Helicobacter pylori seropositivity associates with hyperglycemia, but not obesity, in Danish children and adolescents.

BACKGROUND: Helicobacter pylori colonizes the human stomach and may affect the inflammatory response, hormone production related to energy regulation, and gastrointestinal microbiota composition. Previous studies have explored a potential association between H. pylori infection and pediatric obesity with varying results. Considering the immunomodulatory effects of early-life infection with H. pylori that can confer beneficial effects, we hypothesized that we would find an inverse relationship between H. pylori seropositivity and obesity among Danish children and adolescents. METHODS: We assessed H. pylori seroprevalence in 713 subjects from an obesity clinic cohort and 990 subjects from a population-based cohort, aged 6 to 19 years, and examined its association with obesity and other cardiometabolic risk factors. RESULTS: No association was found between H. pylori and body mass index standard deviation score (BMI SDS). H. pylori seropositivity was, however, significantly associated with higher fasting plasma glucose levels and the prevalence of hyperglycemia. CONCLUSION: While we did not find an association between H. pylori seropositivity and BMI SDS, we observed a significant association with higher fasting plasma glucose levels and increased prevalence of hyperglycemia, suggesting that H. pylori infection may contribute to impaired glucose regulation in Danish children and adolescents.

["Change Agents" Or "Peer Washing": Can Peer Support Workers Contribute To The Transformation Of Psychiatric Institutions?] / "Change Agents" oder "Peer Washing": inwiefern können Peer- und Genesungsbegleiter*innen zur Veränderung von psychiatrischen Einrichtungen beitragen?

OBJECTIVE: Peer support workers (=PSW) are expected to change the culture of a care facility. It is examined whether and how they can implement such a change mandate. METHODS: The material is based on two sub-studies of the ImpPeer-Psy5 study, investigating the implementation of PSW in the German psychiatric care system. Data from 57 problem-centered interviews and two focus groups were analyzed using a thematic analysis. RESULTS: PSW need courage, also as their impulses for change are often insufficiently received. This can lead to the reproduction of exclusion and to the PSWs' adaption to the attitudes of the teams. CONCLUSIONS: To facilitate the implementation of the PSWs' change mandate, power relations and structural discrimination should be reflected, exchange spaces and a culture of critique should be established, and enough time be allowed for the implementation of PSW.

Implementing peer support work in mental health care in Germany: The methodological framework of the collaborative, participatory, mixed-methods study (ImpPeer-Psy5).

BACKGROUND: Starting in the 1990s in the United States, individuals with lived experience of mental health crises and recovery have been employed as peer support workers (PSWs) internationally. However, the implementation of PSW in clinical contexts remains challenging. METHODS: This manuscript presents and discusses the methodological framework of the ImpPeer-Psy5 study on the PSW implementation in the German mental healthcare sector. This study used a mixed-methods and collaborative research approach, as well as participatory research strategies. After describing the study design, populations, teamwork and assessments, the epistemic challenges of its methodological framework will be critically discussed and how it has iteratively shaped the object of study. DISCUSSION AND PRACTICAL IMPLICATIONS: The healthcare, policy and funding context of PSW implementation as well as the study's methodological framework have differently influenced the ways in which the implementation of PSW has been conceived in this study. The choice of a collaborative or participatory methodological framework is advised to better align research questions and procedures to the specific needs and challenges of PSWs and other stakeholders concerned with PSW implementation. PATIENT AND PUBLIC CONTRIBUTION: The research team of the ImpPeer-Psy5 study was collaboratively staffed by a portion of researchers who also identify as users or survivors of psychiatric services. A nonprofit organization for the training of PSWs served as a practice partner throughout the research process. Different participatory formats involve a significant number of diverse stakeholders relevant to PSW implementation.

Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.

Introduction: Extracting beat-by-beat information from electrocardiograms (ECGs) is crucial for various downstream diagnostic tasks that rely on ECG-based measurements. However, these measurements can be expensive and time-consuming to produce, especially for long-term recordings. Traditional ECG detection and delineation methods, relying on classical signal processing algorithms such as those based on wavelet transforms, produce high-quality delineations but struggle to generalise to diverse ECG patterns. Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. However, supervised ML techniques require large annotated datasets for training, and existing datasets for ECG detection/delineation are limited in size and the range of pathological conditions they represent. Methods: This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from "pools" of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples. Results: The proposed approach achieves remarkable performance, with a F 1 -score of 99.38% and delineation errors of 2.19 ± 17.73 ms and 4.45 ± 18.32 ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches. Discussion: Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.

