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Abstract Purpose The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is often time-consuming and resource-intensive. In this study, we introduce a novel precision-preferred comprehensive information extraction and selection procedure to enhance both the efficiency and accuracy of evidence selection for TCM practitioners. Methods We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. The selection process is recorded in real-time, allowing researchers to backtrack and verify its accuracy. This innovative approach was tested on ten high-quality, randomly selected systematic reviews of TCM-related topics written in Chinese. To evaluate its effectiveness, we compared the screening time and accuracy of this approach with traditional evidence selection methods. Results Our finding demonstrated that the new method accurately selected potential literature based on consistent criteria while significantly reducing the time required for the process. Additionally, in some cases, this approach identified a broader range of relevant evidence and enabled the tracking of selection progress for future reference. The study also revealed that traditional screening methods are often subjective and prone to errors, frequently resulting in the inclusion of literature that does not meet established standards. In contrast, our method offers a more accurate and efficient way to select clinical evidence for TCM practitioners, outperforming traditional manual approaches. Conclusion We proposed an innovative approach for selecting clinical evidence for TCM reviews and guidelines, aiming to reduce the workload for researchers. While this method showed promise in improving the efficiency and accuracy of evidence-based selection, its full potential required further validation. Additionally, it may serve as a useful tool for editors to assess manuscript quality in the future.

Abstract The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals’ constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

Abstract Background Self-absorption refers an excessive, persistent, and rigid preoccupation with information regarding the self. This study aims to introduce the Self-Absorption Scale (SAS) into China with an assessment of its latent variable structure, network structure, psychometric properties, and clinical utility in a nonclinical Chinese sample. Methods 209 participants completed the translated SAS as well as the Short General Health Questionnaire (GHQ-12), rumination subscale of the Rumination-Reflection Questionnaire (RRQ), the Mindful Attention Awareness Scale (MAAS), the Private Self-Consciousness Scale (PrSCS), the Obsessive-Compulsive Inventory-Revised (OCI-R) and the Dissociative Experiences Scale (DES-II). In addition, 30 respondents completed the Chinese version of the SAS and retested it 2 weeks later. Results The Chinese version of the SAS (CH-SAS) had a desirable two-correlated-factor structure with the reverse scored item removed, which was invariant across different genders. The core items in the network structure of the CH-SAS were related to excessive self-immersion, uncontrollability and anxiety aspects of self-absorption. The Cronbach’s alpha coefficient for the CH-SAS was 0.903 while the McDonald’s omega coefficient was 0.916 and the test-retest reliability was 0.908. The CH-SAS and its two subscales had moderate positive correlations with the rumination subscale of the RRQ (ranging from 0.474 to 0.616; p .05), and its correlation with the PubSAS was significant at the 0.05 level, with a remarkably low correlation coefficient (r = .157). The hierarchical regression analysis suggested that the CH-SAS can significantly predict the severity of OCD beyond factors such as depression, anxiety, rumination, dissociation, and mindful attention awareness. Conclusions The CH-SAS demonstrates excellent reliability, including internal consistency and test-retest reliability. Additionally, it exhibits favorable structural validity, as well as strong evidence of convergent and divergent validity. Furthermore, the self-absorption measured using the CH-SAS contributed significantly to the prediction of OCD beyond other relevant psychological factors, suggesting its clinical utility.

Abstract Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

Image style transfer is an important way to combine different styles and contents to generate new images, which plays an important role in computer vision tasks such as image reconstruction and image texture synthesis. In style transfer tasks, there are often long-distance dependencies between pixels of different styles and contents, and existing neural network-based work cannot handle this problem well. This paper constructs a generation model for style transfer based on the cycle-consistent network and the attention mechanism. The forward and backward learning process of the cycle-consistent mechanism could make the network complete the mismatch conversion between the input and output of the image. The attention mechanism enhances the model’s ability to perceive the long-distance dependencies between pixels in process of learning feature representation from the target content and the target styles, and at the same time suppresses the style feature information of the non-target area. Finally, a large number of experiments were carried out in the monet2photo dataset, and the results show that the misjudgment rate of Amazon Mechanical Turk (AMT) perceptual studies achieves 45%, which verified that the cycle-consistent network model with attention mechanism has certain advantages in image style transfer.

