Objective To observe the clinical efficacy of withdrawal therapy based on regulating nutritive qi and defensive qi(shortened to Tiaohe Yingwei method)in treating sedative-hypnotic dependent insomnia of disharmony between nutritive qi and defensive qi type.Methods Ninety patients with sedative-hypnotic dependent insomnia of disharmony between nutritive qi and defensive qi type were randomly divided into the treatment group and the control group,with 45 patients in each group.The control group was given oral use of Estazolam by 25%of weekly dose-reduction,while the treatment group was treated with Chinese medicinal decoction of Tiaohe Yingwei Zhumian Prescription based on Tiaohe Yingwei method together with Estazolam.The treatment course for the two groups lasted for 4 weeks.The changes of Pittsburgh Sleep Quality Index(PSQI)scores,total TCM syndrome scores,and Drug-withdrawal Syndrome Scale(DWSS)scores in the two groups were observed before and after treatment.After treatment,the efficacy for improving sleep efficiency value(IUSEV)and clinical safety in the two groups were evaluated.Results(1)During the trial,2 cases fell off in the treatment group,and 43 cases included in the statistics;3 cases fell off in the control group,and 42 cases included in the statistics.(2)After 4 weeks of treatment,the total effective rate for improving IUSEV of the treatment group was 88.37%(38/43),and that of the control group was 61.90%(26/42).The intergroup comparison by non-parametric rank-sum test showed that the efficacy for improving IUSEV in the treatment group was significantly superior to that in the control group(P＜0.05).(3)After treatment,obvious reduction was shown in the overall PSQI scores and the scores of the items of sleep quality,time for falling asleep,sleep time,sleep efficiency,sleep disorder and daytime dysfunction in the two groups when compared with those before treatment(P＜0.05).The intergroup comparison showed that except for the items of sleep disorder and daytime dysfunction,the treatment group had stronger effect on decreasing the scores of the remaining items and the overall PSQI scores than the control group(P＜0.05).(4)After treatment,the total scores of TCM syndromes of both groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease of the total scores of TCM syndrome in the treatment group was significantly superior to that in the control group(P＜0.05).(5)After treatment,the total DWSS scores of the two groups were significantly decreased compared with those before treatment(P＜0.05),and the effect on lowering the scores in the treatment group was significantly superior to that in the control group(P＜0.05).(6)During the course of treatment,no significant adverse reactions occurred in the two groups,or no abnormal changes were found in the safety indexes such as routine test of blood,urine and stool,liver and kidney function,and electrocardiogram of the patients.Conclusion Withdrawal therapy based on Tiaohe Yingwei method exerts certain effect for the treatment of sedative-hypnotic dependent insomnia of disharmony between nutritive qi and defensive qi type.The therapy is effective on improving the quality of sleep and reducing the incidence of drug-withdrawal syndrome,and has a high safety.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

Abnormal tumour cell adhesion is a key step in tumour metastasis， in which weakened homologous and enhanced heterologous adhesion of tumour cells is an important cause of tumour metastasis. Chronic stress can activate the sympathetic nervous system to link and regulate the homologous and heterologous adhesion of tumour cells and exacerbate tumour metastasis. Combining the understanding of traditional Chinese medicine （TCM） and Western medicine on tumour metastasis， it is believed that the mechanism of "qi constraint and stagnation， tumor toxin transmission and retention" in TCM theory is highly related to the abnormal adhesion of tumour cells triggered by chronic stress. Qi constraint and stagnation is closely related to chronic stress and its activation of the sympathetic nervous system， and transmission and retention of tumor toxin explained the mechanism of tumour metastasis due to abnormal adhesion of tumour cells from the perspective of TCM. By regulating the key link of sympathetic nervous system-tumour cell adhesion， application of the formulas of regulating qi and resolving toxin can improve chronic stress and inhibit tumour metastasis.

