. Treatment persistence was 77%, and compliance reached 82% 2 years after initiation. Three patients experienced serious adverse events. TeriLIVE-QoL provides real-world evidence of clinical effectiveness, high treatment satisfaction, consistent safety and improved psychiatric outcomes, associated with elevated treatment persistence and compliance in patients treated with teriflunomide.iance reached 82% 2 years after initiation. Three patients experienced serious adverse events.

Safety and Effectiveness of Cladribine in Multiple Sclerosis: Real-World Clinical Experience From 5 Tertiary Hospitals in Portugal.

OBJECTIVES: Cladribine is a selective and oral immunological reconstitution treatment, approved in Europe for very active multiple sclerosis (MS) with relapses. Aims were to assess the safety and effectiveness of cladribine in real-world setting, during treatment follow-up. METHODS: This was a multicentric, longitudinal, observational study with retrospective and prospective data collection of clinical, laboratory, and imaging data. This interim analysis reports data from July 1, 2018 (study onset), to March 31, 2021. RESULTS: A total of 182 patients were enrolled: 68.7% were female; mean age at onset was 30.1 ± 10.0 years, and mean age at first cycle of cladribine treatment was 41.1 ± 12.1; 88.5% were diagnosed with relapse-remitting MS and 11.5% with secondary progressive MS. Mean disease duration at cladribine start was 8.9 ± 7.7 years. Most patients (86.1%) were not naive, and median number of previous disease-modifying therapies was 2 (interquartile range, 1-3). At 12 months, we observed no significant Expanded Disability Status Scale score worsening ( P = 0.843, Mann-Whitney U test) and a significantly lower annualized relapse rate (0.9 at baseline to 0.2; 78% reduction). Cladribine treatment discontinuation was registered in 8% of patients, mainly (69.2%) due to disease activity persistence. Most frequent adverse reactions were lymphocytopenia (55%), infections (25.2%), and fatigue (10.7%). Serious adverse effects were reported in 3.3%. No patient has discontinued cladribine treatment because of adverse effects. CONCLUSION: Our study confirms the clinical efficacy and the safety profile of cladribine for treating MS patients with a long-term active disease in the real-world setting. Our data contribute to the body of knowledge of the clinical management of MS patients and the improvement of related clinical outcomes.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 µg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 µg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Bone Metastases Detection in Patients with Breast Cancer: Does Bone Scintigraphy Add Information to PET/CT?

BACKGROUND: Positron emission tomography/computed tomography (PET/CT) has become in recent years a tool for breast cancer (BC) staging. However, its accuracy to detect bone metastases is classically considered inferior to bone scintigraphy (BS). The purpose of this work is to compare the effectiveness of bone metastases detection between PET/CT and BS. MATERIALS AND METHODS: Prospective study of 410 female patients treated in a Comprehensive Cancer Center between 2014 and 2020 that performed PET/CT and BS for staging purposes. The image analysis was performed by 2 senior nuclear medicine physicians. The comparison was performed based on accuracy, sensitivity, and specificity on a patient and anatomical region level and was assessed using McNemar's Test. An average ROC was calculated for the anatomical region analysis. RESULTS: PET/CT presented higher values of accuracy and sensitivity (98.0% and 93.83%), surpassing BS (95.61% and 81.48%) in detecting bone disease. There was a significant difference in favor of PET/CT (sensitivity 93.83% vs. 81.48%), however, there is no significant difference in eliminating false positives (specificity 99.09% vs. 99.09%). PET/CT presented the highest accuracy and sensitivity values for most of the bone segments, only surpassed by BS for the cranium. There was a significant difference in favor of PET/CT in the upper limb, spine, thorax (sternum) and lower limb (pelvis and sacrum), and in favor of BS in the cranium. The ROC showed that PET/CT has a higher sensitivity and consistency across the bone segments. CONCLUSION: With the correct imaging protocol, PET/CT does not require BS for patients with BC staging.

Persistent headache attributed to past cervicocephalic artery dissection: clinical characteristics and contributors to headache persistence.

BACKGROUND: Persistent headache/facial/neck pain attributed to past cervicocephalic arterial dissection is under-documented in literature. Our main goal was to evaluate clinical characteristics and contributors to this persistence. METHODS: A retrospective cohort study which included patients with a radiologically confirmed cervicocephalic arterial dissection (2015-2020) in a Portuguese tertiary hospital. Headache persistence was identified through clinical records. A questionnaire aimed to characterize headache in three moments: previous, persistent, and headache at the time of the interview (on average 2.5 years post-event). RESULTS: Ninety-two patients were identified; 24 (26.1%) had headache persistence ≥3 months, and 20 (22.2%) on average after 2.5 years post-event. There were no differences regarding demographics and vascular risk factors among patients with (n = 22) and without (n = 68) headache persistence. The first group had higher previous headache history (68.2% vs 4.4%, p < 0.001), delay in diagnosis (3.6 vs 1.9 days, p < 0.001), and headache/cervicalgia as the first symptom (81.8% vs 41.2%, p < 0.001). At the time of the interview, 20% still reported daily headache. A logistic regression model depicted headache history (OR = 59.8, p < 0.001), acute headache/cervicalgia (odds ratio, OR = 25.4, p = 0.005), posterior circulation dissection (OR = 7.6, p < 0.001), and less than 4 points by National Institutes of Health Stroke Scale score (OR = 5.0, p = 0.025) as contributors to headache persistence. CONCLUSION: Headache persistence post-cervicocephalic arterial dissection is common, and frequently affects patients daily. As it potentially affects functional outcomes and quality of life, the contributors identified in this study may help clinicians manage patients after the acute event.

Metabolic syndrome parameters and multiple sclerosis disease outcomes: A Portuguese cross-sectional study.

BACKGROUND: Metabolic syndrome and multiple sclerosis [MS] share the presence of chronic inflammation in their pathogenic mechanisms. This study aimed to estimate the prevalence of metabolic syndrome parameters in MS and their association with disease disability, cognitive function, and Neurofilament Light chain [NfL] levels. METHODS: Clinical, analytical, and magnetic resonance imaging data were obtained through medical records. Disease disability was measured by the Expanded Disability Status Scale [EDSS], the MS Severity Scale [MSSS] along with cognitive impairment by the Brief International Cognitive Assessment for MS [BICAMS] and Word List Generation test [WLG]. Metabolic syndrome parameters were evaluated by fasting blood glucose, triglycerides, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol, total cholesterol, blood pressure, and waist circumference [WC]. We also analysed serum leptin and ghrelin and cerebrospinal fluid NfL. RESULTS: Our sample included 51 people with MS, 34 (66.7%) females, mean age of 38.20±12.12 years and median disease duration of 3 years (P25=2.0, P75=5.0). Multivariate linear regression analysis confirmed that WC correlates with EDSS (ß=0.04, p=.001) and MSSS (ß=0.07, p=.002) as well as Brief Visuospatial Memory Test-Revised (ß=-0.29, p=.008), WLG (ß=-0.20, p=.039). NfL is also negatively associated with HDL-C (ß=-4.51, p=.038). CONCLUSIONS: Waist circumference is associated with disability and deficits in cognitive tests. A decrease in HDL-C is associated with an increase in NfL. This suggests metabolic syndrome might be an important factor in MS disease course.

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists.

Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using "deep learning techniques," "interpretability" and "oncology" as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.

Impact of the COVID-19 pandemic on percutaneous coronary interventions in Portugal.

INTRODUCTION: The COVID-19 pandemic has imposed an unprecedented burden on healthcare systems worldwide, changing the profile of interventional cardiology activity. OBJECTIVES: To quantify and compare the number of percutaneous coronary interventions (PCIs) performed for acute and chronic coronary syndromes during the first COVID-19 outbreak with the corresponding period in previous years. METHODS: Data on PCI from the prospective multicenter Portuguese Registry on Interventional Cardiology (RNCI) were used to analyze changes in PCI for ST-elevation myocardial infarction (STEMI), non-ST-elevation acute coronary syndromes (NSTE-ACS) and chronic coronary syndromes (CCS). The number of PCIs performed during the initial period of the COVID-19 outbreak in Portugal, from March 1 to May 2, 2020, was compared with the mean frequency of PCIs performed during the corresponding period in the previous three years (2017-2019). RESULTS: The total number of PCIs procedures was significantly decreased during the initial COVID-19 outbreak in Portugal (-36%, p<0.001). The reduction in PCI procedures for STEMI, NSTE-ACS and CCS was, respectively, -25% (p<0.019), -20% (p<0.068) and -59% (p<0.001). CONCLUSIONS: Compared with the corresponding period in the previous three years, the number of PCI procedures performed for STEMI and CCS decreased markedly during the first wave of the COVID-19 pandemic in Portugal.

