A clinical study of drugs associated with acute kidney injury in the Chinese population

Purpose: To carry out a study aimed at comprehensive identificat6ion of classes of drugs which cause acute kidney injury (AKI).Methods: A total of 110,508 patients enrolled in Weihai Central Hospital, Weihai, Shandong, China between March 2014 to April 2018 were asked to provide information on comprehensive prescription drug coverage including antivirals, antibiotics, NSAIDs, diuretics and anti-cancer drugs. Only the active user  of these classes of drugs were included in the study. Daily prescription dose, duration, date and time of each drug were recorded. Furthermore, the characteristics and other conditions of the patients such as hypertension, congestive heart failure, diabetes, liver disease,  angiotensin receptor blockers (ARBs), alpha-receptor blockers, beta-receptor blockers, and calcium channel-blockers were included.Results: A total of 1230 patients presented with AKI during the first 60 days of follow-up, while 1546 (58 %) patients were diagnosed with AKI in the secondary endpoint. Indomethacin, valacyclovir, fluorouracil, levofloxacin, ibuprofen and rofecoxib produced higher frequencies of AKI than the control drug, celecoxib. Indomethacin (OR = 2.97 ; 95 % CI= 1.94 - 3.89) and valacyclovir (OR = 2.85 ; 95 % CI = 1.56 - 3.42) were mostly responsible for AKI, followed by rofecoxib (OR = 2.48 ; 95 % CI = 2.32 - 2.71), fluorouracil (OR = 2.58 ; 95 % CI = 1.94 - 3.11), ibuprofen (OR = 1.68 ; 95 % CI = 1.28 - 2.21) and levofloxacin (OR = 1.58 ; 95 % CI = 1.48 - 2.73), in that orderConclusion: This study has identified various classes of drugs which frequently induced AKI. Therefore, physicians should exercise caution in prescribing these drugs, and should consider other medicines to minimize the risk of AKI. Keywords: Acute kidney injury, Antiviral, NSAID, Toxicit

Mining Weighted Frequent Closed Episodes over Multiple Sequences

Frequent episode discovery is introduced to mine useful and interesting temporal patterns from sequential data. The existing episode mining methods mainly focused on mining from a single long sequence consisting of events with time constraints. However, there can be multiple sequences of different importance as the persons or entities associated with each sequence can be of different importance. Aiming to mine episodes in multiple sequences of different importance, we first define a new kind of episodes, i.e., the weighted frequent closed episodes, to take sequence importance, episode distribution and occurrence frequency into account together. Secondly, to facilitate the mining of such new episodes, we present a new concept called maximal duration serial episodes to cut a whole sequence into multiple maximum episodes using duration constraints, and discuss its properties for episode shrinking processing. Finally, based on the theoretical properties, we propose a two-phase approach to efficiently mine these new episodes. In Phase I, we adopt a level-wise episode shrinking framework to discover the candidate frequent closed episodes with the same prefixes, and in Phase II, we match the candidates with different prefixes to find the frequent close episodes. Experiments on simulated and real datasets demonstrate that the proposed episode mining strategy has good mining effectiveness and efficiency

Resveratrol protects against sepsis induced acute kidney injury in mice by inducing Klotho mediated apoptosis inhibition

Purpose: To investigate the mechanism of resveratrol protection against sepsis-induced acute kidney injury in mice. Methods: A sepsis-induced acute kidney injury model was established in mice by cecal ligation and puncture (CLP). Sixty healthy male ICR mice were randomly divided into the sham operation (sham) group, sepsis-induced acute kidney injury model (CLP) group, CLP + low-dose (20 mg/kg) resveratrol treatment (CLP + ResL) group, CLP + high-dose (40 mg/kg) resveratrol treatment (CLP + ResH) group and CLP + Klotho (0.01 mg/kg) treatment (CLP + Klotho) group. All mice were administered treatment on the day after surgery and once every 24 h for 3 days. Various serum biochemical parameters and protein expressions were evaluated. Results: After CLP, the levels of serum creatinine (Scr) and blood urea nitrogen (BUN) increased and the pathology was exacerbated. The protein and mRNA expression levels of Klotho and Bcl-2 decreased, while those of Bax and Caspase-3 increased (p < 0.05). After resveratrol and Klotho protein intervention, Scr and BUN levels recovered, and pathological changes were alleviated. The protein and mRNA expression levels of Klotho and Bcl-2 increased, while those of Bax and Caspase-3 decreased. The conditions of the mice in CLP + ResH group and the CLP + Klotho group improved more significantly than those of the mice in the CLP + ResL group (p < 0.05). Conclusion: Resveratrol upregulates the expression of endogenous Klotho to exert its antiapoptotic effects, which can protect the kidneys of mice against sepsis-induced acute kidney injury. Thus, the compound has potentials for development for protection against acute kidney injury

Adaptive finite-time control of multi-agent systems with partial state constraints and input saturation via event-triggered strategy

This paper focuses on the finite-time control problem of multi-agent systems with input saturation, unknown nonlinear dynamics, external disturbances and partial state constraints via output feedback. Fuzzy logic system and fuzzy state observer are introduced to approximate the uncertain nonlinearities and estimate the unmeasurable states, respectively. The partial state constraints are dealt with by using the barrier Lyapunov function, so that all states of the system do not exceed the preset boundary values. In order to reduce the computational complexity of the virtual controller and save communication resources, a first-order filter and an event-triggered mechanism are introduced, respectively. It is proved that the Zeno behavior does not occur via the proposed event-triggered controller. By stability analysis, the finite-time convergence of tracking error to a small neighborhood of the origin is proven. The effectiveness of the theoretical results is verified by examples.http://wileyonlinelibrary.com/iet-cthhj2023Electrical, Electronic and Computer Engineerin

Chromium phosphide CrP as highly active and stable electrocatalysts for oxygen electroreduction in alkaline media

Catalysts for oxygen reduction reaction (ORR) are key components in emerging energy technologies such as fuel cells and metal-air batteries. Developing low-cost, high performance and stable electrocatalysts is critical for the extensive implementation of these technologies. Herein, we present a procedure to prepare colloidal chromium phosphide CrP nanocrystals and we test their performance as ORR electrocatalyst. CrP-based catalysts exhibited remarkable activities with a limiting current density of 4.94¿mA¿cm-2 at 0.2¿V, a half-potential of 0.65¿V and an onset potential of 0.8¿V at 1600¿rpm, which are comparable to commercial Pt/C. Advantageously, CrP-based catalysts displayed much higher stabilities and higher tolerances to methanol in alkaline solution. Using density functional theory calculations, we demonstrate CrP to provide a very strong chemisorption of O2 that facilitates its reduction and explains the excellent ORR performance experimentally demonstrated.Postprint (author's final draft

Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon