Fingermarks are trace evidence of great forensic importance, and their omnipresence makes them pivotal in crime investigation. Police and law enforcement authorities have exploited fingermarks primarily for personal identification, but crucial knowledge on when fingermarks were deposited is often lacking, thereby hindering crime reconstruction. Biomolecular constituents of fingermark residue, such as amino acids, lipids and proteins, may provide excellent means for fingermark age determination, however robust methodologies or detailed knowledge on molecular mechanisms in time are currently not available. Here, we address fingermark age assessment by: (i) drafting a first protein map of fingermark residue, (ii) differential studies of fresh and aged fingermarks and (iii), to mimic real-world scenarios, estimating the effects of donor contact with bodily fluids on the identification of potential age biomarkers. Using a high-resolution mass spectrometry-based proteomics approach, we drafted a characteristic fingermark proteome, of which five proteins were identified as promising candidates for fingermark age estimation. This study additionally demonstrates successful identification of both endogenous and contaminant proteins from donors that have been in contact with various bodily fluids. In summary, we introduce state-of-the-art proteomics as a sensitive tool to monitor fingermark aging on the protein level with sufficient selectivity to differentiate potential age markers from body fluid contaminants. While representing a major and valuable source for donor identification through latent fingerprint analysis, fingermarks are also rich in molecules that can be used to acquire toxicological and biological donor profiling information. In addition, exogenous compounds in fingermarks have shown applicability for crime reconstruction and to assess offender and victim activities before, during and after a criminal act. Many of such applications have been reviewed by van Dam et al.4, and Huynh and Halamek5. A major limitation in these respects is the lack of temporal information, which makes it difficult to establish forensic timelines. At present, precise assessment of the age of a fingermark is not possible.Fingermarks are frequently encountered at crime scenes and on related items, and naturally protected against fast degradation due to the abundance of recalcitrant substances, such as fatty acids and constituents of sebum13. Fingermarks likely contain low amounts of endogenous proteins, compared to abundantly present lipids, salts and free amino acids3, and have only attracted limited attention from a proteomics point of view. In contrast, contaminant proteins on fingers have been proved to be highly relevant in studying donor contact with bodily fluids, such as blood10 and vaginal fluid14 by means of mass spectrometry (MS) analysis. From previous studies on fingermarks and epidermal skin layers it can be deduced that fingermark residue holds a wide variety of slowly degradable keratins alongside more typical proteins originating from sweat20. Upon deposition on a substrate, the protein composition of fingermarks is likely to change, whereas rates of degradation as well as alterations originating from e.g. oxidation, deamidation or alkylation processes will vary, depending notably on environmental conditions and time. This makes these biomolecules good targets to assess crucial yet presently unavailable temporal information for crime investigation. Fluorescence-based screening of proteinaceous material in parallel with lipids has recently been introduced as a high-throughput, non-contact methodology to assess the age of fingermarks from forensic contexts21. Despite the successful assessment of potential spectral regions of interest and a preliminary age-estimation model, this study, however, did not focus on compound identification nor on specific degradation/expression pathways. Furthermore, studies that have attempted to identify endogenous proteins from fingermarks were restricted either to indirect methods  or peptide mass fingerprinting combined with putative protein identification from the literature9. Hence, in the strictest sense, no sequence-based proteomics studies have been conducted solely on fingermark residue until now. Therefore, a first proteomic map of fingermark development in time would not only provide further information on the chemical composition of fingermark residue, but likely would also reveal ageing pathways and related biomarkers.Although fingermark residue is recognized as an important source of tell-tale (bio)molecules, it is not yet routinely used for forensic profiling in casework or in court. Nonetheless, recent research efforts have now placed illicit substances (and their metabolites), lipids, amino acids, DNA, RNA and proteins high on the forensic research agendaIn this study, we developed a fingermark sampling and protein extraction protocol easily adaptable to forensic settings. A bottom-up proteomics approach Fig.  was thenHere, a fingermark proteome of about 50 proteins was recorded, whereof a small fraction of keratin proteins and the antimicrobial peptide dermcidin (DCD) exhibited distinct responses during ageing. Ageing effects included an early increase in the frequency of observed amino acid residue modifications as well as protein degradation processes, decreasing the number of observable peptides. In addition, we demonstrate that most of the here described endogenous fingermark proteins can even be measured after contact with bodily fluids. In all, we drafted a first fingermark protein map including candidates that may serve as novel markers for accessing fingermark aging in forensics.The relatively low protein content of fingermark residue as discerned from the literature was confirmed by a separate study using a bicinchoninic acid (BCA) assay and nanodrop spectrophotometric analysis for protein quantification; Protein yields were inferred to range from about 0.2 to 51.0 μg per fingermark . For the ageing study, protein searches against a focussed fingermark protein database resulted in the identification of 52 proteins across all ageing pools of which 31 were redundant between these sample pools. Unique proteins were only identified in fresh fingermark residue (n = 2), and in marks aged for 8 days (n = 1) and 12 days (n = 1)  could not be identified from our data. The recorded proteomic profile is in line with that observed from the skin surface19, with cytokeratins as dominant protein species. These components make up approximately 35% of the fingermark proteome, whilst the remainder is accounted for by an array of antibiotic proteins as well as secreted blood proteins26. Gene ontology (GO) analysis . Cluster 1 consists primarily of keratins and includes a more or less separate group of non-keratins, including Filaggrin and Filaggrin-2 , Caspase-14 (CASPE), Cystatin-A (CYTA) and Desmoplakin (DESP). The second most dominant clusters are represented by serum proteins; ALBU and Hemoglobin subunit beta (HBB), as well as Keratin type I cytoskeletal 19 (K1C19) and redox associated proteins Thioredoxin (THIO) and Phosphoribosylformylglycinamide synthase (PUR4) (cluster 2), and proteins likely representing sweat induced species associated with (non)immune defence against pathogens ) and Fatty acid-binding protein (FABP5) and cluster 4: Zinc-alpha-2-glycoprotein (ZA2G) and Prolactin-inducible protein (PIP)). Other groupings in our data are associated with more specific processes i.e.; chromatin interactions (cluster 5: Chromodomain-helicase-DNA-binding protein 3 (CHD3), Deoxynucleotidyltransferase terminal-interacting protein 1 (TDIF1) and Probable helicase senataxin (SETX) and hair formation  and Keratin, type II, cuticular Hb4 (KRT84)). Other linked proteins in the here observed fingermark proteome were primarily based on interactions between putative homologs found in species other than humans . Lastly, a single-member cluster containing only Junction plakoglobin (PLAK), a protein associated with keratinocyte adhesion, was found to link clusters 1, 8 and 9. The clustering of our data is largely in agreement with origin of fingermark residue i.e.; material shed from skin and sweat, and this further supports that the proteome reported is a genuine molecular representation of fingermarks. As previously observed and confirmed by this work, high abundance proteins in fingermarks are primarily keratins. Specifically, keratins 1, 2, 5, 6, 9, 10, 14 and 17 were observed in almost all individual fingermarks with up to 57% sequence coverage. Other proteins (including some other keratins) are typically represented only by some of the fingermarks , used in this work to monitor background contamination, displayed low coverage of the here observed fingermark proteome Fig. . This miFor the 31 proteins that were identified in all sample pools, we performed ANCOVA using fingermark age, donor sex and finger type as outcome variables. Next, the normalized abundances of these proteins were manually evaluated for changes with increased age. Combined ageing effects and clear temporal expression trends were found for K2C1, Keratin, type II cytoskeletal 2 epidermal (K22E), Keratin, type I cytoskeletal 9 (K1C9), K1C10 and DCD. Here, effects of ageing were all significant, whereas donor sex or finger type effects were non-significant (see Supplementary Table www.unimod.org) as deoxydation, dethiomethylation, deamidation, oxidation or hydroxylation, hydroxymethylation, cys-arg substitution, diethylation and carboxyethylation. These modifications are however not equally distributed amongst the ageing sets and we were unable to assign mass shifts at +52 and +104 Da to potential modification events.Double-blind searches were performed using MODa software and used to detect protein modifications commonly associated with protein damage and ageing. Apart from single, double and triple alkylation , disulphide reduction (mass shift: +2 Da), ammonia loss (−17 Da) and potassium adducts (+39 Da) likely coming from sample preparation or electrospray ionization, top 10 abundant mass shifts common to K2C1, K22E, K1C9, K1C10, and DCD were found to be at: −16, −48, +1, +16, +30, +52, +53, +56, +73, +104 Da. These mass shifts were interpreted using the unimod database  are listed in Supplementary Tables 2 drying instead of protein precipitation, as well as low-volume tryptic digestion we attempted straightforward sample preparation with minimal protein loss. The protein levels obtained from fingermarks (up to 57 μg) were however much lower compared to a previous study (128–542 μg)16. This importantly reveals the effects of ‘grooming’ prior to fingermark deposition, a procedure which was not included in our study so as to better mimic real-world conditions. In addition, Drapel et al.16 measured protein levels directly from the skin surface of fingertips rather than the much more realistic sampling from substrate applied in this work. It is nonetheless evident that the relatively low protein levels in fingermark residue make the application of MS-based protein sequencing challenging, notably in terms of stringent validation constraints. By using effective swab-sampling, low protein loss sample processing, and utilizing two-pass and match-between-runs database search strategies29 we were however able to overcome the limitations dictated by the protein yields from individual fingermarks. As was anticipated and confirmed by our data, our experimental design with maximum validity for crime investigation is likely to reveal high molecular variation between fingermarks. The here recorded data shows that proteome variation was not affected by donor sex nor by finger type. While this is in agreement with previous findings with regards to amino acids and lipids in fingermark residue7, donor sex differentiation has been successfully applied using matrix-assisted-laser-desorption/ionization MS profiling of fingermarks9. However, the precise molecular features that accounted for donor sex variation remained unclear from this study. Despite low and