BackgroundIn the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient.ObjectivesTo expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction.MethodsWe adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China National Knowledge Infrastructure (CNKI), WanFang Data, and VIP databases were selected as the data source for extraction. We then constructed a manually annotated dataset of TCM clinical literature to train the model, including ten evidence elements and 24,244 datapoints. We chose two models, BERT-CRF and BiLSTM-CRF, as the baseline, and compared the training effects with Evi-BERT and Evi-BERT combined with rule expression (RE).ResultsWe found that Evi-BERT combined with RE achieved the best performance (precision score = 0.926, Recall = 0.952, F1 score = 0.938) and had the best robustness. We totally summarized 113 pieces of rule datasets in the regulation extraction procedure. Our model dramatically expands the amount of data that can be searched and greatly improves efficiency without losing accuracy.ConclusionOur work provided an intelligent approach to extracting clinical evidence for TCM RCT data. Our model can help physicians reduce the time spent reading journals and rapidly speed up the screening of clinical trial evidence to help generate accurate clinical reference guidelines. Additionally, we hope the structured clinical evidence and structured knowledge extracted from this study will help other researchers build large language models in TCM.

With the increasing capacity of renewable energy generators, microgrid (MG) systems have experienced rapid development, and the optimal economic operation is one of the most important and challenging issues in the MG field. To reduce the overall generation cost of microgrids, a hybrid butterfly algorithm (HBOA) is proposed to address the optimal economic operation problem in MG systems. This algorithm uses adaptive switching thresholds to balance the global exploration capability and local exploitation capability of the algorithm. It introduces a diversity learning mechanism to enhance information exchange among populations to improve the algorithm's accuracy and proposes an elite-guided guidance strategy to accelerate the convergence speed of the algorithm. Numerical simulation experiments on 10 standard test functions validate that the HBOA algorithm has higher optimization accuracy and faster convergence speed. Simulation experiments are conducted on two operation modes of microgrids: Islanded and grid-connected, and compared with other algorithms. In islanded and grid-connected modes, HBOA can reduce operating costs by up to 11.7% and 17.7%, respectively. The experimental results confirm the applicability and superiority of the proposed algorithm for solving the optimal economic operation problem in microgrids.

For jointed rock mass with anisotropy and discontinuity, the structure of the surrounding rock is constantly developing and changing during tunnel excavation. It is difficult to reasonably predict localized deformation of jointed rock mass by using the existing rock mechanics theory. In this paper, the failure characteristic of pre-holed jointed rock mass with three joint angles is experimentally investigated by adopting the digital image correlation and acoustic emission methods. To avoid the influence of measurement error on Digital Image Correlation (DIC) from discontinuous deformation, parametric studies and an optimized algorithm are also included in DIC tests. Results indicate that the perpendicular-jointed condition (0° joints) is the most dangerous situation because of its comparatively lower strength and brittle failure mode with a shift energy release. For rocks with different jointed angles, localized deformation emerges after the material enters the plasticity. Significant localization occurs after the failure with cracks surrounding the center hole and pre-existing joints.

BackgroundHypertension has now developed into a major public health problem worldwide. Under the existing antihypertensive drug treatment paradigm, problems such as decreasing drug resistance and increasing drug side effects can occur for elderly patients. Acupuncture, a core technique in the non-pharmacological treatment of Chinese medicine, plays an important role in the treatment of elevated blood pressure.ObjectiveThis study aimed to systematically evaluate the effect of acupuncture alone or in combination with antihypertensive drugs on the efficiency of reducing blood pressure and controlling blood pressure in elderly patients with hypertension.MethodsArticles of randomized controlled trials of acupuncture for hypertension in the elderly published before November 2022 were searched in 7 databases. The methodological quality of the literature was evaluated using the Cochrane Risk of Bias Assessment Tool. The primary outcome was the efficiency rate of blood pressure reduction, and the secondary outcome was the change in blood pressure after treatment.ResultsThis study conducted a systematic review and meta-analysis of 12 randomized controlled trials with a total of 1,466 subjects. Among the primary outcome—efficiency rate, acupuncture-only treatment (RR = 1.11, 95% CI: 1.03–1.20, P

