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Abstract Silk fabrics hold immense historical value as precious legacies left by our ancestors, yet they face significant damage during archaeological excavations, necessitating urgent protective measures. However, The current protective materials can’t effectively prevent the degradation of silk fabrics. Nanotechnology has emerged as a promising avenue for the consolidation and preservation of silk fabrics, offering novel concepts and materials. In this study, we propose an innovative and cost-effective method that uses the MOF-818 with a radical scavenging ability to enhance the protection of silk fabrics. The resulting demonstrates that the MOF-818 was the large surface area and porous properties, which exhibited excellent superoxide dismutase (SOD)-like activity at 10 ug/mL. The silk fabrics treated by MOF-818 displays small color difference, reduced the oxidation of functional group and prevents the degradation of silk fabrics. The successful development of this nanocomposite marks a significant advancement in silk protection, opening new horizons for the preservation of silk cultural relics.

Background: Given the wide application of TNF-α inhibitors for the treatment of rheumatoid arthritis (RA), more active tuberculosis cases have been reported in patients with RA. This meta-analysis was performed to evaluate the risk of active tuberculosis (TB) in RA patients undergoing treatment with TNF-α inhibitors. Methods and Results: Thirty randomized controlled trials (RCTs), comprising 18,640 patients, were included in the meta-analysis after searching PubMed, EMBASE, and Web of Science databases. Overall, there was a higher risk of active TB in patients with RA undergoing treatment with TNF-α inhibitors than in the control group (Peto OR, 2.57; 95%CI, 1.42-4.66; P=0.002). Conclusion: Our meta-analysis showed that RA patients treated with TNF-α inhibitors had a higher risk of developing active TB. Tuberculosis screening should be performed before and during the use of TNF-α inhibitors in these patients.

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.

Interleukin-37 (IL-37), previously known as IL-1F7, is a member of the IL-1 family of cytokines. There are five basic subtypes of IL-37, including IL-37a, IL-37b, IL-37c, IL-37d, and IL-37e. Like other members of the IL-1 family, IL-37 is initially expressed as an immature precursor protein that needs to be processed enzymatically by caspase-1 to generate the bioactive protein. However, unlike most other members of the IL-1 family, IL-37 induces anti-inflammatory activities in IL-37 receptor-positive target cells. IL-37 functions as an extracellular protein by binding to the IL-18 receptor, IL-18R, and an intracellular protein via its interaction with SMAD family member-3 (SMAD-3). This article reviews recent findings regarding the IL-37 protein maturation process and the biological functions mediated by this cytokine.

The intake of high-fat diets (HFD) has been shown to diminish the muscle quality of aquatic animals. Sanguinarine, as an excellent additive, exhibits the capability to reduce fat deposition and alleviate inflammation. However, its role in the muscle quality reduction caused by HFD remains unclear. An eight-week trial was conducted to investigate the impacts of dietary supplementation of sanguinarine at 1200 μg/kg (HFDS; crude fat = 10%) on the muscle quality of grass carp (Ctenopharyngodon idellus) in comparison to a basic diet (CON, crude fat = 5%). Each group had 3 replicates, with 40 fish per replicate. This experiment employed one-way ANOVA and Duncan's multiple comparisons of the means. The results showed that the HFD exhibited lower growth performance, reduced protein deposition, myofiber diameter, and muscle hardness, coupled with higher levels of fat deposition and inflammation when compared with the CON. However, HFDS improved growth performance (P < 0.05), fat metabolism (ppar-α ( P = 0.001), lpl (P < 0.001), atgl (P < 0.001), and cpt1 (P = 0.001) expression exhibited a significant elevation), protein deposition (the protein and mRNA levels of AKT (P = 0.004), PI3K (P = 0.027), TOR (P = 0.005), and P70S6K (P = 0.007) demonstrated a marked increase), myofiber diameter, muscle hardness, and the total content of eicosapentaenoic acid and docosahexaenoic acid. Furthermore, the HFDS reduced oxidative damage caused by fat deposition by significantly downregulating nf-κb (P < 0.001), il-1β (P < 0.001), il-6 (P < 0.001), il-8 (P = 0.003), and tnf-α (P < 0.001) expression and markedly upregulated nrf2 (P < 0.001), gpx4 (P < 0.001), cat (P < 0.001), sod (P < 0.001), and gr (P = 0.003) expression. The findings from this study suggest that sanguinarine has the potential to alleviate the adverse effects of HFD on growth and muscle quality, providing a theoretical foundation for its practical implementation.

Summary: Background: Loss of heterozygosity (LOH) diminishes genetic diversity within cancer genomes. A tumour arising in an individual heterozygous for a functional and a loss-of-function (LoF) allele of a gene occasionally retain only the LoF allele. This can result in deficiency of specific protein activities in cancer cells, creating unique differences between tumour cells and normal cells of the individual. Such differences may constitute vulnerabilities that can be exploited through allele-specific therapies. Methods: To discover frequently lost genes with prevalent LoF alleles, we mined the 1000 Genomes dataset for SNVs causing protein truncation through base substitution, indels or splice site disruptions, resulting in 60 LoF variants in 60 genes. From these, the variant rs3892097 in the liver enzyme CYP2D6 was selected because it is located within a genomic region that frequently undergoes LOH in several tumor types including hepatocellular cancers. To evaluate the relationship between CYP2D6 activity and the toxicities of anticancer agents, we screened 525 compounds currently in clinical use or undergoing clinical trials using cell model systems with or without CYP2D6 activity. Findings: We identified 12 compounds, AZD-3463, CYC-116, etoposide, everolimus, GDC-0349, lenvatinib, MK-8776, PHA-680632, talazoparib, tyrphostin 9, VX-702, and WZ-3146, using an engineered HEK293T cell model. Of these, talazoparib and MK-8776 demonstrated consistently heightened cytotoxic effects against cells with compromised CYP2D6 activity in engineered hepatocellular cancer cell models. Moreover, talazoparib displayed CYP2D6 genotype dependent effects on primary hepatocellular carcinoma organoids. Interpretation: Exploiting the loss of drug-metabolizing enzyme gene activity in tumor cells following loss of heterozygosity could present a promising therapeutic strategy for targeted cancer treatment. Funding: This work was funded by Barncancerfonden (T.S, PR2022-0099 and PR2020-0171, X.Z, TJ2021-0111), Cancerfonden (T.S, 211719Pj and D.G, 222449Pj), Vetenskapsrådet (T.S, 2020-02371 and D.G, 2020-04707), and the Erling Persson Foundation (T.S, 2020-0037 and T.S, 2023-0113).

