Prevalence, awareness, and control of hypertension in Greece before and after the COVID-19 pandemic: May Measurement Month survey 2019-2022.

OBJECTIVE: The COVID-19 pandemic had an adverse impact on several cardiovascular risk factors. This study investigated the prevalence, awareness and treatment of hypertension in Greece before and after the pandemic. Data were collected in the context of the May Measurement Month (MMM) global survey initiated by the International Society of Hypertension. METHODS: Adult volunteers (age ≥ 18 years) were recruited through opportunistic screening in public areas across cities in Greece in 2019 and 2022. Medical history and triplicate sitting blood pressure (BP) measurements were taken using validated automated upper-arm cuff devices. The data were uploaded to the international MMM cloud platform. Hypertension was defined as systolic BP ≥ 140 mm Hg and/or diastolic ≥90 mm Hg and/or self-reported use of drugs for hypertension. The same threshold was used to define uncontrolled BP in treated individuals. RESULTS: Data from 12,080 adults were collected (5,727/6,353 in MMM 2019/2022; men 46/49%, p < 0.01; mean age 52.7 ± 16.6/54.8 ± 16.2, p < 0.001; smokers, 24.7/30.5, p < 0.001; diabetics 12/11.5%, p = NS; cardiovascular disease 5/5.8%, p = NS). The prevalence of hypertension was 41.6/42.6% (MMM 2019/2022, p = NS), with 21.3/27.5% of individuals with hypertension being unaware of their condition (p < 0.001), 5.6/2.4% aware untreated (p < 0.001), 24.8/22.1% treated uncontrolled (p < 0.05), and 48.3/47.8% treated controlled (p = NS). CONCLUSION: In Greece, the COVID-19 pandemic did not appear to affect the prevalence and control of hypertension; however, the rate of undiagnosed hypertension was higher after the pandemic. National strategies need to be implemented for the early detection and optimal management of hypertension in the general population in Greece.

AI nutrition recommendation using a deep generative model and ChatGPT.

In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. However, the lack of guidelines from nutritional experts has raised questions on the accuracy and trustworthiness of the nutritional advice provided by such AI systems. This paper aims to address this issue by introducing a novel AI-based nutrition recommendation method that leverages the speed and explainability of a deep generative network and the use of novel sophisticated loss functions to align the network with established nutritional guidelines. The use of a variational autoencoder to robustly model the anthropometric measurements and medical condition of users in a descriptive latent space, as well as the use of an optimizer to adjust meal quantities based on users' energy requirements enable the proposed method to generate highly accurate, nutritious and personalized weekly meal plans. Coupled with the ability of ChatGPT to provide an unparalleled pool of meals from various cuisines, the proposed method can achieve increased meal variety, accuracy and generalization capabilities. Extensive experiments on 3000 virtual user profiles and 84000 daily meal plans, as well as 1000 real profiles and 7000 daily meal plans, demonstrate the exceptional accuracy of the proposed diet recommendation method in generating weekly meal plans that are appropriate for the users in terms of energy intake and nutritional requirements, as well as the easiness with which it can be integrated into future diet recommendation systems.

Effect of DASH vs. mediterranean diet accompanied by a salt restriction on metabolic syndrome and cardiometabolic risk factors in adults with high normal blood pressure or grade 1 hypertension: secondary analyses of a randomized controlled trial.

