Additional file 2: Supplementary Table S1. Full ASV table for samples sequenced by 16S marker gene sequencing. Each sample is in a column, named by individual ID and cycle day, and each ASV in a row. Taxonomic annotations are in the second-to-last column and centroid sequence in the last. Supplementary Table S2. Full taxonomic annotation and feature counts for the samples sequenced by shotgun. Each sample is in a column, named by participant and cycle day, and each taxon in a row. Supplementary Table S3. Differential abundance results for samples in CST-IA from individuals with menses-related dysbiotic or unstable VCD compared to constant eubiotic VCD. Supplementary Table S4. Differential abundance results for samples in CST-IB from individuals with menses-related dysbiotic or unstable VCD compared to constant eubiotic VCD. Supplementary Table S5. Differential abundance results for samples in CST-IIIA from individuals with menses-related dysbiotic or unstable VCD compared to constant dysbiotic VCD. Supplementary Table S6. Differential abundance results for samples in CST-IIIB from individuals with menses-related dysbiotic or unstable VCD compared to constant dysbiotic VCD. Supplementary Table S7. Differential abundance results for all samples in menses-related dysbiotic, unstable and constant dysbiotic VCD against constant eubiotic. Supplementary Table S8. Differential abundance results for all samples in menses-related dysbiotic, unstable and constant eubiotic VCD against constant dysbiotic. Supplementary Table S9. Differential frequency of gene clusters in Lactobacillus spp., contrasting constant eubiotic and menses-related dysbiotic vs. unstable and constant dysbiotic. Supplementary Table S10. Differential frequency of gene clusters in Gardnerella spp., contrasting constant eubiotic and menses-related dysbiotic vs. unstable and constant dysbiotic. Supplementary Table S11. Differential frequency of gene clusters in Prevotella spp., contrasting constant eubiotic and menses-related eubiotic vs. unstable and constant dysbiotic.

Additional file 2: Supplementary Table S1. Full ASV table for samples sequenced by 16S marker gene sequencing. Each sample is in a column, named by individual ID and cycle day, and each ASV in a row. Taxonomic annotations are in the second-to-last column and centroid sequence in the last. Supplementary Table S2. Full taxonomic annotation and feature counts for the samples sequenced by shotgun. Each sample is in a column, named by participant and cycle day, and each taxon in a row. Supplementary Table S3. Differential abundance results for samples in CST-IA from individuals with menses-related dysbiotic or unstable VCD compared to constant eubiotic VCD. Supplementary Table S4. Differential abundance results for samples in CST-IB from individuals with menses-related dysbiotic or unstable VCD compared to constant eubiotic VCD. Supplementary Table S5. Differential abundance results for samples in CST-IIIA from individuals with menses-related dysbiotic or unstable VCD compared to constant dysbiotic VCD. Supplementary Table S6. Differential abundance results for samples in CST-IIIB from individuals with menses-related dysbiotic or unstable VCD compared to constant dysbiotic VCD. Supplementary Table S7. Differential abundance results for all samples in menses-related dysbiotic, unstable and constant dysbiotic VCD against constant eubiotic. Supplementary Table S8. Differential abundance results for all samples in menses-related dysbiotic, unstable and constant eubiotic VCD against constant dysbiotic. Supplementary Table S9. Differential frequency of gene clusters in Lactobacillus spp., contrasting constant eubiotic and menses-related dysbiotic vs. unstable and constant dysbiotic. Supplementary Table S10. Differential frequency of gene clusters in Gardnerella spp., contrasting constant eubiotic and menses-related dysbiotic vs. unstable and constant dysbiotic. Supplementary Table S11. Differential frequency of gene clusters in Prevotella spp., contrasting constant eubiotic and menses-related eubiotic vs. unstable and constant dysbiotic.

Additional file 3: Supplemental Table 1. Qualitative data from DARE medical student alumni collected at two focus group meetings. Appendix 1. References for publications of DARE Fellows and Alumni, 2015-2020, Cohorts 1-5.

Additional file 3: Supplemental Table 1. Qualitative data from DARE medical student alumni collected at two focus group meetings. Appendix 1. References for publications of DARE Fellows and Alumni, 2015-2020, Cohorts 1-5.

Objective: The aim of this study was to expand the spectrum of epilepsy syndromes related to STX1B, encoding the presynaptic protein syntaxin-1B, and establish genotype-phenotype correlations by identifying further disease-related variants. Methods: We used next generation sequencing in the framework of research projects and diagnostic testing. Clinical data and EEGs were reviewed, including already published cases. To estimate the pathogenicity of the variants, we used established and newly developed in silico prediction tools. Results: We describe 15 new variants in STX1B which are distributed across the whole gene. We discerned four different phenotypic groups across the newly identified and previously published patients (49 patients in 23 families): 1) Six sporadic patients or families (31 affected individuals) with febrile and afebrile seizures with a benign course, generally good drug response, normal development and without permanent neurological deficits; 2) two patients with genetic generalized epilepsy without febrile seizures and cognitive deficits; 3) 13 patients or families with intractable seizures, developmental regression after seizure onset and additional neuropsychiatric symptoms; 4) two patients with focal epilepsy. More often we found loss-of-function mutations in benign syndromes, whereas missense variants in the SNARE motif of syntaxin-1B were associated with more severe phenotypes. Conclusion: These data expand the genetic and phenotypic spectrum of STX1B-related epilepsies to a diverse range of epilepsies that span the ILAE classification. Variants in STX1B are protean and contribute to many different epilepsy phenotypes, similar to SCN1A, the most important gene associated with fever-associated epilepsies.

Objective: To determine the diagnostic accuracy and clinical utility of electromagnetic source imaging (EMSI) in presurgical evaluation of patients with epilepsy. Methods: We prospectively recorded magnetoencephalography (MEG) simultaneously with electroencephalography (EEG) and performed EMSI, comprising electric (ESI), magnetic source imaging (MSI) and analysis of combined MEG-EEG datasets (cEMSI), using two different software packages. As reference standard for irritative zone (IZ) and seizure onset zone (SOZ) we used intracranial recordings and for localization accuracy, outcome one year after operation. Results: We included 141 consecutive patients. EMSI showed localized epileptiform discharges (ED) in 94 patients (67%). Most of the ED-clusters (72%) were identified by both modalities, 15% only by EEG and 14% only by MEG. Agreement was substantial between inverse solutions and moderate between software packages. EMSI provided new information that changed the management plan in 34% of the patients, and these changes were useful in 80%. Depending on the method, EMSI had a concordance of 53-89% with IZ and 35%-73% with SOZ. Localization accuracy of EMSI was between 44% and 57%, which was not significantly different from MRI (49-76%) and PET (54-85%). cEMSI achieved significantly higher odds ratio compared to ESI and MSI. Conclusions: EMSI has accuracy similar to established imaging methods and provides clinically useful, new information in 34% of the patients. Classification of Evidence: This study provides Class IV evidence that EMSI had a concordance of 53-89% and 35%-73% (depending on analysis) for the localization of epilepsy as compared with intracranial recordings - IZ zone and SOZ respectively.

Global projections from the World Health Organization rank chronic obstructive pulmonary disease (COPD) and HIV as the third and eighth leading causes of death by 2030, respectively. An increasingly large number of individuals will consequently face a double burden of disease. The incidence of COPD is relatively high in the HIV-infected population, and HIV has been shown to be an independent risk factor.