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Background Postoperative intracranial neurosurgical infections (PINI) complicate < 5% neurosurgeries. Scarce attention was dedicated to the extension and characteristics of its antimicrobial management considering their high morbidity, not negligible mortality, delayed hospital stay and increased healthcare costs. Methods We analyzed retrospectively (2014–2023) 162 PINI from eight Spanish third-level academic hospitals. Results Elective clean craniotomies after tumor or vascular causes were the leading procedures. Epidural abscess (24.7%), scalp infections (19.8%), postsurgical meningitis (16.7%) and cranioplasty infections (16.7%) were the most frequent PINI. Gram negative bacteria (38.6%) and Staphylococcus spp (28.6%) were the predominant isolates. Overall 85.2% patients underwent pus drainage, mostly by craniotomy (40.3%). Interestingly 34% were already receiving antibiotics for extracranial infections before developing PINI while 16.8% did not receive pre-operative antibiotic prophylaxis. In total 77.2% patients started a combined intravenous (IV) antimicrobial therapy, of which 85.2% switched after 5 days to a second-line IV antibiotic regimen, in 41.3% cases combined, after pus culture results, for a median of 21 days. Overall 61.1% patients continued on oral antimicrobials after hospital discharge, 30.3% as a combined regimen, for a median of 42 days. Complete cure was obtained in 81.5% cases, while 11.1% relapsed, 7.4% failed to cure and 6.8% died after PINI complications. In the multivariate analysis oral antimicrobial therapy after hospital discharge ( p  = 0.001) was significantly associated with PINI cure with no effect on survival. Conclusions We conclude that an extended 6 weeks sequential IV and oral antimicrobial therapy in addition to neurosurgical correction increases PINI cure rate with no effect on survival. Clinical trial number Not applicable.

Low-density granulocytes (LDGs) play a key role in the pathogenesis of Systemic Lupus Erythematosus (SLE) and has been associated with vascular complications. Their heightened activation and NETosis may contribute to immune dysregulation and vascular inflammation. In this study, we analyzed a distinct myeloid subset excluded from standard LDG/monocyte gating due to high CD14 and CD15 co-expression. Myeloid cell populations were quantified by flow cytometry in freshly isolated blood mononuclear cells (PBMC) from SLE patients (n=143), non-disease controls (n=42) and non-autoimmune atherosclerotic individuals (n=22). High-dimensional clustering and visualization of myeloid cells were performed using the FlowSOM algorithm. Circulating levels of cellular subsets were analyzed in relation to subclinical carotid arteriopathy determined by ultrasonography and/or previously documented clinical cardiovascular (CV)-disease. High-dimensional analysis identified a distinct CD14 CD15 CD16 population in PBMC from patients and controls, separate from known LDG/monocytes. Phenotyping in 15 SLE patients characterized them as CD66b CD15 neutrophils displaying a CD14 CD16 HLADR CD10 profile, thus naming them APC-like neutrophils (nAPC-like). nAPC-like correlated with Th1 (ρ=0.230; p=0.006), Th17 (ρ=0.244; p=0.004) and Treg (ρ=-0.219; p=0.010) cells in SLE, as well as with SLEDAI and anti-dsDNA titers (all p<0.001). Moreover, nAPC-like were increased in patients and controls with traditional CV-risk factors or clinical/subclinical CV-disease. A minor CD16 nAPC-like subset (CD10 CD66b CD15 ), suggestive of an immature status, was expanded in SLE and correlated with serum IL-6 and BLyS (p<0.05). Our findings reveal a previously unrecognized LDG subset with neutrophil and APC-like features that may contribute to immune dysregulation and subclinical carotid arteriopathy in SLE and non-autoimmune individuals.

We report the case of idiopathic non-cirrhotic portal hypertension associated with systemic lupus erythematosus in a 43-year-old woman who suffered from breast cancer. We review this rare condition, as well as its diagnostic and therapeutic approaches.

