Full Set Research Articles

Probiotics plus vitamin D in irritable bowel syndrome: a prospective multicentric non-interventional study.

Patients with irritable bowel syndrome (IBS) experience recurrent symptoms and anxiety disorders that significantly impact their quality of life (QoL). The aim of the study was to assess in daily practice the benefit of the combination of three probiotic strains (Lactobacillus plantarum CETC 7484 and CETC 7485; Pediococcus acidilactici CECT 7483) plus vitamin D in patients with diarrhea-predominant IBS (IBS-D) or IBS with mixed bowel movements (IBS-M). This was a prospective, multicenter, non-interventional study in adult patients with IBS-D or IBS-M (Rome IV criteria) followed by private-practice gastroenterologists. Patients received daily one sachet of a combination of probiotics (3×109 CFU) and vitamin D (10 μg) for 42 days. The primary endpoint was the responder rate at D42 (≥50% and/or ≥100-point decrease of IBS-Severity Scoring System; IBS-SSS). Gut-related anxiety was measured with the Visceral Sensitivity Index (VSI). The full analysis set population included 246 patients (mean age, 51.2±15.4 years; women, 73.2%; IBS-D, 56.1%; IBS-M, 43.9%). At D42, among the 89 patients with evaluable data, the responder rate was 62.9% with a mean decrease of IBS-SSS of 146.6±125.9 (P<0.0001). Changes of IBS-SSS were significantly correlated with changes of IBS-QoL (r=-0.78; P<0.0001), HAD-anxiety (r=0.46; P<0.0001), HAD-depression (r=0.61; P<0.001) and VSI (r=0.74; P<0.0001). These real-life results are in line with a previous randomized clinical trial demonstrating the benefits of this combination in IBS-D and IBS-M. Symptom relief was associated with improvement of IBS-QoL, anxiety, depression and specifically gut-related anxiety.

Organocatalysis for the Chemical Fixation of Carbon Dioxide to Synthesise N-Heterocycles

Abstract: Organocatalysed reactions are becoming powerful tools in the construction of complex molecular skeletons. It gains extra importance when used as a chemical approach to the chemical fixation of carbon dioxide (CO2). Carbon dioxide is an increasingly dangerous environmental hazard as the global climate temperature rises through the greenhouse effect. Meanwhile, in the past decades, significant advances can be noted in the use of organocatalysis for CO2 capture and its conversion into valuable chemicals. Therefore, herein we review a full set of organocatalysts used in CO2 fixation for the synthesis of N-heterocycles since they are present in several chemical structures with biological relevance.

Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

Nonspecific Effects of the Bacillus Calmette-Guérin Vaccine in Portuguese Children Under 5 Years of Age: Protocol for a Population-Based Historical Birth Cohort Study.

The Bacillus Calmette-Guérin vaccine (BCG) against tuberculosis (TB) shows beneficial nonspecific effects, which are likely related to innate immune training. Until 2016, a single BCG dose was administered to all newborns in Portugal. In July 2016, a clinical guideline established that only children under 6 years belonging to high-risk groups should receive BCG. This might have prevented nonvaccinated children from developing trained immunological responses as effectively as BCG-vaccinated children. This study aims to investigate if there is variation in TB-related and all-cause mortality, and severe, moderate, or mild morbidity in children under 5 years of age, and whether such variation might be explained by the BCG vaccination policy change in 2016. This population-based historical birth cohort study includes children under 5 years of age born in Portugal between July 1, 2010, and June 30, 2021. Newborns with low birth weight, premature status, or known or suspected HIV infection are excluded. The follow-up period is until the completion of 5 years of age or the end of follow-up (June 30, 2021). The study will use secondary data from the National Health Service user registry, death certificate database, vaccination registry, communicable diseases surveillance system, TB surveillance system, diagnosis-related group information system for hospital admissions and emergency department visits, and primary health care information system. The data will be linked. Primary outcomes include person-time incidence rates of death (all causes and TB), TB diagnosis, and all causes and some specific causes of severe, moderate, or mild morbidity, and the incidence rate ratio of nonvaccinated to BCG-vaccinated children. We will compare the probability of surviving the first and fifth years of life or of not having severe, moderate, or mild morbidity during the follow-up period according to exposure (BCG vaccinated or nonvaccinated, number of doses, and time from birth until the first dose), using the log-rank test for assessing differences in survival rates between exposed and nonexposed children and hazard ratios for quantifying the differences. Moreover, we will perform a proportional hazards regression analysis. Ethics approval has been obtained. In March 2022, database owners were contacted to present the project and discuss the request for data. A unique identifier will be used. In July 2023, a process of redefinition of the variables per database was initiated. Data were received in October and November 2023. In November 2023, further work was conducted. By April 2024, we expect to start analyzing the full data set. The results will contribute to the accumulating body of knowledge and might have relevance to guide global BCG vaccination policy. Data linkage can contribute to a swifter mechanism to use available health data to conduct population-based studies and inform policy decision-making. ClinicalTrials.gov NCT05471167; https://clinicaltrials.gov/study/NCT05471167. DERR1-10.2196/55332.