Treatment of acute exacerbations of trigeminal neuralgia in the emergency department: A retrospective case series.

OBJECTIVE: To evaluate the response to treatment of acute trigeminal neuralgia (TN) exacerbations in the emergency department (ED). BACKGROUND: TN is characterized by recurrent and intense pain paroxysms. Some patients experience severe acute exacerbations requiring ED presentation. The optimal management of these episodes is not well established. METHODS: We present a case series of TN exacerbations in adults who presented to the ED of a tertiary center from January 2008 to December 2020. We analyzed demographic and clinical data, including pharmacological management in the ED. The primary outcome was pain relief, classified into "no relief," "partial relief," and "satisfactory relief" based on the qualitative description in the ED's records. RESULTS: Ultimately 197 crisis episodes corresponding to 140 patients were included. Most were women (61%, 121/197) with a median age of 63 years (interquartile range: 52-73). Acute TN exacerbations were treated with opioids in 78% (108/139) of crisis episodes, nonsteroidal anti-inflammatory drugs in 42% (58/139), corticosteroids in 21% (29/139), intravenous phenytoin in 18% (25/139), and intravenous lidocaine in 6% (8/139). Of the 108 cases treated with opioids, 78 (72%) required additional drugs for pain management. Intravenous phenytoin allowed satisfactory pain relief in 64% of cases. CONCLUSION: In our sample, opioids were the most used therapeutic approach in acute TN exacerbations despite their low efficacy and subsequent need for further drug treatment in most cases. Most crisis episodes managed with intravenous phenytoin reached total pain relief. Prospective studies are needed to guide the treatment of acute exacerbations of TN.

Brown-Séquard syndrome in a patient with spondyloarthritis after COVID-19 vaccine: a challenging differential diagnosis.

A 41-year-old woman with pre-radiographic axial and peripheric spondyloarthritis, taking adalimumab since 2010, started motor impairment of the right limbs and numbness of the left leg seven days after the administration of COVID-19 mRNA vaccine. Adalimumab was taken 47 days before clinical onset. A comprehensive study for infectious, autoimmune and neoplastic causes were unremarkable. MRI depicted an acute inflammatory lesion at C2 level with gadolinium enhancement. The patient started methylprednisolone with clinical improvement. Three scenarios should be considered: primary CNS inflammatory disorder or a secondary manifestation of the underlying rheumatologic disease; immune-mediated inflammatory lesion triggered by vaccine; demyelinating event due to adalimumab.

Case report: Challenging post-traumatic pseudoarthrosis of C2 odontoid fracture and extreme C1-C2 subluxation.

Background: Type II C2 odontoid fractures are common traumatic cervical spine lesions and have the highest risk of non-union without fusion. Pseudoarthrosis may lead to extreme anatomic deformation, and poor clinical outcomes. A 50-year-old male, following a traumatic C2 dens fracture treated when the patient was 44 years of age, newly presented at age 50 with pseudoarthrosis and severe C1-C2 subluxation that required a secondary fusion using a three-dimensional (3D) printed model for appropriate surgical planning. Case Description: A 44-year-old male underwent a C1 posterior arch osteotomy to treat cervical myelopathy after a type 2 odontoid fracture. Now at age 50, he newly presented with recurrent myelopathy, and marked cord compression due to a C2 odontoid-dens pseudoarthrosis, and extreme C1 subluxation over C2. A 3D model of the patients' cervical-spine anatomy was created for surgical planning and led to an anterior C1-C2 freeing of the pseudoarthrosis, followed by a posterior C0-C1 decompression, deformity reduction, C0, C2 laminar, and C3/4 trans-articular arthrodesis. Six months postoperatively, the patient improved from a pre-operative mJOA score of 5 to a postoperative mJOA score of 14. Conclusion: A 3D model was successfully utilized to plan a secondary 360° fusion for a pseudoarthrosis diagnosed 6 years after an original C-C2type II odontoid fusion in a now 50-year-old male.

Partial Multiple Imputation With Variational Autoencoders: Tackling Not at Randomness in Healthcare Data.

Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.