A nodule in the right middle lobe of the lung was treated by a combination of cone-beam CT,three-dimensional registration for fusion imaging,and electromagnetic navigation bronchoscopy-guided thermal ablation.The procedure lasted for 90 min,with no significant bleeding observed under the bronchoscope.The total radiation dose during the operation was 384 mGy.The patient recovered well postoperatively,with only a small amount of blood in the sputum and no pneumothorax or other complications.A follow-up chest CT on the first day post operation showed that the ablation area completely covered the lesion,and the patient was discharged successfully.
Humans ; Bronchoscopy/methods* ; Catheter Ablation/methods* ; Cone-Beam Computed Tomography ; Electromagnetic Phenomena ; Imaging, Three-Dimensional ; Lung Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

Objective:To explore the research hotspots and trends of artificial intelligence in elderly delirium, and to provide reference for clinical healthcare professionals.Methods:The Web of Science Core Collection database was used as the data source to obtain literature on "artificial intelligence" and "elderly delirium". The search period was from January 1, 2017 to July 29, 2024. CiteSpace software was used for visual analysis of the included literature.Results:A total of 99 articles were included, with an overall upward trend in the number of publications. The United States had the most publications ( n=43), followed by China ( n=25). The institution with the most publications was Harvard Medical School ( n=8), followed by Sichuan University ( n=7). Journal of the American Geriatrics Society was the most co-cited journal. Based on the keywords, research hotspots mainly focused on the construction and validation of the risk prediction model of elderly delirium, intelligent diagnosis of elderly delirium, and intelligent elderly delirium nursing program. Future studies could explore reliability, ICU and validity. Conclusions:Artificial intelligence is increasingly being researched in elderly delirium to facilitate personalized nursing programs for elderly delirium in all aspects, from disease prediction, risk assessment, early diagnostic monitoring to intervention strategy development. In the future, trends in artificial intelligence research in the field of elderly delirium will revolve around improving the reliability of diagnosis, spreading its use in ICU, and optimizing predictive models.

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To explore the relationship between suggestion presentation, cognitive load, and adherence to nursing interventions for delirium.Methods:A prospective randomized controlled trial was conducted to recruit 47 nurses from 4 ICU units in Xuanwu Hospital, Capital Medical University from October 2021 to January 2022. They were divided into an intelligent group with 24 participants (using the intelligent nursing system for ICU delirium) and a non-intelligent group with 23 participants (using the paper-version implementation plan of delirium) by the random number table method. The cognitive load levels and adherence to nursing interventions were measured on day 7 of the study. AMOS 28.0 software was used to analyze the relationship between suggestion presentation, cognitive load, and adherence. The mediation effect was tested using the Bootstrap method.Results:In the intelligent group, there were 5 male and 19 female nurses, aged (33.33 ± 5.57) years. In the non-intelligent group, there were 1 male and 22 female nurses, aged (32.65 ± 3.46) years. The scores of extraneous cognitive load, germane cognitive load, and adherence to nursing intervention in the intelligent group were (6.42 ± 1.72), (7.15 ± 1.71) and (0.71 ± 0.05)%, respectively, compared with (7.78 ± 2.08), (6.07 ± 1.48) and (0.58 ± 0.08)% in the non-intelligent group. The differences were statistically significant ( t=6.06, 5.35, 41.47, all P<0.05). The Bootstrap method showed that both extraneous cognitive load (effect size=0.043, 95% CI 0.024 to 0.090, P<0.05) and germane cognitive load (effect size=0.026, 95% CI 0.017 to 0.082, P<0.05) played a partial mediating effect between suggestion presentation and adherence to nursing intervention, the total mediating effect accounted for 17.29%. Conclusions:Using the intelligent system to change the presentation of delirium nursing interventions can reduce the extraneous cognitive load and increase the germane cognitive load of ICU nurses, and then improve the adherence of nurses to implement delirium nursing interventions. Our study provides a clinical practice basis for improving the quality of clinical nursing.

Objective:To explore the relationship between suggestion presentation, cognitive load, and adherence to nursing interventions for delirium.Methods:A prospective randomized controlled trial was conducted to recruit 47 nurses from 4 ICU units in Xuanwu Hospital, Capital Medical University from October 2021 to January 2022. They were divided into an intelligent group with 24 participants (using the intelligent nursing system for ICU delirium) and a non-intelligent group with 23 participants (using the paper-version implementation plan of delirium) by the random number table method. The cognitive load levels and adherence to nursing interventions were measured on day 7 of the study. AMOS 28.0 software was used to analyze the relationship between suggestion presentation, cognitive load, and adherence. The mediation effect was tested using the Bootstrap method.Results:In the intelligent group, there were 5 male and 19 female nurses, aged (33.33 ± 5.57) years. In the non-intelligent group, there were 1 male and 22 female nurses, aged (32.65 ± 3.46) years. The scores of extraneous cognitive load, germane cognitive load, and adherence to nursing intervention in the intelligent group were (6.42 ± 1.72), (7.15 ± 1.71) and (0.71 ± 0.05)%, respectively, compared with (7.78 ± 2.08), (6.07 ± 1.48) and (0.58 ± 0.08)% in the non-intelligent group. The differences were statistically significant ( t=6.06, 5.35, 41.47, all P<0.05). The Bootstrap method showed that both extraneous cognitive load (effect size=0.043, 95% CI 0.024 to 0.090, P<0.05) and germane cognitive load (effect size=0.026, 95% CI 0.017 to 0.082, P<0.05) played a partial mediating effect between suggestion presentation and adherence to nursing intervention, the total mediating effect accounted for 17.29%. Conclusions:Using the intelligent system to change the presentation of delirium nursing interventions can reduce the extraneous cognitive load and increase the germane cognitive load of ICU nurses, and then improve the adherence of nurses to implement delirium nursing interventions. Our study provides a clinical practice basis for improving the quality of clinical nursing.

