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The objective of this study was to investigate the immune mechanisms involved in preterm labor (PTL), preterm prelabor rupture of the membranes (PPROM), and normal pregnancies. The second objective was to explore immune profiles in PTL for association with early ( < 34 gestational weeks (gw)) or instant ( < 48 h) delivery. This prospective observational multi-center study included women with singleton pregnancies with PTL (n = 80) or PPROM (n = 40) before 34 gw, women with normal pregnancies scheduled for antenatal visits (n = 44), and women with normal pregnancies in active labor at term (n = 40). Plasma samples obtained at admission were analyzed for cytokine and chemokine quantification using a multiplex bead assay in order to compare the immune profiles between PTL, PPROM, and normal pregnancies. In PTL, CXCL1 and CCL17 were significantly higher compared to gestational age-matched women at antenatal visits, whereas for PPROM, CXCL1 and IL-6 were increased. Women in term labor had a more pronounced inflammatory pattern with higher levels of CXCL1, CXCL8, and IL-6 compared with PTL (p = 0.007, 0.003, and 0.013, respectively), as well as higher levels of CCL17, CXCL1 and IL-6 (all p < 0.001) compared with the women at antenatal visits. In PTL, CXCL8 was higher in women with delivery before 34 gw, whereas CXCL8, GM-CSF, and IL-6 were significantly higher in women with delivery within 48 h. To conclude, PTL and PPROM were associated with a complex pattern of inflammation, both involving Th17 (CXCL1) responses. Although further studies are needed, CXCL8, GM-CSF, and IL-6 may be potential candidates for predicting preterm birth in PTL., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: In order to identify and possibly offer prophylactic treatment to women at risk for preterm birth (PTB), novel prediction models for PTB are needed. Our objective was to utilize high-sensitive plasma protein profiling to investigate whether early prediction of spontaneous PTB (sPTB) before 34 gestational weeks (gw) was possible in a low-risk population., Methods: A case-control study was conducted on 46 women with sPTB before 34 gw and 46 women with normal pregnancies and term deliveries. Prospectively collected plasma sampled at gw 11 (range 7-16) and gw 25 (range 23-30) was analyzed with a high-sensitivity Proximity Extension Assay for levels of 177 inflammation-associated proteins, and statistically processed with multivariate logistic regression analysis., Results: In the first trimester, higher levels of hepatocyte growth factor (HGF) were associated with sPTB <34 gw (OR 1.49 (1.03-2.15)). In the second trimester, higher levels of interleukin (IL)-10 (OR 2.15 (1.18-3.92)), IL-6 (OR 2.59 (1.34-4.99)), and the receptor activator of nuclear factor κB (RANK) (OR 2.18 (1.26-3.77)) were associated with sPTB <34 gw. The area under the curve for the prediction models including these proteins was 0.653 (0.534-0.759) in the first trimester and 0.854 (0.754-0.925) in the second trimester., Conclusion: A combination of inflammation-associated plasma proteins from the second trimester of pregnancy showed a good predictive ability regarding sPTB before 34 gw, suggesting it could be a valuable supplement for the assessment of the clinical risk of sPTB. However, although a high number (n=177) of plasma proteins were analyzed with a high-sensitivity method, the prediction of sPTB in the first trimester remains elusive., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Svenvik, Raffetseder, Brudin, Berg, Hellberg, Blomberg, Jenmalm and Ernerudh.)

