Model selection for component network meta-analysis in connected and disconnected networks: a simulation study.

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.

Empirical evidence of study design biases in nutrition randomised controlled trials: a meta-epidemiological study.

BACKGROUND: Instruments to critically appraise randomised controlled trials (RCTs) are based on evidence from meta-epidemiological studies. We aim to conduct a meta-epidemiological study on the average bias associated with reported methodological trial characteristics such as random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and compliance of RCTs in nutrition research. METHODS: We searched the Cochrane Database of Systematic Reviews, for systematic reviews of RCTs, published between 01 January 2010 and 31 December 2019. We combined the estimates of the average bias (e.g. ratio of risk ratios [RRR] or differences in standardised mean differences) in meta-analyses using the random-effects model. Subgroup analyses were conducted to investigate the potential differences among the RCTs with low versus high/unclear risk of bias with respect to the different types of interventions (e.g. micronutrients, fatty acids, dietary approach), outcomes (e.g. mortality, pregnancy outcomes), and type of outcome (objective, subjective). Heterogeneity was assessed through I2 and τ2, and prediction intervals were calculated. RESULTS: We included 27 Cochrane nutrition reviews with 77 meta-analyses (n = 927 RCTs). The available evidence suggests that intervention effect estimates may not be exaggerated in RCTs with high/unclear risk of bias (versus low) judgement for sequence generation (RRR 0.97, 95% CI 0.93 to 1.02; I2 = 28%; τ2 = 0.002), allocation concealment (RRR 1.00, 95% CI 0.96 to 1.04; I2 = 27%; τ2 = 0.001), blinding of participants and personnel (RRR 0.95, 95% CI 0.91 to 1.00; I2 = 23%; τ2 = 0), selective reporting (RRR 0.97, 95% CI 0.92 to 1.02; I2 = 24%; τ2 = 0), and compliance (RRR 0.95, 95% CI 0.89 to 1.02; I2 = 0%; τ2 = 0). Intervention effect estimates seemed to be exaggerated in RCTs with a high/unclear risk of bias judgement for blinding of outcome assessment (RRR 0.81, 95% CI 0.70 to 0.94; I2 = 26%; τ2 = 0.03), which was predominately driven by subjective outcomes, and incomplete outcome data (RRR 0.92, 95% CI 0.88 to 0.97; I2 = 22%; τ2 = 0.001). For continuous outcomes, no differences were observed, except for selective reporting. CONCLUSIONS: On average, most characteristics of nutrition RCTs may not exaggerate intervention effect estimates, but the average bias appears to be greatest in trials of subjective outcomes. Replication of this study is suggested in this field to keep this conclusion updated.

Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study.

BACKGROUND: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess the treatment effects of medical interventions. To evaluate the agreement of effect estimates between bodies of evidence (BoE) from randomized controlled trials (RCTs) and cohort studies and to identify factors associated with disagreement. METHODS: Systematic reviews were published in the 13 medical journals with the highest impact factor identified through a MEDLINE search. BoE-pairs from RCTs and cohort studies with the same medical research question were included. We rated the similarity of PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) between BoE from RCTs and cohort studies. The agreement of effect estimates across BoE was analyzed by pooling ratio of ratios (RoR) for binary outcomes and difference of mean differences for continuous outcomes. We performed subgroup analyses to explore factors associated with disagreements. RESULTS: One hundred twenty-nine BoE pairs from 64 systematic reviews were included. PI/ECO-similarity degree was moderate: two BoE pairs were rated as "more or less identical"; 90 were rated as "similar but not identical" and 37 as only "broadly similar". For binary outcomes, the pooled RoR was 1.04 (95% CI 0.97-1.11) with considerable statistical heterogeneity. For continuous outcomes, differences were small. In subgroup analyses, degree of PI/ECO-similarity, type of intervention, and type of outcome, the pooled RoR indicated that on average, differences between both BoE were small. Subgroup analysis by degree of PI/ECO-similarity revealed high statistical heterogeneity and wide prediction intervals across PI/ECO-dissimilar BoE pairs. CONCLUSIONS: On average, the pooled effect estimates between RCTs and cohort studies did not differ. Statistical heterogeneity and wide prediction intervals were mainly driven by PI/ECO-dissimilarities (i.e., clinical heterogeneity) and cohort studies. The potential influence of risk of bias and certainty of the evidence on differences of effect estimates between RCTs and cohort studies needs to be explored in upcoming meta-epidemiological studies.

