Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

Bispecific Metabolic Monitoring Platform for Bacterial Identification and Antibiotic Susceptibility Testing

Prompt and reliable bacterial identification and antibiotic susceptibility testing are vital for combating bacterial infections and drug resistance. Herein, we designed a bispecific metabolic monitoring platform that targets enzyme-catalyzed biochemical reactions for bacterial identification and antibiotic susceptibility testing. Specifically, we designed two kinds of coreshell-structured persistent luminescence nanoparticles with surface-confined red and green persistent luminescence, respectively. The persistent luminescence nanoparticles were functionalized with energy acceptors that can be specifically cleaved by bacterial enzymes. The surface-confined persistent luminescence amplified the Förster resonance energy transfer (FRET) efficacy from the nanoparticles to the surface energy acceptors, even though the diameter of the nanoparticles exceeded the critical size of FRET, which improved the sensitivity of bacterial enzyme monitoring. Due to the differentiated expression and secretion of enzymes, different species of bacteria produced discrepant red and green persistent luminescence after incubation with the persistent luminescence nanoprobes. Machine learning models were trained by the characteristic persistent luminescence patterns of bacteria for unknown bacterial identification. Prompt bacteria identification was realized, and the overall accuracy reached 100%. Moreover, the machine learning model could identify the active and inactive states of bacteria treated with antibiotics, which provided a prompt and convenient method to determine whether the bacteria were susceptible to the antibiotics. This study provides a robust method to monitor bacterial metabolism and offers a promising strategy for infection treatment, bacterial communication monitoring, and pathogenicity investigation.

De novo identification of universal cell mechanics gene signatures

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

Simplicity within biological complexity

Motivation

Heterogeneous, interconnected, systems-level, molecular (multi-omic) data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs.

Results

In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods (also called graph representation learning) map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications, and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics, focusing on precision medicine and personalized drug discovery. It will lead to a paradigm shift in the computational and biomedical understanding of data and diseases that will open up ways to solve some of the major bottlenecks in precision medicine and other domains.

The Influence of Gastric Microbiota and Probiotics in Helicobacter pylori Infection and Associated Diseases

The role of microbiota in human health and disease is becoming increasingly clear as a result of modern microbiome studies in recent decades. The gastrointestinal tract is the major habitat for microbiota in the human body. This microbiota comprises several trillion microorganisms, which is equivalent to almost ten times the total number of cells of the human host. Helicobacter pylori is a known pathogen that colonizes the gastric mucosa of almost half of the world population. H. pylori is associated with several gastric diseases, including gastric cancer (GC) development. However, the impact of the gastric microbiota in the colonization, chronic infection, and pathogenesis is still not fully understood. Several studies have documented qualitative and quantitative changes in the microbiota's composition in the presence or absence of this pathogen. Among the diverse microflora in the stomach, the Firmicutes represent the most notable. Bacteria such as Prevotella sp., Clostridium sp., Lactobacillus sp., and Veillonella sp. were frequently found in the healthy human stomach. In contrast, H.pylori is very dominant during chronic gastritis, increasing the proportion of Proteobacteria in the total microbiota to almost 80%, with decreasing relative proportions of Firmicutes. Likewise, H. pylori and Streptococcus are the most abundant bacteria during peptic ulcer disease. While the development of H. pylori-associated intestinal metaplasia is accompanied by an increase in Bacteroides, the stomachs of GC patients are dominated by Firmicutes such as Lactobacillus and Veillonella, constituting up to 40% of the total microbiota, and by Bacteroidetes such as Prevotella, whereas the numbers of H. pylori are decreasing. This review focuses on some of the consequences of changes in the gastric microbiota and the function of probiotics to modulate H. pylori infection and dysbiosis in general.