OBJECTIVE To analyse the prognosis and laryngeal function retention of patients undergoing minimally invasive and open surgery after induction chemotherapy.METHODS The clinical data of 54 hypopharyngeal carcinoma patients who received induction chemotherapy and underwent laryngeal preservation surgery in Beijing Tongren Hospital from 2016 to 2022 were retrospectively analyzed.The laryngeal function recovery and survival rate were compared between the two groups.RESULTS Twenty-eight patients underwent transoral minimally invasive surgery and 26 patients underwent partial laryngectomy and/or partial laryngectomy via external cervical approach.The 3-year survival rates of the two groups were 63%and 59%,respectively,and the difference was not statistically significant(P＞0.05).The differences were statistically significant(P＜0.05).CONCLUSION In patients with downstaged hypopharyngeal carcinoma after induction chemotherapy,the survival rate of transoral minimally invasive surgery is similar to that of open surgery,and the laryngeal function recovery of transoral minimally invasive surgery is better.

Objective To investigate the neurodevelopmental characteristics of children with autism spectrum disorder(ASD),analyze the correlation between neurodevelopmental indicators and cerebral blood flow(CBF),and explore the potential mechanisms of neurodevelopment in ASD children.Methods A retrospective study was conducted on 145 children aged 2-6 years with newly-diagnosed ASD.Scores from the Gesell Developmental Diagnosis Scale and the Autism Behavior Checklist(ABC)and CBF results were collected to compare gender differences in the development of children with ASD and analyze the correlation between CBF and neurodevelopmental indicators.Results Fine motor and personal-social development quotient in boys with ASD were lower than those in girls with ASD(P<0.05).Gross motor development quotient in ASD children was negatively correlated with CBF in the left frontal lobe(r=-0.200,P=0.016),right frontal lobe(r=-0.279,P=0.001),left parietal lobe(r=-0.208,P=0.012),and right parietal lobe(r=-0.187,P=0.025).The total ABC score was positively correlated with CBF in the left amygdala(r=0.295,P<0.001).Conclusions Early intervention training should pay attention to gender and developmental structural characteristics for precise intervention in ASD children.CBF has the potential to become a biological marker for assessing the severity of ASD.

This study investigated the characteristics of gut microbiota imbalance in patients with infectious diarrhea caused by various pathogenic infections,and the role of Bacteroides in maintaining homeostasis in the intestinal environment.The gut microbiota in patients with diarrhea caused by pathogenic infections,such as viral and bacterial infections,was determined through full-length 16S rRNA amplicon sequencing.Patients with diarrhea were grouped and analyzed according to the presence of single bacterial infection,single viral infection,mixed infection,or Clostridioides difficile infection.Bacteroides had the highest absolute number and relative abundance in the gut microbiota in healthy people,whereas patients with infectious diar-rhea showed lower relative abundance of Bacteroides at each phylum/order/family/genus taxonomic level.Alpha diversity anal-ysis indicated no significant differences among groups.NMDS and PCoA indicated formation of distinct clusters in the control group compared with the different infectious diarrhea groups.The diversity of the gut microbiota was higher in the control group than the infectious diarrhea groups.Patients with infec-tious diarrhea caused by different pathogens showed differing predominant gut microbiota.Bifidobacterium predominated in the single viral infection group,Streptococcus predominated in the single bacterial infection group,and Lachnoclostridium predominated in the mixed infection group.Escherichia and Klebsiella were the major gut microbiota in the C.difficile infection group.Meanwhile,the dominant gut microbiota in the healthy population was Bacteroides.COG function prediction revealed that the healthy control group formed a distinct cluster from the different infection groups.The functions of defense mechanisms,cell wall synthesis,protein modification,cellular differentiation,and replication and recombination were signifi-cantly diminished in all infectious diarrhea groups.In general,patients with infectious diarrhea caused by different pathogens showed dysbiosis,with diminished gut microbiota diversity and the emergence of related biomarkers.Our findings indicated that Bacteroides has a key role in maintaining the homeostasis of the human intestinal environment,thus providing new ideas for the subsequent treatment of infectious diarrhea and research in other fields.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.