A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing. ConveIn this work, we propose a novel technique for classifying heavy metals using Mie scattering measurements associated with each ion's characteristic activity with different sizes of carboxylated polystyrene microparticles (PS-COOH). While superior specificity could be achieved through using functional groups specific to each heavy metal ion species, covalent conjugation of such functional groups to polystyrene particles is not always easy and may deprive the particles of their stability. Carboxylated particles, on the other hand, are readily available commercially with relatively low cost, and very stable for a long period. Based on our own observations, particles would not precipitate out from suspension for more than 1 year. The main objective of this work is an exploration of how well statistical inference methods could classify the heavy metal ion species in the water samples using non-specific assays, e.g. with carboxyl groups.Light scattering from these particles falls in the Mie scattering regime, as the wavelength of incident light  is comparable to the size of particles . Ion species of differing atomic mass and oxidation states are expected to interact characteristically with these carboxyl groups on differently sized microparticles. Furthermore, the extent of inter-particle aggregation is expected to vary by the heavy metal ion species and their concentrations. Such particle aggregation significantly alters the extent of Mie scattering, when measured at an optimized detection angle, as Mie scattering changes significantly over the particle size and scattering angle . We hypo2.2.1.−1). Lead nitrate [Pb(II)], zinc sulfate heptahydrate [Zn(II)], copper sulfate pentahydrate [Cu(II)], chromium trioxide [Cr(VI)], potassium chloride (K+) and magnesium chloride hexahydrate (Mg2+) were acquired from Sigma-Aldrich . A cadmium reference standard , nickel chloride hexahydrate [Ni(II)], cobalt chloride hexahydrate [Co(II)] and iron chloride hexahydrate [Fe(III)] were acquired from Thermo Fisher Scientific . Iron chloride tetrahydrate [Fe(II)] was acquired from Honeywell . From these stock solutions, assay solutions of 2000–2 ppb (ng l−1) were prepared through serial dilution in deionized water along with a separate blank sample (0 ppb). Surfactant free, spherical carboxylated polystyrene microspheres (PS-COOH) of diameters D = 0.91, 0.75 and 0.40 µm  were purchased from Magsphere, Inc. . These stocks were diluted to 0.03% w/v samples for all assays before being mixed with equal volumes of heavy metal ion solutions.Separate heavy metal stock solutions were prepared in deionized water at concentrations between 1000 and 300 ppm (mg l2.2.www.philiplaven.com/mieplot.htm) to determine appropriate forward scatter detection angles for each particle size, considering singlets (sphere diameter = D) and aggregates (sphere diameter ≈ 2D) [−4–10−5 particles/μm3 . Detection angle axes were established such that 90° corresponded to scattering perpendicular to the incident light source, 0° corresponded to the direction of forward transmitted light and 180° back to the incident light source.Mie scattering simulations were conducted using MiePlot v4.6  ,12. Thes2.3.+ or Mg2+; chosen from groups 1 and 2 from the periodic table considering their atomic mass and environmental availability) were prepared separately with each PS-COOH sample  in a 50 : 50 ratio. This resulted in assay samples containing 0.015% w/v PS-COOH and mixed separately with 1000, 100, 10, 1 and 0 ppb concentrations of each heavy metal species. Aliquots of 1.5 ml were placed in cleaned cylindrical glass cuvettes and inserted into a 3D printed cuvette holder designed in SolidWorks  and printed using a Makerbot Z18 3D printer . An incident light of 460 nm was supplied and reflection probes were fixed at the two forward scatter angles determined by the above simulations. Scattering intensities were measured using a USB4000 miniature spectrometer running OceanView version 1.4.1 software . Relative scattering intensity was calculated as I/I0 at each detection angle for each particle and metal species/concentration combination. I is the peak scattering intensity from the given 0.015% PS-COOH particles and metal solutions , and I0 is the same measurement from the particles and blank sample (0 ppb). Assays were conducted in triplicates, each time using different samples.Based on scattering simulations and empirical confirmation, the two angles offering the highest scattering peak distinct from the transmitted light for particle singlets and aggregates were used to fix the reflection probes during scattering detection. Assay samples containing single heavy metal species or single reference cation species  and Cu(II) ,14. The et al.  and Adam [et al. . Each mi2.5.k = 11 clusters). The resulting models’ performances were assessed by leave-one-out cross-validation. Following classification of the heavy metal and cation species, we developed separate classification models to predict concentration values. Independence of observations was assumed based on sampling structure. Assumptions of multivariate normality were evaluated graphically through quantile–quantile plots, while assumptions of multicollinearity and homoscedasticity were evaluated through pairwise correlation analysis (|r| < 0.8) and graphical analyses.Using the above scattering data under each particle size and associated detection angle condition, we developed explicit classification functions for each heavy metal and reference cation species across all concentrations through linear discriminant analysis (LDA) based on Mahalanobis distance criteria. These groupings were compared with those provided by K-medians and K-means cluster analyses following Euclidean distance criteria (t-distributed stochastic neighbour embedding (t-SNE) plots, a nonlinear dimension reduction method able to visualize/detect patterns that principal component analysis (PCA) may miss [We also used support vector machines (SVM), a supervised learning method, when categorizing each heavy metal species. For SVM, we divided the data into a training set containing two randomly selected replicates from each particle size and concentration, and a test set consisting of the remaining replicate for each particle size and concentration. The training data, complete with labels for each category, were used to create a classification model based on other parameters within the data. In the case of our data, the categories are the heavy metals, and the parameters on which our model is based are the particle refraction indices for the scattering angles. For test data subjected to SVM, we assessed the correct classification rate of predicted group to actual group using the resulting Cohen's kappa coefficient . Due to may miss .3.3.1.λ/10 and 10λ, or 50 nm and 100 µm for visible wavelengths). Over this range, scattering intensity oscillates as a function of scattering angle, as shown in Mie scattering assays were first conducted with single species of heavy metals mixed with the three sizes of PS-COOH particles, with measurements at two angles for each particle size – the first specific to particle singlets (i.e. non-aggregated) and the second specific to particle aggregates. Mie scattering describes the static light scattering regime for spherical particles in which particle diameter is approximately equal to wavelength (between approx. + and Mg2+) showed the least significant change. The assays using 0.75 µm and 0.91 µm particles showed a similar trend, although the results are different from each other for certain ion species, e.g. Zn(II), Ni(II), Fe(II), Fe(III) and control cations  due to differences in electronegativity and atomic size. Stronger electronegativity and bigger atomic size would lead to larger aggregations of carboxylated particles. While no singular distinguishing characteristic was immediately apparent from the raw scattering measurements, multivariate differences did emerge. These were primarily classified through LDA as well as SVM, described in the following section.Based on these initial analyses, the assays using 0.40 µm particles showed a general decrease in scattering with increasing heavy metals concentrations, at both 90° and 50° detection angles , top. Th cations . We spec3.2.Discriminant analysis is a generative modelling technique in which observations are classified according to an algorithm developed around pattern features extracted from multivariate data . In cont+  for Pb(II), Cd(II), Ni(II), Co(II), Fe(III) and K+ . This waAdditionally, SVM is a discriminative modelling technique where observations are classified according to their location relative to a decision boundary. In contrast with LDA, which is particularly useful for modelling linear relationships, SVM can be used to classify nonlinear data . SpecifiAn SVM model trained on two-thirds of all scattering data available at all angles for all particle sizes and concentrations was tested using the remaining one-third of the dataset . We founThe LDA and SVM models were also compared with K-means and K-medians cluster analyses. K-means and K-medians cluster analysis routines are similar to LDA in that they group observations based on iterative partitioning that minimizes some distance metric between each observation and the centroid of some cluster in the sample space. For K-means and K-medians clustering, these centroids are the cluster means and medians, respectively. Conversely, SVM serves as a classification method rather than a clustering method. From multiple distributions, SVM serves to identify which distribution a new data point lies within.+  and Co(II), followed by Pb(II), Ni(II), Fe(II) and Fe(III) . A simil+ .3.3.+ and Mg2+ excluded) and four concentrations . Results are summarized in In addition to classifying species, SVM analysis was used to classify the concentration of single-species heavy metal ions in solution. An SVM model was created such that a class was created for every heavy metal ion and concentration. This resulted in 36 different potential classification classes from nine heavy metal ions (K3.4.t-distributed stochastic neighbor embedding (t-SNE) plots were produced  containia). It is also noted that as the relative ratio of the concentration of Pb(II) to Cu(II) increases from the ‘Pipe 2’ and ‘Pipe 1’ mixtures that the grouping of the ‘Pipe 1’ mixture grows nearer to the 100 ppb Pb(II) grouping. While SVM demonstrated about the same classification accuracy for Pb(II) and Cu(II), the scattering intensities with Pb(II) decreased more substantially than those with Cu(II) as shown in The t-SNE plot displayed more prevalent interaction between the carboxylated polystyrene particles and Pb(II) for the ‘Pipe 1’ and ‘Pipe 2’ mixtures. This is indicated by the location of the ‘Pipe 1’ and ‘Pipe 2’ mixture groups with respect to the 100 ppb Pb(II) and 1000 ppb Pb(II) grouping compared to that of the 100 ppb Cu(II) and 1000 ppb Cu(II) grouping a. It is et al. [et al. [To better identify multiple heavy metal ion species found in environmental water samples, it may be necessary to expand this method to mixtures of more than three heavy metal ion species. However, the amount of data that would need to be collected to develop such a multiplexed model would be extraordinarily high, requiring extensive labour and resources. For most groundwater and drinking water samples, however, as few as one or two major heavy metal ion species are often observed, as we have simulated in this study (Pb(II) and Cu(II) in pipe leaching and Cd(II) and Ni(II) in subsurface mine tailing models). This is true for other types of groundwater or drinking water samples. Taghipour et al.  reported [et al.  reported4.In conclusion, support vector machine and discriminant analyses indicate that there is a distinguishable difference in the scattering characteristics between different heavy metal ions and control cations. Among these, the most characteristically distinguishable heavy metal ions are Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II), and Fe(III). These findings suggest that the sorption of heavy metals on simple carboxylated polystyrene microparticles differs somewhat between species, particularly for Pb(II), Cd(II), Ni(II) and Co(II), as well as for Fe(II) and Fe(III). These differences may be increasingly distinguishable between an array of particle sizes and detection angles, even among relatively small data sets. With increasing data sets, measurements of additional parameters (e.g. sample conductivity) and a more complex chemical binding group, improved classification of these heavy metal ion species may be possible in samples with diverse mixtures. In addition, it may also be possible to identify heavy metal ion species in real water systems, i.e. with co-existing cations.


