The Human Phospholipase B-II Precursor (HPLBII-P) in Urine as a Novel Biomarker of Glomerular Activity in COVID-19 and Diabetes Mellitus.

Background: The human phospholipase B-II precursor (HPLBII-P) was originally purified from white blood cells but is also found in other cellular structures, such as kidney glomeruli and tubuli. The objective of this report was to investigate the relationship of HPLBII-P in urine to acute kidney injury in patients with COVID-19. Methods: Urine was collected at admission from 132 patients with COVID-19 admitted to the intensive care units (ICUs) because of respiratory failure. HPLBII-P was measured using a sensitive ELISA. For comparison, human neutrophil lipocalin (HNL) was measured in urine, using the ELISA configured with the monoclonal antibody 763/8F, as a sign of tubular affection in addition to routine biomarkers of kidney disease. Results: Overall, the concentrations of urinary HPLBII-P were almost 3-fold higher in patients with COVID-19 compared to healthy controls (p < 0.0001) and with significantly higher concentrations even in patients with COVID-19 without signs of acute kidney injury (AKI) (p < 0.001). HPLBII-P was further increased in patients with AKI (p < 0.02). HPLBII-P was significantly increased in patients with diabetes mellitus (p = 0.0008) and correlated to plasma glucose (r = 0.29, p = 0.001) and urine albumin concentrations (r = 0.55, p < 0.001). Conclusions: Urine concentrations of HPLBII-P are highly raised in the urine of patients with COVID-19 and relate to AKI and diabetes mellitus. HPLBII-P may reflect glomerular injury and/or increased glomerular cell activity in SARS-CoV-2 infections.

The Human Phospholipase B-II Precursor (HPLBII-P) in Urine as a Novel Biomarker of Increased Glomerular Production or Permeability in Diabetes Mellitus?

Background: A previous report showed that the urine output of HPLBII-P in patients with diabetes mellitus and SARS-CoV-2 infection was increased as a sign of glomerular dysfunction. The aim of this report was to investigate the relation of the urine output of HPLBII-P to diabetes mellitus in two large community-based elderly populations, i.e., the ULSAM and PIVUS cohorts. Methods: HPLBII-P was measured by an ELISA in the urine of a community-based cohort of 839 men (ULSAM) collected at 77 years of age and in the urine of a community-based cohort of 75-year-old men, n = 387, and women, n = 401 (PIVUS). KIM-1, NGAL, and albumin were measured in urine and cathepsin S and cystatin C in serum. Results: HPLBII-P was significantly raised among males with diabetes in the ULSAM (p < 0.0001) and PIVUS cohorts (p ≤ 0.02), but not in the female cohort of PIVUS. In the female subpopulation of insulin-treated diabetes, HPLBII-P was raised (p = 0.02) as compared to women treated with oral antidiabetics only. In the ULSAM cohort, HPLBII-P was correlated to NGAL, KIM-1, and albumin in urine both in non-DM (all three biomarkers; p < 0.0001) and in DM (NGAL; p = 0.002, KIM-1; p = 0.02 and albumin; p = 0.01). Plasma glucose and HbA1c in blood showed correlations to U-HPLBII-P (r = 0.58, p < 0.001 and r = 0.42, p = 0.004, respectively). U-HPLBII-P and cathepsin S were correlated in the ULSAM group (r = 0.50, p < 0.001). No correlations were observed between U-HPLBII-P and serum creatinine or cystatin C. Conclusions: The urine measurement of HPLBII-P has the potential to become a novel and useful biomarker in the monitoring of glomerular activity in diabetes mellitus.

LILRB2 promotes immune escape in breast cancer cells via enhanced HLA-A degradation.