OBJECTIVE: Lifestyle interventions are recommended as the first-line treatment to control metabolic syndrome components and improve cardiometabolic risk factors. However, studies directly comparing the cardiometabolic effects of the Dietary Approaches to Stop Hypertension (DASH) vs. the Mediterranean diet (MedDiet) accompanied by salt restriction are currently lacking. Thus, with the present secondary analyses of a randomized trial, we aimed to assess the cardiometabolic effects of a 3-month intensive dietary intervention implementing salt restriction alone or on top of the DASH and MedDiet compared to no/minimal intervention in never drug-treated adults with high normal blood pressure (BP) or grade 1 hypertension. METHODS: We randomly assigned individuals to the control group (CG, n = 60), salt restriction group (SRG, n = 60), DASH diet with salt restriction group (DDG, n = 60), or MedDiet with salt restriction group (MDG, n = 60). RESULTS: According to the intention-to-treat analysis, the DDG and the MDG had lower odds ratio (OR) (95% CI) of metabolic syndrome [0.29 (0.12, 0.72), and 0.15 (0.06, 0.41), respectively] compared to the CG. Moreover, the MDG had lower odds of metabolic syndrome compared to the SRG and lower odds of elevated BP levels than the DDG and the SRG. Moreover, total and LDL-cholesterol, fasting glucose, HbA1c, and systolic/diastolic BP were reduced in all three intervention groups compared to the CG. CONCLUSION: On a background of salt restriction, the MedDiet was superior in BP reduction, but the DASH and MedDiet reduced the prevalence of metabolic syndrome to the same extent.

Can ChatGPT provide appropriate meal plans for NCD patients?

OBJECTIVES: Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. METHODS: Using a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. RESULTS: Experimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. CONCLUSION: Additional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.

Revisiting Cardiovascular Benefits of Blood Pressure Reduction in Primary and Secondary Prevention: Focus on Targets and Residual Risk-A Systematic Review and Meta-Analysis.

BACKGROUND: Previous meta-analyses resurrected the debated statement "the lower, the better" following blood pressure (BP)-lowering treatment. We investigated the benefits of BP-lowering treatment at different BP targets by prevention category. METHODS: The meta-analysis protocol was registered at the International Prospective Register of Systematic Reviews (CRD42022379249). The database included 115 BP-lowering or comparison trials from patients with (n=241 089) or without (n=198 937) previous cardiovascular events. Prevention disease groups were stratified by in-treatment achieved BP, drug class versus placebo, and drug class versus other classes. Risk ratios and 95% CIs of major adverse cardiovascular events were calculated. RESULTS: Following a standard (10/5 mm Hg) BP reduction, major adverse cardiovascular event relative risk reductions were not different between prevention groups (primary, 25% [95% CI, 18%-31%]; secondary, 28% [95% CI, 20%-37%]). For achieved systolic BP of at least 140 mm Hg, between 130 and 140 mm Hg, and <130 mm Hg (nadir, 125 mm Hg), (1) risk ratios of major adverse cardiovascular events and absolute risk reductions were not different between prevention groups across systolic BP strata, and (2) residual risk, though 4.1× greater in secondary than primary prevention, decreased in primary prevention from higher to lower systolic BP targets. The effect of separate drugs versus others on the primary outcome was not different between prevention groups. CONCLUSIONS: BP-lowering treatment benefits did not differ by prevention group to a nadir of 125 mm Hg for systolic BP. Although residual risk in secondary prevention is higher than in primary prevention, it gradually decreases at progressively lower systolic BP targets in primary prevention. REGISTRATION: URL: https://www.crd.york.ac.uk/PROSPERO/; Unique identifier: CRD42022379249.

Safety and Efficacy of Renal Denervation in Patients Taking Antihypertensive Medications.

BACKGROUND: Renal denervation (RDN) reduces blood pressure (BP) in patients with uncontrolled hypertension in the absence of antihypertensive medications. OBJECTIVES: This trial assessed the safety and efficacy of RDN in the presence of antihypertensive medications. METHODS: SPYRAL HTN-ON MED is a prospective, randomized, sham-controlled, patient- and assessor-blinded trial enrolling patients from 56 clinical centers worldwide. Patients were prescribed 1 to 3 antihypertensive medications. Patients were randomized to radiofrequency RDN or sham control procedure. The primary efficacy endpoint was the baseline-adjusted change in mean 24-hour ambulatory systolic BP at 6 months between groups using a Bayesian trial design and analysis. RESULTS: The treatment difference in the mean 24-hour ambulatory systolic BP from baseline to 6 months between the RDN group (n = 206; -6.5 ± 10.7 mm Hg) and sham control group (n = 131; -4.5 ± 10.3 mm Hg) was -1.9 mm Hg (95% CI: -4.4 to 0.5 mm Hg; P = 0.12). There was no significant difference between groups in the primary efficacy analysis with a posterior probability of superiority of 0.51 (Bayesian treatment difference: -0.03 mm Hg [95% CI: -2.82 to 2.77 mm Hg]). However, there were changes and increases in medication intensity among sham control patients. RDN was associated with a reduction in office systolic BP compared with sham control at 6 months (adjusted treatment difference: -4.9 mm Hg; P = 0.0015). Night-time BP reductions and win ratio analysis also favored RDN. There was 1 adverse safety event among 253 assessed patients. CONCLUSIONS: There was no significant difference between groups in the primary analysis. However, multiple secondary endpoint analyses favored RDN over sham control. (SPYRAL HTN-ON MED Study [Global Clinical Study of Renal Denervation With the Symplicity Spyral Multi-electrode Renal Denervation System in Patients With Uncontrolled Hypertension in the Absence of Antihypertensive Medications]; NCT02439775).

DASH vs. Mediterranean diet on a salt restriction background in adults with high normal blood pressure or grade 1 hypertension: A randomized controlled trial.

BACKGROUND & AIMS: Non-pharmacological measures are recommended as the first-line treatment for individuals with high-normal blood pressure (BP) or mild hypertension. Studies directly comparing the BP effects of the Dietary Approaches to Stop Hypertension (DASH) vs. the Mediterranean diet (MedDiet) on a salt restriction background are currently lacking. Thus, our purpose was to assess the BP effects of a 3-month intensive dietary intervention implementing salt restriction either alone or in the context of the DASH, and the MedDiet compared to no/minimal intervention in adults with high normal BP or grade 1 hypertension. METHODS: We randomly assigned never drug-treated individuals to a control group (CG, n = 60), a salt restriction group (SRG, n = 60), a DASH diet with salt restriction group (DDG, n = 60), or a MedDiet with salt restriction group (MDG, n = 60). The primary outcome was the attained office systolic BP difference among the randomized arms during follow-up. RESULTS: A total of 240 patients were enrolled, while 204 (85%) completed the study. According to the intention-to-treat analysis, compared to the CG, office and 24 h ambulatory systolic and diastolic BP were reduced in all intervention groups. A greater reduction in the mean office systolic BP was observed in the MDG compared to all other study groups (MDG vs. CG: mean difference = -15.1 mmHg; MDG vs. SRG: mean difference = -7.5 mmHg, and MDG vs. DDG: mean difference = -3.2 mmHg, all P-values <0.001). The DDG and the MDG did not differ concerning the office diastolic BP and the 24 h ambulatory systolic and diastolic BP; however, both diets were more efficient in BP-lowering compared to the SRG. CONCLUSIONS: On a background of salt restriction, the MedDiet was superior in office systolic BP-lowering, but the DASH and MedDiet reduced BP to an extent higher than salt restriction alone.

The effect of SGLT2 inhibitors on the endothelium and the microcirculation: from bench to bedside and beyond.

AIMS: The beneficial cardiovascular effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors irrespective of the presence of diabetes mellitus are nowadays well established and they already constitute a significant pillar for the management of heart failure, irrespective of the ejection fraction. The exact underlying mechanisms accountable for these effects, however, remain largely unknown. The direct effect on endothelial function and microcirculation is one of the most well studied. The broad range of studies presented in this review aims to link all available data from the bench to bedside and highlight the existing gaps as well as the future directions in the investigations concerning the effects of SGLT2 inhibitors on the endothelium and the microcirculation. METHODS AND RESULTS: An extensive search has been conducted using the MEDLINE/PubMed database in order to identify the relevant studies. Preclinical data suggest that SGLT2 inhibitors directly affect endothelial function independently of glucose and specifically via several interplaying molecular pathways, resulting in improved vasodilation, increased NO production, enhanced mitochondrial homeostasis, endothelial cell viability, and angiogenesis as well as attenuation of oxidative stress and inflammation. Clinical data systematically confirm this beneficial effect on the endothelium, whereas the evidence concerning the effect on the microcirculation is conflicting. CONCLUSION: Preclinical and clinical studies indicate that SGLT2 inhibitors attenuate endothelial and microvascular dysfunction via a combination of mechanisms, which play a role in their beneficial cardiovascular effect.