LYST/CHS1 was cloned and identified in 1996, which showed significant homology to other species such as bovine and mouse. To date, 74 pathogenic or likely pathogenic mutations had been reported.Chediak-Higashi Syndrome (CHS) is a rare autosomal recessive disease caused by loss of function of the lysosomal trafficking regulator protein. The causative gene LYST gene, which was identified in a 4-year-old female patient. The patient showed skin hypopigmentation, sensitivity to light, mild splenomegaly and reduction of platelets in clinical examination. Giant intracytoplasmic inclusions were observed in the bone marrow examination, suggesting the diagnosis of CHS. Amplicon sequencing was performed to detect pathogenic mutation in LYST gene. The result was confirmed by two-generation pedigree analysis base on sanger sequencing.Here we describe a compound heterozygote in LYST gene, consisting of a missense mutation c.5719A > G and an intron mutation c.4863-4G > A, was identified from the patient by using amplicon sequencing. The missense mutation is reported for the first time. Two-generation pedigree analysis showed these two mutations were inherited from the patient’s parents, respectively. Our result demonstrated that amplicon sequencing has great potential for accelerating and improving the diagnosis of rare genetic diseases.A compound heterozygote in  Chediak-Higashi Syndrome  rare but lethal autosomal recessive disorder characterized by multiple clinical features, including hypopigmentation of skin and hair, reduction of platelets and leukocytes, abnormal organelles in circulating granulated cells, and neurological dysfunction . PatientLYST gene (HGNC:1968), consisting of 53 exons with a mRNA transcript of 13,503 bp, was identified to be responsible for this disease [LYST gene is required for sorting endosomal resident proteins into late multivesicular endosomes [LYST gene had been reported  and a intron mutation NC_000001.10:g.235945391C > T (c.4863-4G > A), which was identified in a 4-year-old female patient with CHS. Mutations in LYST gene were detected by using amplicon sequencing within 24 h. We also performed sanger sequencing to confirm the result and identify the source of mutations in the patient’s family. A typical autosomal recessive inheritance pattern was demonstrated by our data. Our finding expands the spectrum of pathogenic mutations in LYST gene and indicates an efficient and accurate approach for diagnosis of genetic diseases.Here we describe a compound heterozygote in The 4-year-old female patient was admitted to Children’s Hospital Affiliated to Zhengzhou University with recurrent upper respiratory infections last for 2 months. Antibiotic treatment had been taken but without any improvement. Patient and her parents are all Han Chinese from Henan province of China, no family history was noticed in records. The study was approved by the ethics committee of Children’s Hospital Affiliated to Zhengzhou University. A written informed consent for publishing data and images was obtained from the parents before performing genetic test.9/L) and white blood cell , the red blood cell was normal . Leukocytes with giant intracytoplasmic inclusions were observed with peripheral smear, which evidenced by bone marrow aspirate , details was described in Additional file A customized panel covering all 53 exons and 10 bp of padding region of LYST gene, resulting an amino acid sequence change of I1907V, is reported for the first time and predicted to be damaging for protein function by SIFT. The result was confirmed by sanger sequencing. Two-generation pedigree analysis was performed, targeting these two mutations. A typical autosomal recessive inheritance pattern was demonstrated, the missense mutation c.5719A > G was inherited from patient’s mother, the other was from father  and a intron mutation NC_000001.10:g.235945391C > T (c.4863-4G > A) . 400 μL of plasma from the patient and her father was used to extract protein, then tested according to manufacturer’s protocol. The result showed that normal LYST protein is undetectable in plasma of the patient, proving the loss of function of LYST protein Fig. .Fig. 3ELYST gene identified from a 4-year-old female patient in China, who was diagnosed with CHS based on clinical manifestations including hypopigmentation of skin, hepatomegaly, reduction of PLT and WBC, and leukocytes with giant intracytoplasmic inclusions. Genetic test showed two heterozygous mutations were inherited from her father and mother, respectively. The girl was treated with enhanced anti-infective therapy and liver protection, the clinical manifestation was stabilized. Further treatment including HSCT will be evaluated based on conditions of prognosis.Here we described a compound heterozygote in LYST gene had been reported in ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/) , Griscelli syndrome  and Familial hemophagocytic lymphohistiocytosis . Amplicon sequencing based on ultrahigh-efficient multiplex PCR provides an approach for detecting mutations from targeted regions on genome. The whole process of DNA preparing, sequencing and data analyzing could be finished in 24 h, which could improve the efficiency in the clinical laboratories.LYST gene was identified from a 4-year-old female patient with CHS, a novel missense mutation was included. The result was confirmed by sanger sequencing. The source of mutations was investigated by two-generation pedigree analysis. Our data expands the spectrum of pathogenic mutations in LYST gene, demonstrates the value of amplicon sequencing as an efficient and accurate approach for diagnosis of genetic diseases.In summary, a compound heterozygote in Additional file 1: Functional prediction of mutations performed by HSF 3.1, including the novel missense LYST:c.5719A > G, the known SNP rs201382097 (LYST:c.4863-4G > A), and two mutations in adjacent sites of rs201382097, which referred to LYST:c.4863-3 T > A and LYST:c.4863-5 T > A.Additional file 2: Table S1. Information of custom-designed amplicon sequencing panel for CHS and related disorders.Additional file 3: Table S2. Global distribution of the mutation LYST:c.4863-4G>A (rs201382097) in gnomAD database.Additional file 4: Table S3. Global distribution of the mutation LYST:c.4863-4G>A (rs201382097) in 1000 genome database.Additional file 5: Table S4. Reported pathogenic and likely pathogenic mutations in the LYST gene from the ClinVar database.


Since January 2016, a resurgence of measles in Romania has led to the third measles epidemic in the past 12 years; 64 deaths have been confirmed so far–the highest number of measles-related deaths since the measles-mumps-rubella (MMR) vaccine was introduced in 2004.To provide an overview on the characterisation on measles in Romania after the introduction of the MMR vaccine with focus on the current outbreak, laboratory and molecular analysis.We performed an analysis of measles incidence and mortality after the introduction of MMR vaccination and a retrospective study using serological and molecular data in three consecutive outbreaks with focus on the current outbreak.In the current outbreak, 17,533 measles cases were notified to the national surveillance system, 93% were unvaccinated. Measles virus was isolated from 429 samples and 283 were genotyped. Genotype B3 was predominant (n = 269) and sporadic measles cases associated with D8 genotype (n = 9) were also observed; genotype D4 and D8 were identified in the previous two measles outbreaks. The detection of several distinct measles virus B3 genotypes suggests multiple virus importations to Romania.The current outbreak is a consequence of insufficient vaccine coverage. Control measures were implemented to improve uptake of MMR vaccine, including administering the first MMR dose at a younger age (9–11 months) and offering catch-up vaccination to children that have not followed the recommended dosing schedule. More measures are needed to improve the surveillance performance and to achieve high routine MMR vaccination coverage. Measles is a highly contagious respiratory infection, a viral disease capable of causing epidemics. Unimmunised children and susceptible adults can get severe infection and die from measles due to complications such as pneumonitis and encephalitis. A rare complication of measles is subacute sclerosing panencephalitis (SSPE) which may occur many years after the primary infection, due to persistence of the virus in the central nervous system .In 2012, the World Health Organization (WHO) set a target for measles to be eliminated by 2020 in all WHO Regions. Of 53 countries in the WHO European Region, 43 have interrupted measles endemic transmission as at the end of 2016 [Prior to the introduction of a measles vaccination policy in May 1979 , Romania had 60,000–150,000 reported measles cases annually . BetweenFollowing the introduction of measles vaccination in 1979, measles epidemics have occurred in 1982, 1986, 1993, 1997, 2003, 2010 and 2016 in Romania, but number of cases and measles-related deaths is much less in comparison to epidemics in the pre-vaccination era .Information about the genotype and virus variant (named strain) is becoming increasingly important to discriminate between endemic and imported virus variants, wild-type and vaccine strains (in recently vaccinated persons) and to track transmission routes as part of routine surveillance . Here, wMeasles is a notifiable disease in Romania requiring physicians and laboratories to immediately notify local public health units of all probable and confirmed cases, according to the national strategy of measles surveillance, approved by Romanian Ministry of Health. Case-based surveillance of measles is conducted continuously and based on the European Union case definition .BoxPossible case: A person with fever and maculopapular rash and at least one of the following symptoms: cough, coryza, or conjunctivitisConfirmed case: A case that was not recently vaccinated and meets the clinical definition and the laboratory criteria:- Detecting measles IgM antibody in serum samples; or- Isolation of measles virus from a clinical specimen; or- Detection of measles-virus specific nucleic acid from a clinical specimen using PCR; or- IgG seroconversion or a significant rise in measles IgG antibody Probable case: A case who met the clinical case definition with an epidemiological link to a laboratory-confirmed case (contact with a confirmed measles case).Blood samples were collected from possible measles cases and tested in the National Measles and Rubella Reference Laboratory in Romania based in Cantacuzino National Military Medical Institute for Research and Development, Regional Laboratories, Iasi, Cluj, Timisoara and Bucharest and in laboratories of infectious diseases hospitals around Romania. All laboratory-confirmed cases are reported to the surveillance system located at National Institute of Public Health, National Centre for Communicable Diseases Surveillance and Control (Bucharest).Throat swabs collected in viral transport medium (VTM), urine, necropsy samples and cerebrospinal fluid (CSF) were transferred for virological testing in Cantacuzino Institute laboratory.Detection of specific anti-measles virus IgM antibodies by enzyme-linked immunosorbent assay (ELISA) in serum is the reference standard for the confirmation of measles. Serum samples were tested using Measles virus IgM micro-capture ELISA  or Enzygnost Anti-Measles Virus/IgM antibody enzyme immunoassay  according to the manufacturer’s instructions. All serum samples with negative results were tested for the rubella-specific IgM using Rubella virus IgM micro-capture ELISA . Measles infection was confirmed when anti-measles IgM antibodies were present. In the event of an equivocal result, a second serum or swab/oral fluid was requested to ascertain seroconversion or to identify the measles virus.Clinical specimens such as swabs, urine, cerebrospinal fluid (CSF), saliva and lung fragments were used for viral RNA extraction directly from 150 μl of sample using the Nucleospin Viral RNA kit  according to the manufacturer’s instructions. Each RNA sample was stored at − 70 °C until amplification by real-time reverse transcription PCR (qRT-PCR). Sequencing of the 450 nt of the C-terminal region of the measles virus nucleoprotein gene (N-450) was attempted to determine the genotype by using the one-step RT-PCR kit according to manufacturer's protocol  .Confirmed measles cases from new small (< 10 cases) outbreaks (index case and two or three secondary cases) with a history of travel abroad during the incubation period (7–21 days) were selected for genotyping; severe cases were also genotyped. To sequence the DNA templates, the PRISM BigDye Terminator v3.1 Ready Reaction Cycle Sequencing kit ) was used on a PRISM 3100-Avant Genetic Analyzer (Applied Biosystems).N-450 sequences were aligned against related sequences retrieved from GenBank database using the BioEdit Sequence alignment Editor. MEGA-X software was used for constructing a Maximum Likelihood Tree using the Tamura-Nei evolutionary model with neighbour-joining using 100 bootstrap replicates. The phylogenetic relationship between virus variants was taken into account when transmission chains were analysed in the European context. The sequences reported in this study have been deposited into the Measles Nt Surveillance database (MeaNS), a tool used to track measles sequence diversity and monitor elimination of virus strains  and GenBOverall, between 1 January 2016 and 1 July 2019, 17,533 cases of measles were notified to the national surveillance system corresponding to an annual incidence of 122.07 per one million population in 2016 increasing to 459.4/million in 2017. From the notified cases, 9,673 were laboratory-confirmed cases and 7,860 were probable cases.Of the 17,533 cases reported, 77%  were among individuals who were eligible for vaccination but were incompletely vaccinated and 23%  had an immunisation status considered ‘up to date for age’ with the vast majority being infants < 1 year  i.e. they were too young to receive their first dose of MMR vaccine or they were adults born before 1979 when the measles-containing vaccine was introduced (n = 371). Infants and young children aged 9 years or less were the most affected age group . Cases were reported in patients aged 3 weeks–59 years (median age: 9 years) .Cases have been reported in all 42 districts of Romania with the districts of Timis , Caras Severin , Arad , Satu Mare (n = 951) and Brasov (n = 867), situated in the west and centre of Romania, reported the highest number of cases .The 2004–07 measles outbreak resulted in 23 fatalities; four cases were registered in 2010–13 . Measles-related deaths were recorded in 64 cases in the 2016–19 outbreak , corresponding to a case fatality ratio (CFR) of 0.4 per 100 measles cases. The deaths occurred as a consequence of severe measles complications; 41 cases (93.3%) had acute pneumonia and four cases (6.7%) suffered acute encephalitis. Fourty cases had a pre-existing medical condition including: anaemia (n = 8), malnutrition (n = 7), cardiac diseases (n = 4), spastic tetra-paresis (n = 4), immunosuppressed (n = 3), born prematurely (n = 2), Down syndrome (n = 1), Niemann Pick disease (n = 1), cystic fibrosis (n = 1), seizure syndrome (n = 1), granulomatous disease (n = 1), pulmonary tuberculosis under treatment (n = 1), intellectual disability , but 24 cases recorded no pre-existing medical condition.All but one fatal case occurred in unvaccinated patients who were aged 1 year or less (n = 45); the exception had hypotrophy and anaemia (aged 4 years) and had been vaccinated with one dose of MMR as per the national scheme.The median age at death was 8 months (range: 21 days–41 years). The most affected counties were Timis (n = 9), Dolj (n = 7), Arad (n = 6), Constanta (n = 5), Iasi (n= 5), Bacau (n = 4), Neamt (n = 4) and Caras Severin (n = 3).From January 2016 to July 2019, 11,713 serum samples collected from possible measles cases were tested, 9,596 (82.5%) of which were IgM positive, 67 with equivocal results (0.6%). Of cases with IgM equivocal results, swab samples were available from 59 all of which were positive for measles virus. A second serum sample was requested from eight cases and their ELISA test results were positive for specific IgM.All 2,040 (16.9%) negative serum samples were tested for the presence of IgM specific for rubella, none of them was positive. All serum samples collected from possible rubella cases (n = 169) were tested for measles IgM, 10 were positive. The initially laboratory-confirmed cases reported no history of travel abroad.For the viral detection, 578 specimens were collected within 6 days of rash onset. The viral RNA was extracted directly from 429 samples . The rest of 149 specimens were negative for the presence of measles virus. The genotype was determined for 283 selected samples  of which 278 showed a wild-type virus with genotype B3 (n = 269) and D8 (n = 9). Five sequences matched a vaccine strain (A-vaccine), being associated with reactions to recent vaccination rather than measles cases. The serological and molecular results were compared with the laboratory results from the previous two measles epidemics after the introduction of MMR vaccination in Romania in 2004 .The two measles outbreaks 2004–07 and 2010–13 were mostly caused by measles strains of genotype D4. In 2004, the outbreak was associated with the strains D4-Bucharest and D4-Hamburg. The MV variant D4-Manchester and sub-variant MVs/Maramures.ROU/3.11 caused prolonged outbreaks associated with secondary spread that resulted in additional outbreaks in 2010–13 (data not shown).Between January 2016 and July 2019, 95% (n = 269) of the sequenced samples belonged to genotype B3.Phylogenetic analysis of these strains showed that the sequences were divergent, with distinct lineages within Romania being identifiable. The first sporadic case identified in the most recent outbreak was detected in January 2016 from a patient with no history of travel aboard (MVs/Olt.ROU/3.16). The sequence is 100% identical to the named strain MVs/Como.ITA/32.15 and sequences from Berlin (Germany) and Agrigendo, Parma   and did A Romanian measles lineage has 42 sequences with 100% identity to the named strain MVs/Niger.NGA/8.13 and a sequence from Coesfeld, Germany . The first sequence from this lineage was obtained in Bistrita Nasaud County in February 2016 (MVs/Bistrita-Nasaud.ROU/8.16) and initiated a small outbreak in the region with 40 cases. The same virus seeded outbreaks in other counties i.e. Iasi, Mures, Constanta, Dolj, Olt, Bucuresti, Calarasi and Teleorman. Variants divergent by one nt were detected from sporadic cases: MVs/Alba.ROU/9.17, MVs/Dolj.ROU/14.17, MVs/Prahova.ROU/19.17 and MVs/Brasov.ROU/37.17. Seventeen identical sequences divergent by one nt evolved into a different lineage from MVs/Niger.NGA/8.13 and were linked to measles outbreaks in Dambovita county and Bucuresti.Another lineage with 93 sequences identical to the named strain MVs/Dublin.IRL/8.16 (KY013331.1) and sequences from Belarus, China, Germany, Italy, Serbia and the US was identified. Some cases were divergent from this lineage by one nt: MVs/Arad.ROU/24.17, MVs/Cluj.ROU/28.17 and MVs/Iasi.ROU/20.18. The strains responsible for outbreaks in the counties of Arges (MVs/Arges.ROU/21.17) and Dolj (MVs/Dolj.ROU/23.17) diverged from the Dublin named strain by two nt. Two sporadic cases with identical N-450 sequences divergent from this third lineage by one nt were also identified in week 3 and 24 in 2017 . Identical sequences were also found in Germany .The third lineage contains 83 sequences identical with named strain MVs/Saint-Denis.FRA/36.17 and sequences from Belgium, the Netherlands, Sweden and the US, . Variants divergent from this strain by a single nt were identified in Vrancea (MVs/Vrancea.ROU/17.18), Prahova (MVs/Prahova.ROU/16.18), Calarasi , Arges (MVs/Arges.ROU/10.18), Ilfov (MVs/Ilfov.ROU/11.18) counties and Bucuresti (MVs/Bucuresti.ROU/19.18) .A sporadic case with measles genotype B3 was also detected in April 2011 in Calarasi County , the case had travelled to Spain. Phylogenetic analysis of this case revealed 100% identity with MVs/Barcelona.ESP/48.10 detected between 2010 and 2011 in Spain. Another case with an identical sequence (MVs/Tulcea.ROU/41.11) was detected 25 weeks later in the neighbouring Tulcea County and apparently did not spread further .Nine of 283 samples collected in the period January 2016–July 2019 belonged to measles genotype D8. The first patient infected with this virus (MVs/Prahova.ROU/18.17/2) had a history of travel to Germany during the incubation period (7-21 days). The transmission pathway could not be traced. Basic Local Alignment Search Tool analysis of the N-450 sequence showed that it was 100% identical to a sequence from Australia . Another two sequences with the same genotype were identified at patients from Bihor County with a history travel in France (MVs/Bihor.ROU/28.18); identical sequences were identified in week 5 (MVs/Maramures.ROU/5.19) and week 16  in 2019. Other four sequences different by one nucleotide were identified in Suceava and Mehedinti Counties , 100% identical with sequences from Amsterdam, Hungary, India, Nederland and US. The phylogenetic analysis showed that the sequences are identical with other public sequences from Germany, Netherland and the US. The genotype D8 was also detected in Romania in November 2011 in Buzau County (MVs/Buzau.ROU/46.11), but no secondary cases were detected. Other measles cases associated with this genotype were detected in 2012 in Gorj, Olt and Suceava counties and included a fatal case: MVs/Suceava.ROU/41.12. These strains belonged to the variant D8-Frankfurt-Main (MVs/Frankfurt Main.DEU/17.11) .Since 2016, Romania has been facing a measles outbreak with three distinct lineages of B3 measles genotype and sporadic D8 genotype being identified. No index case was identified. The molecular epidemiology suggests that there are different chains of transmission. The outbreak occurred primarily among unvaccinated or partially vaccinated individuals ; only 1.2% of cases were in individuals who had received two doses of MMR vaccine and no deaths were reported in the latter group. Children have been most affected by the outbreak, with more than 80% of cases being aged 15 years or less.We showed that despite implemented and ongoing control measures on a national level e.g. lowering the age of first dose of MMR to 9–11 months of age and supplementary vaccination activities, Romania still ranks high among countries with endemic transmission of measles infection. Since the introduction of the MMR vaccine in 2004, epidemics of measles have still occurred every 2–4 years and while in the current outbreak the western part of Romania has been most affected, in the measles outbreak 2010–13 the eastern region was most affected [Changes in the main circulating strains from D4 to D8 and now B3, reflect the reported genotypes in Europe ,17-23. TTo reduce mortality from measles, several strategies have been implemented relating to the vaccination programme in Romania. However, the vaccination coverage remains under 90% allowing for continued measles transmission within the country and the potential for spread to other countries with suboptimal vaccination coverage. A survey of parents conducted in 2015 found that the most common reasons for them not to vaccinate their children were failure to attend a healthcare clinic and refusal of vaccines based on safety concerns – despite the historic and strong evidence of safety and efficacy of vaccines against measles ,25. The Measles transmission is difficult to assess through epidemiological information alone as on average, one third of cases have no identifiable link to another case . PCR-basDuring the ongoing outbreak in Romania, the measles has resulted in a higher number of deaths than any other vaccine-preventable disease among children. The suboptimal MMR coverage rate has led to the accumulation of susceptible individuals. The use of timely genotyping, epidemiological field investigation  to determine the source of infection could lead to a better understanding of the gaps in national surveillance, the root cause(s) of measles outbreaks, how future outbreaks can be prevented in order to reach the goal of measles elimination. In the absence of a travel history or an epidemiological link between most cases, extended genotyping could be used to seek additional evidence about possible chains of transmission to improve measles surveillance.


