Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy.

BACKGROUND: A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery. PATIENTS AND METHODS: We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI). RESULTS: Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN-TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan-Meier (K-M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05). CONCLUSIONS: Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the  AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention.

Clinicopathological features of rhabdomyosarcoma with novel FET::TFCP2 and TIMP3::ALK fusion: report of two cases and literature review.

AIMS: The aim of this study was to evaluate the clinicopathological features, immunophenotype, differential diagnosis, molecular genetic features and prognosis of spindle cell rhabdomyosarcoma with TFCP2 rearrangement. METHODS: Two cases of spindle cell rhabdomyosarcoma with FET::TFCP2 gene fusion were included in this study. Samples were collected and evaluated through histological observation, immunohistochemistry, fluorescence in-situ hybridisation and high-throughput gene sequencing and previous findings. RESULTS: The tumour tissues mainly comprised spindle cells and epithelioid cells, which expressed striated muscle markers, and exhibited high expression levels of CK and ALK protein markers. Molecular detection showed that the FET::TFCP2 gene was fused. A rare case with TIMP3::ALK and FUS::TFCP2 double-fusion was observed in this study. CONCLUSIONS: A case with double fusion of ALK and TFCP2 was reported in rhabdomyosarcoma for the first time in this study, which provides information on the molecular characteristic of the tumour. Spindle cell rhabdomyosarcoma with FET::TFCP2 fusion is characterised by histological, immunohistochemical and genetic changes. The tumour is aggressive, with poor prognosis and poor response to radiotherapy and chemotherapy. The efficacy of targeted therapy for ALK should be explored through more clinical studies.

A study on smart home use intention of elderly consumers based on technology acceptance models.

PURPOSE: Smart home devices have great potential to improve the quality of life and independence of older people, positively impacting their health, safety, and comfort. However, Chinese research in this field is still in its early stages. Therefore, more comprehensive and in-depth studies are needed to comprehend the various aspects influencing the acceptance and use of smart homes by older users. PATIENTS AND METHODS: This study adopted the Technology Acceptance Model (TAM) and included perceived usefulness, perceived ease of use, usage intention, intergenerational technology support, perceived value, and perceived risk as extension variables to delve deeper into the behavioral intentions of older users in smart home services. The study used a convenience sampling method to randomly distribute 236 questionnaires among older adults over the age of 60 in the school's community and neighboring urban communities who have experience in smart home use and who can complete human-computer interactions either independently or with the help of others, mainly focusing on the four sections: user characteristics, family situation, experience of use, and usage intention. The study used structural equation modeling (SEM) and factor analysis to analyze the completion of questionnaires. Finally, we conducted a validation analysis of the rationality and scientificity of the model and derived the six dimensions of the model of the influencing factors on the use of smart home products by the elderly and the weight sizes of their corresponding 13 influencing factors. RESULTS: The results show that perceived usefulness and perceived ease of use have a positive effect on users' intention to use smart homes. Perceived ease of use has a positive effect on the perceived usefulness of smart homes. In addition, intergenerational technology support, perceived value, and perceived risk impact users' perceived usefulness and perceived ease of use of the smart home. CONCLUSION: This research aims to describe the factors influencing older users' willingness to use smart homes. The findings are not only significant for the elderly in China but also of broad value to other regions and countries facing similar demographic challenges. The development of smart homes not only involves the elderly but is also closely related to all segments of society. The government should increase policy support and guide more social forces to participate in the development of the smart home industry. Service providers and designers should fully understand the demand situation and user experience of target users to develop easy-to-use smart home solutions. At the same time, smart homes, as intelligent products for the elderly, need to focus not only on the basic needs of the elderly such as material life and home safety, but also on the spiritual needs of elderly users. Children or caregivers should always pay attention to the psychological state of the elderly and actively guide them to use smart homes to help them realize their self-worth. We look forward to more research focusing on this area in the future and further exploring the specific issues and solutions involved.