The objective of this study is to investigate the effects of free gossypol in cottonseed meal (CM) on the growth and intestinal health of grass carp, as well as to determine whether free gossypol is the primary factor restricting high-level inclusion of cottonseed meal. This study was designed with five groups: the control group (32% soybean concentrate protein), the CM group (44% CM), the cottonseed protein concentrate (CPC) group (33% CPC), and the CPC and control groups with 400 and 600 ​mg/kg gossypol added, designated as CON, CM, CPC, CPC ​+ ​GP, and CON ​+ ​GP, respectively. The experiment lasted eight weeks, and each group had three replicates. This experiment employed one-way ANOVA and Duncan's multiple comparisons of the means. The findings revealed that in comparison to the CON group, the growth of fish in the CM, CPC ​+ ​GP, and CON ​+ ​GP groups significantly decreased. Intestinal inflammation damage was observed. This was indicated by a significant upregulation of pro-inflammatory factors, including tnf-α, nf-κb, il-6, il-8, il-12β, and il-1β, along with significant downregulation of anti-inflammatory factors, including tgf-β1, il-15, and il-10. Additionally, significant downregulation of antioxidant enzyme-related genes, including nrf2, cat, CuZnsod, gpx4, and gpx1, was observed and the intestinal physical barrier function was compromised. In addition, the intestinal microbiota composition was affected, with a significant reduction in Bacillus and Cetobacterium abundances and a remarkable increase in the abundance of Aeromonas, resulting in dysregulation of the intestinal microbiota function. However, in comparison to the CM group, the growth rate of fish in the CPC group exhibited a marked increase; nonetheless, it persisted at a level lower than that observed in the CON group and the degree of intestinal damage was significantly improved. Additionally, the intestinal microbiota structure was found to be similar to that of the CON group. In conclusion, excessive CM negatively affects grass carp growth and intestinal health, whereas replacing CM with CPC mitigates these effects. Adding gossypol equivalent to CM group in CON and CPC diets reduces growth performance and impairs intestinal function, indicating gossypol as a primary limiting factor for high CM inclusion in diets.

Lycopene, a carotenoid with numerous physiological benefits, particularly in its Z-isomer form, faces challenges in its application due to low chemical stability. To address this limitation, high internal phase emulsion was successfully synthesized using ovalbumin-chitosan complexes. The aim was to enhance the stability of lycopene including Z-lycopene. The solubility, particle size, ζ-potential and uniformity of the mixture were dependent on pH value and biopolymer proportion. Notably, optimal ovalbumin-chitosan complex formation occurred at pH 2.5 with a ratio of 4:1 resulting in the highest solubility and optimal uniformity which contributed to its superior emulsification properties. Evaluation of encapsulating efficiency and loading amount revealed 98.19% and 1.7661 mg/g respectively for lycopene in ovalbumin-chitosan stabilized emulsions, inhibiting the transformation from Z-lycopene to (all-E)-lycopene. The encapsulated lycopene possessed UV stability where retention rate remained high at 81.86%. The retention rate was up to 65.37% and 41.82% at 45 °C and 80 °C, respectively.

The Tibetan Plateau (TP) has been the focus of numerous studies examining the energy and water cycle variations, but there is still a lack of long-term, quantitative precise assessments of evapotranspiration. This research first provided two sets of long-term comprehensive observational datasets, and an advanced monitoring technique to measure soil moisture, which can improve the estimation accuracy of evapotranspiration. Subsequently, using microwave data, the Surface Energy Balance System model and Machine Learning methods, it calculated a complete set of long-term evapotranspiration data. At the same time, based on reasonable assumptions, it also estimated the total evaporation from plateau lakes. These findings contribute significantly to the understanding of the relationship between the Asian monsoon, the TP’s physical characteristics, and its atmosphere, thereby improving predictions of water resource variability in the TP. The study’s innovative methodologies and synthesis of diverse data sources provide critical information for informed and sustainable water management strategies in the region.

Abstract Lithium-ion batteries (LIBs) are widely used in portable energy storage. The capacity of commercial graphite is difficult to improve due to the stoichiometry limit of LiC6 of graphite, thus new anodes need to be developed to meet the demand of high-energy–density LIB. The growing interest in graphitized carbon nitride (g-C3N4) stems from its structural resemblance to graphite and its capacity to offer abundant adsorption and intercalation sites. However, g-C3N4, as a semiconductor, has a low lithium transfer rate due to its poor conductivity and high diffusion resistance. Improving the electron transport rate of g-C3N4 and reducing the adsorption energy barrier of Li+ in g-C3N4 are the keys to improving the electrochemical performances of g-C3N4. In this study, lignin and melamine were homogeneously mixed using the spray drying method, followed by the preparation of covalently bonded C3N4/LC material through a one-step carbonization process. The uniform dispersion of g-C3N4 in amorphous carbon can improve the conductivity and reduce the diffusion energy barrier of Li+. As a result, the C3N4/LC-x anode has better electrochemical behavior, including higher reversible capacity, better rate performance, and cycle stability.