In China, the most populous developing country in the world, dementia represents a serious challenge. We performed a large-scale systematic review and meta-regression analysis to elucidate the prevalence of dementia and its subtypes and to identify potential factors underlying the differences between articles.A comprehensive literature search was conducted in the following databases to identify studies published up to December 2015: Cochrane Library, CBMDISK, Chongqing VIP, CNKI, PubMed and EMBASE. All statistical analyses  were performed with R version 3.3.3.In total, 51 surveys were selected. The pooled prevalence rates of dementia and its main subtypes, namely, Alzheimer’s disease (AD) and vascular dementia (VAD), for the population aged 55 years and older were 4.03, 2.44 and 1.09%, respectively. The outcomes showed that the meta-regression analysis was affected by the publication year, sample size, region and diagnostic criteria.Our analysis provided reliable estimates of the prevalence of dementia/ AD/ VD over the past 30 years, which may be affected by education level, and diagnostic criteria. The prevalence of AD/VAD was higher in northern than in southern China, which warrants further study.The online version of this article (10.1186/s12889-019-6840-z) contains supplementary material, which is available to authorized users. Dementia is a neurological condition that mostly occurs in individuals older than 60 years and has become a significant global problem in most developed and developing countries due to population ageing. Dementia can diminish a patient’s quality of life and lead to high healthcare costs. A recent meta-analysis published by Fiest et al.  reportedTwo reviewers independently searched the Cochrane Library, CBMDISK, Chongqing VIP, CNKI, PubMed and EMBASE databases up to December 2015 using ‘Alzheimer’s disease’ or ‘AD’, ‘vascular dementia’ or ‘VAD’, ‘dementia’, and ‘prevalence’ or ‘associated risk’ or ‘infection status’ or ‘epidemic status’ or surveillance’ as the major search terms. There were no language limitations, but the study did not include epidemiological studies in the areas of Hong Kong, Macao and Taiwan because these data were not easily accessible. In addition, the authors screened the reference lists of the identified articles and corresponded with the study investigators using the approach recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.The inclusion criteria were as follows: (i) case collection based on a field survey; (ii) studies published in peer-reviewed journals; (iii) the population in the investigations was 55 years and older; ; and (iv) used a validated method to assess the dementia diagnosis (through both a screening phase and a diagnostic phase). The exclusion criteria were as follows: (i) studies that used the Newcastle-Ottawa scale (NOS); (ii) articles with repeated cases; (iii) articles of inferior quality or with incomplete information; and (iv) unavailable articles .In the screening stage, all samples were screened using brief cognitive tests, including the Mini-Mental State Examination (MMSE), the Blessed Dementia Scale (BDS) and the Hasegawa Dementia Scale (HDS). Positive screens were identified when the samples scored below the cut-off point on one or more tests or were clinically diagnosed with dementia. In the diagnosis stage, positive screens were further diagnosed by experienced physicians with the assistance of laboratory and neuropsychological tests.The clinical diagnostic criteria used in the 50 studies included the DSM-III  , DSM-IIIThe detailed information extracted from the articles included the following: (i) basic information, including the author, type of publication and publication year; (ii) study design and tools, such as screening methods and tools, diagnostic criteria and sampling methods; (iii) characteristics of the study population, including age, location, occupation and education; and (iv) the prevalence of dementia and its subtypes.Full manuscripts of the potentially acceptable studies were screened by two reviewers. The same two authors independently assessed the risk of bias of these nonrandomized studies using a modified version of the Newcastle-Ottawa Scale (NOS), which assesses the quality of nonrandomized studies based on design, content and ease of use to facilitate the task of incorporating quality assessments into the interpretation of meta-analytic results. Disagreements were resolved via consensus after discussion with a third author.p values < 0.05 were considered significant. A p value of Cochrane’s Q test < 0.1 or an I2 > 50% was defined as indicating significant heterogeneity between studies. Due to the high heterogeneity observed (I2 > 75%), random effects models were used for the pooled statistical analyses. To identify factors that influenced the prevalence of dementia and its subtypes (AD and VAD), the characteristics of the cases, including gender, age, occupation, educations, and residence, were divided into diverse groups for analysis. We also performed subgroup analyses based on the specific region or publication year of the study. Publication bias was estimated using Egger’s test.The prevalence was calculated with the variance-stabilizing double arcsine transformation and weighted towards 50% to include studies with a low dementia prevalence . The 95%Due to the high heterogeneity, we further investigated the potential sources of heterogeneity via a meta-regression analysis. All studies were allocated into diverse groups according to their potentially relevant characteristics. The suspected factors were analysed using a univariate model including the (i) publication year (before and including the year 2000 versus after 2000), (ii) geographical region , (iii) living area , (iv) sample size (> 3000 versus ≤3000), and (v) diagnostic method (DSM−/IV/IV-R versus others). Furthermore, we subdivided the geographical regions/diagnostic methods into 7/4 groups, respectively, and analysed these factors using a multivariate analysis model.All the analyses were performed with R version 3.3.3 . The PRISMA guidelines were followed throughout the entire process.All data generated or analysed during this study are included in this published article (and its Supplementary Information files)..We strictly screened 1230 studies in a step-by-step manner based on the inclusion criteria. Ultimately, 51 surveys from 50 articles were included after the search process, including 49 studies that reported one survey and 1 study that reported two surveys due to differences in nationality  A total of 166,068 people aged 55 years or older  were gathered from all 51 surveys; the publication year ranged from 1985 to 2015. The locations of all 51 surveys were distributed into 3 areas, including 7 regions  and then subdivided into 23 provinces and municipalities. Detailed information regarding the study characteristics is shown in Table The quality scores of the studies were 7–9 (average = 8.412) when evaluated using the Newcastle-Ottawa quality assessment criteria; the score components for all 50 individual studies are shown in Additional file All 51 surveys provided the number of patients with dementia, which was divided by the different pathological subtypes . Estimates of the dementia prevalence ranged from 0.75–11.86%, and the pooled prevalence of dementia was 4.03% (95% CI: 3.46–4.70%). The pooled prevalence of AD/VAD was 2.44% (95% CI: 2.06–2.89%)/1.09% (95% CI: 0.91–1.31%), with ranges from 0.19–9.29%/0.18–3.64%, respectively  increased progressively with increasing age in both males and females.The prevalence of dementia depending on age  is shown in Additional file 4. The prevalence of AD was significantly higher in females  than in males , which was not observed for VAD. The prevalence decreased progressively with increasing education level for both AD and VAD. The prevalence rates of AD/VAD were significantly lower in the college group / than in the illiterate group /. The prevalence of dementia was not significantly different between the city and rural groups. The housewife group had a much higher prevalence of AD than the other occupations, but the result was biased by gender, because the term “housewife” inherently referred to females.Other factors, such as sex , education level , residence , and occupation  were calculated and presented in Additional file 5The subgroup analysis based on region showed that the prevalence of AD decreased progressively from north China to south China. The prevalence rates in the northeast/north/northwest areas of north China were 3.62%/3.08%/2.63%, respectively, which were higher than the prevalence rates in the central area . The south area  had the lowest prevalence. The prevalence of VAD in the north region  was higher than the prevalence in the central and south regions  .We divided the studies into 6 groups according to the publication year  and found that the prevalence rates of AD/VAD were 0.87%/1.04, 1.58%/0.79, 2.09%/0.76, 1.88%/0.94, 3.84%/1.61, and 3.77%/1.49%, respectively .The . Depending on the univariate meta-regression results, the prevalence rates of dementia/AD/VAD were higher in studies in which the publication year was after 2000 (p = 0.0002/0.0002/0.0062), and the sample size was greater than 3000 (p < 0.0001/0.0318/0.0025). The prevalence of dementia/AD/VAD was not significantly different according to the living area  or the diagnostic method (DSM-III/IV/IV-R vs. others). However, the prevalence of VAD was higher in studies from north China (p < 0.0001), which was not observed for dementia or AD. Then, we added two risk factors  to the multivariate analysis model (Table p = 0.0006/0.0009/0.0046), which was not observed in dementia or AD. When we set DSM-III/III-R as the reference diagnostic criteria, studies in which DSM-IV/IV-R were used had a higher prevalence of dementia/AD/VAD (p < 0.0001/< 0.0001/0.0008) in the multivariate meta-regression analysis .The outcomes of the univariate meta-regression analysis are presented in Additional file In this systemic review and meta-analysis, we analysed 51 surveys conducted in mainland China from 1985 to 2015. The overall estimate of the dementia prevalence was 4.03% (95% CI: 3.46–4.70%), whereas the pooled prevalence of AD/VAD was 2.44%/1.09%. The prevalence of dementia/AD/VAD was greater than that reported in a previous review , 62; thiNeurotoxicology and Lancet in 2017 also found that the prevalence of dementia was positively related to the level of air pollution [The prevalence of AD followed an obviously increasing pattern from the southern and central to the northern areas of China in the subgroup analysis. The prevalence of VAD was much higher in the northern area of China, but the difference between the central and southern areas of China was not significant. However, the economic discrepancies and differences in education between these areas were not significant, because all three areas included big cities (Beijing/Shanghai/Guangzhou) and less developed provinces ; this finding was consistent with a