PURPOSE: Increased expression of leukocyte immunoglobulin-like receptor subfamily B member 2 (LILRB2) is associated with immune evasion in breast cancer (BC). The aim of this study to elucidate the role of LILRB2 in BC progression. METHODS: LILRB2 expression in tumor tissues was detected by immunohistochemical staining. Human leukocyte antigen A (HLA-A) expression in BC cells was detected by Western blotting, and HLA-A ubiquitination was detected by immunoprecipitation and histidine pulldown assay. An in-situ tumor model was established in nude BALB/c mice to verify the role of LILRB2 in immune escape. Finally, the functions and potential mechanisms of LILRB2 in BC progression were explored using in silico data. RESULTS: LILRB2 was upregulated in BC tissues and cells, and correlated positively with poor prognosis. LILRB2 promoted BC progression by downregulating HLA-A expression. Mechanistically, LILRB2 facilitates the ubiquitination and subsequent degradation of HLA-A by promoting the interaction between the ubiquitin ligase membrane-associated ring finger protein 9 (MARCH9) and HLA-A. In syngeneic graft mouse models, LILRB2-expressing BC cells evaded CD8 + T cells and inhibited the secretion of cytokines by the cytotoxic CD8 + T cells. CONCLUSION: LILRB2 downregulates HLA-A to promote immune evasion in BC cells and is a promising new target for BC treatment.

A Novel Event-Triggered Bipartite Consensus for PDE-Based Multiagent Systems With Switching Topologies and Antagonistic Interactions.

Unlike the traditional studies on multiagent systems (MASs), this article investigates a class of MASs with spatial properties, whose agents are modeled by partial differential equations (PDE), in addition, to make the model more comprehensive, the Markovian switching topology with partially unknown probability is taken into account. The purpose of this article is to achieve the bipartite consensus for the above PDE-based MASs. In fact, the consideration of spatial factors in MASs will exacerbate the network transmission pressure, to overcome this problem, a novel spatiotemporal event-triggered mechanism is developed. Compared with the existing event-triggered mechanism, the proposed one is not only time-dependent but also covers the influence of spatial variables on the trigger conditions, which helps to further reduce the triggering frequency. Finally, three examples are given to verify the validity of the conclusion we proposed.

Neural Preassigned Performance Control for State-Constrained Nonlinear Systems Subject to Disturbances.

This article addresses the finite-time neural predefined performance control (PPC) issue for state-constrained nonlinear systems (NSs) with exogenous disturbances. By integrating the predefined-time performance function (PTPF) and the conventional barrier Lyapunov function (BLF), a new set of time-varying BLFs is designed to constrain the error variables. This establishes conditions for satisfying full-state constraints while ensuring that the tracking error meets the predefined performance indicators (PPIs) within a predefined time. Additionally, the incorporation of the nonlinear disturbance observer technique (NDOT) in the control design significantly enhances the ability of the system to reject disturbances and improves overall robustness. Leveraging recursive design based on dynamic surface control (DSC), a finite-time neural adaptive PPC strategy is devised to ensure that the closed-loop system is semi-globally practically finite-time stable (SPFS) and achieves the desired PPIs. Finally, the simulation results of two practical examples validate the efficacy and viability of the proposed approach.

Boosting the sonophotocatalytic performance of BiOCl by Eu doping: DFT and an experimental study.

Bismuth oxychloride (BiOCl) has a unique layered structure and uneven charge distribution, resulting in an internal electric field under polarization, which promotes the efficient separation and migration of photogenerated carriers. BiOCl could be a candidate for sonophotocatalysts. However, the low utilization of visible light limits the application of BiOCl in photocatalysts. In this study, the photocatalytic performance of rare earth element (Nd, Sm, Eu, Er and Er)-doped BiOCl was studied by density functional theory (DFT) and experimentally to screen high-performance catalysts. The band structure, density of states, and optical properties were calculated by the DFT method to predict the photocatalytic activity of rare earth-doped BiOCl. The built-in electric field formed in Eu-doped BiOCl inhibiting electron and hole recombination can be observed. Subsequently, the activity of the photocatalyst and sonophotocatalysts was evaluated. The results show that the photocatalytic and sonophotocatalytic activity of Eu-doped BiOCl is improved, which is consistent with the theoretical prediction. Combining theoretical calculations with experiments, the sonophotocatalytic activity of Eu-doped BiOCl is enhanced, mainly due to the synergistic effect of inhibiting carrier recombination, and expansion to the visible light absorption region.