Fatty liver index and cardiovascular outcomes in never-treated hypertensive patients: a prospective cohort.

The prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing rapidly worldwide, affecting 25-30% of the population. Fatty liver index (FLI) is a validated marker of NAFLD and can be used as a screening tool for hepatic steatosis. The purpose of the study was to evaluate the relationship between FLI and the risk of major cardiovascular events in never treated hypertensive patients. We included 903 hypertensive patients without a history of cardiovascular disease (mean age 52.7 ± 11.4 years; men 55%; baseline clinic BP 149.8 ± 15.2/95.5 ± 10.1 mmHg). Participants were prospectively evaluated for a mean follow-up period of 5.2 ± 3.2 years with at least one annual visit. Patients were also categorized into two groups using an FLI of 60 units. The incidence of cardiovascular events during follow-up was 8.5% (n = 77). Patients with FLI < 60 (n = 625) had a better BP control compared to their counterparts with FLI ≥ 60 (n = 278) during follow up (43% vs 33%, p = 0.02). Cox-regression analysis indicated that FLI (Hazard Ratio [HR], 1.05; 95% Confidence Interval [CI], 1.03-1.07, p < 0.001), FLI z-scores (HR, 3.66; 95% CI, 2.22-6.04) and high-risk FLI (HR, 7.5; 95% CI, 3.12-18.04) were independent determinants of the outcome after adjustment for baseline and follow-up variables. Stratification by diabetes mellitus indicated that FLI predicted the outcome to a greater extent in those with than those without diabetes (P-interaction < 0.001). In conclusion, FLI has an independent prognostic value for the incidence of cardiovascular events in newly diagnosed, never-treated hypertensive patients. Therefore, FLI might identify higher-risk patients in the primary prevention of hypertension.

Predictive evolution of metabolic phenotypes using model-designed environments.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets.

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system's effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.

Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture.

Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods.

Abdominal cocoon syndrome: Rare cause of intestinal obstruction-Case report and systematic review of literature.

BACKGROUND: Abdominal cocoon or sclerosing encapsulating peritonitis is an uncommon condition in which the small bowel is completely or partially encased by a thick fibrotic membrane. Our study presents a case of sclerosing encapsulating peritonitis and conducts a literature review. METHODS: A bibliographic research was conducted. Our research comprised 97 articles. Gender, age, symptoms, diagnostic procedures, and treatment were all included in the database of patient characteristics. CASE PRESENTATION: A 51-year-old man complaining of a 2-day history of minor diffuse abdominal pain, loss of appetite, and constipation was presented in emergency department. Physical examination was indicative of intestinal obstruction. Laboratory tests were normal. Diffuse intraperitoneal fluid and dilated small intestinal loops were discovered on computed tomography (CT). An exploratory laparotomy was recommended, in which the sac membrane was removed and adhesiolysis was performed. He was discharged on the tenth postoperative day. RESULTS: There were 240 cases of abdominal cocoon syndrome in total. In terms of gender, 151 of 240 (62.9%) were male and 89 of 240 (37%) were female. Ages between 20 and 40 are most affected. Symptoms include abdominal pain and obstruction signs. For the diagnosis of abdominal cocoon syndrome, CT may be the gold standard imaging method. The surgical operation was the treatment of choice in the vast majority of cases (96.7%). Only 69 of 239 patients (28.9%) were detected prior to surgery, and CT was applied in these cases. CONCLUSION: Abdominal cocoon is a rare condition marked by recurrent episodes of intestinal obstruction. Surgical therapy is the most effective treatment option.

Profile and management of hypertensive urgencies and emergencies in the emergency cardiology department of a tertiary hospital: a 12-month registry.