previous study . Air polollution , 73. TheChronologically, the prevalence rates of AD/VAD were higher after 2000 than before 2000. Wu et al.  showed aThe important influence of methodological factors was confirmed by the meta-regression analysis in this study. According to the meta-regression outcomes, studies using the DSM-IV/IV-R had a higher prevalence of dementia/AD/VAD; we suspect that the increased diagnostic level (including the updated diagnostic criteria) partially caused the chronological increases in dementia/AD/VAD.We found that the prevalence rates of dementia/AD/VAD were negatively correlated with education level . A previThe prevalence of AD was much higher in the female group than in the male group, as previously established. However, the prevalence of VAD did not show a significant difference between the two groups, indicating that gender was not a risk factor for VAD. In the analysis of stratified prevalence based on 5–year age groups, we found a positive relationship between dementia/AD/VAD and age in both the male and female groups. Age was considered the pivotal influencing factor of dementia/AD/VAD. However, as we increased the number of elder groups to include those encompassing ages from 85 to 90 and 90+ years, their total sizes were much smaller than those of the other groups, which might have led to a magnified result. Thus, more high-quality studies are needed to confirm this outcome.Jia et al.  and Ji eOur study has several limitations. Significant differences in the publication year, sample size and other factors were apparent, although the quality of the studies indicated their reliability. Depending on the results of the multivariate meta-regression analysis, we observed that the high heterogeneity between studies was caused by these risk factors. However, many risk factors were not considered. To obtain more reliable results, these risk factors and other potential factors should be better controlled. More information is needed from studies with identical designs in the future.Because dementia is a serious societal problem, its prevention deserves more attention. According to the results of our study, some factors, such as air quality and education, are important for the prevention of dementia. However, more high-quality studies are required to resolve the many unknown factors related to dementia.The prevalence of dementia/AD/VAD has increased over the past 30 years and may be affected by education level and diagnostic criteria. The prevalence of AD/VAD was higher in northern than in southern China, which warrants further study.Additional file 1:The quality scores of the studies. (DOC 100 kb)Additional file 2:The pooled prevalence of AD (A) /VAD (B) /dementia (C). (DOC 829 kb)Additional file 3:The prevalence of subtypes of dementia included in the studies. (DOC 108 kb)Additional file 4:The prevalence of dementia in terms of age in China from 1985 to 2015. (DOC 39 kb)Additional file 5:Prevalence of dementia due to sex, education, occupation and residence difference. (DOC 36 kb)Additional file 6:The subgroup analysis the prevalence of AD (A) /VAD (B) /dementia (C) based on region. (DOC 543 kb)Additional file 7:The subgroup analysis the prevalence of AD (A) /VAD (B) /dementia (C) based on published year. (DOC 629 kb)Additional file 8:Univariate meta-regression of the prevalence of dementia/AD/VAD. (DOC 40 kb)


HR), we were able to develop a targeted bioanalytical method for eicosanoid quantification. For this, we optimized the LC-MS/MS conditions and evaluated several parameters, including linearity, limits of quantification, matrix effects and recovery yields. For validation purposes, we looked at the method’s precision and accuracy. A library of high-resolution fragmentation spectra for eicosanoids was developed. Our comprehensive dataset meets benchmark standards for targeted analysis, having been derived using best-practice workflows and rigorous quality assessments. As such, our method has applications for determining complex eicosanoid profiles in the biomedical field.Eicosanoids comprise a class of bioactive lipids derived from a unique group of essential fatty acids that mediate a variety of important physiological functions. Owing to the structural diversity of these lipids, their analysis in biological samples is often a major challenge. Advancements in mass spectrometric have been helpful for the characterization and quantification of these molecular lipid species in complex matrices. However, there are technical limitations to this approach, including low-abundant and/or poorly ionizable lipids. Using high-resolution multiple-reaction monitoring (MRM Recent advances have been enabled by the development of new mass spectrometric tools and protocols for the analysis of molecular lipids in complex matrices2. Polyunsaturated fatty acids (PUFAs) are known to mediate some of their bioactivities through the formation of oxygenated metabolites. These bioactive lipids include eicosanoids, a class of lipid mediators derived from arachidonic acid via cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome-P450-catalyzed reactions (CYP450), and through non-enzymatic lipid peroxidation3 . Collision-activated dissociation (CAD) can be used to determine structural information acquired through the lipid’s ion fragmentation8. LC-MS/MS methods for targeted lipidomics utilize scheduled multiple-reaction monitoring (MRM) to optimize sensitivity, maximize the number of metabolites that can be analyzed at any given time, and reduce the time required for analysis9. The beauty of LC is that it simplifies analysis and offers the greatest chromatographic advantage for separating complex mixtures of lipid mediators because no prior derivatization of the analytes was required6. Despite this, chemical similarities and the presence of isomeric species still make separating lipid mediator species via LC rather difficult.A wide range of techniques has been used for the separation, detection, and quantification of lipid mediators. Currently, progress in liquid-chromatography–electrospray ionization tandem mass spectrometry (LC–ESI-MS/MS) offers a powerful approach for this purpose+ and [M-H]−)10, even though our analysis was conducted in the negative-ion mode. Tandem mass spectrometry (MS/MS) is known to increase the sensitivity and specificity of quantitative lipid analyses1. Since the acquisition mode MRM in most lipidomics studies is used to monitor the analytes through the transition of a select precursor ion to a specific product ion4, there are several lists which feature characteristic eicosanoids MRM transitions performed on triple-quadrupole instruments11.MS detection of the biological molecules was found to be dependent on ESI for ion generation, which produced both positive and negative molecular ion species  instrument can be used to acquire high-resolution MRM (MRMHR)13. In a MRMHR assays, the first quadrupole basically selected the precursor ion passing into the collision cell, and the instrument subsequently monitored all transitions of this precursor ion. The TOF analyzer recorded the resultant high-resolution spectrum. If retention times were known prior to analysis, many precursors would be selected during a scheduled MRMHR experiment; providing higher confidence detection, since all possible transitions can be monitored13. Indeed, targeted eicosanoid lipidomics could be performed using a comprehensive MRMHR strategy, which included important analytical steps, such as the standardization of the operating procedures for sample collection and preparation, as well as the development of both the LC-MS/MS method and the computational process12  were obtained from Milli-Q integral water purification System for ultrapure water from Merck-Millipore . The acetic acid and ammonium hydroxide (NH4OH) used in these experiments came from Sigma-Aldrich .All eicosanoids, fatty acids , and deuterated internal standards (IS) were purchased from Cayman Chemical Co . The details of all analytical-grade standards are listed in file “Metabolite standard details (docx) and Metabolite standard details (pdf), Data Citation 1”. HPLC-grade solvents acetonitrile (ACN), methanol (MeOH), isopropanol were purchased from Merck , and deionized water . Euthanasia protocols were performed under CO2/O2 excess atmosphere. Furthermore, all efforts were made to minimize suffering.Six-week-old male C57BL/6 mice were obtained from Standard Solutions and Calibration Curve: Individual stock solutions of lipid mediators (MWS) and IS were prepared at 10 μg.mL−1 in MeOH. A standard working solution (WS) composed of a mixture of all of the lipids (MWS and IS) was prepared via direct infusion of 100 ng.mL−1 in MeOH/H2O  containing 0.1% NH4OH by volume; this was to establish the MRMHR parameters, such as the declustering potential (DP) and the collision energy (CE). The WS was prepared without adding any isomer compounds to the same solution. For LC parameter studies, the WS was prepared as described with no addition of NH4OH. In another set of experiments, the calibration curves for the validation assay were obtained by diluting the relevant lipids in MeOH/H2O  to the following final concentrations: 2.3, 4.6, 9.2, 18.5, 37.0, 74.0, 148.1, 222.2, 333.3, and 500 ng.mL−1. To determine matrix effect and recovery yields, IS solutions were prepared in MeOH at three concentrations 18.5, 148.1, and 500 ng.mL−1. All solutions were stored in amber glass vials at −80°C and under argonium atmosphere until needed.Solid Phase Extraction (SPE): Pooled plasma and lung tissue samples were obtained from C57BL/6 mice. Dulbecco’s Modified Eagle Medium  was used as the culture medium for the validation assay. Lipid extraction was carried out according to a previously described protocol16, but with slight modifications. In brief, each sample was spiked with 10 μL of the IS solution before being extracted for use either in the recovery assay or the matrix effect assay. Lung samples were prepared via homogenization  of 50 mg of tissue per mL of MeOH/H2O  solution. Plasma samples were made via protein precipitation of 150 μL of the sample in 1.5 mL of MeOH/ACN  at 4 °C, which was left to denature overnight. Afterward, both plasma and lung samples were centrifuged for 10 min at 4 °C and 800 x g. For culture medium extraction a solution in MeOH was preparade . Subsequently, the supernatant samples, from lungs, plasma or culture medium were diluted with water to a maximum solvent concentration of 15%.2O that had been generated using an extraction manifold . After loading the diluted samples, the cartridges were again flushed with 4 mL of H2O to remove hydrophilic impurities. The analytes which had been adsorbed on the SPE sorbent were eluted with 1 mL of MeOH and stored at −80 °C to prevent metabolite degradation. The solvent was removed in vacuo  at room temperature and re-dissolved in 50 μL of MeOH/H2O  for LC-MS/MS analysis.For the first step in the SPE extraction protocol, the cartridge  was washed with 4 mL of MeOH and equilibrated with 4 mL of