KAN0438757, a novel PFKFB3 inhibitor, prevent the progression of severe acute pancreatitis via the Nrf2/HO-1 pathway in infiltrated macrophage.

Acute pancreatitis (AP) is a non-infectious pancreatic enzyme-induced disorder, a life-threatening inflammatory condition that can cause multi-organ dysfunction, characterized by high morbidity and mortality. Several therapies have been employed to target this disorder; however, few happen to be effectively employable even in the early phase. PFKFB3(6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3) is a critical regulator of glycolysis and is upregulated under inflammatory, mitogenic, and hypoxia conditions. Essential information on the targeting of the inflammatory pathway will present the termination of the disorder and recovery. Herein we investigated the protective function of KAN0438757, a potent inhibitor of PFKFB3, and its mechanism of impeding AP induced in mice. KAN0438757 was confirmed to activate the Nrf2/HO-1 inflammatory signaling pathways in response to caerulein induced acute pancreatitis (CAE-AP) and fatty acid ethyl ester induced severe acute pancreatitis (FAEE-SAP). Additionally, KAN0438757 alleviated the inflammatory process in infiltrated macrophage via the Nrf2/HO-1 inflammatory signaling pathway and demonstrated a significant effect on the growth of mice with induced AP. And more importantly, KAN0438757 displayed negligible toxicity in vivo. Taken together our data suggest KAN0438757 directly suppresses the inflammatory role of PFKFB3 and induces a protective role via the Nrf2/HO-1 pathway, which could prove as an excellent therapeutic platform for SAP amelioration.

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine.

Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition.

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD), with an incidence of more than 20%. IDH induces ischemic organ damage and even reduces the ultrafiltration and duration of HD sessions. Frequent attacks of IDH are a risk factor for death in HD patients. Malnutrition is common in HD patients and is also associated with mortality. Although the link between IDH episodes and malnutrition has been observed in practice, it has not been supported by the data. To study the relationship, we propose a promising hybrid model called BSCWJAYA_KELM, which is a wrapper feature selection method based on a variant of the JAYA optimization algorithm (SCWJAYA) and Kernel extreme learning machine (KELM). In this paper, we verify the optimization capability of the SCWJAYA algorithm in the model by comparing experiments with some state-of-the-art methods for IEEE CEC2014, IEEE CEC2017, and IEEE CEC2019 benchmark functions. The prediction accuracy of BSCWJAYA_KELM is validated by the public datasets and the HD dataset. In the experiments on the HD dataset, 1940 HD sessions of 178 HD patients are analyzed by the developed BSCWJAYA_KELM model. The key indicators selected from vast amounts of data are serum uric acid, dialysis vintage, age, diastolic pressure, and albumin. The BSCWJAYA_KELM method is a stable and excellent prediction model that can achieve a more accurate prediction of IDH.

A Multi-Center, Randomized, Blind, Controlled Clinical Trial of the Safety and Efficacy of Micro Radio Frequency Therapy System for the Treatment of Overactive Bladder.

Purpose: The purpose of this study was to evaluate the efficacy and safety of low power micro radiofrequency (RF) therapy (µRFthera®) through urethra in the treatment of overactive bladders (OAB) through a prospective, single-blind, placebo-controlled, multi-center clinical protocol. Materials and Methods: One hundred and fourteen patients with refractory OAB were randomized at 2:1 ratio, treatment to control undergoing same procedures except only the micro-RF treatment group at turned "on" setting in energy. Bladder diaries recorded during the screening period (3 days before enrollment) and during follow-up period on week 1, 3, and 7, respectively. The patients in control could choose receiving an energized treatment during extension stage. Results: The treatment efficacy was 76.1%. There was 49.80% rate improvement compared to control (95%CL 32.48%, 67.13%). The crude rate ration (RR) was 2.89, 95% CI (1.67-5.01) with p < 0.001 in uni-variate analysis, while the RR became 2.94, 95% CI (1.67-5.16) with p < 0.001 after adjusted potential confounding factors in multi-variate analysis. Statistically significant improvements have been demonstrated in the frequency of urination, urgency, nocturia, and quality of life (QoL) scores. Conclusions: Micro RF therapy is safe and effective for the treatment of OAB. The main treatment-related complications were catheterization related complications. Clinical Trial Registration: Zhejiang Device Registration Certificate No. 202090909, www.chictr.org.cn, Clinical Trial Accession Number: ChiCTR2100050096.

An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease-mineral and bone disorders.

Intradialytic hypotension (IDH) is the most common acute complication in hemodialysis (HD) sessions and is associated with increased morbidity and mortality in HD patients. To prevent the episode of IDH, it is critical to predict its occurrence. Chronic kidney disease-mineral and bone disorders (CKD-MBD) induce cardiac and vascular calcification, which impairs the compensatory mechanisms of blood pressure during HD. In this study, we proposed a feature selection framework called BSWEGWO_KELM to analyze 1940 records from 178 HD patients, which was based on an enhanced grey wolf optimization (GWO) algorithm and the kernel extreme learning machine (KELM). Then, global optimization experiments, together with feature selection experiments on public data sets and HD dataset, were performed to verify the effectiveness of the BSWEGWO_KELM method. The experimental results showed that the established BSWEGWO_KELM had the capability of screening out the key indicators such as dialysis vintage, mean arterial pressure (MAP), alkaline phosphatase (ALP), and intact parathyroid hormone (iPTH). Consequently, BSWEGWO_KELM can be applied as a practical and accurate method to predict IDH.

An effective model for predicting serum albumin level in hemodialysis patients.

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.