Adherence to guidelines and central line-associated bloodstream infection occurrence during insertion and maintenance of intravascular catheters: evidence from 20 tertiary hospitals.

OBJECTIVE: To investigate adherence to intravascular catheter (IVC) insertion and maintenance guidelines in Chinese tertiary hospitals. METHODS: A cross-sectional questionnaire survey of adult inpatients with IVC placements was conducted from July to September 2022 in 20 tertiary hospitals in China. One clinical staff member from each department in each hospital was assigned to participate in the survey. Questionnaires were uniformly collected and reviewed after 3 months. RESULTS: This study included 1815 cases (62.69%) of central venous catheter, 471 cases (16.27%) of peripherally inserted central catheter, 461 cases (15.92%) of PORT, and 147 cases (5.08%) of haemodialysis catheter insertions. Statistically significant differences in compliance were observed across the four IVC types, specifically in relation to the insertion checklist, standard operating procedure, and insertion environment (P<0.05). Practice adherence during IVC maintenance differed significantly across the four IVC types in aspects such as availability of IVC maintenance verification forms, daily scrubbing of the catheterised patients, and catheter connection methods (P<0.05). A total of 386 (13.34%) patients developed fever, 1086 (37.53%) were treated with therapeutic antibiotics, 16 (0.55%) developed central line-associated bloodstream infections, two (0.07%) developed local skin infections, and six (0.21%) developed deep vein thrombosis. CONCLUSIONS: Adherence to guidelines regarding insertion and maintenance differed across the four IVC types; there is a gap between the recommended measures and the actual operation of the guideline. Therefore, it is necessary to further enhance training and develop checklists to prevent CLABSIs.

TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method.

Background: Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective: This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods: Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results: The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions: TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.

Prognostic value of genomic mutation signature associated with immune microenvironment in southern Chinese patients with esophageal squamous cell carcinoma.

The genomic landscape of esophageal squamous cell cancer (ESCC), as well as its impact on the regulation of immune microenvironment, is not well understood. Thus, tumor samples from 92 patients were collected from two centers and subjected to targeted-gene sequencing. We identified frequently mutated genes, including TP53, KMT2C, KMT2D, LRP1B, and FAT1. The most frequent mutation sites were ALOX12B (c.1565C > T), SLX4 (c.2786C > T), LRIG1 (c.746A > G), and SPEN (c.6915_6917del) (6.5%). Pathway analysis revealed dysregulation of cell cycle regulation, epigenetic regulation, PI3K/AKT signaling, and NOTCH signaling. A 17-mutated gene-related risk model was constructed using random survival forest analysis and showed significant prognostic value in both our cohort and the validation cohort. Based on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and the MCPcounter algorithm, we found that the risk score calculated by the risk model was significantly correlated with stimulatory immune checkpoints (TNFSF4, ITGB2, CXCL10, CXCL9, and BTN3A1; p < 0.05). Additionally, it was significantly associated with markers that are important in predicting response to immunotherapy (CD274, IFNG, and TAMM2; p < 0.05). Furthermore, the results of immunofluorescence double staining showed that patients with high risk scores had a significantly higher level of M2 macrophage than those with low risk scores (p < 0.05). In conclusion, our study provides insights into the genomic landscape of ESCC and highlights the prognostic value of a genomic mutation signature associated with the immune microenvironment in southern Chinese patients with ESCC.

GhHDZ76, a cotton HD-Zip transcription factor, involved in regulating the initiation and early elongation of cotton fiber development in G. hirsutum.

