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Abstract The suppressors of cytokine signaling 2 (SOCS2) inhibits growth hormone receptor (GHR) signaling by negative feedback in the regulation of metabolism. In this study, we found that GHR upregulates SOCS2 expression, whereas KIT mutations, the key driver mutations of gastrointestinal stromal tumors (GISTs), inhibits SOCS2 expression in GISTs. Furthermore, SOCS2 associated and inhibited the activation of KIT mutations, but not wild-type KIT, in addition to its inhibition of GHR signaling, suggesting that KIT mutations may promote their activation by downregulation of SOCS2 expression. Accordingly, SOCS2 inhibited GIST cell survival and proliferation in vitro. In KITV558A/WT mice, knockout of SOCS2 expression increased the tumorigenesis of GISTs and decreased the life span of the mice. In addition, the presence of SOCS2 increased the inhibition of KIT signaling and GIST cell survival and proliferation by imatinib in vitro, and imatinib treatment further reduced tumor growth in KITV558A/WT mice compared with that in KITV558A/WT/SOCS2-/- mice, indicating the key role of SOCS2 in the sensitivity of GISTs to the targeted therapy. Taken together, our data revealed the key role of SOCS2 in the tumorigenesis of GISTs and the sensitivity of GISTs to the targeted therapy, providing a better basis for the improved treatment strategy.

Abstract Although the triglyceride-glucose (TyG) index has been established as a valuable predictor for cardiovascular disease (CVD) and cardiovascular mortality, there is limited research exploring its association with all-cause or CVD mortality specifically in adults with diabetes aged

Deep rocks encountered in underground engineering are frequently in complex in situ environments and experience both excavation disturbance during construction and cyclic loading throughout the long-term operation. Understanding the fatigue behavior of excavation-disturbed rocks in complex stress environments is critical for assessing the long-term stability of deep rock structures. Hence, an experimental method has been developed to capture the fatigue damage process of rocks while considering the in situ environment and excavation disturbance. Using this method, a series of triaxial fatigue damage experiments were conducted on Jinping deep marble samples from various in situ environments of 100 m, 1000 m, 1800 m, and 2400 m to better understand the variation in fatigue characteristics at different depths. With increasing depth, the samples experienced more cycles and greater fatigue deformation before failure. Further insights were gained into the fatigue damage behavior in terms of stiffness degradation, energy dissipation and irreversible strain accumulation. A decrease in the elastic modulus and an increase in the dissipated energy and irreversible strain exhibit an evolution pattern of initial→stabilization→acceleration, reflecting the nonlinear fatigue process that occurs inside marble. With increasing depth, marble samples have longer fatigue lives but exhibit more significant stiffness loss, energy dissipation and irrecoverable deformation accumulation; thus, evaluating the instability of deep rock structures solely using fatigue life alone is inadequate. Moreover, the previously reported inverted S-shaped evolution of fatigue damage was observed, and it was found that an increase in depth leads to an earlier onset of the accelerated fatigue damage stage with greater dominance of fatigue failure. Based on the nonlinear strain, loading cycle variable and fatigue life, a highly accurate nonlinear fatigue model was developed to describe the complete inverted S-shaped evolution pattern of fatigue damage, which demonstrated excellent practical implications for the theoretical characterization of anisotropic fatigue damage in disturbed Jinping marble.

Abstract Bacterial pneumonia is a significant public health burden, contributing to substantial morbidity, mortality, and healthcare costs. Current therapeutic strategies beyond antibiotics and adjuvant therapies are limited, highlighting the need for a deeper understanding of the disease pathogenesis. Here, we employed single-cell RNA sequencing of 444,146 bronchoalveolar lavage fluid cells (BALFs) from a large cohort of 74 individuals, including 58 patients with mild (n = 22) and severe (n = 36) diseases as well as 16 healthy donors. Enzyme‐linked immunosorbent and histological assays were applied for validation within this cohort. The heterogeneity of immune responses in bacterial pneumonia was observed, with distinct immune cell profiles related to disease severity. Severe bacterial pneumonia was marked by an inflammatory cytokine storm resulting from systemic upregulation of S100A8/A9 and CXCL8, primarily due to specific macrophage and neutrophil subsets. In contrast, mild bacterial pneumonia exhibits an effective humoral immune response characterized by the expansion of T follicular helper and T helper 2 cells, facilitating B cell activation and antibody production. Although both disease groups display T cell exhaustion, mild cases maintained robust cytotoxic CD8+T cell function, potentially reflecting a compensatory mechanism. Dysregulated neutrophil and macrophage responses contributed significantly to the pathogenesis of severe disease. Immature neutrophils promote excessive inflammation and suppress T cell activation, while a specific macrophage subset (Macro_03_M1) displaying features akin to myeloid-derived suppressor cells (M-MDSCs) suppress T cells and promote inflammation. Together, these findings highlight potential therapeutic targets for modulating immune responses and improving clinical outcomes in bacterial pneumonia.

BackgroundThis study aims to investigate the correlation between oxidative balance score (OBS) and all-cause, cardiovascular disease (CVD) and respiratory-related mortality within a cohort that includes older asthma patients with diabetes.MethodsData from the National Health and Nutrition Examination Survey (NHANES) spanning from 2001 to 2018, which included 611 participants, were analyzed. Mortality outcomes were determined by linking the data to National Death Index (NDI) records through December 31, 2019. Cox regression modeling was employed to examine the relationship between OBS and all-cause, CVD and respiratory-related mortality. Restricted cubic splines (RCS), subgroup analyses and interaction tests were also conducted in this study.ResultsOver a median follow-up of 78.96 months, there were 216 all-cause deaths and 57 CVD-related deaths. A significant negative association was found between the OBS and all-cause and CVD mortality. We did not observe OBS could reduce respiratory-related mortality in older asthma patients with diabetes. RCS analysis indicated a linear and inverse association between the OBS and all-cause and CVD mortality. Subgroup analyses and interaction tests indicated the negative association between OBS and CVD mortality was significantly influenced by alcohol consumption.ConclusionIn this sample, higher OBS was associated with lower all-cause and CVD mortality risks. These findings stressed the importance of infection status in assessing oxidative balance’s impact on health.

