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Anna-Lena Klauser,1 Marc Hirschfeld,1– 3 Andrea Ritter,1,2 Gerta Rücker,1,4 Markus Jäger,1,2 Julia Gundarova,1 Daniela Weiss,1,2 Ingolf Juhasz-Böss,1,2 Kai Berner,1,2 Thalia Erbes,1,2 Jasmin Asberger1,2 1Faculty of Medicine, University of Freiburg, Freiburg, Germany; 2Department of Obstetrics and Gynecology, Medical Center – University of Freiburg, Freiburg, Germany; 3Institute of Veterinary Medicine, University of Göttingen, Göttingen, Germany; 4Institute of Medical Biometry and Statistics, Medical Center – University of Freiburg, Freiburg, GermanyCorrespondence: Jasmin AsbergerDepartment of Obstetrics and Gynecology, Faculty of Medicine and Medical Center – University of Freiburg, Hugstetterstr. 55, Freiburg, 79106, GermanyTel +49 761 270 30020Fax +49 761 270 30370Email jasmin.asberger@uniklinik-freiburg.dePurpose: In 2020, breast cancer still represents the most common type of cancer in women worldwide. Depending on the specific molecular subtype, clinical breast cancer management comprises surgery, radiotherapy, chemotherapy and targeted therapy. Furthermore, there are some therapeutic approaches from the field of complementary and alternative medicine. Current research focuses on the elucidation of new therapeutic targets for treatment development. Odorant substances affect apoptosis, proliferation and cell cycle in healthy and cancerous cells. Exact signalling pathways involved are not entirely clear. The present study aims to analyse their therapeutic potential in breast cancer.Methods: This study focuses on the effect of commonly used odorant substances (citral, citrathal R, cyclovertal, para-cymol, hexylacetat, herbavert, dihydromyrcerol and limonen) on the breast cancer cell lines MDA-MB-231, T47-D and BT474. Methodologically, this study applied cell culturing, MTT assay for detection of IC50 of the odorant substance, RNA purification followed by qRT-PCR, protein isolation and Western Blot, as well as immunocytochemistry. Further, this study investigates the role of transient receptor potential channel V1 (TRPV1), involved in the mechanisms of action for some odorant substances. Therefore, capsazepine, a TRPV1 antagonist, was used.Results: The odorant substances citral, citrathal R and cyclovertal have significant pro-apoptotic (p < 0.001), anti-proliferative (p < 0.001) and cell cycle-arresting effects measurable in RNA expression as well as in protein levels and immunocytochemical staining. The combination of citral and capsazepine no longer showed significant pro-apoptotic, antiproliferative, and cell cycle inhibitory effects compared to the compounds alone. This indicates that TRPV1 is necessary for the signal transduction of citral.Conclusion: This present study reveals three odorant substances with effects on cell viability, indicating their potential use in breast cancer therapy.Keywords: citral, citrathal R, cyclovertal, odorant substances, olfactory receptor and breast cancer

Summary: Postoperative nausea and vomiting is a common adverse effect of anaesthesia. Although dozens of different anti‐emetics are available for clinical practice, there is currently no comparative ranking of efficacy and safety of these drugs to inform clinical practice. We performed a systematic review with network meta‐analyses to compare, and rank in terms of efficacy and safety, single anti‐emetic drugs and their combinations, including 5‐hydroxytryptamine3, dopamine‐2 and neurokinin‐1 receptor antagonists; corticosteroids; antihistamines; and anticholinergics used to prevent postoperative nausea and vomiting in adults after general anaesthesia. We systematically searched for placebo‐controlled and head‐to‐head randomised controlled trials up to November 2017 (updated in April 2020). We assessed how trustworthy the evidence was using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) and Confidence In Network Meta‐Analysis (CINeMA) approaches for vomiting within 24 h postoperatively, serious adverse events, any adverse event and drug class‐specific side‐effects. We included 585 trials (97,516 participants, 83% women) testing 44 single drugs and 51 drug combinations. The studies' overall risk of bias was assessed as low in only 27% of the studies. In 282 trials, 29 out of 36 drug combinations and 10 out of 28 single drugs lowered the risk of vomiting at least 20% compared with placebo. In the ranking of treatments, combinations of drugs were generally more effective than single drugs. Single neurokinin‐1 receptor antagonists were as effective as other drug combinations. Out of the 10 effective single drugs, certainty of evidence was high for aprepitant, with risk ratio (95%CI) 0.26 (0.18–0.38); ramosetron, 0.44 (0.32–0.59); granisetron, 0.45 (0.38–0.54); dexamethasone, 0.51 (0.44–0.57); and ondansetron, 0.55 (0.51–0.60). It was moderate for fosaprepitant, 0.06 (0.02–0.21) and droperidol, 0.61 (0.54–0.69). Granisetron and amisulpride are likely to have little or no increase in any adverse event compared with placebo, while dimenhydrinate and scopolamine may increase the number of patients with any adverse event compared with placebo. So far, there is no convincing evidence that other single drugs effect the incidence of serious, or any, adverse events when compared with placebo. Among drug class specific side‐effects, evidence for single drugs is mostly not convincing. There is convincing evidence regarding the prophylactic effect of at least seven single drugs for postoperative vomiting such that future studies investigating these drugs will probably not change the estimated beneficial effect. However, there is still considerable lack of evidence regarding safety aspects that does warrant investigation. [ABSTRACT FROM AUTHOR]

Ravindra Gopaul,1 Robert A Waller,1 Ricci Kalayanamitra,2 Garrett Rucker,3 Andrew Foy4 1Department of Emergency Medicine, Penn State Hershey Medical Center, Hershey, PA, USA; 2Department of Emergency Medicine, Staten Island University Hospital, New York, NY, USA; 3Penn State Milton S. Hershey College of Medicine, Hershey, PA, USA; 4Department of Internal Medicine, Division of Cardiology, Penn State Hershey Medical Center, Hershey, PA, USACorrespondence: Ravindra Gopaul, Email rgopaul@pennstatehealth.psu.eduObjective: The HEART Score is a clinically validated risk stratification tool for patients with chest pain. Using five parameters (History, Electrocardiogram, Age, Risk factors, and Troponin), this instrument categorizes patients as low, moderate, or high risk for major adverse cardiac events within six weeks after evaluation. Of these parameters, History is the most subjective, as providers independently assign their level of clinical suspicion. Overestimation of history, and ultimately the HEART Score, can result in increased resource utilization, expense, and patient risk. We sought to evaluate bias in provider assessment of history when determining the HEART Score.Methods: Emergency medicine (EM) and Cardiology providers received surveys with one of two versions of clinical vignettes randomized at the question level and were asked to estimate the history component of the HEART Score. Vignettes differed by age, risk factors, sex, and socioeconomic status (SES), but both versions should have received the same score for history. Statistical analysis was then used to assess differences in history assessment between vignettes.Results: Of the 884 responses analyzed, most providers overestimated the historical portion of the HEART Score when assessing risk factors, patient distress, age, and lower SES. Many underestimated history with knowledge of a previous negative stress test. When controlling for specialty, the universal theme was overestimation by EM providers and underestimation by cardiologists. Despite the presence of hypertension, gender differences, and the appearance of mild distress, cardiologists were more likely to correctly estimate history compared to EM providers. SES consideration generally led to an underestimation of history by cardiologists. These findings were all statistically significant.Conclusion: Our study demonstrates that both EM and cardiology providers overestimate history when considering prognosticators that are frequently viewed as concerning. Further education on proper usage of the HEART Score is needed for more appropriate scoring of history and improved resource allocation for hospital systems.Keywords: HEART score, physician, provider, history