Effectiveness and safety of anlotinib with or without S-1 in the treatment of patients with advanced hepatocellular carcinoma in a Chinese population: a prospective, phase 2 study.

BACKGROUND: The aim of the study was to observe the safety and efficacy of anlotinib (ANL) alone or combined with S-1 in the first-line treatment of advanced hepatocellular carcinoma (HCC). PATIENTS AND METHODS: Fifty-four patients with untreated advanced HCC who could not be resected were randomly divided into the ANL group (n = 27) and ANL+S-1 group (n = 27). The ANL group was given 10 mg ANL orally once a day for 14 consecutive days, stopped for 1 week, and repeated every 21 days. The ANL+S-1 group was given 10 mg ANL once a day orally and 40 mg S-1 twice a day orally for 14 consecutive days, stopped for 1 week, repeated every 21 days. All patients were treated until the disease progressed or toxicity became unacceptable. For patients who could not tolerate adverse reactions, the ANL dose should be reduced to 8 mg per day. CT or MRI was reviewed every 6 weeks to evaluate the efficacy. RESULTS: A total of 44 patients were included in the results analysis, including 22 patients in the ANL group and 22 patients in the ANL+S-1 group. In the ANL group, the objective response rate (ORR) was 4.5% (1/22), the disease control rate (DCR) was 77.3% (17/22), the median progression-free survival (PFS) was 4.2 months (95% CI: 3.6-6.0) and the median overall survival (mOS) was 7.0 months (95% CI: 6.3-9.0). In the ANL+S-1 group, the ORR was 18.2% (4/22), the DCR was 59.1% (13/22), the median PFS was 4.0 months (95% CI: 3.6-5.4) and the mOS was 6.0 months (95% CI: 5.5-7.4). There was no significant difference in ORR (p = 0.345) or DCR (p = 0.195) between the two groups. Adverse reactions were mainly hypertension, anorexia, fatigue, liver transaminase heightened and hand and foot skin reaction. CONCLUSIONS: ANL monotherapy was effective in the treatment of advanced HCC, and adverse reactions have been able to tolerated.

A data-driven framework for learning hybrid dynamical systems.

The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, which depend on the prior knowledge of the dynamical models. In this work, we propose a novel data-driven framework to discover the hybrid dynamical systems from time series data, without any prior knowledge required of the systems. More specifically, we devise a dual-loop algorithm to peel off the data subject to each subsystem of the hybrid dynamical system. Then, we approximate the subsystems by iteratively training several residual networks and estimate the transition rules by training a fully connected neural network. Several prototypical examples are presented to demonstrate the effectiveness and accuracy of our method for hybrid models with various dimensions and structures. This method appears to be an effective tool for learning the evolutionary governing laws of hybrid dynamical systems from available data sets with wide applications.

Asynchronous Resilent State Estimation of Switched Fuzzy Systems With Multiple State Impulsive Jumps.

This work researches the resilent mixed H∞ and energy-to-peak filter design problem of switched Takagi-Sugeno (T-S) fuzzy systems with asynchronous switching and multiple state impulsive jumps. The novelties include three points. First, a novel mixed H∞ and energy-to-peak performance index is proposed, which covers the H∞ performance index and energy-to-peak performance index as special cases. Second, in addition to designing the switching filters, the filter state jump rules are constructed at filter switching instants. Finally, both system states and filter states jump in a asynchronous manner. The switching law is devised through the mode-dependent average dwell time (MDADT) approach. A new type of Lyapunov-like functionals is constructed, which will increase when the subsystem is running with its mismatched filter and jump while the subsystem or the filter is switching. Then, new conditions are deduced to ensure the filtering error systems with multiple state impulsive jumps to be asymptotically stable with a mixed H∞ and energy-to-peak performance level. Filter design conditions expressing as linear matrix inequality (LMI) are obtained. Finally, the effectiveness of the derived results is illustrated by two examples.