Differences in Prevalence of Histologic Gastric Cancer Subtypes Between Mestizo and Mayan Populations in Guatemala.

PURPOSE: Although the intestinal subtype of gastric cancer (GC) is most prevalent around the world, a relatively high prevalence of the diffuse subtype has been reported in some populations of Central American countries, including Guatemala. This study aimed to investigate whether differences exist in the prevalence of the two GC subtypes in the two main ethnic groups in Guatemala, namely Mayan and Mestizo (known as Ladino in Guatemala), between whom significant socioeconomic disparities exist, and to determine whether there is an association with Helicobacter pylori/CagA seropositivity. MATERIALS AND METHODS: Participants included 65 patients with GC and 135 age-/sex-matched controls. Data on ethnicity, H. pylori and CagA seropositivity status, as well as tumor subtype (diffuse or intestinal) were collected. Logistic regression models were fitted to examine the relationship between predictor variables (age, sex, ethnicity, H. pylori, and CagA) and the binary response variable (tumor type). Model selection was based on the Akaike information criterion. RESULTS: The prevalence of diffuse GC was found to be significantly higher in the Mayan compared with the Mestizo population in Guatemala. Although seropositivity for CagA was significantly higher in patients with GC, there were no significant differences between the two GC subtypes. CONCLUSION: This study suggests that there are differences in the prevalence of intestinal and diffuse GC histologic subtypes between the two main ethnic groups in Guatemala. Further studies are warranted, given the potential higher prevalence of the more severe GC subtype in the most vulnerable population.

Neck Circumference as a Valuable Tool to Identify the Risk of Metabolic Syndrome in Mexican Children.

BACKGROUND/OBJECTIVES: Neck circumference (NC) has been proposed as a simple measurement to identify patients with overweight and obesity. It has been found that adipose tissue at the cervical level is associated with the presence of metabolic alterations. The aim of this study was to estimate the association between NC and indicators of Metabolic Syndrome (MS) to subsequently estimate its capacity to identify the risk of MS compared to waist circumference (WC) and Body Mass Index (BMI). METHODS: A cross-sectional study was carried out with a sample of 286 children 6-9 years old who attended six public primary schools in Jalisco, Mexico. Pearson's correlation coefficients along with sensitivity and specificity tests were performed to analyze the relationship between NC and MS indicators. Odds ratio (OR) and concordance analyses were performed considering the Kappa index. RESULTS: NC showed statistically significant correlations with all MS indicators except for LDL cholesterol and total cholesterol. The cut-off points of NC to identify MS according to sex was >27.4 cm for girls and >29.8 cm for boys. The association of NC with values above the cutoff point and the presence of MS was OR: 21.6 (CI: 7.11-65.74). CONCLUSIONS: NC represents a simple and cost-effective alternative to identify children at risk of MS when compared to BMI and WC.

Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology.

Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fréchet Inception Distance (FID) by 88.6. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively train AI models.

Molecular changes and interactions of wheat flour biopolymers during bread-making: Implications to upcycle bread waste into bioplastics.

This study aims to understand the molecular and supramolecular transformations of wheat endosperm biopolymers during bread-making, and their implications to fabricate self-standing films from stale white bread. A reduction in the Mw of amylopectin (51.8 × 106 vs 425.1 × 106 g/mol) and water extractable arabinoxylans WEAX (1.79 × 105 vs 7.63 × 105 g/mol), and a decrease in amylose length (245 vs 748 glucose units) was observed after bread-baking. The chain length distribution of amylopectin and the arabinose-to-xylose (A/X) ratio of WEAX remained unaffected during bread-making, suggesting that heat- or/and shear-induced chain scission is the mechanism responsible for molecular fragmentation. Bread-making also resulted in more insoluble cell wall residue, featured by water unextractable arabinoxylan of lower A/X and Mw, along with the formation of a gluten network. Flexible and transparent films with good light-blocking performance (<30 % transmittance) and DPPH-radical scavenging capacity (~8.5 %) were successfully developed from bread and flour. Bread films exhibited lower hygroscopicity, tensile strength (2.7 vs 8.5 MPa) and elastic modulus (67 vs 501 MPa) than flour films, while having a 6-fold higher elongation at break (10.0 vs 61.2 %). This study provides insights into the changes in wheat biopolymers during bread-making and sets a precedent for using stale bread as composite polymeric materials.