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset's meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

A rapid-floating natural polysaccharide gel-raft with double-effect for the treatment of gastroesophageal reflux disease

Gastroesophageal reflux disease (GERD) is a prevalent gastrointestinal condition characterized by regurgitating stomach contents into the esophagus, causing mucosal damage or erosion. Clinical physical protection treatment mainly relies on the use of floating rafts. Bletilla striata (BS) is widely regarded as the first-choice drug for treating digestive tract injuries in Chinese Medicine. The rapid-floating gel-raft (B-R) was prepared via a one-step swelling method using natural BS polysaccharide and glyceryl monooleate. Panax notoginseng saponins (PNS) were loaded to further prepare P/B-R according to clinical experience. Possessing hydrophobic dense, stratified porous structure and stable rheological properties, an outperforming floating performance of P/B-R was proven compared with Gaviscon® (alginate-antacid formulation) in vitro. In vivo imaging results showed that P/B-R can retain and adhere to the gastric mucosa of rats for up to 90 min, protecting and repairing the mucosa. Besides physical protection in situ, the systemic effects of antioxidant and anti-inflammatory actions for treating GERD were achieved through the intestinal release of PNS. Acid-labile PNS was protected by P/B-R against gastric acid, attaining the desired release and permeability. A significantly effective mucosa injury protective effect of P/B-R was found in ethanol-induced gastric damage model on rats. Moreover, P/B-R exhibits excellent biosafety at the cellular level.

The role of microbiomes in gastrointestinal cancers: new insights

Gastrointestinal (GI) cancers constitute more than 33% of new cancer cases worldwide and pose a considerable burden on public health. There exists a growing body of evidence that has systematically recorded an upward trajectory in GI malignancies within the last 5 to 10 years, thus presenting a formidable menace to the health of the human population. The perturbations in GI microbiota may have a noteworthy influence on the advancement of GI cancers; however, the precise mechanisms behind this association are still not comprehensively understood. Some bacteria have been observed to support cancer development, while others seem to provide a safeguard against it. Recent studies have indicated that alterations in the composition and abundance of microbiomes could be associated with the progression of various GI cancers, such as colorectal, gastric, hepatic, and esophageal cancers. Within this comprehensive analysis, we examine the significance of microbiomes, particularly those located in the intestines, in GI cancers. Furthermore, we explore the impact of microbiomes on various treatment modalities for GI cancer, including chemotherapy, immunotherapy, and radiotherapy. Additionally, we delve into the intricate mechanisms through which intestinal microbes influence the efficacy of GI cancer treatments.

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community

Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance

Antibiotic treatment can lead to a loss of diversity of gut microbiota and may adversely affect gut microbiota composition and host health. Previous studies have indicated that the recovery of gut microbes from antibiotic-induced disruption may be guided by specific microbial species. We expect to predict recovery or non-recovery using these crucial species or other indices after antibiotic treatment only when the gut microbiota changes. This study focused on this prediction problem using a novel ensemble learning framework to identify a set of common and reasonably predictive recovery-associated bacterial species (p-RABs), enabling us to predict the host microbiome recovery status under broad-spectrum antibiotic treatment. Our findings also propose other predictive indicators, suggesting that higher taxonomic and functional diversity may correlate with an increased likelihood of successful recovery. Furthermore, to explore the validity of p-RABs, we performed a metabolic support analysis and identified Akkermansia muciniphila and Bacteroides uniformis as potential key supporting species for reconstruction interventions. Experimental results from a C57BL/6J male mouse model demonstrated the effectiveness of p-RABs in facilitating intestinal microbial reconstitution. Thus, we proved the reliability of the new p-RABs and validated a practical intervention scheme for gut microbiota reconstruction under antibiotic disturbance.

Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections

Enteric protozoan pathogenic infections significantly contribute to the global burden of gastrointestinal illnesses. Their occurrence is considerable within remote and indigenous communities and regions due to reduced access to clean water and adequate sanitation. The robustness of these pathogens leads to a requirement of harsh treatment methods, such as medicinal drugs or antibiotics. However, in addition to protozoal infection itself, these treatments impact the gut microbiome and create dysbiosis. This often leads to opportunistic pathogen invasion, anti-microbial resistance, or functional gastrointestinal disorders, such as irritable bowel syndrome. Moreover, these impacts do not remain confined to the gut and are reflected across the gut-brain, gut-liver, and gut-lung axes, among others. Therefore, apart from medicinal treatment, nutritional supplementation is also a key aspect of providing recovery from this dysbiosis. Future proteins, prebiotics, probiotics, synbiotics, and food formulations offer a good solution to remedy this dysbiosis. Furthermore, nutritional supplementation also helps to build resilience against opportunistic pathogens and potential future infections and disorders that may arise due to the dysbiosis. Systems biology techniques have shown to be highly effective tools to understand the biochemistry of these processes. Systems biology techniques characterize the fundamental host-pathogen interaction biochemical pathways at various infection and recovery stages. This same mechanism also allows the impact of the abovementioned treatment methods of gut microbiome remediation to be tracked. This manuscript discusses system biology approaches, analytical techniques, and interaction and association networks, to understand (1) infection mechanisms and current global status; (2) cross-organ impacts of dysbiosis, particularly within the gut-liver and gut-lung axes; and (3) nutritional interventions. This study highlights the impact of anti-microbial resistance and multi-drug resistance from the perspective of protozoal infections. It also highlights the role of nutritional interventions to add resilience against the chronic problems caused by these phenomena.

Maternal gut microbiota in the postpartum Period: A Systematic review

Studies have demonstrated the importance of the gut microbiota during pregnancy, and there is emerging literature on the postpartum maternal gut microbiota. The primary objective of this paper was to synthesize the literature on the postpartum gut microbiome composition and diversity measured in stool samples from healthy mothers of predominantly term infants. The secondary objectives were (1) to identify biological and environmental factors that influence postpartum maternal gut microbiota and (2) to assess health conditions and clinical intermediate measures associated with postpartum gut microbiota changes in all mothers. Electronic searches were conducted November 9, 2020 and updated July 25, 2021 without publication time limits on PubMed, Embase, CINHAL, Scopus, Cochrane Library, BioArchives, and OpenGrey.eu. Primary research on maternal gut microbiota in the postpartum (up to one year after childbirth) were eligible. Postpartum gut microbiota comparisons to pregnancy or non-pregnancy gut microbiota were of interest, therefore, studies examining these in addition to the postpartum were included. Studies were excluded if they were only conducted in animals, infants, pregnancy, or microbiome of other body locations (e.g., vaginal). Data extraction of microbial composition and diversity were completed and synthesized narratively. Studies were assessed for risk of bias. A total of 2512 articles were screened after deduplication and 27 were included in this review. Of the 27 included studies, 22 addressed the primary objective. Firmicutes was the predominant phylum in the early (<6 weeks) and late postpartum (6 weeks to 1 year). In early postpartum, Bacteroides was the predominant genus. Findings from longitudinal assessments of alpha and beta diversity from the early to the late postpartum varied. Nineteen of the 27 studies assessed biological and environmental factors influencing the postpartum gut microbial profile changes. Timing of delivery, probiotic supplementation, triclosan exposure, and certain diets influenced the postpartum gut microbiota. Regarding health conditions and intermediate clinical measures assessed in 8 studies; inflammatory bowel disease, postpartum depression, early-onset preeclampsia, gestational diabetes, excessive gestational weight gain, and anthropometric measures such as body mass index and waist-to-hip ratio were related to gut microbiota changes. There is limited data on the maternal postpartum gut microbiota and how it influences maternal health. We need to understand the postpartum maternal gut microbiome, establish how it differs from non-pregnancy and pregnancy states, and determine biological and environmental influencers. Future research of the gut microbiome's significance for the birthing parent in the postpartum could lead to a new understanding of how to improve maternal short and long-term health.

Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes

We introduce in network geometry a measure of geometrical congruence (GC) to evaluate the extent a network topology follows an underlying geometry. This requires finding all topological shortest-paths for each nonadjacent node pair in the network: a nontrivial computational task. Hence, we propose an optimized algorithm that reduces 26 years of worst scenario computation to one week parallel computing. Analysing artificial networks with patent geometry we discover that, different from current belief, hyperbolic networks do not show in general high GC and efficient greedy navigability (GN) with respect to the geodesics. The myopic transfer which rules GN works best only when degree-distribution power-law exponent is strictly close to two. Analysing real networks-whose geometry is often latent-GC overcomes GN as marker to differentiate phenotypical states in macroscale structural-MRI brain connectomes, suggesting connectomes might have a latent neurobiological geometry accounting for more information than the visible tridimensional Euclidean.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

