Serum exosome tRFs as a promising biomarker for active tuberculosis and latent tuberculosis infection.

OBJECTIVE: To analyse the expression profiles of serum exosome tRFs/tiRNAs and to explore their diagnostic value in tuberculosis (TB) activity. METHODS: The serum exosome tRF/tiRNA profile was analysed using high-throughput sequencing technology in 5 active tuberculosis (ATB) patients, 5 latent tuberculosis infection (LTBI) patients and 5 healthy controls (HCs). Then, serum exosome tRFs/tiRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR), and their diagnostic value was evaluated by receiver operating characteristic curve (ROC) and area under the curve (AUC). Finally, bioinformatics analysis was performed to explore and identify the potential biological pathways induced by tRFs/tiRNAs. RESULTS: The sequencing results revealed that serum exosome tRF/tiRNA expression profiles were different among ATB patients, LTBI patients and HCs. Three tRFs (tRF-56:75-Trp-CCA-4, tRF-1:22-chrM.Ser-GCT and tRF-56:76-Val-TAC-1-M2) were selected for qRT-PCR validation. The results demonstrated that the expression level of tRF-1-22-chrM.Ser-GCT was upregulated in ATB patients, while tRF-56-75-Trp-CCA-4 was downregulated, which was consistent with the sequencing data. The AUCs of tRF-56:75-Trp-CCA-4 and tRF-1:22-chrM. Ser-GCT were 0.824 and 1.000, respectively, which have significant values in the diagnosis of ATB patients. Moreover, the expression levels of tRF-56:75-Trp-CCA-4 and tRF-1:22-chrM.Ser-GCT and tRF-56:76-Val-TAC-1-M2 in ATB patients and LTBI were different, which indicated that these three tRFs could effectively distinguish ATB patients and LTBI patients. CONCLUSION: Our findings indicate that serum exosome tRFs can be used as potential markers for the diagnosis of ATB and LTBI.

Enhanced elimination of Cr(VI) from aqueous media by polyethyleneimine modified corn straw biochar supported sulfide nanoscale zero valent iron: Performance and mechanism.

A novel polyethyleneimine modified corn straw biochar supported sulfide nanoscale zero-valent iron (S-nZVI@PBC) was developed to enhance Cr(VI) removal from aqueous media. The characteristics of morphology, chemical composition, and functional groups of S-nZVI@PBC, as well as its kinetics and mechanism for Cr(VI) removal were explored. Characterization verified S-nZVI was successfully loaded onto PEI modified biochar. The adsorption process was well represented pseudo-second-order model (R2 = 0.990) and Langmuir isotherm model (R2 = 0.962), indicating it was a monolayer chemical adsorption process. The Cr(VI) removal was affected by pH and achieved the maximum when pH = 3.0, which may be ascribed to the better corrosion of nZVI and release of Fe(II) from the S-nZVI@PBC in acidic condition. The primary mechanisms were adsorption, reduction, and co-precipitation. S-nZVI@PBC exhibited higher stability and reusability than nZVI, which makes it more promising in environmental application. Overall, S-nZVI@PBC is of great potential for treating Cr(VI)-containing wastewater.

Mode-Dependent Adaptive Event-Triggered Control for Stabilization of Markovian Memristor-Based Reaction-Diffusion Neural Networks.

This article focuses on the design of a mode- dependent adaptive event-triggered control (AETC) scheme for the stabilization of Markovian memristor-based reaction-diffusion neural networks (RDNNs). Different from the existing works with completely known transition probabilities, partly unknown transition probabilities (PUTPs) are considered here. The switching conditions and values of memristive connection weights are all correlated with Markovian jumping. A mode-dependent AETC scheme is newly proposed, in which different adaptive event-triggered mechanisms will be applied for different Markovian jumping modes and memristor switching modes. For each given mode, the corresponding event-triggered mechanism can efficiently reduce the number of transmission signals by adaptively adjusting the threshold. Thus, the mode-dependent AETC scheme can effectively save the limited network communication resources for the considered system. Based on the proposed control scheme, a new stabilization criterion is set up for Markovian memristor-based RDNNs with PUTPs. Meanwhile, a memristor-dependent AETC scheme is devised for memristor-based RDNNs. Finally, simulation results are presented to verify the effectiveness and superiority of the analysis results.

Event-Triggered Impulsive Fault-Tolerant Control for Memristor-Based RDNNs With Actuator Faults.

