BUSCADOR RECOLECTA

Appendix of Emerging Microbes & Infections 13: 2295389 (2024), Rat hepatitis E virus (ratHEV; species Rocahepevirus ratti) is considered a newly emerging cause of acute hepatitis of zoonotic origin. ratHEV infection of people living with HIV (PLWH) might portend a worse, as with hepatitis E virus (HEV; species Paslahepevirus balayani), and consequently this group may constitute a high-risk population. We aimed to evaluate the prevalence of ratHEV by measuring viral RNA and specific IgG antibodies in a large Spanish cohort of PLWH. Multicentre study conducted in Spain evaluating PLWHIV included in the Spanish AIDS Research Network (CoRIS). Patients were evaluated for ratHEV infection using PCR at baseline and anti-ratHEV IgG by dot blot analysis to evaluate exposure to ratHEV strains. Patients with detectable ratHEV RNA were followed-up to evaluate persistence of viremia and IgG seroconversion. Eight-hundred and forty-two individuals were tested. A total of 9 individuals showed specific IgG antibodies against ratHEV, supposing a prevalence of 1.1 (95% CI; 0.5%−2.1%). Of these, only one was reactive to HEV IgG antibodies by ELISA. One sample was positive for ratHEV RNA (prevalence of infection: 0.1%; 95% CI: 0.08%−0.7%). The case was a man who had sex with men exhibiting a slightly increased alanine transaminase level (49 IU/L) as only biochemical alteration. In the follow-up, the patients showed undetectable ratHEV RNA and seroconversion to specific ratHEV IgG antibodies. Our study shows that ratHEV is geographical broadly distributed in Spain, representing a potential zoonotic threat., This work was supported by the Andalusian General Secretariat for Research, Development, and Innovation in Health (PI-0287-2019), the Spanish Ministry of Health (RD12/0017/0012), co-financed by European Regional Development Fund (ERDF), and the Carlos III Health Institute (Research Project grant numbers: PI21/00793 and PI22/01098). Projects “PI21/00793” and “PI22/01098” were funded by Carlos III Health Institute (ISCIII) and co-funded by the European Union. ARJ is the recipient of a “Miguel Servet” Research Contract by the Spanish Ministry of Sciences (CP18/00111). JCG is supported by the CIBERINFEC (CB21/13/00083), Carlos III Health Institute, Spanish Ministry os Science and NextGenerationEU. MCJ is the recipient of a PFIS predoctoral grant (FI22/00180) from the Carlos III Health Institute and co-funded by the European Union. DCM is the recipient of a “Rio-Hortega” (CM22/00176) grant from the Carlos III Health Institute and co-funded by the European Union. PLL is the recipient of a “Margarita Salas” contract funded by NextGeneration EU. TGG is recipient of a “Ramon y Cajal” contract funded by MCIN/AEI/10.13039/501100011033 and NextGeneration EU/PRTR. The laboratory of R.G.U. is supported by DZIF Thematic Translational Unit (TTU) “Emerging Infections” (grant number 01.808; awarded to R.G.U.). The HIV BioBank is supported by Carlos III Health Institute (PT20/00138) and Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, CIBER-BBN (CB22/01/00041). CoRIS cohort is supported by CIBER (CB21/13/00091), Carlos III Health Institute, Spanish Ministry of Sciences NextGenerationEU., Peer reviewed

Data from the paper "Optimization of the baiting strategy for the administration of oral vaccines to wild boar". These data are from experiments done in field work based on the baiting preference of different animal species., Peer reviewed

Supplementary Figure S1. Correlation plots are presented to show association between body composition (X-axis) and cancer complications related to head and neck cancer (Y-axis) of males. Pearson’s correlation was conducted and asterisk indicates significant correlation between variables according to the Pearson’s correlation test (*p<0.05). Abbreviations: BCM: Body cell mass; BCMI: BCM index; FFMI: Fat-free mass index; FM: Fat mass; FMI: FM index; OR: Odds ratio; PA: Phase angle; SMI: skeletal muscle index; SPA: Standardized PA., Supplementary Figure S2. Correlation plots are presented to show association between body composition (X-axis) and cancer complications related to head and neck cancer (Y-axis) of females. Pearson’s correlation was conducted and asterisk indicates significant correlation between variables according to the Pearson’s correlation test (*p<0.05). Abbreviations: BCM: Body cell mass; BCMI: BCM index; FFMI: Fat-free mass index; FM: Fat mass; FMI: FM index; OR: Odds ratio; PA: Phase angle; SMI: skeletal muscle index; SPA: Standardized PA., Supplementary Figure S3. Random forest and Decision tree of variable to predict mortality in males. (A) Decision tree performed with the most important variable in the model. (B) Table to calculate the precision of the model. (C) Random forest analysis to predict the importance of variables, presented as gain. Abbreviations: AUC: area under curve;BCM: Body cell mass; BCMI: BCM index; FFMI: Fat-free mass index; FM: Fat mass; FMI: FM index; OR:Odds ratio; PA: Phase angle; SMI: skeletal muscle index; SPA: Standardized PA., Supplementary Figure S4. Random forest and Decision tree of variable to predict mortality in females. (A) Decision tree performed with the most important variable in the model. (B) Table to calculate the precision of the model. (C) Random forest analysis to predict the importance of variables, presented as gain. Abbreviations: AUC: area under curve; BCM: Body cell mass; BCMI: BCM index; FFMI: Fat-free mass index; FM: Fat mass; FMI: FM index; OR: Odds ratio; PA: Phase angle; SMI: skeletal muscle index; SPA: Standardized PA., Supplementary Figure S5. Kaplan-Meier curves for the variables included in the study. (A) Kaplan-Meier curve cell mass index. (B): Kaplan-Meier curve skeletal mass index; (C) Kaplan-Meier curve fat free mass index (D): Kaplan-Meier curve phase angle; (E): Kaplan-Meier curve phase angle; (F): Kaplan-Meier curve fat mass; (G): Kaplan-Meier curve fat mass index Abbreviations: BCM: Body cell mass; BCMI: BCM index; FFMI: Fat-free mass index; FM: Fat mass; FMI: FM index; OR: Odds ratio; PA: Phase angle; SMI: skeletal muscle index; SPA: Standardized PA., [Introduction] Bioelectrical impedance analysis (BIA) serves as a method to estimate body composition. Parameters such as phase angle (PA), standardized phase angle (SPA), body mass cell (BCM), BCM index (BCMI), and fat-free mass (FFM) might significantly impact the prognosis of head and neck cancer (HNC) patients. The present study aimed to investigate whether bioelectrical parameters can be used to predict survival in the HNC population and establish the optimal cutoff points for predictive accuracy., [Methods] A multicenter observational study was performed across 12 tertiary hospitals in Andalusia (a region from the south of Spain). A total of 494 patients diagnosed with HNC between 2020 and 2022 at different stages were included in this study, with a minimum follow-up period of 12 months. The BIA assessment was carried out during the first 2 weeks of radical radiotherapy treatment with chemotherapy or other systemic treatments. A multivariate logistic regression analysis of overall survival, complications, hospital admission, and palliative care and its relationship with BIA nutritional assessment was performed., [Results] Significant prognostic factors identified in the multivariable analysis encompassed phase angle (PA), standardized phase angle (SPA), body cell mass (BCM), and BCM index (BCMI). Lower PA and BCM values were significantly associated with adverse clinical outcomes. A BCM threshold above 17 kg/m2 was the most significant predictor for predicting survival within the overall HNC population. The PA values of <5.1° in male and <4.8° in female patients showed the best predictive potential for mortality. Increased PA (as a continuous variable) demonstrated a significantly reduced risk for mortality (OR, 0.64; 95% CI, 0.43–0.94; p < 0.05) and a decreased likelihood of hospital admission (OR, 0.75; 95% CI, 0.52–1.07; p < 0.05). Higher BCM correlated with a lower risk of mortality (OR, 0.88; 95% CI, 0.80–0.96; p < 0.01) and a diminished probability of hospital admission (OR, 0.91; 95% CI, 0.83–0.99; p < 0.05)., [Conclusion] BIA is a crucial tool in the nutritional assessment of HNC patients. BCM and PA are the main bioelectrical parameters used to predict clinical outcomes in this population. Future studies are needed to validate BIA variables in a large cohort to ensure whether early intensification of nutritional treatment would improve survival., Peer reviewed