To address the challenges of effective data handling faced by Small and Medium Sized Enterprises (SMEs) a cloud-based infrastructure for accessing and processing of Earth Observation(EO)-data has been developed within the project APPS4GMES(www.apps4gmes.de). To gain homogenous multi mission data access an Input Data Portal (IDP) been implemented on this infrastructure. The IDP consists of an Open Geospatial Consortium (OGC) conformant catalogue, a consolidation module for format conversion and an OGC-conformant ordering framework. Metadata of various EO-sources and with different standards is harvested and transferred to an OGC conformant Earth Observation Product standard and inserted into the catalogue by a Metadata Harvester. The IDP can be accessed for search and ordering of the harvested datasets by the services implemented on the cloud infrastructure. Different land-surface services have been realised by the project partners, using the implemented IDP and cloud infrastructure. Results of these are customer ready products, as well as pre-products (e.g. atmospheric corrected EO data), serving as a basis for other services. Within the IDP an automated access to ESA's Sentinel-1 Scientific Data Hub has been implemented. Searching and downloading of the SAR data can be performed in an automated way. With the implementation of the Sentinel-1 Toolbox and own software, for processing of the datasets for further use, for example for Vista's snow monitoring, delivering input for the flood forecast services, can also be performed in an automated way. For performance tests of the cloud environment a sophisticated model based atmospheric correction and pre-classification service has been implemented. Tests conducted an automated synchronised processing of one entire Landsat 8 (LS-8) coverage for Germany and performance comparisons to standard desktop systems. Results of these tests, showing a performance improvement by the factor of six, proved the high flexibility and computing power of the cloud environment. To make full use of the cloud capabilities a possibility for automated upscaling of the hardware resources has been implemented. Together with the IDP infrastructure fast and automated processing of various satellite sources to deliver market ready products can be realised, thus increasing customer needs and numbers can be satisfied without loss of accuracy and quality. [ABSTRACT FROM AUTHOR]

Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burned. Fuel consumption (FC) depends on the biomass available to burn and the fraction of the biomass that is actually combusted, and can be combined with estimates of area burned to assess emissions. While burned area can be detected from space and estimates are becoming more reliable due to improved algorithms and sensors, FC is usually modeled or taken selectively from the literature. We compiled the peer-reviewed literature on FC for various biomes and fuel categories to understand FC and its variability better, and to provide a database that can be used to constrain biogeochemical models with fire modules. We compiled in total 77 studies covering 11 biomes including savanna (15 studies, average FC of 4.6 t DM (dry matter) ha-1 with a standard deviation of 2.2), tropical forest (n =19, FC=126±77), temperate forest (n =12, FC=58±72), boreal forest (n =16, FC=35±24), pasture (n =4, FC=28±9.3), shifting cultivation (n =2, FC=23, with a range of 4.0-43), crop residue (n =4, FC=6.5±9.0), chaparral (n =3, FC=27±19), tropical peatland (n =4, FC=314±196), boreal peatland (n =2, FC=42 [42-43]), and tundra (n =1, FC=40). Within biomes the regional variability in the number of measurements was sometimes large, with e.g. only three measurement locations in boreal Russia and 35 sites in North America. Substantial regional differences in FC were found within the de?ned biomes: for example, FC of temperate pine forests in the USA was 37% lower than Australian forests dominated by eucalypt trees. Besides showing the differences between biomes, FC estimates were also grouped into different fuel classes. Our results highlight the large variability in FC, not only between biomes but also within biomes and fuel classes. This implies that substantial uncertainties are associated with using biome-averaged values to represent FC for whole biomes. Comparing the compiled FC values with co-located Global Fire Emissions Database version 3 (GFED3) FC indicates that modeling studies that aim to represent variability in FC also within biomes, still require improvements as they have dif?culty in representing the dynamics governing FC. [ABSTRACT FROM AUTHOR]

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in NMA., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

We aimed to assess the performance of Ag-RDT and RT-qPCR with regard to detecting infectious SARS-CoV-2 in cell cultures, as their diagnostic test accuracy (DTA) compared to virus isolation remains largely unknown. We searched three databases up to 15 December 2021 for DTA studies. The bivariate model was used to synthesise the estimates. Risk of bias was assessed using QUADAS-2/C. Twenty studies (2605 respiratory samples) using cell culture and at least one molecular test were identified. All studies were at high or unclear risk of bias in at least one domain. Three comparative DTA studies reported results on Ag-RDT and RT-qPCR against cell culture. Two studies evaluated RT-qPCR against cell culture only. Fifteen studies evaluated Ag-RDT against cell culture as reference standard in RT-qPCR-positive samples. For Ag-RDT, summary sensitivity was 93% (95% CI 78; 98%) and specificity 87% (95% CI 70; 95%). For RT-qPCR, summary sensitivity (continuity-corrected) was 98% (95% CI 95; 99%) and specificity 45% (95% CI 28; 63%). In studies relying on RT-qPCR-positive subsamples (n = 15), the summary sensitivity of Ag-RDT was 93% (95% CI 92; 93%) and specificity 63% (95% CI 63; 63%). Ag-RDT show moderately high sensitivity, detecting most but not all samples demonstrated to be infectious based on virus isolation. Although RT-qPCR exhibits high sensitivity across studies, its low specificity to indicate infectivity raises the question of its general superiority in all clinical settings. Study findings should be interpreted with caution due to the risk of bias, heterogeneity and the imperfect reference standard for infectivity., (© 2024 The Author(s). Reviews in Medical Virology published by John Wiley & Sons Ltd.)

