Berberine promotes immunological outcomes and decreases neuroinflammation in the experimental model of multiple sclerosis through the expansion of Treg and Th2 cells

Abstract Introduction Among the most frequent demyelinating autoimmune disorders of the central nervous system (CNS) is multiple sclerosis. Experimental autoimmune encephalomyelitis (EAE) is used as an animal model of multiple sclerosis. Berberine is an alkaloid found in some medicinal plants with anti‐inflammatory effects. Methods C57BL/6 female mice were used and divided into three groups: (1) The control group received PBS, (2) the low‐dose treatment group received 10 mg/kg of berberine, and (3) The high‐dose treatment group received 30 mg/kg of berberine. Myelin Oligodendrocyte Glycoprotein and complete Freund's adjuvant were subcutaneously administered to induce EAE. Mice were given intraperitoneal injections of pertussis toxin on the day of immunization and 2 days later. Histological studies showed low lymphocyte infiltration and demyelination of CNS in the treated groups. Results The clinical scores of the treatment group with low‐dose berberine (T1: 2 ± 0.13) and high‐dose berberine (T2: 1.5 ± 0.14) were significantly (p < .001) lower than the control group (CTRL: 4.5 ± 0.13). Treatment groups decreased pro‐inflammatory cytokines (IFN‐γ, TNF‐α, interleukin [IL]‐17) (p < .001) as well as increased anti‐inflammatory cytokine expression (IL‐4, IL‐10, IL‐27, IL‐33, IL‐35, TGF‐β) (p < .01) when compared to the CTRL group. Treatment groups with berberine reduced expression of the Th1 and Th17 cytokines and transcription factors (p < .001) and increased expression of transcription factors and Th2 and Treg cytokines (p < .01) in contrast to CTRL group. Conclusion Berberine appears to have a protective effect on disease development and alleviating disease status in EAE, which appears to be due to the cell expansion and function of Treg and Th2 cells in addition to berberine's anti‐inflammatory properties

Profiling delirium progression in elderly patients via continuous-time markov multi-state transition models

none11Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from nodelirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.noneOcagli H.; Azzolina D.; Soltanmohammadi R.; Aliyari R.; Bottigliengo D.; Acar A.S.; Stivanello L.; Degan M.; Baldi I.; Lorenzoni G.; Gregori D.Ocagli, H.; Azzolina, D.; Soltanmohammadi, R.; Aliyari, R.; Bottigliengo, D.; Acar, A. S.; Stivanello, L.; Degan, M.; Baldi, I.; Lorenzoni, G.; Gregori, D

Statistical QoS provisioning for MTC networks under finite blocklength

Abstract This paper analyzes the effective capacity of delay-constrained machine-type communication (MTC) networks operating in the finite blocklength regime. First, we derive a closed-form mathematical approximation for the effective capacity in quasi-static Rayleigh fading channels. We characterize the optimum error probability to maximize the concave effective capacity function with reliability constraint and study the effect of signal-to-interference-plus-noise ratio (SINR) variations for different delay constraints. The trade-off between reliability and effective capacity maximization reveals that we can achieve higher reliability with limited sacrifice in effective capacity specially when the number of machines is small. Our analysis reveals that SINR variations have less impact on effective capacity for strict delay-constrained networks. We present an exemplary scenario for massive MTC access to analyze the interference effect proposing three methods to restore the effective capacity for a certain node which are power control, graceful degradation of delay constraint, and joint compensation. Joint compensation combines both power control and graceful degradation of delay constraint, where we perform the maximization of an objective function whose parameters are determined according to the delay and SINR priorities. Our results show that networks with stringent delay constraints favor power controlled compensation, and compensation is generally performed at higher costs for shorter packets