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The cardiotoxicity during immunotherapy administration leads to mortality by more than 42% and heart disease–related mortality among immunotherapy-linked cancers is still considered to be underestimated. In this study, the advanced stage of non-small cell lung cancer (NSCLC) with heart disease–related death was selected in accordance with immunotherapy approval time. NSCLC was searched on the Surveillance, Epidemiology, and End Results (SEER) program. Results show that 538 advanced NSCLC cases, those dominated by men and elderly people aged more than 70 years, had a high percentage of heart disease–related death in both eras. The difference between contemporary groups was fairly nonsignificant (P = > 0.05). The overall survival (OS) of all-cause mortality difference showed improved survival in the immunotherapy group (P = 0.0001). In the study of heart disease–related death survival with adjusted data, the NSCLC patients show significant lower survival in the immunotherapy era compared with the nonimmunotherapy era (P = 0.003; hazard ratio [HR] = 1.31; 95% CI = 1.099–1.57). In the multivariate analysis of NSCLC-related immunotherapy, histology revealed that the non-squamous cell type had an independent risk for lower OS than the squamous cell type (P = 0.04; HR= 0.74; CI = 0, 55- 0.99). The results demonstrate the survival benefits for NSCLC in immunotherapy; however, in heart disease–related death, immunotherapy in patients with NSCLC shows decreased OS. This study highlights that NSCLC patients should be highly monitored during immunotherapy administration, and further assessment is needed.

The aim of this study was to establish isothermal multiple self-matching initiated amplification (IMSA) and cross-priming amplification (CPA) methods to detect heat-stable I enterotoxin (STa)-producing enterotoxigenic Escherichia coli (ETEC). To avoid cross-contamination of aerosols, a closed independent isothermal amplification tube (IAT) was used to perform the assays. Optimal amplification conditions for IMSA and CPA were selected for specificity and sensitivity, respectively, and for clinical relevance. Both IMSA and CPA assays could specifically recognize all 3-STa positive strains in which they fluoresced green under UV light, but not in the 11 non-STa strains. The results of the sensitivity analysis indicated that the detection limit of the IMSA assay was 1.5 ×102 CFU, comparable to real-time PCR, but 10-fold more sensitive than CPA and LAMP. Further evaluation of the detection methods of swine diarrhea samples demonstrated that both could successfully identify the DNA of STa-producing ETEC in clinical specimens, consistent with LAMP and qPCR methods. The results demonstrated that the IMSA and CPA methods had high specificity and sensitivity with rapid detection of ETEC, so having great potential in clinical applications.

Background and aimsPurchasing Chinese herbal medicine (CHM) without a physician's prescription may have adverse effects on health. However, the recent status of purchasing non-prescribed CHM and the associated factors are not completely understood. We aimed to report the prevalence of purchasing CHM and associated factors.MethodsUsing data from the 2017 Taiwan Survey of Family Income and Expenditure, we identified 16,528 individuals (householders) aged 18 years and older. Sociodemographic factors, expenditures on medical services and health behaviours were compared between people with and without a history of purchasing non-prescribed CHM by calculating adjusted odds ratios (ORs) and 95% confidence intervals (CIs) in a multiple logistic regression analysis.ResultsThe one-year prevalence of purchasing non-prescribed CHM was 74.8% in Taiwan. In addition to sociodemographics, marital status (OR 2.14, 95% CI 1.88-2.44), the use of traditional Chinese medicine (OR 3.62, 95% CI 3.30-3.97), the purchasing of non-prescribed biochemical medications (OR 3.09, 95% CI 2.75-3.48), the purchasing of health foods (OR 2.59, 95% CI 2.33-2.86), the use of folk therapy (OR 2.27, 95% CI 1.95-2.64), and a high level of expenditure on alcohol (OR 3.79, 95% CI 3.29-4.36) were strongly correlated with purchasing non-prescribed CHM.ConclusionThe one-year prevalence of purchasing non-prescribed CHM is very high in Taiwan and is correlated with sociodemographics, health behaviours, and the utilization of medical care. The interaction of non-prescribed CHM with physician-prescribed herbal medicine and biomedical medications requires more attention.

Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery. Traditional machine learning methods have achieved certain beneficial effects in medical image segmentation, but they have problems such as low classification accuracy and poor robustness. Deep learning theory has good generalizability and feature extraction ability, which provides a new idea for solving medical image segmentation problems. However, deep learning has problems in terms of its application to medical image segmentation: one is that the deep learning network structure cannot be constructed according to medical image characteristics; the other is that the generalizability y of the deep learning model is weak. To address these issues, this paper first adapts a neural network to medical image features by adding cross-layer connections to a traditional convolutional neural network. In addition, an optimized convolutional neural network model is established. The optimized convolutional neural network model can segment medical images using the features of two scales simultaneously. At the same time, to solve the generalizability problem of the deep learning model, an adaptive distribution function is designed according to the position of the hidden layer, and then the activation probability of each layer of neurons is set. This enhances the generalizability of the dropout model, and an adaptive dropout model is proposed. This model better addresses the problem of the weak generalizability of deep learning models. Based on the above ideas, this paper proposes a medical image segmentation algorithm based on an optimized convolutional neural network with adaptive dropout depth calculation. An ultrasonic tomographic image and lumbar CT medical image were separately segmented by the method of this paper. The experimental results show that not only are the segmentation effects of the proposed method improved compared with those of the traditional machine learning and other deep learning methods but also the method has a high adaptive segmentation ability for various medical images. The research work in this paper provides a new perspective for research on medical image segmentation.

Sepsis-induced acute kidney injury (AKI) can increase the mortality of critically ill patients and the incidence of chronic kidney disease in critically ill survivors. The main goal was to investigate the possible link between metabolic changes and sepsis-induced AKI development. The experimental animal model of sepsis-induced AKI was established by intraperitoneal injection of lipopolysaccharide in rats. Non-targeted metabolic screening of the renal cortex in the control and sepsis-induced AKI groups was carried out based on gas chromatography coupled with quadrupole time-of-flight mass spectrometry (GC-TOFMS) technology. The data between the two groups were analyzed by combining univariate and multivariate statistical methods, and the metabolites associated with AKI in rats with sepsis were screened. By examining the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, altered metabolic pathways associated with acute renal injury in sepsis were identified. The cross validated scores plot of orthogonal partial least squares discriminant analysis (OPLS-DA) showed a distinct separation trend between the model and control groups in the profile of renal cortex metabolites, indicating a significant change in endogenous metabolites in the rat renal cortex. Further analysis and screening showed that 26 different metabolites were identified in the renal cortex between the two groups, mainly involving taurine and hypotaurine metabolism, pantothenic acid and CoA biosynthesis, phenylalanine metabolism, and other metabolic pathways. The metabolic disorders of taurine, pantothenic acid, and phenylalanine in the renal cortex are related to the development of acute renal injury in sepsis. Correcting these metabolic disorders is expected to prevent and treat sepsis-induced AKI.

Abstract Background Yersinia pestis is a contributing agent to the epidemic disease, plague, which killed an estimated 200 million people during historical times. In this study, a rapid, cheap, sensitive, and specific technique, the lateral flow assay (F1 strips), has been successfully developed to detect this pathogen, by using paired monoclonal antibodies (MAbs) against Y. pestis capsule like fraction 1 (F1) protein. Compared with the polyclonal antibody (PAb) based F1 strips, the Mab-based F1 strips have a remarkable increased detection limitation (10 to 100 folds). Furthermore, besides the limitation and specificity evaluation, the application of this F1 strip on simulated clinical samples indicate the LFA can be a good candidate to detect plague. Methods Recombinant F1 antigen was expressed and purified from a series of works. The various anti-F1 monoclonal antibodies generated from hybridoma cells were screened with the ELISA technique. To evaluate the feasibility of this Y. pestis F1 test strip, the F1 protein/Y. pestis was spiked into simulated clinical samples such as human serum, mouse bronchoalveolar lavage fluids, and mouse blood to mimic natural infection status. Additionally, this technique was applied to detect the Y. pestis in the environment-captured rats, to evaluate the practical usefulness of the strips. Results By using this MAb-based-LFA technique, 4 ng/ml of recombinant F1-protein and 103 CFU/ml of Y. pestis could be detected in less than 10 mins, which is at least 10-folds than that of the PAb format. On the other hand, although various Yersinia strains were applied to the strips, only Y. pestis strain showed a positive result; all other Yersinia species did not produce a positive signal, indicating the high efficiency and specificity of the MAb-based F1-strips. Conclusion Based on our findings, we suggest that the MAb-format-LFA will be valuable as a diagnostic tool for the detection of Y. pestis. This report shows that the F1 strip is sufficient to support not only the detection of plague in simulated clinical samples, but also it may be a good candidate to meet the epidemiological surveillance during an outbreak of the biological warfare.