Abstract The missense variant rs13107325 (C/T, p.Ala391Thr) in SLC39A8 consistently showed robust association with schizophrenia in recent genome-wide association studies (GWASs), suggesting the potential pathogenicity of this non-synonymous risk variant. Nevertheless, how this missense variant confers schizophrenia risk remains unknown. Here we constructed a knock-in mouse model (by introducing a threonine at the 393th amino acid of mouse SLC39A8 (SLC39A8-p.393T), which corresponds to rs13107325 (p.Ala391Thr) of human SLC39A8) to explore the potential roles and biological effects of this missense variant in schizophrenia pathogenesis. We assessed multiple phenotypes and traits (associated with rs13107325) of the knock-in mice, including body and brain weight, concentrations of metal ions (including cadmium, zinc, manganese, and iron) transported by SLC39A8, blood lipids, proliferation and migration of neural stem cells (NSCs), cortical development, behaviors and cognition, transcriptome, dendritic spine density, and synaptic transmission. Many of the tested phenotypes did not show differences in SLC39A8-p.393T knock-in and wild-type mice. However, we found that zinc concentration in brain and blood of SLC39A8-p.393T knock-in mice was dysregulated compared with wild-types, validating the functionality of rs13107325. Further analysis indicated that cortical dendritic spine density of the SLC39A8-p.393T knock-in mice was significantly decreased compared with wild-types, indicating the important role of SLC39A8-p.393T in dendritic spine morphogenesis. These results indicated that SLC39A8-p.393T knock-in resulted in decreased dendritic spine density, thus mimicking the dendritic spine pathology observed in schizophrenia. Our study indicates that rs13107325 might confer schizophrenia risk by regulating zinc concentration and dendritic spine density, a featured characteristic that was frequently reported to be decreased in schizophrenia.

Digitalization has emerged as a new trend in healthcare, with great potential and creating many unique opportunities, as well as many challenges. Cardiovascular disease is one of the major causes of disease-related morbidity and mortality worldwide, and the threat to life posed by acute heart failure is evident. In addition to traditional collegiate therapies, this article reviews the current status and subdisciplinary impact of digital healthcare at the level of combined Chinese and Western medical therapies. It also further discusses the prospects for the development of this approach, with the objective of developing an active role for digitalization in the combination of Western and Chinese medicine for the management of acute heart failure in order to support maintenance of cardiovascular health in the population.

BackgroundAs a common clinical symptom, insomnia has a high incidence of combined mental illness and it is also a risk factor for the development of depression, anxiety and suicide. As a new concept in the field of health in recent years, mindfulness therapy can improve insomnia, anxiety and depression, which is a new way to solve such diseases.ObjectiveThis study aims to systematically evaluate the effects of mindfulness compared with conventional treatment on scores of the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) in people with insomnia and anxiety-depressive symptoms.MethodsArticles published before October 2022 were searched from seven databases and included in randomized controlled trials (RCTs) to evaluate mindfulness therapy. The assessment tool of Cochrane bias risk was used to evaluate the methodological quality of the literature. The main outcome indicators were HAMD and HAMA scores, and the secondary outcome indicators were SDS and SAS scores.ResultsTen randomized controlled trials including 1,058 subjects were systematically evaluated and meta-analyzed in this study. In the main outcome indicators, there was a significant difference between mindfulness therapy and conventional treatment in reducing HAMD score (MD: −3.67, 95% CI: −5.22–2.11, p

Abstract Genome‐wide association studies have identified 3p21.1 as a robust risk locus for schizophrenia. However, the underlying molecular mechanisms remain elusive. Here a functional regulatory variant (rs2535629) is identified that disrupts CTCF binding at 3p21.1. It is confirmed that rs2535629 is also significantly associated with schizophrenia in Chinese population and the regulatory effect of rs2535629 is validated. Expression quantitative trait loci analysis indicates that rs2535629 is associated with the expression of three distal genes (GLT8D1, SFMBT1, and NEK4) in the human brain, and CRISPR‐Cas9‐mediated genome editing confirmed the regulatory effect of rs2535629 on GLT8D1, SFMBT1, and NEK4. Interestingly, differential expression analysis of GLT8D1, SFMBT1, and NEK4 suggested that rs2535629 may confer schizophrenia risk by regulating SFMBT1 expression. It is further demonstrated that Sfmbt1 regulates neurodevelopment and dendritic spine density, two key pathological characteristics of schizophrenia. Transcriptome analysis also support the potential role of Sfmbt1 in schizophrenia pathogenesis. The study identifies rs2535629 as a plausibly causal regulatory variant at the 3p21.1 risk locus and demonstrates the regulatory mechanism and biological effect of this functional variant, indicating that this functional variant confers schizophrenia risk by altering CTCF binding and regulating expression of SFMBT1, a distal gene which plays important roles in neurodevelopment and synaptic morphogenesis.

