Wiley最新文献

Ranking multiple interventions is a crucial task in network meta-analysis (NMA) to guide clinical and policy decisions. However, conventional ranking methods often oversimplify treatment distinctions, potentially yielding misleading conclusions due to inherent uncertainty in relative intervention effects. To address these limitations, we propose a novel Bayesian rank-clustering estimation approach, termed rank-clustering estimation (RaCE), specifically developed for NMA. Rather than identifying a single "best" intervention, RaCE enables the probabilistic clustering of interventions with similar effectiveness, offering a more nuanced and parsimonious interpretation. By decoupling the clustering procedure from the NMA modeling process, RaCE is a flexible and broadly applicable approach that can accommodate different types of outcomes (binary, continuous, and survival), modeling approaches (arm-based and contrast-based), and estimation frameworks (frequentist or Bayesian). Simulation studies demonstrate that RaCE effectively captures rank-clusters even under conditions of substantial uncertainty and overlapping intervention effects, providing more reasonable result interpretation than traditional single-ranking methods. We illustrate the practical utility of RaCE through an NMA application to frontline immunochemotherapies for follicular lymphoma, revealing clinically relevant clusters among treatments previously assumed to have distinct ranks. Overall, RaCE provides a valuable tool for researchers to enhance rank estimation and interpretability, facilitating evidence-based decision-making in complex intervention landscapes.

We present model-informed selection of the recommended dose for expansion (RDE) of investigational oral ATR inhibitor tuvusertib, by integrating clinical pharmacokinetics (PK), pharmacodynamics (PD), and safety data from DDRiver Solid Tumors 301 trial Part A1 (NCT04170153). A population PK (POPPK) model was developed to characterize PK and hemoglobin (HGB) reduction after multicycle treatment was simulated using a semi-mechanistic, multivariate POPPK/PD model of reticulocyte (RET), red blood cell (RBC), and HGB dynamics. A semi-mechanistic PK-efficacy model characterized concentration-dependent tumor growth inhibition (TGI) in ARID1A mutant xenograft models. The clinical exposure-PD relationship was described for phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) as a biomarker of ATR inhibition. POPPK simulations predict the average steady-state concentrations to exceed phosphorylated checkpoint kinase 1 (pCHK1) IC₉₀ at 100-180 mg once daily (QD) and 180 mg QD 2 weeks (w) on/1w off. Exposure-related PD suggested target engagement of ≥80% at ≥130 mg QD. POPPK/PD modeling showed partial HGB recovery and lower rates of Grade ≥3 anemia after multicycle treatment with 180 mg QD 2w on/1w off vs. 130 mg and 180 mg QD. Lesser HGB reduction was predicted for 100 mg QD vs. higher QD doses. Translational modeling indicated no effect of the one-week dosing break on TGI at 180 mg QD. The analysis supports tuvusertib 180 mg QD 2w on/1w off as the RDE and 100 mg QD as the no-regret dose for clinical evaluation. This example underscores the value of integrated quantitative pharmacology analyses to inform dose selection using a totality of evidence approach.

Polypharmacy is increasingly recognized as a relevant issue in diabetes care, but its prevalence and clinical relevance in individuals with type 1 diabetes remain underexplored. This study aimed to determine the prevalence of polypharmacy and to identify associated clinical and psychological factors. Participants were recruited from a tertiary diabetes outpatient clinic between February 2020 and April 2021. Polypharmacy was defined as the concurrent use of five or more medications, including insulin. Clinical, sensor-based, and psychosocial data were collected. Logistic regression was used to identify variables independently associated with polypharmacy. A total of 484 individuals with type 1 diabetes were included (mean age 51.3 ± 15.9 years; 51.2% male; median diabetes duration 30 [IQR 16-40] years; mean HbA_1c 60.3 ± 11.6 mmol/mol). Polypharmacy was present in 175 (36.2%) participants. Individuals with polypharmacy were more often female, were older, and had longer diabetes duration, higher BMI, higher HbA_1c, more complications, and higher rates of hospital admission. They also were more likely to have impaired awareness of hypoglycemia and reported higher levels of fear of hypoglycemia with no differences in hyperglycemia-related worry or behavior or diabetes-related emotional distress. Polypharmacy affects over one-third of individuals with type 1 diabetes and is associated with poorer health status and a greater hypoglycemia-related burden. Future studies should investigate whether targeted medication review and psychological interventions may alleviate some of the burden in this high-risk group.

In the 1980s, researchers discovered the remarkable ability of electrospray plumes to effectively ionize gas-phase molecules via secondary ionization. Around 20 years later-coinciding with the ambient mass spectrometry revolution-secondary electrospray ionization (SESI) and extractive electrospray ionization (EESI) coupled to mass spectrometry were revisited and further developed to analyze complex mixtures of gas and aerosol samples in real-time yet with high sensitivity. During the past two decades, these mass spectrometric techniques have been applied across a broad range of applications, such as the detection of illicit drugs, environmental aerosol analysis, and a series of metabolomic studies through the analysis of volatiles emitted from living organisms. This review offers a comprehensive overview of the progress of SESI and EESI applications since their emergence. Finally, we discuss the opportunities, challenges, along with future directions of SESI and EESI techniques.