AbstractBackground The effectiveness of nirmatrelvir–ritonavir has mainly been shown in non-hospitalized patients with mild-to-moderate coronavirus disease 2019 (COVID-19). The real-world effectiveness of nirmatrelvir-ritonavir urgently needs to be determined using representative in-hospital patients with COVID-19 during the Omicron wave of the pandemic.Methods We performed a multicentre, retrospective study in five Chinese PLA General Hospital medical centers in Beijing, China. Patients hospitalized with COVID-19 from 10 December 2022 to 20 February 2023 were eligible for inclusion. A 1:1 propensity score matching was performed between the nirmatrelvir-ritonavir group and the control group.Results 1010 recipients of nirmatrelvir-ritonavir and 1010 matched controls were finally analyzed after matching. Compared with matched controls, the nirmatrelvir-ritonavir group had a lower incidence rate of all-cause death (4.6/1000 vs. 6.3/1000 person-days, p = 0.013) and a higher incidence rate of clinical improvement (47.6/1000 vs. 45.8/1000 person-days, p = 0.012). Nirmatrelvir-ritonavir was associated with a 22% lower all-cause mortality and a 14% higher incidence of clinical improvement. Initiation of nirmatrelvir-ritonavir within 5 days after symptom onset was associated with a 50% lower mortality and a 26% higher clinical improvement rate. By contrast, no significant associations were identified among patients receiving nirmatrelvir-ritonavir treatment more than 5 days after symptom onset. Nirmatrelvir-ritonavir was also associated with a 50% increase in survival days and a 12% decrease in days to clinical improvement.Conclusion Among hospitalized patients with COVID-19 during the Omicron wave in Beijing, China, the early initiation of nirmatrelvir-ritonavir was associated with clinical benefits of lowering mortality and improving clinical recovery.

Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Abstract Background Patients with severe infection often require careful fluid management. Intravenous linezolid can increase the fluid volume, whereas oral linezolid can effectively limit the fluid volume. However, the differences in the treatment effect and incidence of thrombocytopenia between oral and intravenous linezolid in patients with severe infection remain unclear. Methods Patients who received linezolid in the intensive care unit of PLA General Hospital from December 2010 to December 2020 were divided into an oral group and intravenous group according to the route of administration, and we further established the PO group and PO-match group by propensity score matching. The anti-infection effect of linezolid and incidence of thrombocytopenia were compared between the two groups. Results In total, 177 patients were enrolled in the study (59 in PO group and 118 in PO-match group). The microbial clearance rate and clinical cure rate were similar between the two groups (70.0% vs. 79.3%, P = 0.293; 72.9% vs. 83.9%, P = 0.213). The 30-day mortality rate was slightly higher in the PO group than in the PO-match group, but the difference was not statistically significant (13.6% vs. 6.8%, P = 0.138). There was no difference in the incidence of thrombocytopenia between the two groups (62.7% vs. 65.3%, P = 0.739). Conclusions There were no significant differences in the treatment effect or incidence of thrombocytopenia between oral and intravenous administration of linezolid in patients with severe infection.

Abstract Background Zinc finger SWIM-type containing 4 (ZSWIM4) is a zinc finger protein with its function largely uncharacterized. In this study, we aimed to investigate the role of ZSWIM4 in gastrointestinal stromal tumors (GISTs). Results We found that ZSWIM4 expression is inhibited by the predominantly mutated protein KIT in GISTs, while conversely, ZSWIM4 inhibits KIT expression and downstream signaling. Consistent with the observation, ZSWIM4 inhibited GIST cell survival and proliferation in vitro. RNA sequencing of GISTs from KITV558A/WT mice and KITV558A/WT/ZSWIM4−/− mice showed that loss of ZSWIM4 expression increases the expression of circadian clock pathway member BMAL1 which contributes to GIST cell survival and proliferation. In addition, we found that KIT signaling increases the distribution of ZSWIM4 in the nucleus of GIST cells, and which is important for its inhibition of KIT and BMAL1. In agreement with the results in vitro, the in vivo studies showed that ZSWIM4 deficiency increases the tumorigenesis of GISTs in KITV558A/WT mice. Conclusions Taken together, our results revealed that the entry of ZSWIM4 to the nucleus is important for its inhibition of KIT and BMAL1, ultimately attenuating GIST tumorigenesis. The results provide a novel insight in the understanding of signal transduction in GISTs and lay strong theoretical basis for the advancement of GIST treatment.

Abstract Background Although studies have demonstrated the value of the triglyceride–glucose (TyG) index for cardiovascular disease (CVD) and cardiovascular mortality, however, few studies have shown that the TyG index is associated with all-cause or CVD mortality in young patients with diabetes. This study aimed to investigate the association between the TyG index and all-cause and CVD mortality in young patients with diabetes in the United States. Methods Our study recruited 2440 young patients with diabetes from the National Health and Nutrition Examination Survey (NHANES) 2001–2018. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Cox regression modeling was used to investigate the association between TyG index and mortality in young patients with diabetes. The nonlinear association between TyG index and mortality was analyzed using restricted cubic splines (RCS), and a two-segment Cox proportional risk model was constructed for both sides of the inflection point. Results During a median follow-up period of 8.2 years, 332 deaths from all causes and 82 deaths from cardiovascular disease were observed. Based on the RCS, the TyG index was found to have a U-shaped association with all-cause and CVD mortality in young patients with diabetes, with threshold values of 9.18 and 9.16, respectively. When the TyG index was below the threshold value (TyG index

BackgroundKlebsiella pneumoniae is a major cause of hospital-acquired infections (HAIs), primarily spread through environmental contamination in hospitals. The effectiveness of current chemical disinfectants is waning due to emerging resistance, which poses environmental hazards and fosters new resistance in pathogens. Developing environmentally friendly and effective disinfectants against multidrug-resistant organisms is increasingly important.MethodsThis study developed a bacteriophage cocktail targeting two common carbapenem-resistant Klebsiella pneumoniae (CRKP) strains, ST11 KL47 and ST11 KL64. The cocktail was used as an adjunctive disinfectant in a hospital’s respiratory intensive care unit (RICU) via ultrasonic nebulization. Digital PCR was used to quantify CRKP levels post-intervention. The microbial community composition was analyzed via 16S rRNA sequencing to assess the intervention’s impact on overall diversity.ResultsThe phage cocktail significantly reduced CRKP levels within the first 24 hours post-treatment. While a slight increase in pathogen levels was observed after 24 hours, they remained significantly lower than those treated with conventional disinfectants. 16S rRNA sequencing showed a decrease in the target pathogens’ relative abundance, while overall species diversity remained stable, confirming that phages selectively target CRKP without disrupting ecological balance.DiscussionThe findings highlight the efficacy and safety of phage-based biocleaners as a sustainable alternative to conventional disinfectants. Phages selectively reduce multidrug-resistant pathogens while preserving microbial diversity, making them a promising tool for infection control.

Background: Low temperature pose significant challenges to peach cultivation, causing severe damage to peach buds and restricting production and distribution. Ethylene, an important phytohormone, plays a critical role in enhancing plant cold resistance. Structural genes and transcription factors involved in ethylene biosynthesis and signal transduction pathways are associated with cold resistance. However, no research has specifically addressed their roles in peach cold resistance.Methods: In this study, we aimed for cold-resistance gene discovery in cold-sensitive peach cultivar “21Shiji” (21SJ) and cold-resistance cultivar “Shijizhixing” (SJZX) using RNA-seq and gas chromatography.Results: The findings revealed that under cold stress conditions, ethylene biosynthesis in “SJZX” was significantly induced. Subsequently, a structural gene, PpACO1-1, involved in ethylene biosynthesis in peach buds was significantly upregulated and showed a higher correlation with ethylene release rate. To identify potential transcription factors associated with PpACO1-1 expression and ethylene signal transduction, weighted gene co-expression network analysis was conducted using RNA-seq data. Four transcription factors: PpERF2, PpNAC078, PpWRKY65 and PpbHLH112, were identified.Conclusion: These findings provide valuable theoretical insights for investigating the regulatory mechanisms of peach cold resistance and guiding breeding strategies.