variable protein levels, we were able to explore the fingermark proteome and record temporal trends. This suggests that the proteins identified here are stable constituents of fingermark residue in view of e.g. donor traits and activities; an observation that could also be confirmed by the mere presence of common skin and sweat proteins.In this work, we combined an easily applicable sampling and protein extraction method, and high-end LC-MS analysis to obtain a first draft proteome map of fingermark residue. By means of efficient protein recovery using N31 and regarded as death cells, notwithstanding the fact that they remain biologically active, being filled with keratin proteins and lipids33. The co-expression of many skin keratins, and CASPE, FLA, FLA2, DESP and Calmodulin-like protein 5 (CALL5) also suggests on-going (bio)chemical activity with regards to keratinocyte differentiation and physiology in fingermark residue after deposition39. Many of the less represented proteins in the here examined fingermarks could be associated with skin defence against pathogenic bacteria. Notably, PIP in excreted sweat has been reported to regulate non-immune defence40 probably by binding to IgG, CD4 and/or T cell receptors41. ZA2G has been shown to complex with PIP41 and although its exact function is yet unknown, the co-expression of these proteins suggests that defence mechanisms against bacteria are active in fingermark residue. As ZA2G also binds fatty acids and stimulates lipolysis43, this complex may also be involved in the degradation of fingermark residue. Other antibacterial proteins identified in this work include: S10A7, S10A8 and S10A9. Specifically, S10A7 is targeted against E.coli and has been previously associated with skin defence44, whilst S10A8 and 9 are commonly involved in the regulation of inflammatory processes and immune response45. Moreover, additional anti-inflammatory properties for in vitro S-nitrosylated S10A8 have been suggested46. Since the functioning of S100 family proteins in common keratinocyte proliferation, also in close relation with fatty acid processing, has a large evidence base as well, dual functionality of this protein family is likely. Other protein clusters in our samples highlight ALBU and HBB, probably acting as carrier proteins for keratins and S100 proteins. Albumin has also been recognized to contain regions with antioxidant functionality47. The remaining proteins identified here were not or only putatively associated with other fingermark residue constituents through protein-protein interaction analysis. One of these proteins is DCD, which is known for its involvement in skin defence, due to its antibacterial, antifungal and redox properties48. The latter are also expressed by THIO50. Ubiquitin peptide 52 (RL40) shows involvement of the proteasome system during keratinocyte proliferation51, whilst this is also confirmed by the co-expression of keratin 6b (K2C6B)52. Functioning of other members of the here reported fingermark proteome in regards to the biochemistry of skin surface material could not be inferred from the literature and, yet, remain elusive.According to GO analysis of the fingermark proteome described in this study, most positively identified species could be assigned to extracellular and intermediate filament proteins, which are critical for cell scaffolding, skin development and homeostasis, as well as to a group of keratins which are involved in epidermis development and terminal differentiation of keratinocytes. Keratins may originate from viable and non-viable cells, as previous microscopic and biomolecular studies have revealed that both nucleated keratinocytes and anucleated corneocytes occur in fingermark residue. The latter are however generally observed in higher abundance53.Temporal changes of protein abundance in fingermark residues were found for keratins 1, 2, 9, 10 and DCD. DCD exhibited a clear decrease in abundance with increasing age. Interestingly, most keratins showed, however, a relative increase of their peptide signals during the progress of ageing (after 8 days of ageing). Since our initial hypothesis was that prolonged exposure of fingermarks to the environment results in protein degradation and thus a decrease in protein signal, we assessed alternative scenarios supported by current literature. On the fingertip surface, there is a clear discrepancy in regards to recalcitrant molecular components (e.g. keratin proteins) and constituents that are generally more susceptible towards damage and degradation, such as small proteins, peptides and amino acids. It can thus be hypothesized that upon fingermark ageing the over-all sample matrix complexity decreases in parallel with a mass balance shift towards stable keratinaceous constituents. Therefore, it may be plausible that the observed time-dependent increase in keratin signals originate mostly from the relative better stability of this class of proteinet al.54. In skin, enhanced expression of (keratin) proteins has been reported to be linked to e.g. ageing, UV radiation, irritants, and is suggested to result from stress and inflammatory responses of keratinocytes, apoptosis, transepidermal water loss or skin moister loss56. Such a response can be extrapolated to ageing fingermarks, especially because of the significant loss of water from fingermark residue in time57. It has also been shown that water loss results in increased epidermal calcium (Ca) levels and, in turn, increased protein expression58. As described earlier54, the concentration of ions (including Ca) in fingermark residue or sweat can increase as a result of water loss, and as such the assumption that an effect thereof on keratin expression is reasonable. The identification of specific Ca-binding proteins  in this study also supports such a hypothesis.Other processes which have to be taken under consideration when understanding the ageing trends recorded in this work are based on the assumption that (bio)chemically active skin cells are abundantly present in fingermark residue, as reported by Moret 59. In many biological systems extensive modification may induce protein degradation and decreased protein turnover60, whilst this was not observed for these potential age markers identified. Our data suggest that, relatively low albeit increasing protein modification levels were achieved between 0 and 12 days of ageing. Reversible oxidation of methionine residues of proteins and thereby effectively consuming ROS61 may have contributed to protein accumulation during the ageing of fingermarks. Although more in-depth analysis of modification events of age-related markers would be very useful to extend these results, it is beyond the resolution of our data and the scope of this study. For these reasons, we did not further investigate protein modification in detail. Lastly, as fingermark residue was deposited on a foreign surface, changing biophysical conditions  over time may also have contributed to enhanced cell permeability or rupture62, thereby releasing keratins to the environment or making them more susceptible for extraction. In view of the above discussion, the behaviour of DCD was as expected and showed a temporal decrease of LFQ levels, thereby suggesting peptide degradation or a more or less gradual consumption of this peptide in microbial defence. Modification events for this small protein follow this trend on the peptide level  contaminants and the presence of proteins enabling contaminant sourcing, this additional study could be an important basis for further development of analytical protocols and data processing methods for age-biomarker discovery and validation studies. In prospect of such endeavours, it is noteworthy that keratin proteins are well-known to occupy various bodily fluids, including those studied here. It is, however, unclear how well such exogenous species are actually expressed in fingermark proteomes in view of the limited amount of proteinaceous material generally adhered to fingers.For fingermark residue analysis in forensic contexts, environmental contamination is a critical and difficult issue that analytical protocols should address. Two approaches were taken here; (i) searching MS records against a contaminant database and (ii) assessment of a background proteome by using procedural blanks. As for (i), the contaminant database included all common contaminant proteins reported by the common Repository of Advantitious Proteins with the exception of keratins. Besides porcine trypsin (TRYP), introduced during sample preparation, these searches resulted in the identification of a keratin protein likely originating from sheep wool  and was considered as coming from dust contamination. For the procedural blanks that were used to monitor background contamination (ii), only low protein coverage of the recorded fingermark proteome was observed, with the exception of two medium abundance proteins likely introduced through carry-over action. Further, a keratin protein likely originating from sheep wool  and porcine trypsin were observed in the blanks. These were also regarded as coming from dust contamination and sample preparation. Taken together, it can be assumed that swab-sampling and/or the fingermark substrate can introduce contamination. Yet, the intermittent occurrence of mass spectra originating from these contaminants support the integrity of the data presented here. Although the level contamination was low in this study, the chances of protein contamination occurring during fingermark exposure outside a controlled laboratory environment are generally high and inevitable, especially after longer exposure times. Similar to the procedural blanks used here, assessment of environmental contamination should be straightforward through sampling blank substrate in close vicinity to the target fingermarks. Also, cleaning of swabs prior to sampling by means of washing with a solvent suitable for upstream processing seems to be worthwhile to avoid additional contamination from the sampling procedure itself.Limitations of this study mainly concern the univariate ageing conditions. By using glass as substrate there is a potential bias to detect ageing responses associated with non-reactive materials only. Variations in climatic conditions were also not studied here and this may add to the bias between laboratory conditions and forensic practice. However, the recalcitrance of keratin proteins under various and sometimes extreme conditions presents great promise for future forensic implementation.63 for examining fingermarks. The present study paves the way for applying marker-specific and time-resolved proteomics to obtain evidential and tactical information for crime investigation.In all, we established a sensitive fingermark sampling and proteomics approach, and present a first insight into the fingermark proteome. Moreover, four keratin proteins and the anti-microbial peptide Dermcidin are introduced as high potential biomarkers of fingermark age. Our study demonstrates that regardless of protein trace quantities and the presence of surface contaminants, LC-MS based fingermark proteomics is capable of providing a much deeper insight into the fingermark proteome compared to previously published methods. This is a very important step towards addressing forensic issues, such as age determination and providing additional tools for donor profiling. However, further improvements in sensitivity will expand the repertoire of fingermark proteins and establish a more complete database of the fingermark proteome. In addition, high-throughput targeted LC-MS approaches using multiplexed identification of forensically relevant protein or peptide markers will offer the necessary speedFingermarks for the ageing study were collected by firmly pressing the tip of each finger  onto a glass slide for 5 seconds. The samples were stored on paper towel in open document trays and aged for 4, 8, 12 and 16 days at 20 °C and 61% relative humidity. Each ageing pool, including non-aged samples (t = 0 days) consisted of 10 fingermarks from both male (n = 