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Abstract Background Acute gouty arthritis (AGA) is classified as ‘arthritis’ in traditional Chinese medicine (TCM) theory. Shirebi granules (SGs), derived from the classic prescription SiMiaoWan, exerts satisfying therapeutic efficacy in ameliorating AGA clinically. However, the underlying mechanisms of SGs against AGA remain unclarified. Methods AGA-related biological processes, signal pathways and biomarker genes were mined from the GEO database through bioinformatics. SGs components were systematically recognized using the UPLC-Q-TOF–MS/MS. A correlation network was established based on the biomarker genes and the chemical components, from which the signal pathway used for further study was selected. Finally, we established an AGA model using SD rats injected with monosodium urate (MSU) in the ankle joint for experimental validation. A combination of behavioral tests, H&E, safranin O- fast green, western blotting, and immunofluorescence were employed to reveal the mechanism of action of SGs on AGA. Results The deterioration of AGA was significantly related to the imbalance between immunity and inflammation, neutrophil chemotaxis and inflammatory factor activation. HDAC5, PRKCB, NFκB1, MPO, PRKCA, PIK3CA were identified to be the candidate targets of SGs against AGA, associated with neutrophil extracellular traps (NETs) signal pathway. Animal experiments demonstrated that SGs effectively repaired cartilage damage, blocked TLR4 activation, and inhibited the expression of NETs indicators and inflammatory factors. In addition, SGs prominently alleviated joint redness and swelling, improved joint dysfunction, inhibited inflammatory infiltration of AGA rats. Conclusion Our data reveal that SGs may effectively alleviate the disease severity of AGA by suppressing NETs-promoted imbalance between immunity and inflammation.

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

Objective Tension-type headache is the most common type of primary headache and results in a huge socioeconomic burden. This network meta-analysis (NMA) aimed to compare the efficacy and safety of simple analgesics for the treatment of episodic tension-type headache (ETTH) in adults.Methods We searched the Cochrane Library, PubMed, Web of Science, Embase, Chinese BioMedical Literature database and International Clinical Trials Registry Platform databases for eligible randomized clinical trials reporting the efficacy and/or safety of simple analgesics. A Bayesian NMA was performed to compare relative efficacy and safety. The surface under the cumulative ranking curve (SUCRA) was calculated to rank interventions. PROSPERO registration number: CRD42018090554.Results We highlighted six studies including 3507 patients. For the 2 h pain-free rate, the SUCRA ranking was ibuprofen > diclofenac-K > ketoprofen > acetaminophen > naproxen > placebo. All drugs except naproxen reported a higher 2 h pain-free rate than placebo, with a risk ratio (RR) of 2.86 (95% credible interval, CrI: 1.62–5.42) for ibuprofen and 2.61 (1.53–4.88) for diclofenac-K. For adverse events rate, the SUCRA ranking was: metamizol > diclofenac-K > ibuprofen > lumiracoxib > placebo > aspirin > acetaminophen > naproxen > ketoprofen. The adverse event rates of all analgesics were no higher than those of placebo, except for ketoprofen. Moreover, all drugs were superior to placebo in the global assessment of efficacy. In particular, the RR of lumiracoxib was 2.47 (1.57–4.57). Global heterogeneity I2 between the studies was low.Conclusions Simple analgesics are considered more effective and safe as a placebo for ETTH in adults. Our results suggest that ibuprofen and diclofenac-K may be the two best treatment options for patients with ETTH from a comprehensive point of view (both high-quality evidence).

