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To evaluate the necessity of multiple salivary pepsin tests within a day when diagnosing laryngopharyngeal reflux.Prospective cohort study.Tertiary hospitals.A total of 138 patients with signs and/or symptoms associated with laryngopharyngeal reflux were included. Salivary pepsin was detected on the day of 24-hour pH monitoring, and the results of salivary pepsin detected once in the morning and multiple times in 1 day were compared with the results of pH monitoring.Among the 138 patients, pH monitoring results were positive in 112. Salivary pepsin was positive in 47 cases in the morning, which was not consistent with the results of pH monitoring (kappa value = 0.117). With the pH monitoring results as the standard, the salivary pepsin detected once in the morning had a sensitivity of 38.4% (43/112) and a specificity of 84.6% (22/26) for the diagnosis of laryngopharyngeal reflux. When salivary pepsin was detected multiple times per day, 102 patients tested positive. The consistency with pH monitoring was moderate (kappa value = 0.587). The sensitivity was 86.6% (97/112), and the specificity was 80.8% (21/26). Of the 97 patients with positive results from pH monitoring and salivary pepsin detected multiple times a day, 54 had negative findings for a single detection in the morning, indicating that 55.7% (54/97) of the true positive cases were missed.Although a single detection of salivary pepsin in the morning is more economical, the sensitivity is too low, and it is necessary to detect it multiple times a day.

Deterministic networking for industrial manufacturing is a frontier technology with both theoretical innovativeness and technical practicability; it is significant for promoting technological innovations of the new-generation industrial Internet and for promoting the integrated development of industrial manufacturing and high-quality network, thereby providing opportunities for occupying the commanding height of network technology and seizing new economic growth points. This study investigates the development requirements for deterministic networking for industrial manufacturing from the aspects of manufacturing and communication enterprises, summarizes the development status and trends of the technology in detail, and further addresses development challenges. We classify the development stages of the technology into pilot demonstration, large-scale replication, and spontaneous innovation. Research reveals that the “deterministic network + industrial manufacturing” development in China needs to focus on the following aspects: establishing industry alliances to improve the industrial ecology, maximizing the existing public networks, carefully selecting industry development paths, increasing resource guarantee for application innovation, strengthening research on basic theories and key technologies, and training personnel through collaboration among industry, universities, and research institutes.

To analyze the role of nonacid reflux in laryngopharyngeal reflux diseases (LPRD). From January 2014 to April 2019, 344 patients associated with LPRD underwent 24-h multichannel intraluminal impedance–pH monitoring, and their reflux symptom index (RSI) and reflux finding score (RFS) were recorded. The numbers of acid, weakly acidic and alkaline reflux events in the laryngopharynx were counted, and the consistency analysis of the results with the results of the RSI and RFS was conducted. Among the 344 patients, nonacid reflux events accounted for 74.1% (1367/1845) of the all reflux events. There were 111 patients with ≥ 3 acid reflux events, 218 patients with ≥ 3 any kinds of reflux events, and 257 patients with positive results of RSI or RFS. Taking the results of the RSI and or RFS as a reference, the sensitivity, specificity and consistency test Kappa value for the diagnosis of LPRD according to the existence of ≥ 3 acid reflux events were 41.2%, 94.2% and 0.228, respectively. With the existence of three or more all kinds of reflux events as the standard, the sensitivity, specificity and consistency test Kappa value were 76.7%, 74.7% and 0.449, respectively. The nonacid reflux events account for the highest proportion of laryngopharyngeal reflux events, and the consistency of the results of RSI and or RFS with all reflux events is higher than that with only acid reflux events, that indicates nonacid reflux may play an important role in LPRD.

PURPOSE Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning. METHODS To classify VCLs, our method combined DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN based model. Analysis of F1 score and receiver operating characteristic (ROC) curves were conducted to evaluate the performance of the system. RESULTS Beyond existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an AUC of 0.9557 for four fine-grained classes of VCLs, namely normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent (keratinization, carcinoma) and non-urgent (normal, polyp), with an overall accuracy of 0.939, a sensitivity of 0.887, a specificity of 0.993, and an AUC of 0.9828. The proposed method also outperformed clinicians in the classification of normal, polyps, and carcinoma at an extremely low time cost. CONCLUSION The VCLs diagnosis system succeeded in using DCNN to distinguish the most common VCLs and normal cases, holding a practical potential for improving the overall diagnostic efficacy in VCLs examinations. The proposed VCLs diagnosis system could be appropriately integrated into the conventional work-flow of VCLs laryngoscopy as a highly objective auxiliary method. This article is protected by copyright. All rights reserved.

