The Bifidogenic Effect Revisited—Ecology and Health Perspectives of Bifidobacterial Colonization in Early Life

Extensive microbial colonization of the infant gastrointestinal tract starts after parturition. There are several parallel mechanisms by which early life microbiome acquisition may proceed, including early exposure to maternal vaginal and fecal microbiota, transmission of skin associated microbes, and ingestion of microorganisms present in breast milk. The crucial role of vertical transmission from the maternal microbial reservoir during vaginal delivery is supported by the shared microbial strains observed among mothers and their babies and the distinctly different gut microbiome composition of caesarean-section born infants. The healthy infant colon is often dominated by members of the keystone genus Bifidobacterium that have evolved complex genetic pathways to metabolize different glycans present in human milk. In exchange for these host-derived nutrients, bifidobacteria’s saccharolytic activity results in an anaerobic and acidic gut environment that is protective against enteropathogenic infection. Interference with early-life microbiota acquisition and development could result in adverse health outcomes. Compromised microbiota development, often characterized by decreased abundance of Bifidobacterium species has been reported in infants delivered prematurely, delivered by caesarean section, early life antibiotic exposure and in the case of early life allergies. Various microbiome modulation strategies such as probiotic, prebiotics, synbiotics and postbiotics have been developed that are able to generate a bifidogenic shift and help to restore the microbiota development. This review explores the evolutionary ecology of early-life type Bifidobacterium strains and their symbiotic relationship with humans and discusses examples of compromised microbiota development in which stimulating the abundance and activity of Bifidobacterium has demonstrated beneficial associations with health.

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

Association between gender and deep pit and fissure among index age group 35-45 years

Dental caries is a well-known factor leading to various dental complications, affecting both the health and quality of life. Type of pit and fissures such as "V" and "K" are more susceptible to dental caries where mechanical cleaning becomes a challenge. This study is conducted to evaluate the association of gender with deep pit and fissure among the Index age group 35-44 years old. A retrospective cross-sectional study was conducted using the patient records of the University hospital. A total of 103 consecutive case records of patients who underwent pit and fissure sealant were retrieved and analysed. Descriptive statistics were done to find the prevalence and association of gender and deep pit and fissures among the index age group. The number of teeth with bottomless pit and fissures was higher in males (52.43%) than in females (47.57%). Also, individuals of age group 35-40 years had a larger number of teeth with deep pit and fissures (74.75%) than the age group 41-45 years. No significant association was found between gender and the number of deep pits and fissures (p>0.05). The proportion of deep pit and fissures was high in males compared to females.

Sensitivity analysis of the PALM model system 6.0 in the urban environment

Sensitivity of the PALM model 6.0 is tested in a real urban environment in the vicinity of a typical crossroad in a densely built-up residential area in Prague, Czech Republic. Two types of scenarios are employed. First are the synthetic scenarios altering mainly surface and material parameters such as albedo, emissivity or wall conductivity, testing sensitivity of the model simulations to potentially erroneous setting of model inputs. Second, real-life type scenarios are analyzed, in which commonly considered urban heat island mitigation measures are applied, such as greening of the streets or changing surface materials. For the first-type scenarios, surface parameters used in radiation balance equations are found to be the most sensitive overall followed by volumetric heat capacity and thermal conductivity of walls. Other parameters show limited average effect, however, some can still be significant in some parts of the day, such as surface roughness in the morning hours. Second type, the mitigation scenarios, show urban vegetation to be the most effective measure, especially when considering both physical and biophysical temperature indicators. Influence of both type scenarios was also tested for air quality, specifically PM10 dispersion which generally shows behaviour opposite to thermal indicators, ie., improved thermal comfort brings deterioration of PM10 concentrations.

Prediction of Type 1 Diabetes Mellitus using Datamining Techniques

Type1 diabetes is a sickness occurs when your immune system fighting against infection, affects and erode the insulin generating beta cells of the pancreas. In general, when the blood sugar stage increases, the pancreas makes more insulin. Insulin helps to go sugar out of the blood so it can be used for liveliness. Type 1 diabetes occurs due to the immune system which affects cells in the pancreas that make insulin. The pancreas cannot make adequate insulin, so the blood sugar level continues to increase. According to the children history of type 1 diabetes may enhance risk of their life. Type 1 diabetes cannot be cured, but it can be controlled and managed. In this study we use Naive Bayes, linear regression and k-means algorithm for data analysis and prediction. It predicts the diabetes affected children with maximum level of accuracy 96% by using of data mining algorithms.

Ecological specificity of the metagenome in a set of lower termite species supports contribution of the microbiome to adaptation of the host

Background Elucidating the interplay between hosts and their microbiomes in ecological adaptation has become a central theme in evolutionary biology. A textbook example of microbiome-mediated adaptation is the adaptation of lower termites to a wood-based diet, as they depend on their gut microbiome to digest wood. Lower termites have further adapted to different life types. Termites of the wood-dwelling life type never leave their nests and feed on a uniform diet. Termites of the foraging life type forage for food outside the nest and have access to other nutrients. Here we sought to investigate whether the microbiome that is involved in food substrate breakdown and nutrient acquisition might contribute to adaptation to these dietary differences. We reasoned that this should leave ecological imprints on the microbiome. Results We investigated the protist and bacterial microbiomes of a total of 29 replicate colonies from five termite species, covering both life types, using metagenomic shotgun sequencing. The microbiome of wood-dwelling species with a uniform wood diet was enriched for genes involved in lignocellulose degradation. Furthermore, metagenomic patterns suggest that the microbiome of wood-dwelling species relied primarily on direct fixation of atmospheric nitrogen, while the microbiome of foraging species entailed the necessary pathways to utilize nitrogen in the form of nitrate for example from soil. Conclusion Our findings are consistent with the notion that the microbiome of wood-dwelling species bears an imprint of its specialization on degrading a uniform wood diet, while the microbiome of the foraging species might reflect its adaption to access growth limiting nutrients from more diverse sources. This supports the idea that specific subsets of functions encoded by the microbiome can contribute to host adaptation.