With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.
Humans ; Brain-Computer Interfaces ; Consciousness Disorders/physiopathology* ; Electroencephalography ; Brain/physiopathology* ; Consciousness

OBJECTIVES: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the EGFR and KRAS genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict EGFR and KRAS gene mutation status in NSCLC patients, thereby providing a reference for precision oncology. METHODS: Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting EGFR and KRAS gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation. RESULTS: The models effectively predicted EGFR and KRAS mutations in NSCLC patients, achieving an AUC of 0.95 for EGFR mutations and 0.90 for KRAS. The models also demonstrated high accuracy (EGFR 89.66%; KRAS 87.10%), sensitivity (EGFR 93.33%; KRAS 87.50%), and specificity (EGFR 85.71%; KRAS 86.67%). CONCLUSIONS A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating EGFR and KRAS gene mutation status in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Mutation ; Proto-Oncogene Proteins p21(ras)/genetics* ; ErbB Receptors/genetics* ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Female ; Male ; Neural Networks, Computer ; Middle Aged ; Aged

Objective:To investigate the protective mechanism of TRPV4 channel inhibitor on blood-brain barrier(BBB)damage after traumatic brain injury(TBI).Methods:The TBI rat model was established.TRPV4 channel inhibitor HC067047 or PKC-δ inhibitor Rottlerin was used to detect changes in BBB permeability,neurological function score,and the expression of microvascular endothelial tight junction proteins ZO-1 and ZO-2 in brain injury areas after TBI.Results:Compared with the Sham group,BBB permeability significantly increased,brain neurological function score significantly decreased,and the expression of ZO-1 and ZO-2 significantly decreased in TBI group(P＜0.05).Compared with the TBI group,after administration of HC067047 or Rottlerin,changes in BBB permeability,brain neurological function score,the expression of ZO-1 and ZO-2 were partially reversed(P＜0.05).Conclusions:TBI-induced BBB injury may be mediated by TRPV4 channel regulating PKC-δ signaling pathway to affect the expression of tight junction proteins ZO-1 and ZO-2.Inhibition of TRPV4 channel function or PKC-δ signal molecule can partially alleviate BBB damage induced by TBI.This study may provide new ideas for the treatment of clinical TBI.

An innovative on-site real-time avian influenza virus(AIV)detection method was estab-lished by integratingrecombinase-aided amplification(RAA)with the clustered regularly inter-spaced short palindromic repeats(CRISPR)/CRISPR-associated protein(Cas)system.After analy-zing 120 sequences of the M gene of avian influenza viruses of different subtypes publicly available on NCBI,the RAA primers and crRNA were designed based on the identified highly conserved segment and used for RAA nucleic acid amplification.After the amplified products were transferred to a CRISPR/Cas13a detection system,the fluorescence values were monitored throughout the re-action process to indicate the results.The sensitivity and specificity of the RAA-CRISPR/Cas13a method were validated using gradient dilutions(106-100 copies/μL)of positive plasmids and sev-en other avian viruses.Fifty clinical samples were tested using this method and compared with the national standard fluorescence RT-PCR method.The results indicated that the detection limit for RAA-CRISPR/Cas13a method was 102 copies/μL,a two-fold improvement over the standard RAA.Specificity assay showed the established method only detected AIV with no cross-reactivity with other seven avian viruses.Compared to the national standard fluorescence RT-PCR method,this method exhibited 100％specificity,95.24％accuracy,and 98.00％consistency in detection of clinical samples.In conclusion,a universal and rapid RAA-CRISPR/Cas13a for detection of AIV was established with the capacity of achieving detection within 60 minutes at 37 ℃,which provides a rapid,sensitive,and specific on-site detection method for AIV.

The control strategy of rehabilitation robots should not only adapt to patients with different levels of motor function but also encourage patients to participate voluntarily in rehabilitation training.However,existing control strategies usually consider only one of these aspects.This study proposes a voluntary and adaptive control strategy that solves both questions.Firstly,the controller switched to the corresponding working modes(including challenge,free,assistant,and robot-dominant modes)based on the trajectory tracking error of human-robot cooperative movement.To encourage patient participation,a musculoskeletal model was used to estimate the patient's active torque.The robot's output torque was designed as the product of the active torque and a coefficient,with the coefficient adaptively changing according to the working mode.Experiments were conducted on two healthy subjects and four hemiplegic patients using an ankle rehabilitation robot.The results showed that this controller not only provided adaptive the robot's output torque based on the movement performance of patients but also encouraged patients to complete movement tasks themselves.Therefore,the control strategy has high application value in the field of rehabilitation.

