Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

Objective To develop the functions and optimize the automatic monitoring module for arrhythmias of the adverse drug event active surveillance and assessment system-Ⅱ,in order to continuously improve the performance,enhance the monitoring efficiency,and explore the ways to optimize the module.Methods Expand and optimize the functions of the module,increase the customized configuration,and determine the optimal setting conditions;compare the optimized test data with the results of the evaluation studies on the automatic monitoring of drug-induced arrhythmias in large samples of medicated population previously,and verify the optimization extent as well as the accuracy of the module.Results In the new module optimized according to the characteristics of the monitoring population,the function of"mandatory medical order keywords"was added,and it was determined that the inclusion of 6 electrocardiogram examination-related medical order keywords with a frequency of not less than 2 occurrences was the optimal configuration condition for the optimization of the module;combining the results of the previous automatic monitoring and evaluation researches,the system functions were verified and compared under the conditions of using the whole drugs and 2 kinds of single drug.While there was no loss of true positive cases,the number of cases with system alarms decreased by 30.75％,80.13％and 90.82％,respectively,compared with that before the optimization of the module,and the positive predictive value was significantly improved.Conclusion After the function expansion and optimization,the automatic monitoring module of drug-induced arrhythmias significantly reduces the labor cost of case evaluation and keeps the accuracy of monitoring results constant;the new module can better adapt to the demands of different automatic monitoring modes and operates stably,which is more generalizable and flexible,and provides a new way of considering for the research and development of automatic monitoring modules.

Objective To obtain the occurrence and clinical characteristics of central nervous system adverse drug reactions(CNS-ADR)associated with three kinds of carbapenems,and to provide reference for clinical drug safety.Methods Based on adverse drug event active surveillance and assessment system-Ⅱ(ADE-ASAS-Ⅱ),retrospective automated monitoring of inpatients using imipenem,meropenem,and biapenem in a tertiary hospital from January 2022 to December 2022 was conducted.The incidence of carbapenem related CNS-ADR was calculated,and the basic conditions,disease conditions,drug use,occurrence time of ADR and symptoms of patients with CNS-ADR were analyzed by descriptive statistics.Results A total of 2 482 patients with 2 709 times of medication were included in this study,and a total of 93 positive cases of CNS-ADR occurred,with an overall incidence of 3.43％for all three medications,3.98％for imipenem,3.51％for meropenem,and 2.78％for biapenem.The indications for the 93 positive cases of CNS-ADR were mainly pulmonary infections(59.13％)and abdominal infections(25.80％);they occurred mostly within 7 days of the administration of the medication;with a variety of clinical manifestations,with anxiety/irritability being the most common,and epilepsy appearing most frequently in severe cases.Co-administration of proton pump inhibitors and cephalosporins accounted for a greater proportion of positive cases,50.54％of positive cases had a history of surgery,and 69.89％of positive cases were associated with electrolyte disturbances.Conclusion Clinical use of carbapenems should be based on the actual situation of the patient to develop an individualised drug regimen,and special attention should be paid to patients with comorbidities of renal disease,electrolyte disorders,and a history of previous surgery and neurological disorders,in order to reduce the risk of the occurrence of CNS-ADR.

This is the first reported case of a female with monkeypox infection in Kunming City,Yunnan Province.An epi-demiological investigation was conducted to provide a scientific basis for the prevention and control of monkeypox epidemics in China,especially for early detection in females in accordance with the"Monkeypox prevention and control program(2023 ver-sion)".Diagnosis was performed as described in the"Monkeypox Diagnosis and Treatment Guidelines(2022 version)".Speci-mens were collected for laboratory testing.The epidemiological investigation determined that the female patient had sexual in-tercourse with her newly married husband once before disease onset and the husband hid his history of male homosexual sex.The laboratory test results of the woman and her husband were positive for the nucleic acid of the monkeypox virus.Both had typical clinical symptoms,including rash.The epidemiological investigation,clinical symptoms,laboratory test results,and previous epidemic data of monkeypox in Yunnan province confirmed the woman as the first female infected with monkeypox in Yunnan Province and her husband was the presumed source of infection.