Abstract Background Cardiac arrest (CA) is one of the leading causes of death globally, characterized by high incidence and mortality. It is of particular significance to determine the prognosis of patients with CA early and accurately. Therefore, we aim to investigate the correlation between albumin-corrected calcium (ACC) and the prognosis in patients diagnosed with CA. Methods We retrospectively collected data from medical information mart for intensive care IV database. Patients were divided into two groups (survival and non-survival groups), according to the 90-day prognosis. In the Restricted cubic spline (RCS) analysis, the cut-off values (8.86 and 10.32) were obtained to categorize patients into three groups: low ACC group ( 10.32). The least absolute shrinkage and selection operator with a ten-fold cross-validation regression analysis was performed to identify variables linked to the mortality. The inverse probability treatment weighting (IPTW) was used to address the confounding factors, and a weighted cohort was generated. RCS, Kaplan–Meier curve, and Cox regression analyses were used to explore the relationship between ACC and the mortality. Sensitivity analysis was employed to validate the stability of the results. Results Cut-off values for ACC of 8.86 and 10.32 were determined. RCS analyses showed that there was an overall non-linear trend relationship between ACC and the risk of 90-day and 360-day mortalities. After IPTW adjustment, compared to the moderate ACC group, the 90-day and 360-day mortalities in the high ACC group were higher (P 10.32) were independent risk factors for 90-day and 360-day all-cause mortality in patients with CA (P

Abstract Background Leptospirosis, an important zoonotic bacterial disease, commonly affects resource-poor populations and results in significant morbidity and mortality worldwide. The value of antibiotics in leptospirosis remains unclear, as evidenced by the conflicting opinions published. Methods We conducted a search in the PubMed, Web of Science, and Cochrane Library databases for studies. These studies included clinical trials and retrospective studies that evaluated the efficacy or safety of antibiotics for leptospirosis treatment. The primary outcomes assessed were defervescence time, mortality rate, and hospital stays. Subgroup analyses were performed based on whether there were cases involving children and whether there were cases of severe jaundice. Safety was defined as the prevalence of adverse events associated with the use of antibiotics. p scores were utilized to rank the efficacy of the antibiotics. Results There are included 9 randomized controlled trials (RCTs), 1 control trial (CT), and 3 retrospective studies (RS) involving 920 patients and 8 antibiotics. Six antibiotics resulted in significantly shorter defervescence times compared to the control, namely cefotaxime (MD, − 1.88; 95% CI = − 2.60 to − 1.15), azithromycin (MD, − 1.74; 95% CI = − 2.52 to − 0.95), doxycycline (MD, − 1.53; 95% CI = − 2.05 to − 1.00), ceftriaxone (MD, − 1.22; 95% CI = − 1.89 to − 0.55), penicillin (MD, − 1.22; 95% CI = − 1.80 to − 0.64), and penicillin or ampicillin (MD, − 0.08; 95% CI = − 1.01 to − 0.59). The antibiotics were not effective in reducing the mortality and hospital stays. Common adverse reactions to antibiotics included Jarisch–Herxheimer reaction, rash, headache, and digestive reactions (nausea, vomiting, diarrhea, abdominal pain, and others). Conclusions Findings recommend that leptospirosis patients be treated with antibiotics, which significantly reduced the leptospirosis defervescence time. Cephalosporins, doxycycline, and penicillin are suggested, and azithromycin may be a suitable alternative for drug-resistant cases. Systematic review registration PROSPERO CRD42022354938.

Abstract Immunosenescence increases the risk and severity of diseases in elderly individuals and leads to impaired vaccine-induced immunity. With aging of the global population and the emerging risk of epidemics, developing adjuvants and vaccines for elderly individuals to improve their immune protection is pivotal for healthy aging worldwide. Deepening our understanding of the role of immunosenescence in vaccine efficacy could accelerate research focused on optimizing vaccine delivery for elderly individuals. In this review, we analyzed the characteristics of immunosenescence at the cellular and molecular levels. Strategies to improve vaccination potency in elderly individuals are summarized, including increasing the antigen dose, preparing multivalent antigen vaccines, adding appropriate adjuvants, inhibiting chronic inflammation, and inhibiting immunosenescence. We hope that this review can provide a review of new findings with regards to the impacts of immunosenescence on vaccine-mediated protection and inspire the development of individualized vaccines for elderly individuals.

Abstract Aims Acute myocardial infarction (AMI) is a cardiovascular disease with high morbidity and mortality. We collected patients with AMI from the Medical Information Mart for Intensive Care IV (v2.0) database and explored the association between serum albumin‐corrected anion gap (ACAG) level and mortality in patients with AMI. Methods and results Data of adult patients with AMI were collected. According to the 360 day prognosis, patients were divided into survival and non‐survival groups. Based on the ACAG level, patients were then divided into normal and high ACAG groups. Cox hazard proportional models and restricted cubic splines (RCSs) were used to investigate the correlation between ACAG and mortality. Kaplan–Meier curves were created to compare the cumulative survival rates between the high and normal ACAG groups. The receiver operating characteristic (ROC) curve was used to analyse the predictive value of ACAG for the prognosis of patients with AMI. Sensitivity and subgroup analyses were conducted to revalidate the results. Finally, 1783 patients were included. Elevated ACAG (>20 mmol/L) was significantly associated with 30 and 360 day mortality (P 20 mmol/L) was an independent risk factor of increased all‐cause mortality in patients with AMI (hazard ratio 1.423, 95% confidence interval 1.206–1.678, P

Progress in the systemic therapies, represented by the molecular target therapies and immune checkpoint inhibitors (ICIs), have significantly improved the prognosis of unresectable hepatocellular carcinoma (uHCC), but long term survial remains limitted. As one of the major combination of systemic and surgical therapies, conversion therapy provides potentially curative opportunity to some uHCC patients. It is widely reported from different domestic centers.The first Chinese expert consensus about it was released in 2021. Yet at the same time, there are many key issues in the practice and theory remains to be resolved. Actively thinking, exploring and finally resolving these issues are essential for establishing and promoting the standardized theoretical system of uHCC conversion therapy.