Abstract Background In Singapore, there is currently scarce population-based research informing the recent trends of asthma-related healthcare burdens. In this study, we investigated the past 25-year trends of asthma-related hospitalisations, emergency department (ED) visits and deaths in Singapore and projected the future burdens from 2023 to 2040. Methods We acquired annually-measured data from the Singapore Ministry of Health Clinical and National Disease Registry, containing 25-year asthma-related hospitalisation and death rates as well as 15-year ED visit rates. We conducted change-point analysis and generalised linear modelling to identify time intervals with stable trends and estimate asthma-related healthcare utilisation and mortality rates. To project future asthma-related burdens, we developed a probabilistic model which combined projections of future population size with the estimated rate outcomes from the last stable period. Results Our results show that the asthma hospitalisation rate in Singapore had remained at approximately 80 episodes per 100,000 from 2003 to 2019 and are likely to grow by 1.7% each year (95% CI: 0.7, 5.0%), leading to a total of 163,633 episodes from 2023 to 2040 which corresponds to an estimated $103,075,820 based on 2022 USD. Besides, Singapore’s asthma-related ED visit rate was 390 per 100,000 in 2019 and is expected to decline by 3.4% each year (95% CI: − 5.8, 0.0%), leading to a total of 208,145 episodes from 2023 to 2040 which corresponds to USD$15,053,795. In contrast, the 2019 asthma-related mortality rate in Singapore was approximately 0.57 per 100,000 and is likely to stay stably low (change per year: -1.3, 95% CI: − 11.0, 4.3%). Between 2023 and 2040, Singapore’s estimated total number of asthma-related deaths is 638 episodes. Conclusions Currently, the burden of asthma acute care in Singapore is high; Singapore’s asthma-related hospitalisation and ED visit rates are relatively higher than those of other developed economies, and its asthma admission rate is expected to increase significantly over time, possibly indicating excess resource use for asthma. The established national asthma programme in Singapore, together with recent efforts in reinforcing primary care at the national level, provides opportunities to reduce avoidable asthma admissions.

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

BackgroundTo date, studies investigating the association between pre-biologic biomarker levels and post-biologic outcomes have been limited to single biomarkers and assessment of biologic efficacy from structured clinical trials.AimTo elucidate the associations of pre-biologic individual biomarker levels or their combinations with pre-to-post biologic changes in asthma outcomes in real-life.MethodsThis was a registry-based, cohort study using data from 23 countries, which shared data with the International Severe Asthma Registry (May 2017-February 2023). The investigated biomarkers (highest pre-biologic levels) were immunoglobulin E (IgE), blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO). Pre- to approximately 12-month post-biologic change for each of three asthma outcome domains (i.e. exacerbation rate, symptom control and lung function), and the association of this change with pre-biologic biomarkers was investigated for individual and combined biomarkers.ResultsOverall, 3751 patients initiated biologics and were included in the analysis. No association was found between pre-biologic BEC and pre-to-post biologic change in exacerbation rate for any biologic class. However, higher pre-biologic BEC and FeNO were both associated with greater post-biologic improvement in FEV1 for both anti-IgE and anti-IL5/5R, with a trend for anti-IL4Rα. Mean FEV1 improved by 27-178 mL post-anti-IgE as pre-biologic BEC increased (250 to 1000 cells/µL), and by 43-216 mL and 129-250 mL post-anti-IL5/5R and -anti-IL4Rα, respectively along the same BEC gradient. Corresponding improvements along a FeNO gradient (25-100 ppb) were 41-274 mL, 69-207 mL and 148-224 mL for anti-IgE, anti-IL5/5R, and anti-IL4Rα, respectively. Higher baseline BEC was also associated with lower probability of uncontrolled asthma (OR 0.392; p=0.001) post-biologic for anti-IL5/5R. Pre-biologic IgE was a poor predictor of subsequent pre-to-post-biologic change for all outcomes assessed for all biologics. The combination of BEC + FeNO marginally improved the prediction of post-biologic FEV1 increase (adjusted R2: 0.751), compared to BEC (adjusted R2: 0.747) or FeNO alone (adjusted R2: 0.743) (p=0.005 and

Physical exercise can significantly impact our bodies, affecting our functional capacity, structure establishment, and molecular makeup. The magnitude of these changes depends on the specific exercise protocols used. For instance, low-to-moderate-intensity exercise can activate important molecular targets in the short term, such as BDNF-mediated signaling, while high-intensity exercise can maintain these signaling molecules in the active state for a longer term. This makes it challenging to recommend specific exercises for obtaining BDNF-induced benefits. Additionally, exercise-induced molecular signaling targets can have positive and negative effects, with some exercises blunting these targets and others activating them. For example, increasing BDNF concentration through exercise can be beneficial for brain health, but it may also have a negative impact on conditions such as bipolar disorder. Therefore, a deeper understanding of a specific exercise-mediated mechanistic approach is required. This review will delve into how the sprint exercise-mediated activation of BDNF could help maintain brain health and explore potential molecular interventions.