Kaposi sarcoma (KS) is a mesenchymal tumor characterized by angioproliferation in the setting of infection by human herpesvirus 8 (HHV-8), also known as KS–associated herpesvirus. The disease has several manifestations, the most common of which is a violaceous-to-dark brown macule, plaque, or nodule on the skin. Involvement of the gastrointestinal tract is less common, only affecting 10% of patients but is much more prevalent in the immunocompromised.1 Involvement of visceral organs outside the gastrointestinal tract such as the lungs, liver, and bone is extremely rare in immunocompetent patients. Clinically, most cases of KS fit into 1 of 5 reported subtypes: classical, African endemic, AIDS epidemic, iatrogenic, and nonepidemic. Very rarely do patients present who do not conform to 1 of the 5 subtypes. We present 3 such patients and discuss their fit in the current classification system.

Backgound: Pepsin immunohistochemical (IHC) staining is a promising diagnostic approach of laryngopharyngeal reflux (LPR). The interarytenoid mucosa has been proved to be an effective biopsy area.O...

This article contains a new subnet training method for modular neural networks, proposed with the inspiration of the principle of “an expert with other capabilities”. The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical subnet learning method and its advantage in the general case is theoretically discussed. Both methodology and empirical study of this new method are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical subnet learning method, the average testing error of the proposed method is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated.

Background Purposeful completion (fidelity) more than simple adherence to items in the surgical safety checklist may improve operating room efficiency and patient safety. The purpose of this study was to evaluate intraoperative delays and correlate them with adherence and fidelity to the preincision surgical safety checklist. Methods Trained observers evaluated surgical safety checklist compliance during 3 observation periods from 2014–2016. Degree of adherence, checkpoint verbalization, fidelity, and meaningful completion were assessed. Delays were categorized as missing or malfunctioning equipment, staff error, and medication issues. Descriptive statistics, analysis of variance, logistic regression, χ2 and Student t test were used to analyze results. Results Of the 591 cases observed, 19% (n = 110) had at least one documented, intraoperative delay. The majority of delays were related to missing (50%) or malfunctioning (30%) equipment. Compared with cases without delays, cases with delays did not have a different mean degree of adherence (96.3 ± 7.6% vs 95.6 ± 5.8%, P = .36). Degree of fidelity was different between cases with and without delays (mean fidelity 77.1 ± 14.9% vs 80.5 ± 7.14.2%, P = .03). Conclusion The preincision SSC is a communication tool offering an opportunity to discuss potential concerns and anticipated intraoperative needs. Fidelity rather than adherence to the surgical safety checklist seems to diminish intraoperative delays.

Dynamic modularity is one of the fundamental characteristics of the human brain. Cooperative divide and conquer strategy is a basic problem solving approach. This chapter proposes a new subnet training method for modular neural networks with the inspiration of the principle of “an expert with other capabilities.” The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical method and its advantage is theoretically discussed. Both methodology and empirical study are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical learning method, the average testing error is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated. Finally, a mind-gut frame is proposed.

This paper discusses dynamically weighted model predictive control based on two-timescale neurodynamic optimization. Minimax optimization problems with dynamic weights in objective functions are used in the model predictive control. The minimax optimization problems are solved by using a two-timescale neurodynamic optimization approach. Examples on controlling HVAC (heating, ventilation, and air-conditioning) and CSTR (cooling continuous stirred tank reactor) systems are elaborated to substantiate the efficacy of the control approach.

In this paper, neural networks based on linearly translated features (LTFs) are presented. LTFs including uniform, non-uniform, and multiple translation vectors are embedded into feedforward neural networks. Learning algorithms are presented for the neural networks. Learning capabilities of the neural networks are analyzed. Experimental results on approximation’ identification, and evaluation problems are reported to substantiate the efficacy of the neural networks and learning algorithms.