Background: Nursing-sensitive events (NSEs) are common, accounting for up to 77% of adverse events in hospitalized patients (eg, fall-related harm, pressure ulcers, and health care-associated infections). NSEs lead to adverse patient outcomes and impose an economic burden on hospitals due to increased medical costs through a prolonged hospital stay and additional medical procedures. To reduce NSEs and ensure high-quality nursing care, appropriate nurse staffing levels are needed. Although the link between nurse staffing and NSEs has been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing patient-level exposure to nurse staffing during the hospital stay. Few studies have assessed nurse staffing and patient outcomes using a single-center longitudinal design, with limited generalizability. There is a need for multicenter longitudinal studies with improved potential for generalizing the association between individual nurse staffing levels and NSEs., Objective: This study aimed (1) to determine the prevalence, preventability, type, and severity of NSEs; (2) to describe individual patient-level nurse staffing exposure across hospitals; (3) to assess the effect of nurse staffing on NSEs in patients; and (4) to identify thresholds of safe nurse staffing levels and test them against NSEs in hospitalized patients., Methods: This international multicenter study uses a longitudinal and observational research design; it involves 4 countries (Switzerland, Sweden, Germany, and Iran), with participation from 14 hospitals and 61 medical, surgery, and mixed units. The 16-week observation period will collect NSEs using systematic retrospective record reviews. A total of 3680 patient admissions will be reviewed, with 60 randomly selected admissions per unit. To be included, patients must have been hospitalized for at least 48 hours. Nurse staffing data (ie, the number of nurses and their education level) will be collected daily for each shift to assess the association between NSEs and individual nurse staffing levels. Additionally, hospital data (ie, type, teaching status, and ownership) and unit data (ie, service line and number of beds) will be collected., Results: As of January 2024, the verification process for the plausibility and comprehensibility of patients' and nurse staffing data is underway across all 4 countries. Data analyses are planned to be completed by spring 2024, with the first results expected to be published in late 2024., Conclusions: This study will provide comprehensive information on NSEs, including their prevalence, preventability, type, and severity, across countries. Moreover, it seeks to enhance understanding of NSE mechanisms and the potential impact of nurse staffing on these events. We will evaluate within- and between-hospital variability to identify productive strategies to ensure safe nurse staffing levels, thereby reducing NSEs in hospitalized patients. The TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) study will focus on the optimization of scarce staffing resources., International Registered Report Identifier (irrid): DERR1-10.2196/56262., (©Stefanie Bachnick, Maria Unbeck, Maryam Ahmadi Shad, Katja Falta, Nicole Grossmann, Daniela Holle, Jana Bartakova, Sarah N Musy, Sarah Hellberg, Pernilla Dillner, Fatemeh Atoof, Mohammadhossein Khorasanizadeh, Paula Kelly-Pettersson, Michael Simon. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 22.04.2024.)

Lyme neuroborreliosis (LNB) is a complex neuroinflammatory disorder caused by Borrelia burgdorferi, which is transmitted through tick bites. Epigenetic alterations, specifically DNA methylation (DNAm), could play a role in the host immune response during infection. In this study, we present the first genome-wide analysis of DNAm in peripheral blood mononuclear cells from patients with LNB and those without LNB. Using a network-based approach, we highlighted HLA genes at the core of these DNAm changes, which were found to be enriched in immune-related pathways. These findings shed light on the role of epigenetic modifications in the LNB pathogenesis that should be confirmed and further expanded upon in future studies., Competing Interests: Potential conflicts of interest. The authors report no conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Apolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is unknown. Here we show that the liver responds to a dyslipidemic insult based on inducible models of familial hypercholesterolemia and APOB tracing. An acute transition to atherogenic APOB lipoprotein levels resulted in uptake by Kupffer cells and rapid accumulation of triglycerides and cholesterol in the liver. Bulk and single-cell RNA sequencing revealed a Kupffer-cell-specific transcriptional program that was not activated by a high-fat diet alone or detected in standard liver function or pathological assays, even in the presence of fulminant atherosclerosis. Depletion of Kupffer cells altered the dynamic of plasma and liver lipid concentrations, indicating that these liver macrophages help restrain and buffer atherogenic lipoproteins while simultaneously secreting atherosclerosis-modulating factors into plasma. Our results place Kupffer cells as key sentinels in organizing systemic responses to lipoproteins at the initiation of atherosclerosis., (© 2024. The Author(s).)

Purpose of Review: Update readers on the state of the research on mental health, perinatal loss, and infertility with a focus on providing a comprehensive overview to empower clinicians in treating this population., Recent Findings: Rates of psychiatric illness are increased in people that experience perinatal loss and infertility. The research remains largely below the clear need for focused screening, prevention, and treatment. Clinicians and researchers need to remain attuned to the impact of perinatal loss and infertility on the mental health of patients and families. Screening, referral, and expanded therapeutic and psychiatric resources are imperative to improving the well-being of these patients and families., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS., (© 2023. The Author(s).)

Background: Multiple sclerosis (MS) is a neuroinflammatory disease in which pregnancy leads to a temporary amelioration in disease activity as indicated by the profound decrease in relapses rate during the 3rd trimester of pregnancy. CD4 + and CD8 + T cells are implicated in MS pathogenesis as being key regulators of inflammation and brain lesion formation. Although Tcells are prime candidates for the pregnancy-associated improvement of MS, the precise mechanisms are yet unclear, and in particular, a deep characterization of the epigenetic and transcriptomic events that occur in peripheral T cells during pregnancy in MS is lacking., Methods: Women with MS and healthy controls were longitudinally sampled before, during (1st, 2nd and 3rd trimesters) and after pregnancy. DNA methylation array and RNA sequencing were performed on paired CD4 +  and CD8 +  T cells samples. Differential analysis and network-based approaches were used to analyze the global dynamics of epigenetic and transcriptomic changes., Results: Both DNA methylation and RNA sequencing revealed a prominent regulation, mostly peaking in the 3rd trimester and reversing post-partum, thus mirroring the clinical course with improvement followed by a worsening in disease activity. This rebound pattern was found to represent a general adaptation of the maternal immune system, with only minor differences between MS and controls. By using a network-based approach, we highlighted several genes at the core of this pregnancy-induced regulation, which were found to be enriched for genes and pathways previously reported to be involved in MS. Moreover, these pathways were enriched for in vitro stimulated genes and pregnancy hormones targets., Conclusion: This study represents, to our knowledge, the first in-depth investigation of the methylation and expression changes in peripheral CD4 +  and CD8 +  T cells during pregnancy in MS. Our findings indicate that pregnancy induces profound changes in peripheral T cells, in both MS and healthy controls, which are associated with the modulation of inflammation and MS activity., (© 2023. The Author(s).)