Trichoderma and Mycosynthesis of Metal Nanoparticles: Role of Their Secondary Metabolites.

Nanocompounds are widely used in many fields such as environmental, medicine, or agriculture. Nowadays, these nanocompounds are mainly synthesized by chemical methods, causing environmental pollution and potential health problems. Thus, microorganisms have been investigated as potential nanoparticle green biosynthesizers. The main research is focused on the synthesis of nanoparticles (NPs) using algae, yeast, bacteria, and fungi. Among them, fungi have been the most used, due to their simple and effective mycosynthesis. Fungi as well as other organisms involved in green synthesis of NPs use their secondary metabolites (SMs) to mediate and catalyze the reactions to produce metal nanoparticles (MNPs) as well as being able to act as capping agents producing different physicochemical characteristics and biological activities in the MNPs. Among the various fungi used for mycosynthesis are Trichoderma species, which mediate the production of Ag, Cu, CuO, Zn, ZnO, and other MNPs. Here, we review the main SMs from Trichoderma that have been reported or suggested to contribute to synthesize or act as capping agents and their applications, as well as present the main challenges faced by this type of synthesis.

Delineation of intracavitary electrograms for the automatic quantification of decrement-evoked potentials in the coronary sinus with deep-learning techniques.

Cardiac arrhythmias cause depolarization waves to conduct unevenly on the myocardial surface, potentially delaying local components with respect to a previous beat when stimulated at faster frequencies. Despite the diagnostic value of localizing the distinct local electrocardiogram (EGM) components for identifying regions with decrement-evoked potentials (DEEPs), current software solutions do not perform automatic signal quantification. Electrophysiologists must manually measure distances on the EGM signals to assess the existence of DEEPs during pacing or extra-stimuli protocols. In this work, we present a deep learning (DL)-based algorithm to identify decrement in atrial components (measured in the coronary sinus) with respect to their ventricular counterparts from EGM signals, for disambiguating between accessory pathways (APs) and atrioventricular re-entrant tachycardias (AVRTs). Several U-Net and W-Net neural networks with different configurations were trained on a private dataset of signals from the coronary sinus (312 EGM recordings from 77 patients who underwent AP or AVRT ablation). A second, separate dataset was annotated for clinical validation, with clinical labels associated to EGM fragments in which decremental conduction was elucidated. To alleviate data scarcity, a synthetic data augmentation method was developed for generating EGM recordings. Moreover, two novel loss functions were developed to minimize false negatives and delineation errors. Finally, the addition of self-attention mechanisms and their effect on model performance was explored. The best performing model was a W-Net model with 6 levels, optimized solely with the Dice loss. The model obtained precisions of 91.28%, 77.78% and of 100.0%, and recalls of 94.86%, 95.25% and 100.0% for localizing local field, far field activations, and extra-stimuli, respectively. The clinical validation model demonstrated good overall agreement with respect to the evaluation of decremental properties. When compared to the criteria of electrophysiologists, the automatic exclusion step reached a sensitivity of 87.06% and a specificity of 97.03%. Out of the non-excluded signals, a sensitivity of 96.77% and a specificity of 95.24% was obtained for classifying them into decremental and non-decremental potentials. Current results show great promise while being, to the best of our knowledge, the first tool in the literature allowing the delineation of all local components present in an EGM recording. This is of capital importance at advancing processing for cardiac electrophysiological procedures and reducing intervention times, as many diagnosis procedures are performed by comparing segments or late potentials in subsequent cardiac cycles.

Electrophysiological biomarkers in dual pathology.