Metal cations are one of the important factors affecting the performance of anionic foaming agent.In this paper,based on the different metal cations with sodium dodecyl sulfate (SDS) solution system,the influence of cations on foaming characteristics and interfacial properties of sodium dodecyl sulfate were investigated.The foaming ability and foam stability of different SDS solution systems were analyzed by foam scanner. The foam morphology,diameter and distribution were statistical analyzed by image analysis method.By utilizing of interfacial tension/rheometer,and the interfacial viscoelasticity were tested.The results showed that the cations can reduce the foaming ability and the influence order is Al3+ > Mg2+ > Na+.The half-life of foam decreased firstly and then tends to be gently with the increase concentration of Mg2+ and Na+,but the half-life of foam decreased firstly and then increased,finally tends to be gentle with the increase of Al3+.The adverse effect of Al3+ on the foam structure is much larger than that of Na+ and Mg2+.The ability of Al3+ which reduce the foam diameter significantly.The composite solution surface is viscoelastic,and the effects of cations on dilational modulus is Al3+ > Mg2+ > Na+.

Action recognition is a trending topic and key research direction in computer vision, machine learning, artificial intelligence, and other fields. This research seeks to identify human action in image and video data. Its research results have been widely used in the fields of safety monitoring, disability monitoring, understanding multimedia content, human–computer interaction, virtual reality, and so on. However, the existing traditional human action recognition technology has many limitations in practical application, such as low accuracy and weak adaptive ability. Although the action recognition based on deep learning can self-learn and improve the action recognition accuracy, there are many difficulties in training the deep neural network model, such as gradient disappearance, gradient explosion, and overfitting. Therefore, this paper will reduce the abovementioned difficulties in deep neural network model training from the perspective of deep neural network model parameter initialization and then propose a model parameter initialization method based on the multilayer maxout network activation function to solve the difficulties in deep neural network model training. Then, on this basis, a method of learning the temporal and spatial characteristics of human action based on the deep neural network model is proposed. First, the method detects and tracks the human action and uses the restricted Boltzmann machine (RBM) to encode the temporal and spatial features of various parts of the human body. Second, the temporal and spatial feature codes of various parts of the human body are integrated into a global temporal and spatial feature representation method of the action video through a RBM neural network. Finally, the trained SVM classifiers are used to recognize human action. Experiments show that the human action recognition method proposed in this paper not only has high recognition accuracy but also has great adaptability. Thus, this method extracts temporal and spatial features from the shape feature sequences of various parts of the human body, thus opening up a new way to extract human action features and solving the problem of human action recognition in complex scenes. Its proposal provides an exploratory technical method and approach for self-adaptive recognition of human action. It also gives directional enlightenment to the development and improvement of self-adaptive human action methods.

Gelatin was immobilized selectively on the amide groups-modified bottom of a trench array of a photoresist template with 2 μm resolution by the ethyl(dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide reaction. The gelatin-immobilized line array was brominated to generate a macroinitiator for atom transfer radical polymerization. Poly(methacrylic acid) (PMAA) brushes were grafted from the macroinitiator layer as line arrays of one-dimensional diffraction gratings (DGs) for various grafting polymerization times. A laser beam system was employed to analyze the optical feature with a characteristic diffraction effect of the PMAA DGs at a 45° incident angle along the transverse magnetic and transverse electric polarization. The growth of the PMAA brush lines increased both their heights and widths, leading to a change in the reflective diffraction intensity. The PMAA brushes under various grafting polymerization times were cleaved from the substrate by digestion of gelatin with trypsin, and their molecular weights were obtained by gel permeation chromatography. The change degree of the diffraction intensity varied linearly with the molecular weight of the PMAA brushes over a wide range, from 135 to 1475 kDa, with high correlation coefficients. Molecular weight determination of polymer brushes using the reflective diffraction intensity provides a simple method to monitor their growth in real time without polymer brush cleavage.