Identifying cellular cancer mechanisms through pathway-driven data integration

MOTIVATION

Cancer is a genetic disease in which accumulated mutations of driver genes induce a functional reorganization of the cell by reprogramming cellular pathways. Current approaches identify cancer pathways as those most internally perturbed by gene expression changes. However, driver genes characteristically perform hub roles between pathways. Therefore, we hypothesize that cancer pathways should be identified by changes in their pathway-pathway relationships.

RESULTS

To learn an embedding space that captures the relationships between pathways in a healthy cell, we propose pathway-driven non-negative matrix tri-factorization. In this space, we determine condition-specific (i.e. diseased and healthy) embeddings of pathways and genes. Based on these embeddings, we define our 'NMTF centrality' to measure a pathway's or gene's functional importance, and our 'moving distance', to measure the change in its functional relationships. We combine both measures to predict 15 genes and pathways involved in four major cancers, predicting 60 gene-cancer associations in total, covering 28 unique genes. To further exploit driver genes' tendency to perform hub roles, we model our network data using graphlet adjacency, which considers nodes adjacent if their interaction patterns form specific shapes (e.g. paths or triangles). We find that the predicted genes rewire pathway-pathway interactions in the immune system and provide literary evidence that many are druggable (15/28) and implicated in the associated cancers (47/60). We predict six druggable cancer-specific drug targets.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at: https://gitlab.bsc.es/swindels/pathway_driven_nmtf.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Renaissance for Phage-Based Bacterial Control

Bacteriophages (phages) are an underutilized biological resource with vast potential for pathogen control and microbiome editing. Phage research and commercialization have increased rapidly in biomedical and agricultural industries, but adoption has been limited elsewhere. Nevertheless, converging advances in DNA sequencing, bioinformatics, microbial ecology, and synthetic biology are now poised to broaden phage applications beyond pathogen control toward the manipulation of microbial communities for defined functional improvements. Enhancements in sequencing combined with network analysis make it now feasible to identify and disrupt microbial associations to elicit desirable shifts in community structure or function, indirectly modulate species abundance, and target hub or keystone species to achieve broad functional shifts. Sequencing and bioinformatic advancements are also facilitating the use of temperate phages for safe gene delivery applications. Finally, integration of synthetic biology stands to create novel phage chassis and modular genetic components. While some fundamental, regulatory, and commercialization barriers to widespread phage use remain, many major challenges that have impeded the field now have workable solutions. Thus, a new dawn for phage-based (chemical-free) precise biocontrol and microbiome editing is on the horizon to enhance, suppress, or modulate microbial activities important for public health, food security, and more sustainable energy production and water reuse.

Microbial metabolites in the marine carbon cycle

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

Lactobacillus acidophilus DDS-1 Modulates the Gut Microbial Co-Occurrence Networks in Aging Mice

Age-related alterations in the gut microbiome composition and its impacts on the host’s health have been well-described; however, detailed analyses of the gut microbial structure defining ecological microbe–microbe interactions are limited. One of the ways to determine these interactions is by understanding microbial co-occurrence patterns. We previously showed promising abilities of Lactobacillus acidophilus DDS-1 on the aging gut microbiome and immune system. However, the potential of the DDS-1 strain to modulate microbial co-occurrence patterns is unknown. Hence, we aimed to investigate the ability of L. acidophilus DDS-1 to modulate the fecal-, mucosal-, and cecal-related microbial co-occurrence networks in young and aging C57BL/6J mice. Our Kendall’s tau correlation measures of co-occurrence revealed age-related changes in the gut microbiome, which were characterized by a reduced number of nodes and associations across sample types when compared to younger mice. After four-week supplementation, L. acidophilus DDS-1 differentially modulated the overall microbial community structure in fecal and mucosal samples as compared to cecal samples. Beneficial bacteria such as Lactobacillus, Oscillospira, and Akkermansia acted as connectors in aging networks in response to L. acidophilus DDS-1 supplementation. Our findings provided the first evidence of the DDS-1-induced gut microbial ecological interactions, revealing the complex structure of microbial ecosystems with age.