Study on the effect of different durations of decalcification and depigmentation on programmed death ligand 1 immunohistochemical staining.

Background: Programmed death ligand 1 (PD-L1) score is an important companion diagnosis to predict the response to immunotherapy. Immunohistochemistry can accurately assess the expression of PD-L1 in routine paraffin-embedded tissue. However, whether decalcified or depigmented tissue is still accurate and can be used as a companion diagnosis is controversial. This study attempts to resolve this controversy by analyzing the effects of decalcification and depigmentation at different times on PD-L1 expression. Methods: Placental tissues were selected for tissue microarray, decalcification was performed according to time gradients of 6, 12, 24, 36, and 48 h, and depigmentation was performed according to time gradients of 1, 5, 15, 30, and 60 min. The intensity of PD-L1 expression at different time points was observed and quantified. Ten PD-L1-positive esophageal squamous carcinoma samples were selected for decalcification treatment, and the PD-L1. Combined Positive Score (CPS), Tumor Proportion Score (TPS) and Immunocyte Proportion Score (IPS) and the positivity rates were compared before and after decalcification. Results: After the placenta was decalcified, the intensity of PD-L1 positivity diminished, and the average optical density (AOD) value decreased with the prolongation of decalcification time and decreased significantly (P<0.05) at 24 h compared with the control group, and significantly (P<0.01) at 36 and 48 h compared with the control group. The intensity of PD-L1 positivity was weakened considerably after the treatment with potassium permanganate depigmentation. In addition, the AOD value decreased significantly (P<0.01) after the depigmentation time reached 5 min compared with the control group. Ten cases of PD-L1 positive esophageal squamous carcinoma were treated with 24 h decalcification, although the PD-L1 score decreased to a certain degree (P>0.05), and the positivity rate could reach 90%. After 36 h treatment, PD-L1 scores decreased, the CPS and IPS scores decreased significantly (P<0.05), and the positive rate was only 50%. Conclusions: Potassium permanganate depigmentation significantly reduces PD-L1 expression, even for a shorter time, affecting the accuracy of the results. The accuracy of PD-L1 remained high within 24 h decalcification. The above results have certain reference value for clinical selection of immunotherapy.

Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System.

Indoor motion planning challenges researchers because of the high density and unpredictability of moving obstacles. Classical algorithms work well in the case of static obstacles but suffer from collisions in the case of dense and dynamic obstacles. Recent reinforcement learning (RL) algorithms provide safe solutions for multiagent robotic motion planning systems. However, these algorithms face challenges in convergence: slow convergence speed and suboptimal converged result. Inspired by RL and representation learning, we introduced the ALN-DSAC: a hybrid motion planning algorithm where attention-based long short-term memory (LSTM) and novel data replay combine with discrete soft actor-critic (SAC). First, we implemented a discrete SAC algorithm, which is the SAC in the setting of discrete action space. Second, we optimized existing distance-based LSTM encoding by attention-based encoding to improve the data quality. Third, we introduced a novel data replay method by combining the online learning and offline learning to improve the efficacy of data replay. The convergence of our ALN-DSAC outperforms that of the trainable state of the arts. Evaluations demonstrate that our algorithm achieves nearly 100% success with less time to reach the goal in motion planning tasks when compared to the state of the arts. The test code is available at https://github.com/CHUENGMINCHOU/ALN-DSAC.

Building a 4E interview-grounded theory model: A case study of demand factors for customized furniture.

With the lifestyle change, users' demand for furniture has shown a trend for personalization and diversification. The customized furniture market is growing rapidly and gradually becoming an indispensable choice for lifestyle items. The present qualitative study sought to identify the influencing factors and relationships of user demand for customized furniture. This study constructed a 4E semi-structured interview guide, which means that interviews were conducted from 4 dimensions: essential information, information extraction, user experience, and product expectation. The interview results were coded and analyzed in combination with grounded theory. Based on the identified 38 concepts and 10 categories, we obtain 4 main categories: fundamental condition, operation behaviour, sensory value and emotional value. For the factors that affect the demand of customized furniture users, customized furniture enterprises can start from 2 levels of publicity and product design to meet user demand and improve the user purchase probability.

