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  1. Article ; Online: Cogan syndrome following SARS-COV-2 infection.

    Chen, Longfang / Teng, Jialin / Yang, Chengde / Chi, Huihui

    Clinical rheumatology

    2023  Volume 42, Issue 9, Page(s) 2517–2518

    MeSH term(s) Humans ; Cogan Syndrome ; COVID-19/complications ; SARS-CoV-2 ; Vasculitis
    Language English
    Publishing date 2023-06-04
    Publishing country Germany
    Document type Editorial
    ZDB-ID 604755-5
    ISSN 1434-9949 ; 0770-3198
    ISSN (online) 1434-9949
    ISSN 0770-3198
    DOI 10.1007/s10067-023-06642-4
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    Zs.A 1740: Show issues Location:
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  2. Article ; Online: Semi-supervised learning methods for weed detection in turf.

    Liu, Teng / Zhai, Danlan / He, Feiyu / Yu, Jialin

    Pest management science

    2024  

    Abstract: Background: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop ... ...

    Abstract Background: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass.
    Results: Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated.
    Conclusion: These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.
    Language English
    Publishing date 2024-01-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 2001705-4
    ISSN 1526-4998 ; 1526-498X
    ISSN (online) 1526-4998
    ISSN 1526-498X
    DOI 10.1002/ps.7959
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  3. Article ; Online: SARS-CoV-2 serological cross-reactivity with autoantibodies - Authors' reply.

    Teng, Jialin / Dai, Jin

    The Lancet Rheumatology

    2020  Volume 3, Issue 1, Page(s) e16

    Language English
    Publishing date 2020-12-24
    Publishing country England
    Document type Journal Article
    ISSN 2665-9913
    ISSN (online) 2665-9913
    DOI 10.1016/S2665-9913(20)30359-3
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  4. Article ; Online: Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

    Sun, Hu / Liu, Teng / Wang, Jinxu / Zhai, Danlan / Yu, Jialin

    Pest management science

    2024  

    Abstract: Background: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting ... ...

    Abstract Background: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds.
    Results: The precision, recall, F1-score, mAP
    Conclusion: The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.
    Language English
    Publishing date 2024-02-07
    Publishing country England
    Document type Journal Article
    ZDB-ID 2001705-4
    ISSN 1526-4998 ; 1526-498X
    ISSN (online) 1526-4998
    ISSN 1526-498X
    DOI 10.1002/ps.7990
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  5. Article ; Online: Levels and influencing factors of mental workload among intensive care unit nurses: A systematic review and meta-analysis.

    Teng, Mei / Yuan, Zhongqing / He, Hong / Wang, Jialin

    International journal of nursing practice

    2023  , Page(s) e13167

    Abstract: Aim: The purpose of this systematic review was to determine the levels and influencing factors of mental workload in intensive care unit nurses.: Background: Intensive care unit nurses have a high mental workload level. To our knowledge, no meta- ... ...

    Abstract Aim: The purpose of this systematic review was to determine the levels and influencing factors of mental workload in intensive care unit nurses.
    Background: Intensive care unit nurses have a high mental workload level. To our knowledge, no meta-analytic research investigating the levels of mental workload in intensive care unit nurses and related factors has yet been performed.
    Design: This article is a systematic review and meta-analysis.
    Methods: Eleven electronic databases were searched from the database setup dates until 31 December 2022. The research team independently conducted study selection, quality assessments, data extractions and analysis of all included studies. The PRISMA guideline was used to guide reportage of the systematic review and meta-analysis.
    Results: Seventeen studies were included. In these studies, the pooled mean score of mental workload was 68.07 (95%CI:64.39-71.75). Furthermore, subgroup analyses indicated that intensive care unit nurses' mental workload differed significantly by countries, sample size and publication year. The mental workload influential factors considered were demographic, work-related and psychological factors.
    Conclusion: Hospital administrators should develop interventions to reduce mental workload to enhance the mental health of intensive care unit nurses and nursing care quality. Hospital managers should pay attention to the mental health of nurses and guide them to correctly relieve occupational stress and reduce mental workload.
    Language English
    Publishing date 2023-05-31
    Publishing country Australia
    Document type Journal Article ; Review
    ZDB-ID 1381116-2
    ISSN 1440-172X ; 1322-7114
    ISSN (online) 1440-172X
    ISSN 1322-7114
    DOI 10.1111/ijn.13167
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  6. Article ; Online: Nurses' mental workload and public health emergency response capacity in COVID-19 pandemic: A cross-sectional study.