To reveal the dynamic mechanical characteristics of deep rocks, a series of impact tests under triaxial static stress states corresponding to depths of 300–2400 m were conducted. The results showed that both the strain rates and the stress environments in depth significantly affect the mechanical characteristics of rocks. The sensitivity of strain rate to the dynamic strength and deformation modulus shows a negative correlation with depth, indicating that producing penetrative cracks in deep environments is more difficult when damage occurs. The dynamic strength shows a tendency to decrease and then increase slightly, but decreases sharply finally. Transmissivity demonstrates a similar trend as that of strength, whereas reflectivity indicates the opposite trend. Furthermore, two critical depths with high dynamically induced hazard possibilities based on the China Jinping Underground Laboratory (CJPL) were proposed for deep engineering. The first critical depth is 600–900 m, beyond which the sensitivity of rock dynamic characteristics to the strain rate and restraint of circumferential stress decrease, causing instability of surrounding rocks under axial stress condition. The second one lies at 1500–1800 m, where the wave impedance and dynamic strength of deep surrounding rocks drop sharply, and the dissipation energy presents a negative value. It suggests that the dynamic instability of deep surrounding rocks can be divided into dynamic load dominant and dynamic load induced types, depending on the second critical depth.

Abstract In the current global context of environmental degradation and resource constraints, the pursuit of sustainable development has become an imperative. One avenue that holds promise for achieving this objective is the application of digital technologies, which have the potential to decouple economic growth from its carbon footprint. However, it is crucial to ensure that these technologies are designed and governed in a prudent manner, with a strong alignment to environmental priorities. This study focuses on exploring the potential roles of blockchain and artificial intelligence (AI) in supply chain coordination and impact mitigation. Furthermore, they have the capacity to incentivize recycling and circular business models, as well as facilitate carbon accounting and offsetting. To fully realize these benefits, it is essential to deploy these technologies within inclusive collaborative frameworks that take into consideration social and ecological considerations. The study also offers policy recommendations that highlight key leverage points for digital innovation, enabling countries to embark on smart and green industrial transformation pathways. By harnessing the potential of blockchain and AI in supply chains, governments can promote transparency, traceability, and accountability, thereby fostering sustainable practices and reducing environmental impacts. Incorporating blockchain and AI technologies into supply chain approaches leads to a substantial improvement in efficiency, as demonstrated by a numerical analysis. In conclusion, the integration of innovative digital technologies offers significant opportunities to optimize production systems and economic activity while prioritizing sustainability objectives for the betterment of society and the environment. These technologies have the potential to mitigate environmental externalities by addressing information imbalances within global supply chains. However, it is essential to prioritize inclusive governance that emphasizes democratic participation to mitigate any unintended negative consequences, especially for vulnerable communities. By ensuring inclusive decision-making processes, we can maximize the positive impact of these technologies while minimizing potential harm.

Abstract Background and aim Two oral antivirals (Nirmatrelvir- ritonavir and Azvudine) are widely used in China practice during the Omicron wave of the pandemic. However, little evidence regarding the real-world effectiveness of these two oral antivirals in in-hospital patients. We aimed to evaluate the clinical effectiveness of nirmatrelvir-ritonavir versus azvudine among adult hospitalized patients with COVID-19. Methods This retrospective cohort study used data from three Chinese PLA General Hospital medical centres. Hospitalized patients with COVID-19 treated with azvudine or nirmatrelvir-ritonavir from Dec 10, 2022, to February 20, 2023, and did not require invasive ventilation support on admission were eligible for inclusion. Results After exclusions and propensity-score matching, the final analysis included 486 azvudine recipients and 486 nirmatrelvir-ritonavir recipients. By 28 days of initiation of the antivirus treatment, the crude incidence rate of all-cause death was similar in both types of antivirus treatment (nirmatrelvir-ritonavir group 2.8 events 1000 person-days [95% CI, 2.1–3.6] vs azvudine group 3.4 events/1000 person-days [95% CI, 2.6–4.3], P = 0.38). Landmark analysis showed that all-cause death was lower in the nirmatrelvir-ritonavir (3.5%) group than the azvudine (6.8%, P = 0.029) within the initial 10-day admission period, while no significant difference was observed for results between 10 and 28 days follow-up. There was no significant difference between the nirmatrelvir-ritonavir group and the azvudine group in cumulative incidence of the composite disease progression event (8.6% with nirmatrelvir-ritonavir vs. 10.1% with azvudine, HR, 1.22; 95% CI 0.80–1.86, P = 0.43). Conclusion Among patients hospitalized with COVID-19 during the omicron wave in Beijing, similar in-hospital clinical outcomes on 28 days were observed between patients receiving nirmatrelvir-ritonavir and azvudine. However, it is worth noticing that nirmatrelvir-ritonavir appears to hold an advantage over azvudine in reducing early mortality. Further randomized controlled trials are needed to verify the efficacy of those two antivirus medications especially in early treatment.

The deep earth, deep sea, and deep space are the main parts of the national “three deep” strategy, which is in the forefront of the strategic deployment clearly defined in China’s 14th Five-Year Plan (2021–2025) and the Long-Range Objectives Through the Year 2035. It is important to reveal the evolutionary process and mechanism of deep tectonics to understand the earth’s past, present and future. The academic connotation of Geology in Time has been given for the first time, which refers to the multi-field evolution response process of geological bodies at different time and spatial scales caused by geological processes inside and outside the Earth. Based on the deep in situ detection space and the unique geological environment of China Jinping Underground Laboratory, the scientific issue of the correlation mechanism and law between deep internal time-varying and shallow geological response is given attention. Innovative research and frontier exploration on deep underground in situ geo-information detection experiments for Geology in Time are designed to be carried out, which will have the potential to explore the driving force of Geology in Time, reveal essential laws of deep earth science, and explore innovative technologies in deep underground engineering.