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy.

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

Efficacy and safety of tibial nerve stimulation using a wearable device for overactive bladder.

To evaluate the efficacy and safety of a wearable, smartphone-controlled, rechargeable transcutaneous tibial nerve stimulation (TTNS) device in patients with overactive bladder (OAB). This multicentre, prospective, single-blind, randomised clinical trial included eligible patients with OAB symptoms who were randomly assigned to the stimulation group or sham group. The primary efficacy outcome was change from baseline in voiding frequency/24 h after 4 weeks of treatment. The secondary efficacy outcomes included changes in bladder diary outcomes (urgency score/void, nocturia episodes/day, micturition volume/void, and incontinence episodes/day), questionnaires on Overactive Bladder Symptom Score (OABSS), Patient Perception of Bladder Condition (PPBC), and American Urological Association Symptom Index Quality of Life Score (AUA-SI-QoL) at baseline and after 4 weeks of treatment. Device-related adverse events (AEs) were also evaluated. In the full analysis set (FAS), the mean (sd) change of voiding frequency/24 h in the stimulation group and sham group at 4 weeks were -3.5 (2.9) and -0.6 (2.4), respectively (P < 0.01). Similar results were obtained in the per-protocol set (PPS): -3.5 (2.9) vs -0.4 (2.3) (P < 0.01). In the FAS and PPS, micturition volume/void significantly improved at 4 weeks (P = 0.01 and P = 0.02). PPBC improvement almost reached significance in the FAS (P = 0.05), while it was significant in the PPS (P = 0.02). In the FAS and PPS, AUA-SI-QoL significantly improved at 4 weeks in the two groups (P < 0.01 and P < 0.01), whereas there were no significant differences in urgency score/void, nocturia episodes/day or OABSS between the groups. Also, no device-related serious AEs were reported. The non-invasive neuromodulation technique using the novel ambulatory TTNS device is effective and safe for treating OAB. Its convenience and easy maintenance make it a new potential home-based treatment modality. Future studies are warranted to confirm its longer-term efficacy.

Photoelastic Properties of Stoichiometric Lithium Niobate from First‐Principles Calculations

The linear optical response of lithium niobate under compressive as well as tensile stress is calculated from first principles. For this purpose, the elastic constants are computed with different exchange‐correlation functionals and compared with available experimental values. A good agreement with the measured quantities is obtained. A full set of the piezo‐optic and photoelastic coefficients is determined from the dependence of the dielectric permeability in the linear regime on the considered stress. The calculated coefficients agree with the experimental values as well. In contrast to the numerous experimental investigations, the coefficients are not only given for a single excitation wavelength but also within the technically relevant optical range. From the dispersion of the piezo‐optic and photoelastic coefficients, conclusions can be drawn about the refractive indices and the birefringence under strain for different excitation energies. It can be concluded that the birefringence in the long wavelength range increases under stress in the z direction. For uniaxial stress in x and y directions, the lattice symmetry is broken and the crystal becomes biaxial in the x and y directions as well.