Jiang-flavor Baijiu is popular among consumers because of its profound cultural deposits, traditional solid-state brewing process, unique flavor, and sensory experience. In this study, 537 sensory descriptors for Jiang-flavor Baijiu were obtained from 121 articles and 96 standards regarding ‘Jiang-flavor’ and ‘Jiang-flavor Baijiu’ retrieved from China National Knowledge Infrastructure (CNKI), the National Public Service Platform for Standards Information and the Foodmate Website. By sorting out and analyzing these literature data, this paper briefly describes the formation, development, trend and standardization of the sensory quality descriptions of Jiang-flavor Baijiu, summarizes the relationship between the flavor substances and the odor characteristics of Jiang-flavor Baijiu, analyzes the problems with the existing descriptors, and proposes ideas and suggestions for constructing a sensory quality description system for Jiang-flavor Baijiu and using modern sensory evaluation techniques to characterize the sensory quality of Jiang-flavor Baijiu.

Abstract Inflammation plays a key role in pathogenesis and rupture of aneurysms. Non-invasively and dynamically monitoring aneurysm inflammation is critical. This study evaluated myeloperoxidase (MPO) as an imaging biomarker and therapeutic target for aneurysm inflammation using an elastase-induced rabbit model treated with or without 4-aminobenzoic acid hydrazide (ABAH), an irreversible inhibitor of MPO. Myeloperoxidase-sensitive magnetic resonance imaging (MRI) using Mn-TyrEDTA, a peroxidase activity-dependent contrast agent, revealed weak contrast enhancement in contralateral arteries and decreased contrast enhancement in aneurysm walls with ABAH treatment, indicating MPO activity decreased and inflammation mitigated. This was supported by reduced immune cell infiltration, matrix metalloproteinases (MMP-2 and − 9) activity, ROS production and arterial wall destruction on histology. Finally, the aneurysm expansion rate remained

Background To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.Methods The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves).Results Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram (https://chsyh2006.shinyapps.io/dynnomapp/). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value.Conclusions Our study selected the optimal model and visualized it as a static and dynamic nomogram integrating clinical predictors, so that clinicians can personalized predict the in-hospital outcome of critically ill patients with AKI undergoing CRRT upon ICU admission.

Background The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT).Methods Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed.Results In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p 1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

Objective To explore the relationship between lactate-to-albumin ratio (LAR) at ICU admission and prognosis in critically ill patients with acute kidney injury (AKI).Methods A retrospective analysis was conducted. Patients were divided into low (

ABSTRACTPancreatic ductal adenocarcinoma (PDAC) is currently difficult to treat, even when therapies are combined with immune checkpoint blockade (ICB). A novel strategy for immunotherapy would be to maximize the therapeutic potential of oncolytic viruses (OVs), which have been proven to engage the regulation of tumor microenvironment (TME) and cause-specific T-cell responses. To boost tumor sensitivity to ICB therapy, this study aimed to investigate how glutathione peroxide 4 (GPX4)-loaded OVs affect CD8+ T cells and repair the immunosuppressive environment. Here, we successfully constructed a novel recombinant oncolytic vaccinia virus (OVV) encoding the mouse GPX4 gene. We found the OVV-GPX4 effectively replicated in tumor cells and prompted the expression of GPX4 in T cells. Our research indicated that OVV-GPX4 could reshape the TME, rectify the depletion of CD8+T cells, and enhance the antitumor effects of ICB therapy.

Summary: Continuous renal replacement therapy (CRRT) is a commonly utilized treatment modality for individuals experiencing severe acute kidney injury (AKI). The objective of this research was to construct and assess prognostic models for the timely discontinuation of CRRT in critically ill AKI patients receiving this intervention. Data were collected retrospectively from the MIMIC-IV database (n = 758) for model development and from the intensive care unit (ICU) of Huzhou Central Hospital (n = 320) for model validation. Nine machine learning models were developed by utilizing LASSO regression to select features. In the training set, all models demonstrated an AUROC exceeding 0.75. In the validation set, the XGBoost model exhibited the highest AUROC of 0.798, leading to its selection as the optimal model for the development of an online calculator for clinical applications. The XGBoost model demonstrates significant predictive capabilities in determining the discontinuation of CRRT.

Introduction Ixodes ticks are pivotal in transmitting diseases like Lyme disease and human granulocytic anaplasmosis, caused by Borrelia burgdorferi and Anaplasma phagocytophilum, respectively. These pathogens not only affect humans through single or multiple tick bites but also pose risks to animal hosts, leading to potential coinfections. Despite regional studies indicating significant prevalence, their global coinfection data remain sparse. This study aims to bridge this gap through a systematic review and meta-analysis of B. burgdorferi and A. phagocytophilum coinfections in Ixodes ticks worldwide. Addressing data limitations and study variability, it seeks to provide a nuanced understanding of coinfection patterns, their epidemiological implications and inform targeted prevention strategies.Methods and analysis Following Preferred Reporting Items for Systematic Review and Meta-analysis Protocols 2015 guidelines and PROSPERO registration, this study will undertake a thorough database search without constraints on language or publication date, using standardised screening and data extraction protocols. The quality and bias of studies will be evaluated using Joanna Briggs Institute tools. In the statistical analysis phase, conducted in R, we will initially determine the use of fixed or random-effects models based on the assessment of data heterogeneity. This choice will guide the framework for subsequent analyses. Within the selected model’s framework, we will perform subgroup analyses and meta-regression to investigate the effects of various factors, ensuring that each step is tailored to the initial model selection to maintain analytical consistency.Ethics and dissemination As this study does not involve clinical research or data collection from subjects, ethical approval is not required. We will uphold ethical standards in synthesising and reporting data. Study outcomes will be published in peer-reviewed journals, communicating findings to the scientific community and contributing to the understanding of Ixodes tickborne diseases.PROSPERO registration number CRD42023449735.