The LFK index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the LFK index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the LFK index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the LFK index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the LFK index test if the false positive rate of the LFK index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the LFK index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The LFK index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis., (© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

We conducted a systematic review investigating the efficacy and tolerability of adrenocorticotropic hormone (ACTH) and corticosteroids in children with epilepsies other than infantile epileptic spasm syndrome (IESS) that are resistant to anti-seizure medication (ASM). We included retrospective and prospective studies reporting on more than five patients and with clear case definitions and descriptions of treatment and outcome measures. We searched multiple databases and registries, and we assessed the risk of bias in the selected studies using a questionnaire based on published templates. Results were summarized with meta-analyses that pooled logit-transformed proportions or rates. Subgroup analyses and univariable and multivariable meta-regressions were performed to examine the influence of covariates. We included 38 studies (2 controlled and 5 uncontrolled prospective; 31 retrospective) involving 1152 patients. Meta-analysis of aggregate data for the primary outcomes of seizure response and reduction of electroencephalography (EEG) spikes at the end of treatment yielded pooled proportions (PPs) of 0.60 (95% confidence interval [CI] 0.52-0.67) and 0.56 (95% CI 0.43-0.68). The relapse rate was high (PP 0.33, 95% CI 0.27-0.40). Group analyses and meta-regression showed a small benefit of ACTH and no difference between all other corticosteroids, a slightly better effect in electric status epilepticus in slow sleep (ESES) and a weaker effect in patients with cognitive impairment and "symptomatic" etiology. Obesity and Cushing's syndrome were the most common adverse effects, occurring more frequently in trials addressing continuous ACTH (PP 0.73, 95% CI 0.48-0.89) or corticosteroids (PP 0.72, 95% CI 0.54-0.85) than intermittent intravenous or oral corticosteroid administration (PP 0.05, 95% CI 0.02-0.10). The validity of these results is limited by the high risk of bias in most included studies and large heterogeneity among study results. This report was registered under International Prospective Register of Systematic Reviews (PROSPERO) number CRD42022313846. We received no financial support., (© 2024 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

The placebo effect is the 'effect of the simulation of treatment that occurs due to a participant's belief or expectation that a treatment is effective'. Although the effect might be of little importance for some conditions, it can have a great role in others, mostly when the evaluated symptoms are subjective. Several characteristics that include informed consent, number of arms in a study, the occurrence of adverse events and quality of blinding may influence response to placebo and possibly bias the results of randomised controlled trials. Such a bias is inherited in systematic reviews of evidence and their quantitative components, pairwise meta-analysis (when two treatments are compared) and network meta-analysis (when more than two treatments are compared). In this paper, we aim to provide red flags as to when a placebo effect is likely to bias pairwise and network meta-analysis treatment effects. The classic paradigm has been that placebo-controlled randomised trials are focused on estimating the treatment effect. However, the magnitude of placebo effect itself may also in some instances be of interest and has also lately received attention. We use component network meta-analysis to estimate placebo effects. We apply these methods to a published network meta-analysis, examining the relative effectiveness of four psychotherapies and four control treatments for depression in 123 studies., Competing Interests: Competing interests: TAF reports personal fees from Boehringer Ingelheim, DT Axis, Kyoto University Original, Shionogi and SONY, and a grant from Shionogi, outside the submitted work. In addition, TAF has patents 2020-548587 and 2022-082495 pending, and intellectual properties for Kokoro-app licensed to Mitsubishi Tanabe., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Shivering is a common side effect after general anesthesia. Risk factors are hypothermia, young age and postoperative pain. Severe complications of shivering are rare but can occur due to increased oxygen consumption. Previous systematic reviews are outdated and have summarized the evidence on the topic using only pairwise comparisons. The objective of this manuscript was a quantitative synthesis of evidence on pharmacological interventions to treat postanesthetic shivering., Evidence Acqusition: Systematic review and frequentist network meta-analysis using the R package netmeta. Endpoints were the risk ratio (RR) of persistent shivering at one, five and 10 minutes after treatment with saline/placebo as the comparator. Data were retrieved from Medline, Embase, Central and Web of Science up to January 2022. Eligibility criteria were: randomized, controlled, and blinded trials comparing pharmacological interventions to treat shivering after general anesthesia. Studies on shivering during or after any type of regional anesthesia were excluded as well as sedated patients after cardiac surgery., Evidence Synthesis: Thirty-two trials were eligible for data synthesis, including 28 pharmacological interventions. The largest network included 1431 patients. The network geometry was two-centered with most comparisons linked to saline/placebo or pethidine. The best interventions were after one minute: doxapram 2 mg/kg, tramadol 2 mg/kg and nefopam 10 mg, after 5 minutes: tramadol 2 mg/kg, nefopam 10 mg and clonidine 150 µg and after 10 minutes: nefopam 10 mg, methylphenidate 20 mg and tramadol 1 mg/kg, all reaching statistical significance. Pethidine 25 mg and clonidine 75 µg also performed well and with statistical significance in all networks., Conclusions: Nefopam, tramadol, pethidine and clonidine are the most effective treatments to stop postanesthetic shivering. The efficacy of doxapram is uncertain since different doses showed contradictory effects and the evidence for methylphenidate is based on a single comparison in only one network. Furthermore, both lack data on side effects. Further studies are needed to clarify the efficacy of dexmedetomidine to treat postanesthetic shivering.

Background: Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA., Methods: We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities., Results: CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist., Conclusions: CNMA methods are feasible for connected networks but questionable for disconnected networks., (© 2023. The Author(s).)

Objective: To determine equianalgesic potency ratios for opioids with an -evidence-based approach without the use of pre-existing potency tables., Design: Frequentist network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing opioids in patient-controlled analgesia (PCA)., Setting: A systematic review., Data Sources: A systematic search of MEDLINE, EMBASE, the Cochrane Library (CENTRAL), and Web of Science identified relevant RCTs from start of recording to 2019., Eligibility Criteria: RCTs comparing opioids via intravenous PCA in acute pain, with comparable resulting pain scores and identical treatment with coanalgesics at study level. The quality of studies was assessed using the Cochrane risk of bias tool with six items., Results: 52 RCTs were identified with data for 16 opioids. Primary endpoint was the inverted ratio of means of the total consumption administered via PCA, which resembles the analgesic potency. The calculated analgesic potencies were sufentanil 423 [95 percent CI 334.99; 532.96], fentanyl 58 [48.22; 68.60], buprenorphine 37 [26.66; 50.81], remifentanil 13 [9.37; 19.13], alfentanil 7 [4.02; 11.01], hydromorphone 6 [4.96; 8.43], oxymorphone 6 [4.46; 8.84], butorphanol 4.5 [3.05; 6.73], diamorphine 2.2 [1.16; 4.10], morphine 1, oxycodone 0.9 [0.65; 1.34], piritramide 0.9 [0.55; 1.56], nalbuphine 0.7 [0.54; 0.95], pethidine 0.12 [0.10; 0.15], meptazinol 0.08 [0.03; 0.20], and tramadol 0.08 [0.07; 0.10]., Conclusions: The results in part contradict the values from the literature, which have been criticized for their imprecision. From clinical experience however, our findings seem very plausible. Short-acting opioids are less potent compared to longer acting drugs, eg, morphine, probably due to shorter intervals for -readministration.