5) and female (n = 5) donors, and were processed and analysed individually. Glass slides without fingermarks  were processed as below and analysed as procedural blanks. For the donor contact study , the tip of each finger was briefly (1 min) brought in contact with the bodily fluids by the action of either mouth  or vaginal  insertion. As for mimicking contamination with urine, fingertips (n = 6 samples) were briefly dipped in urine, previously collected in plastic tubes. Fingertips were allowed to dry for approximately 10 min and fingermarks were collected on glass slides as above. Oral approval of this study was obtained from the TU Delft Research Ethics Committee and informed consent was obtained from all donors. All methods were performed in accordance with the relevant guidelines and regulations of the TU Delft and the Netherlands Forensic Institute.2 flow. Prior to swab sampling, all swabs were washed with 50% ACN in water (v/v) and centrifuged for 1 min  using spinbaskets. This was repeated twice and then the swabs were further dried at RT for 1 h.Sampling of the slides was performed using polyester swabs  moistened with 50 μl extraction fluid containing 50% acetonitrile (ACN) in water (v/v). After removing the swabhandles using scissors, swabheads were placed in protein low-binding Eppendorf tubes and 200 μl of extraction fluid was added to each tube. Fingermark proteins were extracted from the swabheads by pulsed vortexing (10 mins) followed by sonication (10 min). The swabheads were then centrifuged for 10 min  using spinbaskets. For each sample, the liquid permeate was dried under a N2 flow and either processed immediately or stored at −20 °C for a maximum of 24 h.Dried fingermark proteins were dissolved in 25.5 μl ammonium bicarbonate (50 mM) by pulsed vortexing (10 min) and sonication (10 min). Then the proteins were incubated at 95 °C for 5 min with 1.5 μl of DTT (100 mM). After cooling the samples to RT, 3 μl of IAA (100 mM) was added to each sample and incubated in the dark at RT for 20 min. Proteins were digested o/n at 37 °C with 0.5 μg trypsin. After digestion, the samples were either processed immediately or stored at −20 °C for a maximum of 24 h. The peptide solutions were purified and concentrated using solid-phase extraction (SPE). Here, C18 SPE spin-tips  were conditioned twice with 50 μl of a solution containing 70% ACN, 29.9% water, 0.1% FA (v/v/v) and equilibrated three times with 50 μl 0.1% FA in water (v/v). After loading and binding of the peptides, the tips were washed three times with 50 μl 0.1% FA in water (v/v) and finally the peptides were eluted twice with 50 μl of a solution containing 70% ACN, 29.9% water and 0.1% FA (v/v/v). Reagents and samples were slowly forced through the spin-tips using air pressure. The eluted peptides were dried under a Net al.64). Internal calibration was performed using two mass signals (m/z 121.12 and 922.01) from premixed standard solutions (HP-0921 and Purine) prepared according to manufacturer’s instructions.All samples were analysed by reversed-phase C18 liquid chromatography tandem MS (LC-MS/MS), using an Agilent HPLC system  connected to a high-resolution Agilent 6530 series electrospray ionization quadrupole time-of-flight mass spectrometer (ESI-QTOF-MS). Dried peptides were dissolved in 25 μl 0.1% FA in water (v/v) and 20 μl of each sample was loaded and separated on an Agilent AdvanceBio Peptide plus column  at 40 °C. A binary solvent system was used consisting of mobile phases A  and B . Each run lasted 65 min and was composed of the following gradient: 5% A , 5–40% A , 40–95% A , 95% A . The flow rate was set to 0.3 ml/min. The mass spectrometer was operated at 2 GHz in extended dynamic range mode . Data dependent acquisition in positive ion mode was applied for MS analysis and was set to automatically select max. 3 precursor ions with intensities above 4500 counts for each cycle. Initial precursor selection was based on charge  followed by signal intensity. Peptide ions were fragmented by collision induced dissociation using collision energies based on charge state, m/z value and an offset value . Full MS scans were acquired over a 200–1700 m/z range. Mass signals were selected for fragmentation at a scan-rate of 3 Hz, when exceeding a target count threshold of 25000 (counts/spectrum). Dynamic exclusion of precursor ions was set to 30 sec and static exclusion was used to reject mass signals from common non-peptidic fingermark contaminants, such as polyethylene glycols and detergents 66, MSGF + (v2017.01.13)67 and Andromeda (v1.5.6.0)68 algorithms through SearchGUI (v3.2.20) and Peptideshaker (v1.16.15) software69 using the following settings: proteolytic enzyme: trypsin, precursor mass tolerance: 20 ppm, fragment mass tolerance: 40 ppm, max. Missed cleavages: 2, charge range: 2–4, variable modification: Oxidation (M), fixed modification: Carbamidomethylation. All peaklists were searched against a target/decoy protein database containing all reviewed Uniprot human protein sequences  and 20,316 reversed decoy sequences. Search results were based on a complement of the posterior error probability (PEP)70 as described in Vaudel et al.69 and filtered at 25% FDR  on both peptide and protein levels with a minimum number of peptide-spectrum-matches of ≥1 for each peptide and a minimum number of peptide matches ≥2 for each protein group. Based on the combined consensus results of these searches, a focussed database was constructed containing all sequences of the identified protein groups. This database (130 sequences) was appended with 19 sequences from common non- contaminant proteins  and used for a second search (Andromeda engine only) combined with label-free quantification (LFQ) using MaxQuant software (v. 1.6.0.16)71. MaxQuant searches were performed using raw data files and with default settings . Mass signals from the procedural blanks were searched as above. Protein modifications were determined using multi-blind unrestrictive searches using the MODa software tool (v1.61)72 and databases containing K2C1, K22E, K1C9, K1C10, and DCD sequences only. Settings for MODa were: data format: mgf, MS/MS instrument: ESI-QTOF, Target-Decoy: search with decoy proteins, AutoPMCorrection = 1, Fragment Tol.: 0.1 Da, Modified mass range: −150 to +400 Da, BlindMode = 2, HighResolution = ON, Enzyme: Trypsin. For the donor contact study, MS/MS recordings were interrogated using a 2-pass search; first the records were searched against the focussed fingermark database and then all peptide-spectrum-matches that remained unassigned were extracted and used for a second search against the above Uniprot human protein database. All searches for this study were performed using the Andromeda engine and processed using SearchGUI and peptideshaker software with identical parameter settings as described above apart from the FDR filters, which were set to ≤1%. In general, only proteins represented by two or more peptides occurring in at least two samples were regarded as positively identified. All MS data and MaxQuant results have been submitted to the proteomics identifications database (PRIDE) via ProteomeXchange, with dataset identifier PXD009706.Raw files (Agilent.d files) were converted to peaklists using msconvert software v3.0.11174)117465, w73 (v. 3.3.3). A value of p < 0.05 was considered significant. Normality of our data was tested using quantile-quantile plots.Effect sizes of ageing, donor sex and finger type were assessed using analysis of covariance (ANCOVA) and Bonferroni adjusted pairwise t-tests in R softwarehttps://string-db.org).Protein functional networks and GO enrichments were obtained through the STRING database and accompanying software (Supplementary informationTableS3TableS4TableS5TableS6TableS7MaxQuant settings


Dysnatremias are common electrolyte disturbances with significant morbidity and mortality. In chronic dysnatremias a slow correction rate (<10 mmol/L/24 h) is indicated to avoid neurological complications. In acute dysnatremias (occurring <48 h) a rapid correction rate may be indicated. Most guidelines do not differ between acute and chronic dysnatremias. In this review, we focus on the evidence-based treatment of acute dysnatremias.A literary search in PubMed and Embase. A total of 72 articles containing 79 cases were included, of which 12 cases were excluded due to lack of information.Of 67 patients (70% women) with acute dysnatremia, 60 had hypo- and 7 had hypernatremia. All patients with hyper- and 88% with hyponatremia had a rapid correction rate (> 10 mmol/L/24 h). The median time of correction was 1 day in patients with hypo- and 2.5 days in patients with hypernatremia. The mortality was 7% in patients with hypo- and 29% in patients with hypernatremia.Severe acute dysnatremias have significant mortality and require immediate treatment. A rapid correction rate may be lifesaving and is not associated with neurological complications. Chronic dysnatremias, on the other hand, are often compensated and thus less severe. In these cases a rapid correction rate may lead to severe cerebral complications.The online version of this article (10.1186/s13049-019-0633-3) contains supplementary material, which is available to authorized users. Dysnatremias are common electrolyte disturbances with significant morbidity and mortality , 2.Hyponatremia <135 mmol/L) has a prevalence of 15–30% 35 mmol/L, and hypAcute dysnatremias (<48 h) are usually caused by excessive intake of water or salt within a short period of time. Renal and extra-renal water loss, as well as salt loss or retention usually takes more time to develop. Groups at risk include psychiatric patients, children, elderly with reduced cognitive functions and endurance athletes.Severe dysnatremias, usually defined as serum-sodium < 125 mmol/L or > 165 mmol/L, are associated with neurological signs and symptoms with increased mortality. Nausea, falling and asthenia are common in severe hyponatremia while decreased consciousness and confusion are common in severe hypernatremia. Other signs and symptoms include headache, dizziness, agitation and seizures . HypernaIn chronic dysnatremias a slow correction rate (< 10 mmol/L/24 h) is preferred to avoid neurological complications  mainly fThis review is based on a search in PubMed and EMBASE. The search had no time limit and was ended April 6th 2017.Search terms were acute OR severe AND hyper-, hypo-, dysnatremia, polydipsia, water intoxication, salt poisoning OR exercise-associated hyponatremia, both as MeSH  in PubMed and EMTREE Subject Heading in EMBASE and as regular words in title and abstracts in both databases. We used filters for language and age groups. Complete search string is available in the Additional file The search resulted in 5413 articles (1743 in PubMed and 3670 in EMBASE). After the removal of doublets 3884 articles remained. Relevant articles were selected based on title and abstract. Inclusion criteria: Adults ≥18 years old and duration of symptoms <48 h before admission. Patients developing hypo- or hypernatremia due to treatment in hospital were excluded. As were articles lacking information about the treatment given or the correction rate, some of these are still cited in the discussion. All articles were in English, Norwegian, Swedish or Danish. We included 263 articles and 72 articles were read in full text.The search resulted in 72 articles, containing 79 cases of whom 12 were excluded due to lacking information about treatment and correction rate. The flow chart , had onset of signs and symptoms <24 h before admission. In hyponatremia, 77% (46/60) had decreased consciousness and 67% (40/60) had seizures  were treated with hypertonic saline infusions, 33% were given isotonic infusions, one patient was given hypertonic infusions and dialysis, and one was not given saline but mannitol 20% infusion. The rest were treated with fluid restriction. In patients with hypernatremia, 86% (6/7) received hypotonic infusions. One were given isotonic infusions and water by nasogastric tube.Median time of correction was 1  day in hTable 2O) will