H2, Shimadzu-Kyoto, HO, Japan) was equipped with a binary pump system, a SIL-30AC autosampler, a DGU-20A degasser and a CBM-20A controller. The conditions used for chromatography are outlined in a previously published work17. However, some modifications have been implemented to improve the eicosanoids quantifications. For this propose, the method was optimized using an Ascentis Express C18 column  with an i.d. of 100 ×4.6 mm and a particle size of 2.7 μm. Elution was conducted under a binary gradient system which consisted of Phase A, H2O/ACN/acetic acid  at pH 5.8 (adjusted with NH4OH), and Phase B, an ACN/isopropanol . Gradient elution was carried out for 25 min at a flow rate of 0.6 mL.min−1. Gradient conditions were as follows: 0 to 2.0 min, 0% B; 2.0 to 5.0 min, 15% B; 5.0 to 8.0 min, 20% B; 8.0 to 11.0 min, 35% B; 11.0 to 15.0 min, 70% B; and 15.0 to 19 min, 100% B. At 19.0 min, the gradient returned to the initial condition of 0% B, and the column was re-equilibrated until 25.0 min. Over the course of the analyses, the column was kept at 25 °C, and the samples were maintained at 4°C in the autosampler. A 10 μL aliquot of each sample was injected onto the column. The pH of the mobile phase A was optimized to achieve the best sensitivity and chromatographic separation of lipid metabolites. As such, the pH values of phase A throughout the experiment were 3.8, 5.8, and 6.4.The Ultra-High-Performance Liquid Chromatography (UHPLC) system  equipped with a Turbo-V IonSpray. An Atmospheric-Pressure Chemical Ionization probe (APCI) was used for external calibrations of the Calibrant Delivery System (CDS). Automatic mass calibration (<2 ppm) was performed using APCI Negative Calibration Solution  injected via direct infusion at 300 μL.min−1 flow rate; this was done periodically after each of the five sample injections. An Electrospray Ionization (ESI) source in the negative ion mode was utilized for MRMHR scanning. For optimization of MRMHR channel settings, CE and DP values were determined for each analyte via direct infusion of MWS and IS solutions at a flow rate of 10 μL.min−1. Compounds were fragmented via CAD using nitrogen as the collision gas. Additional instrumental parameters were as follows: nebulizer gas (GS1), 50 psi; turbo-gas (GS2), 50 psi; curtain gas (CUR), 25 psi; electrospray voltage (ISVF), −4.0 kV; and turbo ion spray source temperature, 550°C. The mass range of the product ion experiments was from m/z 50 to 700, the dwell time was 10 ms, and a mass resolution of 35,000 was achieved at m/z 400. Data acquisitions were performed using Analyst Software .The UHPLC system was interfaced with a TripleTOF5600m/z value for each analyte. PeakView software is a stand-alone application for the qualitative review of LC-MS data files. In addition to its general application in exploring and interpreting qualitative data, the software also provides the tools for analyzing accurate and nominal mass data, structures, and multiple samples at any given time. Data processing usually proceeds through multiple steps, including filtering, feature detection, alignment, and normalization. Feature detection is conducted to identify all signals caused by true ions and to avoid the detection of false positives, thereby interpolating theoretical information from the molecular data file and experimental fragments. In our study, alignment to confirm retention time differences between runs was conducted through a comparison of the IS chromatography peaks. Normalization steps help in removing factors, which cause unwanted systematic bias and can be used to determine extraction efficiency during sample preparation via ion intensities and/or peak area between measurements. This was performed by extrapolation of the area ratio among the IS and biological analyte peaks.Identification of the lipid species obtained in the LC-MS analysis was performed using PeakView 2.1 . Briefly, this software combines information in three dimensions to give a representation of the retention time, signal intensity, and HR channels were created during computational data processing. These were instructed to select product ions of higher intensity and selectivity in order to achieve greater analytical sensitivity and avoid cross-identification. The MRMHR optimized parameters used in LC-MS/MS analysis are presented in For quantitative analysis, we used MultiQuant. This software uses robust integration algorithms, such as MQ4 and advanced SignalFinder, and is able to generate reliable integration results faster with less user intervention in order to maximize productivity. The use of reference standards for each of the target lipid species or class was important in quantifying the lipids under investigation. In this field, it is common practice to normalize the individual molecular ion-peak intensities using an internal standard for each lipid class. The calculated ratio of analyte to internal standard is then multiplied by the concentration of said internal standard so as to obtain the concentration of a particular analyte present in the sample. To quantify the lipid mediators in our work, MRM18. The parameters of interest were the analyte’s linearity, the lower-limits of quantification (LLOQ), inter- and intraday precision and accuracy, recovery and the matrix effects in the culture medium and both the plasma and lung tissue samples.The method was partially validated in accordance with Food and Drug Administration (FDA) recommendations1/x weighted) were constructed by plotting the standard area against its nominal concentration (File: ‘Retention time for lipid mediators panel under different UHPLC-MS/MS chromatography conditions (pdf)’, Data Citation 1). The acceptance criterion for the coefficient of variation (CV) was ≤20% for the LLOQ level and ≤15% for levels above the LLOQ. The LLOQ was defined as the lowest concentration level at which the signal-to-noise ratio (S/N) is ≥10 and the CV is ≤20%.These were assessed by means of 10-point standard curves prepared for five replicate samples within a linear range . Standar−1 for low-concentration quality control (LQC) samples; 1.5 ng.mL−1 for medium-concentration quality control (MQC) samples; and 5 ng.mL−1 for high-concentration quality control (HQC) samples. Precision and accuracy were expressed in percentage as the Relative Standard Deviation (RSD) and the Relative Error (RE), respectively. Accuracy was defined as the difference between the expected concentration (Cex) and the average measured concentration (Cav) as calculated by the formula (Cex−Cav/Cex)×100. The precision, RSD, was calculated using the formula (SD/Cav)×100. The method was only considered to be precise if the RSD was ≤15% (≤20% for LLOQ) and accurate if the RE of the analyte concentration measured was ≤15% of the expected value (≤20% for LLOQ). The intraday precision (repeatability) and accuracy were established for five replicates for the LQC, MQC, and HQC over the course of the same day using the same calibration curve. For the interday analysis (reproducibility), the assay was conducted for three consecutive days. Interday accuracy and precision were assessed by running analysis for five calibration curves in quintuplicate over consecutive days. According to FDA guidelines, the conventionally accepted range of inter- and intraday accuracy is from 80 to 120%. Accuracy and precision results for the lipid mediators are given in These were evaluated at three concentration levels for the quality control samples (QC): 0.15 ng.mL2O, 7:3, v/v). Recovery was expressed as the percentage of the expected value, as calculated by the average area ratio between the extraction samples and the standard samples. The extent of recovery of the IS should be consistent, precise, and reproducible. Matrix-dependent recovery was established using different matrices, such as lung tissue homogenate, plasma, and culture medium; these were spiked with IS at three concentrations  after SPE extraction. The peak areas after extraction were compared with the corresponding peak areas of the pure IS solutions. Samples for matrix effect and recovery studies were prepared in quintuplicate, and a CV of ≤15% was used as the accepted criteria for quality control. The recovery and matrix effect percentages for lung tissue, plasma, and culture cell are shown in To determine the extraction efficiency of SPE for the analytes in the biological matrix studied, recovery was assessed by means of the IS solutions. Plasma and lung tissue samples, as well as the culture medium, were spiked with IS solution before SPE extraction. Recovery was determined by comparing the analytical response of the IS for the extracted samples at three concentrations  to the IS that had not been extracted and that had been prepared in solvent (MeOH/HData Record 1–(Metabolite standard details (docx) and Metabolite standard details (pdf), Data Citation 1) gives for each metabolite standard purchase from Cayman Chemical all details, such as molecular weight, molecular formula, CAS number and supplied solution, as well biological information, such as fatty acid precursor, biosynthetic pathway and chemical class. The file include Internal lipid standards, with matching natural lipid (MWS), used in for extraction control, matrix effect and recovery experiments, also standards for calibration curves (MWS) in UHPLC-MS/MS and for quantitative calculation of lipid species.Data Record 2– for eicosanoid panel (pdf), Data Citation 1), described tandem mass spectral libraries in high resolution (HR-MS/MS) for eicosanoid panel. Also, the file provided information about collision energy, molecular formula and common name, as well theoretical mass-to-charge ratio (m/z) of precursors and fragments ions . Experimental spectrum showed numerical values that correspond to the m/z (x-axis) and relative intensity (y-axis) for each detected metabolite. The precursors (red) and fragments (blue) ions used for quantification analysis were highlight in the spectra.Data Record 3–(Retention time for lipid mediators panel under different UHPLC-MS/MS chromatography conditions (pdf), Data Citation 1) demonstrated the retention time for lipid mediators panel under different UHPLC-MS/MS chromatography conditions. The chromatography parameters differ by mobile phase pH . Using a elution method of UHPLC with a reversed-phase column (C18), a mixture of commercially lipid mediators standards elutes according to their polarity, number of double bonds and chain length, allowing the separation of most isomeric and isobaric species (represented by blue and red lines respectively) dependent of pH and according to identifying fragmentation patterns described in file ‘Tandem mass spectral libraries in high resolution (HR-MS/MS) for eicosanoid panel (pdf), Data Citation 1). An asterisk represented the non-detect metabolites.Data Record 4– and linear range of concentration for each analyte (xlsx) and Linear regression equation, correlation coefficient (r2) and linear range of concentration for each analyte (pdf), Data Citation 1), showed the