Objective To calculate the pre-stretching of the microscopic components of the aortic wall under physiological homeostasis by considering a three-dimensional(3D)residual stress field.Methods The aortic wall was simplified into a double-layer ideal circular tube,and the 3D residual stress field of the vascular wall was calculated based on a 3D expansion angle experiment.Then,the in vivo stress distribution characteristics under mean blood pressure and the pre-stretching of each microscopic constituent of the vascular wall under a physiologically steady state were obtained.The inverse problem was constructed according to the internal pressure-radius relationship measured in vivo.Physiological homeostasis of the aorta was considered the reference state,and inversion identification of the material parameters of the aorta in vivo was realized while integrating the three residual stress fields.Results When residual stress was not considered,the mean stress of the middle membrane was greater than that of the outer membrane.When residual stress was considered,the outer membrane bore more stress than the middle membrane,and the outer membrane protected the middle membrane.The pre-stretching of the middle film with residual stress was lower than that without residual stress,whereas the pre-stretching of the outer film was higher than that without residual stress.Moreover,the pre-stretching of the outer membrane collagen fibers was greater than that of the middle membrane collagen fibers.The in vivo calculations of the material parameters of the aorta were performed using physiological homeostasis as the reference configuration,and the proportion of each component was consistent with the experimental results.However,the proportion of elastin in the outer membrane was significantly overestimated when the non-stress configuration was used as the initial configuration,which was inconsistent with the experimental results.Conclusions Residual stress significantly influences the pre-stretching and physiologically steady mechanical states of the microscopic components of the aortic wall.Therefore,it is necessary to fully consider the influence of residual stress to establish the physiologically steady state of the aortic wall accurately.Furthermore,it is also necessary to fully consider the 3D characteristics and layer specificity of residual stress in the in vivo identification of material parameters.

Objective To propose a SSA-BP algorithm based on the back propagation(BP)neural network and sparrow search algorithm(SSA)to predict battery remaining life accurately.Methods Firstly,the total number of the weights and thresholds was determined with the structure of the BP neural network;secondly,the initial weights and thresholds were optimized using the SSA algorithm and assigned to the BP neural network;and finally,the predicted output values were obtained by training the input samples.The data of 18650 model lithium batteries at different ambient temperatures(4,24,43 ℃)were selected for testing,and the prediction accuracy of the SSA-BP neural network algorithm and BP neural network algorithm on the remaining life of medical device batteries was verified by the mean absolute error,root mean square error and mean absolute percentage error.Results The SSA-BP algorithm had the average absolute error,root mean square error and mean absolute percentage error lower than those of the BP neural network when used to predict battery remaining life.Conclusion The SSA-BP algorithm can effectively predict battery remaining life,and enhances battery reliability during practical application.[Chinese Medical Equipment Journal,2024,45(8):21-25]

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.

Roasting is a characteristic traditional Chinese medicine(TCM)processing technology, which has a long history. A large number of ancient books recorded the varieties and processing methods of roasted TCM. However, with the evolution of the times, there are relatively few studies on this processing technology in modern times. By consulting works related to herbs of the past, this study collected a total of 119 kinds of TCM roasting methods recorded in 123 ancient books and systematically summarized the historical evolution and development of TCM roasting methods. At the same time, the inclusion of roasted varieties in Chinese Pharmacopoeia of different editions, National Traditional Chinese Medicine Processing Specification(1988 edition), and provincial TCM processing specifications was sorted out. This paper reviews the research progress of the process, quality control, chemical composition, pharmacological action, and clinical application of roasted TCM and analyzes the evolution of the roasting technology, in order to provide a literature basis for optimizing roasting process parameters, establishing the quality standard of roasted decoction pieces, explaining the processing theory of roasting, and promoting rational clinical application.
Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/history* ; Humans ; Quality Control ; History, Ancient ; Hot Temperature

Objective:To explore the value of subspecialty group collaboration combined with disease checklist-driven learning in overcoming the impact of the specialized disease treatment mode in subspecialty establishment on the cultivation of professional postgraduate students.Methods:In the teaching of general surgery and gastroenterology, sixty professional postgraduate students of grade 2019 were randomly divided into control group and experimental group, with 30 students in each group. The control group received traditional teaching, while the experimental group received the teaching mode of subspecialty group collaboration combined with disease checklist-driven learning. The teaching effectiveness and the degree of satisfaction with teaching were compared between the two groups. The data were analyzed using the t test and the chi-squared test using SPSS 20.0. Results:In actual teaching, compared with the control group, the experimental group showed significantly higher scores of theoretical assessment (71.51±11.32 vs. 87.23±10.51, P<0.05) and case analysis (73.61±6.82 vs. 92.37±6.87, P<0.05). The rates of satisfaction with theoretical knowledge learning, application of clinical thinking ability for diseases, teaching organization forms, and teaching effectiveness were 90.00%(27/30), 86.67%(26/30), 96.67%(29/30), and 93.33%(28/30) in the experimental group, respectively, which were significantly higher than those of the control group [40.00%(12/30), 23.33%(7/30), 40.00%(12/30), and 46.67%(14/30), respectively; all P<0.05]. Conclusions:The subspecialty group collaboration combined with disease checklist-driven learning mode can overcome the problems of "narrow disease spectrum and narrow knowledge scope" in specialized postgraduate education, and guide students to break the teaching barriers generated by subspecialty construction to create a new form of comprehensive and multi-disease learning, with good prospects for promotion and application.