RESUMEN
Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.
RESUMEN
In the era of big data, ecological research is experiencing a transformative shift, yet advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration allows researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. Moreover, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. Detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.
RESUMEN
OBJECTIVE: A nationwide vaccination operation against Coronavirus disease 2019 (COVID-19) using the BNT162b2 mRNA vaccine commenced in Israel in December 2020. People older than 60 were prioritized, and most were vaccinated shortly after. Seizures are not infrequently attributed to the vaccine despite a lack of supporting evidence. People with epilepsy (PWE) are often reluctant to get the vaccine due to concerns of seizure aggravation. We aim to examine the effect of the vaccine effort on the frequency of both new-onset seizures and recurrent seizures in PWE. METHODS: All adults who presented to the emergency department (ED) of Tel Aviv Sourasky Medical Center between January 1st and May 31st, 2017-2021, and were diagnosed with seizures were included. Demographic, clinical, and vaccination status parameters were collected using MDClone, a data acquisition tool. Vaccination rates in the general population were obtained from official governmental publications. Statistics included a sub-analysis of patients with the highest vaccination rate, people older than 60. RESULTS: 1675 cases were included. The numbers of ED visits and hospital admissions due to seizures in 2021 were comparable to preceding years after adjusting for the total number of ED visits at the same time. Out of 339 cases in 2021, 134 patients older than 60 years old presented to the ED (39.5%) compared to 124-151 in 2017-2019 (37-44%) and 103 in 2020 (33%). The vaccination rate among patients hospitalized due to seizures was similar to the general population of the same age group during the same period in Israel. There was no temporal relation between vaccination and hospitalization due to a seizure. SIGNIFICANCE: Despite very high vaccination rates in the general population in Israel and especially among people older than 60 years, no increase was observed in ED presentations due to seizures. No temporal relation was observed between vaccination and hospitalization due to a seizure. We conclude that the mass vaccination with the Pfizer BioNTech mRNA vaccine is not associated with increased seizure propensity.
RESUMEN
PURPOSE: Postexercise recovery rate is a vital component of designing personalized training protocols and rehabilitation plans. Tracking exercise-induced muscle damage and recovery requires sensitive tools that can probe the muscles' state and composition noninvasively. METHODS: Twenty-four physically active males completed a running protocol consisting of a 60-min downhill run on a treadmill at -10% incline and 65% of maximal heart rate. Quantitative mapping of MRI T2 was performed using the echo-modulation-curve algorithm before exercise, and at two time points: 1 h and 48 h after exercise. RESULTS: T2 values increased by 2%-4% following exercise in the primary mover muscles and exhibited further elevation of 1% after 48 h. For the antagonist muscles, T2 values increased only at the 48-h time point (2%-3%). Statistically significant decrease in the SD of T2 values was found following exercise for all tested muscles after 1 h (16%-21%), indicating a short-term decrease in the heterogeneity of the muscle tissue. CONCLUSION: MRI T2 relaxation time constitutes a useful quantitative marker for microstructural muscle damage, enabling region-specific identification for short-term and long-term systemic processes, and sensitive assessment of muscle recovery following exercise-induced muscle damage. The variability in T2 changes across different muscle groups can be attributed to their different role during downhill running, with immediate T2 elevation occurring in primary movers, followed by delayed elevation in both primary and antagonist muscle groups, presumably due to secondary damage caused by systemic processes.