This paper addresses receding horizon control of a heating, ventilation, and air-conditioning (HVAC) system based on neurodynamic optimization. The receding horizon control problem for the HVAC system is formulated as sequential quadratic programs, which are solved by using a neurodynamic optimization model. Simulation results on temperature setpoint regulation of the HVAC system are discussed to substantiate the efficacy of the approach.

OBJECTIVE To observe the clinical effect of catheter-directed thrombolysis on deep venous thrombosis (DVT) of lower extremity after burn. STUDY DESIGN An observational study. PLACE AND DURATION OF STUDY Invasive Technology Department, Binzhou City Center Hospital, China, from January 2015 to November 2017. METHODOLOGY Eighty-two patients with lower extremity DVT after burn were selected as the study object. All patients received catheter-directed thrombolysis and the clinical efficacy was evaluated. The blood coagulation parameters including plasma prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrin original (FIB) and inflammatory response factors including interleukin (IL)-6, C-reactive protein (CRP) and D-dimer were compared before and after treatment. RESULTS All interventional surgeries that the patients received were successful. The effective rate of catheter-directed thrombolysis was 98.78% (81 cases). After 6 days of treatment, compared with that before treatment, serum PT, APTT and TT were up-regulated (all p

Background The debriefing phase of the surgical safety checklist (SSC) provides the operative team an opportunity to share pertinent intraoperative information and communicate postoperative plans. Prior quality improvement initiatives at our institution focused on the preincision phase of the SSC; however, the debriefing phase has not been evaluated. We aimed to assess adherence to the debrief checklist at our institution and identify areas for improvement. Materials and methods An observational study was conducted from 2014 to 2016 with a convenience sample of pediatric surgery cases at an academic children's hospital over 8-wk periods annually to evaluate the debriefing checklist across 14 subspecialties. Intraoperative team members' adherence to eight prespecified checkpoints was assessed. Descriptive statistics, Pearson's chi square, Kruskal–Wallis rank test, and Cohen's kappa for interrater reliability were used ( P Results A total of 603 cases were observed (2014 n = 191; 2015 n = 195; 2016 n = 217). The debriefing checklist was conducted in 90.6%, 90.3%, and 94.9% of observed cases each year respectively with the median number of checklist items completed relatively unchanged (8, 7, and 7, range 0-8). However, the checklist was only fully completed in 55%, 48%, and 50% of cases over the study period ( P = 0.001) with no debriefing at all in approximately 9% of cases in 2014 and 2015 versus 5% in 2016 ( P 0.65. Conclusions Despite slight increases annually in overall compliance to the debriefing checklist, only half of all checklists were completed in full. Future efforts to augment adherence are needed and will include interventions targeting the debriefing phase and increasing operating room efficiency.

This paper mainly aims at reducing relative learning complexity of subnets originate from “be Expert in Multiple aspects and Good at Many” (EMGM) modular neural network (MNN). Firstly, subnet learning algorithm which has a pure sequential execution style is built and convergence analysis is given. Secondly, in EMGM MNN system, an equivalent learning condition, which satisfies the criterion needed for the efficient training algorithm designed before, is founded for every subnet. Three identification problems have been involved to test the effectiveness and efficiency of the new framework in dealing with low dimension data. Both theoretical and experimental results show the new framework will reduce relative learning complexity of every subnet. The experiment result also shows new framework can achieve comparable generalization capability with original one. Furthermore, Bias Variance analysis shows maximum ability of generalization performance improvement of EMGM MNN may exist and the improvement comes from the improvement of bias estimation accuracy.

Based on the thought of """"to be expert in one aspect and good at many"""", a new training method of modular neural network (MNN) is presented. The key point of this method is a subnet learns the neighbor data sets while fulfiling its main task : learning the objective data set. Both methodology and empirical study of this new method are presented. Two examples (static approximation and nonlinear dynamic system pre-diction) are tested to show the new method's effectiveness: average testing error is dramatically decreased compared to original algorithm.