Abstract The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infected a substantial proportion of Chinese population, and understanding the factors underlying the severity of the disease and fatality is valuable for future prevention and clinical treatment. We recruited 64 patients with invasive ventilation for COVID-19 and performed metatranscriptomic sequencing to profile host transcriptomic profiles, plus viral, bacterial, and fungal content, as well as virulence factors and examined their relationships to 28-day mortality were examined. In addition, the bronchoalveolar lavage fluid (BALF) samples from invasive ventilated hospital/community-acquired pneumonia patients (HAP/CAP) sampled in 2019 were included for comparison. Genomic analysis revealed that all Omicron strains belong to BA.5 and BF.7 sub-lineages, with no difference in 28-day mortality between them. Compared to HAP/CAP cohort, invasive ventilated COVID-19 patients have distinct host transcriptomic and microbial signatures in the lower respiratory tract; and in the COVID-19 non-survivors, we found significantly lower gene expressions in pathways related viral processes and positive regulation of protein localization to plasma membrane, higher abundance of opportunistic pathogens including bacterial Alloprevotella, Caulobacter, Escherichia-Shigella, Ralstonia and fungal Aspergillus sydowii and Penicillium rubens. Correlational analysis further revealed significant associations between host immune responses and microbial compositions, besides synergy within viral, bacterial, and fungal pathogens. Our study presents the relationships of lower respiratory tract microbiome and transcriptome in invasive ventilated COVID-19 patients, providing the basis for future clinical treatment and reduction of fatality.

Objectives: To enhance the daily training quality of athletes without inducing significant physiological fatigue, aiming to achieve a balance between training efficiency and load. Design methods: Firstly, we developed an activity classification training model using the random forest algorithm and introduced the “effective training rate” (the ratio of effective activity time to total time) as a metric for assessing athlete training efficiency. Secondly, a method for rating athlete training load was established, involving qualitative and quantitative analyses of physiological fatigue through subjective fatigue scores and heart rate data. Lastly, an optimization system for training efficiency and load balance, utilizing multiple inertial sensors, was created. Athlete states were categorized into nine types based on the training load and efficiency ratings, with corresponding management recommendations provided. Results: Overall, this study, combining a sports activity recognition model with a physiological fatigue assessment model, has developed a training efficiency and load balance optimization system with excellent performance. The results indicate that the prediction accuracy of the sports activity recognition model is as high as 94.70%. Additionally, the physiological fatigue assessment model, utilizing average relative heart rate and average RPE score as evaluation metrics, demonstrates a good overall fit, validating the feasibility of this model. Conclusions: This study, based on relative heart rate and wearable devices to monitor athlete physiological fatigue, has developed a balanced optimization system for training efficiency and load. It provides a reference for athletes’ physical health and fatigue levels, offering corresponding management recommendations for coaches and relevant professionals.

This paper studies the blade cooling under the co-effect of external film extraction and internal impingement cooling with crossflow diverters. The influence of spanwise angle (α) and diameter (Df) of film hole on flow and cooling performance are analyzed when the impingement jet Reynolds number ranges (Rei) from 15,000 to 45,000. From the results, if a row of film holes is set on the target surface of an impingement cooling with crossflow diverters, the overall cooling effectiveness will be greatly improved, especially the pressure side. When the α ranges from 35° to 115°, no matter pressure side or suction side, the film jet injection towards blade hub is more beneficial to cooling, and the smaller the α, the more beneficial. When the Df ranges from 0.25 mm to 1.00 mm, at suction side, as Df increases, overall cooling effectiveness gradually increases; at pressure side, when the Df ranges from 0.25 mm to 0.75 mm, overall cooling effectiveness increases, but if the Df continues to increase, it will decrease, which means the Df of 0.75 mm can obtain the most excellent cooling performance. This study provides a theoretical basis for the comprehensive application of film extraction, impingement cooling, and crossflow diverters in blade cooling design.

Abstract Introduction The mortality rate of hospitalized patients with severe hospital-acquired pneumonia (SHAP) remains high. Empirical broad-spectrum antibiotic coverage and the misuse of high-grade antibiotics could lead to the emergence of multi-drug and even pandrug-resistant bacteria. In addition to metagenomic next-generation sequencing (mNGS), microbiological rapid on-site evaluation (M-ROSE) might be a useful technique to identify the pathogens in the early stage; however, the effect of M-ROSE guiding anti-infection treatment on prognostic outcomes of SHAP patients is still unclear. Methods/design This is a multicenter, single-blind, prospective, randomized controlled trial to evaluate the effect of M-ROSE guiding anti-infection treatment in SHAP patients, which will provide new strategies for the prevention and control of clinical multi-drug resistance bacteria. A total of 166 patients with SHAP, aged 18 years and over, will be recruited from seven centers in Beijing and randomly assigned to the intervention group (M-ROSE combined with mNGS) or the control group (mNGS only) in a 1:1 ratio using the central randomization system. Patients in the intervention group will accept M-ROSE and mNGS analysis, and the control group will accept mNGS analysis. Individualized anti-infective treatment and routine treatment will be selected according to the analysis results. The primary outcome is the ICU outcome (mortality). The safety of the intervention measures will be evaluated during the entire trial period. This trial will be the first randomized controlled trial to evaluate the effect of M-ROSE guiding treatment on mortality in patients with SHAP and may change the prevalence of multi-drug resistant bacteria. Ethics and dissemination This trial adheres to the Declaration of Helsinki and guidelines of Good Clinical Practice. Signed informed consent will be obtained from all participants. The trial has been approved by the Chinese PLA General Hospital (Approval Number: 20220322001). Trial registration ClinicalTrials.gov NCT05300776. Registered on 25 March 2022.

Background: Linezolid-associated thrombocytopenia (LAT) leads to drug withdrawal associated with a poor prognosis. Some risk factors for LAT have been identified; however, the sample size of previous studies was small, data from elderly individuals are limited, and a simple risk score scale was not established to predict LAT at an early stage, making it difficult to identify and intervene in LAT at an early stage. Methods: In this single-center retrospective case-control study, we enrolled elderly patients treated with linezolid in the intensive care unit from January 2015 to December 2020. All the data of enrolled patients, including demographic information and laboratory findings at baseline, were collected. We analyzed the incidence and risk factors for LAT and established a nomogram risk prediction model for LAT in the elderly population. Results: A total of 428 elderly patients were enrolled, and the incidence of LAT was 35.5% (152/428). Age ≥80 years old (OR=1.980; 95% CI: 1.179–3.325; P=0.010), duration of linezolid ≥ 10 days (OR=1.100; 95% CI: 1.050–1.152; P 150 was defined as high risk, and the probability of LAT was >67.5%. Conclusions: We created the ADPLCP risk score scale to predict the occurrence of LAT in elderly individuals. ADPLCP is simple and feasible and is helpful for the early determination of LAT to guide drug withdrawal or early intervention.

To ensure the timely execution of hard real-time applications, scheduling analysis techniques must consider safe upper bounds on the possible execution durations of tasks or runnables, which are referred to as Worst-Case Execution Times (WCET). Bounding WCET requires not only program path analysis but also modeling the impact of micro-architectural features present in modern processors. In this paper, we model the ARMv8 ISA and micro-architecture including instruction cache, branch predictor, instruction prefetching strategies, out-of-order pipeline. We also consider the complex interactions between these features (e.g., cache misses caused by branch predictions and branch misses caused by instruction pipelines) and estimate the WCET of the program using the Implicit Path Enumeration Technique (IPET) static WCET analysis method. We compare the estimated WCET of benchmarks with the observed WCET on two ARMv8 boards. The ratio of estimated to observed WCET values for all benchmarks is greater than 1, demonstrating the security of the analysis.