Humans ; COVID-19 ; HIV Infections/drug therapy* ; Risk Factors

COVID-19 has brought a significant impact to the global health system, and also opportunities and challenges to epidemiological researches. Theoretical epidemiological models can simulate the process of epidemic in scenarios under different conditions. Therefore, modeling researches can analyze the epidemical trend of COVID-19, predict epidemical risks, and evaluate effects of different control measures and vaccine policies. Theoretical epidemiological modeling researches provide scientific advice for the prevention and control of infectious diseases, and play a crucial role in containing COVID-19 over the past year. In this study, we review the theoretical epidemiological modeling researches on COVID-19 and summarize the role of theoretical epidemiological models in the prevention and control of COVID-19, in order to provide reference for the combination of mathematical modeling and epidemic control.
COVID-19 ; Communicable Diseases/epidemiology* ; Humans ; Models, Theoretical ; SARS-CoV-2

Objective:To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients.Methods:Based on the patients’ admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model.Results:In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94 %, peripheral white blood cells count >8.0×10 9, change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7 %, and the missed diagnosis rate was 38.3 %. The prediction sensitivity of the model based on the test set was 75.0 %, and the missed diagnosis rate was 25.0 %. Conclusions:Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model’s prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.

Objective To explore the epidemiological situation and characteristics of human brucellosis in Jiangsu Province from 2006 to 2017,and to provide scientific evidences for its prevention and control.Methods Using a retrospective study,data of brucellosis epidemic in Jiangsu Province from 2006 to 2017 were collected.The epidemic data of brucellosis and the population data were derived from China Disease Prevention and Control Information System,etiological data of brucellosis from 2011 to 2017 were derived from the surveillance report of brucellosis in Jiangsu Province.The overall incidence of brucellosis,pathogen research and regional,seasonal,and population distribution characteristics of cases were analyzed.Results A total of 607 prevalent cases of brucellosis were reported in Jiangsu Province from 2006 to 2017,with an average annual incidence rate of 0.065/100 000,including 595 incident cases.The number of reported case showed a sharp upward trend in 2011-2017,the incidence rates were in an increasing trend (t =5.623,P ＜ 0.01).A total of 132 Brucella strains were isolated from serum samples in Jiangsu Province in 2011-2017,all of the strains were sheep breeds,of which sheep type 3 accounted for 84.85％ (112/132).The top five cities with annual incidence of brucellosis from 2006 to 2017 were Xuzhou,Lianyungang,Suzhou,Huai'an and Suqian,with the incidence rates of 0.223/100 000,0.210/100 000,0.128/100 000,0.108/100 000 and 0.102/100 000,respectively.Brucellosis cases were distributed in each month,with the 540 cases (90.76％,540/595) from January to September.There were 446 males and 161 females in 607 cases of brucellosis,the sex ratio was 2.77:1.00.A total of 579 cases were reported in the age group of 20-74 years old,accounting for 95.39％.The occupational distribution was mainly peasants,which accounting for 60.13％ (365/607).Conclusions The epidemic situation of human brucellosis in Jiangsu Province is becoming increasingly serious recently.Males and peasants are main incidence population.Thus,we should pay more attention to the livestock quarantine and the surveillance and control of brucellosis cases in high risk population so as to control the epidemic situation of brucellosis effectively.

Objective This study aimed to investigate the role of pyrroloquinoline quinone (PQQ) in repairing oxidative nerve cells,and to study the antioxidant capacity of PQQ on the oxidative damage of rats caused by apolexis,as well as the effects on learning and memory abilities of apolexis rats.Methods Oxidative damage of PC12 was induced by H2O2,and the repairing rate of PQQ on oxidative PC12 cells was tested by methylthiazolyldiphenyl-tetrazolium bromide assay kit.The 18-month-old male SD rats were administered PQQ (0,10,20,40 mg/kg).After 4 weeks,Morris water maze test was used to test the learning and memory ability.After 6 weeks,serum and brain tissue related indicators and antioxidant capacity were recorded.Results The survival rate of PC12 cells increased from 59.1％ to 90.5％ with 200 nmol/L PQQ.Compared with the apolexis model group,the latency of the PQQ group (20,40 mg/kg) was shortened in the Morris water maze experiment,the swimming distance was reduced,pass-through counts were increased,and the first secure platform pass-through was reduced.Meanwhile,the levels of malondialdehyde and lipofuscin in serum and brain tissue of PQQ group decreased,the levels of superoxide dismutase,glutathione peroxidase vitality,antioxidant capacity of PQQ group (20,40 mg/kg) were enhanced.Conclusion PQQ could repair the oxidative damage of nerve cells,and it was confirmed that PQQ could play the same antioxidant effect in body and brain,and increase the learning and memory ability of apolexis rats.