BackgroundSubclinical hypothyroidism (SCH) is a common endocrine subclinical disorder, the main adverse consequences of which are the development of clinical hypothyroidism and the promotion of ischemic heart disease. Metabolic syndrome (MetS) is a collection of metabolic problems. The goal of this meta-analysis was to evaluate the relationship between MetS and SCH.MethodsSuitable publications were identified using PubMed, Embase, and the Cochrane Library. The meta-analysis included only studies in English that reported odds ratio (OR) data for MetS and SCH. Two researchers combined data using a random-effects model. OR and 95% confidence intervals (CIs) were used to present the results.ResultsMetS was associated with an elevated risk of developing SCH (OR 2.56, 95% CI 1.44–4.55). However, the individual components of MetS were not associated with the risk of SCH. Subgroup analysis revealed that different definitions of MetS had varying effects on SCH. Sensitivity analysis confirmed that our results were robust.ConclusionsThis meta-analysis indicates that patients with MetS have an increased risk of SCH, while there is no significant association between the five individual components of MetS and the risk of SCH.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023454415.

IntroductionThe precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields.MethodsFirst, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd’s purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCW-YOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed.Results and discussionThe ablation experiment results showed that the CSCW-YOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications.

Abstract This study investigates the responses of the urban atmospheric thermal environment to two distinct heat waves in Hefei, China, and explores potential changes associated with future urban expansion. During the Event 1, characterized by clear and dry conditions, the western Pacific subtropical high limits water vapor influx, resulting in a significant cooling effect in rural area due to higher surface latent heat flux. The urban heat island (UHI) intensity, calculated using surface temperature and 2‐m temperature, reaches 5.2°C and 1.7°C during the Event 1, respectively. Although Event 2, characterized by cloudy and humid conditions, exhibits weaker UHI and urban dry island effects, it remains highly unfavorable for human comfort. During distinct heat waves, the vertical extent of the warming effect induced by future urban expansion varies, which can be attributed to environmental factors, such as atmospheric stability and near‐surface wind speed.

BackgroundGiven the rising interest in complementary therapeutic strategies for autism spectrum disorder (ASD), this research aims to provide a comprehensive analysis of the impact of animal-assisted activities and therapies (AAAT) on various ASD symptoms.MethodsA meticulous search of databases, including Scopus and PubMed, was conducted to gather relevant research on AAAT for ASD. This process led to the selection of 45 studies encompassing 1,212 participants. The chosen studies were then subjected to a meta-analysis to evaluate the efficacy of AAAT in alleviating core ASD symptoms.ResultsThe meta-analysis revealed significant improvements in several core ASD symptoms due to AAAT. Notably, there were improvements in social communication (MD = −4.96, 95% CI [−7.49, −2.44]), irritability (MD = −2.38, 95% CI [−4.06, −0.71]), hyperactivity (MD = −4.03, 95% CI [−6.17, −1.89]), and different word usage skills (MD = 20.48, 95% CI [7.41, 33.55]). However, social awareness (MD = −1.63, 95% CI [−4.07, 0.81]), social cognition (MD = −3.60, 95% CI [−9.36, 2.17]), social mannerisms (MD = −0.73, 95% CI [−2.55, 1.09]), social motivation (MD = −1.21, 95% CI [−2.56, 0.13]), lethargy (MD = −1.12, 95% CI [−3.92, 1.68]), and stereotypical behaviors (MD = −0.23, 95% CI [−1.27, 0.80]) did not significantly improve.ConclusionThe study demonstrates the potential of AAAT in improving certain core symptoms of ASD, such as social communication, irritability, hyperactivity, and word usage skills. However, the effectiveness of AAAT in other ASD symptom domains remains uncertain. The research is limited by the absence of long-term follow-up data and a high risk of bias in existing studies. Therefore, while the findings indicate the promise of AAAT in specific areas, caution is advised in generalizing its efficacy across all ASD symptoms.

Abstract Acetoin, a versatile platform chemical and popular food additive, poses a challenge to the biosafety strain Bacillus subtilis when produced in high concentrations due to its intrinsic toxicity. Incorporating the PHB synthesis pathway into Bacillus subtilis 168 has been shown to significantly enhance the strain’s acetoin tolerance. This study aims to elucidate the molecular mechanisms underlying the response of B. subtilis 168-phaCBA to acetoin stress, employing transcriptomic and metabolomic analyses. Acetoin stress induces fatty acid degradation and disrupts amino acid synthesis. In response, B. subtilis 168-phaCBA down-regulates genes associated with flagellum assembly and bacterial chemotaxis, while up-regulating genes related to the ABC transport system encoding amino acid transport proteins. Notably, genes coding for cysteine and d-methionine transport proteins (tcyB, tcyC and metQ) and the biotin transporter protein bioY, are up-regulated, enhancing cellular tolerance. Our findings highlight that the expression of phaCBA significantly increases the ratio of long-chain unsaturated fatty acids and modulates intracellular concentrations of amino acids, including l-tryptophan, l-tyrosine, l-leucine, l-threonine, l-methionine, l-glutamic acid, l-proline, d-phenylalanine, l-arginine, and membrane fatty acids, thereby imparting acetoin tolerance. Furthermore, the supplementation with specific exogenous amino acids (l-alanine, l-proline, l-cysteine, l-arginine, l-glutamic acid, and l-isoleucine) alleviates acetoin’s detrimental effects on the bacterium. Simultaneously, the introduction of phaCBA into the acetoin-producing strain BS03 addressed the issue of insufficient intracellular cofactors in the fermentation strain, resulting in the successful production of 70.14 g/L of acetoin through fed-batch fermentation. This study enhances our understanding of Bacillus’s cellular response to acetoin-induced stress and provides valuable insights for the development of acetoin-resistant Bacillus strains.