thus, uncompensated, lead to a change in cell size. In physiological osmoregulation, thirst and the secretion of antidiuretic hormone (ADH) is suppressed by serum-sodium approximately < 135 mmol/L, and is stimulated by serum-sodium approximately > 135 mmol/L [To maintain homeostasis, cell function and size, the physiological regulation of osmolality is important. Osmolality is regulated by the intake and excretion of water and salts. Cell membranes contain water channels (aquaporines) and water diffuses freely between the extracellular and intracellular space, implicating that extra- and intracellular osmolality is approximately the same. Changes in serum osmolality , while hyponatremia in most cases leads to decreased serum osmolality (hypotonicity) . ExceptiSerum-sodium will fall rapidly if the amount of water intake, or hypotonic fluids given intravenously, exceeds the renal capacity of water excretion. In healthy adults a water intake of 1 L/hour may lead to hyponatremia . In caseConversely, serum-sodium will rise rapidly if concentrated salt (sodium-chloride) is eaten or given intravenously. Loss of electrolyte-free water due to aquaresis or osmotic diuresis, that is not replaced, will also cause increased serum-sodium .Due to its specialized function and the space limiting anatomy of the skull, the brain is particularly vulnerable to changes in osmolality and cell size. Brain cells continuously compensate gradual changes in extracellular osmolality by pumping in, or out, electrolytes and endogenous osmolytes . SustainPrimary polydipsia is a condition with increased thirst and water intake, often secondary to psychiatric disorders occurring in 20–47% of schizophrenic patients of whom up to 7% develop hyponatremia [natremia . Many psnatremia  increasiWater intoxication without psychiatric cause is reported in advance of urinary samples [ samples , medical samples , concurr samples , and con samples .Ecstasy-associated hyponatremia is complicated and due to large water intake, often secondary to ecstasy mediated hyperthermia with loss of water and electrolytes, and non-osmotic ADH-secretion mediated by ecstasy and its metabolites [abolites , 16. Womabolites .Exercise-associated hyponatremia (EAH) is defined as hyopnatremia occuring during, or within 24 h of prolonged physical activity [activity . Severe activity , 18. Twoactivity , 19. Speactivity , 19.The treatment of severe cases of primary polydipsia, water intoxication and ecstasy-associated hyponatremia will be the same, namely intravenous bolus of hypertonic (3%) saline, or salty (NaCl) items given orally. The treatment goal is not full correction, but counteracting cerebral edema. A rapid increase in serum sodium of 4–6 mmol/L has been shown to be sufficient in most patients , 4.Salt intoxication occurs in attempts of suicide, to induce emesis, or when salt is mistaken for sugar. Groups at risk include psychotic patients, children, disabled people and elderly with dementia. Campbell et al. [l et al.  found 35Most acute dysnatremias are hyponatremias with onset of symptoms <24 h before hospital arrival. Decreased consciousness and seizures are common, and women are overrepresented. In most patients serum-sodium is corrected faster than recommended for chronic dysnatremias. Acute hypernatremias have a higher mortality than acute hyponatremias.The overrepresentation of women among patients with acute hyponatremias, including exercise-associated hyponatremia  and ecstIn patients who died of acute hypernatremia a rapid correction was attempted, but the patients died within a short period of time. Among patients excluded due to lacking information about treatment given or correction rate, the mortality was 3/12 (25%). All fatal salt intoxications (serum-sodium 175–255 mmol/L) happened within 4–36 h after hospital arrival. We also excluded several other cases due to lacking information about duration of symptoms, including several autopsy reports. Our numbers is thus not representative for the actual mortality in acute dysnatremias.Most patients with hyponatremia, and all with hypernatremia, had faster correction rates than 10 mmol/L/24 h. A significant proportion were corrected as fast as 20–50 mmol/L/24 h. This is up to five times as fast as most guidelines recommend in chronic dysnatremias , 23. HowAmong the surviving patients with the fastest correction rate, the following are emphasized: a young man with serum-sodium 196 mmol/L received 6 l of glucose solution intravenously, within half an hour, with a documented drop in serum-sodium of 37 mmol/L . A man wA difficult clinical challenge is to distinguish between acute and acute-on-chronic hyponatremia in patients with hyponatremic encephalopathy. It also occurs that assumed acute hyponatremia has underlying causes  and thus also may have a chronic (and partly compensated) component. These patients may get complications if a rapid correction rate is used. Our experience is that a head CT with signs of cerebral edema supports an acute genesis Fig. . Both EuAlthough the search resulted in 3884 articles, only 67 cases made our inclusion criteria. Many articles and cases were excluded due to unknown duration of symptoms or underlying risk factors and/or insufficient information about the treatment given, or the correction rate. For the same reason no reviews or randomized controlled studies were included. Although some larger studies have compared survival and correction rate in dysnatremias, these do not distinguish clearly between acute and chronic dysnatremias. To our knowledge no randomized controlled trial regarding the correction rate of acute dysnatremias has been published.In this review, we did not have access to the hour-by-hour correction rate for all patients. Thus we cannot know if the patients had a rapid correction of serum-sodium which then decreased, or if they had a steady correction rate. Publication bias may have contributed to the high survival and the rapid correction rate in our material. However, we believe it is worth noticing the survival- and correction rate in context with the fact that none of the authors reported signs or symptoms consistent with osmotic demyelination syndrome. Only a small number of patients received a follow-up MRI, thus we cannot rule out that some patients may have had radiological deviations, but in our review we have not found any cases of osmotic demyelination syndrome secondary to rapid correction of acute hyponatremia.Severe acute dysnatremias have high mortality and require immediate treatment. Many guidelines, textbooks and reviews recommend a correction rate of 0.5–1.0 mmol/L/h, with a maximum correction of 8–12 mmol/L/24 h , 23. ThiAdditional file 1:Acute Dysnatremias. (DOCX 247 kb)


TM Microbiome DNA Purification Kit, ZR Fecal DNA MiniPrepTM Kit, NucleoSpin® DNA Stool Kit, and IHMS protocol Q) on bacterial and fungal gut microbiome recovery using (i) a defined system of germ-free mice feces spiked with bacterial or fungal strains, and (ii) non-spiked human feces. In our experimental setup, we confirmed that the examined methods significantly differed in efficiency and quality, which affected the identified stool microbiome composition. In addition, our results indicated that fungal DNA extraction might be prone to be affected by reagent/kit contamination, and thus an appropriate blank control should be included in mycobiome research. Overall, standardized IHMS protocol Q, recommended by the International Human Microbiome Consortium, performed the best when considering all the parameters analyzed, and thus could be applied not only in bacterial, but also in fungal microbiome research.Our understanding of human gut microbiota in health and disease depends on accurate and reproducible microbial data acquisition. The critical step in this process is to apply an appropriate methodology to extract microbial DNA, since biases introduced during the DNA extraction process may result in inaccurate microbial representation. In this study, we attempted to find a DNA extraction protocol which could be effectively used to analyze both the bacterial and fungal community. We evaluated the effect of five DNA extraction methods (QIAamp DNA Stool Mini Kit, PureLink The human gut microbiome has been a subject of study for more than a century , but witClostridium difficile infection after fecal microbiota transplantation  in human health has recently emerged. Mycobiota represents only 0.1% of total gut microbiota , but theantation .During the intensive worldwide study of gut microbiomes, the use of different methodologies to prepare samples resulted in numerous microbiome studies with contradictory results. Therefore, one of the presumptions for reproducible and comparable microbiome research is the use of appropriate methods to collect specimens, and extract and store DNA . In an e® DNA Isolation kit) on fungal or combined bacterial and fungal communities were investigated. Despite their effort, mycobiome research is still extremely underexplored and more studies focused on assessing and validating methodology are needed. Most importantly, a protocol capable of accurately extracting DNA from both microbial communities needs to be established.Therefore, gut mycobiome research is facing the same lack of the standardization as bacterial microbiome research in the past. To our knowledge, only two studies have attempted to evaluate the effect of different extraction protocols on the outcome of human gut mycobiome research . In thesTM Microbiome DNA Purification Kit, ZR Fecal DNA MiniPrepTM Kit, NucleoSpin® DNA Stool Kit, and IHMS protocol Q) were evaluated using (i) a defined system of germ-free mice feces spiked with bacterial or fungal strains, and (ii) non-spiked human feces; in order to compare the efficiency of microbial DNA recovery and microbial composition profiles. The presence of kit/reagent fungal contamination was also addressed.In this study, we aimed to expand on current knowledge of how DNA extraction methods affect both bacterial and fungal gut community recovery. Five DNA extraction methods  and fungal  strains to evaluate the methods’ yields using real-time PCR. The strains were chosen as typical representatives of gram-positive, yeast and filamentous microorganisms, respectively. Human feces serving as vehicle controls were analyzed to compare DNA extraction methods in terms of integrity, purity, DNA quantity and microbial composition. Use of the single human stool sample was intended to avoid the effect of inter-individual variability observed elsewhere  germ-free mice feces, for their absence of viable microbiota and very low background microbial DNA load originating from diet and bedding sterilized by irradiation , confirmlsewhere . The detGerm-free mice fecal samples were obtained from the Laboratory of Gnotobiology, Institute of Microbiology, Academy of Sciences of the Czech Republic. This laboratory is well-established in germ-free animal research . A singl®  in 18 mL of autoclaved PBS, divided into 160 μl aliquots (n = 85) and frozen at -80°C until used. Then, the stool aliquots were separated into groups for two independent sets of DNA extraction. For the first set, the aliquots (n = 30) were simultaneously spiked with 20 μl of two different C. albicans concentrations  and 20 μl of E. faecalis concentrations (7.7 × 105 or 7.7 × 107 cells/ml), resulting in a 200 μl total sample volume (n = 30) were spiked with 20 μl of two different amounts of A.fumigatus (106 or 108 cells/ml approximately) and 20 μl of sterile water  to preserve an equal sample volume. Stool aliquots spiked with 40 μl sterile water (n = 25) were included in both groups as the baseline microbial DNA load controls.Immediately after delivery, two grams of mice feces were homogenized using TissueRuptore volume . For then = 15), and frozen at -80°C until used. Dilutions were performed to make samples homogeneous and pipettable to ensure exactly the same volume (the same amount of microbiota) in all samples and thus, identical conditions for each extraction method tested.In addition, we used 200 mg of human feces, serving