With the addition of Bluetooth AOA/AOD direction-finding capabilities in the Bluetooth 5.1 protocol and the introduction of antenna array technology into the Bluetooth platform to further enhance positioning accuracy, Bluetooth has gradually become a research hotspot in the field of indoor positioning due to its standard protocol specifications, rich application ecosystem, and outstanding advantages such as low power consumption and low cost compared to other indoor positioning technologies. However, current indoor positioning based on Bluetooth AOA/AOD suffers from overly simplistic core algorithm implementations. When facing different application scenarios, the standalone AOA or AOD algorithms exhibit weak applicability, and they also encounter challenges such as poor positioning accuracy, insufficient real-time performance, and significant effects of multipath propagation. These existing problems and deficiencies render Bluetooth lacking an efficient implementation solution for indoor positioning. Therefore, this paper proposes a study on Bluetooth AOA and AOD indoor positioning algorithms. Through an analysis of the principles of Bluetooth’s newly added direction-finding functionality and combined with research on array signal DOA estimation algorithms, the paper ultimately integrates the least squares algorithm to optimize positioning errors in terms of accuracy and incorporates an anti-multipath interference algorithm to address the impacts of multipath effects in different scenarios. Experimental testing demonstrates that the indoor positioning algorithms applicable to Bluetooth AOA and AOD can effectively mitigate accuracy errors and overcome multipath effects, exhibiting strong applicability and significant improvements in real-time performance.

Geophysical logging plays a very important role in reservoir evaluation. In the actual production process, some logging data are often missing due to well wall collapse and instrument failure. Therefore, this paper proposes a logging reconstruction method based on improved sand cat swarm optimization (ISCSO) and a temporal convolutional network (TCN) and bidirectional gated recurrent unit network with attention mechanism (BiGRU-AM). The ISCSO-TCN-BiGRU-AM can process both past and future states efficiently, thereby extracting valuable deterioration information from logging data. Firstly, the sand cat swarm optimization (SCSO) improved by the variable spiral strategy and sparrow warning mechanism is introduced. Secondly, the ISCSO’s performance is evaluated using the CEC–2022 functions and the Wilcoxon test, and the findings demonstrate that the ISCSO outperforms the rival algorithms. Finally, the logging reconstruction method based on the ISCSO-TCN-BiGRU-AM is obtained. The results are compared with the competing models, including the back propagation neural network (BPNN), GRU, and BiGRU-AM. The results show that the ISCSO-TCN-BiGRU-AM has the best performance, which verifies its high accuracy and feasibility for the missing logging reconstruction.

Abstract Background Multiple organ dysfunction syndrome (MODS) is one of the leading causes of death in critically ill patients. MODS is the result of a dysregulated inflammatory response that can be triggered by various causes. Owing to the lack of an effective treatment for patients with MODS, early identification and intervention are the most effective strategies. Therefore, we have developed a variety of early warning models whose prediction results can be interpreted by Kernel SHapley Additive exPlanations (Kernel-SHAP) and reversed by diverse counterfactual explanations (DiCE). So we can predict the probability of MODS 12 h in advance, quantify the risk factors, and automatically recommend relevant interventions. Methods We used various machine learning algorithms to complete the early risk assessment of MODS, and used a stacked ensemble to improve the prediction performance. The kernel-SHAP algorithm was used to quantify the positive and minus factors corresponding to the individual prediction results, and finally, the DiCE method was used to automatically recommend interventions. We completed the model training and testing based on the MIMIC-III and MIMIC-IV databases, in which the sample features in the model training included the patients’ vital signs, laboratory test results, test reports, and data related to the use of ventilators. Results The customizable model called SuperLearner, which integrated multiple machine learning algorithms, had the highest authenticity of screening, and its Yordon index (YI), sensitivity, accuracy, and utility_score on the MIMIC-IV test set were 0.813, 0.884, 0.893, and 0.763, respectively, which were all maximum values of eleven models. The area under the curve of the deep–wide neural network (DWNN) model on the MIMIC-IV test set was 0.960, and the specificity was 0.935, which were both the maximum values of all these models. The Kernel-SHAP algorithm combined with SuperLearner was used to determine the minimum value of glasgow coma scale (GCS) in the current hour (OR = 0.609, 95% CI 0.606–0.612), maximum value of MODS score corresponding to GCS in the past 24 h (OR = 2.632, 95% CI 2.588–2.676), and maximum score of MODS corresponding to creatinine in the past 24 h (OR = 3.281, 95% CI 3.267–3.295) were generally the most influential factors. Conclusion The MODS early warning model based on machine learning algorithms has considerable application value, and the prediction efficiency of SuperLearner is superior to those of SubSuperLearner, DWNN, and other eight common machine learning models. Considering that the attribution analysis of Kernel-SHAP is a static analysis of the prediction results, we introduce the DiCE algorithm to automatically recommend counterfactuals to reverse the prediction results, which will be an important step towards the practical application of automatic MODS early intervention.

To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the nonlinear inertia weight factor, Cauchy mutation, and sparrow warning mechanism are introduced to improve the pelican optimization algorithm (POA). Secondly, the superiority of IPOA is verified by using the CEC–2022 benchmark test functions. In addition, the Wilcoxon test is applied to evaluate the experimental results, which proves the superiority of IPOA against other popular algorithms. Finally, BiLSTM-AM is optimized by IPOA, and IPOA-BiLSTM-AM is used for porosity prediction in the Midlands basin. The results show that IPOA-BiLSTM-AM has the smallest prediction error for the verification set samples (RMSE and MAE were 0.5736 and 0.4313, respectively), which verifies its excellent performance.

Abstract Background Existing data suggest that cerebral autoregulation (CA) varies among different subtypes of ischaemic stroke. CA is globally impaired in patients with small artery occlusion (SAO). However, the factors influencing CA impairment in patients remains to be elucidated. Methods Stroke patients with SAO who underwent brain magnetic resonance imaging (MRI) were prospectively studied. Within 7 days after stroke onset, CA was recorded from the middle cerebral artery blood flow velocity and arterial blood pressure was simultaneously measured. Transfer function analysis was used to derive CA parameters, including gain and phase. Clinical characteristics, mean arterial pressure (MAP), biochemical findings, and cerebral small vessel disease (CSVD) markers on MRI were assessed in each patient. Factors associated with CA parameters were investigated. Univariate and multivariate linear regression analyses were conducted to determine the relationship between clinical factors and CA parameters. Results Sixty-three SAO patients (age, 56.3 ± 9.9 years; 55 men) were enrolled in the study. In the multiple linear regression analysis, after controlling for relevant clinical factors, MAP on admission (ipsilateral OR = 0.99 and contralateral OR = 0.99, both P 105 mmHg on admission (OR = 0.77, P = 0.019) was significantly associated with ipsilateral gain. Diabetes mellitus was a significant predictive factor for bilateral gain (ipsilateral OR = 1.32 and contralateral OR = 1.22, both P 105 mmHg on admission tended to have better ipsilateral CA. Diabetes mellitus appears to be an independent risk factor for CA impairment in patients with SAO-related stroke. CSVD may not be the main factor affecting bilateral CA in patients with SAO.