In this study, the whole HD-Zip family members of G. hirsutum were identified, and GhHDZ76 was classified into the HD-Zip IV subgroup. GhHDZ76 was predominantly expressed in the 0-5 DPA of fiber development stage and localized in the nucleus. Overexpression of GhHDZ76 significantly increased the length and density of trichomes in Arabidopsis thaliana. The fiber length of GhHDZ76 knockout lines by CRISPR/Cas9 was significantly shorter than WT at the early elongation and mature stage, indicating that GhHDZ76 positively regulate the fiber elongation. Scanning electron microscopy showed that the number of ovule surface protrusion of 0 DPA of GhHDZ76 knockout lines was significantly lower than WT, suggesting that GhHDZ76 can also promote the initiation of fiber development. The transcript level of GhWRKY16, GhRDL1, GhEXPA1 and GhMYB25 genes related to fiber initiation and elongation in GhHDZ76 knockout lines were significantly decreased. Yeast two-hybrid and Luciferase complementation imaging (LCI) assays showed that GhHDZ76 can interact with GhWRKY16 directly. As a transcription factor, GhHDZ76 has transcriptional activation activity, which could bind to L1-box elements of the promoters of GhRDL1 and GhEXPA1. Double luciferase reporter assay showed that the GhWRKY16 could enhance the transcriptional activity of GhHDZ76 to pGhRDL1, but it did not promote the transcriptional activity of GhHDZ76 to pGhEXPA1. GhHDZ76 protein may also promote the transcriptional activity of GhWRKY16 to the downstream target gene GhMYB25. Our results provided a new gene resource for fiber development and a theoretical basis for the genetic improvement of cotton fiber quality.

Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform.

BACKGROUND: Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity. METHODS: We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961). RESULTS: A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757-0.926) and 0.914 (0.851-0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility. CONCLUSIONS: We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.

ML GSP model based on machine learning has good severity discrimination in both training and validation cohorts (0.916 (0.872­0.958), 0.914 (0.851­0.978), respectively).We built an online user-friendly platform for the ML GSP model to help clinicians better identify the severity of GSP.

Is online shopping addiction still a depressive illness? -- the induced consumption and traffic trap in live E-commerce.

While immersive shopping has injected new vitality into China's e-commerce, it has also resulted in consumers' over-reliance on online shopping. Psychological studies have linked online shopping addiction with depression, but business practices challenge this conclusion. This study, grounded in addiction theory, developed a theoretical model, and conducted an online survey with 214 live-streaming shoppers using structural equation modeling for validation. The primary focus was on determining whether consumers truly become addicted to online shopping in the four stages of the addiction model. The study unveils the process of consumers becoming addicted to online shopping. It explores the moderating role of perceived risk in the relationship between utilitarian and hedonic purchases and online shopping addiction. The findings suggest that through tactics such as traffic promotion, traffic trapping, anchor feature utilization, and incorporation of consumer aesthetics, merchants may induce utilitarian and hedonic purchases, leading to addiction to live-streaming shopping among consumers. Furthermore, perceived risk significantly and negatively moderates the relationship between utilitarian purchases and online shopping addiction. Our research indicates that merchants intentionally create external stimuli, enticing consumers to indulge in online shopping, suggesting that online shopping addiction is not merely a simple psychological state but may be influenced by external factors. This study provides novel insights into the phenomenon of online shopping addiction while offering valuable recommendations for consumers seeking to avoid succumbing to its allure.

Elucidating the changes in the heterogeneity and function of radiation-induced cardiac macrophages using single-cell RNA sequencing.

Purpose: A mouse model of irradiation (IR)-induced heart injury was established to investigate the early changes in cardiac function after radiation and the role of cardiac macrophages in this process. Methods: Cardiac function was evaluated by heart-to-tibia ratio, lung-to-heart ratio and echocardiography. Immunofluorescence staining and flow cytometry analysis were used to evaluate the changes of macrophages in the heart. Immune cells from heart tissues were sorted by magnetic beads for single-cell RNA sequencing, and the subsets of macrophages were identified and analyzed. Trajectory analysis was used to explore the differentiation relationship of each macrophage subset. The differentially expressed genes (DEGs) were compared, and the related enriched pathways were identified. Single-cell regulatory network inference and clustering (SCENIC) analysis was performed to identify the potential transcription factors (TFs) which participated in this process. Results: Cardiac function temporarily decreased on Day 7 and returned to normal level on Day 35, accompanied by macrophages decreased and increased respectively. Then, we identified 7 clusters of macrophages by single-cell RNA sequencing and found two kinds of stage specific macrophages: senescence-associated macrophage (Cdkn1ahighC5ar1high) on Day 7 and interferon-associated macrophage (Ccr2highIsg15high) on Day 35. Moreover, we observed cardiac macrophages polarized over these two-time points based on M1/M2 and CCR2/major histocompatibility complex II (MHCII) expression. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses suggested that macrophages on Day 7 were characterized by an inflammatory senescent phenotype with enhanced chemotaxis and inflammatory factors, while macrophages on Day 35 showed enhanced phagocytosis with reduced inflammation, which was associated with interferon-related pathways. SCENIC analysis showed AP-1 family members were associated with IR-induced macrophages changes. Conclusion: We are the first study to characterize the diversity, features, and evolution of macrophages during the early stages in an IR-induced cardiac injury animal model.