The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results. [ABSTRACT FROM AUTHOR]

Reducing regional economic disparities in China is an important task to facilitate inclusive and sustainable long-term economic growth. This paper investigates the role of technological innovation in facilitating regional economic convergence in China (i.e., narrowing the gap between more developed and less developed provinces). We utilize a province-level panel data set from 1998 to 2017 to calculate the so-called sigma (σ) and beta (β) regional economic convergence indexes, which allows us to examine the patterns of convergence in China over time. In particular, we use panel data regression models to calculate β indexes and examine how various measures of technological innovation influence regional economic convergence in China. Findings from our empirical analysis suggest that the provincial economies were diverging from 1998 to 2006. However, we observe regional economic convergence in China after 2006. Moreover, we find that technological innovation promoted regional economic convergence post-2006, and there is evidence that technological innovation spills over and benefits nearby surrounding provinces. We also find that transfer payments made by the Chinese central government to the provinces help boost regional economic growth and narrow regional economic disparities. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the roles of Toll-like receptor 4 (TLR4) and microRNA-7 (miR-7) in colorectal cancer (CRC) development and progression. We assessed TLR4 and miR-7 expression in CRC cells and tissues using reverse transcription-quantitative polymerase chain reaction. The relationship between miR-7 and TLR4 was analyzed through dual luciferase reporter assays. MTT, wound healing, and cell invasion assays were conducted to examine the effects of TLR4 and miR-7 on CRC cell proliferation, migration, and invasion. Western blotting was used to explore the involvement of the TRAF6/NF-κB signaling pathway. miR-7 was underexpressed in CRC, while TLR4 levels were increased. miR-7 negatively regulated TLR4 expression and its knockdown enhanced CRC cell proliferation, migration, and invasion. TLR4 knockdown had the opposite effects. The TRAF6/NF-κB pathway was linked to TLR4’s role in tumor progression. miR-7 might inhibit TRAF6/NF-κB target a signaling pathway of TLR4 and promote CRC occurrence. miR-7 may therefore be used as a sensitive biomarker in CRC patients. [ABSTRACT FROM AUTHOR]

Carboxylesterase (CES), a main hydrolysis enzyme family in the human body, plays a crucial role in drug metabolism. Among them, CES1 and CES2 are the primary subtypes, and each exhibits distinct distribution and functions. However, convenient and non-invasive methods for distinguishing them and the real-time monitoring of CES2 are relatively rare, hindering the further understanding of physiological functions and underlying mechanisms. In this study, we have designed, synthesized, and evaluated the first selective bioluminescent probe (CBP 1) for CES2 with high sensitivity, high specificity and rapid reactivity. This probe offers a promising approach for the real-time detection of CES2 and its dynamic fluctuations both in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Background: Metastasis has emerged as the major reason of treatment failure and mortality in patients with nasopharyngeal carcinoma (NPC). Growing evidence links abnormal DNA methylation to the initiation and progression of NPC. However, the precise regulatory mechanism behind these processes remains poorly understood. Methods: Bisulfite pyrosequencing, RT-qPCR, western blot, and immunohistochemistry were used to test the methylation and expression level of NEURL3 and its clinical significance. The biological function of NEURL3 was examined both in vitro and in vivo. Mass spectrometry, co-immunohistochemistry, immunofluorescence staining, and ubiquitin assays were performed to explore the regulatory mechanism of NEURL3. Results: The promoter region of NEURL3, encoding an E3 ubiquitin ligase, was obviously hypermethylated, leading to its downregulated expression in NPC. Clinically, NPC patients with a low NEURL3 expression indicated an unfavorable prognosis and were prone to develop distant metastasis. Overexpression of NEURL3 could suppress the epithelial mesenchymal transition and metastasis of NPC cells in vitro and in vivo. Mechanistically, NEURL3 promoted Vimentin degradation by increasing its K48-linked polyubiquitination at lysine 97. Specifically, the restoration of Vimentin expression could fully reverse the tumor suppressive effect of NEURL3 overexpression in NPC cells. Conclusions: Collectively, our study uncovers a novel mechanism by which NEURL3 inhibits NPC metastasis, thereby providing a promising therapeutic target for NPC treatment. [ABSTRACT FROM AUTHOR]

