In the context of burgeoning economic trade, there has been a remarkable increase in the number of ships travelling between domestic and international ports. Inbound and outbound cargo ships, cruise liners, ferries, and other types of vessels have emerged as critical vehicles for the dispersal of vector organisms and the spread of infectious disease agents. Public health concerns associated with these vessels represent a significant threat to human welfare. In this paper, we comprehensively review the hazards posed by vector organisms on ships, the latest advances in prevention and control strategies, and proposes appropriate countermeasures. The aim is to provide effective guidance for the prevention and control of public health security issues caused by ship-borne vector organisms.

Objective To investigate the influence of the and mechanism of liver failing to convey and disperse on age-related changes in attentional search based on ERPs.Methods oddball attention search task was administrated to record and analyze behavioral and EEG data(N2pc、SPCN、N2pc-Ptc components)of 120 subjects.Results Compared with liver controlling conveyance and dispersion group,the accuracy in subjects with liver failing to convey and disperse decreased significantly(P<0.05).The elderly group had a lower accuracy(P<0.001)and a longer reaction time(P<0.001)compared to the young group.The N2pc amplitude in subjects with liver failing to convey and disperse was significantly greater than that in subjects with liver controlling conveyance and dispersion(P<0.05).The interaction effect of SPCN amplitude between age and liver failing to convey and disperse status was significant(P=0.024).And in the elderly group,SPCN amplitude in subjects with liver dysregulation was significantly smaller than that of liver controlling conveyance and dispersion(P=0.042).The N2pc-Ptc peak to peak amplitude interaction effect between age and liver regulation status was marginal significant(P=0.087),and in liver failing to convey and disperse group,N2pc-Ptc peak to peak amplitude of the elderly was significantly smaller than that of the young(P=0.008).Conclusion Attention search ability is impaired in the elderly with liver failing to convey and disperse,and the electrophysiological abnormalities,such as directed attention allocation,spatiotemporal dynamic cohesion and short-term memory maintenance,may be part of the mechanism.

Objective To investigate the influence of the and mechanism of liver failing to convey and disperse on age-related changes in attentional search based on ERPs.Methods oddball attention search task was administrated to record and analyze behavioral and EEG data(N2pc、SPCN、N2pc-Ptc components)of 120 subjects.Results Compared with liver controlling conveyance and dispersion group,the accuracy in subjects with liver failing to convey and disperse decreased significantly(P<0.05).The elderly group had a lower accuracy(P<0.001)and a longer reaction time(P<0.001)compared to the young group.The N2pc amplitude in subjects with liver failing to convey and disperse was significantly greater than that in subjects with liver controlling conveyance and dispersion(P<0.05).The interaction effect of SPCN amplitude between age and liver failing to convey and disperse status was significant(P=0.024).And in the elderly group,SPCN amplitude in subjects with liver dysregulation was significantly smaller than that of liver controlling conveyance and dispersion(P=0.042).The N2pc-Ptc peak to peak amplitude interaction effect between age and liver regulation status was marginal significant(P=0.087),and in liver failing to convey and disperse group,N2pc-Ptc peak to peak amplitude of the elderly was significantly smaller than that of the young(P=0.008).Conclusion Attention search ability is impaired in the elderly with liver failing to convey and disperse,and the electrophysiological abnormalities,such as directed attention allocation,spatiotemporal dynamic cohesion and short-term memory maintenance,may be part of the mechanism.

Objective:To explore drug detoxification motivation in methamphetamine-dependent female youth and its relationship with time perspective,perceived social support and emotion regulation strategies.Methods:To-tally 200 methamphetamine-dependent female youths under compulsory isolation were assessed with the Drug De-toxification Motivation Scale,Zimbardo Time Perspective Inventory-Chinese(ZTPI-C),Emotion Regulation Scale(ERS),and Perceived Social Support Scale(PSSS).Results:Multiple linear regression analysis showed that drug detoxification motivation scores were positively correlated with the scores of ERS reevaluation and expression inhi-bition,PSSS family support scores and level of education(β=0.24,0.16,0.20,0.16).Conclusion:Time perspec-tive,perceived social support and emotion regulation strategies are closely related to drug detoxification motivation in methamphetamine-dependent female youth.

Objective:To explore drug detoxification motivation in methamphetamine-dependent female youth and its relationship with time perspective,perceived social support and emotion regulation strategies.Methods:To-tally 200 methamphetamine-dependent female youths under compulsory isolation were assessed with the Drug De-toxification Motivation Scale,Zimbardo Time Perspective Inventory-Chinese(ZTPI-C),Emotion Regulation Scale(ERS),and Perceived Social Support Scale(PSSS).Results:Multiple linear regression analysis showed that drug detoxification motivation scores were positively correlated with the scores of ERS reevaluation and expression inhi-bition,PSSS family support scores and level of education(β=0.24,0.16,0.20,0.16).Conclusion:Time perspec-tive,perceived social support and emotion regulation strategies are closely related to drug detoxification motivation in methamphetamine-dependent female youth.

Objective To propose strategies for developing clinical predictive models,aiming to assist researchers in conducting standardized clinical prediction model studies.Methods Literature review was conducted to summarize the operational steps and content for developing clinical predictive models.Then,a methodological framework was summarized and refined through expert consultation.Results The 11-step methodological framework for developing clinical predictive models was obtained by synthesizing the experience of 456 clinical predictive modeling studies and expert consultation,and the details were analyzed and elaborated.Conclusions This study presents methodological strategies and recommendations for the development of clinical predictive models,intended to serve as a guide for researchers.

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.

Humans ; Leukemia, Mast-Cell ; Bone Marrow ; Leukemia

BACKGROUND: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Humans ; Retrospective Studies ; Pneumonia/diagnostic imaging* ; Analysis of Variance ; Tomography, X-Ray Computed ; Lymphoma/diagnostic imaging*

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers