Musculoskeletal disorders (MSDs) are characterized by extensive pathological involvement and high prevalence and cause a significant disease burden. Long-term drug administration often causes by adverse effects with poor therapeutic efficacy. Low-intensity pulsed ultrasound (LIPUS), as a specialized therapeutic modality, delivers acoustic energy at a low intensity in a pulsed wave mode, thus ensuring stable energy transmission to the target tissues while minimizing thermal effects. This non-invasive approach has demonstrated significant potential for MSD treatment by delivering effective physical stimulations. Extensive animal and clinical studies have demonstrated the efficacy of LIPUS for accelerating the healing process of fresh fractures and nonunions, promoting soft tissue regeneration and suppressing inflammatory responses. Emerging evidence suggests promising applications of LIPUS in skeletal muscle injury treatment and promoting tissue regeneration and repair. This review outlines the recent advancements and mechanistic studies of LIPUS for treatment of common MSDs including fractures, nonunions, muscle injuries, and osteoarthritis, addressing also the technical parameters of commercially available LIPUS devices, current therapeutic approaches, the existing challenges, and future research directions.
Humans ; Ultrasonic Therapy/methods* ; Musculoskeletal Diseases/therapy* ; Ultrasonic Waves ; Osteoarthritis/therapy* ; Muscle, Skeletal/injuries*

A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.
Humans ; Imagination/physiology* ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; Brain/physiology* ; Movement/physiology* ; Motor Activity/physiology* ; Psychomotor Performance/physiology*

In recent years, the ongoing development of transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) has demonstrated significant potential in the treatment and rehabilitation of various brain diseases. In particular, the combined application of TES and TMS has shown considerable clinical value due to their potential synergistic effects. This paper first systematically reviews the mechanisms underlying TES and TMS, highlighting their respective advantages and limitations. Subsequently, the potential mechanisms of transcranial electromagnetic combined stimulation are explored, with a particular focus on three combined stimulation protocols: Repetitive TMS (rTMS) with transcranial direct current stimulation (tDCS), rTMS with transcranial alternating current stimulation (tACS), and theta burst TMS (TBS) with tACS, as well as their clinical applications in brain diseases. Finally, the paper analyzes the key challenges in transcranial electromagnetic combined stimulation research and outlines its future development directions. The aim of this paper is to provide a reference for the optimization and application of transcranial electromagnetic combined stimulation schemes in the treatment and rehabilitation of brain diseases.
Humans ; Transcranial Magnetic Stimulation/methods* ; Transcranial Direct Current Stimulation/methods* ; Brain Diseases/therapy*

[Objective]To construct a prediction model for brucellosis by using a deep neural network algorithm to improve the early detection.[Methods]We collected the clinical data of 202 brucellosis patients and 319 non-brucellosis patients admitted to Hohhot Occupational Disease Prevention and Treatment Hospital in 2023,and analyzed data such as gender,age,blood routine indices and clinical diagnosis.A prediction model for brucellosis was constructed by using a deep neural network algorithm and optimized through 10-fold cross-validation.Performance metrics included sensitivity,false negative rate,specificity,false positive rate,accuracy,positive predictive value,negative predictive value,F1 score,and area under the receiver operating characteristic curve(AUC).The optimal model was interpreted by using SHapley Additive exPlanations(SHAP)to clarify decision-making logic and feature influencing mechanisms.[Results]Data visualization analysis revealed no significant difference between brucellosis and non-brucellosis groups.The optimal model demonstrated good performance:sensitivity(85.3%),specificity(92.1%),accuracy(89.5%),AUC(96.6%),95%CI(0.937,0.977).SHAP analysis identified age,platelet count,mean platelet volume,basophil ratio,red blood cell distribution width,and absolute basophil count as significant predictors of brucellosis.[Conclusions]The deep neural network prediction model constructed in this study has good performance and can provide reliable support for the early diagnosis,prevention and control of brucellosis.Identification of key brucellosis-related influencing features will help further understand the pathogenesis of the disease,and this model holds promise for broad clinical application in the future.