The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Three distinct phenotypes were derived in the derivation cohort (n=2667)—phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])—and reproduced in the internal validation cohort (n=1368)—phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4–4·3) for patients with phenotype A, 30·5% (28·5–32·6) for patients with phenotype B, and 60·7% (53·7–67·2) for patients with phenotype C (log-rank test p<0·0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5·3% [95% CI 3·4–8·1] for phenotype A, 31·3% [28·5–34·2] for phenotype B, and 59·5% [48·8–69·3] for phenotype C; external validation cohort: 3·7% [2·0–6·4] for phenotype A, 23·7% [21·8–25·7] for phenotype B, and 51·4% [41·9–60·7] for phenotype C). Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated. Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA.

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disease in humans and is characterized by the presence of hyperactive immune cells and aberrant antibody responses to nuclear and cytoplasmic antigens, including characteristic anti–double-stranded DNA antibodies. We performed a cross-sectional study in order to determine if an SLE-associated gut dysbiosis exists in patients without active disease. A group of 20 SLE patients in remission, for which there was strict inclusion and exclusion criteria, was recruited, and we used an optimized Ion Torrent 16S rRNA gene-based analysis protocol to decipher the fecal microbial profiles of these patients and compare them with those of 20 age- and sex-matched healthy control subjects. We found diversity to be comparable based on Shannon’s index. However, we saw a significantly lower Firmicutes / Bacteroidetes ratio in SLE individuals (median ratio, 1.97) than in healthy subjects (median ratio, 4.86; P < 0.002). A lower Firmicutes / Bacteroidetes ratio in SLE individuals was corroborated by quantitative PCR analysis. Notably, a decrease of some Firmicutes families was also detected. This dysbiosis is reflected, based on in silico functional inference, in an overrepresentation of oxidative phosphorylation and glycan utilization pathways in SLE patient microbiota. IMPORTANCE Growing evidence suggests that the gut microbiota might impact symptoms and progression of some autoimmune diseases. However, how and why this microbial community influences SLE remains to be elucidated. This is the first report describing an SLE-associated intestinal dysbiosis, and it contributes to the understanding of the interplay between the intestinal microbiota and the host in autoimmune disorders. Growing evidence suggests that the gut microbiota might impact symptoms and progression of some autoimmune diseases. However, how and why this microbial community influences SLE remains to be elucidated. This is the first report describing an SLE-associated intestinal dysbiosis, and it contributes to the understanding of the interplay between the intestinal microbiota and the host in autoimmune disorders.

While the type I interferon (IFN-I) pathway is crucial in autoimmunity, its role in antiphospholipid antibody (aPL)-positive subjects, including aPL carriers and antiphospholipid syndrome (APS) patients, is poorly understood. This study aims at characterizing IFN-I pathway activation within the spectrum of aPL-positive subsets. A total of 112 patients [29 aPL carriers, 31 primary APS (PAPS), 25 secondary APS (SAPS), 27 systemic lupus erythematosus (SLE) patients without aPL, and 44 healthy controls (HCs)] were recruited. IFI6, IFI44, IFI44L, MX1, IFI27, OAS1, and RSAD2 gene expression was evaluated in whole blood, and a composite index (IFN score) was calculated. An overall activation of the IFN-I pathway was observed across the entire APS spectrum, with differences among genes based on the specific disease subset. The composite score revealed quantitative differences across subsets, being elevated in aPL carriers and PAPS patients compared to HCs (both < 0.050) and increasing in SAPS ( < 0.010) and SLE patients ( < 0.001). An unsupervised cluster analysis identified three clusters, and correspondence analyses revealed differences in clusters usage across APS subsets ( < 0.001). A network analysis revealed different patterns characterizing different subsets. The associations between IFN-I pathway activation and clinical outcomes differed across APS subsets. Although no differences in gene expression were observed in systemic APS, the network analyses revealed specific gene-gene patterns, and a distinct distribution of the clusters previously identified was noted ( = 0.002). IFN-I pathway activation is a common hallmark among aPL-positive individuals. Qualitative and quantitative differences across the APS spectrum can be identified, leading to the identification of distinct IFN-I signatures with different clinical values beyond traditional categorization.