On the emergence of cooperative industrial and labour relations

AbstractWe explore the long‐run determinants of current differences in the degree of co‐operative labour relations at the local level. We do this by estimating the effect of the medieval communes –that were established in certain cities in Centre‐Northern Italy towards the end of the 11th century – and that contributed to the emergence of a co‐operative attitude in the population on various proxies for current co‐operative labour relations. Conditional on a large set of firm and municipality level controls, as well as a full set of province fixed effects, we find that firms located in municipalities that had been a free medieval commune in the past have higher current probabilities to adopt two‐tier bargaining structures and to be unionized. We also report instrumental variables (IV) and propensity score estimates that confirm our main results.

Digital reading comprehension instruction in English for children with English as an additional language: A systematic review

BackgroundDigital literacy instruction is increasingly common in contemporary practices and can accommodate learners with a range of needs. This systematic review explores the use and effects of technology during reading comprehension instruction involving school‐aged children learning English as an additional language (EAL). Our aim was to provide broad, global coverage that reflects the state of research in the field.MethodsWe searched databases using terms related to EAL, reading comprehension and digital literacy instruction to identify relevant studies published on the topic between 2010 and 2023. Data were extracted regarding study context, participant characteristics, methodology, instruction characteristics and outcomes. We quantified the outcomes of instruction using standardised measures of effect size where possible and appraised the research quality of each study.ResultsA total of 4382 studies were identified. Of these, 53 met our inclusion criteria. Studies were conducted across a variety of low‐ and high‐resource environments, including countries where English is a majority language and those where it is a non‐majority language. Learners from kindergarten through 11th grade were included across the studies. A range of reading comprehension strategies were utilised within the context of digital instruction, with multiple‐strategy instruction the most common. Digital instruction resulted in statistically significant gains in reading comprehension in 32 studies (small to large effect sizes). Our research quality analysis revealed variability in terms of rigour, further adding to the diversity across studies. We conducted a sub‐analysis of eight studies that met the highest standards of research quality. Like the full set of 53 studies, these eight studies reported on diverse types of digital instruction and diverse reading comprehension outcomes but tended to result in smaller effect sizes.ConclusionsDigital reading comprehension instruction can result in reading comprehension gains for children learning EAL, though findings are mixed. Further high‐quality research is needed.

10-year follow-up results of the European Achalasia Trial: a multicentre randomised controlled trial comparing pneumatic dilation with laparoscopic Heller myotomy

ObjectiveAs achalasia is a chronic disorder, long-term follow-up data comparing different treatments are essential to select optimal clinical management. Here, we report on the 10-year follow-up of the European Achalasia...

A toolbox for decoding BCI commands based on event-related potentials.

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.

Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models.

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

Investigation of stair ascending and descending activities on the lifespan of hip implants

Total hip arthroplasty (THA) surgeries among young patients are on the increase, so it is crucial to predict the lifespan of hip implants correctly and produce solutions to improve longevity. Current implants are designed and tested against walking conditions to predict the wear rates. However, it would be reasonable to include the additional effects of other daily life activities on wear rates to predict convergent results to clinical outputs. In this study, 14 participants are recruited to perform stair ascending (AS), descending (DS), and walking activities to obtain kinematic and kinetic data for each cycle using marker based Qualisys motion capture (MOCAP) system. AnyBody Modeling System using the Calibrated Anatomical System Technique (CAST) full body marker set are performed Multibody simulations. The 3D generic musculoskeletal model used in this study is a marker-based full-body motion capture model (AMMR,2.3.1 MoCapModel) consisting of the upper extremity and the Twente Lower Extremity Model (TLEM2). The dynamic wear prediction model detailing the intermittent and overall wear rates for CoCr-on-XLPE bearing couple is developed to investigate the wear mechanism under 3D loading for AS, DS, and walking activities over 5 million cycles (Mc) by using finite element modelling technique. The volumetric wear rates of XLPE liner under AS, DS, and walking activities over 5-Mc are predicted as 27.43, 23.22, and 18.84 mm3/Mc respectively. Additionally, the wear rate was predicted by combining stair activities and gait cycles based on the walk-to-stair ratio. By adding the effect of stair activities, the volumetric wear rate of XLPE is predicted as 22.02 mm3/Mc which is equivalent to 19.41% of walking. In conclusion, in this study, the effect of including other daily life activities is demonstrated and evidence is provided by matching them to the clinical data as opposed to simulator test results of implants under ISO 14242 boundary conditions.