Existing traditional Chinese medicine (TCM)-related databases are still insufficient in data standardization, integrity and precision, and need to be updated urgently. Herein, an Encyclopedia of Traditional Chinese Medicine version 2.0 (ETCM v2.0, http://www.tcmip.cn/ETCM2/front/#/) was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books, 9872 Chinese patent drugs, 2079 Chinese medicinal materials and 38,298 ingredients. To facilitate the mechanistic research and new drug discovery, we improved the target identification method based on a two-dimensional ligand similarity search module, which provides the confirmed and/or potential targets of each ingredient, as well as their binding activities. Importantly, five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0, which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy, to summarize the rules of prescription use, and to find alternative drugs for endangered Chinese medicinal materials. Moreover, ETCM v2.0 provides an enhanced JavaScript-based network visualization tool for creating, modifying and exploring multi-scale biological networks. ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs, the TCM-derived drug discovery and repurposing, and the pharmacological mechanism investigation of TCMs against various human diseases.

Background: Longitudinal predictors of persistent poor asthma control in severe asthma (SA) cohort remain scarce. The predictive value of the asthma severity scoring system (ASSESS) in the SA cohort outside the original study and in the Asian population is unknown. Objective: We sought to determine the 5-year longitudinal outcome of patients with SA and validate the use of ASSESS score in predicting future outcomes in SA. Methods: A prospective longitudinal observational study of patients with SA attending the multidisciplinary specialist SA clinic of the Singapore General Hospital from 2011 to 2021 was conducted. The number of exacerbations and asthma control test results were recorded yearly for 5 consecutive years. The ASSESS score was computed at baseline, and the area under the receiver-operating characteristic curve for predicting persistent poor asthma control was generated. Results: Of the 489 patients recruited into the study, 306 patients with 5-year follow-up data were analyzed. Seventy-three percent had type 2 inflammation with increased overall exacerbations over 5 years (rate ratio, 2.55; 95% CI, 1.31-4.96; P = .006) relative to non–type 2 SA. In the multivariate model, bronchiectasis, gastroesophageal reflux disease, and an asthma control test score of less than 20 were significantly associated with persistent poor asthma control over 5 years. ASSESS scores were good at predicting persistent poor asthma control with an area under the receiver-operating characteristic curve of 0.71 (95% CI, 0.57-0.84). Conclusions: Bronchiectasis and gastroesophageal reflux disease are predictors for persistent poor asthma control and targeted traits for precision medicine in SA. The ASSESS score has a good prediction for persistent poor asthma control over 5 years.

ObjectiveThe aims of this study were to evaluate the quality of osteoporosis guidelines on traditional Chinese medicine (TCM) drug therapies and to analyze the specific recommendations of these guidelines.MethodsWe systematically collected guidelines, evaluated the quality of the guidelines using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool, and summarized the recommendations of TCM drug therapies using the Patient-Intervention-Comparator-Outcome (PICO) model as the analysis framework.Results and conclusionsA total of 20 guidelines were included. Overall quality evaluation results revealed that four guidelines were at level A, four at level B, and 12 at level C, whose quality needed to be improved in the domains of “stakeholder involvement”, “rigor of development”, “applicability” and “editorial independence”. Stratified analysis suggested that the post-2020 guidelines were significantly better than those published before 2020 in the domains of “scope and purpose”, “stakeholder involvement” and “editorial independence”. Guidelines with evidence systems were significantly better than those without evidence systems in terms of “stakeholder involvement”, “rigor of development”, “clarity of presentation” and “applicability”. The guidelines recommended TCM drug therapies for patients with osteopenia, osteoporosis and osteoporotic fracture. Recommended TCM drugs were mainly Chinese patent medicine alone or combined with Western medicine, with the outcome mainly focused on improving bone mineral density (BMD).

Abstract Background Individualized prediction of treatment response may improve the value proposition of advanced treatment options in severe asthma. This study aimed to investigate the combined capacity of patient characteristics in predicting treatment response to mepolizumab in patients with severe asthma. Methods Patient-level data were pooled from two multinational phase 3 trials of mepolizumab in severe eosinophilic asthma. We fitted penalized regression models to quantify reductions in the rate of severe exacerbations and the 5-item Asthma Control Questionnaire (ACQ5) score. The capacity of 15 covariates towards predicting treatment response was quantified by the Gini index (measuring disparities in treatment benefit) as well as observed treatment benefit within the quintiles of predicted treatment benefit. Results There was marked variability in the ability of patient characteristics to predict treatment response; covariates explained greater heterogeneity in predicting treatment response to asthma control than to exacerbation frequency (Gini index 0.35 v. 0.24). Key predictors for treatment benefit for severe exacerbations included exacerbation history, blood eosinophil count, baseline ACQ5 score and age, and those for symptom control included blood eosinophil count and presence of nasal polyps. Overall, the average reduction in exacerbations was 0.90/year (95%CI, 0.87‒0.92) and average reduction in ACQ5 score was 0.18 (95% CI, 0.02‒0.35). Among the top 20% of patients for predicted treatment benefit, exacerbations were reduced by 2.23/year (95% CI, 2.03‒2.43) and ACQ5 score were reduced by 0.59 (95% CI, 0.19‒0.98). Among the bottom 20% of patients for predicted treatment benefit, exacerbations were reduced by 0.25/year (95% CI, 0.16‒0.34) and ACQ5 by -0.20 (95% CI, -0.51 to 0.11). Conclusion A precision medicine approach based on multiple patient characteristics can guide biologic therapy in severe asthma, especially in identifying patients who will not benefit as much from therapy. Patient characteristics had a greater capacity to predict treatment response to asthma control than to exacerbation. Trial registration ClinicalTrials.gov number, NCT01691521 (registered September 24, 2012) and NCT01000506 (registered October 23, 2009).

Abstract Background Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy. Methods An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot. Results The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot. Conclusions The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

ObjectiveThis study aimed to quantify heterogeneity in the value for money of precision medicine (PM) by application types across contexts and conditions and to quantify sources of heterogeneity to areas of particular promises or concerns as the field of PM moves forward.MethodsA systemic search was performed in Embase, Medline, EconLit, and CRD databases for studies published between 2011 and 2021 on cost-effectiveness analysis (CEA) of PM interventions. Based on a willingness-to-pay threshold of one-time GDP per capita of each study country, the net monetary benefit (NMB) of PM was pooled using random-effects meta-analyses. Sources of heterogeneity and study biases were examined using random-effects meta-regressions, jackknife sensitivity analysis, and the biases in economic studies checklist.ResultsAmong the 275 unique CEAs of PM, publicly sponsored studies found neither genetic testing nor gene therapy cost-effective in general, which was contradictory to studies funded by commercial entities and early stage evaluations. Evidence of PM being cost-effective was concentrated in a genetic test for screening, diagnosis, or as companion diagnostics (pooled NMBs, $48,152, $8,869, $5,693, p

Summary: Tianhe Zhuifeng Gao (TZG) shows a satisfying therapeutic efficacy in treating arthromyodynia, which shares similar etiology to myofascial pain syndrome (MPS). We herein aim to explore whether TZG could be a potential prescription for MPS therapy. An MPS rat model was successfully established presenting with reduced pain thresholds, abnormal local switch responses, etc., which was effectively reversed by TZG treatment externally. A transcriptome sequencing based on the active MTrPs samples of rats, combined with network analysis revealed that TZG might ameliorate the progression of MPS by impairing neutrophil extracellular traps (NETs) release through inhibiting PI3K-RAC2 signaling to reduce NADPH oxidase-originated ROS. Experimentally, the expression levels of inducers, biomarkers of NETs formation and vessel injury, and p-PI3K, p-P47, and RAC2 proteins were all significantly up-regulated in affected tissues, which were markedly reversed by TZG. Our results not only shed light into broadening the clinical indications of TZG, but benefit MPS therapy.