Microgrid operation planning is crucial for ensuring the safe and efficient output of distributed energy resources (DERs) and stable operation of the microgrid power system. The integration of hydrogen fuel cells into microgrids can increase the absorption rate of renewable energy, while the incorporation of lithium batteries facilitates the adjustment of microgrid power supply voltage and frequency, ensuring the three-phase symmetry of the system. This paper proposes an economic scheduling method for a grid-connected microgrid that considers demand response and combines hydrogen and electricity. Based on the operating costs of renewable energy, maintenance and operation costs of nonrenewable energy, interaction costs between the microgrid and main grid, and pollution control costs, an optimization model for dispatching a hydrogen–electric hybrid microgrid under grid-connected mode is established. The primary objective is to minimize the operating cost, while the secondary objective is to minimize the impact on the user’s power consumption comfort. Therefore, an improved demand response strategy is introduced, and an enhanced sparrow search algorithm (ISSA) is proposed, which incorporates a nonlinear weighting factor and improves the global search capability based on the sparrow search algorithm (SSA). The ISSA is used to solve the optimal operation problem of the demand-response-integrated microgrid. After comparison with different algorithms, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), sooty tern optimization algorithm (STOA), and dingo optimization algorithm (DOA), the results show that the proposed method using demand response and ISSA achieves the lowest comprehensive operating cost for the microgrid, making the microgrid’s operation safer and with minimum impact on user satisfaction. Therefore, the feasibility of the demand response strategy is demonstrated, and ISSA is proved to have better performance in solving optimal operation planning problems for hydrogen–electric hybrid microgrids.

ObjectiveThe goal of this study was to systematically summarize and categorize the syndrome differentiation, medication rules, and acupoint therapy in the domestic traditional Chinese medicine (TCM) literature on type 2 diabetes mellitus (T2DM), such that guidelines and new insights can be provided for future practitioners and researchers.MethodsTaking randomized controlled trials (RCTs) on the treatment of T2DM in TCM as the research theme, we searched for full-text literature in three major clinical databases, including CNKI, Wan Fang, and VIP, published between 1990 and 2020. We then conducted frequency statistics, cluster analysis, association rules extraction, and topic modeling based on a corpus of medical academic words extracted from 3,654 research articles.ResultsThe TCM syndrome types, subjective symptoms, objective indicators, Chinese herbal medicine, acupuncture points, and TCM prescriptions for T2DM were compiled based on invigorating the kidney and Qi, nourishing Yin, and strengthening the spleen. Most TCM syndrome differentiation for T2DM was identified as “Zhongxiao” (the lesion in the spleen and stomach) and “Xiaxiao” (the lesion in the kidney) deficiency syndromes, and most medications and acupoint therapies were focused on the “Spleen Channel” and “Kidney Channel.” However, stagnation of liver Qi was mentioned less when compared with other syndromes, which did not have symptomatic medicines.ConclusionThis study provides an in-depth perspective for the TCM syndrome differentiation, medication rules, and acupoint therapy for T2DM and provides practitioners and researchers with valuable information about the current status and frontier trends of TCM research on T2DM in terms of both diagnosis and treatment.

Microgrid operations planning is one of the keys to ensuring the safe and efficient outputs of distributed energy resources (DERs) and the stable operation of a power system in a microgrid (MG). In this study, for the symmetry in renewable energy and microgrid systems, and coordinated control based on a storage battery system, an MG dispatching model with DER conditions and integrated costs is established in grid-connected mode, on the basis of MG operation costs, interaction costs, and pollutant emissions costs. Moreover, an optimization objective for minimizing integrated costs is established. Therefore, based on the original whale optimization algorithm (WOA), an improved whale optimization algorithm (IWOA) with adaptive weight strategy and Levy flight trajectory is proposed in this paper, to solve the optimal operations planning problem of MGs. Finally, in computing comparisons with methods such as the genetic algorithm (GA), particle swarm algorithm (PSO), WOA, wild horse optimizer (WHO), and enhanced whale optimization algorithm (EWOA), the results show that the IWOA computation had lower integrated costs and higher operational efficiency. Moreover, it is verified that the IWOA performed better in solving the MG operations planning problem.