This article focuses on designing an event-triggered impulsive fault-tolerant control strategy for the stabilization of memristor-based reaction-diffusion neural networks (RDNNs) with actuator faults. Different from the existing memristor-based RDNNs with fault-free environments, actuator faults are considered here. A hybrid event-triggered and impulsive (HETI) control scheme, which combines the advantages of event-triggered control and impulsive control, is newly proposed. The hybrid control scheme can effectively accommodate the actuator faults, save the limited communication resources, and achieve the desired system performance. Unlike the existing Lyapunov-Krasovskii functionals (LKFs) constructed on sampling intervals or required to be continuous, the introduced LKF here is directly constructed on event-triggered intervals and can be discontinuous. Based on the LKF and the HETI control scheme, new stabilization criteria are derived for memristor-based RDNNs. Finally, numerical simulations are presented to verify the effectiveness of the obtained results and the merits of the HETI control method.

Application of Non-Invasive Ventilator in Treatment of Severe COVID-19 Patients.

BACKGROUND: Since December 2019, there has been a global outbreak of COVID-19. As of the end of July 2020, more than 600,000 deaths had been reported globally. The purpose of this paper is to further explore the application of non-invasive ventilation in severe COVID-19 patients. METHODS: A retrospective study was conducted to included 57 confirmed COVID-19 patients, among which 36 cases were severe. According to different oxygen inhalation methods, they were divided into non-invasive ventilator assisted ventilation group with 21 cases (group A) and 15 cases of nasal catheter oxygen inhalation group (group B). The data of respiration (RR), heart rate (HR), partial arterial pressure of oxygen (PaO2), partial arterial pressure of carbon dioxide (PaCO2), and oxygenation index (OI) before the treatment of noninvasive ventilator assisted ventilation or nasal catheter oxygen treatment at 24, 48, and 72 hours of treatment of the 2 groups were collected and analyzed to determine whether the above indicators were statistically different in each time period. RESULTS: After 24 hours of treatment with noninvasive ventilator assisted ventilation in group A, RR gradually decreased, PaO2 and OI were significantly higher than before treatment, while after 24 hours of treatment, PaO2, RR, HR and other indexes in group B showed no significant improvement, and OI increased gradually after 48 hours of treatment, with statistically significant difference compared with that before treatment. CONCLUSIONS: Early adoption of non-invasive ventilation can effectively improve the hypoxic state of patients with severe COVID-19. The combination of underlying diseases will not prolong the use of non-invasive ventilation.

Adaptive Event-Triggered Synchronization of Reaction-Diffusion Neural Networks.

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

Novel Inequalities to Global Mittag-Leffler Synchronization and Stability Analysis of Fractional-Order Quaternion-Valued Neural Networks.

This article is concerned with the problem of the global Mittag-Leffler synchronization and stability for fractional-order quaternion-valued neural networks (FOQVNNs). The systems of FOQVNNs, which contain either general activation functions or linear threshold ones, are successfully established. Meanwhile, two distinct methods, such as separation and nonseparation, have been employed to solve the transformation of the studied systems of FOQVNNs, which dissatisfy the commutativity of quaternion multiplication. Moreover, two novel inequalities are deduced based on the general parameters. Compared with the existing inequalities, the new inequalities have their unique superiorities because they can make full use of the additional parameters. Due to the Lyapunov theory, two novel Lyapunov-Krasovskii functionals (LKFs) can be easily constructed. The novelty of LKFs comes from a wider range of parameters, which can be involved in the construction of LKFs. Furthermore, mainly based on the new inequalities and LKFs, more multiple and more flexible criteria are efficiently obtained for the discussed problem. Finally, four numerical examples are given to demonstrate the related effectiveness and availability of the derived criteria.

Fuzzy Sampled-Data Control for Synchronization of T-S Fuzzy Reaction-Diffusion Neural Networks With Additive Time-Varying Delays.

This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components.

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.

Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.

Late-onset bronchopleural fistula after lobectomy and adjuvant chemotherapy for lung cancer: A case report and review of the literature.