    He, Hong / Wang, Jialin / Yuan, Zhongqing / Teng, Mei / Wang, Shuping

    Journal of advanced nursing

    2023  Volume 80, Issue 4, Page(s) 1429–1439

    Abstract: Aims: The aim of this study was to assess the level of mental workload of Chinese nurses through a latent profile analysis and to explore its relationship with public health emergency response capacity.: Design: A cross-sectional design with a ... ...

    Abstract Aims: The aim of this study was to assess the level of mental workload of Chinese nurses through a latent profile analysis and to explore its relationship with public health emergency response capacity.
    Design: A cross-sectional design with a convenience sample.
    Methods: A convenience sample of nurses from five tertiary hospitals in Chengdu between May and December 2022. Demographic, work-related information, Nurse's version of NASA's Task Load Index Scale and Nurse's Public Health Emergency Response Capacity Scale were used in this study.
    Results: The mean scores for mental workload and emergency response capacity for nurses were (57.19 ± 15.67) and (3.58 ± 0.77) respectively. We found that the mental workload of nurses fell into three potential categories. In addition, there were differences in psychological training and supply of epidemic prevention materials in the department among nurses with different mental workload subtypes. There was a moderate negative correlation between nurses' mental workload and public health emergency response capacity.
    Conclusion: Our results show that there is still a strong mental workload on a proportion of nurses, and enhanced psychological training and material supply support are beneficial in relieving nurses' mental workload. The better the nurses' capacity to cope with public health emergencies, the lower their mental workload.
    Impact: Nursing managers should pay ongoing attention to the mental workload status of nurses in the latter stages of a pandemic and individual differences in nurses' mental workload. In addition, nursing managers should be aware of the impact of public health emergency response capacity on nurses' mental workload. They can intervene in nurses mental workload from a new perspective.
    Patient or public contribution: 560 registered nurses participated in this study.
    MeSH term(s) Humans ; COVID-19/epidemiology ; Cross-Sectional Studies ; Pandemics ; Public Health ; Nurses ; Surveys and Questionnaires
    Language English
    Publishing date 2023-11-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 197634-5
    ISSN 1365-2648 ; 0309-2402
    ISSN (online) 1365-2648
    ISSN 0309-2402
    DOI 10.1111/jan.15929
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  7. Article ; Online: Correspondence on '2023 ACR/EULAR antiphospholipid syndrome classification criteria'.

    Tang, Zihan / Shi, Hui / Liu, Hong-Lei / Cheng, Xiaobing / Ye, Junna / Su, Yutong / Hu, Qiongyi / Meng, Jianfen / Pan, Haoyu / Yang, Chengde / Teng, Jialin / Liu, Tingting

    Annals of the rheumatic diseases

    2024  Volume 83, Issue 3, Page(s) e4

    MeSH term(s) Humans ; Antiphospholipid Syndrome/diagnosis ; Sjogren's Syndrome ; Rheumatology
    Language English
    Publishing date 2024-02-15
    Publishing country England
    Document type Letter
    ZDB-ID 7090-7
    ISSN 1468-2060 ; 0003-4967
    ISSN (online) 1468-2060
    ISSN 0003-4967
    DOI 10.1136/ard-2023-225013
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  8. Article: Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf.

    Jin, Xiaojun / Liu, Teng / McCullough, Patrick E / Chen, Yong / Yu, Jialin

    Frontiers in plant science

    2023  Volume 14, Page(s) 1096802

    Abstract: Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect ...

    Abstract Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect and distinguish every weed species with high accuracy. Training convolutional neural networks for detecting weeds susceptible to herbicides can offer a new strategy for implementing site-specific weed detection in turf. DenseNet, EfficientNet-v2, and ResNet showed high F
    Language English
    Publishing date 2023-02-01
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2023.1096802
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  9. Article ; Online: The levels and related factors of mental workload among nurses: A systematic review and meta-analysis.