A forward search algorithm for detecting extreme study effects in network meta-analysis.

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.

The Effect of Transfusion of Two Units of Fresh Frozen Plasma on the Perioperative Fibrinogen Levels and the Outcome of Patients Undergoing Elective Endovascular Repair for Abdominal Aortic Aneurysm.

BACKGROUND: We aimed to investigate whether the transfusion of 2 units of fresh frozen plasma (FFP) immediately post aneurysm exclusion has any effect on the perioperative fibrinogen levels and the outcome of patients undergoing elective endovascular repair (EVAR) of abdominal aortic aneurysm (AAA). METHODS: Consecutive infrarenal AAA patients undergoing elective EVAR with the bifurcated Endurant-II stent-graft (Medtronic) were recruited from 2 vascular units. The first unit has a routine policy of administering 2 units of FFP immediately upon aneurysm exclusion (FFP group), whereas the second unit has no such policy (control group). Serum fibrinogen levels were measured on admission and 24 hr post-EVAR and the perioperative change in fibrinogen (Δfib) was calculated (24-hr postoperative minus preoperative fibrinogen). The 2 groups were compared with regards to the perioperative fibrinogen levels (preoperative, 24-hr postoperative, and Δfib) and the outcome (endoleaks, reinterventions, major adverse cardiovascular events, death) during follow up. RESULTS: A total of 70 patients (41 in the FFP group, 29 controls) were examined. There were 68 men, the mean age was 70 ± 7 years and the maximum AAA diameter was 63.3 ± 13.8 mm. During the follow up (34 ± 19 months), a total of 6 endoleaks were recorded (2 type Ia, 2 type Ib and 1 type II). Mean preoperative fibrinogen, 24-hr postoperative fibrinogen and Δfib was 391.1 ± 92.8 mg/dL, 367.7 ± 97.8 mg/dL and -23.5 ± 51.02 mg/dL, respectively. There was a trend for the fibrinogen to fall 24 hr postprocedure, but this was not statistically significant (P = 0.07). There was a weak negative association between Δfib and endoleaks (P = 0.007, r = -0.29). Compared to controls, the FFP group had a higher 24-hr postoperative fibrinogen (401.8 ± 112.9 mg/dL vs. 319.3 ± 34.9 mg/dL, P < 0.0001) and a lower Δfib (-3.00 ± 56.01 mg/dL vs. -52.48 ± 21.15 mg/dL, P < 0.0001). No significant difference was observed between the 2 groups with regards to endoleaks, reinterventions, major adverse cardiovascular events, or deaths. CONCLUSIONS: Transfusion of 2 units of FFP postaneurysm exclusion prevents a significant drop in plasma fibrinogen 24 hr post-EVAR, but the impact on clinical outcome has yet to be defined.

Network meta-analysis of multicomponent interventions.

In network meta-analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta-analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.

Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review.

BACKGROUND: Network meta-analysis (NMA) has become a popular method to compare more than two treatments. This scoping review aimed to explore the characteristics and methodological quality of knowledge synthesis approaches underlying the NMA process. We also aimed to assess the statistical methods applied using the Analysis subdomain of the ISPOR checklist. METHODS: Comprehensive literature searches were conducted in MEDLINE, PubMed, EMBASE, and Cochrane Database of Systematic Reviews from inception until April 14, 2015. References of relevant reviews were scanned. Eligible studies compared at least four different interventions from randomised controlled trials with an appropriate NMA approach. Two reviewers independently performed study selection and data abstraction of included articles. All discrepancies between reviewers were resolved by a third reviewer. Data analysis involved quantitative (frequencies) and qualitative (content analysis) methods. Quality was evaluated using the AMSTAR tool for the conduct of knowledge synthesis and the ISPOR tool for statistical analysis. RESULTS: After screening 3538 citations and 877 full-text papers, 456 NMAs were included. These were published between 1997 and 2015, with 95% published after 2006. Most were conducted in Europe (51%) or North America (31%), and approximately one-third reported public sources of funding. Overall, 84% searched two or more electronic databases, 62% searched for grey literature, 58% performed duplicate study selection and data abstraction (independently), and 62% assessed risk of bias. Seventy-eight (17%) NMAs relied on previously conducted systematic reviews to obtain studies for inclusion in their NMA. Based on the AMSTAR tool, almost half of the NMAs incorporated quality appraisal results to formulate conclusions, 36% assessed publication bias, and 16% reported the source of funding. Based on the ISPOR tool, half of the NMAs did not report if an assessment for consistency was conducted or whether they accounted for inconsistency when present. Only 13% reported heterogeneity assumptions for the random-effects model. CONCLUSIONS: The knowledge synthesis methods and analytical process for NMAs are poorly reported and need improvement.

Pain Management Modalities after Total Knee Arthroplasty: A Network Meta-analysis of 170 Randomized Controlled Trials.

BACKGROUND: Optimal analgesia for total knee arthroplasty remains challenging. Many modalities have been used, including peripheral nerve block, periarticular infiltration, and epidural analgesia. However, the relative efficacy of various modalities remains unknown. The authors aimed to quantify and rank order the efficacy of available analgesic modalities for various clinically important outcomes. METHODS: The authors searched multiple databases, each from inception until July 15, 2016. The authors used random-effects network meta-analysis. For measurements repeated over time, such as pain, the authors considered all time points to enhance reliability of the overall effect estimate. Outcomes considered included pain scores, opioid consumption, rehabilitation profile, quality of recovery, and complications. The authors defined the optimal modality as the one that best balanced pain scores, opioid consumption, and range of motion in the initial 72 postoperative hours. RESULTS: The authors identified 170 trials (12,530 patients) assessing 17 treatment modalities. Overall inconsistency and heterogeneity were acceptable. Based on the surface under the cumulative ranking curve, the best five for pain at rest were femoral/obturator, femoral/sciatic/obturator, lumbar plexus/sciatic, femoral/sciatic, and fascia iliaca compartment blocks. For reducing opioid consumption, the best five were femoral/sciatic/obturator, femoral/obturator, lumbar plexus/sciatic, lumbar plexus, and femoral/sciatic blocks. The best modality for range of motion was femoral/sciatic blocks. Femoral/sciatic and femoral/obturator blocks best met our criteria for optimal performance. Considering only high-quality studies, femoral/sciatic seemed best. CONCLUSIONS: Blocking multiple nerves was preferable to blocking any single nerve, periarticular infiltration, or epidural analgesia. The combination of femoral and sciatic nerve block appears to be the overall best approach. Rehabilitation parameters remain markedly understudied.

A comparison of 20 heterogeneity variance estimators in statistical synthesis of results from studies: a simulation study.

When we synthesize research findings via meta-analysis, it is common to assume that the true underlying effect differs across studies. Total variability consists of the within-study and between-study variances (heterogeneity). There have been established measures, such as I2 , to quantify the proportion of the total variation attributed to heterogeneity. There is a plethora of estimation methods available for estimating heterogeneity. The widely used DerSimonian and Laird estimation method has been challenged, but knowledge of the overall performance of heterogeneity estimators is incomplete. We identified 20 heterogeneity estimators in the literature and evaluated their performance in terms of mean absolute estimation error, coverage probability, and length of the confidence interval for the summary effect via a simulation study. Although previous simulation studies have suggested the Paule-Mandel estimator, it has not been compared with all the available estimators. For dichotomous outcomes, estimating heterogeneity through Markov chain Monte Carlo is a good choice if an informative prior distribution for heterogeneity is employed (eg, by published Cochrane reviews). Nonparametric bootstrap and positive DerSimonian and Laird perform well for all assessment criteria for both dichotomous and continuous outcomes. Hartung-Makambi estimator can be the best choice when the heterogeneity values are close to 0.07 for dichotomous outcomes and medium heterogeneity values (0.01 , 0.05) for continuous outcomes. Hence, there are heterogeneity estimators (nonparametric bootstrap DerSimonian and Laird and positive DerSimonian and Laird) that perform better than the suggested Paule-Mandel. Maximum likelihood provides the best performance for both types of outcome in the absence of heterogeneity.

The use of immobilized artificial membrane chromatography to predict bioconcentration of pharmaceutical compounds.

The potential of immobilized artificial membrane chromatography (IAM) to predict bioconcentration factors (BCF) of pharmaceutical compounds in aquatic organisms was studied. For this purpose, retention factors extrapolated to pure aqueous phase, logkw(IAM), of 27 drugs were measured on an IAM stationary phase, IAM.PC.MG type. The data were combined with retention factors on two IAM columns, IAM.PC.MG and IAM.PC.DD2 types, reported previously by our research group and correlated with logBCF values predicted by Estimation Program Interface (EPI Suite) Software. Linear models were established upon exclusion of ionic or highly hydrophilic nonionic drugs, for which a constant value of logBCF equal to 0.50 was arbitrarily assigned by EPI Suite Software. As additional physicochemical parameter BioWin5 proved to be statistically significant, expressing the decrease of bioaccumulation potential as a result of biodegradation in the aquatic environment. The constructed IAM model was successfully validated by application to a set of pharmaceuticals, whose experimental BCF values are available. Better predictions compared to EPI Suite Software were achieved for the dataset under study. Since bioconcentration process involves electrostatic interactions, IAM retention may be a better measure for BCF values, especially for ionic species, compared to octanol-water partition coefficients widely implemented in environmental sciences. The developed approach can be considered as a novel tool for the prediction of bioconcentration of pharmaceutical compounds in aquatic organisms in order to minimize further experimental assays in the future.

Particulate matter emissions, and metals and toxic elements in airborne particulates emitted from biomass combustion: The importance of biomass type and combustion conditions.

The aim of this study was to investigate the impact of biomass combustion with respect to burning conditions and fuel types on particulate matter emissions (PM10) and their metals as well as toxic elements content. For this purpose, different lab scale burning conditions were tested (20 and 13% O2 in the exhaust gas which simulate an incomplete and complete combustion respectively). Furthermore, two pellet stoves (8.5 and 10 kW) and one open fireplace were also tested. In all cases, 8 fuel types of biomass produced in Greece were used. Average PM10 emissions ranged at laboratory-scale combustions from about 65 to 170 mg/m3 with flow oxygen at 13% in the exhaust gas and from 85 to 220 mg/m3 at 20% O2. At pellet stoves the emissions were found lower (35 -85 mg/m3) than the open fireplace (105-195 mg/m3). The maximum permitted particle emission limit is 150 mg/m3. Metals on the PM10 filters were determined by several spectrometric techniques after appropriate digestion or acid leaching of the filters, and the results obtained by these two methods were compared. The concentration of PM10 as well as the total concentration of the metals on the filters after the digestion procedure appeared higher at laboratory-scale combustions with flow oxygen at 20% in the exhaust gas and even higher at fireplace in comparison to laboratory-scale combustions with 13% O2 and pellet stoves. Modern combustion appliances and appropriate types of biomass emit lower PM10 emissions and lower concentration of metals than the traditional devices where incomplete combustion conditions are observed. Finally, a comparison with other studies was conducted resulting in similar results.

PM10 emissions and PAHs: The importance of biomass type and combustion conditions.

The aim of the present work was to investigate the impact of biomass combustion with respect to conditions and fuel types on particle emissions (PM10) and their PAHs content. Special concern was on sampling, quantification and characterization of PM using different appliances, fuels and operating procedures. For this purpose different lab-scale burning conditions, two pellets stoves (8.5 and 10 kW) and one open fireplace were tested by using eight fuel types of biomass. An analytical method is described for the quantitative determination of 16 PAHs using liquid-liquid extraction and subsequent measurement by gas chromatography coupled to a mass spectrometer (GC-MS). Average PM10 emissions ranged from about 65 to 170 mg/m(3) at lab-scale combustions with flow oxygen at 13% in the exhaust gas, 85-220 mg/m(3) at 20% O2, 47-83 mg/m(3) at pellet stove of 10 kW, 34-69 mg/m(3) at pellet stove of 8.5 kW and 106-194 mg/m(3) at the open fireplace. The maximum permitted particle emission limit is 150 mg/m(3). Pellets originated from olive trees and from nonmixture trees were found to emit the lowest particulate matter in relation to the others, so they are considered healthiest and suitable for domestic heating reasons. In general, the results show that biomass open burning is an important PM10 and PAHs emission source.