Effect of Comfort Care on Perioperative Outcomes and Postoperative Recovery of Breast Cancer Patients.

Objective: The objective of this study was to evaluate the effects of comfort care on perioperative outcomes and postoperative recovery of breast cancer patients. Evaluating comfort care is important in the context of breast cancer surgery because it can potentially alleviate pain, improve patient comfort, enhance postoperative recovery, and reduce complications, ultimately leading to better patient outcomes. Methods: Between March 2020 and December 2021, 78 patients undergoing breast cancer surgery at our hospital were randomly assigned to receive either routine nursing (routine group) or comfort care (experimental group). The comfort care intervention included various components such as health education, preoperative care, intraoperative care, postoperative care, pain care, and psychological care. The routine group received standard nursing care following medical advice. Results: The patient characteristics between the two groups were comparable. Comfort care resulted in significantly higher visual analog scale (VAS) scores, indicating reduced pain, and better improvement in functional recovery of the upper limb compared to routine nursing. Comfort care was also associated with better postoperative recovery, as evidenced by lower self-rating depression scale (SDS) and self-rating anxiety scale (SAS) scores. The experimental group had a significantly lower incidence of complications compared to the routine group. Additionally, the experimental group reported better 24-hour comfort and higher nursing satisfaction. Conclusion: In conclusion, comfort care effectively reduces postoperative pain, promotes postoperative recovery, improves patient emotions, lowers the incidence of complications, and enhances comfort and care satisfaction in breast cancer patients undergoing radical surgery. These findings highlight the importance of incorporating comfort care interventions in the perioperative management of breast cancer patients. Further research and implementation of comfort care strategies may have implications for improving clinical practice and patient outcomes in the future.

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer.

OBJECTIVE: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

Effects of Isochlorogenic Acid on Ewes Rumen Fermentation, Microbial Diversity and Ewes Immunity of Different Physiological Stages.

The effects of isochlorogenic acid (ICGA) on ewes rumen environment, microbial diversity, and immunity at different physiological stages (estrus, pregnancy and lactation) were studied in this experiment. Twenty healthy female Hu lambs of 1.5 months with similar body weight (17.82 ± 0.98 kg) and body condition were selected and randomly divided into two groups: the control group (CON) and the ICGA group (ICGA). The lambs of CON were fed a basal diet, while the lambs of ICGA were supplemented with 0.1% ICGA based on the basal diet. Lambs rumen fermentation characteristics, microbial diversity and immunity at estrus, pregnancy, and lactation stages were determined and analyzed, respectively. The results showed that the rumen pH in CON increased first and then decreased as lambs grew (p < 0.05). However, it showed the opposite change in ICGA. The content of ammonia nitrogen (NH3-N) showed the highest at estrus stage in both groups, but it was significantly higher in ICGA than that in CON (p < 0.05). The Acetic acid/propionic acid (A/P) ratio at estrus stage and the volatile fatty acids (VFAs) at pregnancy stage in ICGA were significantly higher than those of the CON (p < 0.05). The 16S rDNA sequencing analysis showed that the Shannon, Chao 1 and ACE indexes of the ICGA were significantly higher than those of the CON both at estrus and lactation stages (p < 0.05), while they showed higher at the pregnancy stage in CON (p > 0.05). Principal component analysis (PCA) showed that there were significant differences in rumen microorganism structure between CON and ICGA at all physiological stages (p < 0.01). At the phylum level, compared with the CON, Firmicutes relative abundance of three physiological stages decreased (p > 0.05) while Bacteroidota increased (p > 0.05). The relative abundance of Synergistota at estrus stage and Patescibacteria at the lactation stage increased significantly too (p < 0.05). At the genus level, compared with the CON, the relative abundance of Prevotella at three stages showed the highest (p > 0.05), while the relative abundance of Succiniclasticum, unclassified_Selenomonadaceae and Rikenellaceae_RC9_gut_group showed different abundances at different physiological stages in ICGA. Compared with the CON, the lambs of the ICGA showed higher blood IgG, IgM, and TNF- α contents at three physiological stages and higher IL-6 contents at pregnancy stage (p < 0.05). Conclusion: Adding ICGA could regulate ewes rumen fermentation mode at different physiological stages by increasing rumen NH3-N at estrus, VFAs at pregnancy, and the ratio of A/P at lactation. It optimizes rumen microbial flora of different physiological stages by increasing Bacteroidota relative abundance while reducing Firmicutes relative abundance, maintaining rumen microbial homeostasis at pregnant stage, increasing the number of beneficial bacteria in later lactating and ewes blood immunoglobulins content at three physiological stages.

