It is believed that the root cause of vascular dementia lies in insufficient primal yang and weakness in the circulation of blood. The key pathological factors are the mutual stagnation of phlegm and blood stasis， and the obstruction of blood vessels. The general treatment principle is to tonify qi and promote yang， expel pathogenic factors， and unblock yang. This involves supplementing the innate and acquired yang qi of the spleen and kidneys， removing pathogenic factors like phlegm， turbidity， and blood stasis， in order to restore the function of yang qi and promote the circulation of blood and qi. The basic prescription for tonifying qi and promoting yang includes Heishunpian （Lycium Ruthenicum）， Zhichuanwu （Aconitum Carmichaelii）， Guizhi （Cinnamomum Cassia）， Renshen （Panax Ginseng）， and Huangqi （Astragalus Membranaceus）. Depending on the type of pathogenic excess， the treatment can be modified with Tongqiao Huoxue Decoction （通窍活血汤）， adding Yujin （Curcuma Longa） and Xiangfu （Cyperus Rotundus） to invigorate blood and promote yang， or with Fabanxia （Pinellia Ternata）， Chenpi （Citrus Reticulata）， Zhishi （Citrus Aurantium） plus Sijunzi Decoction （四君子汤） to resolve phlegm and promote yang. Both the root and branch are treated simultaneously. When yang qi is activated， blood and qi are nourished， and when pathogenic excess is expelled， the blood vessels are unblocked. This approach aims to provide a treatment strategy for vascular dementia.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To compare the changes in fracture displacement under different vertical loadings between the 2 different internal fixation modalities for vertically unstable femoral neck fractures of Pauwels type Ⅲ by a mechanical study and a finite element analysis.Methods:Twelve biomimic bones were transversely dissected from 10 cm below the lesser trochanter of the femur to create femoral neck fracture models with a Pauwels angle of 70° using a swing saw. The models were equally divided into 2 groups ( n=6): group A was fixed with 3 cannulated screws after fracture reduction (scheme A), and group B with 3 cannulated screws plus a self-designed anteromedial support plate after fracture reduction (scheme B). Continuous vertical force was applied using a mechanical testing machine. Changes in displacement were recorded and load-displacement curves were plotted. One volunteer (female, 28 years old, 168 cm in height and 65 kg in weight) was selected for finite element analysis of her CT images of both lower limbs to examine the maximum displacement and the maximum Mises stress in scheme A and scheme B respectively. Results:In groups A and B respectively: All the 6 biomimic mimetic bones had similar load and displacement curves, and similar fracture displacements (Dx) at different loading points (N X); the curves of 6 biomimic bones were highly fitted with S-shaped curve equation (the r-square value was close to 1). At the initial loading stage (0 N0.05). When the load was 100 N

Objective:To investigate the influence of cytochrome P450 2A6 (CYP2A6) gene polymorphisms on liver function injury in patients with hyperthyroidism treated with methimazole.Methods:The study selected 90 patients with hyperthyroidism who were treated with methimazole in the Department of Thyroid Surgery at the First Affiliated Hospital of Zhengzhou University from Sept. 2023 to Aug. 2024 as the research subjects. Based on the occurrence of liver injury, they were divided into a liver injury group ( n=36) and a non-liver injury group (n=54). Peripheral blood DNA was extracted from the patients, and the CYP2A6 gene genotypes (rs8192725, rs8192720, and rs28399433) were detected using the polymerase chain reaction (PCR) amplification method. The association between CYP2A6 gene polymorphisms and liver injury induced by methimazole treatment in hyperthyroidism was analyzed. Results:The comparison of genotype distribution frequencies at the rs8192725 locus between the liver injury group and the non-liver injury group showed a statistically significant difference ( P<0.05). The AG and GG genotypes at the rs8192725 locus were protective factors against liver injury in patients with hyperthyroidism (AG vs. AA, OR: 0.21; 95% CI: 0.08-0.57; P<0.05; GG vs. AA, OR: 0.24; 95% CI: 0.06-0.89; P<0.05; AG+GG vs. AA, OR: 0.22; 95% CI: 0.09-0.54; P<0.05). The frequency of the G allele of rs8192725 in the liver injury group was significantly lower than that in the non-liver injury group (G vs. A, OR: 0.36; 95% CI: 0.19-0.70; P<0.05), indicating that it is a protective factor for liver injury in hyperthyroid patients receiving methimazole treatment. Conclusions:The CYP2A6 gene polymorphism at the rs8192725 locus is associated with the occurrence of liver injury in patients with hyperthyroidism treated with methimazole. The G allele may be a protective factor against liver injury in patients with hyperthyroidism, suggesting that individualized treatment plans can be developed based on the patient's genotype.