Estimation of the residual bromine concentration after disinfection of cooling water by statistical evaluation.

A statistical model based on multiple linear regression is developed, to estimate the bromine residual that can be expected after the bromination of cooling water. Make-up water sampled from a power plant in the Greek territory was used for the creation of the various cooling water matrices under investigation. The amount of bromine fed to the circuit, as well as other important operational parameters such as concentration at the cooling tower, temperature, organic load and contact time are taken as the independent variables. It is found that the highest contribution to the model's predictive ability comes from cooling water's organic load concentration, followed by the amount of bromine fed to the circuit, the water's mean temperature, the duration of the bromination period and finally its conductivity. Comparison of the model results with the experimental data confirms its ability to predict residual bromine given specific bromination conditions.

Does High-Velocity Resistance Exercise Elicit Greater Physical Function Benefits Than Traditional Resistance Exercise in Older Adults? A Systematic Review and Network Meta-Analysis of 79 Trials.

BACKGROUND: A systematic review and network meta-analysis was undertaken to examine the effectiveness of different modes of resistance exercise velocity in fast walking speed, timed-up and go, 5-times sit-to-stand, 30-second sit-to-stand, and 6-minute walking tests in older adults. METHODS: CINAHL, Embase, LILACS, PubMed, Scielo, SPORTDiscus, and Web of Science databases were searched up to February 2022. Eligible randomized trials examined the effects of supervised high-velocity or traditional resistance exercise in older adults (ie, ≥60 years). The primary outcome for this review was physical function measured by fast walking speed, timed-up and go, 5-times sit-to-stand, 30-second sit-to-stand, and 6-minute walking tests, while maximal muscle power and muscle strength were secondary. A random-effects network meta-analysis was undertaken to examine the effects of different resistance exercise interventions. RESULTS: Eighty articles describing 79 trials (n = 3 575) were included. High-velocity resistance exercise was the most effective for improving fast walking speed (standardized mean difference [SMD] -0.44, 95% confidence interval [CI]: 0.00 to 0.87), timed-up and go (SMD -0.76, 95% CI: -1.05 to -0.47), and 5-times sit-to-stand (SMD -0.74, 95% CI: -1.20 to -0.27), while traditional resistance exercise was the most effective for 30-second sit-to-stand (SMD 1.01, 95% CI: 0.68 to 1.34) and 6-minute walking (SMD 0.68, 95% CI: 0.34 to 1.03). CONCLUSION: Our study provides evidence that resistance exercise velocity effects are specific in older adults, as evidenced by physical function test dependence. We suggest that prescriptions based on the velocity of contraction should be individualized to address the specific functional needs of participants.

Application of micellar liquid chromatography to model ecotoxicity of pesticides. Comparison with immobilized artificial membrane chromatography and n-octanol-water partitioning.

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC50/ EC50 values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD50 values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Effects of Dairy Intake on Markers of Cardiometabolic Health in Adults: A Systematic Review with Network Meta-Analysis.

