Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

Forced oscillation technique (FOT) enables early diagnosis of chronic obstructive pulmonary disease (COPD) by quantifying the impedance of COPD patients during normal breathing and reflecting the airway obstruction and distribution of the patients. The flow sensors using in FOT for diagnosis of COPD mainly include differential pressure flow sensors, ultrasonic flow sensors, hot wire gas flow sensors, fiber-optic flow sensors and flow sensors based on friction nanoelectricity technology. In this review, the principles, characteristics, and current application status of these five types of flow sensors were summarized, and their future development prospects were prospected.

In recent years, a variety of innovative fast algorithm techniques have emerged in the field of transcranial magnetic stimulation (TMS) electric field solving, which show great potential to meet the requirements of real-time clinical applications. In this review, the crucial processes of TMS electric field modeling were summarized, focusing on two prominent fast algorithm techniques, namely the basis function method and the deep neural network (DNN)-based method. The advantages and limitations of these two techniques were analyzed in detail. Commonly used software tools for electric field modeling were discussed, and a prospective discussion of future developments was offered, aiming to provide a reference for the further development of TMS electric field modeling technology.

Transcranial ultrasound stimulation (TUS) is a non-invasive brain stimulation technique that demonstrates great potential in treatment of neurological disorders due to its high spatial resolution and focused penetration ability, enabling millimetre-level precision modulation of specific brain regions with ultrasound to achieve neuromodulation of targeted areas. At present, numerous animal experiments and human studies have been carried out on the regulation of Alzheimer′s disease by TUS. In this review, the regulation effects of TUS on Alzheimer′s disease were introduced from the perspective of targeting different brain regions, including the hippocampus, frontal lobe and whole brain.

Alzheimer′s disease (AD) is a degenerative disorder of the central nervous system. In the early stages of AD, i.e. when cognitive impairment is mild or absent but biomarkers are present, patients show abnormal neuroelectric activity. Repetitive transcranial magnetic stimulation (rTMS) is an important method of non-invasively regulating neuroelectric activity in the brain. It does so by inducing microcurrents to change the membrane potential of nerve cells in the brain based on electromagnetic induction. In this review, the characteristics of neuroelectric activity of AD patients and AD model mice in the early stage of AD were mainly introduced, and the regulation of rTMS on the neuroelectric activity of early AD was discussed.

Emotion is defined as a physiological and psychological state that encompasses human thoughts, behaviors, and feelings. This phenomenon is also regarded as a spontaneous physiological and psychological response generated by the human body to external stimuli. Given the established correlation between electroencephalogram signal and cerebral activity, it is possible to extrapolate the emotional state of subjects by means of electroencephalogram signal analysis. In this review, emotion models, datasets, and popular machine learning and deep learning methods in recent years used in emotion recognition research were summarized. In addition, the research progress of emotion recognition based on electroencephalogram signal was reviewed, with the aim of assisting subsequent researchers in understanding developments in electroencephalogram signal domain and offering insights for addressing clinical challenges in emotion recognition.

The prevalence of neurological diseases is increasing, and conventional clinical treatments frequently exhibit suboptimal efficacy and substantial adverse effects. Transcranial magnetic stimulation (TMS) has garnered significant interest due to its non-invasiveness and favorable tolerability. In this review, the etiology of neurological disorders was described, including Alzheimer's disease, stroke, epilepsy, Parkinson's disease and neuropathic pain. The application methods and therapeutic effects of TMS in these diseases were reviewed as well. While TMS demonstrates considerable promise in the treatment of neurological disorders, further large-scale studies and additional research on its mechanisms and personalized treatment protocols are necessary.

Objective To compare the mid- and long-term clinical results of mitral valve plasty (MVP) and mitral valve replacement (MVR) in the treatment of functional mitral regurgitation (FMR). Methods Patients with FMR who underwent surgical treatment in the Department of Cardiovascular Surgery of the General Hospital of Northern Theater Command from 2012 to 2021 were collected. The patients who underwent MVP were divided into a MVP group, and those who underwent MVR into a MVR group. The clinical data and mid-term follow-up efficacy of two groups were compared. Results Finally 236 patients were included. There were 100 patients in the MVP group, including 53 males and 47 females, with an average age of (61.80±8.03) years. There were 136 patients in the MVR group, including 72 males and 64 females, with an average age of (61.29±8.97) years. There was no statistical difference in baseline data between the two groups (P＞0.05). There was no statistical difference between the two groups in the extracorporeal circulation time, aortic occlusion time, postoperative hospital and ICU stay, intraoperative blood loss, or hospitalization death (P＞0.05), but the time of mechanical ventilation in the MVP group was significantly shorter than that in the MVR group (P=0.022). The total follow-up rate was 100.0%, the longest follow-up was 10 years, and the average follow-up time was (3.60±2.55) years. There were statistical differences in the left atrial diameter, left ventricular end-diastolic diameter, left ventricular end-systolic diameter and cardiac function between the two groups compared with those before surgery (P<0.05). The postoperative left ventricular ejection fraction in the MVP group was statistically higher than that before surgery (P=0.002), but there was no statistical difference in the MVR group before and after surgery (P=0.658). The left atrial diameter in the MVP group was reduced compared with the MVR group (P=0.026). The recurrence rate of mitral regurgitation in the MVP group was higher than that in the MVR group, and the difference was statistically significant (10.0% vs. 1.5%, P=0.003). There were 14 deaths in the MVP group and 19 in the MVR group. The cumulative survival rate (P=0.605) and cardiovascular events-free survival rate (P=0.875) were not statistically significant between the two groups by Kaplan-Meier survival analysis. Conclusion The safety, and mid- and long-term clinical efficacy of MVP in the treatment of FMR patients are better than MVR, and the left atrial and left ventricular diameters are statistically reduced, and cardiac function is statistically improved. However, the surgeon needs to be well aware of the indications for the MVP procedure to reduce the rate of mitral regurgitation recurrence.

Objective We improved the segmentation model TransUNet based on deep learning methods to construct a multitask model that can both segment regions of interest and predict classification,which made it possible to identify sensitive populations of patients undergoing neoadjuvant therapy for rectal cancer.Methods The multitask model added classification structure on the basis of TransUNet,including the fully connected layer(input of 512,output of 256),the ReLU activation function,and the fully connected layer(input of 256,output of 3),to achieve the prediction of triple classification outcomes(stable disease(SD),partial response(PD),and complete response(CR)).The 3D MRI of 71 rectal cancer patients before neoadjuvant chemotherapy admitted to the Harbin Medical University Cancer Hospital from 2015 to 2017 were extracted into 2D images as data for the study.Dice coefficient and Hausdorff distance were used to evaluate thesegmentation performance,and accuracy,micro-precision,micro-recall,and micro-F1 score were used for classification performance.Results The model was trained for 100 epochs,and the average Dice coefficient and average Hausdorff distance for the segmentation task on the test set were 0.851 and 10.806,respectively.For the classification task,the accuracy,micro-precision,micro-recall,and micro-F1 score on the test set were all 0.651 from the slicing perspective,and all four metrics were 0.857 from the patient perspective.Conclusion Our model works well in the segmentation task.Although the model performs poorly on the classification task at the slice level,the performance was acceptable at the patient level taking into account the tripartite classification results.The multitask model has the potential to be used in the clinic for assisted diagnosis.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.