ABSTRACTInfection with a novel H10N8 influenza virus in humans was first described in China in December 2013, which raised concerns related to public health. This novel virus was subsequently confirmed to have originated from a live poultry market. However, whether this virus can infect other mammals remains unclear. In the present study, antibody specific for H10N8 influenza virus was detected in swine herds in southern China during serological monitoring for swine influenza virus. The pathogenicity and transmissibility of this H10N8 influenza virus to swine was examined. The results showed that swine are susceptible to infection with human-origin H10N8 influenza virus, which causes viral shedding, severe tissue lesions, and seroconversion, while infection with avian-origin H10N8 influenza virus causes only seroconversion and no viral shedding. Importantly, human-origin H10N8 influenza virus can inefficiently be transmitted between swine and cause seroconversion through direct contact. This study provides a new perspective regarding the ecology of H10N8 influenza virus and highlights the importance of epidemiological monitoring of the H10N8 influenza virus in different animal species, which will be helpful for preventing and controlling future infections by this virus.

AimThe aim of this study was to assess the clinical efficacy and safety of Tripterygium-derived glycosides (TG) after 3-month and 6-month of treatments of diabetic nephropathy (DN) and to resolve the conflict between medicine guidance and clinical practice for TG application.MethodsWe conducted a systematic review and meta-analysis of randomized controlled trials involving TG application in treating DN. We extensively searched PubMed, Cochrane Library, CNKI, VIP, Wan-Fang, CBM, Chinese Clinical Trial Registry, and WHO International Clinical Trial Registration Platform till November 2020, along with grey literature for diabetes and all other relevant publications to gather eligible studies. Based on the preset inclusion and exclusion criteria, document screening, quality assessment of methodology, and data extraction was conducted by two researchers independently. The methodological quality was assessed by the Cochrane risk test from the Cochrane Handbook 5.2, and then analyses were performed by Review Manager 5.3 (Rev Man 5.3). The quality of output evidence was classified by GRADE.ResultsThirty-one eligible studies (2764 patients) were included for this meta-analysis. Our study results showed a comparable significant decrease in the 24 h-UTP and blood creatinine levels in DN patients from both 3-month and 6-month TG treatment groups, compared with the routine symptomatic treatment alone. To the contrary of the findings from the included studies, our results showed that the occurrence of serious adverse reaction events was significantly higher in the TG treated group with 6 months of treatment duration compared to that of 3 months of the treatment course. However, the total AR ratio was slightly varied while increasing the percent of severe adverse events. GRADE assessment indicated that the quality of evidence investigating TG-induced adverse reactions was moderate and that for 24 h-UTP and blood creatinine indicators were considerably low.ConclusionCombinatorial treatment regimen including TG can significantly decrease the pathological indicators for DN progression, while it can also simultaneously predispose the patient to a higher risk for developing severe adverse events, as the medicine guidance indicates. Notably, even in 3-month of course duration smaller percent of severe adverse events can get to a fatal high percent and is likely to increase proportionally as the TG treatment continues. This suggests that TG-mediated DN treatment duration should be optimized to even less than 3 continuous months to avoid adverse event onset-associated further medical complications in DN patients. In clinical practice, serious attention should be paid to these severe side-effects even in a course normally considered safe, and importantly more high-quality studies are urgently warranted to obtain detailed insights into the balance between the efficacy and safety profiles of TG application in treating DN.

Exoskeletons are increasingly used in rehabilitation and daily life in patients with motor disorders after neurological injuries. In this paper, a realistic human knee exoskeleton model based on a physical system was generated, a human–machine system was created in a musculoskeletal modeling software, and human–machine interactions based on different assistive strategies were simulated. The developed human–machine system makes it possible to compute torques, muscle impulse, contact forces, and interactive forces involved in simulated movements. Assistive strategies modeled as a rotational actuator, a simple pendulum model, and a damped pendulum model were applied to the knee exoskeleton during simulated normal and fast gait. We found that the rotational actuator–based assistive controller could reduce the user's required physiological knee extensor torque and muscle impulse by a small amount, which suggests that joint rotational direction should be considered when developing an assistive strategy. Compared to the simple pendulum model, the damped pendulum model based controller made little difference during swing, but further decreased the user's required knee flexor torque during late stance. The trade-off that we identified between interaction forces and physiological torque, of which muscle impulse is the main contributor, should be considered when designing controllers for a physical exoskeleton system. Detailed information at joint and muscle levels provided in this human–machine system can contribute to the controller design optimization of assistive exoskeletons for rehabilitation and movement assistance.