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

Objective:To investigate the influence of cytochrome P450 2A6 (CYP2A6) gene polymorphisms on liver function injury in patients with hyperthyroidism treated with methimazole.Methods:The study selected 90 patients with hyperthyroidism who were treated with methimazole in the Department of Thyroid Surgery at the First Affiliated Hospital of Zhengzhou University from Sept. 2023 to Aug. 2024 as the research subjects. Based on the occurrence of liver injury, they were divided into a liver injury group ( n=36) and a non-liver injury group (n=54). Peripheral blood DNA was extracted from the patients, and the CYP2A6 gene genotypes (rs8192725, rs8192720, and rs28399433) were detected using the polymerase chain reaction (PCR) amplification method. The association between CYP2A6 gene polymorphisms and liver injury induced by methimazole treatment in hyperthyroidism was analyzed. Results:The comparison of genotype distribution frequencies at the rs8192725 locus between the liver injury group and the non-liver injury group showed a statistically significant difference ( P<0.05). The AG and GG genotypes at the rs8192725 locus were protective factors against liver injury in patients with hyperthyroidism (AG vs. AA, OR: 0.21; 95% CI: 0.08-0.57; P<0.05; GG vs. AA, OR: 0.24; 95% CI: 0.06-0.89; P<0.05; AG+GG vs. AA, OR: 0.22; 95% CI: 0.09-0.54; P<0.05). The frequency of the G allele of rs8192725 in the liver injury group was significantly lower than that in the non-liver injury group (G vs. A, OR: 0.36; 95% CI: 0.19-0.70; P<0.05), indicating that it is a protective factor for liver injury in hyperthyroid patients receiving methimazole treatment. Conclusions:The CYP2A6 gene polymorphism at the rs8192725 locus is associated with the occurrence of liver injury in patients with hyperthyroidism treated with methimazole. The G allele may be a protective factor against liver injury in patients with hyperthyroidism, suggesting that individualized treatment plans can be developed based on the patient's genotype.

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To compare the changes in fracture displacement under different vertical loadings between the 2 different internal fixation modalities for vertically unstable femoral neck fractures of Pauwels type Ⅲ by a mechanical study and a finite element analysis.Methods:Twelve biomimic bones were transversely dissected from 10 cm below the lesser trochanter of the femur to create femoral neck fracture models with a Pauwels angle of 70° using a swing saw. The models were equally divided into 2 groups ( n=6): group A was fixed with 3 cannulated screws after fracture reduction (scheme A), and group B with 3 cannulated screws plus a self-designed anteromedial support plate after fracture reduction (scheme B). Continuous vertical force was applied using a mechanical testing machine. Changes in displacement were recorded and load-displacement curves were plotted. One volunteer (female, 28 years old, 168 cm in height and 65 kg in weight) was selected for finite element analysis of her CT images of both lower limbs to examine the maximum displacement and the maximum Mises stress in scheme A and scheme B respectively. Results:In groups A and B respectively: All the 6 biomimic mimetic bones had similar load and displacement curves, and similar fracture displacements (Dx) at different loading points (N X); the curves of 6 biomimic bones were highly fitted with S-shaped curve equation (the r-square value was close to 1). At the initial loading stage (0 N0.05). When the load was 100 N

Objective To propose a frequency-domain pulse wave prediction model based on fusion attention mechanism,improving the low classification accuracy and poor generalization performance of hypertension time-domain pulse wave classification based on artificial intelligence technology.Methods Firstly,the time-domain pulse wave was transformed into frequency-domain Mel-scale frequency cepstral coefficient features to enhance its discriminability.Then,temporal convolutional network and Transformer structures were employed to extract the deep features of pulse waves,and self-attention mechanism and selective kernel attention were combined for decision fusion to extract relevant features.Floodings regularization method was used to indirectly control the training loss and prevent overfitting.A 5-fold cross-verification experiment was conducted based on 527 clinical pulse diagnosis data provided by Longhua Hospital,Shanghai University of Traditional Chinese Medicine and Shanghai Traditional Chinese Medicine-Integrated Hospital.Additionally,the extreme gradient boosting algorithm was employed to calculate the contribution rate ranking of frequency-domain pulse wave features,and the key factors affecting the classification accuracy of the model were analyzed to provide reference for the clinical auxiliary diagnosis of traditional Chinese medicine.Results The evaluation metrics accuracy,F1 score,precision,recall rate and area under curve value of the model proposed in this study were 0.939 6,0.924 9,0.940 9,0.929 5,and 0.993 4,respectively.The static characteristics of the pulse wave,the contribution rate of the first-order difference and the second-order difference coefficients were relatively balanced,indicating that the degree of hypertension risk was not only related to the static characteristics of the pulse wave,but also to the dynamic characteristics of the pulse wave.Conclusion The proposed model has higher classification accuracy and generalization performance compared to typical pulse wave classification models.