Figure S1 Responses to the question “Do you consider the return of the brown bear to Portugal likely in short/mid-term (10–20 years)?” Figure S2. Sector perceived as positively (a) and negatively (b) affected by the return of the brown bear to Portugal by the stakeholder group. Table S1 Structure and content of the questionnaire regarding general information (I), perceptions and attitudes (II), and action plan design preferences (III). Table S2. Respondents in each group of stakeholders and overall by age, gender, and education class. Table S3. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of the GFN to statement “I consider the brown bear to be a dangerous animal for human beings.” Table S4. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of the GFN to question “I would feel safe living in areas where the brown bear occurs.” Table S5. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of the GFN to question “I believe that I would be able to handle correctly the situation in case I find myself with a brown bear in the wild.” Table S6. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to question “What is your general perception of the return of the brown bear to Portugal?” Table S7. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to statement “The return of the brown bear to Portugal constitutes an OPPORTUNITY for local development.” Table S8. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to statement “The return of the brown bear to Portugal constitutes a THREAT for local development.” Table S9. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to statement “I am willing to make ADJUSTMENTS in my activity (without cost) to adapt to the presence of the brown bear in Portugal.” Table S10. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to question “I am willing to assume COSTS (within reasonable limits) in my activity associated with the return of the brown bear to Portugal”. Table S11. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to the statement “People whose economic activity is affected by the return of the brown bear in Portugal will receive EXCLUSIVELY DIRECT COMPENSATION (monetary) for damages or other negative effects caused by the brown bear in my professional activity.” Table S12. Group fuzzy number (GFN), lower group dispersion (LGD), upper group dispersion (UGD), and width of GFN of answers to the statement “People whose economic activity is affected by the return of the brown bear in Portugal will receive EXCLUSIVELY INDIRECT COMPENSATION (tax benefits, land rights, social benefits in terms of housing, transport, education, etc.) for losses or other negative effects caused by the brown bear in my professional activity.”, Peer reviewed

Table S1. Data used in the analysis and data sources. Table S2. Sample sizes for SSD and explanatory variables: mating system, sexual display, clutch size, and diet overlap. Table S3. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in body mass in relation to diet overlap (Morisita's index) as explanatory variable. Table S4. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in beak length in relation to diet overlap (Morisita's index) as explanatory variable. Table S5. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in wing length in relation to diet overlap (Morisita's index) as explanatory variable. Table S6. Phylogenetic generalized least squares (PGLS) testing sexual size dimorphism (SSD) in tarsus in relation to diet overlap (Morisita's index) as explanatory variable. Table S7. Candidates PGLS "full models" for SSD in body mass, beak length, wing length and tarsus length. Table S8. Ranking of all path models using the R packages ‘piecewiseSEM’ and ‘lavaan’. Table S9. Ranking of all path models tested based on CICc values using the R package ‘phylopath’. Figure S1. Distribution of SSD, calculated as log 10 (male trait / female trait) in four morphological traits: body mass, beak length, wing length and tarsus length. Figure S2. Sexual size dimorphism (SSD) according to mating systems: monogamy (MG, greeen) and polygyny (PG, orange). Monogamy corresponded to mating system values of 2 and 3 (i.e., monogamy and <5% polygyny) and polygyny to values of 4 and 5 (i.e., <15 polygyny and lek)., Peer reviewed

