Objective: To identify early clinical predictors associated with dysphagia and delayed swallowing recovery in patients with traumatic brain injury (TBI). Methods: In this retrospective study, we enrolled adult TBI patients admitted to the rehabilitation unit of a tertiary medical center between June 2019 and June 2023. Data on baseline characteristics, neurological status, imaging findings, and rehabilitation-related variables were collected. Swallowing function was assessed using two indicators: (1) nasogastric (NG) tube retention and (2) the Functional Oral Intake Scale (FOIS) scores at 1, 4, and 12 weeks post-injury. Regression analyses were conducted to identify predictors associated with dysphagia and swallowing recovery. Results: A total of 160 patients were included. At 1 week post-injury, longer intensive care unit (ICU) stay, poor initial sitting balance and use of sedative medication in ICU were associated with NG tube retention. At 4 weeks, lower initial Rancho Los Amigos Scale (RLAS) scores, immobility-related complications, longer hospitalization, and temporal lobe hematomas were associated with persistent NG tube dependence. By 12 weeks, older age, delayed ability to follow commands, and poor initial sitting balance remained associated with NG tube retention. FOIS outcomes were also associated with older age, delayed time to follow commands, impaired initial sitting balance, prolonged ICU stay, temporal lobe hematomas, lower initial RLAS scores, immobility-related complications, prolonged endotracheal tube placement and extended hospital stays. Conclusion Impaired cognitive status, poor physical function, immobility-related complications, and temporal lobe hematomas were key factors associated with dysphagia and delayed oral intake in individuals with TBI.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To investigate the current situation of skin tests for β-lactams in 155 hospitals in Beijing.Methods The questionnaires were sent to medical institutions in Beijing to statistically described the current situation of β-lactams skin test,and the relevant influencing factors were analyzed by Fisher exact test,and Chi-square test.Results A total of 3 097 questionnaires were received from 155 medical institutions,of which 3 057 were valid questionnaires(effective rate is 98.71％).In this study,122 hospitals conducted skin test before using intravenous penicillins,accounting for 99.19％of 123 hospitals with intravenous penicillins.Hospitals with infectious disease pharmacists conducted more skin tests before using intravenous penicillins(98.59％vs 88.46％,P＜0.001),and some hospitals didn't conduct skin tests before using oral penicillins.Seventy hospitals(45.16％)had not yet cancelled skin tests for cephalosporins,more hospitals with infectious disease pharmacists cancelled routine skin test(59.74％vs 50.00％,P＜0.05),and more tertiary hospitals cancelled cephalosporin skin test(80.65％vs 51.40％,29.41％,all P＜0.01).After the promulgation of the"Guiding Principle for Skin Test of β-lactams(2021 edition)",more primary hospitals have developed or updated the rules or regulation for skin test of β-lactams according to the Guiding Principle,compared with secondary hospitals(72.22％vs 41.18％,P＜0.05).Hospitals with infectious disease pharmacists developed or updated the rules or regulation for skin test of β-lactams in their hospitals(78.57％vs 60.00％,P＜0.05).Infectious disease pharmacists had promoting effect on the publicity of the rules or regulation of skin test in the hospital(92.65％vs 80.46％,P＜0.05).Conclusion Many hospitals in Beijing have not cancelled cephalosporins skin test,and the current situation of cancelling cephalosporins skin test in tertiary hospitals is better than that in primary and secondary hospitals.