as a natural sample type control for the third independent DNA extraction set. The stool was homogenized in 4 mL autoclaved PBS, divided into 200 μl aliquots , ZR Fecal DNA MiniPrepTM Kit , NucleoSpin® DNA Stool Kit  and non-commercial protocol Q, recommended by the International Human Microbiome Consortium (abbreviated as IHMS). All protocol procedures were performed according to the manufacturer’s instructions with minor differences , PureLink® 3.0 Fluorometer  using the QubitTM dsDNA HS Assay Kit. DNA purity was determined via 260/280 and 260/230 ratios measured on the NanoDrop 1000 . DNA integrity was determined by 0.6% agarose gel electrophoresis and visualized  was used to evaluate the bacterial yield using the ABI 7500 fast Real-Time PCR System .Commercial real-time PCR kit for specific The fungal yield was evaluated using a broad-range real-time PCR assay as described previously  using thIn both real-time PCR assays, all the analyzed samples were performed in duplicates with serially diluted calibration curves. Sterile water served as no template control. Pure culture extracts served as positive controls for species verification. Altogether, 394 real-time PCR reactions for germ-free fecal samples and 60 real-time PCR reactions for human fecal samples (controls included) were performed.p-values lower than 0.05 were considered significant. Log transformed data were used for fold change analysis.Statistical analyses were conducted and visualized in R (3.4.2). One-way analysis of variance  followed by Tukey’s multiple comparison test (function Tukey HSD in R) was used to evaluate the DNA yield and real-time PCR data. 16S rDNA and ITS1 rDNA amplicon sequencing using the Illumina MiSeq sequencing platform. Primers with unique barcode sequences for PCR amplification over the bacterial 16S rDNA gene’s V3/V4 region were designed as described previously  of the rRNA operon  under the following conditions: initial denaturation at 95°C for 15 min, followed by 30 cycles consisting of denaturation at 95°C for 40 s, annealing at 55°C for 45 s and extension at 72°C for 60 s, with a final extension step at 72°C for 5 min. From each reaction, 5 μl were analyzed by 2% agarose gel electrophoresis. PCR products were purified using AMPure XP magnetic beads  diluted into an equimolar concentration and pooled according to their unique barcode sequence, which enables multiplexing. Only samples with different barcode sequences were pooled together. Next, Illumina dual-index barcodes were added to the pooled PCR products with the Nextera XT Index Kit . Indexed PCR products were purified and pooled into the equimolar concentration prior to paired-end sequencing with MiSeq Reagent Kit v3 (600 – cycle) (Illumina), following the manufacturer’s directions.2 , RNase-Free Water under the following conditions: initial denaturation at 95°C for 15 min, followed by 40 cycles consisting of denaturation at 95°C for 30 s, annealing at 56°C for 45 s and extension at 72°C for 60 s, with a final extension step at 72°C for 10 min. In the first clean up step, we increased the bead amounts to 35 μl per sample to match the input volume. Then the purified PCR products were diluted to an equimolar concentration and samples with different barcode sequences were pooled together. Next, Illumina dual-index barcodes were added to the pools with the Nextera XT Index Kit . Indexed PCR pools were purified and pooled into the equimolar concentration prior to paired-end sequencing with MiSeq Reagent Kit v3 (600 – cycle) (Illumina) following the manufacturer’s directions.The ITS1 Library was constructed in a similar way to the 16S Library, with the following modifications: PCR was performed with HotStarTaq Master Mix , 4 mM MgClde novo. A set of sequences representing OTUs was created, and taxonomy was assigned (using script: assign_taxonomy.py) to each sequence using the Greengenes database (v. gg_13_8_otus) and Uclust (v. 1.2.22q) (2 for fungi (input sequences were searched for against a BLAST database of pre-assigned reference sequences from UNITE). This process resulted in the OTU table in BIOM format with the singletons discarded. The bacterial OTU table was filtered for OTUs, with the number of sequences less than 0.005% of the total number of sequences  pipeline . 16S and1.2.22q)  for bactequences . The funequences . Alpha- p-values were adjusted according to the Benjamini–Hochberg procedure. p-values lower than 0.05 were considered significant. To characterize method specific composition profiles, “core microbiome” measurements were performed.Statistical analyses were performed using online Calypso software (version 8.82) . Data weTwo types of fecal material were used to compare five selected DNA extraction methods. To examine method yield, DNA was extracted from germ-free mice stool samples spiked with a known amount of clinical microbial cultures and processed using specific real-time PCR.Moreover, the human stool samples were independently analyzed for purity, integrity, yield and microbial composition to establish the most effective simultaneous bacterial and fungal DNA extraction protocol for downstream microbiome analysis .To compare the DNA extraction methods, an artificial system was constructed. The homogenized germ-free mice stool aliquots were spiked with verified clinical bacterial and/or fungal cultures, each in two defined concentrations differing in one to two orders of magnitude. Non-spiked stool aliquots were used as a baseline microbial load, and sterile water was used as a blank control. All extractions were performed in triplicates and compared using bacterial- and fungal-specific real-time PCR, resulting in 394 reactions.All methods varied in terms of technical reproducibility, but the variability among replicates was considerably lower than among the same samples extracted by different methods. Extraction from the low-concentrated (LC) and high-concentrated (HC) communities showed similar patterns . Howeverp < 0.001 and p < 0.05, respectively)  . Moreovectively) .C. albicans and A. fumigatus, selected as typical yeast and mold representatives. For both species, the IHMS and ZR methods provided the highest DNA yields consistently (p < 0.001) were produced by QIA and PL methods in both concentration levels. The QIA method produced higher DNA yields for C. albicans  across methods, with values lower than 1 copy . However, only the ZR method significantly differed (p < 0.05) from every other method, resulting in a higher fungal DNA yield. In addition, fungal DNA was also detected in the blank controls (n = 8/10) with values lower than 10 copies for four methods  and 100 copies for the ZR method (During n 1 copy . TherefoR method .p < 0.05). In addition, DNA extracted with the IHMS, PL, or NS methods were not contaminated with RNA, protein, or organic contents, while ZR extraction showed the lowest DNA purity  and chimera removal. These reads  were distributed into 60 bacterial taxa assigned at genus level (n = 1018) of sequencing reads. However, Acinetobacter reads (n = 943) detected in the ZR method’s blank control, were also reflected as increased relative abundance of Acinetobacter in the ZR method’s microbial composition profile (1.2% in ZR vs. 0.003% in others). The complete taxa list detected in the blank controls is shown in The pooled 15 samples  returned a total of 384579 us level . Only 21us level . The blap < 0.001; Bacterial alpha-diversity was similar between the IHMS, NS, PL, and ZR methods, while the QIA method showed the lowest rarefaction curve . Principp-values according to the Benjamini–Hochberg procedure. We found that the examined taxa  varied greatly between extraction methods. We detected significant differences (p < 0.05) in 19/52 taxa applying the Kruskal–Wallis test and 42/52 taxa applying the GLM test  were not assigned. Two taxa  with an average relative abundance of 99% were dominant in all extracted samples and the other 70 taxa,  varied between methods. Among them, a majority (n = 47) of the taxa were consistent between replicates  were analyzed for their ITS1 target. Chimeric and singleton sequence removal resulted in a total 483224 sequence reads. These reads  were distributed into 72 fungal taxa assigned at genus level. Only 0.06% of the total reads  were uniquely detected by only one method. This variable fraction was probably responsible for the observed alpha-diversity differences  were tested. We detected significant differences (p < 0.05) in 15/16 taxa by applying a GLM test, although there were no significant differences after applying a Kruskal–Wallis test , which represents a limitation in fungal diversity analyses, as only 1% influenced the outcome. Both taxa were previously detected in human stool samples , the presence of the reagents’ fungal contamination may be more common in mycobiome datasets than bacterial ones. It has been previously discussed that the bacterial contamination of kits/laboratory reagents may be an issue when analyzing a sample with low biomass , contrarOur understanding of the human gut microbiome role in health and disease depends on obtaining reliable and comparable microbial data of both bacterial and fungal communities. To achieve this goal, using an appropriate methodology is an important step. Previous efforts in standardizing methodology for bacterial microbiome research lead to the recommendation of using the IHMS protocol Q worldwide, to ensure data reproducibility and comparability. In this study, we evaluated and compared the impact of five different DNA extraction methods including the IHMS protocol Q, on the representation of fecal bacterial and fungal communities, with an emphasis on applying the method for use on both communities. In our experimental design, the evaluated DNA extraction methods significantly differed in the efficiency and quality of the isolated DNA, which affected the identified stool microbiome composition. We also discussed the impact of fungal contaminations revealed in kit reagents. Overall, based on the data obtained, we suggest using the DNA extraction protocol “IHMS protocol Q,” which is suitable for simultaneously analyzing both the bacterial and fungal gut community.This study was carried out in accordance with the recommendations of Committee of the Centre for Cardiovascular Surgery and Transplantation (Protocol no. 201603), with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by Committee of the Centre for Cardiovascular Surgery and Transplantation.KF and TF conceived the initial project design with inputs from EN and MC. KF performed all experiments and statistical analyses of the data with significant contributions from EN and HG. MR performed the bioinformatics analysis of the data. ML provided bioinformatics assistance. KF, HG, and TF wrote the manuscript. JB and DS significantly contributed to the final preparation of the manuscript. All authors revised and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To better understand the impact of seasonal influenza in pregnant women we analyzed data collected during four seasons at a hospital for acute respiratory infection that specializes in treating pregnant women.This was a single-center active surveillance study of women 15–44 years of age hospitalized for acute respiratory diseases between 2012/2013 and 2015/2016 in Moscow, Russian Federation. Women had to have been hospitalized within 7 days of the onset of symptoms. Swabs were taken within 48 h of admission, and influenza was detected by reverse transcription-polymerase chain reaction.p <  0.001). This pattern was homogenous across seasons . Influenza A(H1N1)pdm09 was the dominant strain in 2012/2013, A(H3N2) in 2013/2014, B/Yamagata lineage and A(H3N2) in 2014/2015, and A(H1N1)pdm09 in 2015/2016. Influenza-positive pregnant admissions went to the hospital sooner than influenza-negative pregnant admissions (p <  0.001). The risk of influenza increased by 2% with each year of age and was higher in women with underlying conditions . Pregnant women positive for influenza were homogeneously distributed by trimester . Frequencies of stillbirth, delivery, preterm delivery, and caesarean delivery did not significantly differ between influenza-positive and influenza-negative hospitalized pregnant women or between subtypes/lineages.During the four seasons, of the 1992 hospitalized women 1748 were pregnant. Laboratory-confirmed influenza was detected more frequently in pregnant women  than non-pregnant women   contains supplementary material, which is available to authorized users. Pregnant women are at increased risk of severe influenza illness and influenza-related death –3 and, dDue to small study populations and designs that limit interpretation, the real impact of influenza on pregnant women remains uncertain , 13, 14.The present investigation was conducted as part of the Global Influenza Hospital Surveillance Network (GIHSN) that aims to generate data on the impact of influenza virus infection during hospitalization. The GIHSN is an international collaboration launched in 2012 to improve understanding of influenza epidemiology and better inform public health policy decisions . The GIHSince 2012, the Federal Budget Institute of Health “Clinical Hospital for Infectious Diseases No. 1” (CHID#1) in Moscow, Russian Federation has participated in the GIHSN. CHID#1 is one of two reference hospitals for acute respiratory diseases in Moscow and specializes in treating pregnant women. Here, we analyzed data collected from CHID#1 during the four seasons since its inclusion in the GIHSN (2012/2013 to 2015/2016) to describe the epidemiology of influenza in hospitalized pregnant women, and evaluate the clinical symptoms and outcomes of influenza-associated acute respiratory illness in this population.This was a prospective, active surveillance hospital-based study conducted during the 2012/2013 to 2015/2016 influenza seasons at CHID#1 . CHID#1 is a unique, specialized department hospital for pregnant women with infectious diseases. The study was performed due to the study site’s participation in the GIHSN international influenza surveillance project and its use of the standardized GIHSN protocols ) or B/Yamagata lineage  . The adjusted probability of a positive result decreased 13%  for each day between onset of symptoms and swabbing . Time to swabbing differed significantly between subtypes/lineages, although the median was 2 days in all cases (Table The probability of laboratory-confirmed influenza decreased with the time between symptom onset and swabbing Table . The prop <  0.001), cough (p <  0.001), and myalgia (p <  0.001) were reported as presenting complaints more often in influenza-positive than influenza-negative pregnant admissions (Table p <  0.001)  Table . Proportp <  0.001) (Table p <  0.001).Hospital admission occurred sooner after the onset of symptoms in influenza-positive than influenza-negative pregnant admissions (1) Table . This wa1) Table . In contp = 0.001; data not shown). The median hospital stay also differed by subtype/lineage (Table p = 0.025 [data not shown]) or A(H1N1)pdm09  but did not differ for those infected with A(H3N2) .The overall length of hospital stay was not significantly longer for pregnant admissions positive for influenza than those negative for influenza Table ; howeverge Table  and was n = 923; 52.8%) followed by influenza  , and 23 stillbirths (Table p = 0.003), although the frequency did not significantly differ by subtype/lineage  is usually , but notInfluenza infection, confirmed by RT-PCR, accounted for nearly half of the admissions over the four influenza seasons. The risk of influenza infection was higher in pregnant than non-pregnant women , irrespective of subtype/lineage and trimester. This agrees with a larger multi-country study by the GIHSN during the 2012/2013 influenza season, which found an aOR of 3.84  for influenza-related hospitalization in pregnant vs. non-pregnant women . It alsoUnderlying conditions, especially anemia, obesity, and asthma, increase the risk of influenza-related hospitalization in pregnant women , 28. AltInfluenza-positive pregnant admissions were hospitalized sooner after the onset of symptoms and stayed slightly longer in the hospital than influenza-negative pregnant admissions. Pregnant admissions positive for influenza also more frequently complained of cough, myalgia, and especially fever than those who were negative for influenza. This suggests that influenza causes more severe illness in pregnant women than other kinds of acute respiratory infection.Influenza viruses varied substantially between seasons, although all subtypes and lineages resulted in hospitalization. In addition, demographics, clinical manifestations, and rates of stillbirth differed slightly between subtypes/lineages. For example, the risk of influenza infection increased slightly with the mother’s age. Also, admissions with influenza A(H1N1)pdm09 or B/Victoria lineage occurred mostly in the first or second trimester, whereas admissions with influenza A(H3N2) or B/Yamagata lineage occurred mostly in the second or third trimester. Although influenza B has been reported to be more frequent in the second trimester than influenza A , these rInfluenza, especially A(H1N1), is considered a risk for stillbirth and low birth weight , 30–32. These data confirm that pregnant women are at increased risk from seasonal influenza A and B viruses. With more than 1700 pregnant admissions, this study provides important and detailed information about the impact of influenza in pregnant women that can be used to inform and support vaccination policies in this susceptible population. Furthermore, our study provides pregnancy outcome data, which are rarely included in epidemiological studies of influenza in pregnant women, and have not been published before.However, this study had some limitations. The main analysis was based on comparing hospitalized influenza-positive with hospitalized influenza-negative pregnant women. We were unable to compare pregnant admissions to non-pregnant admissions because of insufficient numbers of non-pregnant women, who are more frequently admitted for influenza-like illness to other hospitals in Moscow. Nonetheless, the unique nature of the CHID#1 study site allowed us to recruit a substantial number of pregnant women. CHID#1 receives the most pregnant admissions from any of the hospitals in the GIHSN network . Another limitation was that we could not assess the long-term effects of influenza on pregnant women, or pregnancy outcome beyond the current admission, because data were collected only from women while they were hospitalized, and follow-up evaluations until the end of pregnancy were not within the study protocol. Finally, the study could have been limited by increasing rates of hospitalization for pregnant women following the 2009 pandemic due to increased awareness of the risks. However, this was probably accounted for by recruiting consecutive admissions without previous knowledge of influenza status. Furthermore, influenza positivity rates were similar over the four influenza seasons, suggesting that this was not a problem.Our results confirm that pregnant women are at increased risk from influenza infection irrespective of season, circulating viruses, or trimester. This supports recommendations by the World Health Organization  and manyAdditional file 1:a) trimester, (b-e) subtype/lineage, and (f) overall according to age group and presence of underlying conditions. Conditional plots examining interactions between trimester and patient age or presence of underlying conditions for the risk of admission with any influenza or with each subtype/lineage. (PDF 35 kb)Predicted probability of admission with influenza by (


In 22% (n=86), information was incomplete, most frequently affecting T stage , followed by N stage  and M stage . Full NSCLC-lesion segmentation required a median time of 13.3 min, while the median of the shortest estimator of the text-based reporting time (R1) was 18.1 min (p=0.01). Tumor stage , lesion size (p < 0.001), and lesion count  correlated significantly with the segmentation time, but not with the estimators of text-based reporting time. Numerous text-based reports are lacking staging information. A segmentation-based reporting approach tailored to the staging task improves report quality with manageable processing time and helps to avoid erroneous therapy decisions based on incomplete reports. Furthermore, segmented data may be used for multimedia enhancement and automatization.Results of PET/CT examinations are communicated as text-based reports which are frequently not fully structured. Incomplete or missing staging information can be a significant source of staging and treatment errors. We compared standard text-based reports to a manual full 3D-segmentation-based approach with respect to TNM completeness and processing time. TNM information was extracted retrospectively from 395 reports. Moreover, the RIS time stamps of these reports were analyzed. 2995 lesions using a set of 41 classification labels (TNM features + location) were manually segmented on the corresponding image data. Information content and processing time of reports and segmentations were compared using descriptive statistics and modelling. The TNM/UICC stage was mentioned explicitly in only 6% ( Non-small-cell lung cancer (NSCLC) is a common malignant tumor and the leading cause of cancer-related death worldwide . NSCLC iAccurate image-based staging is key for further diagnostic workup and therapy management. However, the discordance between preoperative staging using PET/CT and surgical pathology is considerable: according to Cerfolio and Bryant, approximately 32% of patients are preoperatively understaged . FurtherNext to the discordance as shortcoming of the current reading process, it can be argued that this process does not extract all potentially relevant information from imaging data. Despite being only partially reflected in the current staging system, factors like tumor burden are of great prognostic relevance for patients with NSCLC. Oh et al. have shown that, in patients with brain metastases, the overall survival is inversely correlated with the volume of all metastases . MoreoveThe aim of this study was to quantify the amount of TNM information missing in conventional text-based PET/CT reports for staging of NSCLC, to outline an implementation for structured, multimedia-enhanced segmentation-based reporting of imaging findings in NSCLC, and to compare this approach to conventional, text-based reporting in terms of staging accuracy and processing time.The local ethics committee approved this retrospective, observational study. All work was conducted in accordance with the Declaration of Helsinki (1964).From 1327 FDG-PET/CTs examinations that were performed with the ICD-10 diagnosis code C34 between 01/2008 and 12/2016, 395 were selected according to the inclusion criteria “histologically proven NSCLC” and “primary staging situation.” Exclusion criteria are listed in PET/CT examinations were performed on an integrated PET/CT system with 16-slice CT  from 01/2008 to 11/2015 and on a PET/CT with 128-slice CT  from 12/2015 to 12/2016. Before tracer injection, patients were fasting for at least 6 h. Scans were obtained 1 h after intravenous injection of 5 MBq FDG/kg body weight at glycaemic levels below 10 mmol/L. All text-based reports were created by a resident in nuclear medicine in daily clinical practice using electronic reports with findings structured by anatomic regions  and wereA dual-board-certified radiologist and nuclear medicine physician (G.S.) interpreted and extracted the TNM stage by analyzing the text-based reports for T (1–4), N (0–3), and M (0-1) descriptors or other text information that are stage defining without access to other clinical information or PET/CT images. It was also recorded whether the TNM or UICC stage was mentioned in the report explicitly. The descriptor was reported as missing when neither the TNM descriptor nor equivalent stage-defining information such as tumor size was found. From the extracted TNM, we derived the UICC (7th