AIMS: Currently there are scarce epidemiological data regarding prevalence, clinical phenotype, and therapy of hypertensive urgencies (HU) and emergencies (HE). The aim of this article was to record the prevalence, clinical characteristics, and management of patients with HU and HE assessed in an emergency department (ED) of a tertiary hospital. METHODS AND RESULTS: The population consisted of patients presenting with HE and HU in the ED (acute increase in systolic blood pressure (BP) ≥ 180 mmHg and/or diastolic BP ≥120 mmHg with and without acute target organ damage, respectively). Of the 38 589 patients assessed in the ED during a 12-month period, 353 (0.91%) had HU and HE. There were 256 (72.5%) cases presented as HU and 97 (27.5%) as HE. Primary causes for both HU and HE were stress/anxiety (44.9%), increased salt intake (33.9%), and non-adherence to medication (16.2%). Patients with HU reported mainly dizziness/headache (46.8%) and chest pain (27.4%), whereas those with HE presented dyspnoea (67%), chest pain (30.2%), dizziness/headache (10.3%), and neurological disorders (8.2%). In HE, the underlying associated conditions were pulmonary oedema (58%), acute coronary syndrome (22.6%), and neurological disorders/stroke (7.2%). All HE cases were hospitalized and received intensive healthcare, including dialysis. CONCLUSION: This 1-year single-centre registry demonstrates a reasonable prevalence of HU and HE contributing to the high volume of visits to the ED. Stress, increased salt intake and non-adherence were main triggers of HE and HU. Dizziness and headache were the prevalent symptoms of HU patients while heart failure was the most common underlying disease in patients with HE.

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Artificial Intelligence Technologies for Sign Language.

AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion.

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Recurrent neural network pruning using dynamical systems and iterative fine-tuning.

Network pruning techniques are widely employed to reduce the memory requirements and increase the inference speed of neural networks. This work proposes a novel RNN pruning method that considers the RNN weight matrices as collections of time-evolving signals. Such signals that represent weight vectors can be modelled using Linear Dynamical Systems (LDSs). In this way, weight vectors with similar temporal dynamics can be pruned as they have limited effect on the performance of the model. Additionally, during the fine-tuning of the pruned model, a novel discrimination-aware variation of the L2 regularization is introduced to penalize network weights (i.e., reduce the magnitude), whose impact on the output of an RNN network is minimal. Finally, an iterative fine-tuning approach is proposed that employs a bigger model to guide an increasingly smaller pruned one, as a steep decrease of the network parameters can irreversibly harm the performance of the pruned model. Extensive experimentation with different network architectures demonstrates the potential of the proposed method to create pruned models with significantly improved perplexity by at least 0.62% on the PTB dataset and improved F1-score by 1.39% on the SQuAD dataset, contrary to other state-of-the-art approaches that slightly improve or even deteriorate models' performance.

Joint ESH excellence centers' national meeting on renal sympathetic denervation: A Greek experts' survey.

OBJECTIVE: The efficacy of renal sympathetic denervation (RDN) has been affirmed by a number of recent clinical studies, despite controversies in this field over the last five years. Therefore, it is of paramount importance that hypertension experts debate the merits of RDN by revealing and expressing their personal beliefs and perspectives regarding this procedure. METHODS: A cross-sectional survey was conducted among Greek leaders of the Hypertension Excellence Centers with the use of a closed-type questionnaire specifically designed to elicit information and evaluate the respondent's views and perspectives about RDN efficacy, safety and ideal target patient population. RESULTS: A total of 36 participants completed the survey. Based on the results, RDN was considered efficient (91.7%) and safe (94.5%), while the overwhelming majority of the participants felt confident in the long-term efficacy (88.9%) of the intervention and that it lacks reliable predictors of blood pressure response (94.5%). Patients with resistant (91.7%), ultra-resistant (94.4%), and uncontrolled hypertension (80.6%) were suggested as ideal candidates for RDN. Establishing a close co-operation between interventionalists and hypertension experts was considered essential to ensure the efficacy (97.2%) as well as the safety (97.3%) of the procedure. CONCLUSION: The vast majority of Greek hypertension experts surveyed were convinced of the efficacy and safety of RDN based on the preponderance of available scientific and clinical data. Identification of the ideal patient group remains controversial. Respondents generally agreed on the necessity of building close collaborative relationships between interventionalists and hypertension experts in order to improve RDN clinical outcome.