Effect of different postures and loads on joint motion and muscle activity in older adults during overhead retrieval.

Introduction: Pain is a common health problem among older adults worldwide. Older adults tend to suffer from arm, lumbar, and back pain when using hanging cabinets. Methods: This study used surface electromyography to record muscle activity and a motion capture system to record joint motion to research effects of different loads and retrieval postures on muscle activity and joint range of motion when older adults retrieve objects from a high place, to provide optimised feedback for the design of hanging cabinet furniture. Results: We found that: 1) The activity of BB (Biceps brachii) on the side of the body interacting with the cabinet door was greater than that of UT (Upper trapezius) and BR (Brachial radius) when retrieving objects from a high place, the activity of UT on the side of the body interacting with a heavy object was greater than that of BB and BR. 2) The activity of UT decreases when the shoulder joint angle is greater than 90°, but the activity of BB increases as the angle increases. In contrast, increasing the object's mass causes the maximum load on the shoulder joint. 3) Among the different postures for overhead retrieval, alternating between the right and left hand is preferable for the overhead retrieval task. 4) Age had the most significant effect on overhead retrieval, followed by height (of person), and load changes were significantly different only at the experiment's left elbow joint and the L.BR. 5) Older adults took longer and exerted more effort to complete the task than younger adults, and static exercise in older adults may be more demanding on muscle activity in old age than powered exercise. Conclusion: These results help to optimise the design of hanging cabinet furniture. Regarding the height of hanging cabinets, 180 cm or less is required for regular retrieval movements if the human height is less than 150 cm. Concerning the depth of the hanging cabinets, different heights chose different comfort distances, which translated into the depth of the hanging cabinets; the greater the height, the greater the depth of the hanging cabinets to use.

Impact of swiping direction on the interaction performance of elderly-oriented smart home interface: EEG and eye-tracking evidence.

Introduction: Smart home technology is increasingly popular, yet not all seniors are receptive and comfortable with it. This situation recognizes that the usability of smart home interfaces is particularly important. Most studies on interface swiping direction demonstrate the advantages of horizontal over vertical swiping, but the findings lack age-based as well as gender-specific judgments. Methods: In this paper, we use cognitive neural techniques of EEG and eye-tracking, combined with a subjective preference questionnaire, to analyze the preference of older persons for the swiping direction of smart home interfaces in a multimodal manner. Results: The EEG data showed that swiping direction had a significant effect on potential values (p = 0.001). Also, the mean power in the δ and the θ band was enhanced during vertical swiping. Gender had no significant effect on potential values (p = 0.085), but the cognitive task was more EEG stimulating for females. The eye-tracking metrics data showed a significant effect of swiping direction on fixation duration (p = 0.047) and a non-significant effect on pupil diameter (p = 0.576). These results were consistent with the results of the subjective preference questionnaire, both demonstrating a preference for vertical swiping among participants. Discussion: This paper uses three research tools simultaneously, combining objective perceptions as well as subjective preferences, to make the findings more comprehensive and reliable. Gender differences were also taken into account and differentiated in the data processing. The findings of this paper are different from most previous studies and better reflect the preference of elderly people for swiping directions, which can provide a reference for the future elderly-friendly smart home interface design.

An empirical study on the collaborative usability of age-appropriate smart home interface design.