Abstract A novel active fiber cavity ringdown (FCRD) technique using frequency-shifted interferometry (FSI) is proposed for the first time. Using this scheme, external parameters can be monitored in the space domain by measuring the ringdown distance instead of ringdown time. A bidirectional erbium-doped fiber amplifier (Bi-EDFA) is employed to compensate the inherent cavity loss for achieving higher sensitivity. And two band-pass filters are used to reduce the amplified spontaneous emission (ASE) noise of the Bi-EDFA. Compared with the well-known time-domain active FCRD scheme, our proposed method enables us to avoid using pulsed laser needed in time-domain active FCRD, it uses continuous-wave laser to inject into the fiber cavity and stabilize the optical power in the fiber cavity, which can suppress the baseline drift of ringdown signal caused by the gain fluctuations of the EDFA and thus improve the detecting precision. Moreover, this novel method enables us to use differential detection method for further reducing the ASE noise, and thus eliminating the baseline drift of ringdown signal. A magnetic field sensor was developed as a proof-of-concept demonstration. The experimental results demonstrate that the proposed sensor with a sensitivity of 0.01537 (1/km·Gs) was achieved. This is the highest magnetic field sensitivity compared to the time-domain active FLRD method. Due to the reduced ASE noise, the stability of the proposed sensing system was also greatly improved.

In order to deal with the volatility caused by the high proportion of renewable energy, Europe has established a cross-regional balancing market. The European experience and practices in the cross-regional balancing market can bring reference for the construction of China’s power market. To this end, this article conducts an in-depth study on the operation mode and settlement of the unified European balance market, and reviews the key issues and the corresponding solutions faced by the settlement level in the European integration of the balancing markets of various countries. Finally, founded on the actual situation of China’s inter-regional power market, the European balancing market is proposed for the construction of China’s inter-regional market.

Introduction Severe asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool.Methods and analysis The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds.Ethics and dissemination This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal.Trial registration number European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).

Abstract Background The economic burden of interstitial lung disease (ILD) is unknown, limiting informed resource allocation and planning. We sought to conduct the first systematic review on the direct, indirect, and overall costs associated with ILD and to evaluate the cost-effectiveness of current therapies globally. Methods We conducted systematic reviews of ILD disease cost studies and cost-effectiveness analyses (CEAs) using MEDLINE, Embase, and Web of Science databases between 2000 and 2020. We compared ILD costs between countries according to the share of costs towards each country’s respective gross domestic product (GDP) per capita. Costs are reported in 2020 USD. Results We identified 25 disease cost studies and 7 CEAs. The direct medical costs ranged between $1824 and $116,927 annually per patient (median $32,834; 14–180% of GDP per capita in Western countries). The leading drivers of direct costs were inpatient (55%), outpatient (22%), and medication costs (18%), based on pooled estimates. Annual indirect costs ranged from $7149 to $10,902 per employed patient (median $9607; 12–23% of GDP per capita). Among the 7 CEAs, only 1 study (14%) showed an ILD therapy (ambulatory oxygen) was cost-effective compared to best supportive care. Conclusion The direct and indirect costs associated with ILD are consistently high in all countries with available data, with cost-effectiveness profiles of new therapies generally undesirable. Globally, the median total direct cost for ILD equates to 51% of a country’s GDP per capita and has been increasing over time.