edition) stage.http://www.slicer.org, Boston, USA) [n=168) with 9 years' experience in PET/CT reading as well as a supervised radiology resident with 2 years of professional experience . Each lesion was segmented as a 3D volume defined by multiple 2D regions of interest (ROIs) that were drawn on contiguous transversal slices of the CT component of the dataset. Fused PET information was used in addition whenever the boundaries of a lesion were not clearly definable on CT.For each patient, the PET/CT image dataset was loaded to a 3D Slicer-based segmentation software  . This soOutput files were saved as JavaScript Object Notation (JSON) files, including time measurement registries and annotations . From these, TNM and UICC were automatically derived.For comparison, we focused on TNM/UICC stage as qualitative and on time as quantitative measures.The TNM information extracted from the text-based reports was analyzed for the frequency of missing information using Excel 2010 .The RIS timestamps were recorded since 05/2010 and registered in 393 of 395 cases. Since reporting time cannot be derived directly from RIS time entries, we used three timestamps for estimation: starting speech recognition, first saving, and saving for second reading. The consistency between RIS time entries and real-time was confirmed by testing 5 sample reports.R1. Cases in which no speech recognition was used (n=257) and registries >800 min  were excluded.As a lower estimator of the text-based reporting time, we defined the time between starting speech recognition and first saving as R2. Cases without speech recognition which were only saved once (n=47) and registries >800 min  were excluded.As an upper estimator of the text-based reporting time, we defined the time between the start of speech recognition or first saving until the saving for second reading as n=14239) (R1) and upper (R2) boundaries for verification. Using a Gaussian mixture model, we identified registries >800 min as outliers. Then, we developed a mathematical simulation using R  to differentiate interruptions from real reporting time (R1 and R2). This model was used to test the upper (R2) and lower estimators (R1). Further information including the R code can be found in Supplementary Materials modelling for reporting time estimation.To evaluate if these estimators are representative, we used all oncological PET/CTs from 05/2010 to 01/2018 (n=14239) . A modelSegmentation time per lesion was extracted from the JSON file. 99.6% (2995/3007) lesions were segmented in <175 min. 12 lesions segmented in >800 min were excluded as outliers. Registries were analyzed regarding reader, lesion count, TNM, and UICC. Statistically significant impact factors of segmentation time were tested on RIS time registries for comparison.rs) coefficient was used for ordinal  and Pearson's correlation coefficient (r) for interval-scaled data  to evaluate correlation. For linear models, we used ANOVA  to show significance. To evaluate multifactorial impact, we used automatic linear modelling in SPSS . To include impact factors, we used a 95% confidence level and Akaike information criterion (AIC). We used the Wilcoxon signed-rank test to compare two related samples such as dictation and segmentation time of the same patient. For differences in distribution, we used Mann–Whitney U test (U) for independent samples like reader dependency or incomplete versus complete reports and Kruskal–Wallis if there were more than two variables. To test normal distribution, Kolmogorov–Smirnov was used. The t-test was used to determine significant differences in normal distributed samples. P < 0.05 was set as the level for statistical significance.For descriptive statistics, median, arithmetic mean, and median test were used. For statistical analysis of segmentation, we pooled the data from readers 1 and 2. For outlier detection, we utilized mixture modelling with maximum likelihood estimation for RIS time registries. Spearman's rank correlation (n=395) comprised 28% female and 72% male patients with ages between 38 and 97 years (71.7 ± 10.5 years). An example of the annotation and segmentation process of NSCLC lesions is shown in Our NSCLC study population  as shown in n=22) and in 2% (n=9) for the M descriptor. In four cases (1%), TNM information was missing completely. An explicit mention of the absence of metastasis was present in 20% for nodal (n=80) and in 32% (n=126) for distant metastasis. A statement on the specific TNM or UICC stage was made in only 6% (n=22) of the text-based reports.Due to lack of information, TNM extraction was not possible for 86 out of 395 text-based reports (22%). Of these, the T stage was most frequently affected  and 151.6 min for R2 (n=248) (n=3700) for R1 as the lower benchmark and 146.1 min for R2 (n=7190) as the upper benchmark were found  but between the sampled and modeled R2s . UICC stage and lesion count were neither correlated with R1  nor with R2  (R1: 19.5 min) than text-based reports with no or incomplete TNM information (R1: 14.8 min).The reporting time of the extracted RIS reports was estimated from  (n=248) . To asse (n=248) . Here, are found . There wp=0.061) . Those tp=0.184) showed no significant difference, even if the differences in distribution show a slightly faster segmentation by reader 1 (U=22113p=0.002). The central tendencies regarding T (U=0.091p=0.927), N , and UICC  stages and age  do not differ significantly between both readers. M stage shows that reader 2 (36.6%) segmented more cases with distant metastases than reader 1 , which in part explains longer segmentation time periods. Results from both readers were used for further analysis.In contrast to the text-based reports, TNM and UICC could be defined readily in all cases by annotation and segmentation. Reader 1  required a median of 13.8 min, and reader 2  needed a median of 17.2 min per case. The median test , whereas time per lesion slightly decreased  .The time registries showed that segmentation-based staging was dependent on the lesion count and tumor stage. As the lesion count increased, the total segmentation time increased linearly (< 0.001) . AccordiF=13.0, p < 0.001) on average. In contrast to the T stage (average count: 1.7), average N (average count: 2.9) and M (average count: 5.6) lesion counts were higher. On the other hand, average diameters of N (12.2 ± 4.8 mm) and M (13.0 ± 4.6 mm) lesions were smaller. Subsequently, segmentation times per metastatic and nodal lesion were approximately half of T lesions . lesions  , N , and M  stages and thus also with the UICC stage . N  and M  stages have a greater impact on total staging time as the T stage , estimating 67.3% for N stage, 23.4% for M stage, and only 9.3% for T stage. A median of 5.1 min segmentation time was needed for UICC I/II versus 6.8 min for UICC III/IV per T stage .The total time for the segmentation correlated with the T  was not longer than for those with no or incomplete TNM information in the text-based reports (13.3 min for each group).R1 = 18.1 min) are neither correlated with the tumor stage nor lesion count.Our objective was to analyze the amount of TNM information missing in text-based PET/CT reports for staging of NSCLC and to compare this conventional reporting with a new segmentation and annotation approach of the total tumor burden. The most important findings can be summarized as follows: TNM stage was frequently missing in structured text-based PET/CT reports (22%). Annotated image segmentation always includes tumor stage and thus enhances the quality of the diagnosis. Segmentation time (median = 16.3 min) increases with the TNM and UICC stage as well as the lesion count, whilst text-based reporting times (lower boundary estimator Level 1: use of common headingsLevel 2: use of subheadings specifying organs or organ systems (“itemized”)Level 3: use of standardized language (“clickable”)Definitions and implementations of free text versus structured text reporting are currently under debate. According to Weiss et al., structured reporting can be divided into the following three steps :Level 1:Most guidelines for PET/CT suggest 3 principal style formats of reporting: order of importance, anatomic site, and hybrid . In our A first approach that might come to mind as a potential remedy is the introduction of level 3 structured reporting approaches. However, as direct links between the text and the image are missing, this approach offers limited options in terms of reporting tumor burden and communicating measurements. Therefore, according to Folio et al., the use of image-based annotated measurements in a standardized format would significantly improve the report quality even beyond the results of text-based structuring alone .However, as the time available per image becomes increasingly shorter  and incrR1 between the two groups are comparable, R2 of the samples differed significantly from the simulation. This suggests the lower benchmark (R1) to be more reliable because of the small difference between the sample and modelling. According to a web-based survey performed by Karantanis et al. [R1–R2. Overall, based on our data, the reference values published in literature, and from our own personal experience, it seems justified to estimate the reading time for a PET/CT exam in NSCLC in between 20 and 30 minutes. Interestingly, the time requirements for conventional text-based reporting in our analysis were independent of factors such as lesion number or TNM stage.To estimate the time required for normal text-based reporting as a reference value, we used a time stamp-based approach on the sample. Since time stamps only give a rough estimate of the true reporting time, we tried to fortify our estimate with a modeled timing based on an extensive sample of PET/CTs. While the median of s et al. , most PEs et al. . This isWe have analyzed factors that influence the time needed for segmentation. Here, the time required can be estimated by case complexity and is dependent on lesion number, tumor size, infiltration, and metastasis. The relevance of total tumor burden, expressed as total tumor volume or lesion count, for patient prognosis has been shown by several studies , 25, 26.Beyond that, segmentation enables a multitude of new applications. It goes without saying that these are neither limited to NSCLC as a disease entity nor to tumor staging as a diagnostic task. Full tumor segmentations may be used for staging, restaging, and follow-up assessment of various kinds of malignancies, e.g., lymphoma, breast cancer, and prostate cancer –30, and There are some limitations in our study. Evaluation of text-based RIS reports, collection of their reporting duration, and segmentation were retrospectively performed. Therefore, there was an unavoidable selection bias. In contrast to a prospective survey of real-time reporting, it was not possible to evaluate external factors and interruptions influencing the duration of a report. Since segmentation of each case was not performed by more than one reader, inter-reader agreement cannot be evaluated. However, given the fact that text-based reports were previously performed in clinical routine and served as basis for tumor segmentation, the variability is certainly lower compared to segmentation without clinical or radiological information. Although the median segmentation time of both readers was comparable, differences in distribution were found linked to slightly different patient groups and readers' experience. In addition, segmentations were performed with a manual approach and not using semi- or automatic PET or CT segmentation that can improve the objectivity of tumor volume measurements, e.g., in head and neck cancer . FurtherIn current text-based PET/CT reports, TNM staging information is frequently incomplete. Structured reporting with annotated image segmentation provides enhanced report quality with complete TNM information with manageable additional workload. Moreover, annotated image segmentation opens the door towards training artificial intelligence algorithms and better integration of imaging data in clinical workflows.