The cornerstone of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is reverse-transcription polymerase chain reaction (RT-PCR) of viral RNA. As a surrogate assay SARS-CoV-2 RNA detection does not necessarily imply infectivity. Only virus isolation in permissive cell culture systems can indicate infectivity. Here, we review the evidence on RT-PCR performance in detecting infectious SARS-CoV-2. We searched for any studies that used RT-PCR and cell culture to determine infectious SARS-CoV-2 in respiratory samples. We assessed (i) diagnostic accuracy of RT-PCR compared to cell culture as reference test, (ii) performed meta-analysis of positive predictive values (PPV) and (iii) determined the virus isolation probabilities depending on cycle threshold (Ct) or log 10 genome copies/ml using logistic regression. We included 55 studies. There is substantial statistical and clinical heterogeneity. Seven studies were included for diagnostic accuracy. Sensitivity ranged from 90% to 99% and specificity from 29% to 92%. In meta-analysis, the PPVs varied across subgroups with different sampling times after symptom onset, with 1% (95% confidence interval [CI], 0%-7%) in sampling beyond 10 days and 27% (CI, 19%-36%) to 46% (CI, 33%-60%) in subgroups that also included earlier samples. Estimates of virus isolation probability varied between 6% (CI, 0%-100%) and 50% (CI, 0%-100%) at a Ct value of 30 and between 0% (CI, 0%-22%) and 63% (CI, 0%-100%) at 5 log 10 genome copies/ml. Evidence on RT-PCR performance in detecting infectious SARS-CoV-2 in respiratory samples was limited. Major limitations were heterogeneity and poor reporting. RT-PCR and cell culture protocols need further standardisation., (© 2022 The Authors. Reviews in Medical Virology published by John Wiley & Sons Ltd.)

Purpose: Ovarian cancer is the seventh most frequent form of malignant diseases in women worldwide and over 150,000 women die from it every year. More than 70 percent of all ovarian cancer patients are diagnosed at a late-stage disease with poor prognosis necessitating the development of sufficient screening biomarkers. MicroRNAs displayed promising potential as early diagnostics in various malignant diseases including ovarian cancer. The presented study aimed at identifying single microRNAs and microRNA combinations detecting ovarian cancer in vitro and in vivo., Methods: Intracellular, extracellular and urinary microRNA expression levels of twelve microRNAs (let-7a, let-7d, miR-10a, miR-15a, miR-15b, miR-19b, miR-20a, miR-21, miR-100, miR-125b, miR-155, miR-222) were quantified performing quantitative real-time-PCR. Therefore, the three ovarian cancer cell lines SK-OV-3, OAW-42, EFO-27 as well as urine samples of ovarian cancer patients and healthy controls were analyzed., Results: MiR-15a, miR-20a and miR-222 showed expression level alterations extracellularly, whereas miR-125b did intracellularly across the analyzed cell lines. MicroRNA expression alterations in single cell lines suggest subtype specificity in both compartments. Hypoxia and acidosis showed scarce effects on single miRNA expression levels only. Furthermore, we were able to demonstrate the feasibility to clearly detect the 12 miRNAs in urine samples. In urine, miR-15a was upregulated whereas let-7a was down-regulated in ovarian cancer patients., Conclusion: Intracellular, extracellular and urinary microRNA expression alterations emphasize their great potential as biomarkers in liquid biopsies. Especially, miR-15a and let-7a qualify for possible circulating biomarkers in liquid biopsies of ovarian cancer patients., (© 2021. The Author(s).)

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Introduction: Patients with inflammatory bowel diseases (IBD) often report psychological problems, unemployment, disability, sick leave and compromised quality of life. The effect of psychological interventions on health-related outcomes in IBD is controversial as previous reviews faced the obstacle of high heterogeneity among provided multimodular interventions. The heterogeneity can be addressed with network meta-analysis (NMA) and (multi)component NMA (CNMA). We aim to investigate whether psychological interventions can improve quality of life, clinical and social outcomes in IBD using NMA and CNMA. This is the study protocol., Methods and Analysis: We will consider randomised, quasi-randomised and non-randomised controlled trials, including cluster randomised and cross-over trials with 2 months of minimum follow-up. The conditions to be studied comprise Crohn's disease and ulcerative colitis in children, adolescents and adults. We will include any psychological intervention aiming to change the health status of the study participant.We will search Medline, Embase, Web of Science, CENTRAL, LILACS, Psyndex, PsycINFO, Google Scholar and trial registries from inception (the search will be updated before the review completion). Two authors will independently screen all references based on titles and abstracts. For data extraction, standard forms are developed and tested before extraction. All information will be assessed independently by at least two reviewers, and disagreements solved by consensus discussion or a third rater if necessary.The data synthesis will include a pairwise meta-analysis supported by meta-regression. We will conduct NMA (all treatments will constitute single nodes of the network) and CNMA (we will define all treatments as sums of core components, eg, cognitive +behaviour, or cognitive +behaviour + relaxation, and additionally consider interactions) using the R Package netmeta., Ethics and Dissemination: No ethical approval is required. Reports will include the final report to the funder, conference presentation, peer-reviewed publication and a patient report., Prospero Registration Number: CRD42021250446., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To (a) identify methodological and application papers reporting a model developed specifically for diagnostic test accuracy network meta-analysis (DTA-NMA) or a hierarchical meta-regression method for comparing at least three index tests; (b) review and summarize the characteristics of the methods and the application papers; and (c) compare DTA-NMA and hierarchical meta-regression methods empirically., Study Design and Setting: We performed a scoping review and searched major databases until March 3rd, 2021. We assessed the characteristics of the identified methods, conducted a descriptive analysis of characteristics of the application articles, and applied the DTA-NMA and meta-regression methods to the available data., Results: We included 49 articles (plus one companion report), of which nine were methodological (describing 11 DTA-NMA methods) and 40 were application papers (data available for 32 DTA-NMAs). Our results showed a steep increase in recent years in DTA-NMA publications. DTA-NMA models may lead to different results. Although sensitivity estimates were comparable between meta-regression and DTA-NMA models, specificity estimates were higher in meta-regression., Conclusion: The choice of a DTA-NMA model will depend on the available data, including the use of different thresholds for test positivity, different study designs, and software familiarity. Selection between the methods may impact on the NMA results, especially for specificity., (Copyright © 2022. Published by Elsevier Inc.)

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called "evidence flow network" for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this article, we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random "hops" between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach has none of the associated ambiguity of the existing algorithm., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: There are no systematic reviews of cerebrospinal fluid and blood biomarkers for sporadic Creutzfeldt-Jakob disease (sCJD) in specialized care settings that compare diagnostic accuracies in a network meta-analysis (NMA)., Methods: We searched Medline, Embase, and Cochrane Library for diagnostic studies of sCJD biomarkers. Studies had to use established diagnostic criteria for sCJD and for diseases in the non-CJD groups, which had to represent a consecutive population of patients suspected as a CJD case, as reference standard. Risk of bias was assessed with QUADAS-2. We conducted individual biomarker meta-analyses with generalized bivariate models. To investigate heterogeneity, we performed subgroup analyses based on QUADAS-2 quality and clinical criteria. For the NMA, we applied a Bayesian beta-binomial ANOVA model. The study protocol was registered at PROSPERO (CRD42019118830)., Results: Of 2976 publications screened, we included 16 studies, which investigated 14-3-3β (n = 13), 14-3-3γ (n = 3), neurofilament light chain (NfL, n = 1), neuron-specific enolase (n = 1), p-tau181/t-tau ratio (n = 2), RT-QuIC (n = 7), S100B (n = 3), t-tau (n = 12), and t-tau/Aβ42 ratio (n = 1). Excluded diagnostic studies had strong limitations in study design. In the NMA, RT-QuIC (0.91; 95% CI [0.83, 0.95]) and NfL (0.93 [0.78, 0.99]) were the most sensitive biomarkers for the diagnosis of definite, probable, and possible sCJD cases. RT-QuIC was the most specific biomarker (0.97 [0.89, 1.00]). Heterogeneity in accuracy estimates was high between studies., Conclusions: We identified RT-QuIC as the most accurate biomarker, partially confirming currently applied diagnostic criteria. The shortcomings identified in many diagnostic studies for sCJD biomarkers need to be addressed in future studies., (© 2022 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question., Methods: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics., Results: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%., Conclusions: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments., (© 2022. The Author(s).)