Butyrate, produced by gut microbe during dietary fiber fermentation, has anti-inflammatory and antioxidant effects on chronic inflammation diseases, yet it remains to be explored whether butyrate has protective effects against viral infections. Here, we demonstrated that butyrate alleviated tissue injury in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected golden hamsters supplemented with butyrate before and during the infection. Butyrate-treated hamsters showed augmentation of type I interferon (IFN) response and activation of endothelial cells without exaggerated inflammation. In addition, butyrate regulated redox homeostasis by enhancing the activity of superoxide dismutase (SOD) to inhibit excessive apoptotic cell death. Therefore, butyrate exhibited effective prevention against SARS-CoV-2 by upregulating antiviral immune responses and promoting cell survival.

Abstract Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.

Abstract Background Low body weight is associated with poor prognosis in patients with chronic obstructive pulmonary disease (COPD). However, it is not known whether gender modifies this relationship. Methods We pooled data of 8686 COPD patients from 7 studies with a median length of 36-months of follow up. Using a longitudinal natural cubic spline regression model, we examined the dose–response relationship between body mass index (BMI) and the rate of decline in forced expiratory volume in one second (FEV1) in patients with GOLD 1 and 2 disease, stratified by gender and adjusted for age, smoking status, and cohort effects. Results There was an inverse linear relationship between BMI and the rate of FEV1 decline in GOLD Grades 1 and 2, which was modified by gender (p

Introduction Precision medicine (PM) involves gene testing to identify disease risk, enable early diagnosis or guide therapeutic choice, and targeted gene therapy. We aim to perform a systematic review and meta-analysis to quantify the cost-effectiveness profile of PM stratified by intervention type, identify sources of heterogeneity in the value-for-money of PM.Methods and analysis We will perform a systematic search in Embase, MEDLINE, EconLit and CRD databases for studies published in English language or with translation in English between 1 January 2011 and 8 July 2021 on the topic of cost-effectiveness analysis of PM interventions. The focus will be on studies that reported health and economic outcomes. Study quality will be assessed using the Biases in Economic Studies checklist. The incremental net benefit of PM screening, diagnostic, treatment-targeting and therapeutic interventions over conventional strategies will be respectively pooled across studies using a random-effect model if heterogeneity is present, otherwise a fixed-effect model. Subgroup analyses will be performed based on disease area, WHO region and World Bank country-income level. Additionally, we will identify the potential sources of heterogeneity with random-effect meta-regressions. Finally, biases will be detected using jackknife sensitivity analysis, funnel plot assessment and Egger’s tests.Ethics and dissemination For this type of study ethics approval or formal consent is not required. The results will be disseminated at various presentations and feedback sessions, in conference abstracts and manuscripts that will be submitted to peer-reviewed journals.PROSPERO registration number CRD42021272956.

The study’s objective is to clarify the influence of drop height on elastic energy’s utilization of the lower extremity, to indicate the correlations between elastic energy’s utilization and personal best, and to determine the optimal loading height for elastic energy’s utilization for male high jumpers. Ten male athletes who belong to high jump events work out the drop jump at different drop heights (0.3 m, 0.45 m, 0.6 m, and 0.75 m). Two AMTI force platforms were used to capture the dynamics data for the lower extremity. Drop height has obvious influence on utilization ratio for elastic energy (P0.05). In this study, the optimal loading height for utilization ratio of elastic energy was 0.75 m. The optimal loading height can be determined in terms of the elastic energy utilization ratio for each high jumper to enhance their training effects.

Accurate demand forecast can help improve teleconsultation efficiency. But teleconsultation demand forecast has not been reported in existing literature. For this purpose, the study proposes a novel model based on deep learning algorithm for daily teleconsultation demand forecast to fill in the research gap. Because of the significant effect of holidays on teleconsultation demand, holiday-related variables, and specific prediction technologies were selected to treat it. The technologies attention mechanism and bidirectional long short-term memory (BILSTM) were used to construct a novel forecasting methodology, i.e., ensemble attention-based BILSTM (EA-BILSTM), for the accurate forecasts. Based on actual teleconsultation data, the effectiveness of variable selection is verified by importing different inputs into models, and the superiority of EA-BILSTM is verified by comparison with nine benchmark models. Empirical results show that importing selected variables can lead to better forecasts and EA-BILSTM model can get lowest forecasting errors on two sub-datasets. This indicates that the proposed forecasting model is a high potential approach for teleconsultation demand prediction in the influence of sparse trait, like holiday effects.