RATIONALE: Late-onset bronchopleural fistula (BPF) induced by chemotherapy after lobectomy for lung cancer is rarely reported, lacking reliable preventive approaches. A timely identification and individualized treatment is essential for prognosis. PATIENT CONCERNS: A 52-year-old female patient complained of fever, productive cough, and fatigue 1 week after adjuvant chemotherapy following right lower lobectomy and systemic mediastinal lymph node dissection. Chest computed tomography (CT) indicated pneumothorax and thick-walled empyema cavity within her right-sided thorax. DIAGNOSES: The patient was diagnosed as late-onset BPF based on clinical manifestation and chest radiography. INTERVENTIONS: In addition to antibiotics, a chest tube was reinserted under CT guidance, and vacuum suction was utilized for continuous drainage. Next cycle of adjuvant chemotherapy was terminated. OUTCOMES: The empyema cavity was gradually closed in 1 month after conservative treatment, and the patient survived with good condition up to now. LESSONS: Late-onset BPF should be kept in mind when the patient suffered from productive cough and chills during postoperative chemotherapy. And a prompt conservative management might be effective.

Late-onset chylothorax during chemotherapy after lobectomy for lung cancer: A case report and review of the literature.

RATIONALE: Chylothorax is usually diagnosed within a few days after lobectomy. Late-onset chylothorax following trauma or thoracic surgery is rare but potentially lethal, lacking reliable preventive methods. PATIENT CONCERNS: A 54-year-old male patient complained of dyspnea during adjuvant chemotherapy on the 35th postoperative day after right middle lobectomy and systemic lymph node dissection (SLND) for lung cancer. His computed tomography indicated massive pleural effusion filling in the right chest cavity. DIAGNOSES: The patient was primarily diagnosed as late-onset chylothorax, without definite evidence to exclude spontaneous chylous leakage. INTERVENTIONS: Uniportal video-assisted thoracoscopic thoracic duct ligation (TDL) was performed for him, as conservative treatment using octreotide with fat-free diet turned out to be ineffective. OUTCOMES: His pleural effusion was gradually diminished after reoperation, and the patient was discharged 9 days after TDL. LESSONS: Postoperative late-onset or spontaneous chylothorax should be kept in mind after pulmonary resection and SLND, and the exclusion of chylous leakage could be considered as a precondition of chest tube removal.

New Results on Stability Analysis for Delayed Markovian Generalized Neural Networks With Partly Unknown Transition Rates.

The stability of delayed Markovian generalized neural networks is studied where the transition rates of the modes are partly unknown. The partly unknown transition rates generalize the traditional works that are with all known transition rates. Then, a Lyapunov-Krasovskii functional (LKF) with a delay-product-type (DPT) term is constructed. The DPT term is not only simple but also fully utilizes the information of time delay. Based on the new DPT LKF, stability criteria are presented, which are with lower computational complexity and less conservative. In the end, the validity and superiorities of the analytical results are verified by several examples.

A New Approach to Stabilization of Chaotic Systems With Nonfragile Fuzzy Proportional Retarded Sampled-Data Control.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

A New Approach to Stochastic Stability of Markovian Neural Networks With Generalized Transition Rates.

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

Therapeutic effects of recombinant human interleukin 2 as adjunctive immunotherapy against tuberculosis: A systematic review and meta-analysis.

BACKGROUND: Interleukin 2 (IL-2) is a cytokine secreted by activated T cells. Studies exploring recombinant human interleukin 2 (rhuIL-2) as an adjunctive immunotherapeutic agent to treat tuberculosis (TB) have shown variable results; however, the true therapeutic efficacy of rhuIL-2 administration in TB patients has not been determined. METHODS: A systematic review to identify publications exploring the association between rhuIL-2-based immunotherapy for TB and outcomes (sputum culture conversion, sputum smear conversion, radiographic changes, and leukocyte phenotype changes) in patients with pulmonary TB published before June 8, 2018 was performed. Data were extracted and analyzed by two investigators independently. RESULTS: A total of 2,272 records were screened. Four randomized controlled trials (RCTs) comprising 656 pulmonary TB patients were finally included. The rhuIL-2 treatment could significantly improve the sputum culture conversion of TB (RR, 1.18; 95%CI: 1.03-1.36; I2 < 0.01; P = 0.019) after at least 3 months of anti-TB therapy and the sputum smear conversion of TB during anti-TB therapy. Treating multidrug-resistant tuberculosis (MDR-TB) with rhuIL-2 could improve the sputum culture conversion (RR, 1.28; 95%CI: 1.05-1.57; I2 < 0.01; P = 0.016) and smear conversion (RR, 1.28; 95%CI: 1.09-1.51; I2 < 0.01; P = 0.003) at the end of anti-TB treatment. Meanwhile, rhuIL-2-based adjunctive immunotherapy could expand the proliferation and conversion of CD4+ and natural killer (NK) cells. Three of the included studies suggested that radiographic changes could not be improved by the use of rhuIL-2 as adjunctive immunotherapy. Publication bias did not exist. CONCLUSIONS: Based on this first meta-analysis, rhuIL-2-based adjunctive immunotherapy appears to expand the proliferation and conversion of CD4+ and NK cells, as well as improve the sputum culture (at 3 months and later) and smear conversion of TB patients.

Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays.

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.

Improved results on state feedback stabilization for a networked control system with additive time-varying delay components' controller.

This paper investigates the problems of stability and stabilization for a networked control system (NCS) with additive time-varying delay components' controller. Firstly, stability of a NCS with additive time-varying delays is investigated. A novel approach with free parameters is proposed. By constructing a new Lyapunov-Krasovskii functional (LKF) with two free parameters, stability criteria are obtained. The obtained stability criteria depend not only on upper bounds of delays but also free parameters. In addition, input-output method is extended to study the stability problem for the NCS. Compared with other approaches such as input-output method, the free-parameter approach is more flexible and effective in reducing the conservatism. Then, based on the stability results, a state feedback controller is designed to guarantee the asymptotically stable of the closed-loop systems. Finally, numerical examples are provided to show the effectiveness and less conservatism of the proposed results.

A rare case of cavitary lung cancer complicated with mycotic pneumonia and bullous emphysema: A case report and review of the literature.

RATIONALE: The accurate diagnosis and staging of cavitary lung cancer is challenging but essential for the choice of therapy; therefore, the differential diagnosis of cystic pulmonary lesions needs to be elucidated. PATIENT CONCERNS: A patient was admitted with multifocal thin-walled cystic lesions in chest computed tomography. DIAGNOSES: The patient had been diagnosed as heterogeneous bullous emphysema pathologically about 3 years ago. His diagnosis turned out to be metastatic cavitary lung cancer complicated with fungal pneumonia this time. INTERVENTIONS: The patient underwent lung volume reduction surgery during his first hospitalization. Concurrent systemic chemotherapy and whole brain radiotherapy were administered after the diagnosis of cystic lung cancer. OUTCOMES: The patient was lost to follow-up after the chemoradiotherapy. LESSONS: Cavitary lung cancer should always be kept in mind during differential diagnosis of pulmonary cystic lesions. Pathological diagnosis by biopsy and surgery could be considered to avoid delayed treatment of malignancy.

A cross-sectional and follow-up study of leukopenia in tuberculosis patients: prevalence, risk factors and impact of anti-tuberculosis treatment.

BACKGROUND: To investigate the prevalence of and risk factors for leukopenia in tuberculosis patients and the impact of anti-tuberculosis regimens on the occurrence of leukopenia in newly treated tuberculosis patients. METHODS: A total of 1,904 tuberculosis patients were included in the study. A cross-sectional survey of the prevalence of leukopenia was initially conducted, and then factors influencing leukopenia were identified using Logistic regression analysis. Non-treatment factors influencing peripheral blood leukocyte counts were analyzed using univariate COX proportional hazards models. Covariate analysis was used to assess the independent effect of different anti-tuberculosis regimens on peripheral blood leukocyte counts. RESULTS: Being female, advanced age and longer duration of previous anti-tuberculosis treatment (>6 month) were risk factors for leukopenia in tuberculosis patients, while secondary pulmonary tuberculosis, higher body mass index (BMI: 24-27.9 kg/m(2)), and higher degree of education (senior high school or above) were protective factors. Gender, vegetable consumption, drinking, pulmonary infection, other chronic diseases, and use of antibiotics were significantly associated with the development of leukopenia in patients on anti-tuberculosis treatment. In tuberculosis patients treated with anti-tuberculosis regimens not containing antibiotics, peripheral blood leukocyte levels gradually declined with the prolongation of treatment duration. In tuberculosis patients treated with anti-tuberculosis regimens containing antibiotics, peripheral blood leukocyte levels showed a declining trend. CONCLUSIONS: Female patients, patients at advanced age and recurrent tuberculosis patients having longer previous anti-tuberculosis treatment are high-risk populations for leukopenia. Attention should be paid to the influence of vegetable consumption and drinking, co-morbidities and use of antibiotics during anti-tuberculosis treatment.