Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective:To analyze the epidemiological characteristics of cases involving monkey injuries at medical institutions surrounding Qianlingshan Park in Guiyang City, and to provide a reference basis for preventive measures to reduce monkey injuries and standardized post-exposure treatment.Methods:A retrospective cross-sectional study was conducted, collecting 1 900 cases of monkey-induced injuries in Qianlingshan Park treated at the outpatient clinic of Guizhou Provincial Center for Disease Control and Prevention and the Department of Surgery at Qianling Hospital, Guiyang City, from 2021 to 2024. Statistical analysis was performed using Pearson′s chi-square test.Results:Total of 1 900 cases of monkey-related injuries in Qianlingshan Park were collected from 2021 to 2024. The exposure time distribution exhibited significant seasonality, with 48.58% of cases occurring during July and August, totaling 923 cases, indicating a peak in the summer. There were 774 male patients and 1 126 female patients, with a ratio of 1∶1.45.and significant differences were observed between different age groups and genders (χ2=195.00, P<0.001), with the highest number of cases occurring in the 0-9 and 20-29 age groups, accounting for 22.05%(419 cases) and 21.79%(414 cases), respectively. The upper limbs were the most common injury site, accounting for 50.84% of the total cases(966 cases in total), with significant differences between gender and injury location (χ2=22.00, P<0.001), Among females, the proportion of injuries to the upper and lower limbs (30.11% and 16.47%, respectively) was higher than that among males (20.74% and 8.63%, respectively). The majority of injuries were classified as Grade Ⅲ, making up 57.38% of cases(1 069 cases in total). Self-treatment after exposure was the most common approach(60.44%), with significant differences observed between wound severity and treatment method (χ2=6.90, P=0.032), Patients with Grade Ⅱ and Grade Ⅲ wounds were more likely to choose self-management (26.84% and 33.23%, respectively) than outpatient management (15.14% and 24.15%). Approximately 98.05% (1 863 cases) of monkey-injured patients had received rabies vaccinations. Conclusions:This study analyzes monkey-related injuries in Qianlingshan Park from 2021 to 2024, clarifying the temporal distribution of injuries, demographic characteristics, injury sites, and treatment methods. The findings provide references for optimizing human-monkey conflict management and the prevention and control of zoonotic diseases in urban ecological parks.

Objective To observe the value of deep learning(DL)reconstruction algorithm for improving imaging quality of accelerated T2WI of cervical tumors.Methods A total of 43 patients with suspected cervical tumors were prospectively enrolled.Cervical conventional T2WI and accelerated T2WI based on DL reconstruction(DL-T2WI)were acquired.The imaging quality was subjectively assessed by 2 physicians using a 4-point system,including overall image quality,artifact,noise,sharpness and lesion detectability scores,and then were compared between conventional T2WI and DL-T2WI.Results The acquisition of conventional T2WI took 116 s,while of DL-T2WI took 101 s.The inter-observer consistency of subjective evaluation results on the overall image quality,artifact,noise,sharpness and lesion detectability scores were all excellent(Kappa=0.851-0.969).No significant difference of subjective evaluation results on overall image quality nor lesion detectability scores was found between conventional T 2WI and DL-T2WI(both P＞0.05),while the artifact and sharpness scores of DL-T2WI were significantly higher but the noise score was significantly lower than those of conventional T2WI(all P＜0.05).Conclusion DL reconstruction algorithm was helpful for improving imaging quality of accelerated T 2WI of cervical tumors.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Next generation sequencing (NGS) technology is playing an increasingly important role in the diagnosis of genetic diseases. Whole exome sequencing (WES), which targets the coding regions of the genome, has been widely used in the diagnosis of genetic diseases for its low cost and high efficiency. However, compared to conventional methods, the Next Generation Sequencing (NGS) process is intricate, and there is variability in the expertise of data analysts and variant interpreters, which may lead to inconsistencies in the outcomes. To ensure the quality of testing and enhance the diagnostic rate of diseases, this consensus has provided recommendations regarding the laboratory setup, operational procedures, data analysis, result interpretation, and quality control for WES, with an aim to standardize its application in the detection of genetic disorders.

Objective:To evaluate the efficacy and safety of obinutuzumab combined with glucocorticoid-based therapy in patients with relapsed immune thrombotic thrombocytopenic purpura (iTTP).Methods:This study analyzed the efficacy and adverse reactions of four patients with relapsed iTTP who were treated with a combination of obinutuzumab and glucocorticoids to assess the effectiveness and safety of the treatment.Results:All four patients had a history of multiple relapses and had previously undergone treatment with rituximab and bortezomib. Three patients exhibited additional autoantibodies. Following the combined therapy, all patients achieved clinical remission, with ADAMTS13 activity returning to normal levels and inhibitors testing negative. During a median follow-up period of 11 months (range: 3–17 months), all patients maintained sustained remission. No severe adverse events were reported during treatment or follow-up.Conclusion:The combination of obinutuzumab and glucocorticoid-based therapy is effective and safe for treating relapsed iTTP.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.