Lack of permissive mechanisms and abundance of inhibitory molecules in the lesioned central nervous system of adult mammals contribute to the failure of functional recovery after injury, leading to severe disabilities in motor functions and pain. Peripheral nerve injury impairs motor, sensory, and autonomic functions, particularly in cases where nerve gaps are large and chronic nerve injury ensues. Previous studies have indicated that the neural cell adhesion molecule L1 constitutes a viable target to promote regeneration after acute injury. We screened libraries of known drugs for small molecule agonists of L1 and evaluated the effect of hit compounds in cell-based assays in vitro and in mice after femoral nerve and spinal cord injuries in vivo. We identified eight small molecule L1 agonists and showed in cell-based assays that they stimulate neuronal survival, neuronal migration, and neurite outgrowth and enhance Schwann cell proliferation and migration and myelination of neurons in an L1-dependent manner. In a femoral nerve injury mouse model, enhanced functional regeneration and remyelination after application of the L1 agonists were observed. In a spinal cord injury mouse model, L1 agonists improved recovery of motor functions, being paralleled by enhanced remyelination, neuronal survival, and monoaminergic innervation, reduced astrogliosis, and activation of microglia. Together, these findings suggest that application of small organic compounds that bind to L1 and stimulate the beneficial homophilic L1 functions may prove to be a valuable addition to treatments of nervous system injuries.

The objective of the present study was to characterize lactobacilli isolates from the feces of male Wistar rats. Various physiological features of the candidate probiotic isolates were preliminarily investigated, including tolerance to simulated gastric juice and bile salts, antimicrobial activity, antibiotic susceptibility and in vitro aggregation. Based on their morphological and biochemical characteristics, four potential probiotic isolates (CS2, CS3, CS4, and CS7) were screened. The isolates showed good tolerance to stimulated gastric juice and bile salts. CS4 and CS7 exhibited strong antibacterial activities against the pathogens tested as assessed in neutral pH culture supernatants. All lactobacilli isolates were susceptible to all the tested antibiotics, except vancomycin. Moreover, the isolate CS4 and CS7 were found to possess stronger cell surface traits such as hydrophobicity, auto-aggregation and co-aggregation capacity. In addition, CS4 and CS7 had greater b-galactosidase activities than the others. Biochemical tests and 16S rRNA gene sequencing confirmed that CS2, CS3, CS4 and CS7 are Lactobacillus intestinalis PJ2, L. sakei PJ3, L. helveticus PJ4, and L. plantarum PJ7, respectively. Based on the obtained results, L. helveticus PJ4 and L. plantarum PJ7 are ideal in vitro probiotic candidates and require further in vivo evaluation.

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

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