Objective:To investigate the impact of immunosuppressive therapy on the severity of SARS-CoV-2 infection and cytokine levels in pediatric patients with kidney diseases.Methods:A retrospective analysis was conducted on the clinical data of 40 hospitalized pediatric patients who were diagnosed with SARS-CoV-2 infection at the 900th Hospital of PLA Joint Logistic Support Force from December 2022 to February 2023. Based on their immunosuppressive status prior to SARS-CoV-2 infection, these patients were categorized into immunosuppressive group and non-immunosuppressive group. Independent sample t-tests, Mann-Whitney U tests, and χ2 test were employed to compare the clinical baseline characteristics and laboratory data, the severity of SARS-CoV-2 infection, and the levels of cytokines between the 2 groups. Results:Among the 40 patients, 11 were in the immunosuppressive group (aged 13 (8, 14) years, 9 males and 2 females) and 29 in the non-immunosuppressive group (aged 2 (1, 4) years, 15 males and 14 females). In the immunosuppressive group, 2 were asymptomatic cases, 8 were mild cases, and 1 was moderate case, and there was no severe or critical cases. In the non-immunosuppressive group, 8 were mild cases, 5 were moderate, 15 were severe cases, 1 was critical case, and no asymptomatic cases. The underlying diseases in the immunosuppressive group included nephrotic syndrome (6 cases), IgA vasculitis nephritis (2 cases), lupus nephritis (1 case), post-renal transplantation (1 case), and renal failure (1 case), with a mean total immunosuppression score (TIS) of (3.6±1.4) points. In the non-immunosuppressive group, 2 patients had a history of epilepsy, and the remaining 27 cases had no underlying conditions, all with TIS scores of 0. Compared to the children in the non-immunosuppressive group, those in the immunosuppressive group were more likely to exhibit asymptomatic or mild infection, with lower risks of severe disease, cytokine storm, fever, and cough, but a higher risk of fatigue ( OR=1.22, 2.66, 0.48, 0.12, 0.12, 0.13, 1.22; 95% CI 0.93-1.62, 0.99-7.15, 0.33-0.70, 0.03-0.57, 0.03-0.57, 0.03-0.65, 0.93-1.62; all P<0.05). The levels of cytokine IL-6, interferon-α and interferon-γ in the immunosuppressive group were all lower than those in the non-immunosuppressive group ( Z=2.23, 2.51, 2.92, respectively; all P<0.05). Conclusion:Pediatric patients with kidney diseases receiving appropriate immunosuppressive therapy may mitigate the severity of SARS-CoV-2 infection by suppressing the expression of cytokines.

[Objective]To construct a prediction model for brucellosis by using a deep neural network algorithm to improve the early detection.[Methods]We collected the clinical data of 202 brucellosis patients and 319 non-brucellosis patients admitted to Hohhot Occupational Disease Prevention and Treatment Hospital in 2023,and analyzed data such as gender,age,blood routine indices and clinical diagnosis.A prediction model for brucellosis was constructed by using a deep neural network algorithm and optimized through 10-fold cross-validation.Performance metrics included sensitivity,false negative rate,specificity,false positive rate,accuracy,positive predictive value,negative predictive value,F1 score,and area under the receiver operating characteristic curve(AUC).The optimal model was interpreted by using SHapley Additive exPlanations(SHAP)to clarify decision-making logic and feature influencing mechanisms.[Results]Data visualization analysis revealed no significant difference between brucellosis and non-brucellosis groups.The optimal model demonstrated good performance:sensitivity(85.3%),specificity(92.1%),accuracy(89.5%),AUC(96.6%),95%CI(0.937,0.977).SHAP analysis identified age,platelet count,mean platelet volume,basophil ratio,red blood cell distribution width,and absolute basophil count as significant predictors of brucellosis.[Conclusions]The deep neural network prediction model constructed in this study has good performance and can provide reliable support for the early diagnosis,prevention and control of brucellosis.Identification of key brucellosis-related influencing features will help further understand the pathogenesis of the disease,and this model holds promise for broad clinical application in the future.

Objective:To investigate the impact of immunosuppressive therapy on the severity of SARS-CoV-2 infection and cytokine levels in pediatric patients with kidney diseases.Methods:A retrospective analysis was conducted on the clinical data of 40 hospitalized pediatric patients who were diagnosed with SARS-CoV-2 infection at the 900th Hospital of PLA Joint Logistic Support Force from December 2022 to February 2023. Based on their immunosuppressive status prior to SARS-CoV-2 infection, these patients were categorized into immunosuppressive group and non-immunosuppressive group. Independent sample t-tests, Mann-Whitney U tests, and χ2 test were employed to compare the clinical baseline characteristics and laboratory data, the severity of SARS-CoV-2 infection, and the levels of cytokines between the 2 groups. Results:Among the 40 patients, 11 were in the immunosuppressive group (aged 13 (8, 14) years, 9 males and 2 females) and 29 in the non-immunosuppressive group (aged 2 (1, 4) years, 15 males and 14 females). In the immunosuppressive group, 2 were asymptomatic cases, 8 were mild cases, and 1 was moderate case, and there was no severe or critical cases. In the non-immunosuppressive group, 8 were mild cases, 5 were moderate, 15 were severe cases, 1 was critical case, and no asymptomatic cases. The underlying diseases in the immunosuppressive group included nephrotic syndrome (6 cases), IgA vasculitis nephritis (2 cases), lupus nephritis (1 case), post-renal transplantation (1 case), and renal failure (1 case), with a mean total immunosuppression score (TIS) of (3.6±1.4) points. In the non-immunosuppressive group, 2 patients had a history of epilepsy, and the remaining 27 cases had no underlying conditions, all with TIS scores of 0. Compared to the children in the non-immunosuppressive group, those in the immunosuppressive group were more likely to exhibit asymptomatic or mild infection, with lower risks of severe disease, cytokine storm, fever, and cough, but a higher risk of fatigue ( OR=1.22, 2.66, 0.48, 0.12, 0.12, 0.13, 1.22; 95% CI 0.93-1.62, 0.99-7.15, 0.33-0.70, 0.03-0.57, 0.03-0.57, 0.03-0.65, 0.93-1.62; all P<0.05). The levels of cytokine IL-6, interferon-α and interferon-γ in the immunosuppressive group were all lower than those in the non-immunosuppressive group ( Z=2.23, 2.51, 2.92, respectively; all P<0.05). Conclusion:Pediatric patients with kidney diseases receiving appropriate immunosuppressive therapy may mitigate the severity of SARS-CoV-2 infection by suppressing the expression of cytokines.