The health effects of dairy products are still a matter of scientific debate owing to inconsistent findings across trials. Therefore, this systematic review and network meta-analysis (NMA) aimed to compare the effects of different dairy products on markers of cardiometabolic health. A systematic search was conducted in 3 electronic databases [MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science; search date: 23 September 2022]. This study included randomized controlled trials (RCTs) with a ≥12-wk intervention comparing any 2 of the eligible interventions [e.g., high dairy (≥3 servings/d or equal amount in grams per day), full-fat dairy, low-fat dairy, naturally fermented milk products, and low dairy/control (0-2 servings/d or usual diet)]. A pairwise meta-analysis and NMA using random-effects model was performed in the frequentist framework for 10 outcomes [body weight, BMI, fat mass, waist circumference, low-density lipoprotein cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, fasting glucose, glycated hemoglobin, and systolic blood pressure]. Continuous outcome data were pooled using mean differences (MDs) and dairy interventions ranked using the surface under the cumulative ranking curve. Nineteen RCTs with 1427 participants were included. High-dairy intake (irrespective of fat content) showed no detrimental effects on anthropometric outcomes, blood lipids, and blood pressure. Both low-fat and full-fat dairy improved systolic blood pressure (MD: -5.22 to -7.60 mm Hg; low certainty) but, concomitantly, may impair glycemic control (fasting glucose-MD: 0.31-0.43 mmol/L; glycated hemoglobin-MD: 0.37%-0.47%). Full-fat dairy may increase HDL cholesterol compared with a control diet (MD: 0.26 mmol/L; 95% CI: 0.03, 0.49 mmol/L). Yogurt improved waist circumference (MD: -3.47 cm; 95% CI: -6.92, -0.02 cm; low certainty), triglycerides (MD: -0.38 mmol/L; 95% CI: -0.73, -0.03 mmol/L; low certainty), and HDL cholesterol (MD: 0.19 mmol/L; 95% CI: 0.00, 0.38 mmol/L) compared with milk. In conclusion, our findings indicate that there is little robust evidence that a higher dairy intake has detrimental effects on markers of cardiometabolic health. This review was registered at PROSPERO as CRD42022303198.

Exploring the elution mechanism of selenium species on liquid chromatography.

In the present work, the chromatographic behavior of eight selenium species, namely selenites (Se(IV)), selenates (Se(VI)), seleno-DL-methionine (Se-Met), selenocystine (Se-Cyst), selenocystamine (Se-CM), selenourea (Se-U), dimethylselenide ((CH(3))(2) Se) and dimethyldiselenide ((CH(3))(2) Se(2)), was investigated under different stationary and mobile phase conditions, in an effort to unravel secondary interferences in their underlying elution mechanism. For this purpose, two end-capped and a polar-embedded reversed-phase stationary phases were employed using different mobile phase conditions. Retention factors (log k(w)) were compared with octanol-water distribution coefficients (log D) as well as with log k(w) values on two immobilized artificial membrane (IAM) columns and two immobilized artificial plasma proteins stationary phases, obtained in our previous work. The role of electrostatic interactions was confirmed by introducing the net charge of the investigated Se species as an additional term in the log k(w)/log D interrelation, which in most cases proved to be statistically significant. Principal component analysis of retention factors on all stationary phases and octanol-water log D values, however, showed that the elution of the investigated selenium species is mainly governed by partitioning mechanism under all different chromatographic conditions, while the pH of the mobile phase and the special column characteristics have only a minor effect.

A review of methods for addressing components of interventions in meta-analysis.

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.

Biomimetic chromatographic analysis of selenium species: application for the estimation of their pharmacokinetic properties.

The retention behavior of selenites, selenates, seleno-DL-methionine, selenocystine, selenocystamine, selenourea, dimethyl selenide, and dimethyl diselenide was investigated by means of biomimetic liquid chromatography. For this purpose, two immobilized artificial membrane (IAM) columns, namely, IAM.PC.DD2 and IAM.PC.MG, and two immobilized plasma protein columns, human serum albumin (HSA) and alpha(1)-acid glycoprotein (AGP) columns, were employed using different mobile phase conditions in respect to pH and buffer composition. In general, satisfactory interrelations between retention factors obtained with the two IAM stationary phases and HSA/AGP columns were obtained. Large differences were observed between biomimetic retention factors and octanol-water logD values, since the latter fail to describe electrostatic interactions. In contrast, despite the column diversity, the net retention outcome on all four biomimetic columns was quite similar, especially in the presence of phosphate-buffered saline, which by its effective shielding alleviates the differences between the stationary phases. Of the two IAM columns, IAM.PC.DD2 showed better performance when compared with HSA and AGP columns as well as to octanol-water partitioning. Biomimetic chromatographic indices were further used to estimate the percentage of human oral absorption and plasma protein binding of the eight selenium species investigated, according to equations previously reported in the literature. The estimated values of human oral absorption imply moderate absorption only for dimethyl diselenide, which also may exhibit considerable plasma protein binding. Moderate affinity for plasma proteins should also be expected for dimethyl selenide and selenocystamine.