Despite the great advances in macroautophagy/autophagy research in the last years, the in vivo role of the different members of the four mammalian orthologs of yeast Atg4 protease (ATG4A-D) remain unclear. To gain further insights into the functional relevance of Atg4 orthologs, we have generated mutant mice deficient in Atg4c. These mice are viable and fertile, and do not display any obvious abnormalities, indicating that they are able to develop the autophagic response required during the early neonatal period. However, they show tissue-specific autophagy alterations, including reduced autophagic flux in diaphragm and show decreased breathing and locomotor activity after fasting. In addition, atg4c-/- mice show reduced number of circulating T and B lymphocytes, which is associated with accumulation of apoptotic cells in the spleen and an increased susceptibility to develop chemically-induced fibrosarcomas. Moreover, through the analysis of cells and mice simultaneously deficient for ATG4C and ATG4D proteases we also reveal a role for ATG4C in mATG8 proteins delipidation. ATG4 (autophagy related 4 cysteine peptidase); ATG4A (autophagy related 4A cysteine peptidase); ATG4B (autophagy related 4B cysteine peptidase); ATG4C (autophagy related 4C cysteine peptidase); ATG4D (autophagy related 4D cysteine peptidase); Atg8 (autophagy related 8); GABARAP (GABA type A receptor-associated protein); GABARAPL1(GABA type A receptor-associated protein like 1); GABARAPL2 (GABA type A receptor-associated protein like 2); MAP1LC3A/LC3A (microtubule associated protein 1 light chain 3 alpha); MAP1LC3B/LC3B (microtubule associated protein 1 light chain 3 beta); mATG8 (mammalian Atg8); PE (phosphatidylethanolamine); PS (phosphatydylserine); SQSTM1/p62 (sequestosome 1)., This work was supported by grants from Ministerio Ciencia eInnovación (Spain) (PID2021-127534OB-I00), the South-Eastern 1315 Norway Regional Health Authority (2021088 to N.E.) and Instituto de Salud Carlos III (RTICC Spain). Jesús Prieto-Lloret is funded by Programa Estrategico IBGM, Escalera de Excelencia, ref. CCVC8485, Consejería de Educación, Junta de Castilla y León (Spain). Funding for open Access Charge: Roche Farma”, as the aricle will be published via Open access and the OA costs will be funded by Roche Farma., Peer reviewed

Table S1. Reagents used in this work. Table S2: Patient Cohort characteristics. Figure S1. PLD2 expression in OC patients and patient survival in OC public databases. (A) PLD2 expression in the OC patient databases GSE12172, GSE9891, GSE63885 and GSE2109 indicating OC subtype. (B) PLD2 expression in the OC patient databases GSE18520, GSE40595, and GSE38666, indicating OC subtype. (C) Kaplan-Meier plots showing overall survival (OS) of patients with high (red) or low (green) PLD2 expression levels in three OC databases with survival data: GSE13876 (advanced HGSOC); GSE23554 (advanced serous epithelial OC); and GSE31245 (92% serous, 2% endometroid, 6% clear cell). Data were analyzed with the log-rank test, and the associated P-values are shown in the graphs. (D) Kaplan-Meier plots showing overall survival (OS) of patients with high (red) or low (green) risk. PLD2 expression for each group is shown on the right of each graph. (E) Kaplan-Meier plots generated with Kaplan-Meier Plotter showing PFS (left column), PPS (middle column) and OS (right column) by splitting patients according to PLD2 expression in all OC patients (top row) or only HGSOC patients (bottom row). Expression levels are shown as log2 transformed values from the R2 database. Data were analyzed using Student’s t-test. *, P <0.05; **, P < 0.01; ***, P < 0.001. Figure S2. HIF1a levels in OC cell lines in response to hypoxia. (A) Representative images of HIF-1a protein levels by immunofluorescence in SKOV3, OVCAR8 and ES-2 cells under normoxia and hypoxia or in the presence of the HIF-hydroxylase inhibitor DMOG. (B) Western blot showing HIF-1a and alpha-tubulin protein levels in SKOV3, OVCAR8 and ES-2 cells under normoxia and hypoxia or in the presence of the HIF-hydroxylase inhibitor DMOG. A minimum of three independent experiments were performed. Figure