linear regression graphics. MS files were processed for each metabolite quantified in MRMHR method with mobile phase pH 5.8. The graphics describe the linear regression equation, correlation coefficient (r2) and linear range of concentration for each analyte assessed by means of 10-point standard curves prepared for five replicate samples. Also the retention time were described in this file.Data Record 5– assay (xlsx) and Values obtained for each replicate of quality control  assay (pdf), Data Citation 1) presented all values obtained for each replicate of quality control  assay. The individual values from quality control assay were used to determine the average (n=5) of precision and accuracy of the MRMHR method presented in Data Record 6– (xlsx) and Values obtained for each replicate of matrix effects and recovery samples  (pdf), Data Citation 1) demonstrated all values obtained for each replicate of matrix effects and recovery samples . In these experiments the analytes corresponded to internal standards (IS- deutered compounds) for eliminate the interference of matrix endogenous metabolites. The individual values were used to determine the average (n=6) of matrix effect and recovery of the extraction method presented in Data Record 7–All data files from TripleTOF®5600+ (.wiff) have been deposited to the MetaboLights metabolomics repository19 (Data Citation 1). Instrument RAW data files (121 files) contain total ion current, spectra and metadata for each replicate. This data set was used to provide the linear range of concentration, retention time, LLOQ  and linear range of concentration for each analyte (xlsx) and Linear regression equation, correlation coefficient (r2) and linear range of concentration for each analyte (pdf), Data Citation 1), intra- and interday precision and accuracy  assay (xlsx) and Values obtained for each replicate of quality control  assay (pdf)’, Data Citation 1). Also, the MTBLS641 archive (.zip) contains others files: ‘i_Investigation.txt’ that provided summary information and project descriptors; ‘s_Study.txt’ with describes the metadata related to the samples; and ‘a_study_id_metabolite_profiling_mass_spectrometry.txt’, ‘a_study_id_metabolite_profiling_mass_spectrometry-1.txt’ (Data Citation 2) which afford the contextual data for the MS analyses from data acquisition through to the multiple processing steps. All raw data files were uploaded using an ISA-tab format.+ system) in MRMHR mode was carried out before biological sample measurements were taken; this was previously described in more detail14. To this end, the pre-defined MRM transitions (pairs of precursor/fragment ions) in a low-resolution mass spectrometer typically used for routine lipidomics analysis in our lab17 were optimized for high-resolution mass spectra  for eicosanoid panel (pdf)”, Data Citation 1). The processing software, MultiQuant, was capable of generating these MRM-like channels post-acquisition, thus allowing that each transition was create in accordance with the MS/MS data obtained during analysis. This meant that there was no need for pre-selection of the candidate lipid fragments in the mass analyzer. In addition, time-resolved mass measurements enabled proper alignment of the detected fragment ions with their respective precursor ions for accurate identification. HR mode, which were done using the most sensitive and most selective MRMHR transitions. These parameters allowed us to accurately discern between the individual lipid mediators and the background interferences for each analyte.Technical validation of the UHPLC-MS/MS method for the comprehensive analysis of the lipid mediators on a high-resolution mass spectrometer ; these included the pairs of lipids PGE2/PGD2, LTD4/trans-LTD4, and LTB4/trans-LTB4. This observation forced us to look at the effect of pH on the elution behavior of the analytes.Developing an LC-MS/MS methodology that would efficiently assay all of the target analytes was filled with challenges, especially when it came to finding the right balance between the method’s sensitivity and chromatographic resolution. To compound matters, chromatographic separation was required for isomers that were not differentiated through MRMHR transition or their respective chromatographic retention times, this analytical method could not be used to quantify lipids like LTC4, AA, and EPA under these conditions. As such, no chromatographic peaks were detected for the aforementioned lipids. Moreover, the shape of the peak obtained for 6-keto-PGF1α was too broad. Given this, a range of pH levels was evaluated using the developed LC method in order to optimize sensitivity and selectivity. At pH 6.4, the analytical response for all lipid species was improved, including those for LTC4, AA, and EPA. It is theorized that, at this pH, the lipid mediators that contained free carboxyl moieties were abundant and present as the negative [M−H]− species. On the other hand, isomers with identical MRMHR channels were co-eluted with the same retention time under these LC conditions. Using a mobile phase with pH of 5.8 could resolved these critical co-elution isomers while still enabling the detection of LTC4, AA, and EPA. As evidence of this, it was noted that the peak shape of 6-keto-PGF1α was improved under these conditions  of up to 0.99 . After download, the data can be available for analysis by different software packages. For this, Sciex  offer a tool called MS Data Converter, free download at https://sciex.com/software-support/software-downloads. Output can be a simple MGF peak list or an mzML file containing a fairly complete representation of the raw data. Choosing an exact translation of the instrument recorded or converts to a processed version, reducing the data down to peak lists. The mzML format is the single XML standard mass spectrometry format that was created by the merger of the older mzXML and mzData formats20. However, the conversion of data in mzML output files result in larger files than the original raw data20. To require mzXML format than mzML, it was recommended to first convert to mzML using the MS Data Converter and then use a public tool such as msconvert in the ProteoWizard package to convert mzML to mzXML, avaible in http://proteowizard.sourceforge.net/downloads.shtml. Informatics challenge associated with analysing MS data required a wide variety of data file formats to encode the complex data types associated with MS workflows20. Each company has developed individual formats of its own and continually extends them, as emerging instrumentation requires new features20. Sciex  instruments are saved as files with the ‘.wiff’ extension. These files might sometimes contain all information in a run or alternatively might contain only metadata and be paired with a file having ‘.wiffscan’ extension that contains the spectra20. To solution this, the new format mzML were created that would include the best features from both mzXML and mzData and eventually replace them20. Likely the most common text format is the Mascot Generic Format (MGF) file. It is still common convert the binary mass spectrometer output files into simple text files containing only the MS/MS spectra20. Finally, targeted and quantitative data obtained by UHPLC-MS/MS method technology in this data descriptor can be automatically processed and rapid evaluation. Also, the data sets documented her will allow draught subsets of lipid species suggestive for a physiological and metabolic condition.The eicosanoids data file acquired by the TripleTOF5600How to cite this article: Sorgi, C. A. et al. Comprehensive High-Resolution Multiple-Reaction Monitoring Mass Spectrometry for Targeted Eicosanoid Assays. Sci. Data 5:180167 doi: 10.1038/sdata.2018.167 (2018).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Stroke is a major global public health burden. Unfortunately, stroke invariably leads to functional limitations, consequently, most stroke survivors are hugely dependent on family members/informal caregivers in carrying out essential daily activities. The increased demands of caregiving negatively impact caregivers’ mental health. Nevertheless, caregivers who receive an adequate amount of social support are likely to adjust better to the caregiving role. We sought to determine the impact of social support on the mental wellbeing of 71 caregivers of patients with stroke in Zimbabwe, a low-resourced country.The mean caregiver age was 41.5 (SD 13.8) years. Patients had a mean age of 65.2 (SD 15.3) years with most being functionally dependent (93.2%). 45.1% of the caregivers showed excessive psychiatric morbidity. The mean Multidimensional Scale of Perceived Social Support (MSPSS) score was 44 (SD 9.4), denoting high levels of social support. Caregivers who received an adequate amount of social support were likely to report of lower psychiatric morbidity . Furthermore, caregiver who were; poorer, were caring for more functionally-dependent patients, and did not receive additional assistance were likely to report of poor mental health functioning. There is therefore a strong need to implement context-specific caregivers wellness programs.The online version of this article (10.1186/s13104-019-4551-2) contains supplementary material, which is available to authorized users. Globally, fifteen million people suffer from stroke yearly, and of these, five million are permanently disabled . AlthougRegrettably, the increased demands of caregiving negatively impact caregivers’ mental health –11. For 0 = 0.76, p1 = 0.8, α = 0.05 and β = 0.90, the minimum sample size was 70. We recruited; adult (≥ 18 years), primary, unpaid caregivers who voluntarily provided written consent for participation. Caregivers who were suffering from a chronic condition (e.g. HIV/AIDS), had a history of mental illness prior to current caregiving role that led to seeking medical help, and were taking care of another patient with a chronic condition at home were similarly excluded from the study as these contextual factors were potential covariates. Further, we only recruited caregivers proficient in either English or Shona (a Zimbabwean native language), the study outcomes were only available and validated in these languages.Data were collected cross-sectionally from caregivers of stroke patients receiving outpatient rehabilitation care at Chitungwiza Central Hospital (CCH), Parirenyatwa Group of Hospitals (PGH) and St Giles Medical Rehabilitation Centre (SGMRC). CCH and PGH are two of the five largest, public tertiary hospitals in Zimbabwe. SGMRC is a private medical rehabilitation centre located in Harare. An almost similar study yielded a 76% prevalence of common mental disorders (CMDs) in Indian caregivers of patients with stroke . AssuminAn ad-hoc sociodemographic questionnaire was designed to extract potential covariates (patient and caregiver characteristics). Psychiatric morbidity and social support were measured using the Shona Symptoms Questionnaire (SSQ) and the Multidimensional Scale of Perceived Social Support (MSPSS) respectively. The SSQ is a 14-item, self-administered, and psychometrically robust screening tool. A reported experience of any of the enlisted 14 symptoms in the past fortnight is rated/scored as 1 with no symptom being scored a 0 giving a cumulative total of 14; scores ≥ 8 are indicative of a high risk of psychiatric morbidity , 21. TheApproval to carry out the study was granted by study sites institutional review boards, and by the Joint Research Ethical Committee for the University of Zimbabwe, College of Health Sciences (JREC Ref: 380/17). The principal investigator (PI) approached prospective participants in the treatment waiting areas. The study aims were briefly explained, thereafter, consenting participants voluntarily completed the study questionnaires. The questionnaires were self-administered, nevertheless, the PI was available to assist the participants whenever necessary. Questionnaires were completed and collected on the same day.Data were entered and cleaned in Microsoft Excel, and thereafter exported to SPSS (Version 21) for analysis. Descriptive statistics  were used to describe participants’ demographics and spread of responses on the MSPSS and SSQ. Data were firstly checked for normality using the Shapiro–Wilk test before deciding on the appropriate statistical tests. Consequently, the spearman correlation co-efficient was used to determine the relationship between social support and psychiatric morbidity. Chi square test and student t-test were used to analyse the associations between categorical variables (e.g. gender) and numeric variables (e.g. age) with summative (numeric) scores on the MSPSS and SSQ respectively.The mean caregiver age was 41.5 (SD 13.8) years. Most of the caregivers were; females (70.4%), married (56.3%), children to the stroke survivors 52.1%), attained at least secondary education (90.1%), received assistance in caregiving (76.1%), had provided care for at least 4 months, and over 80% of carers reported of inadequate finances. Patients had a mean age of 65.2 (SD 15.3) years, and most of them; were females (53.5%), married (56.3%), suffered from left-sided stroke (52.1%), had lived with the condition for at least 6 months, required assistance in functional activities (93.2%), and hypertension was most common comorbid condition (80.3%). 45.1% of the caregivers showed excessive psychiatric morbidity, the median SSQ score was 7 (IQR: 3–9)  and caregivers . Trained caregivers are likely better adjustment to the demands of caring for a patient with stroke.Data were collected cross-sectionally, causality cannot be inferred.Participants were conveniently selected, ideally, we should have randomly selected. Randomization was not possible given the small volumes of patients presenting at the research sites during the study duration.Employment of convenience sampling may limit the study’s external validity.A lack of a comparison group may potentially limit the study’s external validity. It would have been ideal to compare caregivers’ outcomes with a matched, normative sample.Participants’ health status was self-reported, it was therefore difficult to apply a strict/verifiable selection criterion.Study outcomes need to be interpreted with precautions given the following methodological limitations:Additional file 1. Frequencies of responses on the MSPSS, N = 71. Table denotes frequencies of responses on the MSPSS, a 12-item social support outcome measure. Responses are rated on a five-point Likert scale, ranging from “strongly disagree = 1” to “strongly agree = 5”.Additional file 2. Frequencies of responses on the SSQ, N = 71. Table denotes frequencies of responses on the SSQ, a 14-item, binary common mental disorders (CMDs) screen. Respondents indicate if they had experienced any of the enlisted symptoms in the last seven days. A yes response is scored as “one” and no as “zero”, a score ≥ 8 is indicative of risk of CMDs.


The existence of a sarcoidosis-lymphoma syndrome has been previously proposed since the relation between sarcoidosis and an increased risk of lymphoproliferative disorders is well established. Multiple myeloma is a malignant multifocal proliferation of clonal plasma cells within the bone marrow, and its association with sarcoidosis has been rarely described. We present a concurrent diagnosis of sarcoidosis and multiple myeloma and make a brief analysis of the reported cases in the literature. A 65-year-old woman underwent surgery for the excision of a wrist mass that presented 3 years before. Histological analysis showed sarcoid-type epithelioid granulomas without necrosis, establishing soft tissue sarcoidosis. Further evaluation revealed marked interstitial lung parenchyma lesions and large intrathoracic adenopathies. Bronchofibroscopy with transbronchial biopsy confirmed lung sarcoidosis. In addition, blood analysis showed monoclonal IgG kappa gammopathy. A bone marrow biopsy confirmed hypercellularity with 60% plasma cells and plasmocyte infiltration. Thus, the diagnosis of systemic sarcoidosis and multiple myeloma was established simultaneously. In a brief review of the literature, we identified 33 reports of cases with both sarcoidosis and multiple myeloma. We point out the importance of a high level of suspicion for the association of sarcoidosis with malignant haematological diseases such as multiple myeloma. Sarcoidosis is a T-cell-mediated immunological response against an unknown environmental trigger in a susceptible host. Noncaseating granulomas, consisting of compact and centrally organized collections of CD4+ T cells, macrophages, and epithelioid cells, are the hallmark of sarcoidosis and the outcome of an unbalanced T-helper 1 immune response . MultiplA 65-year-old Caucasian women, retired cook, was referred to our outpatient clinic due to a three-year history of a painless mass on the dorsal side of the right wrist. Her medical background was positive for type 2 diabetes mellitus, dyslipidaemia, bronchitis, and venous thrombosis of the right eye. The wrist lesion had been growing gradually and affected finger movement. There was no history of blunt or penetrating trauma.erythema nodosum). There were no other palpable lymph nodes, hepatosplenomegaly, fever, night sweats, or constitutional symptoms.An ultrasound, complemented with nuclear magnetic resonance, identified a lesion on the dorsal plane of the right wrist, extending to the inner face, but without vascular or tendinous invasion, measuring about 100 × 60 × 18 mm in diameter and having well-defined limits . After s9/L (reference range 4–11 × 109/L), C-reactive protein of 0.87 mg/dL (reference range <0.30 mg/dL), erythrocyte sedimentation rate of 83 mm (reference range <20 mm), serum angiotensin converting enzyme (ACE) of 141.28 U/L (reference range 12–68 U/L), albumin of 3.66 g/dL (reference range 3.97–4.94 g/dL), and total serum protein level of 8.40 g/dL (reference range 6.40–8.20 g/dL). Serum protein electrophoresis revealed a monoclonal band, confirmed by immunofixation to be immunoglobulin IgG kappa. Further quantification of serum immunoglobulins showed an elevated IgG level of 3120 mg/dL (reference range 70–1600 mg/dL), with normal IgM and IgA, and beta-2 microglobulin of 2.09 mg/dL (reference range 0.67–1.31 mg/dL). Mantoux assay and Bence Jones proteinuria quantification were negative, and both renal function and serum calcium were within the normal range. The screening for HIV and hepatitis B and C was negative.Laboratory studies showed haemoglobin 9.7 g/dL (reference range 11.5–16.5 g/dL), white blood cells count of 4.6 × 10A high-resolution computed tomography of the chest showed large mediastinal and axillary adenopathies with extensive conglomerates, associated with marked and diffuse permeability of the entire pulmonary parenchyma, thickening of the interlobular septa, and fine bronchovascular and subpleural micronodularity .The patient's lung function testing revealed a mild restrictive pattern with decreased diffusion capacity. A bronchofibroscopy with transbronchial biopsy and bronchioalveolar lavage was performed. Respiratory tract fluid analysis showed an increased CD4/CD8 ratio of 9.02, and both acid-fast staining (Ziehl–Neelsen coloration) and mycobacterial culture were negative. Bronchial biopsy histological results revealed mild chronic inflammatory infiltrate with multiple nonnecrotizing sarcoid-type epithelioid granulomas and giant multinucleated Langerhans cells Figures . The patHence, a simultaneous diagnostic of systemic sarcoidosis and multiple myeloma stage I according to the International Staging System was established. The patient was started on prednisolone 60 mg/day with clinical and biochemical improvement. Bone marrow plasma cell infiltration dropped to 28%, and IgG immunoglobulin and ACE returned to normal values. Although a slow reduction of steroid therapy (to 5 mg/day) was achieved in time, previous oral antidiabetic therapy became insufficient and prompted insulin introduction.Only few cases of sarcoidosis in association with MM have been reported in the literature, so there is not enough scientific evidence available to support a causal relation for the occurrence of these two diseases. Nonetheless, epidemiological data highlight the higher risk for lymphoproliferative disease development in sarcoidosis patients –8.Brincker and Wilbek  publisheIn our review of the literature, 33 cases with both sarcoidosis and MM were identified although patient characteristics and diagnostic information were not available for all of them , 6–25. DH17 cells and Treg cells in sarcoidosis and MM immune mechanisms [The explanation for the relationship between sarcoidosis and lymphoproliferative disorders, particularly MM, is still controversial and unknown. One possible link between sarcoidosis and MM is the polyclonal hypergammaglobulinemia induced by sarcoidosis chronic inflammatory response. This continuous state of stimulation results in prolonged B cell and plasma cell half-life, which in addition to immune system imbalance in sarcoidosis patients, predisposes for genetic mutations and might lead to haematological malignancy development , 20. RecIn our case, sarcoidosis diagnosis was first made after a soft tissue lesion excision in a 65-year-old woman. Although both sarcoidosis and MM were diagnosed simultaneously, previous respiratory symptoms suggest that sarcoidosis pulmonary involvement was already present years before and misdiagnosed as bronchitis. This fact might also account for the older age at the time of the diagnosis of sarcoidosis. No usual abnormalities related to MM, such as lytic lesions or renal function impairment, were present at the time of diagnosis, and only the serum protein electrophoresis monoclonal band called the attention to a possible underlying disease other than sarcoidosis. We aim to call the attention to the possibility of LD development in patients with sarcoidosis. A high level of suspicion should always be present both at the time of diagnosis as well as during follow-up since LD seems to develop years after the diagnosis of sarcoidosis.