A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups’ separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon index: p-values: active UC vs HCs = 0.0005; active UC vs inactive UC = 0.0273; HCs vs inactive UC = 0.0260). In particular, patients with active UC showed the lowest values, followed by patients with inactive UC, and HCs. At species level, we found high levels of Bifidobacterium adolescentis and Haemophilus parainfluenzae in inactive UC and active UC, respectively. A specific microbiota profile was found for each group and was confirmed with sparse partial least squares discriminant analysis, a machine learning-supervised approach. The latter allowed us to observe a perfect class prediction and group separation using the complete information (full Operational Taxonomic Unit table), with a minimal loss in performance when using only 5% of features. A machine learning approach to 16S rRNA data identifies a bacterial signature characterizing different degrees of disease activity in UC. Follow-up studies will clarify whether such microbiota profiling are useful for diagnosis and management

Evaluation and quantification of antimicrobial residues and antimicrobial resistance genes in two Italian swine farms

Antimicrobial resistance genes (ARGs) are considered emerging environmental pollutants, posing potential risks for human and animal health: the misuse of antimicrobials in food-producing animals could favour the maintenance and spread of resistances in bacteria. The occurrence of ARGs in Italian swine farming \u2013 which has specific characteristics \u2013 was investigated in order to explore resistance spread dynamics. Two farrow-to-finish pig farms were longitudinally monitored: faecal samples from animals and environmental samples were collected. DNA was extracted and tetA, ermB, qnrS and mcr1 ARGs were analysed by qPCR for their ability to confer resistance to highly or critically important antimicrobials (CIAs). Moreover, 16SrDNA gene was analysed to assess bacterial abundance. ermB and tetA genes were found in animal samples and manure samples. On the contrary, mcr1 was exclusively found in weaners, while qnrS occurred in all animal categories but sows and finishers. Among the analysed genes, ermB and tetA showed the highest absolute and relative abundances. Our results indicate that ermB and tetA ARGs are widely disseminated in the explored farms, suggesting efficient maintenance among bacteria and persistence in the environment. Interestingly, the presence of qnrS and mcr1, limited to just a few animal categories, highlights inefficient dissemination of these genes in the farm environment, in particular for mcr1, a stable plasmid gene conferring resistance to the last-resort antimicrobial, colistin. Paying close attention only to the finishing phase would have hampered the discovery of resistances to CIAs at farm level, which we instead identified thanks to an intensive longitudinal monitoring programme

A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups’ separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon index: p-values: active UC vs HCs = 0.0005; active UC vs inactive UC = 0.0273; HCs vs inactive UC = 0.0260). In particular, patients with active UC showed the lowest values, followed by patients with inactive UC, and HCs. At species level, we found high levels of Bifidobacterium adolescentis and Haemophilus parainfluenzae in inactive UC and active UC, respectively. A specific microbiota profile was found for each group and was confirmed with sparse partial least squares discriminant analysis, a machine learning-supervised approach. The latter allowed us to observe a perfect class prediction and group separation using the complete information (full Operational Taxonomic Unit table), with a minimal loss in performance when using only 5% of features. A machine learning approach to 16S rRNA data identifies a bacterial signature characterizing different degrees of disease activity in UC. Follow-up studies will clarify whether such microbiota profiling are useful for diagnosis and management

The location of the cochlear amplifier: Spatial representation of a single tone on the guinea pig basilar membrane.

Acoustic stimulation vibrates the cochlear basilar membrane, initiating a wave of displacement that travels toward the apex and reaches a peak over a restricted region according to the stimulus frequency. In this characteristic frequency region, a tone at the characteristic frequency maximally excites the sensory hair cells of the organ of Corti, which transduce it into electrical signals to produce maximum activity in the auditory nerve. Saturating, nonlinear, feedback from the motile outer hair cells is thought to provide electromechanical amplification of the travelling wave. However, neither the location nor the extent of the source of amplification, in relation to the characteristic frequency, are known. We have used a laser- diode interferometer to measure in vivo the distribution along the basilar membrane of nonlinear, saturating vibrations to 15 kHz tones. We estimate that the site of amplification for the 15 kHz region is restricted to a 1.25 mm length of basilar membrane centered on the 15 kHz place

Identification of different subtypes of auditory neuropathy using electrocochleography

Currently, the physiological mechanisms underlying auditory neuropathy are unclear, and there are likely to be multiple sites of lesion. A better understanding of the disruption in individual cases may lead to more effective management and device selection. Frequency-specifi c round-window electrocochleography (ECochG) waveforms were used to assess local hair cell, dendritic, and axonal currents generated within the cochlea in 15 subjects with auditory neuropathy (16 ears). These results were compared with electrically evoked auditory brainstem response (EABR) measured after cochlear implantation. The results of this study demonstrate that predominantly two patterns of ECochG waveforms can be identifi ed: (i) a prolonged latency of the hair cell summating potential (SP) waveform with or without residual CAP activity and (ii) a normal latency SP, typically followed by a dendritic potential (DP). We show that seven of eight subjects with a prolonged SP showed a normal EABR waveform, consistent with a presynaptic lesion, whereas six of seven subjects with a normal latency SP showed poor morphology or absent EABR waveforms, consistent with a postsynaptic lesion. We suggest that a presynaptic and postsynaptic type of auditory neuropathy exist, which may have implications for the fi tting of cochlear implants.16 page(s

Imaging hair cell transduction at the speed of sound: Dynamic behavior of mammalian stereocilia

The cochlea contains two types of sensory cells, the inner and outer hair cells. Sound-evoked deflection of outer hair cell stereocilia leads to fast force production that will enhance auditory sensitivity up to 1, 000-fold. In contrast, inner hair cells are thought to have a purely receptive function. Deflection of their stereocilia produces receptor potentials, transmitter release, and action potentials in the auditory nerve. Here, we describe a method for rapid confocal imaging. The method was used to image stereocilia during simultaneous sound stimulation in an in vitro preparation of the guinea pig cochlea. We show that inner hair cell stereocilia move because they interact with the fluid surrounding the hair bundles, but stereocilia deflection occurs at a different phase of the stimulus than is generally expected. In outer hair cells, stereocilia deflections were ≈1/3 of the reticular lamina displacement. Smaller deflections were found in inner hair cells. The ratio between stereocilia deflection and reticular lamina displacement is important for auditory function, because it determines the stimulus applied to transduction channels. The low ratio measured here suggests that amplification of hair-bundle movements may be necessary in vivo to preserve transduction fidelity at low stimulus levels. In the case of the inner hair cells, this finding would represent a departure from traditional views on their function

Using the Cochlear Microphonic as a Tool to Evaluate Cochlear Function in Mouse Models of Hearing

The cochlear microphonic (CM) can be a useful analytical tool, but many investigators may not be fully familiar with its unique properties to interpret it accurately in mouse models of hearing. The purpose of this report is to develop a model for generation of the CM in wild-type (WT) and prestin knockout mice. Data and modeling results indicate that in the majority of cases, the CM is a passive response, and in the absence of outer hair cell (OHC) damage, mice lacking amplification are expected to generate WT levels of CM for inputs less than ~30 kHz. Hence, this cochlear potential is not a useful metric to estimate changes in amplifier gain. This modeling analysis may explain much of the paradoxical data in the literature. For example, various manipulations, including the application of salicylate and activation of the crossed olivocochlear bundle, reduce the compound action potential but increase or do not change the CM. Based on this current evaluation, CM measurements are consistent with early descriptions where this AC cochlear potential is dominated by basal OHCs, when recorded at the round window