S3. Differential analyses of chromatin accessibility in OC cells. (A) Volcano plot showing differential analyses of chromatin accessibility between SKOV3 cells carrying Ev and plasmid overexpressing PLD2 in normoxia. (B) Heatmaps and average profiles plotting normalized ATAC-seq signal in SKOV3 cells carrying Ev and overexpressing PLD2 for the differentially accessible regions (DARs) in (A). (C) Gene Ontology term enrichment analyses of biological processes for the genes associated with DARs in SKOV3 cells carrying EV in normoxia versus hypoxia. (D) Gene Ontology term enrichment analyses of biological processes for the genes associated with DARs in SKOV3 cells carrying Ev versus overexpressing PLD2 in normoxia. Figure S4. Motif and footprint analyses of transcription factor binding in OC cells. (A-B) Motif enrichment analyses of the increased and decreased ATAC peaks in OC cells carrying Ev in hypoxia vs. normoxia (A) and +/- PLD2 expression in normoxia (B). The three motifs with the lowest p values are shown in each case. (C) Venn diagrams plotting the overlap between TFs with increased binding in hypoxia and expressing PLD2 in normoxia (top) or the overlap between TFs with increased binding in hypoxia and expressing shPLD2 in hypoxia (bottom). (D) Distribution of fold changes in the ATAC peaks containing the motifs FOS::JUN in Ev normoxia vs. Ev hypoxia (top) or in Ev hypoxia vs. shPLD2 hypoxia. (E) Aggregate footprint signal of the peaks containing the motifs in (D). Figure S5. Clustering of differentially accessible regions in OC cells. (A) Heatmaps plotting normalized ATAC-seq signal at peaks associated with stemness genes in SKOV3 cells carrying Ev or plasmid expressing PLD2 in normoxia and carrying Ev or plasmid expressing shPLD2 in hypoxia, for the differentially accessible regions (DARs) clustered using k-means method in 4 clusters. (B) Tracks with ATAC-seq in SKOV3 cells carrying Ev or expressing PLD2 in normoxia and carrying Ev or shPLD2 in hypoxia, at the JAG1 locus. Figure S6. Expression of stemness and hypoxia genes correlated with PLD2 in OC patients. Heatmaps showing the expression z-scores of stemness-associated genes or hypoxia-response genes whose expression correlated with PLD2 in GSE40595 and GSE38666 OC patient databases. Figure S7. Hypoxia induces CSCs in ovarian cancer cells. (A) Top, Representative images of tumorspheres formed by SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. Bottom, quantification of the number and size of tumorspheres. Scale bars: 250 μm. (B) Percentage of holoclones formed by SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. At least 200 individual clones were analyzed. (C) Analysis of the expression of NANOG, SOX2, CD44 and EPCAM stemness-associated genes by RT-qPCR in SKOV3, OVCAR8 and ES-2 cells in normoxia or hypoxia. The mRNA expression was calculated as 2-∆Ct relative to the ACTB gene. (D) Percentage of CD133 positive cells measured by FACS in SKOV3, OVCAR8 and ES-2 cells in normoxia and hypoxia The average and SD of three independent experiments are shown in all cases. A minimum of three independent experiments were performed and the data were compared using Student’s t tests. Asterisks indicate statistical significance with respect to normoxia. *p < 0.05; **p < 0.01; ***p < 0.001. 13 Figure S8. Relative protein quantification of PLD2 normalized to alpha-tubulin from the western blots in Figure 4B. Figure S9. PLD2 expression and clone analyses. (A) Western blot showing PLD2 and alpha-tubulin protein levels in SKOV3, OVCAR8 and ES-2 OC cells carrying Ev, a plasmid expressing shPLD2 or plasmids expressing shPLD2 and PLD2. (B) Percentage of paraclones, meroclones and holoclones formed by SKOV3, OVCAR8 and ES-2 cells carrying Ev or plasmids expressing PLD2, shPLD2 or both in hypoxia or normoxia. At least 200 individual clones were analyzed. The average of three independent experiments is shown. A dotted line represents the percentage of holoclones in Ev carrying cells as a reference. Data were compared using Student’s t tests. Asterisks indicate statistical significance with respect to Ev carrying cells. *p< 0.05. (C) Left, determination of PLD2 protein levels by immunofluorescence in tumorspheres formed by OC cells carrying