This study explored the growth-promoting effect and mechanism of the endophytic bacterium Kocuria rosea on Rehmannia glutinosa, aiming to provide a scientific basis for the development of green bacterial fertilizer. R. glutinosa 'Jinjiu' was treated with K. rosea, and the shoot parameters including leaf length, leaf width, plant width, and stem diameter were measured every 15 days. After 120 days, the shoots and roots were harvested. The root indicators(root number, root length, root diameter, root fresh weight, root dry weight, root volume, and root vitality) and secondary metabolites(catalpol, rehmannioside A, rehmannioside D, verbascoside, and leonuride) were determined. The R. glutinosa growth-promoting mechanism of K. rosea was discussed from the effect of K. rosea on the nutrient element content in R. glutinosa and rhizosphere soil and the genome information of this plant. After application of K. rosea, the maximum increases in leaf length, leaf width, plant width, and stem diameter were 35.67%(60 d), 25.39%(45 d), 40.17%(60 d), and 113.85%(45 d), respectively. The root number, root length, root diameter, root volume, root fresh weight, root dry weight, and root viability increased by 41.71%, 45.10%, 48.61%, 94.34%, 101.55%, 147.61%, and 42.08%, respectively. In addition, the content of rehmannioside A and verbascoside in the root of R. glutinosa increased by 76.67% and 69.54%, respectively. K. rosea promoted the transformation of nitrogen(N), phosphorus(P), and potassium(K) in the rhizosphere soil into the available state. Compared with that in the control, the content of available N(54.60 mg·kg~(-1)), available P(1.83 μmol·g~(-1)), and available K(83.75 mg·kg~(-1)) in the treatment with K. rosea increased by 138.78%, 44.89%, and 14.34%, respectively. The content of N, P, and K in the treatment group increased by 293.22%, 202.63%, and 23.80% in the roots and by 23.60%, 107.23%, and 134.53% in the leaves of R. glutinosa, respectively. K. rosea carried the genes related to colonization(rbsB, efp, bcsA, and gmhC), N, P, and K metabolism(narG, narH, narI, nasA, nasB, GDH2, pyk, aceB, ackA, CS, ppa, ppk, ppk2, pstS, pstA, pstB, and pstC), and indole-3-acetic acid and zeatin synthesis(iaaH and miaA). Further studies showed that K. rosea could colonize the roots of R. glutinosa and secrete indole-3-acetic acid(3.85 μg·mL~(-1)) and zeatin(0.10 μg·mL~(-1)). In summary, K. rosea promotes the growth of R.ehmannia glutinosa by enhancing the nutrient uptake, which provides a theoretical basis for the development of plant growth-promoting microbial products.
Rehmannia/metabolism* ; Endophytes/metabolism* ; Plant Roots/growth & development* ; Micrococcaceae/genetics* ; Data Mining ; Plant Leaves/metabolism* ; Genomics ; Rhizosphere

Based on whole genome data, the identification and expression pattern analysis of the Atropa belladonna WRKY transcription factor family were conducted to provide a theoretical foundation for studying the biological functions and mechanisms of these transcription factors. In this study, bioinformatics methods were employed to identify members of the A. belladonna WRKY gene family and to predict their physicochemical properties, conserved motifs, promoter cis-acting elements, and chromosomal localization. Additionally, the expression patterns of the A. belladonna WRKY gene family under the regulation of environmental factors such as light quality and temperature were analyzed. The results revealed a total of 28 AbWRKY transcription factors, randomly distributed across 16 chromosomes, encoding 324-707 amino acids. Most AbWRKY proteins were acidic, unstable, and hydrophilic. Based on multiple sequence alignment and phylogenetic analysis, the WRKY gene family members were classified into two subfamilies. Conserved motif and domain analysis indicated that WRKY transcription factors in the same subfamily possessed conserved structural features. Promoter analysis predicted that the A. belladonna WRKY family contained light-responsive elements, hormone-responsive elements, and stress-responsive elements. Collinearity analysis showed that AbWRKY24 plays a crucial role in the expansion of the AbWRKY gene family. Then qRT-PCR results indicated that AbWRKY6, AbWRKY8, AbWRKY14, and AbWRKY24 responded to red light stress, while AbWRKY8, AbWRKY14, and AbWRKY24 responded to yellow light/low-temperature combined stress. AbWRKY6 and AbWRKY8 were significantly expressed in leaves and stems, AbWRKY27 and AbWRKY28 were significantly expressed in fibrous roots, and AbWRKY25 was significantly expressed in flowers. This study is the first to identify and analyze the WRKY gene family in A. belladonna and to examine its expression patterns under light and temperature regulation, laying a foundation for in-depth analysis and functional validation of the molecular mechanisms of A. belladonna WRKY transcription factors in responding to light quality and temperature environmental factors.
Transcription Factors/chemistry* ; Plant Proteins/metabolism* ; Phylogeny ; Gene Expression Regulation, Plant ; Light ; Temperature ; Atropa belladonna/metabolism* ; Multigene Family/genetics* ; Promoter Regions, Genetic/genetics* ; Sequence Alignment ; Amino Acid Sequence ; Genome, Plant/genetics*

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.