Abstract Aims There has been a lack of research examining the relationship between red cell distribution width (RDW) and the prognosis of cardiac arrest (CA) patients. The prognostic value of the changes in RDW during intensive care unit (ICU) hospitalization for CA patients has not been investigated. This study aims to investigate the correlation between RDW measures at ICU admission and RDW changes during ICU hospitalization and the prognosis of CA patients and then develop a nomogram that predicts the risk of mortality of these patients. Methods and results A retrospective cohort study is used to collect clinical characteristics of CA patients (>18 years) that are on their first admission to ICU with RDW data measured from the Medical Information Mart for Intensive Care IV Version 2.0 database. Patients are randomly divided into a development cohort (75%) and a validation cohort (25%). The primary outcome is 30 and 360 day all‐cause mortality. ΔRDW is defined as the RDW on ICU discharge minus RDW on ICU admission. A multivariate Cox regression model is applied to test whether the RDW represents an independent risk factor that affects the all‐cause mortality of these patients. Meanwhile, the dose–response relationship between the RDW and the mortality is described by restricted cubic spine (RCS). A prediction model is constructed using a nomogram, which is then assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). A total of 1278 adult CA patients are included in this study. We found that non‐survivors have a higher level of RDW and ΔRDW compared with survivors, and the mortality rate is higher in the high RDW group than in the normal RDW group. The Kaplan–Meier survival curve indicates that patients in the normal RDW group had a higher cumulative survival rate at 30 and 360 days than those in the high RDW group (log‐rank test, χ2 = 36.710, χ2 = 54.960, both P values 15.50%) is an independent predictor of 30 [hazard ratio = 1.451, 95% confidence interval (CI) = 1.181–1.782, P

Land surface temperature (LST) is a crucial physical parameter for hydrological, meteorological, climatological, and climate change studies. To encourage the use of satellite-derived LST products in a wide range of applications, providing feedback on product performance over regional and global scales is an urgent task. However, considering that the uncertainty of newly released LST products is still unclear, it is urgently necessary to perform a comprehensive validation and error analysis, especially in areas with special geographical and weather conditions, such as the Tibetan plateau (TP). In particular, fewer studies have been concerned with the degraded LST retrieval accuracy over the TP because of the sparse ground measurements. In this study, moderate-resolution imaging spectroradiometer (MODIS) LST products (C6.1) were comprehensively evaluated based on the independent ground observation systems with different atmospheric and LST conditions. The in situ measurements collected from the Tibetan Observation and Research Platform and surface radiation systems are located on the American Plain and the TP, respectively, incorporating various land-cover types, including barren land, grassland, cropland, shrubland, and sparse and dense vegetation, among others. The spatial representativeness evaluation indicated that relatively high-quality in situ LSTs can be obtained during nighttime. Compared with the North American Plain (with a mean RMSE of 1.56 K), MODIS LST retrievals have larger discrepancies (mean RMSE of 2.34 K) over the TP with complex terrain and weather conditions. Emissivity determination is the primary source of the uncertainty in the generalized split-window (GSW) algorithm. Moreover, simulation settings of atmospheric and LST conditions in the GSW algorithm cannot cover a wide range of conditions at a global scale. It is expected to develop new LST retrieval algorithm to meet the quality specifications of users over the TP. Overall, this study identifies critical further research needs and improves the understanding of LST product performance under complex circumstances.

The increasing network traffic from connected vehicles is putting a strain on the limited bandwidth resources of cellular networks. Delay-tolerant networking (DTN) over vehicle-to-vehicle (V2V) communications has been considered as an effective means of offloading the cellular data traffic, while its quantitative performance in urban road traffic remains unclear in many aspects. In this paper, we unveil the benefits of data offloading over vehicular DTNs by city-scale network simulations in Nagoya, Japan. The simulation scenario embraces more than 8 million vehicle trips over five consecutive days. The vehicle routes are carefully calibrated against public statistics on the road traffic volume to enable realistic simulations of V2V communication opportunities between vehicles on the road. The results indicate the strong potential of vehicular DTNs in mixed urban road traffic, comprised of both public transport and privately owned vehicles – a large amount data traffic can be offloaded from cellular networks to V2V communication networks even with the limited ratio of vehicles participating the vehicular DTNs.