【Objective】 To explore the effectiveness of an image recognition system based on artificial intelligence (AI) in diagnosing benign and malignant endometrial cell clumps. 【Methods】 We selected endometrial cytological specimens from The First Affiliated Hospital of Xi’an Jiaotong University and Xi’an Daxing Hospital from August 2021 to February 2023; histopathology was used as the gold standard. We compared and analyzed the sensitivity, specificity, positive predictive value, negative predictive value, accuracy and diagnostic time of AI image recognition system (AI diagnosis) and professional pathologists’ manual diagnosis (manual diagnosis) of benign and malignant endometrial cell clumps. 【Results】 Among the 126 patients included in the analysis, the overall coincidence rate of AI diagnosis and histological diagnosis was 92.1% (116/126), which was highly consistent with histopathological results (Kappa=0.841). The overall coincidence rate of manual diagnosis and histological diagnosis was 94.4% (119/126), which was highly consistent with histopathological results (Kappa=0.889). There was no statistically significant difference between AI diagnosis and manual diagnosis methods (χ²=0.568, P=0.451). The sensitivity, specificity, positive predictive value, and negative predictive value of AI diagnosis were 91.8%, 92.3%, 91.8%, and 92.3%, respectively. There were 126 cytology sections, each of which required 6.67 minutes for manual diagnosis and 5.00 minutes for AI diagnosis. 【Conclusion】 The AI image recognition system has high diagnostic accuracy, sensitivity and specificity, which is equivalent to the manual diagnosis level of professional pathologists. Therefore, this system has application value in the diagnosis of benign and malignant endometrial cell clumps.

Objective:To investigate the predictive value of early thyroid function changes on the efficacy of patients with Graves′ disease (GD) after 131I therapy. Methods:Data of patients with GD (59 males, 214 females; age (37.4±11.4) years) who underwent single therapy of 131I in Tianjin Medical University General Hospital from November 2017 to January 2019 were retrospectively analyzed. Symptoms, signs and laboratory tests (serum free triiodothyronine (FT 3) and serum free thyroxine (FT 4)) of patients were observed to assess the efficacy of 131I treatment. Efficacy was divided into complete remission (CR), partial remission (PR), non-remission (NR) or relapse. The changes of thyroid function (ΔFT 3=FT 3 before treatment-FT 3 after treatment)/FT 3 before treatment×100%; ΔFT 4=FT 4 before treatment-FT 4 after treatment)/FT 4 before treatment×100%) 1 month after 131I therapy in each efficacy group and differences among them were compared by using independent-sample t test, χ2 test, one-way analysis of variance and the least significant difference t test. ROC curves were drawn to analyze the predictive values of early thyroid function changes on the efficacy of 131I treatment for GD. Logistic regression analyses were performed to identify the influencing factors for the efficacy of 131I therapy. Results:CR rate and total effective rate of 273 GD patients after single therapy of 131I were 67.03%(183/273) and 92.67%(253/273), respectively. After 1 month, CR rate of euthyroidism group ( n=95) was significantly higher than that of hyperthyroidism group ( n=178; 81.05%(77/95) vs 59.55%(106/178); χ2=4.60, P=0.032). ΔFT 3 and ΔFT 4 at the first month were statistically significant and decreased sequentially in the CR group ( n=183), PR group ( n=70), NR or relapse groups ( n=20; F values: 15.40, 12.54, both P<0.001). ROC curve analysis showed that patients with ΔFT 3≥73.64% and (or) ΔFT 4≥59.03% had a higher probability of achieving CR, with sensitivities of 84.3% and 86.7%, and specificities of 62.6% and 62.6%, respectively. Logistic regression analysis showed that 24 h radioactive iodine uptake (odds ratio ( OR)=1.095, 95% CI: 1.031-1.139), dose of 131I given per gram of thyroid tissue ( OR=1.562, 95% CI: 1.321-1.694), ΔFT 3 ( OR=1.354, 95% CI: 1.295-1.482), ΔFT 4 ( OR=1.498, 95% CI: 1.384-1.608) were factors affecting the outcome of patients with GD treated with 131I treatment (all P<0.05). Conclusion:Effects of 131I treatment can be predicted based on the change of the thyroid function at the first month after 131I treatment in patients with GD.