TRPV4 channels mediate the infrared laser-evoked response in sensory neurons

Infrared laser irradiation has been established as an appropriate stimulus for primary sensory neurons under conditions where sensory receptor cells are impaired or lost. Yet, development of clinical applications has been impeded by lack of information about the molecular mechanisms underlying the laser-induced neural response. Here, we directly address this question through pharmacological characterization of the biological response evoked by midinfrared irradiation of isolated retinal and vestibular ganglion cells from rodents. Whole cell patch-clamp recordings reveal that both voltage-gated calcium and sodium channels contribute to the laser-evoked neuronal voltage variations (LEVV). In addition, selective blockade of the LEVV by micromolar concentrations of ruthenium red and RN 1734 identifies thermosensitive transient receptor potential vanilloid channels as the primary effectors of the chain reaction triggered by midinfrared laser irradiation. These results have the potential to facilitate greatly the design of future prosthetic devices aimed at restoring neurosensory capacities in disabled patients

Effect of a redesigned fracture management pathway and 'virtual' fracture clinic on ED performance

Objectives Collaboration between the orthopaedic and emergency medicine (ED) services has resulted in standardised treatment pathways, leaflet supported discharge and a virtual fracture clinic review. Patients with minor, stable fractures are discharged with no further follow-up arranged. We aimed to examine the time taken to assess and treat these patients in the ED along with the rate of unplanned reattendance. Design A retrospective study was undertaken that covered 1 year before the change and 1 year after. Prospectively collected administrative data from the electronic patient record system were analysed and compared before and after the change. Setting An ED and orthopaedic unit, serving a population of 300 000, in a publicly funded health system. Participants 2840 patients treated with referral to a traditional fracture clinic and 3374 patients managed according to the newly redesigned protocol. Outcome measures Time for assessment and treatment of patients with orthopaedic injuries not requiring immediate operative management, and 7-day unplanned reattendance. Results Where plaster backslabs were replaced with removable splints, the consultation time was reduced. There was no change in treatment time for other injuries treated by the new discharge protocol. There was no increase in unplanned ED attendance, related to the injury, within 7 days (p=0.149). There was a decrease in patients reattending the ED due to a missed fracture clinic appointment. Conclusions This process did not require any new time resources from the ED staff. This process brought significant benefits to the ED as treatment pathways were agreed. The pathway reduced unnecessary reattendance of patients at face-to-face fracture clinics for a review of stable, self-limiting injuries.</p

Overview of the Diversity of Extremely Saline Soils from a Semi-Arid Region Using 16S rRNA Gene Sequencing: A Case Study of the Sebkhas in Algerian High Plateaus

Sebkha is an Arabic word referring to a closed ground depression temporarily occupied by a salt lake. Very few studies on the composition of the microbial communities from these ecosystems in the Algerian High Plateaus have been carried out. To fill this gap, four sebkhas in the eastern High Plateaus of two different Algerian provinces were sampled, in the winter 2020. We employed the 16S rRNA amplicon sequencing to understand the distribution and diversity of prokaryotic communities in these hypersaline soils. Our results indicate that the overall archaeal community in the hypersaline soils was dominated by members of the class Halobacteria followed by members of the yet uncultured phyla Hadarchaeota and Nanohaloarchaeota. Among the bacterial classes, Alphaproteobacteria was by far the most frequently recovered from all samples, whereas the Cyanobacteria phylum dominated in one of the sebkhas. It was evident from data that Halorubrum and Halapricum were the most abundant archaeal genera, whilst Rhodovibrio and Limimonas predominated among Bacteria, and these were present in all samples. Remarkably, the most abundant archaeal OTUs belonged to the families Haloarculaceae (16.6%) and Halobacteriaceae (16.3%)

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already

Realizing the clinical potential of computational psychiatry: Report from the Banbury Center Meeting, February 2019

Computational psychiatry is an emerging field that examines phenomena in mental illness using formal techniques from computational neuroscience, mathematical psychology, and machine learning. These techniques can be used in a theory-driven manner to gain insight into neural or cognitive processes and in a data-driven way to identify predictive and explanatory relationships in complex datasets. The approaches complement each other: theory-driven models can be used to infer mechanisms, and the resulting measurements can be used in data-driven approaches for prediction. Recent computational studies have successfully described and measured novel mechanisms in a range of disorders, have framed disorders in new and informative ways, and have identified predictors of treatment response. These methods hold the potential to improve identification of relevant clinical variables and could be superior to classification based on traditional behavioral or neural data alone. However, these promising results have been slow to influence clinical practice or to improve patient outcomes