Event-Triggered Adaptive Output-Feedback Control for Nonlinearly Parameterized Uncertain Systems With Quantization and Input Delay.

An output-feedback-based event-triggered control issue of a class of uncertain nonlinear systems considering state quantization and input delay is investigated. In this study, by constructing the state observer and adaptive estimation function, a discrete adaptive control scheme is designed based on the dynamic sampled and quantized mechanism. With the aid of the Lyapunov-Krasovskii functional method and a stability criterion, the global stability of the time-delay nonlinear systems is ensured. Additionally, the Zeno behavior will not happen in the event-triggering. Finally, a numerical example and a practical example are presented to verify the effectiveness of the designed discrete control algorithm with input time-varying delay.

BiOCl Heterojunction photocatalyst: Construction, photocatalytic performance, and applications.

BiOCl semiconductors have attracted extensive amounts of attention and have substantial potential in alleviating energy shortages, improving sterilization performance, and solving environmental issues. To improve the optical quantum efficiency of layered BiOCl, the lifetimes of photogenerated electron-hole pairs, and BiOCl reduction capacity. During the past decade, researchers have designed many effective methods to weaken the effects of these limitations, and heterojunction construction is regarded as one of the most promising strategies. In this paper, BiOCl heterojunction photocatalysts designed and synthesized by various research groups in recent years were reviewed, and their photocatalytic properties were tested. Among them, direct Z-scheme and S-scheme photocatalysts have high redox potentials and intense redox capabilities. Hence, they exhibit excellent photocatalytic activity. Furthermore, the applications of BiOCl heterojunctions for pollutant degradation, CO2 reduction, water splitting, N2 fixation, organic synthesis, and tumor ablation are also reviewed. Finally, we summarize research on the BiOCl heterojunctions and put forth new insights on overcoming their present limitations.

Adaptive Event-Triggered Finite-Time Control for Uncertain Time Delay Nonlinear System.

In this article, adaptive event-triggered finite-time control is explored for uncertain nonlinear systems with time delay. First, to handle the time-varying state delays, the Lyapunov-Krasovskii function is used. Fuzzy-logic systems are used to deal with the unknown nonlinearities of the system. Notice that compared to the reporting achievements, our proposed virtual control laws are derivable by using the novel switch function, which avoids "singularity hindrance" problem. Moreover, the dynamic event-triggered controller is designed to reduce the communication pressure and we prove that the controller is Zeno free. Our proposed control strategy ensures that the tracking error is arbitrarily small in finite time and all variables of the closed-loop system remain bounded. Finally, to show the effectiveness of our control strategy, the simulation results are given.

Pinning Controller Design for Set Reachability of State-Dependent Impulsive Boolean Networks.

Considered the stimulation of tumor necrosis factor as an impulsive control, an apoptosis network is modeled as a state-dependent impulsive Boolean network (SDIBN). Making cell death normally means driving the trajectory of an apoptosis network out of states that indicate cell survival. To achieve the goal, this article focuses on the pinning controller design for set reachability of SDIBNs. To begin with, the definitions of reachability and set reachability are introduced, and their relation is illustrated. For judging whether the trajectory of an SDIBN leaves undesirable states, a necessary and sufficient condition is presented according to the criteria for the set reachability. In addition, a series of algorithms is provided to find all possible sets of pinning nodes for the set reachability. Note that attractors containing in all undesirable states are studied to make SDIBNs set reachable via controlling the smallest states. For the purpose of determining pinning nodes for one-step set reachability, the Hamming distance is presented under scalar forms of states. Pinning nodes with the smallest cardinality for the set reachability are derived by deleting some redundant nodes. Compared with the existing results, the state feedback gain can be obtained without solving logical matrix equations. The computation complexity of the proposed approach is lower than that of the existing methods. Moreover, the method of designing pinning controllers is used to discuss apoptosis networks. The experimental result shows that apoptosis networks depart from undesirable states by controlling only one node.