Ev and expressing PLD2 or shPLD2. Right, quantification of the percentage of cells with PLD2 expression in tumorspheres. Scale bars: 100 μm. Figure S10. Analyses of the EMT in OC cells in response to hypoxia and/or PLD2 expression. (A) Heatmaps showing the expression z-scores of epithelial-to-mesenchymal transition-associated genes obtained from TaqMan Arrays. Genes are sorted according to decreasing z-scores in the Ev-carrying cells under normoxia. (B) Heatmaps showing the z-scores of EMT genes expression levels in SKOV3 cells carrying EV or plasmid overexpressing PLD2 under normoxia conditions and carrying EV or plasmid expressing shPLD2 under hypoxia condition. Hierarchical clustering of the samples is shown. (C) Expression levels of SNAI1, VIM, CDH1 and CDH2 EMT-associated genes in cells carrying Ev or plasmids expressing PLD2 under normoxia or shPLD2 under hypoxia conditions. (D) Left, representative images of the Boyden chamber migration assays in SKOV3 and OVCAR8 cells carrying Ev or plasmids expressing PLD2 or shPLD2 under normoxic or hypoxic conditions. Right, quantification of the Boyden chamber migration assays. A minimum of three independent experiments were performed and the data were analyzed using Student’s t-test. *, P < 0.05; **, P < 0.01; ***, P < 0.001., Supplementary Material 1: Essential role of PLD2 in hypoxia-induced stemness and therapy resistance in ovarian tumors., Funding: Consejo Superior de Investigaciones Cientificas (CSIC)., Peer reviewed

Experimental studies and clinical trials of nanoparticles for treating diseases are increasing continuously. However, the reach to the market does not correlate with these efforts due to the enormous cost, several years of development, and off-target effects like cardiotoxicity. Multicellular organisms such as the Caenorhabditis elegans (C. elegans) can bridge the gap between in vitro and vertebrate testing as they can provide extensive information on systemic toxicity and specific harmful effects through facile experimentation following 3R EU directives on animal use. Since the nematodes’ pharynx shares similarities with the human heart, we assessed the general and pharyngeal effects of drugs and polypyrrole nanoparticles (Ppy NPs) using C. elegans. The evaluation of FDA-approved drugs, such as Propranolol and Racepinephrine reproduced the arrhythmic behavior reported in humans and supported the use of this small animal model. Consequently, Ppy NPs were evaluated due to their research interest in cardiac arrhythmia treatments. The NPs’ biocompatibility was confirmed by assessing survival, growth and development, reproduction, and transgenerational toxicity in C. elegans. Interestingly, the NPs increased the pharyngeal pumping rate of C. elegans in two slow-pumping mutant strains, JD21 and DA464. Moreover, the NPs increased the pumping rate over time, which sustained up to a day post-excretion. By measuring pharyngeal calcium levels, we found that the impact of Ppy NPs on the pumping rate could be mediated through calcium signaling. Thus, evaluating arrhythmic effects in C. elegans offers a simple system to test drugs and nanoparticles, as elucidated through Ppy NPs., Peer reviewed

(A) An asynchronous culture of GAL-SIC1ΔNT GLN3-9MYC (YMF4471) was grown at 30 °C in medium lacking galactose. After the addition of nocodazole, culture was synchronised in G2-M phase for 1 generation time. Cells were then transferred to fresh medium containing galactose to allow overexpression of SIC1ΔNT. Cells were maintained as well as in nocodazole. Cell extract were made over the course of 2 h to examine Gln3 mobility. Raw data for blots can be found in Supporting information (S1 Raw Images). (B) TUB1-GFP cdc15-2 (YMF3976) cells were grown in YPD and arrested in late anaphase by raising the temperature to 37 °C before the addition of rapamycin to half of the culture. Subsequently, to allow progression through the cell cycle, cells were released in the absence (−) or presence (+) of rapamycin. Samples were taken at the indicated times. Using fluorescence microscopy, the proportion of cells with anaphase spindles in the absence (i) or presence (ii) of rapamycin was investigated. Examples of TUB1-GFP cdc15-2 cells at 120 min after the