The rapid increase in diagnostic and screening techniques has urged the need to choose among multiple diagnostic tests. For the majority of diseases, there is more than a single test available, and studies usually compare a subset of these tests. In such cases, a separate meta-analysis of each test cannot provide a reliable answer on the relative accuracy of the multiple available tests. Extensions of standard (hierarchical) meta-analysis to network meta-analysis (NMA) models for the comparison of at least three diagnostic tests have been the subject of methodological research in recent years. NMA can be used to jointly analyze the totality of evidence in order to provide estimates of relative accuracy (sensitivity and specificity ), to compare tests that have not been compared head-to-head, and to obtain a ranking of all competing tests in order to further facilitate the decision-making process.In this chapter, we illustrate current methodology for meta-analysis of multiple test comparisons, introduce NMA methods of diagnostic tests as an extension to the standard meta-analysis of diagnostic test accuracy (DTA) studies, and present existing approaches to rank tests according to their accuracy, specificity , and sensitivity . We also describe the basic concepts, underlying assumptions, and challenges in NMA of multiple diagnostic tests., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

The meta-analysis of single proportions has become a popular application over the last two decades. Especially, systematic reviews of prevalence studies are conducted in various fields of science, including medicine, ecology, psychology, or social sciences. In this chapter, we illustrate meta-analysis methods to pool single proportions and to compare proportions from two groups. We introduce classic approaches based on the inverse variance method as well as generalized linear mixed models taking the binary structure of the data into account. The most common transformations of proportions and their back-transformations are described both for individual studies and in the meta-analysis setting., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

In a quantitative synthesis of studies via meta-analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta-analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data-generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta-analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

The Peto odds ratio is a well-known effect measure in meta-analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi-arm studies and in network meta-analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi-arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta-analysis including only two-arm studies and translates to indirect comparisons of pairwise meta-analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta-analysis or indirect comparisons of pairwise meta-analyses., (© 2021 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Background: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics., Methods: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models., Results: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model., Conclusions: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power., (© 2021. The Author(s).)

Background: Healthcare decisions are ideally based on clinical trial results, published in study registries, as journal articles or summarized in secondary research articles. In this research project, we investigated the impact of academically and commercially sponsored clinical trials on medical practice by measuring the proportion of trials published and cited by systematic reviews and clinical guidelines., Methods: We examined 691 multicenter, randomized controlled trials that started in 2005 or later and were completed by the end of 2016. To determine whether sponsorship/funding and place of conduct influence a trial's impact, we created four sub-cohorts of investigator initiated trials (IITs) and industry sponsored trials (ISTs): 120 IITs and 171 ISTs with German contribution compared to 200 IITs and 200 ISTs without German contribution. We balanced the groups for study phase and place of conduct. German IITs were funded by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), or by another non-commercial research organization. All other trials were drawn from the German Clinical Trials Register or ClinicalTrials.gov. We investigated, to what extent study characteristics were associated with publication and impact using multivariable logistic regressions., Results: For 80% of the 691 trials, results were published as result articles in a medical journal and/or study registry, 52% were cited by a systematic review, and 26% reached impact in a clinical guideline. Drug trials and larger trials were associated with a higher probability to be published and to have an impact than non-drug trials and smaller trials. Results of IITs were more often published as a journal article while results of ISTs were more often published in study registries. International ISTs less often gained impact by inclusion in systematic reviews or guidelines than IITs., Conclusion: An encouraging high proportion of the clinical trials were published, and a considerable proportion gained impact on clinical practice. However, there is still room for improvement. For publishing study results, study registries have become an alternative or complement to journal articles, especially for ISTs. IITs funded by governmental bodies in Germany reached an impact that is comparable to international IITs and ISTs., (© 2021. The Author(s).)

Obesity management guidelines consistently advise aerobic training for weight loss, whereas recommendations for other training modalities are sparse. This systematic review and network meta-analysis (NMA) aimed to compare the long-term effects of different training modalities on anthropometric outcomes in patients with obesity. MEDLINE, Cochrane CENTRAL, and Web of Science were searched to identify the following: (1) randomized controlled trials (RCTs); (2) conducted in adults with a mean body mass index (BMI) ≥30 kg/m 2 ; (3) comparing aerobic, resistance, combined, or high-intensity interval training head-to-head or to control for ≥6 months; and (4) reporting changes in body weight (BW), BMI, waist circumference (WC), fat mass (FM), or fat-free mass (FFM). Random-effects NMA models were fitted in a frequentist approach. GRADE framework was used to assess certainty of evidence. Thirty-two RCTs with 4774 participants with obesity were included in this review. Aerobic training was ranked as best for improving BW, BMI, and WC and combined training for improving FM, as well as equally with resistance training most effective for improving FFM. Low to moderate certainty of evidence supports use of aerobic training to improve anthropometric outcomes in obesity, and its combination with resistance training provides additional benefit for reducing FM and increasing FFM., (© 2021 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.)

Background: Overweight and obesity in children and adolescents are major public health challenges affecting quality of life and representing important risk factors in the development of non-communicable diseases. School environments provide great possibilities for the primary prevention of overweight and obesity and different school-based nutrition interventions are available. However, existing research on school-based nutrition interventions has important limitations and no network meta-analysis (NMA) has been performed yet to compare all available interventions. Therefore, the present research project aims to investigate the impact of different nutrition interventions in the school setting by comparing and ranking them using NMA methodology., Methods/design: A systematic literature search will be performed in 11 electronic databases (PubMed, the Cochrane Library, Web of Science, ERIC, PsycINFO, CAB Abstracts, Campbell Library, BiblioMap EPPI, Australian Education Index, Joanna Briggs Institute Evidence-Based Practice Database and Practice-based Evidence in Nutrition Database). Parallel or cluster randomized controlled trials (RCTs) meeting the following criteria will be included: (1) generally healthy school students aged 4-18 years, (2) school-based intervention with ≥ 1 nutrition component, and (3) assessed anthropometric (overweight/obesity risk, body weight change, weight Z-score, [standardized] body mass index, body fat, waist circumference) and/or diet-quality measures (daily intake of fruits and vegetables, fat, and sugar-sweetened beverages). Random effects pairwise and NMA will be performed for these outcomes and surface under the cumulative ranking curve (SUCRA) estimated (P-score). Where possible, component NMA (CNMA) will be used additionally. Subgroup analyses are carried out for intervention duration, gender, age of school students, socioeconomic status, and geographical location, and sensitivity analyses by excluding high risk of bias RCTs., Discussion: This systematic review and NMA will be the first to both directly and indirectly compare and rank different school-based nutrition interventions for the primary prevention of overweight and obesity in childhood and adolescence. Our analyses will provide important insights about the effects of the different interventions and show which are the most promising. The results of our study can help inform the design of new studies and will be of value to anyone interested in developing successful, evidence-based nutrition interventions in school settings., Systematic Review Registration: PROSPERO: CRD42020220451 .