Demographic change concurrent with medical progress leads to an increasing number of elderly patients in intensive care units (ICUs). Antibacterial treatment is an important, often life-saving, aspect of intensive care but burdened by the associated antimicrobial resistance risk. Elderly patients are simultaneously at greater risk of infections and may be more restrictively treated because, generally, treatment intensity declines with age. We therefore described utilization of antibacterials in ICU patients older and younger than 80 years and examined differences in the intensity of antibacterial therapy between both groups.We analysed 17,464 valid admissions from the electronic patient data management system of our surgical ICU from April 2006 – October 2013. Antibacterial treatment rates were defined as days of treatment (exposed patient days) relative to patient days of ICU stay and calculated for old and young patients. Rates were compared in zero-inflated Poisson regression models adjusted for patients’ sex, mean SAPS II- and TISS-scores, and calendar years yielding adjusted rate ratios (aRRs). Rate ratios exceeding 1 represent higher rates in old patients reflecting greater treatment intensity in old compared to younger patients.Observed antibacterial treatment rates were lower in patients 80 years and older compared to younger patients . No difference in treatment intensity, however, was found from zero-inflated Poisson regression models permitting more adequate consideration of patient days with low treatment probability: for all antibacterials the adjusted rate ratio (aRR) was 1.02 (95%CI: 0.98–1.07). Treatment intensities were higher in elderly patients for penicillins (aRR 1.37 (95%CI: 1.26–1.48)), cephalosporins (aRR 1.20 (95%CI: 1.09–1.31)), carbapenems (aRR 1.35 (95%CI: 1.20–1.50)), fluoroquinolones (aRR 1.17 (95%CI: 1.05–1.30), and imidazoles (aRR 1.34 (95%CI: 1.23–1.46)).Elderly patients were generally less likely to be treated with antibacterials. This observation, however, did not persist in patients with comparable treatment probability. In these, antibacterial treatment intensity did not differ between younger and older ICU patients, for some antibacterial classes treatment intensity was even higher in the latter. Patient-level covariates are instrumental for a nuanced evaluation of age-effects in antibacterial treatment in the ICU.The online version of this article (10.1186/s12913-019-4204-0) contains supplementary material, which is available to authorized users. Severe infectious complications such as sepsis are common and increasing in critically ill patients in intensive care units (ICUs) , 2. ConcIn this study we therefore compared antibacterial therapy in adult ICU patients above and below 80 years, a cut-off applied previously for studies in this setting , 16, 18.The data used for this study were extracted from the patient data management system  of the Department of Anesthesiology and Surgical Intensive Care Medicine at University Hospital Mannheim, Germany. The same data source has been used previously . The avaThe referring department of each admission was extracted. We additionally analysed microbiological test results of blood cultures and bronchoalveolar lavages from all patients in the ICU between 26/03/2007 and 31/12/2013 to establish time trends in bacteria isolated on our ward. In excluding other samples, such as routine admission swabs, we focused on results with relevance for antibacterial treatment. Furthermore, the Charlson comorbidity score  for all Entries of antibacterials in the electronic medical record during follow-up were identified and categorized into antibacterial drug classes according to the WHO Anatomical Therapeutic Chemical Classification System (ATC). The following classes were administered: tetracyclines (J01AA12), penicillins , cephalosporins , carbapenems , macrolides , lincosamides (J01FF01), aminoglycosides , fluoroquinolones , glycopeptides (J01XA01), imidazoles (J01XD01), and oxazolidinones (J01XX08). In addition, some other antibacterials were administered which do not belong to one of the aforementioned classes (colistin (J01XB01), fosfomycin (J01XX01), daptomycin (J01XX09), and rifampicin (J04AB02)). The 368 records for these were only considered in analyses of all antibacterials.Antibacterial prescriptions represented individual entries of antibacterials in our electronic patient record. These comprised separate doses, e.g., infusions or flow rates of syringe pumps. A patient could therefore have multiple entries of the same antibacterial on a given day depending on how the daily dose was split up. Consequently, ranking and comparability of prescriptions between classes is limited. We therefore also analysed (2) antibacterial therapy days, defined as days in which antibacterial prescriptions of one or more particular classes were recorded. If a patient received antibacterials of two or more different drug classes in one calendar day, two or more therapy days were counted. Thus, this measure reflects both combination and change of antibacterial classes and mainly served for illustration of time trends in treatment complexity. Finally, we defined (3) exposed patient days as calendar days in which at least one antibacterial agent, irrespective of the class, was administered, i.e., at least one antibacterial prescription was recorded. This measure, relative to days of patient stay, allowed calculation of antibacterial exposure rates. A calendar day in which a patient stayed at least 1 min in the ICU was defined as a patient day. Consequently, a patient could have more antibacterial prescriptions than therapy days and more therapy days than exposed patient days.There are several possibilities to measure antibacterial exposure and consumption, all of which have their advantages and disadvantages –24. In aThese measures were determined for all patients and all antibacterials. In addition, all antibacterials excluding the macrolide erythromycin due to its nearly exclusive off-label use as a prokinetic agent in our ICU were quantified accordingly. Furthermore, exposed patient days were counted for all antibacterial drug classes separately. Macrolides were analysed both with and without erythromycin, with clarithromycin being the only other macrolide administered to our cohort. In the category ‘all antibiotics’ and ‘all antibiotics except erythromycin’ the abovementioned other antibacterials not belonging to one of the drug classes were also included.The respective age group was the main covariate of interest. In addition, we included the patient’s sex, the year of treatment, severity of the acute illness as expressed by the Simplified Acute Physiology Score II (SAPS II) , as wellThe statistical models were adjusted for the above-mentioned cofactors to minimize differences in disease severity or other characteristics impacting infection risk between the age groups that are potentially associated with antibacterial treatment . These cofactors mainly reflected acute disease severity and intensity of therapeutic measures received by the patient. The SAPS II-score, however, additionally considered metastatic malignant tumours, haematological malignancy and AIDS and thus also served as a rough surrogate for significant chronic comorbidity. Our models did not include the treatment indication, i.e., the type of infection and causative bacterial agent. They were not adjusted for individual chronic comorbid conditions affecting infection risk, such as diabetes, and immunocompromised states, e.g., due to drug therapy. The models also contained no information on body weight, smoking, or alcohol intake.2-tests respectively. We used standard Poisson regression to calculate the rates of exposed patient days (numerator of rate) per 100 patient days (denominator of rate) with 95% confidence intervals (95%CI) for all patients and for each age group separately. Furthermore, we calculated rate ratios for patients aged ≥80 compared to < 80 years of age yielding exposed patient days for patients ≥80 per 100 exposed patient days for patients < 80 by using zero-inflated Poisson regression. We chose the zero-inflation model since most patients of our cohort did not receive any antibacterial treatment and so had zero exposed patient days, i.e., there were excess zeros in the data.We examined differences between medians (due to skewed distributions) and frequencies for basic cohort parameters for all patients and both age groups with Mann-Whitney-U-tests and Pearson’s ChiThe rates and rate ratios comprise the whole study period and were calculated for all antibacterial drug classes, all drug classes except erythromycin and for each drug class separately. The rate ratios were both unadjusted and adjusted for the patients’ sex, mean SAPS II- and TISS-score, and year of treatment. As a sensitivity analysis the subgroup of patients with more than 48 h of ICU treatment was described and analysed in the same way, with erythromycin excluded from the exposure definition. The analyses were conducted with SAS 9.4 .p < 0.0001).Of 17,464 valid admissions to our ICU between April 2006 and October 2013 the majority were male (57.1%) and 2640 (15.1%) belonged to the age group of 80 and above , for which penicillins accounted for the most Table . This isIn patients 80 years and older the largest proportion was exposed to cephalosporins, imidazoles and penicillins, in the younger group the largest fraction of patients was exposed to cephalosporins, macrolides and imidazoles. Antibacterial prescriptions, however, were mostly recorded for penicillins and macrolides in both age groups. Penicillins, followed by imidazoles and macrolides, accounted for the most exposed patient days in elderly patients, whereas younger patients were by far most often exposed to macrolides, cephalosporins and carbapenems.Overall, in the cohort there were 38.70 (95%CI: 38.26–39.15) exposed patient days per 100 patient days , and 32.81 (95%CI: 32.41–33.22) exposed patient days per 100 patient days when excluding erythromycin  of patients treated for > 48 h received antibacterials within their first 48 h, a significant proportion of infections present on admission or soon thereafter is likely. Moreover, as our results reflect a single surgical ICU in Germany their generalizability is limited. Infectious disease incidence and infectious agents expectedly vary in other intensive care settings, such as neurological or cardiac surgery ICUs, and the difference between age groups may vary accordingly. In addition, antibacterial prescription practice can already differ between ICUs of the same country , 29. ConWhile a binary age classification facilitated a simple interpretation of the exposure rate ratios, in further studies a greater number of age subgroups should be investigated, e.g., as previously defined as young-old (65–74 years), old-old (75–84 years), and very-old (> 84 years) . MoreoveIn our surgical ICU elderly patients were less often treated with antibacterials than patients younger than 80 years. In those likely to be treated, however, intensity was at least similar in older compared to younger patients. These findings support differentiation between age as a factor for treatment decision and treatment implementation. This requires confirmation in studies from other surgical and non-surgical ICUs, which ideally should include data on treatment indication.Additional file 1:Figure S1. Illustration of antibacterial utilization measures. Table S1. Referring departments of the ICU cohort. Table S2. Most common microbiological isolates from blood cultures and bronchioalveolar lavages 2006–2013. Table S3. Basic characteristics of ICU patients with length of stay > 48 h, all and those treated with antibacterials excluding erythromycin. Table S4. Rate ratios for antibacterial classes (patients with length of stay > 48 h). (DOC 135 kb)