Land surface phenology that reflects periodical changes in terrestrial vegetation plays an important role in influencing the functioning and services of ecosystems. Satellite-derived metrics of land surface phenology have been widely used to assess the patterns in land cover dynamics. There are still limited satellite-based datasets of global land surface phenology available to date due to uneven qualities of satellite data. Here, we developed a global annual land surface phenology dataset based on Advanced Very High Resolution Radiometer (AVHRR) data from 1982 to 2018. The number of global vegetation growing seasons for each individual pixel was counted for each year from 1982 to 2018. We implemented four phenology retrieval methods, including the amplitude threshold method, the second-order derivative method, the third-order derivative method, and the curvature change rate method, to retrieve four key phenological metrics for each vegetation growth cycle, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS). The results show that the phenological metrics retrieved using different phenology retrieval methods have similar patterns of global spatial distribution. From 1982 to 2018, the timing of both maturity and senescence within a year advanced in the Northern Hemisphere and the timing of SOS delayed in the Southern Hemisphere. The phenological metrics extracted from in-situ data such as the USA National Phenology Network (USA-NPN), the Pan European Phenology Database (PEP725), and flux tower measurements were positively correlated with satellite-derived phenological metrics. Despite the effects of mismatching spatial scales, the retrieved phenological metrics based on 5 km AVHRR data were consistent with those based on 500 m Moderate-resolution Imaging Spectroradiometer (MODIS) data and 30 m fused satellite data at both site-scale and regional-scale. The developed dataset released for usages and applications on global land surface dynamics offers potential end users options for choosing desirable phenological metrics derived from different methods.

Giant magnetostrictive actuators (GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations, including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function (E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 μm and decreases the root-mean-square error in displacement to within 0.5 μm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data; it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.

Abstract Pulmonary microbial diversity may be influenced by biotic or abiotic conditions (e.g., disease, smoking, invasive mechanical ventilation (MV), etc.). Specially, invasive MV may trigger structural and physiological changes in both tissue and microbiota of lung, due to gastric and oral microaspiration, altered body posture, high O2 inhalation-induced O2 toxicity in hypoxemic patients, impaired airway clearance and ventilator-induced lung injury (VILI), which in turn reduce the diversity of the pulmonary microbiota and may ultimately lead to poor prognosis. Furthermore, changes in (local) O2 concentration can reduce the diversity of the pulmonary microbiota by affecting the local immune microenvironment of lung. In conclusion, systematic literature studies have found that invasive MV reduces pulmonary microbiota diversity, and future rational regulation of pulmonary microbiota diversity by existing or novel clinical tools (e.g., lung probiotics, drugs) may improve the prognosis of invasive MV treatment and lead to more effective treatment of lung diseases with precision.

Abstract Background Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS), in addition, etiology-associated heterogeneity in ARDS has become an emerging topic quite recently; however, the intersection between the two, which is early prediction of target conditions in etiology-specific ARDS, has not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of moderate-to-severe condition of inhalation-induced ARDS. Methods Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 h that ended 6 h prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 200 mmHg). Results The trained random forest classifier was validated using two independent validation cohorts, with an area under the curve of 0.9127 (95% confidence interval 0.8713–0.9542) and 0.9026 (95% confidence interval 0.8075–1), respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 h prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test). Conclusions This newly established random forest‑based interpretable model shows good predictive ability for moderate-to-severe inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.

Based on the DEA model, this paper evaluates the efficiency of poverty alleviation through tourism in Jiangxi Province based on three aspects: comprehensive efficiency, technical efficiency, and scale efficiency. The results show that the overall efficiency of poverty alleviation through tourism in Jiangxi Province is gradually progressing toward a high level, with the average value in recent years reaching 0.765. Compared to other cities in the Province, Ganzhou and Fuzhou have the highest ranking by level of tourism poverty alleviation. However, the tourism development at present is not truly just or even, resulting in a gap between the efficiency levels of regions. There are some areas that have been in the effective state for a long time, while others are far lower than the average efficiency due to low technical efficiency in these areas. The spatial differentiation of tourism poverty alleviation efficiency across regions shows that the tourism poverty alleviation efficiency in the eastern region of Jiangxi Province is low compared to other parts. Although the overall efficiency of tourism poverty alleviation in Jiangxi Province does not fluctuate much, there are still some areas where the efficiency of tourism poverty alleviation continues to show a downward trend, indicating that the allocation of tourism resources in Jiangxi Province needs to be optimized and leaving much room for improvement in the efficiency of poverty alleviation.

Abstract Background In sepsis, vitamin D binding protein (VDBP) has been shown to be low-expressed. The current study examined the relationship between serum VDBP level and liver injury in sepsis patients, as well as in a mouse model for sepsis and in cultured liver epithelial cell line exposed to lipopolysaccharide (LPS). Methods The human study included 78 sepsis patients and 50 healthy volunteers. Sepsis patients were categorized into sepsis survivor group (n = 43) and sepsis non-survivor group (n = 35) based on 28-day mortality for data analysis. Adult male C57BL/6 mice were subjected to cecal ligation and puncture (CLP). Serum samples were collected on day 1, 3, 5 and 7 to determine the levels of VDBP, 25-hydroxyvitamin D [25(OH)D3], 1,25-dihydroxyvitamin D [1,25(OH)2D3], interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Potential protective effects of VDBP overexpression against LPS-induced liver damage were examined in cultured THLE2 cells. Results Serum levels of VDBP, 25(OH)D3, and 1,25(OH)2D3 were significantly lower in sepsis patients vs. the healthy control (P

The distribution of natural fractures in coal seams and their effect on the anisotropy of the stress sensitivity of permeability are key issues that are commonly encountered in the simultaneous extraction of coal and gas. To fully understand the law of anisotropy of the effective porosity and stress sensitivity of coal permeability, a modified permeability model and the corresponding anisotropic evaluation index of the stress sensitivity were established considering the influence of the natural fracture distribution of coal. Based on anisotropic coal samples obtained from the Tashan coal mine, the effective porosity and permeability in three orthogonal directions under different stress conditions were systematically evaluated, and the anisotropic evolution law of the stress sensitivity of the permeability was quantitatively analyzed. The results show that the natural fracture system of coal exhibits complex heterogeneity, and the compressibility of the fractures in different directions is different, resulting in significant anisotropic permeability and stress sensitivity. The effective porosity sensitivity of the coal permeability exhibits a power relation with the effective porosity, and a higher effective porosity corresponds to a more notable anisotropy of the effective porosity sensitivity. Under a low stress, the coal stress sensitivity in the direction perpendicular to the bedding planes is the largest. When the stress increases to 19 MPa, the dominant direction of the stress sensitivity changes, and the direction parallel to the face cleats experiences the maximum stress sensitivity of permeability at a high stress level. The power index of the correlation between the coal permeability and effective porosity in different directions of the coal samples in the Tashan coal mine ranges from 2.42 to 9.18, and a constant value of 3 is not observed. This result can be attributed to the influence of the pore size, fracture width and difference in the pore–fracture distribution.