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder., Competing Interests: The authors have declared that no competing interests exist.

Network meta-analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching-adjusted indirect comparisons methods. However, nonrandomized comparisons may be associated with an unclear risk of bias. Schmitz et al. used single-arm observational studies for bridging the gap between two disconnected networks of treatments for multiple myeloma. We present a reanalysis of these data using component network meta-analysis (CNMA) models entirely based on RCTs, utilizing the fact that many of the treatments consisted of common treatment components occurring in both networks. We discuss forward and backward strategies for selecting appropriate CNMA models and compare the results to those obtained by Schmitz et al. using their matching method. CNMA models provided a good fit to the data and led to treatment rankings that were similar, though not fully equal to that obtained by Schmitz et al. We conclude that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model. Such models, exclusively based on evidence from RCTs, are a promising alternative to matching approaches that require additional evidence from observational studies. CNMA models are implemented in the R package netmeta., (© 2020 The Authors. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

In the era of the "reproducibility crisis" and the "P-value controversy" new ways of presentation and interpretation of the results of a meta-analysis are desirable. One suggestion that has been made for single studies almost six decades ago and taken up now and then is the P-value function. For a given outcome, this function assigns a P-value to each possible hypothetical value, given the data. Moreover, the P-value function simultaneously provides two-sided confidence intervals for all possible alpha levels. An application to meta-analysis, while suggested early, has not been widely established. We introduce the drapery plot that presents the P-value function for all individual studies and pooled estimates in a meta-analysis as curves and the prediction range for a single future study. We also present a scaled variant with the test statistic on the y-axis. Both plots visualize the full information of a pairwise meta-analysis. We see a drapery plot as a complementary figure to a forest plot. It may be even an alternative in meta-analyses with many studies where forest plots tend to become very large and complex., (© 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Breast cancer (BC) represents the most common type of cancer in females worldwide. Endocrine therapy evolved as one of the main concepts in treatment of hormone-receptor positive BC. Current research focuses on the elucidation of tumour resistance mechanisms against endocrine therapy. In a translational in vitro approach, potential regulatory effects of clinically implemented BC anti-oestrogens on ERα, its coactivators DDX5, DDX17 and other DEADbox proteins as well as on the proliferation markers cyclin D1 and Ki67 were investigated on both the RNA and protein level. BC in vitro models for hormone-receptor positive (MCF-7, T-47D) and hormone-receptor negative cells (BT-20) were subjected to endocrine therapy. Anti-oestrogen-dependent expression regulation of target genes on the transcriptional and translational level was quantified and statistically assessed. Endocrine therapy decreases the expression levels of Ki67, cyclin D1 and ERα in hormone-receptor positive cells. In the hormone-receptor negative cells, the three parameters remained stable after endocrine therapy. Endoxifen triggers a downregulation of DDX5 and DDX23 in MCF-7 cells. Fulvestrant treatment downregulates the expression levels of all investigated DEADbox proteins in MCF-7 cells. In T-47D cells, endoxifen and fulvestrant lead to a decrease of all target gene expression levels. Interestingly, endocrine therapy affects DEADbox RNA expression levels in BT-20 cells, too. However, this result could only be confirmed for DDX1, immunocytologically. The investigated DEADbox proteins appear to correlate with the oestrogen-dependent tumourigenesis in hormone-receptor positive BC and show expression alterations after endocrine treatment.

Early diagnosis and therapy in the first stages of a malignant disease is the most crucial factor for successful cancer treatment and recovery. Currently, there is a high demand for novel diagnostic tools that indicate neoplasms in the first or pre‑malignant stages. MicroRNAs (miRNA or miR) are small non‑coding RNAs that may act as oncogenes and downregulate tumor‑suppressor genes. The detection and mutual discrimination of the three common female malignant neoplasia types breast (BC), ovarian (OC) and endometrial cancer (EC) could be enabled by identification of tumor entity‑specific miRNA expression differences. In the present study, the relative expression levels of 25 BC, EC and OC‑related miRNAs were assessed by reverse transcription‑quantitative PCR and determined using the 2‑ΔΔCq method for normalization against the mean of four housekeeping genes. Expression levels of all miRNAs were analyzed by regression against cell line as a factor. An expression level‑based discrimination between BC and OC cell types was obtained for a subgroup of ten different miRNA types. miR‑30 family genes, as well as three other miRNAs, were found to be uniformly upregulated in OC cells compared with BC cells. BC and EC cells could be distinguished by the expression profiles of six specific miRNAs. In addition, four miRNAs were differentially expressed between EC and OC cells. In conclusion, miRNAs were identified as a potential novel tool to detect and mutually discriminate between BC, OC and EC. Based on a subset of 25 clinically relevant human miRNA types, the present study could significantly discriminate between these three female cancer types by means of their expression levels. For further verification and validation of miRNA‑based biomarker expression signatures that enable valuable tumor detection and characterization in routine screening or potential therapy monitoring, additional and extended in vitro analyses, followed by translational studies utilizing patients' tissue and liquid biopsy materials, are required.

Selective cutoff reporting in primary diagnostic accuracy studies with continuous or ordinal data may result in biased estimates when meta-analyzing studies. Collecting individual participant data (IPD) and estimating accuracy across all relevant cutoffs for all studies can overcome such bias but is labour intensive. We meta-analyzed the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared results for two statistical methods proposed by Steinhauser and by Jones to account for missing cutoffs, with results from a series of bivariate random effects models (BRM) estimated separately at each cutoff. We applied the methods to a dataset that contained information only on cutoffs that were reported in the primary publications and to the full IPD dataset that contained information for all cutoffs for every study. For each method, we estimated pooled sensitivity and specificity and associated 95% confidence intervals for each cutoff and area under the curve (AUC). The full IPD dataset comprised data from 45 studies, 15 020 subjects, and 1972 cases of major depression and included information on every possible cutoff. When using data available in publications, using statistical approaches outperformed the BRM applied to the same data. AUC was similar for all approaches when using the full IPD dataset, though pooled estimates were slightly different. Overall, using statistical methods to fill in missing cutoff data recovered the receiver operating characteristic (ROC) curve from the full IPD dataset well when using only the published subset. All methods performed similarly when applied to the full IPD dataset., (© 2020 John Wiley & Sons Ltd.)