IAS-FET: An intelligent assistant system and an online platform for enhancing successful rate of in-vitro fertilization embryo transfer technology based on clinical features.

BACKGROUND: Among all of the assisted reproductive technology (ART) methods, in vitro fertilization-embryo transfer (IVF-ET) holds a prominent position as a key solution for overcoming infertility. However, its success rate hovers at a modest 30% to 70%. Adding to the challenge is the absence of effective models and clinical tools capable of predicting the outcome of IVF-ET before embryo formation. Our study is dedicated to filling this critical gap by aiming to predict IVF-ET outcomes and ultimately enhance the success rate of this transformative procedure. METHODS: In this retrospective study, infertile patients who received artificial assisted pregnancy treatment at Gansu Provincial Maternity and Child-care Hospital in China were enrolled from 2016 to 2020. Individual's clinical information were studied by cascade XGBoost method to build an intelligent assisted system for predicting the outcome of IVF-ET, called IAS-FET. The cascade XGBoost model was trained using clinical information from 2292 couples and externally tested using clinical information from 573 couples. In addition, several schemes which will be of help for patients to adjust their physical condition to improve their success rate on ART were suggested by IAS-FET. RESULTS: The outcome of IVF-ET can be predicted by the built IAS-FET method with the area under curve (AUC) value of 0.8759 on the external test set. Besides, this IAS-FET method can provide several schemes to improve the successful rate of IVF-ET outcomes. The built tool for IAS-FET is addressed as a free platform online at http://www.cppdd.cn/ART for the convenient usage of users. CONCLUSIONS: It suggested the significant influence of personal clinical features for the success of ART. The proposed system IAS-FET based on the top 27 factors could be a promising tool to predict the outcome of ART and propose a plan for the patient's physical adjustment. With the help of IAS-FET, patients can take informed steps towards increasing their chances of a successful outcome on their journey to parenthood.

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors.

Background: We aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors. Materials and methods: Clinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from 18F-FDG PET and enhanced plan CT images. Four machine learning algorithms, including SVM (Support Vector Machine), Random Forest (RF), and eXtreme Gradient Boosting (XGB) and logistic regression (LR), were applied using k-fold cross-validation to predict pCR after nCRT. The predictive ability of the models was assessed using receiver operating characteristics (ROC) curve analysis. Results: A total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of 18 F-FDG PET, CT and clinical features with SVM machine learning had the highest AUC of 0.852 in the test set when compared to single-modality models in various algorithms. The other combined models had AUC ranged 0.716 to 0.775. Conclusion: Machine learning models utilizing radiomics features from 18F-FDG PET and enhanced plan CT exhibit promising performance in predicting pCR in ESCC after nCRT and anti-PD-1 inhibitors. The fusion of features from multiple modalities radiomics and clinical features enhances the better predictive performance compared to using a single modality alone.

TaqMan-based real-time polymerase chain reaction for the detection of feline chaphamaparvovirus.

Feline chaphamaparvovirus (FeChPV) is a new viral strain detected in Chinese Mainland in recent years. The symptoms mainly include diarrhea and bloody stool in young cats, which can lead to death in severe cases. In this study, a TaqMan-based real-time quantitative PCR (qPCR) with specific primers and TaqMan probes based on the VP1 gene sequence of FeChPV was performed to detect the virus. The established qPCR indicated that there is no cross-reaction of FeChPV with other common feline viruses. The minimum detection limit of the established qPCR method is 3.75 × 10 copies/µL, while conventional PCR is 3.75 × 103 copies/µL. The result that the proposed qPCR protocol was shown to be 100 times more sensitive than conventional PCR. The correlation coefficients exceeded 0.995, and the amplification efficiency was 98%. The difference within and between groups is less than 5%, indicating that the established method has good repeatability. The results of clinical sample detection shown that 16 positive samples were detected from 45 stool samples by the established qPCR method. The conventional PCR method only detected 3 positive samples. In conclusion, the established qPCR method is fast and effective in identifying FeChPV, with higher specificity and sensitivity. It could be used as a diagnostic tool to quantitatively detect the virus content, which is conducive to disease monitoring and epidemiological investigation.