Event-Triggered Distributed Stochastic Mirror Descent for Convex Optimization.

This article is concerned with the distributed convex constrained optimization over a time-varying multiagent network in the non-Euclidean sense, where the bandwidth limitation of the network is considered. To save the network resources so as to reduce the communication costs, we apply an event-triggered strategy (ETS) in the information interaction of all the agents over the network. Then, an event-triggered distributed stochastic mirror descent (ET-DSMD) algorithm, which utilizes the Bregman divergence as the distance-measuring function, is presented to investigate the multiagent optimization problem subject to a convex constraint set. Moreover, we also analyze the convergence of the developed ET-DSMD algorithm. An upper bound for the convergence result of each agent is established, which is dependent on the trigger threshold. It shows that a sublinear upper bound can be guaranteed if the trigger threshold converges to zero as time goes to infinity. Finally, a distributed logistic regression example is provided to prove the feasibility of the developed ET-DSMD algorithm.

Case report: Novel junctional sarcoplasmic reticulum protein 1 intergenic region-anaplastic lymphoma kinase fusion in a patient with lung adenocarcinoma responds to alectinib.

Novel anaplastic lymphoma kinase (ALK) fusions are still being discovered in non-small cell lung cancer (NSCLC). Most patients with ALK+ NSCLC respond favorably to ALK tyrosine kinase inhibitors. In this case report, we identified a novel nonreciprocal ALK fusion, namely, junctional sarcoplasmic reticulum protein 1 (JSRP1) intergenic region-ALK fusion (Jintergenic: A20) via next-generation sequencing in a female patient initially diagnosed with stage IV B lung adenocarcinoma. Further examination of biopsy specimen and analysis of clinical samples by a multidisciplinary team confirmed the diagnosis of ALK+ NSCLC. At the 2- and 4-months follow-up after receiving alectinib, the patient responded rapidly, implying that alectinib had a remarkable therapeutic effect. We identified a novel JSRP1 intergenic region-ALK fusion as a carcinogenic mutation that responds to alectinib, thereby expanding the spectrum of ALK fusion partners in ALK + NSCLC. This study may help clinicians detect oncogenic mutations and provide timely treatment to patients with ALK+ NSCLC.

Amyotrophic lateral sclerosis in seven provinces of Chinese mainland: A cross-sectional survey from 2015 to 2016.

Background: The large-scale survey about amyotrophic lateral sclerosis (ALS) based on both population and hospitals in the Chinese mainland has been deficient at present. To this end, we conducted a cross-sectional survey about ALS based on the population and hospitals in seven provinces of the Chinese mainland in 2015-2016. Methods: We surveyed patients with ALS in seven provinces in eastern, middle, and western China. Among them, 13 prefecture-level cities, 13 municipal districts, 13 counties, 26 streets, 52 communities, 39 towns, and 78 administrative villages were selected for the population-based survey. Totally, 13 class-3 general hospitals, 13 class-2 general hospitals, and 26 street health centers or community health service centers in urban districts, and 13 county-level general hospitals, 39 township health centers, and 78 village clinics in rural districts were recruited for the hospital-based survey. Results: Among the Chinese mainland, the total prevalence of ALS was slightly lower than that of the world's other nations or districts. The male patients were more than female patients. The prevalence of rural residents was more than that of urban residents. The prevalence of farmers was higher than that of other professions. The majority of ALS was not accompanied by other chronic diseases. The peak onset age of ALS was higher, familial ALS (fALS) cases were slightly more, and the average survival duration of sporadic ALS (sALS) was slightly lower compared with the previous investigation data. The hospitalization expenses of almost 60% of ALS were not more than 10,000 Chinese Yuan. Conclusion: Hospitalization expenses in our survey objectives were the lowest in the current reported countries and districts. A farmer was a possible higher risk profession for ALS, the majority of ALS were not accompanied by other chronic diseases. Our survey provided more information on the epidemiology of ALS worldwide and supplied the deficiency of epidemiology survey about ALS from the Chinese mainland.

