Technical Variations and Considerations around OAGB in IFSO-APC and IFSO-MENAC Chapters, an Expert Survey.

OBJECTIVE: This study aimed to evaluate the technical variations of one-anastomosis gastric bypass (OAGB) among IFSO-APC and MENAC experts. BACKGROUND: The multitude of technical variations and patient selection challenges among metabolic and bariatric surgeons worldwide necessitates a heightened awareness of these issues. Understanding different perspectives and viewpoints can empower surgeons performing OAGB to adapt their techniques, leading to improved outcomes and reduced complications. METHODS: The scientific team of IFSO-APC, consisting of skilled bariatric and metabolic surgeons specializing in OAGB, conducted a confidential online survey. The survey aimed to assess technical variations and considerations related to OAGB within the IFSO-APC and IFSO-MENAC chapters. A total of 85 OAGB experts participated in the survey, providing their responses through a 35-question online format. The survey took place from January 1, 2024, to February 15, 2024. RESULTS: Most experts do not perform OAGB for children and adolescents younger than 18 years. Most experts create the gastric pouch over a 36-40-F bougie and prefer to create a gastrojejunostomy, at the posterior wall of the gastric pouch. An anti-reflux suture during OAGB is performed in all patients by 51.8% of experts. Most experts set a common limb length of > 4 m in revisional and conversional OAGBs to prevent nutritional complications. CONCLUSION: The ongoing debate among metabolic and bariatric surgeons regarding the technical variations and patient selection in OAGB remains a significant point of discussion. This survey demonstrated the variations in technical aspects and patient selection for OAGB among MBS surgeons in the IFSO-APC and IFSO-MENAC chapters. Standardizing the OAGB technique is crucial to ensure optimal safety and efficacy in this procedure.

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

Relationship between anaerobic capacity and changes in speed in the 100-m breaststroke stroke in short-distance breaststroke swimmers: the mediating role of technical change

Objective:By analyzing and summarizing the relationship between anaerobic capacity, technical changes of 100m breaststroke en route and speed changes of short distance breaststroke athletes, the interrelationship and internal pathways between the three are revealed to provide reference for improving athletic performance of short distance breaststroke athletes and provide theoretical basis for anaerobic capacity training.Method:Fifteen male short-distance breaststroke athletes (age 19.67±2.61 years, height 178.4±7.04 cm, weight 71.6±7.79 kg) were selected to perform anaerobic power cycling and 100 m breaststroke tests on the upper and lower extremities. The correlations and intrinsic linkage pathways between the three were explored by calculating Pearson correlation coefficients and using a mediating effects model.Result:Significant differences existed in speed, stroke rate, cycle time per stroke, and swim efficiency index in the 100 m breaststroke all-out test. There were significant correlations between the rate of anaerobic power decrease in the upper limb and the changes in stroke amplitude, cycle time per stroke, and speed. There were significant correlations between the change in mean stroke rate, the change in cycle time per stroke, the change in swim efficiency index and the change in speed. Anaerobic power indirectly influenced the speed variation during the en-route swim, which was mediated by the technical variation in cycle time per stroke.Conclusion:The upper limb anaerobic fatigue resistance of short distance breaststroke athletes is a key factor affecting the technique and speed stability of the 100m breaststroke en route, and the rate of decline in upper limb anaerobic power leads to a decrease in speed by affecting the change in time per stroke cycle. (AU)

Variability Analysis in Judo Para Athletes With Visual Impairments: Match-Outcome Performance in the Tokyo 2020 Paralympic Games With Evidence From the New Classification System.

Match-related performance analysis in judo Para athletes with visual impairments is important to coaches and staff to identify technical-tactical profiles of their athletes and opponents but also to identify whether there are similar characteristics in each visual class. Thus, this study explores the match-related performance in judo Para athletes and verifies the relationship between performance using the old and new classification systems. The match-derived variables were analyzed using different statistical methods considering a total of 182 matches from the Tokyo 2020 Paralympic Games. The results indicated that performance was affected by sex and degree of impairment. The new classification system seems suitable for grouping Para judo athletes, as it differentiates performance between the two proposed classes (J1 and J2), since athletes from each group compete separately. Furthermore, different variability index measures were correlated with competitive performance, demonstrating a specific performance profile for each sport class in judo.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies.

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

scMC learns biological variation through the alignment of multiple single-cell genomics datasets.

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

Variations in the microbiome due to storage preservatives are not large enough to obscure variations due to factors such as host population, host species, body site, and captivity.

The study of the primate microbiome is critical in understanding the role of the microbial community in the host organism. To be able to isolate the main factors responsible for the differences observed in microbiomes within and between individuals, confounding factors due to technical variations need to be removed. To determine whether alterations due to preservatives outweigh differences due to factors such as host population, host species, body site, and habitat, we tested three methods (no preservative, 96% ethanol, and RNAlater) for preserving wild chimpanzee (fecal), wild lemur (fecal), wild vervet monkey (rectal, oral, nasal, otic, vaginal, and penile), and captive vervet monkey (rectal) samples. All samples were stored below - 20°C (short term) at the end of the field day and then at - 80°C until DNA extraction. Using 16S rRNA gene sequencing, we show a significant preservative effect on microbiota composition and diversity. Samples stored in ethanol and RNAlater appear to be less different compared with samples not stored in any preservative (none). Our differential analysis revealed significantly higher amounts of Enterococcaceae and Family XI in no preservative samples, Prevotellaceae and Spirochaetaceae in ethanol and RNAlater preserved samples, Oligosphaeraceae in ethanol-preserved samples, and Defluviitaleaceae in RNAlater preserved samples. While these preservative effects on the microbiome are not large enough to remove or outweigh the differences arising from biological factors (e.g., host species, body site, and habitat differences) they may promote misleading interpretations if they have large enough effect sizes compared to the biological factors (e.g., host population).