Objective:To explore the research hotspots and trends of artificial intelligence in elderly delirium, and to provide reference for clinical healthcare professionals.Methods:The Web of Science Core Collection database was used as the data source to obtain literature on "artificial intelligence" and "elderly delirium". The search period was from January 1, 2017 to July 29, 2024. CiteSpace software was used for visual analysis of the included literature.Results:A total of 99 articles were included, with an overall upward trend in the number of publications. The United States had the most publications ( n=43), followed by China ( n=25). The institution with the most publications was Harvard Medical School ( n=8), followed by Sichuan University ( n=7). Journal of the American Geriatrics Society was the most co-cited journal. Based on the keywords, research hotspots mainly focused on the construction and validation of the risk prediction model of elderly delirium, intelligent diagnosis of elderly delirium, and intelligent elderly delirium nursing program. Future studies could explore reliability, ICU and validity. Conclusions:Artificial intelligence is increasingly being researched in elderly delirium to facilitate personalized nursing programs for elderly delirium in all aspects, from disease prediction, risk assessment, early diagnostic monitoring to intervention strategy development. In the future, trends in artificial intelligence research in the field of elderly delirium will revolve around improving the reliability of diagnosis, spreading its use in ICU, and optimizing predictive models.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the distribution characteristics of urinary tract pathogens in patients with community-acquired urinary tract infection and their sensitivity to nenoxacin and levofloxacin.Methods:This prospective, multicenter clinical trial included patients with community-acquired urinary tract infection who were admitted to urological clinics at 9 clinical research centers from November 2021 to August 2022.Inclusion criteria: Patients aged 18-70 years with community-acquired acute uncomplicated cystitis(AUC), recurrent acute episodes of urinary tract infection(rUTI), and non-febrile complicated urinary tract infection(cUTI) with signs of urinary tract irritation and abnormal elevation of routine white blood cells in urine. Exclusion criteria: ①Patients who received effective antimicrobial therapy within 72 h before enrollment and lasted for more than 24 h. ②Fever (>37.3℃) or symptoms of upper urinary tract infection such as low back pain, tapping pain in the kidney area, etc. ③Indwelling urinary catheter. At the first visit, clean midstream urine samples were taken for bacterial culture, and the distribution characteristics of urinary pathogens of different types of urinary tract infections were analyzed. Extended spectrum β-lactamases (ESBLs) were measured for Gram-negative bacteria. The susceptibility of nenoxacin and levofloxacin to urinary tract pathogens was determined by disk diffusion method. Drug resistance rate, sensitivity rate were analyzed between different disease groups.Results:There were 404 enrolled patients from 9 hospitals, including 364 (90.1%) females and 40 (9.9%) males. A total of 177 strains of pathogenic bacteria were isolated, among which the highest proportion of Escherichia coli was 66.1% (117/177).Klebsiella pneumoniae was followed by 6.8% (12/177) and Streptococcus agalactis 5.1% (9/177). The bacterial spectrum distribution of AUC and rUTI were similar, and the proportions of Escherichia coli were 70.6% (85/119) and 65.9% (29/44), respectively. However, the proportions of Escherichia coli isolated from cUTI patients were only 28.6% (4/14) and Enterococcus faecalis 7.1%(1/14). The overall detection rate of ESBLs in Gram-negative bacteria was 30.9%(43/139). The sensitivity rate of nenoxacin was 74.6%(91/122), and the resistance rate was 25.4%(31/122). The overall sensitivity rate of levofloxacin was 44.9%(70/156) and the resistance rate was 36.5%(57/156). The rate of resistance of urinary tract pathogens to levofloxacin was 48.2% (27/56) in patients with previous urinary tract infection history, and 30.0% (30/100) in patients with no previous urinary tract infection history, the difference was statistically significant( P=0.023).The sensitivity rate of Gram-negative bacteria to nenofloxacin was 70.7% (65/92) and that to levofloxacin was 50.0% (46/92, P<0.001). The sensitivity of Gram-positive bacteria to nenofloxacin was 80.0% (16/20), and that to levofloxacin was 70.0% (14/20, P=0.009). Conclusions:The bacterial profile of out-patient community acquired urinary tract infection varies greatly according to different diseases. The proportion of Escherichia coli in AUC and rUTI patients is higher than that in cUTI. The detection rate of ESBLs in Gram-negative bacteria was lower than the domestic average.Patients with a history of urinary tract infection had a high risk of treatment failure with levofloxacin. The sensitivity of common urinary tract pathogens to nenofloxacin was higher than levofloxacin.