Distributed Online Constrained Optimization With Feedback Delays.

We investigate multiagent distributed online constrained convex optimization problems with feedback delays, where agents make sequential decisions before being aware of the cost and constraint functions. The main purpose of the distributed online constrained convex optimization problem is to cooperatively minimize the sum of time-varying local cost functions subject to time-varying coupled inequality constraints. The feedback information of the distributed online optimization problem is revealed to agents with time delays, which is common in practice. Every node in the system can interact with neighbors through a time-varying sequence of directed communication topologies, which is uniformly strongly connected. The distributed online primal-dual bandit push-sum algorithm that generates primal and dual variables with delayed feedback is used for the presented problem. Expected regret and expected constraint violation are proposed for measuring the performance of the algorithm, and both of them are shown to be sublinear with respect to the total iteration span T in this article. In the end, the optimization problem for the power grid is simulated to justify the proposed theoretical results.

Distributed Online Stochastic-Constrained Convex Optimization With Bandit Feedback.

This article studies the distributed online stochastic convex optimization problem with the time-varying constraint over a multiagent system constructed by various agents. The sequences of cost functions and constraint functions, both of which have dynamic parameters following time-varying distributions, are unacquainted to the agent ahead of time. Agents in the network are able to interact with their neighbors through a sequence of strongly connected and time-varying graphs. We develop the adaptive distributed bandit primal-dual algorithm whose step size and regularization sequences are adaptive and have no prior knowledge about the total iteration span T . The adaptive distributed bandit primal-dual algorithm applies bandit feedback with a one-point or two-point gradient estimator to evaluate gradient values. It is illustrated in this article that if the drift of the benchmark sequence is sublinear, then the adaptive distributed bandit primal-dual algorithm exhibits sublinear expected dynamic regret and constraint violation using both two kinds of gradient estimator to compute gradient information. We present a numerical experiment to show the performance of the proposed method.

Practical Stabilization of Networked Takagi-Sugeno Fuzzy Systems via Improved Jensen Inequalities.

This work addresses the problem of aperiodically sampled control for the networked Takagi-Sugeno (T-S) fuzzy systems, where the aperiodically sampled input is generated by a periodic sampler and an event-triggered mechanism (ETM). The purpose of ETM is used to reduce the computational and communication burdens. For guaranteeing controller robustness, the practical stability of T-S fuzzy systems is considered by using the Lyapunov method and linear matrix inequality (LMI) technique. As one of the most powerful inequalities for deriving stability criteria using LMIs, Jensen's inequality has recently been improved by various authors for the stability analysis of delayed systems. However, these results are conservative to obtain lower bounds for integrals with an exponential term. Inspired by this, improved integral inequalities are derived in this work, and they are applied to obtain practical stability criteria for aperiodically sampled control. Finally, a numerical example on flight control of a helicopter is given to illustrate the effectiveness of the obtained practical stability criteria. Furthermore, the effectiveness of the improved Jensen inequalities on the exponential stability criteria is illustrated by numerical comparisons.

Rendezvous With Connectivity Preservation Problem of Linear Multiagent Systems via Parallel Event-Triggered Control Strategies.

This article studies the rendezvous problem of linear multiagent systems by parallel event-triggered connectivity-preserving control strategies. There are two distinguished features of our design. First, the event-triggered control laws can not only guarantee the convergence of the tracking error as existing event-triggered consensus control strategies but also have the additional ability to maintain the connectivity of the time-varying and position-dependent communication network as rendezvous control laws. Second, by combining the potential function technique, output regulation theory, and adaptive control technique, an event-triggered observer is applied to estimate both the leader's system matrix and trajectory, which can work in parallel with the connectivity-preserving event-triggered controller. The executive time instants for the observer and the controller are asynchronous and generated by different triggering functions based on their own locally available measurement errors.