GhLCYε-3 characterized as a lycopene cyclase gene responding to drought stress in cotton.

Drought stress significantly affects crop productivity. Carotenoids are essential photosynthetic pigment for plants, bacteria, and algae, with signaling and antioxidant functions. Lutein is a crucial branch product in the carotenoid synthesis pathway, which effectively improves the stress tolerance of higher plants. lycopene cyclase, a central enzyme for lutein synthesis, holds great significance in regulating lutein production. This research establishes a correlation between lutein content and stress resistance by measuring the drought resistance and lutein content of various cotton materials. To identify which crucial genes are associated with lutein, the lycopene cyclase family (LCYs) was analyzed. The research found that LCYs form a highly conserved family divided into two subfamilies, LCY-ε (lycopene ε-cyclase) and LCY-ß (lycopene ß-cyclase). Most members of the LCY family contain photoresponsive elements and abscisic acid elements. qRT-PCR demonstrates showed that most genes responded positively to drought stress, and GhLCYε-3 was expressed significantly differently under drought stress. Virus-induced gene silencing (VIGS) assay showed that the content of GhLCYε-3 was significantly increased with MDA and PRO, and the contents of chlorophyll and lutein were significantly decreased in pYL156 plants. The decrease in GhLCYε-3 expression is speculated to lead to reduced lutein content in vivo, resulting in the accumulation of reactive oxygen species (ROS) and decreased drought tolerance. This research enriched the understanding of LCY gene family and lutein function, and provided a new reference for cotton planting in arid areas. Synopsis: Lycopene cyclase plays an important role in enhancing the ability of scavenging ROS and drought resistance of plants.

Characteristics and associated factors of health information-seeking behaviour among patients with inflammatory bowel disease in the digital era: a scoping review.

BACKGROUND: Health Information-Seeking Behaviour (HISB) is necessary for self-management and medical decision-making among patients with inflammatory bowel disease (IBD). With the advancement of information technology, health information needs and seeking are reshaped among patients with IBD. This scoping review aims to gain a comprehensive understanding of HISB of people with IBD in the digital age. METHODS: This scoping review adhered to Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews frameworks (PRISMA-ScR). A comprehensive literature search was conducted in PubMed, Embase, Web of Science, PsycINFO, CINAHL, and three Chinese databases from January 1, 2010 to April 10, 2023. Employing both deductive and inductive content analysis, we scrutinized studies using Wilson's model. RESULTS: In total, 56 articles were selected. Within the information dimension of HISB among patients with IBD, treatment-related information, particularly medication-related information, was identified as the most critical information need. Other information requirements included basic IBD-related information, daily life and self-management, sexual and reproductive health, and other needs. In the sources dimension, of the eight common sources of information, the internet was the most frequently mentioned source of information, while face-to-face communication with healthcare professionals was the preferred source. Associated factors were categorized into six categories: demographic characteristics, psychological aspects, role-related or interpersonal traits, environmental aspects, source-related characteristics, and disease-related factors. Moreover, the results showed five types of HISB among people with IBD, including active searching, ongoing searching, passive attention, passive searching, and avoid seeking. Notably, active searching, especially social information seeking, appeared to be the predominant common type of HISB among people with IBD in the digital era. CONCLUSION: Information needs and sources for patients with IBD exhibit variability, and their health information-seeking behaviour is influenced by a combination of diverse factors, including resource-related and individual factors. Future research should focus on the longitudinal changes in HISB among patients with IBD. Moreover, efforts should be made to develop information resources that are both convenient and provide credible information services, although the development of such resources requires further investigation and evaluation.

In situ sonochemical synthesis of flower-like N-graphyne/BiOCl0.5Br0.5 microspheres for efficient removal of levofloxacin.