release at 24 °C are shown in the absence (iii) or presence (iv) of rapamycin. Scale bars indicate 5 μm. (C) cdc15-2 cells (CC2274) were grown in parallel with strains for Fig 2A, but instead or releasing cells at the permissive temperature of 24 °C after the addition of rapamycin like in Fig 2A, cells were maintained at the restrictive temperature of 37 °C in the presence of rapamycin (i). Samples were taken at the indicated times to determine DNA content by flow cytometry analysis (ii) and cell morphology at the end of the experiment (iii). Scale bars indicate 5 μm. (D) cdc14-1 cdc15-2 cells (CC6441) were grown in YPD and arrested in late anaphase by shifting the temperature to 37 °C before the addition of rapamycin to half of the culture (ii). Then, cells were released at 24 °C in the absence (i) or presence (ii) of rapamycin. Samples were taken at the specified times to determine DNA content by FACS analysis. Using light microscopy, we studied cell morphology in the absence (iii) or presence (iv) of rapamycin at the 120 min time point after the release from late anaphase arrest. Scale bars indicate 5 μm. (E) INN1-GFP cdc15-2 (YMF3162) cells were grown as in A. Samples were taken at the indicated times. The proportion of cells with total Inn1-GFP signal in the absence (i) or presence (ii) of rapamycin was determined using fluorescence microscopy. The percentage of cells with either medial rings or spots of Inn1-GFP was calculated too. Examples of cells expressing Inn1-GFP 30 min after the release are shown in the absence (iii) or presence (iv) of rapamycin. Scale bars indicate 5 μm. (F) 3GFP-RAS2 cdc14-1 cells (CC6296) were grown as described in A. Cells are shown at 120 min after the release in the absence (i) or the presence (ii) of rapamycin. Red arrows denote cells where examination of each z-level at the bud neck showed divided cytoplasm and new buds are marked with white asterisks. Scale bars indicate 5 μm. Underlying data for all the graphs can be found in S1 Data file. FACS graphs can be found in the supplementary FACS file (S1 File)., journal.pbio.3002263.s001.pdf, Peer reviewed

Coordinators: Natividad Benito (GEIO-SEIMC), Juan Carlos Martínez-Pastor (SECOT), Jesús Saavedra-Lozano (SEIP).-- Methods: Eighteen clinical questions were formulated under the three major headings of Diagnostics, Therapeutics and Follow-up. The coordinators assigned each clinical question to a subgroup of panellists. For each clinical question, all significant scientific literature was reviewed and summarised in comprehensive tables following the PICO system (P– Populations/People/Patient/Problem; I– Intervention(s); C-Comparison; O– Outcome). The criteria used to evaluate the strength of the recommendation and the quality of the evidence are summarised in Table 1. The coordinators wrote the first draft based on the sections submitted by each subgroup of panellists. The draft was reviewed by all members of the panel of experts, controversial issues were debated, and a final version was prepared. Before its final approval, the document was posted on the SEIMC intranet and left open for suggestions and comments from members. All authors have approved the contents of the document and the final recommendations. Possible conflicts of interest associated with all members of the panel of experts are listed at the end of the document., Infectious arthritis is an infection of one or more joints caused by bacteria (including mycobacteria), fungi and viruses. Only a small number of viruses directly infect the joint, mainly as part of a self-limiting systemic infection. While the term septic arthritis (SA) usually refers to bacterial or fungal arthritis, bacteria are by far the most common agents of these infections. The annual incidence of SA is between 2 and 7 cases per 100,000 inhabitants in most Western European series. 2–7 Higher rates have been observed in other areas, such as Australasia. 8–10 Even though SA remains relatively rare in the general population, the overall incidence is increasing, due to the rising rate among older patients, who have more underlying co-morbidities and joint disorders, undergo more invasive procedures with increased use of immunosuppressive treatments., Peer reviewed