Background: The COVID-19 pandemic has created an epidemic of distress-related mental disorders such as depression, while simultaneously necessitating a shift to virtual domains of mental health care; yet, the evidence to support the use of virtual interventions is unclear., Objective: The purpose of this study was to evaluate the efficacy of virtual interventions for depressive disorders by addressing three key questions: (1) Does virtual intervention provide better outcomes than no treatment or other control conditions (ie, waitlist, treatment as usual [TAU], or attention control)? (2) Does in-person intervention provide better outcomes than virtual intervention? (3) Does one type of virtual intervention provide better outcomes than another?, Methods: We searched the PubMed, EMBASE, and PsycINFO databases for trials published from January 1, 2010, to October 30, 2021. We included randomized controlled trials of adults with depressive disorders that tested a virtual intervention and used a validated depression measure. Primary outcomes were defined as remission (ie, no longer meeting the clinical cutoff for depression), response (ie, a clinically significant reduction in depressive symptoms), and depression severity at posttreatment. Two researchers independently selected studies and extracted data using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Risk of bias was evaluated based on Agency for Healthcare and Research Quality guidelines. We calculated odds ratios (ORs) for binary outcomes and standardized mean differences (SMDs) for continuous outcomes., Results: We identified 3797 references, 24 of which were eligible. Compared with waitlist, virtual intervention had higher odds of remission (OR 10.30, 95% CI 5.70-18.60; N=619 patients) and lower posttreatment symptom severity (SMD 0.81, 95% CI 0.52-1.10; N=1071). Compared with TAU and virtual attention control conditions, virtual intervention had higher odds of remission (OR 2.27, 95% CI 1.10-3.35; N=512) and lower posttreatment symptom severity (SMD 0.25, 95% CI 0.09-0.42; N=573). In-person intervention outcomes were not significantly different from virtual intervention outcomes (eg, remission OR 0.84, CI 0.51-1.37; N=789). No eligible studies directly compared one active virtual intervention to another., Conclusions: Virtual interventions were efficacious compared with control conditions, including waitlist control, TAU, and attention control. Although the number of studies was relatively small, the strength of evidence was moderate that in-person interventions did not yield significantly better outcomes than virtual interventions for depressive disorders., (©Crystal Edler Schiller, Julianna Prim, Anna E Bauer, Linda Lux, Laura Claire Lundegard, Michelle Kang, Samantha Hellberg, Katherine Thompson, Theresa Webber, Adonay Teklezghi, Noah Pettee, Katherine Gaffney, Gabrielle Hodgins, Fariha Rahman, J Nikki Steinsiek, Anita Modi, Bradley N Gaynes. Originally published in JMIR Mental Health (https://mental.jmir.org), 09.01.2023.)

There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier ( ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.

In vitro-in vivo prediction results for hepatic metabolic clearance (CL H ) and intrinsic CL H (CL int ) vary widely among studies. Reasons are not fully investigated and understood. The possibility to select favorable reference data for in vivo CL H and CL int and unbound fraction in plasma (f u ) is among possible explanations. The main objective was to investigate how reference data selection influences log in vitro and in vivo CL int -correlations (r 2 ). Another aim was to make a head-to-head comparison vs an in silico prediction method. Human hepatocyte CL int -data for 15 compounds from two studies were selected. These were correlated to in vivo CL int estimated using different reported CL H - and f u -estimates. Depending on the choice of reference data, r 2 from two studies were 0.07 to 0.86 and 0.06 to 0.79. When using average reference estimates a r 2 of 0.62 was achieved. Inclusion of two outliers in one of the studies resulted in a r 2 of 0.38, which was lower than the predictive accuracy (q 2 ) for the in silico method (0.48). In conclusion, the selection of reference data appears to play a major role for demonstrated predictions and the in silico method showed higher accuracy and wider range than hepatocytes for human in vivo CL int -predictions., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