Abstract Background To identify biomarkers for guiding therapy and predicting clinical response of Tripterysium Glycosides Tablets (TGT) treatment is an urgent task due to individual differences in TGT response across rheumatoid arthritis (RA) patients. Competing endogenous RNA (ceRNA) regulatory system may influence drug response with involvement in diverse biological processes. Herein, we aimed to identify a TGT response‐related ceRNA axis. Methods A TGT response‐related ceRNA axis was screened according to clinical cohort‐based RNA expression profiling, lncRNA‐mRNA coexpression, and ceRNA network analyses. Its clinical relevance was evaluated by computational modeling. Regulatory mechanisms of ceRNA axis were also experimentally investigated. Results The ceRNA regulatory axis combined with lncRNA ENST00000494760, miR‐654‐5p, and C1QC was identified as a candidate biomarker for RA patients' response to TGT. Both ENST00000494760 and C1QC mRNA expression were significantly lower, while miR‐654‐5p expression was dramatically higher in TGT responders than nonresponders. Its clinical relevance was verified by computational modeling based on both independent clinical validation cohort and collagen‐induced arthritis (CIA) mice. Mechanistically, miR‐654‐5p directly bound to the 3′‐untranslated region of both ENST00000494760 and C1QC mRNA to inhibit their expression. Moreover, miR‐654‐5p suppressed C1QC mRNA expression, but ENST00000494760 bound to miR‐654‐5p and relieved its repression on C1QC mRNA, leading to RA aggressive progression and weak TGT response. Conclusions LncRNA ENST00000494760 overexpression may sponge miR‐654‐5p to promote C1QC expression in RA patients. This novel ceRNA axis may serve as a biomarker for screening the responsive RA patients to TGT treatment, which will allow improved personalized healthcare.

Abstract Tie luo (affixed hanging) was an expressive form of traditional Chinese calligraphy or paintings, and popular in the imperial palaces for interior decorations in Qing Dynasty (1644–1911 C.E.). A piece of calligraphic tie luo, written by an eminent calligrapher Gu Gao (1763–1832 C.E.), was restored recently in the Palace Museum, Beijing, China. The paper with pigment coating specially made for this calligraphic piece was named as fenjian. This article presented results from materials identification prior to the restoration. Multianalysis with scientific approaches revealed how tie luo was manufactured. It could be concluded that the investigated tie luo used a paper made from bast fibers of mulberry trees. And the decorative ground layer for the piece was also studied, which indicated that a type of organic red pigments were used for the masterpiece. White lead was considered as the main pigment, mixed with a red dye extracted from sappanwood. Meanwhile, animal glue, drying oils and beeswax were confirmed as the organic binding media. In dye analysis, protosappanin B and brazilin as well as brazilein were identified, which implied that sappanwood was used for the organic manufacture of pigments. In addition, both Nowik type A and C were found in the research, which were characteristic of sappanwood for identification.

To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. The proposed ensemble CNN attention-based BILSTM model (ECA-BILSTM) combines shallow convolutional neural networks (CNNs), attention mechanisms, and bidirectional long short-term memory (BILSTM). Moreover, additional variables are selected according to the characteristics of teleconsultation demand and added to the inputs of forecasting models. To verify the superiority of ECA-BILSTM and the effectiveness of additional variables, two actual teleconsultation datasets collected in the National Telemedicine Center of China (NTCC) are used as the experimental data. Results showed that ECA-BILSTMs can significantly outperform corresponding benchmark models. And two key additional variables were identified for teleconsultation demand prediction improvement. Overall, the proposed ECA-BILSTM model with effective additional variables is a feasible promising approach in teleconsultation demand forecasting.