Abstract Background The liver plays an important role in production and metabolism of homocysteine (Hcy), which has been reported to be involved in liver injury. In our previous work, we confirm that Hcy can induce liver injury by activating endoplasmic reticulum (ER) stress. However, the underlying mechanisms remain largely unknown. Results In present study, we established the Hcy-induced liver injury model by feeding cbs +/− mice with high methionine diet, and found that a considerable mass of disordered arrangement of hepatocytes and enlarged space between hepatocytes were frequently occurred in the liver of cbs +/− mice, accompanied with elevated expression levels of apoptosis-related proteins. In addition, Hcy could activate ER stress both in cbs +/− mice and hepatocytes. Mechanistically, Hcy promoted the expression levels of proteasome 26S subunit non-ATPase 10 (PSMD10) in hepatocytes; and the expression of ER stress indicators and apoptosis-associated proteins were significantly suppressed when PSMD10 was silenced in hepatocytes under Hcy treatment. Moreover, bioinformatics analysis and luciferase reporter assay demonstrated that PSMD10 was a target gene of miR-212-5p. Consistently, miR-212-5p overexpression could inhibit ER stress-mediated apoptosis of hepatocytes under Hcy treatment. With the help of co-immunoprecipitation assay, we identified that the interaction between PSMD10 and GRP78 accelerated ER stress-mediated hepatic apoptosis induced by Hcy. Conclusions Our findings indicate that miR-212-5p directly targets PSMD10 and subsequently activates ER stress to promote Hcy-induced apoptosis of hepatocytes. We propose that endogenous PSMD10 physically interacts with GRP78 to regulate ER stress. Our study may provide the therapeutic target for the liver injury induced by Hcy.

Internet technology, as a product of the development of the network era, connects a large number of hardware devices, but the connection of many devices makes its corresponding security be greatly tested. The network security technology and the corresponding computer hardware security technology have become the focus of people's attention. At the same time, the construction of multimedia database based on this related technology has become the development trend of people's research and attention. Most of the conventional computer security technology focuses on the security construction of the operating system, which has certain openness, so its corresponding reliability is very low, but also uncontrollable. Based on this, this paper proposes a design scheme of the public security architecture on the embedded terminal, and realizes the design of the multimedia security broadcast control system based on this scheme, and realizes the play and storage of multimedia files, as well as the interaction between the corresponding media servers. The wireless network embedded security terminal designed in this paper includes client terminal identity authentication, corresponding media content protection and storage, and real-time broadcast content monitoring. Finally, the experiment is carried out based on the proposed design scheme. The experimental results show that the embedded security terminal proposed in this paper has obvious performance advantages, and its correctness and availability have been verified in practice.

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

Abstract Background Classic Kaposi sarcoma (CKS) is a vascular sarcoma associated with human herpesvirus 8 (HHV-8), which is known to be more common in Mediterranean elderly men and is characterized by indolent clinical behavior. Xinjiang province in China is considered an endemic region for Kaposi’s sarcoma-associated herpesvirus (KSHV), with higher incidence among adults of Kazak and Uyghur ethnicities. Cases of CKS are rarely reported in inland China. Here, we followed a case of CKS for 7 years in a patient of Miao ethnic background in southwestern China. Case presentation A 63-year-old Miao (southwestern China) man was initially diagnosed with CKS in 2010, having a history of limb lesions for 37 years, with left eyelid and binaural lesions for 9 years. He did not have sexual contact with men and was human immunodeficiency virus (HIV)-negative. Due to his lumbago and fever, spinal tuberculosis in the lumbar vertebra was highly suspected after computed tomography (CT) scan. However, diagnostic antituberculosis treatment for 4 weeks failed. The patient was followed up in 2016, when the rash was recovering as the systemic symptoms improved. A new CT was performed, which showed a partial response despite the absence of any medical treatment. The open reading frame (ORF)-K1 of KSHV from skin tissue of the foot was amplified and sequenced, and K1 belonged to subtype A. This genotype is consistent with the typical subtype present in Xinjiang. Conclusions We describe spontaneous partial regression of CKS in a patient of Miao ethnicity in inland China. Our sample may represent an unknown, novel genotype. Surveillance and regulating the immune state may represent a valuable approach for this rare disease.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a critical clinical syndrome with high morbidity and mortality. Currently, the primary treatment for ALI/ARDS is mainly symptomatic therapy such as mechanical ventilation and fluid management. Due to the lack of effective treatment strategies, most ALI/ARDS patients face a poor prognosis. The discovery of exosomes has created a promising prospect for the treatment of ALI/ARDS. Exosomes can exert anti-inflammatory effects, inhibit apoptosis, and promote cell regeneration. The microRNA contained in exosomes can participate in intercellular communication and play an immunomodulatory role in ALI/ARDS disease models. This review discusses the possible mechanisms of exosomes in ALI/ARDS to facilitate the development of innovative treatments for ALI/ARDS.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) develops rapidly and has high mortality. ALI/ARDS is mainly manifested as acute or progressive hypoxic respiratory failure. At present, there is no effective clinical intervention for the treatment of ALI/ARDS. Mesenchymal stromal cells (MSCs) show promise for ALI/ARDS treatment due to their biological characteristics, easy cultivation, low immunogenicity, and abundant sources. The therapeutic mechanisms of MSCs in diseases are related to their homing capability, multidirectional differentiation, anti-inflammatory effect, paracrine signaling, macrophage polarization, the polarization of the MSCs themselves, and MSCs-derived exosomes. In this review, we discuss the pathogenesis of ALI/ARDS along with the biological characteristics and mechanisms of MSCs in the treatment of ALI/ARDS.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a critical clinical syndrome with high morbidity and mortality that poses a major challenge in critical care medicine. The development of ALI/ARDS involves excessive inflammatory response, and macrophage autophagy plays an important role in regulating the inflammatory response in ALI/ARDS. In this paper, we review the effects of autophagy in regulating macrophage function, discuss the roles of macrophage autophagy in ALI/ARDS, and highlight drugs and other interventions that can modulate macrophage autophagy in ALI/ARDS to improve the understanding of the mechanism of macrophage autophagy in ALI/ARDS and provide new ideas and further research directions for the treatment of ALI/ARDS.