Introduction: The smart home has become a popular product, but with the development of the aging population, the differentiated characteristics of the elderly smart home products in terms of demand and use are becoming more and more significant. The existing smart products are complicated to operate and cumbersome to interact with, which increases the cognitive load of the elderly group and hinders the daily use and user experience feeling of the elderly. The purpose of this paper is to study the interface data information and interface visual design starting from hardware and software, interface interaction, to explore the better interface data information and interface visual design, and to output, a new prototype of the operating interface of smart home system for the elderly, so that the smart products can be better used by the elderly. Methods: Thirty-two participants aged 55-75 were invited to conduct the test, and subjective evaluation was conducted at the end of the test. Through the tests, the operability of the prototype structure for smart furniture systems for the elderly was demonstrated. Results: In terms of functionality a new task based on a combination of icons and text is proposed. In the control of devices, the switching status of devices, etc., needs to be clearly distinguished visually, eye-protective bright colors are used, paired with low saturation to highlight the focus, and high bright colors with gray to distinguish the device status. In terms of the density of the content, an appropriate proportion of images and text were used to make the information less dense. ln the arrangement of web content, information content relevant to users was placed first as much as possible. Discussion: Based on this, a secondary optimal design was carried out to improve the interactive design of the smart home for the elderly and output it as a prototype interactive interface. Thus, the operability, rationality, and aesthetic comfort of the prototype design of smart home interaction in an age-friendly scenario are improved, allowing the elderly to have a better experience when using the smart home.

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma.

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04-4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77-3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.

Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and HRV Bioinformatic Feedback.

Human-computer interaction tends to be intelligent and driven by technological innovation. However, there is a digital divide caused by usage barriers for older users when interacting with complex tasks. To better help elderly users efficiently complete complex interactions, a smart home's operating system's interface is used as an example to explore the usage characteristics of elderly users of different genders. This study uses multi-signal physiological acquisition as a criterion. The results of the study showed that: (1) Older users are more attracted to iconic information than textual information. (2) When searching for complex tasks, female users are more likely to browse the whole page before locating the job. (3) Female users are more likely to browse from top to bottom when searching for complex tasks. (4) Female users are more likely to concentrate when performing complex tasks than male users. (5) Males are more likely to be nervous than females when performing complex tasks.

An Empirical Study on the Influence of Smart Home Interface Design on the Interaction Performance of the Elderly.

The concept of the smart home has been widely recognized and accepted, but the differentiated characteristics of elderly smart products in terms of demand and use are becoming more and more prominent. The lack of an efficient navigation design of the smart product interface increases the cognitive burden of elderly users, and how to better meet the needs of the elderly with smart products gradually becomes the focus of attention. This study was conducted for the elderly group, using the scenario-based design method to analyze the needs of elderly users, combining the research results of scenario theory with the smart home interaction design research method, focusing on how to make the style of interface navigation, sliding layout and button size more suitable for the cognitive behavior of elderly users. The purpose of this research is to realize an age-friendly smart home interaction design in terms of functional design and interface design. The experiment is divided into two stages: in stage 1, two different layouts and operation methods are commonly used for the age-friendly smart home interface: up and down sliding and left and right sliding; in stage 2, the functional buttons are square, where 4 styles are selected, and the side lengths are set to 10 mm, 12 mm, 15 mm, 18 mm and 22 mm. The sliding and retrieval test and retrieval and click test results show that for different sliding layout methods, the interactive performance and subjective evaluation of the interface with the up-and-down sliding layout are better. Among all functional button styles, the interaction performance and subjective evaluation of the simple button style with lines are the best. Among the function keys with a size of 10-22 mm, the interaction performance is better from 12 mm to 18 mm. The conclusion of the better interface data information obtained from this experiment improves the rationality of the age-friendly smart home interface and makes the smart home interface better for the age-friendly scenario.

The impact of different age-friendly smart home interface styles on the interaction behavior of elderly users.