Detection of characteristic species released from over-heated PVC cables may enable early warning of electrical fire. This work identified the major volatile species for over-heated PVC cables, and verified their potential as fire signatures with metal oxide gas sensors. Semi-volatile Dioctyl phthalate (DOP) and 2-Ethylhexanol (2-EH) were found to be ubiquitously present in the cable vapors as major species. Detection of these species and the vapors of overheated cables was accomplished with a metal oxide gas sensor. The response of the gas sensor to the cable vapors resulted mainly from DOP and 2-EH, demonstrating them as effective signature gases for over-heated PVC cables. A gas sensor based on SnO2 nanofibers was prepared with greatly enhanced response to the signature gases. Large-scale simulation tests showed that the nanofiber gas sensor could effectively detect the cable overheating at an early stage.

Increasing evidence suggests that the NLRP3 (nucleotide oligomerization domain-like receptor family, pyrin domain containing 3) inflammasome participates in cardiovascular diseases. However, its role and activation mechanism during hypertension remains unclear. In this study, we tested the role and mechanism of calcium-sensing receptor (CaSR) in NLRP3 inflammasome activation during hypertension. We observed that the expressions of CaSR and NLRP3 were increased in spontaneous hypertensive rats (SHRs) along with aortic fibrosis. In vascular smooth muscle cells (VSMCs), the activation of NLRP3 inflammasome associated with CaSR and collagen synthesis was induced by angiotensin II (Ang II). Furthermore, inhibition of CaSR and NLRP3 inflammasome attenuated proinflammatory cytokine release, suggesting that CaSR-mediated activation of the NLRP3 inflammasome may be a therapeutic target in aortic dysfunction and vascular inflammatory lesions.

BACKGROUND:Bronchial thermoplasty (BT) is a recently developed treatment for patients with moderate-to-severe asthma. A few studies have suggested the clinical efficacy of this intervention. However, no study has evaluated the cost-effectiveness of BT compared to other alternative treatments for moderate-to-severe allergic asthma, which currently include omalizumab and standard therapy. OBJECTIVE:To evaluate the cost-effectiveness of standard therapy, BT, and omalizumab for moderate-to-severe allergic asthma in the USA. METHODS:A probabilistic Markov model with weekly cycles was developed to reflect the course of asthma progression over a 5-year time horizon. The study population was adults with moderate-to-severe allergic asthma whose asthma remained uncontrolled despite using high-dose inhaled corticosteroids (ICS, with or without long-acting beta-agonists [LABA]). A perspective of the health-care system was adopted with asthma-related costs as well as quality-adjusted life years (QALYs) and exacerbations as the outcomes. RESULTS:For standard therapy, BT, and omalizumab, the discounted 5-year costs and QALYs were $15,400 and 3.08, $28,100 and 3.24, and $117,000 and 3.26, respectively. The incremental cost-effectiveness ratio (ICER) of BT versus standard therapy and omalizumab versus BT was $78,700/QALY and $3.86 million/QALY, respectively. At the willingness-to-pay (WTP) of $50,000/QALY and $100,000/QALY, the probability of BT being cost-effective was 9%, and 67%, respectively. The corresponding expected value of perfect information (EVPI) was $155 and $1,530 per individual at these thresholds. In sensitivity analyses, increasing the costs of BT from $14,900 to $30,000 increased its ICER relative to standard therapy to $178,000/QALY, and decreased the ICER of omalizumab relative to BT to $3.06 million/QALY. Reducing the costs of omalizumab by 25% decreased its ICER relative to BT by 29%. CONCLUSIONS:Based on the available evidence, our study suggests that there is more than 60% chance that BT becomes cost-effective relative to omalizumab and standard therapy at the WTP of $100,000/QALY in patients with moderate-to-severe allergic asthma. However, there is a substantial uncertainty in the underlying evidence, indicating the need for future research towards reducing such uncertainty.