•Prediction of activities of daily living (ADL) is crucial for post-stroke patients.•No robust prediction models are currently available.•A machine learning-based approach to predict ADL is proposed ...based on the assessments from rehabilitation ward of a reference hospital.•ADL of post-stroke patients could be accurately predicted by the approach.
Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.
Participants of a Post-acute Care-Cerebrovascular Diseases (PAC-CVD) program from a reference hospital in Taiwan between 2014 and 2016 were enrolled in this study. Based on 15 rehabilitation assessments, machine learning (ML) methods, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), were used to predict the Barthel index (BI) status at discharge. Furthermore, SVM and linear regression were used to predict the actual BI scores at discharge.
A total of 313 individuals (men: 208; women: 105) were enrolled in the study. All the classification models outperformed single assessments in predicting the BI statuses of the patients at discharge. The performance of the LR and RF algorithms was higher (area under ROC curve (AUC): 0.79) than that of SVM algorithm (AUC: 0.77). In addition, the mean absolute errors of both SVM and linear regression models in predicting the actual BI score at discharge were 9.86 and 9.95, respectively.
The proposed ML-based method provides a promising and practical computer-assisted decision making tool for predicting ADL in clinical practice.
This study aimed to investigate the perceived work stress and its influencing factors among hospital staff during the novel coronavirus (COVID‐19) pandemic in Taiwan. A web‐based survey was conducted ...at one medical center and two regional hospitals in southern Taiwan, targeting physicians, nurses, medical examiners, and administrators. The questionnaire included items on the demographic characteristics of hospital staff and a scale to assess stress among healthcare workers caring for patients with a highly infectious disease. A total of 752 valid questionnaires were collected. The hospital staff reported a moderate level of stress and nurses had a highest level of stress compared to staff in the other three occupational categories. The five highest stress scores were observed for the items “rough and cracked hands due to frequent hand washing and disinfectant use,” “inconvenience in using the toilet at work,” “restrictions on eating and drinking at work,” “fear of transmitting the disease to relatives and friends,” and “fear of being infected with COVID‐19.” Discomfort caused by protective equipment was the major stressor for the participants, followed by burden of caring for patients. Among participants who experienced severe stress (n = 129), work stress was higher among those with rather than without minor children. The present findings may serve as a reference for future monitoring of hospital staff's workload, and may aid the provision of support and interventions.
Cordycepin is an adenosine derivative isolated from Cordyceps sinensis, which has been used as an herbal complementary and alternative medicine with various biological activities. The general ...anti-cancer mechanisms of cordycepin are regulated by the adenosine A3 receptor, epidermal growth factor receptor (EGFR), mitogen-activated protein kinases (MAPKs), and glycogen synthase kinase (GSK)-3β, leading to cell cycle arrest or apoptosis. Notably, cordycepin also induces autophagy to trigger cell death, inhibits tumor metastasis, and modulates the immune system. Since the dysregulation of autophagy is associated with cancers and neuron, immune, and kidney diseases, cordycepin is considered an alternative treatment because of the involvement of cordycepin in autophagic signaling. However, the profound mechanism of autophagy induction by cordycepin has never been reviewed in detail. Therefore, in this article, we reviewed the anti-cancer and health-promoting effects of cordycepin in the neurons, kidneys, and the immune system through diverse mechanisms, including autophagy induction. We also suggest that formulation changes for cordycepin could enhance its bioactivity and bioavailability and lower its toxicity for future applications. A comprehensive understanding of the autophagy mechanism would provide novel mechanistic insight into the anti-cancer and health-promoting effects of cordycepin.
Higher sustainability with extended product lifecycle is a tireless pursuit in companies’ product design/development endeavours. In this regard, two prevailing concepts, namely the smart circular ...system and smart product-service system (Smart PSS), have been introduced, respectively. However, most existing studies only focus on the sustainability of physical materials and components, without considering the cyber-physical resources as a whole, let alone an integrated strategy towards the so-called Sustainable Smart PSS. To fill the gap, this paper discusses the key features in Sustainable Smart PSS development from a broadened scope of cyber-physical resources management. A data-driven reversible framework is hereby proposed to sustainably exploit high-value and context-dependent information/knowledge in the development of Sustainable Smart PSS. A four-step context-aware process in the framework, including requirement elicitation, solution recommendation, solution evaluation, and knowledge evolvement, is further introduced to support the decision-making and optimization along the extended or circular lifecycle. An illustrative example is depicted in the sustainable development of a smart 3D printer, which validates the feasibility and advantages of the proposed framework. As an explorative study, it is hoped that this work provides useful insights for Smart PSS development with sustainability concerns in a cyber-physical environment.