Background: Postoperative nausea and vomiting (PONV) is a common adverse effect of anaesthesia and surgery. Up to 80% of patients may be affected. These outcomes are a major cause of patient dissatisfaction and may lead to prolonged hospital stay and higher costs of care along with more severe complications. Many antiemetic drugs are available for prophylaxis. They have various mechanisms of action and side effects, but there is still uncertainty about which drugs are most effective with the fewest side effects., Objectives: • To compare the efficacy and safety of different prophylactic pharmacologic interventions (antiemetic drugs) against no treatment, against placebo, or against each other (as monotherapy or combination prophylaxis) for prevention of postoperative nausea and vomiting in adults undergoing any type of surgery under general anaesthesia • To generate a clinically useful ranking of antiemetic drugs (monotherapy and combination prophylaxis) based on efficacy and safety • To identify the best dose or dose range of antiemetic drugs in terms of efficacy and safety SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, and reference lists of relevant systematic reviews. The first search was performed in November 2017 and was updated in April 2020. In the update of the search, 39 eligible studies were found that were not included in the analysis (listed as awaiting classification)., Selection Criteria: Randomized controlled trials (RCTs) comparing effectiveness or side effects of single antiemetic drugs in any dose or combination against each other or against an inactive control in adults undergoing any type of surgery under general anaesthesia. All antiemetic drugs belonged to one of the following substance classes: 5-HT₃ receptor antagonists, D₂ receptor antagonists, NK₁ receptor antagonists, corticosteroids, antihistamines, and anticholinergics. No language restrictions were applied. Abstract publications were excluded., Data Collection and Analysis: A review team of 11 authors independently assessed trials for inclusion and risk of bias and subsequently extracted data. We performed pair-wise meta-analyses for drugs of direct interest (amisulpride, aprepitant, casopitant, dexamethasone, dimenhydrinate, dolasetron, droperidol, fosaprepitant, granisetron, haloperidol, meclizine, methylprednisolone, metoclopramide, ondansetron, palonosetron, perphenazine, promethazine, ramosetron, rolapitant, scopolamine, and tropisetron) compared to placebo (inactive control). We performed network meta-analyses (NMAs) to estimate the relative effects and ranking (with placebo as reference) of all available single drugs and combinations. Primary outcomes were vomiting within 24 hours postoperatively, serious adverse events (SAEs), and any adverse event (AE). Secondary outcomes were drug class-specific side effects (e.g. headache), mortality, early and late vomiting, nausea, and complete response. We performed subgroup network meta-analysis with dose of drugs as a moderator variable using dose ranges based on previous consensus recommendations. We assessed certainty of evidence of NMA treatment effects for all primary outcomes and drug class-specific side effects according to GRADE (CINeMA, Confidence in Network Meta-Analysis). We restricted GRADE assessment to single drugs of direct interest compared to placebo., Main Results: We included 585 studies (97,516 randomized participants). Most of these studies were small (median sample size of 100); they were published between 1965 and 2017 and were primarily conducted in Asia (51%), Europe (25%), and North America (16%). Mean age of the overall population was 42 years. Most participants were women (83%), had American Society of Anesthesiologists (ASA) physical status I and II (70%), received perioperative opioids (88%), and underwent gynaecologic (32%) or gastrointestinal surgery (19%) under general anaesthesia using volatile anaesthetics (88%). In this review, 44 single drugs and 51 drug combinations were compared. Most studies investigated only single drugs (72%) and included an inactive control arm (66%). The three most investigated single drugs in this review were ondansetron (246 studies), dexamethasone (120 studies), and droperidol (97 studies). Almost all studies (89%) reported at least one efficacy outcome relevant for this review. However, only 56% reported at least one relevant safety outcome. Altogether, 157 studies (27%) were assessed as having overall low risk of bias, 101 studies (17%) overall high risk of bias, and 327 studies (56%) overall unclear risk of bias. Vomiting within 24 hours postoperatively Relative effects from NMA for vomiting within 24 hours (282 RCTs, 50,812 participants, 28 single drugs, and 36 drug combinations) suggest that 29 out of 36 drug combinations and 10 out of 28 single drugs showed a clinically important benefit (defined as the upper end of the 95% confidence interval (CI) below a risk ratio (RR) of 0.8) compared to placebo. Combinations of drugs were generally more effective than single drugs in preventing vomiting. However, single NK₁ receptor antagonists showed treatment effects similar to most of the drug combinations. High-certainty evidence suggests that the following single drugs reduce vomiting (ordered by decreasing efficacy): aprepitant (RR 0.26, 95% CI 0.18 to 0.38, high certainty, rank 3/28 of single drugs); ramosetron (RR 0.44, 95% CI 0.32 to 0.59, high certainty, rank 5/28); granisetron (RR 0.45, 95% CI 0.38 to 0.54, high certainty, rank 6/28); dexamethasone (RR 0.51, 95% CI 0.44 to 0.57, high certainty, rank 8/28); and ondansetron (RR 0.55, 95% CI 0.51 to 0.60, high certainty, rank 13/28). Moderate-certainty evidence suggests that the following single drugs probably reduce vomiting: fosaprepitant (RR 0.06, 95% CI 0.02 to 0.21, moderate certainty, rank 1/28) and droperidol (RR 0.61, 95% CI 0.54 to 0.69, moderate certainty, rank 20/28). Recommended and high doses of granisetron, dexamethasone, ondansetron, and droperidol showed clinically important benefit, but low doses showed no clinically important benefit. Aprepitant was used mainly at high doses, ramosetron at recommended doses, and fosaprepitant at doses of 150 mg (with no dose recommendation available). Frequency of SAEs Twenty-eight RCTs were included in the NMA for SAEs (10,766 participants, 13 single drugs, and eight drug combinations). The certainty of evidence for SAEs when using one of the best and most reliable anti-vomiting drugs (aprepitant, ramosetron, granisetron, dexamethasone, ondansetron, and droperidol compared to placebo) ranged from very low to low. Droperidol (RR 0.88, 95% CI 0.08 to 9.71, low certainty, rank 6/13) may reduce SAEs. We are uncertain about the effects of aprepitant (RR 1.39, 95% CI 0.26 to 7.36, very low certainty, rank 11/13), ramosetron (RR 0.89, 95% CI 0.05 to 15.74, very low certainty, rank 7/13), granisetron (RR 1.21, 95% CI 0.11 to 13.15, very low certainty, rank 10/13), dexamethasone (RR 1.16, 95% CI 0.28 to 4.85, very low certainty, rank 9/13), and ondansetron (RR 1.62, 95% CI 0.32 to 8.10, very low certainty, rank 12/13). No studies reporting SAEs were available for fosaprepitant. Frequency of any AE Sixty-one RCTs were included in the NMA for any AE (19,423 participants, 15 single drugs, and 11 drug combinations). The certainty of evidence for any AE when using one of the best and most reliable anti-vomiting drugs (aprepitant, ramosetron, granisetron, dexamethasone, ondansetron, and droperidol compared to placebo) ranged from very low to moderate. Granisetron (RR 0.92, 95% CI 0.80 to 1.05, moderate certainty, rank 7/15) probably has no or little effect on any AE. Dexamethasone (RR 0.77, 95% CI 0.55 to 1.08, low certainty, rank 2/15) and droperidol (RR 0.89, 95% CI 0.81 to 0.98, low certainty, rank 6/15) may reduce any AE. Ondansetron (RR 0.95, 95% CI 0.88 to 1.01, low certainty, rank 9/15) may have little or no effect on any AE. We are uncertain about the effects of aprepitant (RR 0.87, 95% CI 0.78 to 0.97, very low certainty, rank 3/15) and ramosetron (RR 1.00, 95% CI 0.65 to 1.54, very low certainty, rank 11/15) on any AE. No studies