This study explores how bioactive compounds in green coffee beans (Coffea arabica L.) vary across different geographic regions, addressing the key question of how environmental factors shape coffee biochemistry and adaptation mechanisms to diverse conditions. Identifying these variations provides insight into how environmental and processing factors influence coffee's sensory quality. Samples from six major coffee-producing regions were analysed for key bioactive compounds, including biogenic amines, caffeine, trigonelline, sucrose, free amino acids, and phenolics. Total polyphenol content and polyamine concentrations were measured, and PCA was used to differentiate samples based on chemical composition. A correlation analysis was specifically conducted for Brazilian samples, using meteorological and environmental data. Total polyphenol content ranged from 44.8 to 70.7 mg GAeq g^-1 FW, with Brazilian samples having the highest levels. Putrescine, the most abundant polyamine, varied significantly (0.02-1.9 μg g^-1 FW). PCA highlighted Ethiopian samples with high sucrose and low caffeine. Brazilian samples showed distinct separation based on key compounds, including putrescine, trigonelline, and amino acids. Environmental factors in Brazil correlated with polyamine and amino acid composition, suggesting associations with heat and drought tolerance. Environmental factors, particularly heat and drought, influence the biochemical profile of coffee beans. Polyamine levels correlate with stress tolerance, while amino acid composition reflects adaptations for osmotic protection. These findings enhance our understanding of coffee's biochemical adaptation to diverse climates and offer valuable insights for optimizing cultivation strategies in the face of climate change.

Critical appraisal is a core component of JBI qualitative evidence synthesis, offering insights into the quality of included studies and their potential influence on synthesized findings. However, limited guidance exists on whether, when, and how to exclude studies based on appraisal results. This study examined the methods used in JBI qualitative systematic reviews and the implications for synthesized findings. In this study, a systematic analysis of qualitative reviews published between 2018 and 2022 in JBI Evidence Synthesis was conducted. Data on decisions and their justifications were extracted from reviews and protocols. Descriptive and content analysis explored variations in the reported methods. Forty-five reviews were included. Approaches reported varied widely: 24% of reviews included all studies regardless of quality, while others applied exclusion criteria (36%), cutoff scores (11%), or multiple methods (9%). Limited justifications were provided for the approaches. Few reviews cited methodological references to support their decisions. Review authors reported their approach in various sections of the review, with inconsistencies identified in 18% of the sample. In addition, unclear or ambiguous descriptions were also identified in 18% of the included reviews. No clear differences were observed in ConQual scores between reviews that excluded studies and those that did not. Overall, the variability raises concerns about the credibility, transparency, and reproducibility of JBI qualitative systematic reviews. Decisions regarding the inclusion or exclusion of studies based on critical appraisal need to be clearly justified and consistently reported. Further methodological research is needed to support rigorous decision-making and to improve the reliability of synthesized findings.

Trans-acting small interfering RNA (tasiRNAs) are a special type of endogenous small RNAs (sRNAs) found only in plants. Their biogenesis requires an initial miRNA-mediated cleavage of RNA precursors transcribed from TAS genes. TasiRNAs act in trans to silence gene expression by cleaving mRNAs with sequences partially complementary to their own. While Arabidopsis thaliana contains several TAS genes not found in other plants, the miR390-TAS3-ARF pathway is highly conserved among land plant lineages. This pathway exerts its function by silencing a subgroup of Auxin Response Factor (ARF) genes; these tasiRNAs are termed tasiR-ARFs. Many downstream auxin signals are mediated by ARFs acting as transcription factors to confer sensitivity and robustness to the hormone responses in diverse development contexts. These pathway functions are critical for plant growth, developmental timing, and correct organ patterning, such as leaf morphology and polarity, lateral root architecture, and flowering, as well as coping with stress. The phenotypes caused by mutations affecting tasiR-ARF production vary across plant species, showing pleiotropic effects, suggesting a co-opted process where the tasiR-ARF pathway evolution occurred to serve different functions, depending on plant developmental cues. One way to unify the diverse roles of this pathway would be through auxin response integration, possibly by exploring the evolution of ARF3 transcription factors and downstream genes. In this review, we discuss versatility of the tasiR-ARF pathway in land plants according to known developmental and environmental responses where the phytohormone auxin plays an essential role.

One of the great triumphs of mass spectrometry-based peptide and protein characterization is the characterization of their modifications as most modifications have a characteristic mass shift. What happens when the modification does not change the mass of the peptide? Here, the characterization of several peptide and proteins modifications that do not involve a mass shift are highlighted. Protein and peptide synthesis on ribosomes involves L-amino acids; however, posttranslational modifications (PTMs) can convert these L-amino acids into their D-isomers. As another example, nonenzymatic PTM of aspartate leads to the formation of three different isomers, with isoaspartate being the most prevalent. Both modifications do not alter the mass of the peptide and yet can have profound impact on the physicochemical characteristics of the peptide. Several MS and ion mobility techniques are highlighted, as are other methods such as chromatography, enzymatic enrichment, and labeling. The challenges inherent to these analytical methods and prospective developments in bioinformatics and computational strategies are discussed for these zero-dalton PTMs.

Mass spectrometry (MS) is a powerful analytical technique that typically involves sample preparation and online analytical separation before MS detection. Traditional methods often face bottlenecks in sample preparation and analytical separation, despite the rapid detection capabilities of MS. This review explores the integration of electrokinetic manipulations directly with the ionization step to enhance MS performance, focusing on methods that eliminate or simplify sample preparation and separation processes. Techniques such as paper spray, electrophoresis in nanoelectrospray ionization (nESI) emitters, induced nESI, counterflow gradient electrofocusing, and in-syringe electrokinetics are highlighted for their ability to combine extraction and ionization in a single step, significantly improving throughput. The review delves into the use of electric fields during sample preparation and separations for these methods, demonstrating the efficiency of electrophoretic methods in driving extractions, crude separations, desalting, and enhanced sensitivity. The integration of these methods directly with MS ionization aims to enhance the analytical capabilities of mass spectrometry, while reducing costs and increasing throughput relative to traditional approaches.