Gut microbiome predicts cognitive function and depressive symptoms in late life.

Depression in older adults with cognitive impairment increases progression to dementia. Microbiota is associated with current mood and cognition, but the extent to which it predicts future symptoms is unknown. In this work, we identified microbial features that reflect current and predict future cognitive and depressive symptoms. Clinical assessments and stool samples were collected from 268 participants with varying cognitive and depressive symptoms. Seventy participants underwent 2-year follow-up. Microbial community diversity, structure, and composition were assessed using high-resolution 16 S rRNA marker gene sequencing. We implemented linear regression to characterize the relationship between microbiome composition, current cognitive impairment, and depressive symptoms. We leveraged elastic net regression to discover features that reflect current or future cognitive function and depressive symptoms. Greater microbial community diversity associated with lower current cognition in the whole sample, and greater depression in participants not on antidepressants. Poor current cognitive function associated with lower relative abundance of Bifidobacterium, while greater GABA degradation associated with greater current depression severity. Future cognitive decline associated with lower cognitive function, lower relative abundance of Intestinibacter, lower glutamate degradation, and higher baseline histamine synthesis. Future increase in depressive symptoms associated with higher baseline depression and anxiety, lower cognitive function, diabetes, lower relative abundance of Bacteroidota, and lower glutamate degradation. Our results suggest cognitive dysfunction and depression are unique states with an overall biological effect detectable through gut microbiota. The microbiome may present a noninvasive readout and prognostic tool for cognitive and psychiatric states.

Meaning and Clinical Interest of Minor Malformations and Normal Variants in Neonatology.

Congenital malformations can be found in all organ systems of a newborn. Almost two-thirds of congenital malformations have an unknown cause. There are minor (mM) and major (MM) congenital malformations. Searching for minor malformations has its vital place in everyday neonatology practice. Minor malformations are defined as physical variants that have no medical consequences and are mostly located on the face and distal parts of the extremities and are easily noticed. Minor malformations occur in approximately 15% of newborns. Minor congenital malformations are of great importance because they can be an indicator of the existence of major congenital malformations and syndromes. In a one-year retrospective study that analyzed the occurrence of 38 minor malformations through the year 2023 at the University Clinical Hospital of Mostar, there was an incidence of 10.59% of minor malformations. The most frequently recorded minor malformation was deep a sacral dimple at 44.72%, then poorly modeled ears at 15.08%, and moderate rectal diastasis at 14.58%. Three or more minor congenital malformations indicate one or more major congenital malformations. Major congenital malformations are severe structural defects of tissues and organs that endanger life, create serious functional disturbances and hinder the development of the child. In our country, there is currently a recorded incidence of 8.04%. The search for minor malformations in the newborn period is of great importance to children and the whole family, and the search must not be neglected.

Pre-processing Applied to Instrumental Data in Analytical Chemistry: A Brief Review of the Methods and Examples.

The field of analytical chemistry has been significantly advanced by the availability of state-of-the-art instrumentation, allowing for the development of novel applications in this field. However, in many cases, the direct interpretation of the recorded data is often not straightforward, hence some level of pre-processing is required (e.g., baseline correction, derivatives, normalization, smoothing). These techniques have become a critical first step for the successful analysis of the data recorded, and it is recommended to use them before the application of chemometrics (e.g., classification, calibration development). The aim of this paper is to provide with an overview of the most used pre-processing methods applied to instrumental analytical methods (e.g., spectroscopy, chromatography). Examples of their application in near infrared and UV-VIS spectroscopy as well as in gas chromatography will be also discussed. Overall, this paper provides with a comprehensive understanding of pre-processing techniques in analytical chemistry, highlighting their importance during the analysis and interpretation of data, as well as during the development of accurate and reliable chemometric models.