INTRODUCTION: The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology. METHODS: A narrative review of the literature. A bibliographic search was performed on the PubMed, Science Direct, OVID, BIREME and Scielo databases, with the keywords: electrophysiological biomarker and substance use disorder, electrophysiological biomarker and mental disorders, biomarker and dual pathology, biomarker and substance use disorder, electroencephalography, and substance use disorder or comorbid mental disorder. RESULTS: Given the greater amount of literature found in relation to electroencephalography as a biomarker of mental illness and substance use disorders, and the few articles found on dual pathology, the evidence is organised as a biomarker in psychiatry for the diagnosis and prediction of risk and as a biomarker for dual pathology. CONCLUSIONS: Although the evidence is not conclusive, it suggests the existence of a subset of sites and mechanisms where the effects of psychoactive substances and the neurobiology of some mental disorders could overlap or interact.

Cost of SARS-CoV-2 self-test distribution programmes by different modalities: a micro-costing study in five countries (Brazil, Georgia, Malaysia, Ethiopia and the Philippines).

OBJECTIVE: Diagnostic testing is an important tool to combat the COVID-19 pandemic, yet access to and uptake of testing vary widely 3 years into the pandemic. The WHO recommends the use of COVID-19 self-testing as an option to help expand testing access. We aimed to calculate the cost of providing COVID-19 self-testing across countries and distribution modalities. DESIGN: We estimated economic costs from the provider perspective to calculate the total cost and the cost per self-test kit distributed for three scenarios that differed by costing period (pilot, annual), the number of tests distributed (actual, planned, scaled assuming an epidemic peak) and self-test kit costs (pilot purchase price, 50% reduction). SETTING: We used data collected between August and December 2022 in Brazil, Georgia, Malaysia, Ethiopia and the Philippines from pilot implementation studies designed to provide COVID-19 self-tests in a variety of settings-namely, workplace and healthcare facilities. RESULTS: Across all five countries, 173 000 kits were distributed during pilot implementation with the cost/test distributed ranging from $2.44 to $12.78. The cost/self-test kit distributed was lowest in the scenario that assumed implementation over a longer period (year), with higher test demand (peak) and a test kit price reduction of 50% ($1.04-3.07). Across all countries and scenarios, test procurement occupied the greatest proportion of costs: 58-87% for countries with off-site self-testing (outside the workplace, for example, home) and 15-50% for countries with on-site self-testing (at the workplace). Staffing was the next key cost driver, particularly for distribution modalities that had on-site self-testing (29-35%) versus off-site self-testing (7-27%). CONCLUSIONS: Our results indicate that it is likely to cost between $2.44 and $12.78 per test to distribute COVID-19 self-tests across common settings in five heterogeneous countries. Cost-effectiveness analyses using these results will allow policymakers to make informed decisions on optimally scaling up COVID-19 self-test distribution programmes across diverse settings and evolving needs.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

Evaluation of a Training Program on Gender Mainstreaming in Health Research Evaluation at the Senegalese National Research Ethics Committee.

Health research must be of high ethical and scientific quality and consider the needs and experiences of women, men, and nonbinary individuals. National Research Ethics Committees (RECs) are in a strategic position to impede sex- and gender-blind research. In 2020 and 2021, training programs on gender mainstreaming and sex and gender approaches in research evaluation were launched in Senegal. They were evaluated through a mixture of qualitative and quantitative methods. Knowledge acquisition was 16.67%, 8.54%, and 28.42% for the trainees of 2021, 2020, and those who attended the training in both years, respectively. Gender mainstreaming was reported as pertinent in research ethics by 74% of participants. This training is expected to catalyze gender-transformative research ethics in West Africa.

Self-Consistent Field Approach for the Variational Quantum Eigensolver: Orbital Optimization Goes Adaptive.