In this work, N-graphyne is in situ coupled with BiOCl0.5Br0.5via a facile one-step sonochemical method. To our knowledge, both the synthesis strategy for BiOCl0.5Br0.5 and the N-graphyne/BiOCl0.5Br0.5 photocatalytic system are new developments. A collection of characterization methods is adopted to detect the morphologies, structures, and electronic and optical properties. The results demonstrate that wrinkle-like N-graphyne nanosheets successfully enwind around or on flower-like BiOCl0.5Br0.5 microspheres, which are regularly assembled by BiOCl0.5Br0.5 nanosheets. Compared with pristine BiOCl0.5Br0.5, N-graphyne/BiOCl0.5Br0.5 composites exhibit superior adsorption capacity and visible-light-driven photocatalytic degradation of levofloxacin. In particular, the optimal N-graphyne amount for ameliorating the photocatalytic performance of BiOCl0.5Br0.5 is ascertained. In addition, the good stable performance for photocatalysis is confirmed by four cycling experiments. The dominant active species is confirmed to be O2˙- during photodegradation. The improved photocatalytic activity is attributed to the enhanced visible light response and the accelerated transfer/separation of photogenerated carriers by N-graphyne, which are verified using UV-vis absorption spectra, photocurrents, photopotentials, Nyquist plots, and Mott-Schottky curves. This study develops a new perspective for the synthesis and modification of BiOX solid solution, which can be used as an efficient photocatalyst.

Transcriptome analysis of Artemisia argyi following methyl jasmonate (MeJA) treatment and the mining of genes related to the stress resistance pathway.

Artemisia argyi Lev. et Vant. (A. argyi) is a perennial grass in the Artemisia family, the plant has a strong aroma. Methyl jasmonate (MeJA) is critical to plant growth and development, stress response, and secondary metabolic processes. The experimental material Artemisia argyi was utilized in this study to investigate the treatment of A. argyi with exogenous MeJA at concentrations of 100 and 200 µmol/L for durations of 9 and 24 h respectively. Transcriptome sequencing was conducted using the Illumina HiSeq platform to identify stress resistance-related candidate genes. Finally, a total of 102.43 Gb of data were obtained and 162,272 unigenes were identified. Differential analysis before and after MeJA treatment resulted in the screening of 20,776 differentially expressed genes. The GO classification revealed that the annotated unigenes were categorized into three distinct groups: cellular component, molecular function, and biological process. Notably, binding, metabolic process, and cellular process emerged as the most prevalent categories among them. The results of KEGG pathway statistical analysis revealed that plant hormone signal transduction, MAPK signaling pathway-plant, and plant-pathogen interaction were significant transduction pathways in A. argyi's response to exogenous MeJA-induced abiotic stress. With the alteration of exogenous MeJA concentration and duration, a significant upregulation was observed in the expression levels of calmodulin CaM4 (ID: EVM0136224) involved in MAPK signaling pathway-plant and auxin response factor ARF (ID: EVM0055178) associated with plant-pathogen interaction. The findings of this study establish a solid theoretical foundation for the future development of highly resistant varieties of A. argyi.

Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm.

OBJECTIVE: The differential diagnosis between adult-onset Still's disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. METHODS: All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. RESULTS: The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still's disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . CONCLUSION: We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Independent Protection and Influence of the Spike-Specific Antibody Response of SARS-CoV-2 Nucleocapsid Protein (N) in Whole-Virion Vaccines.

Of all of the components in SARS-CoV-2 inactivated vaccines, nucleocapsid protein (N) is the most abundant and highly conserved protein. However, the function of N in these vaccines, especially its influence on the targeted spike protein's response, remains unknown. In this study, the immunization of mice with the N protein alone was shown to reduce the viral load, alleviating pulmonary pathological lesions after challenge with the SARS-CoV-2 virus. In addition, co-immunization and pre-immunization with N were found to induce higher S-specific antibody titers rather than compromise them. Remarkably, the same trend was also observed when N was administered as the booster dose after whole inactivated virus vaccination. N-specific IFN-γ-secreting T cell response was detected in all groups and exhibited a certain relationship with S-specific IgG antibody improvements. Together, these data indicate that N has an independent role in vaccine-induced protection and improves the S-specific antibody response to inactivated vaccines, revealing that an interplay mechanism may exist in the immune responses to complex virus components.