reporting any AE were available for fosaprepitant. Class-specific side effects For class-specific side effects (headache, constipation, wound infection, extrapyramidal symptoms, sedation, arrhythmia, and QT prolongation) of relevant substances, the certainty of evidence for the best and most reliable anti-vomiting drugs mostly ranged from very low to low. Exceptions were that ondansetron probably increases headache (RR 1.16, 95% CI 1.06 to 1.28, moderate certainty, rank 18/23) and probably reduces sedation (RR 0.87, 95% CI 0.79 to 0.96, moderate certainty, rank 5/24) compared to placebo. The latter effect is limited to recommended and high doses of ondansetron. Droperidol probably reduces headache (RR 0.76, 95% CI 0.67 to 0.86, moderate certainty, rank 5/23) compared to placebo. We have high-certainty evidence that dexamethasone (RR 1.00, 95% CI 0.91 to 1.09, high certainty, rank 16/24) has no effect on sedation compared to placebo. No studies assessed substance class-specific side effects for fosaprepitant. Direction and magnitude of network effect estimates together with level of evidence certainty are graphically summarized for all pre-defined GRADE-relevant outcomes and all drugs of direct interest compared to placebo in http://doi.org/10.5281/zenodo.4066353., Authors' Conclusions: We found high-certainty evidence that five single drugs (aprepitant, ramosetron, granisetron, dexamethasone, and ondansetron) reduce vomiting, and moderate-certainty evidence that two other single drugs (fosaprepitant and droperidol) probably reduce vomiting, compared to placebo. Four of the six substance classes (5-HT₃ receptor antagonists, D₂ receptor antagonists, NK₁ receptor antagonists, and corticosteroids) were thus represented by at least one drug with important benefit for prevention of vomiting. Combinations of drugs were generally more effective than the corresponding single drugs in preventing vomiting. NK₁ receptor antagonists were the most effective drug class and had comparable efficacy to most of the drug combinations. 5-HT₃ receptor antagonists were the best studied substance class. For most of the single drugs of direct interest, we found only very low to low certainty evidence for safety outcomes such as occurrence of SAEs, any AE, and substance class-specific side effects. Recommended and high doses of granisetron, dexamethasone, ondansetron, and droperidol were more effective than low doses for prevention of vomiting. Dose dependency of side effects was rarely found due to the limited number of studies, except for the less sedating effect of recommended and high doses of ondansetron. The results of the review are transferable mainly to patients at higher risk of nausea and vomiting (i.e. healthy women undergoing inhalational anaesthesia and receiving perioperative opioids). Overall study quality was limited, but certainty assessments of effect estimates consider this limitation. No further efficacy studies are needed as there is evidence of moderate to high certainty for seven single drugs with relevant benefit for prevention of vomiting. However, additional studies are needed to investigate potential side effects of these drugs and to examine higher-risk patient populations (e.g. individuals with diabetes and heart disease)., (Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Introduction: Obesity is highly prevalent in older adults aged 65 years or older. Different lifestyle interventions (diet, exercise, self-management) are available but benefits and harms have not been fully quantified comparing all available health promotion interventions. Special consideration must be given to functional outcomes and possible adverse effects (loss of muscle and bone mass, hypoglycaemia) of weight loss interventions in this age group. The objective of this study is to synthesise the evidence regarding the effects of different types and modalities of lifestyle interventions, or their combinations, on physical function and obesity-related outcomes such as body composition in older adults with obesity., Methods and Analyses: Six databases (Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Psychinfo and Web of Science) and two trial registries (Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform) will be searched for randomised controlled trials of lifestyle interventions in older adults with obesity. Screening (title/abstract and full-text) and data extraction of references as well as assessment of risk of bias and rating of the certainty of evidence (Grading of Recommendations, Assessment, Development and Evaluation for network meta-analyses) will be performed by two reviewers independently. Frequentist random-effects network meta-analyses will be conducted to determine the pooled effects from each intervention., Ethics and Dissemination: We will submit our findings to peer-reviewed journals and present at national and international conferences as well as in scientific medical societies. Patient-targeted dissemination will involve local and national advocate groups., Prospero Registration Number: CRD42019147286., Competing Interests: Competing interests: LS is a member of the GRADE working group. JAB is funded by the National Institute on Aging and Office of Dietary Supplements of the National Institutes of Health under Award Number K23AG051681, and the R01 AG067416., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Insomnia disorder, defined by nocturnal and daytime symptoms, is highly prevalent worldwide and is associated with the onset of mental illness. Although daytime symptoms are often the reason insomnia patients seek help, it is not clear whether recommended treatment is effective on daytime symptoms. We aimed to investigate the efficacy of cognitive and behavior therapies for insomnia (CBT-I) on all daytime symptoms explored in the literature using both direct and indirect data. 86 studies (15,578 participants) met inclusion criteria. Results showed significant effects of CBT-I administered face-to-face individually, in group and different self-help settings on depressive symptoms, anxiety, daytime sleepiness, fatigue, quality of life, daytime and social functioning and mental state, with Cohen's d's ranging from -0.52 and 0.81. Our results suggest that CBT-I is effective in the treatment of daytime symptoms, albeit with predominantly small to moderate effects compared to far stronger effects on the core symptoms of insomnia. Effects may be biased for depressive and anxiety symptoms, since many included studies excluded patients with severe levels of these complaints. Further, small to moderate effects may reflect that CBT-I, by improving nighttime symptoms, has a positive effect on daytime symptoms, but it does not target the daytime symptoms directly. Future studies may benefit from adding therapeutic techniques that address daytime symptoms more directly., Competing Interests: Declaration of Competing Interest All authors declare that they have no conflicts of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements of a hat matrix. It is, however, not fully clear how to generalize this to the percentage contribution of each study to a NMA estimate., Methods: We define the importance of each study for a NMA estimate by the reduction of the estimate's variance when adding the given study to the others. An equivalent interpretation is the relative loss in precision when the study is left out. Importances are values between 0 and 1. An importance of 1 means that the study is an essential link of the pathway in the network connecting one of the treatments with another., Results: Importances can be defined for two-stage and one-stage NMA. These numbers in general do not add to one and thus cannot be interpreted as 'percentage contributions'. After briefly discussing other available approaches, we question whether it is possible to obtain unique percentage contributions for NMA., Conclusions: Importances generalize the concept of weights in pairwise meta-analysis in a natural way. Moreover, they are uniquely defined, easily calculated, and have an intuitive interpretation. We give some real examples for illustration.