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Evaluating uncertainty in CO2 injection projections often requires numerous high-resolution geological realizations (GRs) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations (RGRs) as an efficient approach to capture the uncertainty range of the full set while reducing computational costs. A predetermined number of RGRs is selected using an integrated unsupervised machine learning (UML) framework, which includes Euclidean distance measurement, multidimensional scaling (MDS), and a deterministic K-means (DK-means) clustering algorithm. In the context of the intricate 3D aquifer CO2 storage model, PUNQ-S3, these algorithms are utilized. The UML methodology selects five RGRs from a pool of 25 possibilities (20% of the total), taking into account the reservoir quality index (RQI) as a static parameter of the reservoir. To determine the credibility of these RGRs, their simulation results are scrutinized through the application of the Kolmogorov–Smirnov (KS) test, which analyzes the distribution of the output. In this assessment, 40 CO2 injection wells cover the entire reservoir alongside the full set. The end-point simulation results indicate that the CO2 structural, residual, and solubility trapping within the RGRs and full set follow the same distribution. Simulating five RGRs alongside the full set of 25 GRs over 200 years, involving 10 years of CO2 injection, reveals consistently similar trapping distribution patterns, with an average value of Dmax of 0.21 remaining lower than Dcritical (0.66). Using this methodology, computational expenses related to scenario testing and development planning for CO2 storage reservoirs in the presence of geological uncertainties can be substantially reduced.

Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Extending a Physics-informed Machine-learning Network for Superresolution Studies of Rayleigh–Bénard Convection

Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine-learning (ML) techniques have demonstrated promise in upscaling resolution in both image analysis and numerical simulations (i.e., superresolution). Here we employ and further develop a physics-constrained convolutional neural network ML model called “MeshFreeFlowNet” (MFFN) for superresolution studies of turbulent systems. The model is trained on both the simulation images and the evaluated partial differential equations (PDEs), making it sensitive to the underlying physics of a particular fluid system. We develop a framework for 2D turbulent Rayleigh–Bénard convection generated with the Dedalus code by modifying the MFFN architecture to include the full set of simulation PDEs and the boundary conditions. Our training set includes fully developed turbulence sampling Rayleigh numbers (Ra) of Ra = 106–1010. We evaluate the success of the learned simulations by comparing the power spectra of the direct Dedalus simulation to the predicted model output and compare both ground-truth and predicted power spectral inertial range scalings to theoretical predictions. We find that the updated network performs well at all Ra studied here in recovering large-scale information, including the inertial range slopes. The superresolution prediction is overly dissipative at smaller scales than that of the inertial range in all cases, but the smaller scales are better recovered in more turbulent than laminar regimes. This is likely because more turbulent systems have a rich variety of structures at many length scales compared to laminar flows.

The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing

Negative information selection is an approach to protect the privacy by using negative information to replace original information. In this paper, we prove some bounds for negative information selection. Those bounds reveal the privacy protection strength of quantitative probability analysis. We also analyzed the reconstruction probability of original information from available negative information. The formal analysis can specify the bound on the strength of security and utility for negative information selection. Besides, we simulate brute force attacks under different data leakage ratios. Specifically, we calculate the attacker’s guess times before and after the data leakage. Experimental results indicate that the data leakage of over 30% can put the original information in a dangerous situation. Furthermore, we found that the leakage possibility has little relevance to the number of elements in the full set, but it is influenced by the ratio of the leaked information.