We present a self-consistent field (SCF) approach within the adaptive derivative-assembled problem-tailored ansatz variational quantum eigensolver (ADAPT-VQE) framework for efficient quantum simulations of chemical systems on near-term quantum computers. To this end, our ADAPT-VQE-SCF approach combines the idea of generating an ansatz with a small number of parameters, resulting in shallow-depth quantum circuits with a direct minimization of an energy expression that is correct to second order with respect to changes in the molecular orbital basis. Our numerical analysis, including calculations for the transition-metal complex ferrocene [Fe (C5H5)2], indicates that convergence in the self-consistent orbital optimization loop can be reached without a considerable increase in the number of two-qubit gates in the quantum circuit by comparison to a VQE optimization in the initial molecular orbital basis. Moreover, the orbital optimization can be carried out simultaneously within each iteration of the ADAPT-VQE cycle. ADAPT-VQE-SCF thus allows us to implement a routine analogous to the complete active space SCF, a cornerstone of state-of-the-art computational chemistry, in a hardware-efficient manner on near-term quantum computers. Hence, ADAPT-VQE-SCF paves the way toward a paradigm shift for quantitative quantum-chemistry simulations on quantum computers by requiring fewer qubits and opening up for the use of large and flexible atomic orbital basis sets in contrast to earlier methods that are predominantly based on the idea of full active spaces with minimal basis sets.

Implementing a pilot study of COVID-19 self-testing in high-risk populations and remote locations: results and lessons learnt.

BACKGROUND: Rapid antigen-detection tests for SARS-CoV-2 self-testing represent a useful tool for pandemic control and expanding access to community-level case screening. COVID-19 self-tests have been extensively used in high-income countries since 2021; however, their introduction and programmatic implementation in low- and middle-income countries was delayed. We aimed to identify and continuously improve a weekly COVID-19 self-testing model among staff at healthcare facilities and schools. METHODS: This mixed-methods, observational prospective study was conducted in 5 healthcare centres and 24 schools in Georgia, between June and December 2022. The study comprised the integration of COVID-19 self-testing into the national mandatory testing programme for high-risk groups, with primary distribution of self-tests among staff performed weekly, plus secondary distribution to their household members. These use cases were selected because NCDC was seeking to strengthen their already strong weekly testing programme, by investigating self-testing to ease the burden of testing in the healthcare system. Online surveys and semi-structured interviews were used for data collection. RESULTS: In total, 2156 participants were enrolled (1963 female, 72%). At baseline and mid- and end-points, 88%, 97% and 99%, respectively, of participants agreed/strongly agreed they would self-test. Similarly, the majority were willing to report their self-testing results (88%, 98% and 96% at baseline and mid- and end-points, respectively). Weekly reporting of test results to the national COVID-19 database was high during all the implementation. There were 622 COVID-19 positive results reported, and linked to care, from 601 individuals (282 participants and 319 household members). Findings from qualitative interviews showed great satisfaction with self-testing for its convenience, ease of use, trust in the results, no need to travel for diagnostics, and increased perception of safety. CONCLUSIONS: Our findings contribute to the evidence-base regarding self-testing strategies conducted via workplaces and secondary distribution to households. Willingness to perform a COVID-19 self-test increased after implementation. This pilot enhanced pandemic preparedness through expansion of the national self-testing reporting system, development of communications materials, changes in the national legal framework and coordination mechanisms, and improved perceptions around self-care in the community. The lessons learnt can inform operational aspects of the introduction and scale-up of self-care strategies.

User requirements for non-invasive and minimally invasive glucose self-monitoring devices in low-income and middle-income countries: a qualitative study in Kyrgyzstan, Mali, Peru and Tanzania.

AIMS: Development of non-invasive and minimally invasive glucose monitoring devices (NI-MI-GMDs) generally takes place in high-income countries (HICs), with HIC's attributes guiding product characteristics. However, people living with diabetes (PLWD) in low-income and middle-income countries (LMICs) encounter different challenges to those in HICs. This study aimed to define requirements for NI-MI-GMDs in LMICs to inform a target product profile to guide development and selection of suitable devices. METHODS: This was a multiple-methods, exploratory, qualitative study conducted in Kyrgyzstan, Mali, Peru and Tanzania. Interviews and group discussions/activities were conducted with healthcare workers (HCWs), adults living with type 1 (PLWD1) or type 2 diabetes (PLWD2), adolescents living with diabetes and caregivers. RESULTS: Among 383 informants (90 HCW, 100 PLWD1, 92 PLWD2, 24 adolescents, 77 caregivers), a range of differing user requirements were reported, including preferences for area of glucose measurement, device attachment, data display, alert type and temperature sensitivity. Willingness to pay varied across countries; common requirements included ease of use, a range of guiding functions, the possibility to attach to a body part of choice and a cost lower than or equal to current glucose self-monitoring. CONCLUSIONS: Ease-of-use and affordability were consistently prioritised, with broad functionality required for alarms, measurements and attachment possibilities. Perspectives of PLWD are crucial in developing a target product profile to inform characteristics of NI-MI-GMDs in LMICs. Stakeholders must consider these requirements to guide development and selection of NI-MI-GMDs at country level, so that devices are fit for purpose and encourage frequent glucose monitoring among PLWD in these settings.