Smart homes create a beneficial environment for the lives of elderly people and enhance the quality of their home lives. This study aims to explore the design of age-friendly interfaces that can meet the emotional needs of self-care elderly people from the perspective of functional realization of the operating interface. Sixteen elderly users aged fifty-five and above were selected as subjects with healthy eyes and no excessive drooping eyelids to obscure them. Four representative age-friendly applications with different interface designs were selected from the Android application market as the stimulus material for the experiment, and a total of fifteen pages were browsed independently. During the experiment, the ErgoLAB human-computer environment synchronization cloud platform was used to monitor and record the subjects' multidimensional physiological indicators of eye movements, skin electricity, and heart rate variability when using different styles of mobile application interfaces. Combined with the post-experimental PAD emotion scale data, the preferences of emotional interface design elements of the self-care elderly were analyzed to guide the subsequent design practice. The results show that: on the layout characteristics of the home page, a simple and intuitive multi-column layout or card layout combined with a bottom navigation bar type main navigation is used; on the choice of the main color, a single color with low saturation is used as the main color, with high saturation color accents to play a role in highlighting key information; on the information density of the home page, a moderate proportion of graphics and text is adopted, with low information density; on the arrangement of the page content, they try to give priority to information content with high user relevance.

The Impact of Interface Design Element Features on Task Performance in Older Adults: Evidence from Eye-Tracking and EEG Signals.

It is crucial that the interface design of mobile apps be age-appropriate at this stage of global aging, as the new epidemic has resulted in a higher sense of isolation among older persons. In this study, four typical senior social service mobile applications were chosen to give older persons the ability to complete user login duties. The participants were 16 older adults (7 men and 9 women) aged 55 to 76. Both objective and subjective data, including task completion time, gaze length, pupil diameter changes, EEG wave amplitude changes, and subjective sensations of older persons, were gathered using a combination of eye-movement and EEG signal approaches. The program was created to investigate the effects of interface design aspects on older people's task performance, including interface layout, interface color, information density, icon size and position, etc. The study's findings revealed that when the user task completion time and average fixation duration were shorter, the line of sight was more equally distributed, the visual focus was closer to the login button, and the average EEG amplitude of the user changed more, the older adults performed better. The palace layout had a more positive effect on job completion among older individuals when it came to interface layout. In terms of interface color, colored (contrasting) colors should serve to highlight the interface's essential information points while they can be removed. In terms of interface information density, a low-density level interface design can simplify and lower the cognitive load of task execution for older people. The first level of icons in the interface and their position in the visual center of the interface is the best interface design for older persons in terms of icon size and position. The results of this study have theoretical ramifications for a thorough understanding of the factors influencing older people's task performance, practical ramifications for the design of older people-centered interfaces, and they contribute to our understanding of the characteristics of older people's interface interaction behavior.

Recognition and Analysis of an Age-Friendly Intelligent Sofa Design Based on Skeletal Key-Points.

The aging population has a higher level of consumption willingness, higher quality of life demands, and more diversified spiritual pursuits. In recent years, age-friendly industries have entered a 'blue ocean of development'; intelligent recreation and age-friendly industries have become new growth points in the double-cycle situation; however, the current generalized design of the market is not enough to meet the needs of its elderly users. Through an extensive research and demand analysis focused on the highly-frequent use of sofas by elderly individuals, an AHP analysis showed that smart sofa design guidelines are among the most important functional indicators; user 'pain points' focused on low seat surfaces caused by the difficulty in getting up. To further refine this action behavior, a Kinect-based experimental device was used to capture the behavior of the elderly user during the sit-to-stand transition. The experimental data were collected from 25 key skeletal points in the human body and further investigated by converting the skeletal points into 12 key joint angles to refine the joint transition threshold risk during the sit-to-stand transition for the elderly user. The test results show that the most important joint angle affecting the sit-to-stand transition process is the trunk thigh angle was θ5-2, with an important value of 0.122. The two-dimensional body data of the elderly user was mapped to the joint angles under the three-dimensional activity threshold to build a comfort model of the elderly user's sofa, providing a theoretical basis for the design parameters of the aging sofa. In response to the research results, an intelligent age-friendly sofa with three forms was designed and prototyped independently, meeting the universal size of elderly users in terms of dimensional parameters, and iterating leisure and assisted standing transformation in terms of function (to reduce the risk levels of the sitting and standing transformations of elderly users).

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.