The effects of cyclodextrin on plaque accumulation in healthy Japanese adults: A randomized, placebo-controlled, double-blind, parallel-group comparison study

Background: Cyclo-isomalto-oligosaccharides (CI) have been reported to inhibit glucosyltransferases (GTF) in vitro. GTF is an enzyme related to oral plaque synthesis. However, there are few clinical studies to investigate the effect of CI on oral plaque via inhibiting GTF activity. Objective: The purpose of this study was to evaluate the inhibitory effect of consuming CI-Dextran mix on plaque accumulation in healthy Japanese adults. Methods: This randomized, placebo-controlled, double-blind, parallel-group comparison study was conducted from May 26, 2022, to September 16, 2022. Individuals who agreed to participate in the study were randomly assigned to the CI-Dextran mix group with 60 mg/day (n = 22), the CI-Dextran mix group with 600 mg/day (n = 11), or the placebo group (n = 22). The intervention period was three days, and the outcome of this study was the plaque index (PlI), an indicator of plaque accumulation. Results: Our results showed that the CI-Dextran mix group (600mg/day) had significantly lower post-intervention PlI values than the placebo group in a full analysis set (FAS). In individuals identified as prone to plaque accumulation among the FAS, both CI-Dextran mix groups showed significantly lower PlI values than the placebo group. No adverse events were observed during the study period, and consumption of the test food under the study conditions was considered safe. Conclusions: Our results clearly indicated that a high dose of CI-Dextran mix (600 mg/day) could significantly reduce plaque formation in healthy Japanese adults, while a marginal reduction was noted for the group taking a low dose (60 mg/day). In particular, intake of both doses (CI-Dextran mix 60 mg/day and 600 mg/day) in individuals who were prone to plaque accumulation, could significantly inhibit plaque formation. Keywords: CI-Dextran mix, cyclodextran, plaque inhibition, functional carbohydrates, isomaltooligosaccharide, Randomized Controlled Trial Trial registration: UMIN000047901. Foundation: Nissin Sugar Co., Ltd.

The effects of cyclodextrin on plaque accumulation in healthy Japanese adults: A randomized, placebo-controlled, double-blind, parallel-group comparison study

Background: Cyclo-isomalto-oligosaccharides (CI) have been reported to inhibit glucosyltransferases (GTF) in vitro. GTF is an enzyme related to oral plaque synthesis. However, there are few clinical studies to investigate the effect of CI on oral plaque via inhibiting GTF activity. Objective: The purpose of this study was to evaluate the inhibitory effect of consuming CI-Dextran mix on plaque accumulation in healthy Japanese adults. Methods: This randomized, placebo-controlled, double-blind, parallel-group comparison study was conducted from May 26, 2022, to September 16, 2022. Individuals who agreed to participate in the study were randomly assigned to the CI-Dextran mix group with 60 mg/day (n = 22), the CI-Dextran mix group with 600 mg/day (n = 11), or the placebo group (n = 22). The intervention period was three days, and the outcome of this study was the plaque index (PlI), an indicator of plaque accumulation. Results: Our results showed that the CI-Dextran mix group (600mg/day) had significantly lower post-intervention PlI values than the placebo group in a full analysis set (FAS). In individuals identified as prone to plaque accumulation among the FAS, both CI-Dextran mix groups showed significantly lower PlI values than the placebo group. No adverse events were observed during the study period, and consumption of the test food under the study conditions was considered safe. Conclusions: Our results clearly indicated that a high dose of CI-Dextran mix (600 mg/day) could significantly reduce plaque formation in healthy Japanese adults, while a marginal reduction was noted for the group taking a low dose (60 mg/day). In particular, intake of both doses (CI-Dextran mix 60 mg/day and 600 mg/day) in individuals who were prone to plaque accumulation, could significantly inhibit plaque formation. Keywords: CI-Dextran mix, cyclodextran, plaque inhibition, functional carbohydrates, isomaltooligosaccharide, Randomized Controlled Trial Trial registration: UMIN000047901. Foundation: Nissin Sugar Co., Ltd.

Using machine learning to determine factors affecting product and product–service innovation

PurposeMachine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).Design/methodology/approachThe ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.FindingsThe analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.Research limitations/implicationsThe analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.Originality/valueThe novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.