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.

Objective To investigate the effect of Xiao Chaihu Decoction and Xiangsha Liujunzi Decoction on the changes of gastric mucosal pathological score and gastrointestinal hormones in patients with chronic atrophic gastritis(CAG)of liver stagnation and spleen deficiency type.Methods A total of 156 cases of CAG patients with liver stagnation and spleen deficiency syndrome were randomly divided into a control group and an observation group,78 cases in each group.The control group was treated with conventional western medicine,and the observation group was treated with Xiao Chaihu Decoction and Xiangsha Liujunzi Decoction on the basis of treatment for the control group.The course of treatment covered four weeks.The changes in the scores of traditional Chinese medicine(TCM)symptoms such as epigastric distention and pain,poor appetite,loose stools,limb weakness,belching and acid regurgitation,gastric mucosal pathological scores and serum levels of gastrointestinal hormones of motilin(MTL)and gastrin(GAS)in the two groups were observed before and after treatment.The negative conversion rate of Helicobacter pylori(Hp)in the two groups was compared,and the clinical efficacy and safety of the two groups were evaluated.Results(1)After four weeks of treatment,the total effective rate of the observation group was 93.59％(73/78),which was significantly higher than 82.05％(64/78)of the control group,and the difference between the two groups was statistically significant(P＜0.05).(2)After treatment,the scores of TCM symptoms such as epigastric distention and pain,poor appetite,loose stool,limb weakness,belching and acid regurgitation in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.05).(3)After treatment,the pathological scores of gastric mucosa in the two groups were significantly decreased when compared with those before treatment(P＜0.05),and the decrease in the observation group was more significant than that in the control group(P＜0.01).(4)After four weeks of treatment,the negative conversion rate of Hp in the observation group was 91.03％(71/78),which was significantly higher than that in the control group(75.64％,59/78),and the difference between the two groups was statistically significant(P＜0.05).(5)After treatment,the level of serum GAS in the two groups was significantly decreased(P＜0.05)and the serum MTL level was significantly increased compared with that before treatment(P＜0.05);the decrease of serum GAS level and the increase of serum MTL level in the observation group were significantly superior to those in the control group(P＜0.05).(6)There were no obvious abnormalities in the routine test of blood,urine,stool,kidney function,and liver function,electrocardiograph and other safety indicators during the treatment of the two groups of patients,no adverse reactions such as dizziness,rash and chest distress occurred either,with high safety.Conclusion Xiao Chaihu Decoction combined with Xiangsha Liujunzi Decoction exerts a significant therapeutic effect on GAS of liver stagnation and spleen deficiency type,which can effectively relieve the clinical symptoms,improve the pathological changes of gastric mucosa and promote Hp negative conversion.The therapeutic mechanism may be related to the regulation of gastrointestinal hormone levels.

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.

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.

Shegan Mahuang Decoction has been used in clinical practice for thousands of years, and is a classical formula for treating asthma and other respiratory diseases, with the effects of ventilating lung, dispersing cold, and relieving cough and asthma. This paper summarized the history, clinical application and mechanism of Shegan Mahuang Decoction, and predicted its quality markers(Q-markers) based on the "five principles" of Q-markers. The results suggested that irisflorentin, tectoridin, tectorigenin, irigenin, ephedrine, pseudoephedrine, asarinin, methyleugenol, shionone, epifriedelanol, tussilagone, 6-gingerol, trigonelline, cavidine, schizandrin, and schizandrin B could be used as Q-markers of Shegan Mahuang Decoction, which provided a basis for the quality control and subsequent research and development of Shegan Mahuang Decoction.
Humans ; Ephedra sinica ; Drugs, Chinese Herbal/pharmacology* ; Asthma/drug therapy* ; Lung ; Cough/drug therapy*