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is a common condition with high mortality. ALI/ARDS is caused by multiple etiologies, and the main clinical manifestations are progressive dyspnea and intractable hypoxemia. Currently, supportive therapy is the main ALI/ARDS treatment, and there remains a lack of targeted and effective therapeutic strategies. Macrophages are important components of innate immunity. M1 macrophages are pro-inflammatory, while M2 macrophages are anti-inflammatory and promote tissue repair. Mesenchymal stem cells (MSCs) are stem cells with broad application prospects in tissue regeneration due to their multi-directional differentiation potential along with their anti-inflammatory and paracrine properties. MSCs can regulate the balance of M1/M2 macrophage polarization to improve the prognosis of ALI/ARDS. In this paper, we review the mechanisms by which MSCs regulate macrophage polarization and the signaling pathways associated with polarization. This review is expected to provide new targets for the treatment of ALI/ARDS.

We demonstrated numerically the generation of broadband, coherent supercontinuum (SC) spectra in the mid-infrared region using dispersion-engineered all-chalcogenide microstructured fibers (MOFs). The 1-cm-long hexagonal fiber can be made with Ge11.5As24S64.5 glass as a low-refractive-index material embedded in a Ge11.5As24Se64.5 glass matrix. By optimizing the structural parameters, we determined a broad and flat all-normal-dispersion characteristic up to 14 μm. A highly coherent broadband SC with an intensity greater than −3 dB in the range from 5973 to 8695 nm is obtained when the fiber is pumped by a 7 μm laser with pulse duration of 50 fs and peak power of 6 kW. Flat-top SC of −30 dB level can be observed utilizing identical pump pulse parameters, covering wavelengths from 3823 to 13577 nm (>1.5 octaves). This broadband coherent MIR SC source can be applied in frequency metrology, optical coherence tomography, biomedical imaging, and few-cycle pulse compression.

BackgroundMalignant hyperthermia (MH) is a rare life-threatening anesthetic emergency. With respect to the high fatality rate, difficulty in early recognition, and the lack of disease-specific drug (ie, dantrolene) in China, more effort is needed to strengthen early diagnosis and effective treatment of MH emergencies. Nowadays, mobile health (mHealth) apps are changing the way of medical practice; they can serve as an accessible tool to help anesthesiologists deal with MH crises. However, no related mHealth-based emergency system is available currently. ObjectiveThe aim of this study is to outline the protocol for the development of a WeChat applet used to design a National Remote Emergency System for Malignant Hyperthermia (MH-NRES) in China, as well as the protocol for the evaluation of the user experience and perception of the system. MethodsThe system adopts the client-server architecture, with a custom user interface operating as clients and the back-end system operating as the server. The client-side software was developed using uni-app technology with Vue.js-based framework, which consists of 6 modules: Quick Diagnosis, Dantrolene Mobilization, Instruction on Dantrolene Use, MH Treatment, Recovery Period Treatment, and DNA Test and Biopsy. The back-end system was developed based on the Spring framework. The system will be evaluated by administrating a modified user version of the Mobile App Rating Scale. Pilot testing will be conducted in Sichuan Province, China, and a subsequent evaluation on a national scale is planned. ResultsThe theoretical framework design of this system was completed in August 2021. The development of the system was completed in February 2022, and the refinement is currently ongoing. Pilot testing after the implementation of the system in Sichuan Province is planned to take 2 months, and the subsequent evaluation on a national scale is planned to take 2 months. ConclusionsWe have described a novel approach using the WeChat applet to develop the MH-NRES. Findings from the usability testing process in the current study may lead to refinements and is expected to suggest that this system is both feasible and welcomed by anesthesiologists. Depending on the availability of research funding, this system will be extended nationally across China. International Registered Report Identifier (IRRID)PRR1-10.2196/37084

Abstract Acute respiratory distress syndrome (ARDS) develops rapidly and has a high mortality rate. Survivors usually have low quality of life. Current clinical management strategies are respiratory support and restricted fluid input, and there is no suggested pharmacological treatment. Mesenchymal stromal cells (MSCs) have been reported to be promising treatments for lung diseases. MSCs have been shown to have a number of protective effects in some animal models of ARDS by releasing soluble, biologically active factors. In this review, we will focus on clinical progress in the use of MSCs as a cell therapy for ARDS, which may have clinical implications during the coronavirus disease 2019 (COVID-19) pandemic.

Abstract Due to its significance for gas hydrate accumulation, the gas hydrate stability zone (GHSZ) is an essential condition for successful gas hydrate exploration and is usually controlled by three main factors: gas components, geothermal gradient, and permafrost thickness. Based on the core and conventional log data of gas hydrate potential region, the CSMHYD program was adopted to simulate the influences of above three factors on the thickness of the GHSZ. When the gas components of the gas hydrate were CH4, C2H6, and C3H8, with the CH4 percentage ranging from 50% to 100%, the average GHSZ thickness can reach 1000 m according to the forward simulation results. When the gas hydrate was composed of CH4 and one of C2H6, C3H8, H2S, and CO2, the influence order of the above four gases on the thickness of GHSZ is as follows: H2S ＞ C2H6 ＞ C3H8 ＞ CO2. Under the gas components of 90% CH4, 5% C2H6, and 5% C3H8, the thickness of GHSZ decreased rapidly as the geothermal gradient increased following an exponential relation with a correlation of 99.92% and increased slowly as permafrost thickness increased following a linear relation with a correlation of 99.9%. In addition, the thickness of a gas hydrate favorable reservoir was determined by comprehensive log methods as 500 m. We conclude that a reservoir within 500 m below the permafrost layer is favorable for gas hydrate reservoir formation.

IntroductionAbdominal cocoon is a unique peritoneal disease that is frequently misdiagnosed. The occurrence of the abdominal cocoon with a jejuno-ileo-colonic fistula has not been previously reported.Case PresentationWe admitted a 41-year-old female patient with an abdominal cocoon and a jejuno-ileo-colonic fistula. She was admitted to our hospital for the following reasons: “the menstrual cycle is prolonged for half a year, and fatigue, palpitations, and shortness of breath for 2 months”. On the morning of the 4th day of admission, the patient experienced sudden, severe, and intolerable abdominal pain after defecating. An emergency abdominal CT examination revealed intestinal obstruction. Surgery was performed, and the small intestine and colon were observed to be conglutinated and twisted into a mass surrounded by a fibrous membrane, and an enteroenteric fistula was observed between the jejunum, ileum, and sigmoid colon. We successfully relieved the intestinal obstruction and performed adhesiolysis. The patient was discharged from our hospital on the 6th postoperative day, then she recovered and was discharged from Feicheng People's Hospital after another 11 days of conservative treatment, and she recovered well-during the 2-month follow-up period.ConclusionAbdominal cocoon coexisting with a jejuno-ileo-colonic fistula is very rare. During the process of abdominal cocoon treatment, the patient's medical history should be understood in detail before the operation, and the abdominal organs should be carefully evaluated during the operation to avoid missed diagnoses.