The gastrointestinal uptake of macrocyclic compounds is not fully understood. Here we applied our previously validated integrated system based on machine learning and conformal prediction to predict the passive fraction absorbed (f a ), maximum fraction dissolved (f diss ), substrate specificities for major efflux transporters and total fraction absorbed (f a,tot ) for a selected set of designed macrocyclic compounds (n = 37; MW 407-889 g/mol) and macrocyclic drugs (n = 16; MW 734-1203 g/mole) in vivo in man. Major aims were to increase the understanding of oral absorption of macrocycles and further validate our methodology. We predicted designed macrocycles to have high f a and low to high f diss and f a,tot , and average estimates were higher than for the larger macrocyclic drugs. With few exceptions, compounds were predicted to be effluxed and well absorbed. A 2-fold median prediction error for f a,tot was achieved for macrocycles (validation set). Advantages with our methodology include that it enables predictions for macrocycles with low permeability, Caco-2 recovery and solubility (BCS IV), and provides prediction intervals and guides optimization of absorption. The understanding of oral absorption of macrocycles was increased and the methodology was validated for prediction of the uptake of macrocycles in man., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Urban Fagerholm, Sven Hellberg and Ola Spjuth declare shares in Prosilico AB, a Swedish company that develops solutions for human clinical ADME/PK predictions. Ola Spjuth declares shares in Aros Bio AB, a company developing the CPSign software., (Copyright © 2022 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4 +  T-cells in non-pregnant and pregnant women, during the 1 st  and 2 nd  trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2 nd  trimester. A network-based modular approach identified several genes,  e.g., CD28, FYN, VAV1  and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases. Abbreviations : BMIQ: beta-mixture quantile dilation; DMGs: differentially methylated genes; DMPs: differentially methylated probes; FE: fold enrichment; FDR: false discovery rate; GO: gene ontology; GWAS: genome-wide association studies; MDS: multidimensional scaling; MS: multiple sclerosis; PBMC: peripheral blood mononuclear cells; PBS: phosphate buffered saline; PPI; protein-protein interaction; RA: rheumatoid arthritis; SD: standard deviation; SLE: systemic lupus erythematosus; SNP: single nucleotide polymorphism; T H : CD4 + T helper cell; VIStA: diVIsive Shuffling Approach.

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4 + T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Magnusson, Rundquist, Kim, Hellberg, Na, Benson, Gomez-Cabrero, Kockum, Tegnér, Piehl, Jagodic, Mellergård, Altafini, Ernerudh, Jenmalm, Nestor, Kim and Gustafsson.)

Multiple sclerosis (MS) is a chronic autoimmune neuroinflammatory and neurodegenerative disorder of the central nervous system. Pregnancy represents a natural modulation of the disease course, where the relapse rate decreases, especially in the 3 rd trimester, followed by a transient exacerbation after delivery. Although the exact mechanisms behind the pregnancy-induced modulation are yet to be deciphered, it is likely that the immune tolerance established during pregnancy is involved. In this study, we used the highly sensitive and specific proximity extension assay technology to perform protein profiling analysis of 92 inflammation-related proteins in MS patients (n=15) and healthy controls (n=10), longitudinally sampled before, during, and after pregnancy. Differential expression analysis was performed using linear models and p-values were adjusted for false discovery rate due to multiple comparisons. Our findings reveal gradual dynamic changes in plasma proteins that are most prominent during the 3 rd trimester while reverting post-partum. Thus, this pattern reflects the disease activity of MS during pregnancy. Among the differentially expressed proteins in pregnancy, several proteins with known immunoregulatory properties were upregulated, such as PD-L1, LIF-R, TGF-β1, and CCL28. On the other hand, inflammatory chemokines such as CCL8, CCL13, and CXCL5, as well as members of the tumor necrosis factor family, TRANCE and TWEAK, were downregulated. Further in-depth studies will reveal if these proteins can serve as biomarkers in MS and whether they are mechanistically involved in the disease amelioration and worsening. A deeper understanding of the mechanisms involved may identify new treatment strategies mimicking the pregnancy milieu., Competing Interests: IT has served at advisory board for Pfizer Inc. JM has received honoraria for Advisory boards for Sanofi Genzyme and Merck and lecture honorarium from Merck. TO has received grant support from the Swedish Research Council, the Swedish Brain foundation, The Knut and Wallenberg foundation and Margaretha af Ugglas foundation and has received compensation for lectures/advisory boards, and unrestricted MS research grants from Biogen, Novartis, Sanofi and Merck. JE has received compensation for lectures from AbbVie, Biogen and Merck. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SS declared a past co-authorship with one of the authors JE to the handling editor., (Copyright © 2022 Papapavlou Lingehed, Hellberg, Huang, Khademi, Kockum, Carlsson, Tjernberg, Svenvik, Lind, Blomberg, Vrethem, Mellergård, Gustafsson, Jenmalm, Olsson and Ernerudh.)