Abstract Background Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of the disease’s severe course. Disorders of lipid metabolism are highly correlated with the occurrence and severity of AP. We aimed to characterize the contribution and immunological characteristics of lipid metabolism-related genes (LMRGs) in non-mild acute pancreatitis (NMAP) and identify a robust subtype and biomarker for NMAP. Methods The expression mode of LMRGs and immune characteristics in NMAP were examined. Then LMRG-derived subtypes were identified using consensus clustering. The weighted gene co-expression network analysis (WGCNA) was utilized to determine hub genes and perform functional enrichment analyses. Multiple machine learning methods were used to build the diagnostic model for NMAP patients. To validate the predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) were used. Using gene set variation analysis (GSVA) and single-cell analysis to study the biological roles of model genes. Results Dysregulated LMRGs and immunological responses were identified between NMAP and normal individuals. NMAP individuals were divided into two LMRG-related subtypes with significant differences in biological function. The cluster-specific genes are primarily engaged in the regulation of defense response, T cell activation, and positive regulation of cytokine production. Moreover, we constructed a two-gene prediction model with good performance. The expression of CARD16 and MSGT1 was significantly increased in NMAP samples and positively correlated with neutrophil and mast cell infiltration. GSVA results showed that they are mainly upregulated in the T cell receptor complex, immunoglobulin complex circulating, and some immune-related routes. Single-cell analysis indicated that CARD16 was mainly distributed in mixed immune cells and macrophages, and MGST1 was mainly distributed in exocrine glandular cells. Conclusions This study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.

For patients with chronic spinal cord injury, the conventional treatment is rehabilitation and treatment of spinal cord injury complications such as urinary tract infection, pressure sores, osteoporosis, and deep vein thrombosis. Surgery is rarely performed on spinal cord injury in the chronic phase, and few treatments have been proven effective in chronic spinal cord injury patients. Development of effective therapies for chronic spinal cord injury patients is needed. We conducted a randomized controlled clinical trial in patients with chronic complete thoracic spinal cord injury to compare intensive rehabilitation (weight-bearing walking training) alone with surgical intervention plus intensive rehabilitation. This clinical trial was registered at ClinicalTrials.gov (NCT02663310). The goal of surgical intervention was spinal cord detethering, restoration of cerebrospinal fluid flow, and elimination of residual spinal cord compression. We found that surgical intervention plus weight-bearing walking training was associated with a higher incidence of American Spinal Injury Association Impairment Scale improvement, reduced spasticity, and more rapid bowel and bladder functional recovery than weight-bearing walking training alone. Overall, the surgical procedures and intensive rehabilitation were safe. American Spinal Injury Association Impairment Scale improvement was more common in T7–T11 injuries than in T2–T6 injuries. Surgery combined with rehabilitation appears to have a role in treatment of chronic spinal cord injury patients.

With the aid of endmember spectral libraries, sparse unmixing plays a critical role in interpreting hyperspectral remote sensing data. Integrating spatial clues from hyperspectral data into sparse unmixing frameworks is pivotal for enhancing unmixing capabilities. As such, extracting and harnessing spatial signatures from imagery has emerged as a prevalent tactic to optimize unmixing. In real-world scenarios, hyperspectral images are susceptible to noise, which poses great challenges to the separability of ground objects. As a result, most sparse unmixing models are ill-equipped to handle this issue properly, facing risks of failure. To tackle this challenge, we proposed a sparse unmixing technique with robust multiscale spectral–spatial regularization (RMSR). In the proposed RMSR model, an abundance estimation error reduction regularizer and a spectral–spatial weighted sparse regularizer are consolidated into a unified framework, which excavates the spatial information of the image from multiple perspectives. Specifically, in the first part, the abundance estimation error is defined as the difference between the precomputed abundance maps at the superpixel level and the expected abundances calculated from the original data. Then, the $\ell _{2,1}$ norm is applied to it as a regularization term, which enhances the robustness of the model against image noise and outliers. In the second part, image level spectral weighting coefficients and local spatial weighting terms are leveraged to individually enhance the sparsity of the abundance maps and their piecewise smoothness. The experimental results reveal the algorithm's considerable capabilities in noise immunity and improved unmixing abilities.

Abstract Background Patients admitted to the surgical intensive care unit (SICU) often suffer from multi-organ dysfunction and have a high mortality rate. Therefore, finding a simple but effective clinical indicator to predict the prognosis of patients is essential to improve their survival. The aim of this study was to investigate the relationship between blood urea nitrogen to serum albumin ratio (B/A) and short-term mortality among patients from the SICU. Methods All eligible adult patients admitted to the SICU from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were recruited for this study. Participants were divided into a death group (n = 638) and a survival group (n = 2,048) based on the 90-day prognosis, and then grouped by B/A quartiles. We used restricted cubic splines (RCS) to visually analyze the correlation of B/A with 30- and 90-day risk of death. Cumulative survival rates were estimated using Kaplan–Meier survival curves according to B/A quartiles and evaluated using the log-rank test. Cox proportional risk models were developed and sensitivity analyses were performed to explore whether B/A was independently associated with short-term outcomes in SICU patients. Receiver operating characteristic (ROC) curves were analyzed to ascertain the value of B/A for prognosticating 90-day outcome. Results A total of 2686 participants were included in the final study, and their 30-day and 90-day all-cause mortality rates were 17.61% and 23.75%, respectively. The differences in 30-day and 90-day mortality rates were statistically significant among the four groups of patients (all p 9.69) was an independent risk factor for 30-day and 90-day all-cause mortality in SICU patients. The analysis of ROC curves demonstrated that B/A exhibited a significant predictive ability for 90-day mortality, with an optimal threshold of 6.587, a sensitivity of 56.9%, and a specificity of 64.8%. Conclusions Elevated B/A (> 9.69) on admission was an independent risk factor for short-term mortality in SICU patients, and clinicians should pay more attention to this group of patients and intervene clinically at an early stage to reduce mortality.