Medial prefrontal and occipito-temporal activity at encoding determines enhanced recognition of threatening faces after 1.5 years

Studies demonstrated that faces with threatening emotional expressions are better remembered than non-threatening faces. However, whether this memory advantage persists over years and which neural systems underlie such an effect remains unknown. Here, we employed an individual difference approach to examine whether the neural activity during incidental encoding was associated with differential recognition of faces with emotional expressions (angry, fearful, happy, sad and neutral) after a retention interval of > 1.5 years (N = 89). Behaviorally, we found a better recognition for threatening (angry, fearful) versus non-threatening (happy and neutral) faces after a delay of > 1.5 years, which was driven by forgetting of non-threatening faces compared with immediate recognition after encoding. Multivariate principal component analysis (PCA) on the behavioral responses further confirmed the discriminative recognition performance between threatening and non-threatening faces. A voxel-wise whole-brain analysis on the concomitantly acquired functional magnetic resonance imaging (fMRI) data during incidental encoding revealed that neural activity in bilateral inferior occipital gyrus (IOG) and ventromedial prefrontal/orbitofrontal cortex (vmPFC/OFC) was associated with the individual differences in the discriminative emotional face recognition performance measured by an innovative behavioral pattern similarity analysis (BPSA). The left fusiform face area (FFA) was additionally determined using a regionally focused analysis. Overall, the present study provides evidence that threatening facial expressions lead to persistent face recognition over periods of > 1.5 years, and that differential encoding-related activity in the medial prefrontal cortex and occipito-temporal cortex may underlie this effect.

Progress in elucidating the relationship between Helicobacter pylori infection and intestinal diseases

Helicobacter pylori (H. pylori) infection causes changes to the intestinal flora, such as small intestinal bacterial overgrowth, and increases gastric acid secretion-stimulating gastrointestinal hormones, mainly gastrin, due to a decrease in gastric acid caused by atrophic gastritis. In addition, the cellular components of H. pylori travel through the intestinal tract, so the bacterial infection affects the immune system. Therefore, the effects of H. pylori infection are observed not only in the stomach and the proximal duodenum but also in the small and large intestines. In particular, meta-analyses reported that H. pylori-infected individuals had an increased risk of colorectal adenoma and colorectal cancer. Moreover, a recent study reported that the risk of developing colorectal cancer was increased in subjects carrying H. pylori vacuolating cytotoxin A antibody. In addition, it has been reported that H. pylori infection exacerbates the symptoms of Fabry’s disease and familial Mediterranean fever attack and is involved in irritable bowel syndrome and small intestinal ulcers. On the other hand, some studies have reported that the frequency of ulcerative colitis, Crohn’s disease, and celiac disease is low in H. pylori-infected individuals. Thus, H. pylori infection is considered to have various effects on the small and large intestines. However, few studies have reported on these issues, and the details of their effects have not been well elucidated. Therefore, additional studies are needed.

Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19

Governments continue to update social intervention strategies to contain COVID-19 infections. However, investigation of COVID-19 severity indicators across the population might help to design more precise strategies, balancing the need to keep people safe and to reduce the socio-economic burden of generalized restriction precedures. Here, we propose a method for age-sex population-adjusted analysis of disease severity in epidemics that has the advantage to use simple and repeatable variables, which are daily or weekly available. This allows to monitor the effect of public health policies in short term, and to repeat these calculations over time to surveille epidemic dynamics and impact. Our method can help to define a risk-categorization of likeliness to develop a severe COVID-19 disease which requires intensive care or is indicative of a higher risk of dying. Indeed, analysis of suitable open-access COVID-19 data in three European countries indicates that individuals in the 0-40 age interval and females under 60 are significantly less likely to develop a severe condition and die, whereas males equal or above 60 are more likely at risk of severe disease and death. Hence, a combination of age-adaptive and sex-balanced guidelines for social interventions could represent key public health management tools for policymakers.