Background: Patient compliance is crucial to maintaining the awake prone position, which has been found to be intolerable by several patients. Addressing patients' compliance while being treated with awake prone positioning and prolonging its duration may be prerequisites for further research. Aim: This study was conducted to explore the personal experiences of non‐intubated patients during the implementation of awake prone positioning. Study Design: We conducted a qualitative study recruiting a purposive sample of twelve adult patients diagnosed with COVID‐19 who were previously treated with awake prone positioning. They were recruited from an intermediate care unit (that provides pre‐ and post‐intensive care) in a COVID‐19‐designated hospital in Nanjing, China from July 15, 2021, to September 20, 2021. Data were collected through individual in‐depth interviews and then analysed using Colaizzi's phenomenological method. Results: Content analysis of the interviews revealed two main themes, each containing three and four sub‐themes, respectively, as follows: (1) barriers to the implementation of awake prone positioning, including (i) generalised discomfort, (ii) a lack of both understanding and trust, and (iii) low satisfaction with the implementation plan; (2) factors promoting the implementation of awake prone positioning, including (i) health education and supervision, (ii) self‐motivation and support from healthcare staff and family members, (iii) finding a comfortable position and having access to entertainment, and (iv) symptom improvement. Conclusions: Healthcare staff should clarify the mechanism underlying the effectiveness of the awake prone position to patients to eliminate any doubts that they may have. Psychological support and appropriate supervision should be emphasised. Pain relief programmes should also be established, including the use of a comfortable prone posture in conjunction with access to entertainment. Finally, individualised awake prone positioning protocols should be formulated according to patients' living habits and attempted in combination with out‐of‐bed activities. These measures may help to enhance the experience and compliance of patients undergoing the treatment. Relevance to Clinical Practice: For future instances involving the implementation of awake prone positioning, several measures should be established and implemented alongside it, including a health education programme, supervision programme, support system, and pain relief programme. Personalisation should also be considered during its implementation. These measures may help to enhance the compliance of patients and prolong the duration of the awake prone position. [ABSTRACT FROM AUTHOR]

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Intermolecular carbophosphination reaction of alkynes or alkenes with unreactive C−P bonds remains an elusive challenge. Herein, we used a Ni−Al bimetallic catalyst to realize an intermolecular carbophosphination reaction of alkynes with 5‐membered phosphole oxides, providing a series of 7‐membered phosphepines in up to 94 % yield. [ABSTRACT FROM AUTHOR]

Over the past year we have researched the development of a network-based real-time laser-based nonintrusive field-deployable detection for the delineation of moving vehicles. The primary goal of this project is to develop a roadway detection system that can be used to gather reliable travel time data non-intrusively. A powerful Rabbit 3200, instead of multi-microchips, is used to control digitally controlled potentiometers (DCP), which adjust the gain of the sensors' signals. Utilizing digitally controlled potentiometers allow for quick and easy adjustment on the highway with only the need of pushing a button. The adjustment, which used to take half or an hour, now only takes several seconds. The Rabbit 3200 is also used as a data sender to a computer through the Ethernet. The rabbit digital input ports are triggered to collect the signals by an interrupt pulse from a PWM signal, which also acts as the laser source trigger. The Rabbit 3200 will then pack all of the data into TCP packages and send them to a remote computer over a network. The Rabbit 3200 is also able to filter noise after having finished the task of adjustment. The software of the system has been modified for 8 channels. Compared with the previous 4 channel code, the new system is able to obtain more information on vehicles, including the profile of a passing vehicle. In order to improve the precision of the system, we improved the mechanical design, optical design and electric circuit design. We also use an interrupt to sample signals instead of a fixed sampling interval strategy. This method ensures every signal generated by the APD will be captured by the Rabbit 3200. The laser sources are pulsed at 10 kHz and the sampling rate of the Rabbit 3200 is synchronized to the 10 kHz laser pulse. This report describes the design and implementation of each functional component of the field-deployable system, the configuration of the field detection system, software design and implementation, and a signal calibration method to obtain higher system precision. It also demonstrates four different ways to test the field-deployable system and the results from each way of testing for future improvement.