Nonrandom RNAseq gene expression associated with RNAlater and flash freezing storage methods.

RNA sequencing is a popular next-generation sequencing technique for assaying genome-wide gene expression profiles. Nonetheless, it is susceptible to biases that are introduced by sample handling prior gene expression measurements. Two of the most common methods for preserving samples in both field-based and laboratory conditions are submersion in RNAlater and flash freezing in liquid nitrogen. Flash freezing in liquid nitrogen can be impractical, particularly for field collections. RNAlater is a solution for stabilizing tissue for longer-term storage as it rapidly permeates tissue to protect cellular RNA. In this study, we assessed genome-wide expression patterns in 30-day-old fry collected from the same brood at the same time point that were flash-frozen in liquid nitrogen and stored at -80°C or submerged and stored in RNAlater at room temperature, simulating conditions of fieldwork. We show that sample storage is a significant factor influencing observed differential gene expression. In particular, genes with elevated GC content exhibit higher observed expression levels in liquid nitrogen flash-freezing relative to RNAlater storage. Further, genes with higher expression in RNAlater relative to liquid nitrogen experience disproportionate enrichment for functional categories, many of which are involved in RNA processing. This suggests that RNAlater may elicit a physiological response that has the potential to bias biological interpretations of expression studies. The biases introduced to observed gene expression arising from mimicking many field-based studies are substantial and should not be ignored.

Quantification of variation and the impact of biomass in targeted 16S rRNA gene sequencing studies.

BACKGROUND: Recent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research. RESULTS: Data were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values. CONCLUSIONS: Well-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.

Systematic Selection of Reference Genes for the Normalization of Circulating RNA Transcripts in Pregnant Women Based on RNA-Seq Data.

RNA transcripts circulating in peripheral blood represent an important source of non-invasive biomarkers. To accurately quantify the levels of circulating transcripts, one needs to normalize the data with internal control reference genes, which are detected at relatively constant levels across blood samples. A few reference gene candidates have to be selected from transcriptome data before the validation of their stable expression by reverse-transcription quantitative polymerase chain reaction. However, there is a lack of transcriptome, let alone whole-transcriptome, data from maternal blood. To overcome this shortfall, we performed RNA-sequencing on blood samples from women presenting with preterm labor. The coefficient of variation (CV) of expression levels was calculated. Of 11,215 exons detected in the maternal blood whole-transcriptome, a panel of 395 genes, including PPP1R15B, EXOC8, ACTB, and TPT1, were identified to comprise exons with considerably less variable expression level (CV, 7.75-17.7%) than any GAPDH exon (minimum CV, 27.3%). Upon validation, the selected genes from this panel remained more stably expressed than GAPDH in maternal blood. This panel is over-represented with genes involved with the actin cytoskeleton, macromolecular complex, and integrin signaling. This groundwork provides a starting point for systematically selecting reference gene candidates for normalizing the levels of circulating RNA transcripts in maternal blood.

Evaluation of the technical variations and the suitability of a hydrophilic interaction liquid chromatography-high resolution mass spectrometry (ZIC-pHILIC-Exactive orbitrap) for clinical urinary metabolomics study.

Although many hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) methods have been developed and applied for untargeted metabolite profiling in clinical metabolomics, according to the literature, the suitability of these HILIC-HRMS methods has not been fully evaluated with respect to their performance when they are subjected to statistical analysis. In this study, using a series of human urine samples we investigated the effect of technical variations on multivariate and univariate analysis of the data collected using a previously developed HILIC-HRMS method for untargeted urinary metabolite profiling in clinical metabolomics. The technical variation introduced by sample preparation was more significant than that produced by the HILIC-HRMS method. By using an orthogonal partial least squares (OPLS) model, subtle fold-changes were accurately measured in the urine samples spiked with (13)C and (15)N isotope labelled amino acids at different concentrations. The robustness of this HILIC method was also evaluated by analysing the obtained data from a single urine sample following manipulation of several primary LC parameters. High reproducibility in the chromatographic performance of three ZIC-pHILIC columns with different batch numbers indicated the reliability of the polymer based zwitterionic stationary phase allowing column replacement without compromising the performance of the method.

Sources of Experimental Variation in 2-D Maps: The Importance of Experimental Design in Gel-Based Proteomics.

The success of proteomic studies employing 2-D maps largely depends on the way surveys and experiments have been organized and performed. Planning gel-based proteomic experiments involves the selection of equipment, methodology, treatments, types and number of samples, experimental layout, and methods for data analysis. A good experimental design will maximize the output of the experiment while taking into account the biological and technical resources available. In this chapter we provide guidelines to assist proteomics researchers in all these choices and help them to design quantitative 2-DE experiments.