Improved production of the antidiabetic metabolite montbretin A in Nicotiana benthamiana: discovery, characterization, and use of Crocosmia shikimate shunt genes.

The plant-specialized metabolite montbretin A (MbA) is being developed as a new treatment option for type-2 diabetes, which is among the ten leading causes of premature death and disability worldwide. MbA is a complex acylated flavonoid glycoside produced in small amounts in below-ground organs of the perennial plant Montbretia (Crocosmia × crocosmiiflora). The lack of a scalable production system limits the development and potential application of MbA as a pharmaceutical or nutraceutical. Previous efforts to reconstruct montbretin biosynthesis in Nicotiana benthamiana (Nb) resulted in low yields of MbA and higher levels of montbretin B (MbB) and montbretin C (MbC). MbA, MbB, and MbC are nearly identical metabolites differing only in their acyl moieties, derived from caffeoyl-CoA, coumaroyl-CoA, and feruloyl-CoA, respectively. In contrast to MbA, MbB and MbC are not pharmaceutically active. To utilize the montbretia caffeoyl-CoA biosynthesis for improved MbA engineering in Nb, we cloned and characterized enzymes of the shikimate shunt of the general phenylpropanoid pathway, specifically hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyl transferase (CcHCT), p-coumaroylshikimate 3'-hydroxylase (CcC3'H), and caffeoylshikimate esterase (CcCSE). Gene expression patterns suggest that CcCSE enables the predominant formation of MbA, relative to MbB and MbC, in montbretia. This observation is supported by results from in vitro characterization of CcCSE and reconstruction of the shikimate shunt in yeast. Using CcHCT together with montbretin biosynthetic genes in multigene constructs resulted in a 30-fold increase of MbA in Nb. This work advances our understanding of the phenylpropanoid pathway and features a critical step towards improved MbA production in bioengineered Nb.

Loss of sodium current caused by a Brugada syndrome-associated variant is determined by patient-specific genetic background.

BACKGROUND: Brugada syndrome (BrS) is an inherited cardiac arrhythmogenic disease that predisposes patients to sudden cardiac death. It is associated with mutations in SCN5A, which encodes the cardiac sodium channel alpha subunit (NaV1.5). BrS-related mutations have incomplete penetrance and variable expressivity within families. OBJECTIVE: The purpose of this study was to determine the role of patient-specific genetic background on the cellular and clinical phenotype among carriers of NaV1.5_p.V1525M. METHODS: We studied sodium currents from patient-specific human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and heterologously transfected human embryonic kidney (HEK) tsA201 cells using the whole-cell patch-clamp technique. We determined gene and protein expression by quantitative polymerase chain reaction, RNA sequencing, and western blot and performed a genetic panel for arrhythmogenic diseases. RESULTS: Our results showed a large reduction in INa density in hiPSC-CM derived from 2 V1525M single nucleotide variant (SNV) carriers compared with hiPSC-CM derived from a noncarrier, suggesting a dominant-negative effect of the NaV1.5_p.V1525M channel. INa was not affected in hiPSC-CMs derived from a V1525M SNV carrier who also carries the NaV1.5_p.H558R polymorphism. Heterozygous expression of V1525M in HEK-293T cells produced a loss of INa function, not observed when this variant was expressed together with H558R. In addition, the antiarrhythmic drug mexiletine rescued INa function in hiPSC-CM. SCN5A expression was increased in the V1525M carrier who also expresses NaV1.5_p.H558R. CONCLUSION: Our results in patient-specific hiPSC-CM point to a dominant-negative effect of NaV1.5_p.V1525M, which can be reverted by the presence of NaV1.5_p.H558R. Overall, our data points to a role of patient-specific genetic background as a determinant for incomplete penetrance in BrS.