Abstract Sepsis represents a critical condition characterized by multiple‐organ dysfunction resulting from inflammatory response to infection. Disulfidptosis is a newly identified type of programmed cell death that is intimately associated with the actin cytoskeleton collapse caused by glucose starvation and disulfide stress, but its role in sepsis is largely unknown. The study was to adopt a diagnostic and prognostic signature for sepsis with disulfidptosis based on the differentially expressed genes (DEGs) between sepsis and healthy people from GEO database. The disulfidptosis hub genes associated with sepsis were identified, and then developed consensus clustering and immune infiltration characteristics. Next, we evaluated disulfidptosis‐related risk genes by using LASSO and Random Forest algorithms, and constructed the diagnostic sepsis model by nomogram. Finally, immune infiltration, GSVA analysis and mRNA‐miRNA networks based on disulfidptosis‐related DEGs were screened. There are five upregulated disulfidptosis‐related genes and seven downregulated genes were filtered out. The six intersection disulfidptosis‐related genes including LRPPRC, SLC7A11, GLUT, MYH9, NUBPL and GYS1 exhibited higher predictive ability for sepsis with an accuracy of 99.7%. In addition, the expression patterns of the critical genes were validated. The study provided a comprehensive view of disulfidptosis‐based signatures to predict the prognosis, biological features and potential treatment directions for sepsis.
Background: Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients. Methods: Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). Results: This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability. Conclusion: This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.
Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. It attracts great concerns recently due to the practical meanings. Meta-learning has been demonstrated to be an effective paradigm for this task. In general, methods based on meta-learning employ an additional support branch to encode novel examples (a.k.a. support images) into class prototypes, which are then fused with query branch to facilitate the model prediction. However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance.New methods are required to capture the distinctive local context for more robust novel object detection. To this end, we propose to distill the most representative support features into fine-grained prototypes. These prototypes are then assigned into query feature maps based on the matching results, modeling the detailed feature relations between two branches. This process is realized by our Fine-Grained Feature Aggregation (FFA) module. Moreover, in terms of high-level feature fusion, we propose Balanced Class-Agnostic Sampling (B-CAS) strategy and Non-Linear Fusion (NLF) module from differenct perspectives. They are complementary to each other and depict the high-level feature relations more effectively. Extensive experiments on PASCAL VOC and MS COCO benchmarks show that our method sets a new state-of-the-art performance in most settings. Our code is available at https://github.com/wangchen1801/FPD.
To analyze the association between blood pressure during vasopressor weaning and in-hospital mortality in patients admitted to an intensive care unit (ICU).Observational retrospective single-center study including patient data registered in the Medical Information Mart for Intensive Care, version 4. The outcome was in-hospital mortality. We used restricted cubic spline (RCS) functions to analyze the associations between mortality and systolic and diastolic blood pressures and mean arterial pressure (SBP, DBP, and MAP, respectively) during weaning from vasopressors. The data was stratified a ccording t o SBP, DBP, and MAP, and sensitivity was assessed with Cox regression analysis.Data for 8294 patients were analyzed. The RCS functions showed that SBP, DBP, and MAP values had nonlinear U-shaped associations with in-hospital mortality. Patients were classified into the following subgroups according to points of intersection of SBP, DBP, and MAP reference values: SBP 110, 110-150, or >150 mmHg; DBP 60, 60-85, or >85 mmHg; and MAP 75, 75-110, or >110 mmHg. In the lowest blood pressure group the hazard ratio was 0.59 (95% CI, 0.52-0.66) for SBP in the 110-150 mmHg range; 0.62 (95% CI, 0.55-0.70) for DBP in the 60-85 mmHg range; and 0.64 (95% CI, 0.57-0.72) for MAP in the 75-110 mmHg range during weaning. The analysis of subgroups also indicated that blood pressures during weaning interacted with cerebral vascular disease and chronic obstructive pulmonary disease.Higher blood pressures during vasopressor weaning are associated with longer in-hospital survival in ICU patients. The optimum pressure ranges are SBP, 110-150 mmHg; DBP, 60-85 mmHg; and MAP, 75-110 mmHg. Blood pressures may behave differently according to diagnosis.Analizar la relación entre la presión arterial (PA) durante el destete de fármacos vasopresores y la mortalidad intrahospitalaria en pacientes ingresados en una unidad de cuidados intensivos (UCI).Estudio de cohorte observacional, retrospectivo y unicéntrico. Incluyó pacientes del registro MIMIC-IV. La variable de resultado fue la mortalidad intrahospitalaria. Se utilizaron splines cúbicos restringidos (SCR) para estudiar la relación entre la PA durante el destete de los vasopresores [sistólica (DPAS), diastólica (DPAD), media (DPAM)] y la mortalidad. Los pacientes se clasificaron en diferentes subgrupos según sus cifras de DPAS, DPAD y DPAM. Se realizó un análisis de sensibilidad mediante regresión de Cox.Se analizaron 8.294 pacientes. El SCR mostró que DPAS, DPAD y DPAM tenían una relación no lineal, en forma de “U”, con la mortalidad intrahospitalaria. Basándose en los puntos de intersección de los valores de referencia, los pacientes se clasificaron en los siguientes grupos: DPAS 110, 110-150 y > 150 mmHg; DPAD 60, 60-85 y > 85mmHg; y DPAM 75, 75-110 y > 110 mmHg. En comparación con el grupo de PA más baja, de DPAS entre 110-150 mmHg tenía una hazard ratio (HR) de 0,59 (IC 95% 0,52-0,66), DPAD entre 60-85 mmHg una HR de 0,62 (IC 95% 0,55-0,70) y DPAM entre 75-110 mmHg una HR de 0,64 (IC 95% 0,57-0,72). El análisis de subgrupos diagnósticos mostró que la PA durante el destete interactuaba con la enfermedad vascular cerebral y con la enfermedad pulmonar crónica.Valores altos de PA durante el destete de los fármacos vasopresores se asocian a una mayor supervivencia intrahospitalaria en pacientes ingresados en una UCI. Los valores óptimos son: DPAS 110-150 mmHg, DPAD 60-85 mmHg y DPAM 75-110 mmHg. La PA óptima puede ser diferente en función del diagnóstico del paciente.
The diet of top predators is vital information needed to determine their ecological function and for their conservation management. However, the elusive habit and low population density of many predators constrains determination of their diets. While the morphological identification of scat contents is the traditional method, DNA metabarcoding has lately proven a more efficient and accurate method of identifying prey taxa. We applied DNA metabarcoding to analyzing the diet of the Eurasian otter ( Lutra lutra ), a top predator in freshwater ecosystems, using 12S and 16S rRNA mitochondrial primers target vertebrate prey. Diet did not vary among different data removal thresholds of 0.1, 1, 3, and 5%, comprising fishes (>90%), amphibians and birds (>2%), and occasionally mammals (<2%). Both 12S and 16S primers revealed similar otter diets, indicating that a single set of primers with a higher threshold is cost-effective for detecting the main prey taxa. Using 12S primers and a 5% threshold, we found no seasonal variation of otter diet in the Tangjiahe National Nature Reserve. A different prey community was found outside the reserve, which resulted in different prey composition for otters. However, prey taxon richness was not different between otters in- and outside the reserve. Otters preferred Schizothorax spp., the largest-sized fish species in the reserve, whereas they mainly preyed on Triplophysa bleekeri , a small-sized fish species, outside the reserve. Otters’ flexible feeding strategy reflect their high adaptability. However, greater human disturbance outside the reserve may present significant challenges to otters by altering prey communities and reducing prey profitability. Combining fecal DNA metabarcoding and local fish survey will provide opportunities for more detailed studies on the impact of different levels of human disturbances on prey communities and otters.
Abstract It is necessary to evaluate the construction effect of viaducts and identify the viaducts that play a key role in road networks. Based on the node deletion method, this paper proposes a method to identify the importance of viaducts in road networks. After applying this method to simulate the importance of viaducts in the highway networks in Yunnan and Guizhou Provinces from 2001 to 2020, the results show the following: (1) The viaducts with high importance were mainly built in 2002, 2009, 2015 and 2016. They are mainly distributed on expressways, such as the HUKUN Expressway, HANGRUI Expressway and YINBAI Expressway. Among the viaducts, the Mengzhai Bridge and Beipanjiang Bridge Hukun are the most important. (2) The importance of viaducts will increase, decrease or increase first and then decrease after viaduct construction. Among the years studied, 2012 and 2016 are important time nodes for change. The trend of changes is affected by the construction of highways and viaducts in other locations. In this road network, there are strong coupling relationships between nodes. (3) The importance of some viaducts is not prominent in the whole region, but that does not mean that their construction value is low. They may have a high connectivity effect on specific regions from a local perspective.
Multi-output regression aims to utilize the correlation between outputs to achieve information transfer between dependent outputs, thus improving the accuracy of predictive models. Although the Bayesian support vector machine (BSVR) can provide both the mean and the predicted variance distribution of the data to be labeled, which has a large potential application value, its standard form is unable to handle multiple outputs at the same time. To solve this problem, this paper proposes a multi-output Bayesian support vector machine model (MBSVR), which uses a covariance matrix to describe the relationship between outputs and outputs and outputs and inputs simultaneously by introducing a semiparametric latent factor model (SLFM) in BSVR, realizing knowledge transfer between outputs and improving the accuracy of the model. MBSVR integrates and optimizes the parameters in BSVR and those in SLFM through Bayesian derivation to effectively deal with the multi-output problem on the basis of inheriting the advantages of BSVR. The effectiveness of the method is verified using two function cases and four high-dimensional real-world data with multi-output.
ABSTRACT Aims and Objectives To develop and validate a prediction model for high‐flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF). Background AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non‐invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction. Design Multicentre observational study. Methods This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset ( n = 180). A random forest (RF)‐based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models. Results The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis. Conclusions This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF. Relevance to Clinical Practice Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC. Reporting Method TRIPOD checklist for prediction model studies was followed in this study. Patient or Public Contribution Patients were involved in the sample of the study.
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