    Yuan, Zhongqing / Wang, Jialin / Feng, Fen / Jin, Man / Xie, Wanqing / He, Hong / Teng, Mei

    International journal of nursing practice

    2023  Volume 29, Issue 5, Page(s) e13148

    Abstract: Aim: The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses.: Background: Mental workload is a key element that affects nursing performance. However, there exists no review ... ...

    Abstract Aim: The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses.
    Background: Mental workload is a key element that affects nursing performance. However, there exists no review regarding mental workload assessed using the NASA-TLX tool, focusing on nurses.
    Design: A systematic review and meta-analysis.
    Data sources: PubMed, MEDLINE, Web of Science, EMBASE, PsycINFO, Scopus, CINAHL, CNKI, CBM, Weipu and WanFang databases were searched from 1 January 1998 to 30 February 2022.
    Review methods: Following the PRISMA statement recommendations, review methods resulted in 31 quantitative studies retained for inclusion which were evaluated with the evaluation criteria for observational studies as recommended by the Agency for Healthcare Research and Quality. The data were pooled and a random-effects meta-analysis conducted.
    Results: Findings showed the pooled mental workload score was 65.24, and the pooled prevalence of high mental workload was 54%. Subgroup analysis indicated nurses in developing countries and emergency departments experienced higher mental workloads, and the mental workloads of front-line nurses increased significantly during the COVID-19 pandemic.
    Conclusion: These findings highlight that nurses experience high mental workloads as assessed using the NASA-TLX tool and there is an urgent need to explore interventions to decrease their mental workloads.
    MeSH term(s) Humans ; Pandemics ; COVID-19 ; Workload ; Databases, Factual
    Language English
    Publishing date 2023-03-23
    Publishing country Australia
    Document type Meta-Analysis ; Systematic Review ; Journal Article ; Review
    ZDB-ID 1381116-2
    ISSN 1440-172X ; 1322-7114
    ISSN (online) 1440-172X
    ISSN 1322-7114
    DOI 10.1111/ijn.13148
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  10. Article ; Online: Psychological capital among clinical nurses: A latent profile analysis.

    Teng, Mei / Wang, Jialin / Jin, Man / Yuan, Zhongqing / He, Hong / Wang, Shuping / Ren, Qianqian

    International nursing review

    2023  

    Abstract: Aim: To determine the psychological capital level of nurses and explore the latent profiles of nurses regarding their psychological capital scores.: Background: The use of individual-centered analysis for the connotation of nurses' psychological ... ...

    Abstract Aim: To determine the psychological capital level of nurses and explore the latent profiles of nurses regarding their psychological capital scores.
    Background: The use of individual-centered analysis for the connotation of nurses' psychological capital structure is less studied and still needs to be further explored.
    Methods: By the convenience sampling method, 494 clinical nurses from 7 general hospitals in Sichuan province were selected. The study was conducted from December 2022 to February 2023. Latent profile analysis was used for data analysis. We followed STROBE guidelines in this research.
    Results: The total mean score of nurses' psychological capital is 5.17 (SD = 0.8). The following four latent profiles were identified: "poor" (4.5%), "medium" (22.9%), "well-off" (41.5%), and "rich" (31.1%). Multiple logistic regression showed that the number of hours worked per day and the number of night shifts per month were negative predictors of psychological capital, and psychological training and job satisfaction were protective factors of psychological capital.
    Discussion: Our study found that the four profiles can be distinguished by "poor," "well-off," "medium," and "rich" levels of psychological capital. Among them, more than 70% of the nurses belonged to the well-off and rich profiles, and the number of the poor profile was the lowest.
    Conclusion: The overall psychological capital of clinical nurses is at a medium-high level. Each profile is influenced by multiple sociodemographic factors (i.e., age, working hours, monthly income, psychological training, and job satisfaction).
    Implications for nursing and health policy: Administrators should develop enhancement strategies to improve the mental health of nurses based on the characteristics of their psychological capital profiles.
    Language English
    Publishing date 2023-12-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 80342-x
    ISSN 1466-7657 ; 0020-8132
    ISSN (online) 1466-7657
    ISSN 0020-8132
    DOI 10.1111/inr.12918
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