Most of existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require long recording time and heavily ...depend on eye trackers. In an effort to facilitate proactive detection of mental fatigue, we introduced a complemental fatigue indicator, named gaze-bin analysis, which simply presents the eye-tracking data with histograms. A method which engaged the gaze-bin analysis as inputs of semisupervised bagged trees was developed. A case study in a vessel traffic service center demonstrated that this approach can alleviate the burden of manual labeling as well as improve the performance of fatigue detection model. In addition, the results show that the approach can achieve an excellent accuracy of 89%, which outperformed other methods. In general, this study provided a complemental indicator for detecting mental fatigue as well as enabled the application of a low sampling rate eye tracker in the traffic control center.
The rapid development and implementation of smart, connected products (SCPs) in the engineering field has triggered a promising manufacturing paradigm of servitization, i.e. smart product-service ...systems (Smart PSS). As a complex solution bundle in both system and product level, its engineering change management differs from the existing ones mainly in two aspects. Firstly, massive in-context stakeholder-generated/product-sensed data during usage stage can be leveraged to enable its success in a data-driven manner. Secondly, the digitalized services, consisting of both hardware and software solutions, can also be changed in a more flexible way other than the physical components alone. Nevertheless, scarcely any work reports on how to conduct engineering change in such context, let alone a systematic approach to support the automatic generation of its change prediction or recommendation. Aiming to fill these gaps, this work proposes an occurrence-based design structure matrix (DSM) approach together with a three-way based cost-sensitive learning approach for automatic engineering change management in the Smart PSS environment. This informatics-based research, as an explorative study, overcomes the subjectivity and tedious assessment of the experts in the conventional approaches, and can offer useful guidelines to the manufacturing companies for managing their engineering changes for product-service innovation process.
The compound 6‐azaindole undergoes self‐assembly by formation of N(1)−H⋅⋅⋅N(6) hydrogen bonds (H bonds), forming a cyclic, triply H‐bonded trimer. The formation phenomenon is visualized by scanning ...tunneling microscopy. Remarkably, the H‐bonded trimer undergoes excited‐state triple proton transfer (ESTPT), resulting in a proton‐transfer tautomer emission maximized at 435 nm (325 nm of the normal emission) in cyclohexane. Computational approaches affirm the thermodynamically favorable H‐bonded trimer formation and the associated ESTPT reaction. Thus, nearly half a century after Michael Kasha discovered the double H‐bonded dimer of 7‐azaindole and its associated excited‐state double‐proton‐transfer reaction, the triply H‐bonded trimer formation of 6‐azaindole and its ESTPT reaction are demonstrated.
Trinity Roots: 6‐azaindole undergoes self‐assembly by formation of N(1)−H⋅⋅⋅N(6) hydrogen bonds (H bonds), to form a cyclic, triply H‐bonded trimer. The H‐bonded trimer undergoes excited‐state triple proton transfer (ESTPT), resulting in a proton‐transfer tautomer emission maximized at 435 nm.
The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, ...connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.
Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum ...tumour markers has been frequently discussed. However, the risk-benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population.
AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated.
To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003-0.008; the ARIs were between 0.119-0.306.
Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis.
Acanthamoeba castellanii is a free-living protist that feeds on diverse bacteria. A. castellanii has frequently been utilized in studies on microbial interactions. Grazing bacteria also exhibit ...diverse effects on the physiological characteristics of amoebae, such as their growth, encystation, and cytotoxicity. Since the composition of amoebae amino acids is closely related to cellular activities, it can indicate the overall responses of A. castellanii to various stimuli.
A. castellanii was exposed to different culture conditions in low-nutrient medium with heat-killed DH5α to clarify their effects. A targeted metabolomic technique was utilized to evaluate the concentration of cellular amino acids. The amino acid composition and pathways were analyzed by two web-based tools: MetaboAnalyst and Pathview. Then, long-term exposure to A. castellanii was investigated through in silico and in vitro methods to elucidate the homeostasis of amino acids and the growth of A. castellanii.
Under short-term exposure, all kinds of amino acids were enriched in all exposed groups. In contrast to the presence of heat-killed bacteria, the medium exhibited obvious effects on the amino acid composition of A. castellanii. After long-term exposure, the amino acid composition was more similar to that of the control group. A. castellanii may achieve amino acid homeostasis through pathways related to alanine, aspartate, citrulline, and serine.
Under short-term exposure, compared to the presence of bacteria, the type of medium exerted a more powerful effect on the amino acid composition of the amoeba. Previous studies focused on the interaction of the amoeba and bacteria with effective secretion systems and effectors. This may have caused the effects of low-nutrient environments to be overlooked.
When A. castellanii was stimulated in the coculture system through various methods, such as the presence of bacteria and a low-nutrient environment, it accumulated intracellular amino acids within a short period. However, different stimulations correspond to different amino acid compositions. After long-term exposure, A. castellanii achieved an amino acid equilibrium by downregulating the biosynthesis of several amino acids.
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