Observational studies indicate that variations in peripheral blood mononuclear cell (PBMC) subsets are associated with an increased risk of pulmonary tuberculosis (PTB) and coronavirus disease 2019 (COVID-19), but causal validation is lacking. Here, we combined single-cell expression quantitative trait locus (sc-eQTL) and two-sample mendelian randomization (MR) analyses to elucidate the causal relationship between PBMC subsets and the occurrence of PTB and COVID-19 and verified by RT-qPCR. We observed an increase in the CD4+ Effective Memory T Cell (CD4+ TEM) cluster in both PTB and COVID-19 patients according to the single-cell transcriptional landscape of PBMC. Through MR analysis using an inverse variance weighted (IVW) method, we found strong evidence of positive correlations between CD4+ TEM cell markers (GBP2, TRAV1-2, and ODF2L) and PTB, and between markers (LAG3 and SLFN5) and COVID-19, especially highlighted by lead eQTL-SNPs of GBP2 (rs2256752, p = 4.76321 × 10−15) and LAG3 (rs67706382, p = 6.16× 10−16). Similar results were observed in validation sets, and no pleiotropy was detected in sensitivity analyses including weighted median (WM), MR-Egger, MR-pleiotropy residual sum and outlier, and leave-one-out analyses (all p > 0.05). We visualized the colocalization of marker-eQTLs and markers of PTB and COVID-19 genome-wide association study (GWAS) associations. Based on CellChat analyses, monocytes communicated predominantly with CD4+ TEM cells positively expressing PTB markers (GBP2, TRAV1-2, and ODF2L) and COVID-19 markers (LAG3 and SLFN5) in both PTB and COVID-19. Our data suggest a causal effect between two key CD4+ TEM cell markers (GBP2 and LAG3) and the risk for PTB and COVID-19 infection. Our findings provide novel insights into the biological mechanism for PTB and COVID-19 infection, but future single-cell studies are necessary to further enhance understanding of this find.

Tuberculosis (TB) is a global infectious threat, and the emergence of multidrug-resistant TB has become a major challenge in eradicating the disease that requires the discovery of new treatment strategies. This study aimed to elucidate the immune infiltration and molecular regulatory network of T cell-interacting activating receptors on myeloid cell 1 (TARM1)-related genes based on a bioinformatics analysis. The GSE114911 dataset was obtained from the Gene Expression Omnibus (GEO) and screened to identify 17 TARM1-related differentially expressed genes (TRDEGs). Genes interacting with the TRDEGs were analyzed using a Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A gene set enrichment analysis (GSEA) was used to identify the biological pathways significantly associated with a Mycobacterium tuberculosis (Mtb) infection. The key genes were obtained based on Cytoscape’s cytoHubba plug-in. Furthermore, protein–protein interaction (PPI) networks were analyzed through STRING, while mRNA–RNA-binding protein (RBP) and mRNA–transcription factor (TF) interaction networks were developed utilizing the StarBase v3.0 and ChIPBase databases. In addition, the diagnostic significance of key genes was evaluated via receiver operating characteristic (ROC) curves, and the immune infiltration was analyzed using an ssGSEA and MCPCounter. The key genes identified in the GSE114911 dataset were confirmed in an independent GSE139825 dataset. A total of seventeen TRDEGs and eight key genes were obtained in a differential expression analysis using the cytoHubba plug-in. Through the GO and KEGG analysis, it was found that these were involved in the NF-κB, PI3K/Akt, MAPK, and other pathways related to inflammation and energy metabolism. Furthermore, the ssGSEA and MCPCounter analysis revealed a significant rise in activated T cells and T helper cells within the Mtb infection group, which were markedly associated with these key genes. This implies their potential significance in the anti-Mtb response. In summary, our results show that TRDEGs are linked to inflammation, energy metabolism, and immune cells, offering fresh insights into the mechanisms underlying TB pathogenesis and supporting further investigation into the possible molecular roles of TARM1 in TB, as well as assisting in the identification of prospective diagnostic biomarkers.

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness.

Background: Sepsis is a prevalent and severe medical condition which is frequently observed in the intensive care unit (ICU). Although numerous biomarkers have been identified to predict the prognosis of sepsis, the lactate dehydrogenase to albumin ratio (LDH/ALB ratio) has not been extensively investigated. The principal objective of this study is to assess the relationship between LDH/ALB ratio and all-cause mortality in patients with sepsis. Methods: This study included all adult critically ill patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.0) database. Propensity score matching (PSM) analysis was conducted to mitigate bias, and Kaplan-Meier curves were performed to evaluate the cumulative survival across different groups. The association between the LDH/ALB ratio and mortality was examined through restricted cubic spline (RCS) analysis and Cox regression analysis. The robustness of the findings was confirmed through subgroup analyses. Additionally, the prognostic capability of the LDH/ALB ratio was further evaluated using receiver operating characteristic (ROC) curve analysis. Results: There were 6059 adult patients with sepsis enrolled in the final analysis. RCS revealed a non-linear relationship between the LDH/ALB ratio and an increased risk of ICU all-cause mortality (χ2 = 46.900, P

Abstract Intelligent Transportation has seen significant advancements with Deep Learning and the Internet of Things, making Traffic Signal Control (TSC) research crucial for reducing congestion, travel time, emissions, and energy consumption. Reinforcement Learning (RL) has emerged as the primary method for TSC, but centralized learning poses communication and computing challenges, while distributed learning struggles to adapt across intersections. This paper presents a novel approach using Federated Learning (FL)-based RL for TSC. FL integrates knowledge from local agents into a global model, overcoming intersection variations with a unified agent state structure. To endow the model with the capacity to globally represent the TSC task while preserving the distinctive feature information inherent to each intersection, a segment of the RL neural network is aggregated to the cloud, and the remaining layers undergo fine-tuning upon convergence of the model training process. Extensive experiments demonstrate reduced queuing and waiting times globally, and the successful scalability of the proposed model is validated on a real-world traffic network in Monaco, showing its potential for new intersections.