BackgroundThe definition and grading system of post-pancreatectomy acute pancreatitis (PPAP) has recently been proposed by ISGPS. This study aimed to put this definition and classification into practice and investigate the potential risk factors and clinical impacts of PPAP.MethodsDemographic and perioperative data of consecutive patients who underwent pancreaticoduodenectomy (PD) from January 2019 to July 2021 were collected and analyzed retrospectively. The diagnostic criteria of PPAP published by ISGPS, consisting of biochemical, radiologic, and clinical parameters, were adopted. The risk factors were analyzed by univariate and multivariate analyses.ResultsA total of 298 patients were enrolled in this study, and the total incidence of PPAP was 52.4% (150 patients). Stratified by clinical impacts of PPAP, the incidences of grades B and C PPAP were 48.9% and 3.5%, respectively. PPAP after PD was significantly associated with pancreatic fistula and other unfavorable complications. Soft pancreatic texture (OR 3.0) and CRP ≥ 180 mg/L (OR 3.6) were the independent predictors of PPAP, AUC 0.613. Stratified by the grade of PPAP, soft pancreatic texture (OR 2.7) and CRP ≥ 180 mg/L (OR 3.4) were the independent predictors of grade B PPAP, and soft pancreatic texture (OR 19.3), operation duration >360 min (OR 13.8), and the pancreatic anastomosis by using conventional duct to mucosa methods (OR 10.4) were the independent predictors of grade C PPAP. PPAP complicated with pancreatic fistula significantly increased the severe complications and mortality compared to only PPAP occurrence.ConclusionPPAP was not an uncommon complication after PD and was associated with unfavorable clinical outcomes, especially since it was complicated with pancreatic fistula. Soft pancreatic texture and CRP ≥ 180 mg/L were the independent predictors of PPAP. Higher-volume multicenter and prospective studies are strongly needed.
Hypertension is one of the most prevalent cardiovascular disorders worldwide, affecting 1.13 billion people, or 14% of the global population. Hypertension is the single biggest risk factor for cerebrovascular dysfunction. According to the American Heart Association, high blood pressure (BP), especially in middle-aged individuals (~ 40 to 60 years old), is associated with an increased risk of dementia, later in life. Alzheimer’s disease and cerebrovascular disease are the two leading causes of dementia, accounting for around 80% of the total cases and usually combining mixed pathologies from both. Little is known regarding how hypertension affects cognitive function, so the impact of its treatment on cognitive impairment has been difficult to assess. The brain renin-angiotensin system (RAS) is essential for BP regulation and overactivity of this system has been established to precede the development and maintenance of hypertension. Angiotensin II (Ang-II), the main peptide within this system, induces vasoconstriction and impairs neuro-vascular coupling by acting on brain Ang-II type 1 receptors (AT 1 R). In this review, we systemically analyzed the association between RAS and biological mechanisms of cognitive impairment, from the perspective of AT 1 R located in the central nervous system. Additionally, the possible contribution of brain AT 1 R to global cognition decline in COVID-19 cases will be discussed as well.
Hypertension is one of the most prevalent cardiovascular diseases worldwide and is known to be dominated by the sympathetic nervous system (SNS). Previously, we found that targeting A Disintegrin and Metalloproteinase 17 (ADAM17) in glutamatergic neurons was able to blunt Ang-II-induced excitation of pre-autonomic neurons, and blocked DOCA-salt treatment-induced sympatho-excitation in mice, thereby alleviating their development of hypertension. However, how ADAM17 support the activation of pre-autonomic neurons remains unknown. Beyond the glutamatergic neurons, we found in the current study that microglial activation and increase of pro-inflammatory cytokine levels in the hypothalamus, induced by DOCA-salt treatment, were blunted in those mice with ADAM17 knockout in glutamatergic neurons (A17G; number of CD11b+cells per line: NT+DOCA vs. A17G+DOCA= 11±2 vs. 6±1, P= 0.0017; normalized TNFα level: NT+DOCA vs. A17G+DOCA= 1.00±0.37 vs. 0.67±0.11, P< 0.05; normalized IL-6 level: 1.00±0.32 vs. 0.59±0.20, P< 0.05). Especially, it also reversed DOCA-salt-induced downregulation of Gad67 mRNA expression (NT= 1.00±0.11, NT+DOCA= 0.66±0.12, A17G= 1.02±0.15, A17G+DOCA= 1.67±0.24 fold of change, NT vs. NT+DOCA and A17G vs. A17G+DOCA: P< 0.05), and normalized the decreased level of GABA in hypothalamus (normalized GABA levels: NT= 1.00±0.15, NT+DOCA= 0.84±0.09, A17G+DOCA= 1.09±0.17, NT vs. NT+DOCA: P< 0.05), suggesting that in addition to the effect of ADAM17 on the neuron itself, it might also regulate the SNS through altering the neuronal microenvironment. In L-NAME hypertensive model, though A17G mice showed no improvement in NOS-blockade-induced dysfunction of neuro-vascular coupling (increase of CBF induced by whisker-stimulation: NT vs. A17G= 24.34±11.20 vs. 24.87±15.78 PU%), it did exhibit improved performance in Barnes maze test (latency time: NT vs. A17G= 32.77±19.36 vs. 95.54±59.03 s, P= 0.002), suggesting that ADAM17 in glutamatergic is critical for the process of neuronal damage induced by hypoxia and ischemia, possibly through modulating the homeostasis of neuronal microenvironment.
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase -space reconstruction. The output of PRBF is a weighted sum of all RBF subnets and represents the prediction value for each new input vector. The chaotic time series data from Lorenz simulation signal and hydraulic pump vibration signal was used to verify the proposed method. Both GrassbergerProcaccia (G-P) algorithm and Takens' method were employed to calculate the minimum embedding dimension of chaotic time series. Finally, the prediction accuracy and result were compared between RBF and PRBF. It is shown that PRBF network is more effective and feasible for the iterative prediction of chaotic time series.
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