The crystallinity and acoustic-vibration parameters of Picea jezoensis, including specific Young’s modulus (E/ρ),coefficient of sound-radiation resistance (R), sound resistance (ω), and the ratio of Young’s modulus to the dynamic stiffness modulus (E/G), before and after heat treatment were measured and characterized. Conditions for the heat treatment included N2 as the protection gas and temperatures of 170 °C, 190 °C, and 210 °C with holding times of 2 h, 3 h, and 4 h. The results showed that specific Young’s modulus, the coefficient of sound-radiation resistance, and the ratio of Young’s modulus to the dynamic stiffness modulus increased, whereas sound resistance decreased, thereby improving the acoustic performance of the wood. The maximum increments were 5.7% for specific Young’s modulus (210 °C, 3 h), 8.8% for the coefficient of sound-radiation resistance (210 °C, 3 h), and 13.8% for the ratio of Young’s modulus to the dynamic stiffness modulus (210 °C, 4 h). Conversely, the maximum decrease in sound resistance was 5.6% (170 °C, 2 h). The crystallinity of heat-treated samples universally increased, and the maximum reached 60.67% (210 °C, 4 h), which was 9.9% higher than that of the control group. Moreover, the sound resistance decreased within increasing crystallinity growth, indicating that these two parameters were negatively correlated. Overall, the acoustic-vibration performance of P. jezoensis was improved through heat treatment, with the best vibration performance obtained at 210 °C with a holding time of 4 h.

This study demonstrates the preparation, structure, and properties of polymer-impregnated wood (PIW) based on novel Muchelia macclurei wood through the in-situ polymerization of vinyl and functional monomers. Mixed monomers of glycidyl methacrylate (GMA) and ethylene glycol dimethacrylate (EGDMA) (2:1 molar ratio) were effectively vacuum/pressure impregnated into the cellular structure of the fast-growing wood, and then they underwent a catalytic-thermal polymerization process to form a polymer-impregnated wood composite. Scanning electron microscope (SEM) observations showed that the polymer formed from the monomers occupying the cell lumens and formed tight bonds with the wood substrate. Fourier transform infrared (FTIR) microscopy analysis indicated that the in-situ generated polymer probably chemical bonded to the wood cell wall. The resultant PIW exhibited remarkably improved mechanical properties and durability relative to untreated wood. This was attributed to the possible grafting of the polymer onto the wood cell wall and the resultant reinforcement of the wood by the polymer.

Background: Prior research has indicated a potential connection between psychological stress and how individuals perceive their own age. Building on this foundation, the current study explores the relationship between negative emotions and self-perceived age. Methods: We conducted a cross-sectional analysis using data from the UK Biobank, a comprehensive cohort study representing the UK population. The analysis included 347 892 participants, aged between 39 and 73 years, of which 184 765 were women, accounting for 53.1% of the sample. Participants were categorized into three groups based on their self-perceived age: feeling younger than their chronological age (group Younger), feeling older than their chronological age (group Older), and feeling as old as their actual age (group Same). To investigate the relationship between negative emotions and self-perceived age, we utilized a multinomial logistic regression model with the Younger group serving as the reference category. Results: Of 347 892 participants, after adjusted for covariates, the results showed that participants with irritability, nervous feelings, worrier/anxious feelings or fed-up feelings, worry too long and loneliness/isolation are more likely to be rated as "about your age" or "older than you are," with "younger than you are" as the reference group, indicating that negative emotions may influence one's self-perceived age. Among those negative emotions, irritability has the most significant impact self-perceived age, with the odds ratios (ORs) being 1.44 (95% CI: 1.35-1.54) and 1.11 (95% CI: 1.09-1.14). Conclusion: Negative emotions are associated with older self-perceived age, and irritability has the greatest impact. Further studies analyzing self-perceived age are needed to take psychological factors into consideration. [ABSTRACT FROM AUTHOR]