To address the common clinical problems associated with warm needling moxibustion, such as burns, bending of needle handles, and the inability to perform moxibustion during oblique needling, an adjustable warm needling moxibustion device is designed and has been granted a national patent. This device consists of five components: a moxa cylinder, an adjustable arm, a supporting tube, a temperature alarm, and a fixing strap. It allows infrared heat radiation from the moxa to pass through while blocking falling ash, thereby ensuring therapeutic efficacy and preventing burns. The device accommodates both perpendicular and oblique needling angles and adapts to various body positions, effectively avoiding deformation of the needle handle. It is easy to operate and offers high safety.
Moxibustion/methods* ; Humans ; Equipment Design ; Needles

Objective:To investigate the drug resistance gene characteristics, plasmid characteristics and genome tracing of Shigella sonnei causing a bacillary dysentery outbreak in Shandong Province. Methods:Sixty-five Shigella sonnei strains isolated from a 2021 outbreak in a county of Shandong Province were analyzed using antimicrobial susceptibility testing, whole genome sequencing (WGS), characterization of resistance and virulence genes, plasmid profiling, core genome multilocus sequence typing (cgMLST), and single nucleotide polymorphism (SNP) analysis. Results:All isolates had the same resistance phenotype and genotypes and were multidrug-resistant ESBL-producing Shigella sonnei, carrying important virulence genes. Plasmid analysis revealed a conserved genetic arrangement, pil( M/ N/ O2/ P)-tra( F/ H/ J/ K/ N/ O/ P/ Q)-IS Ecp1- blaCTX-M-14-Tn 903- yub( J/ I/ F/ G/ E/ D), and shared across strains from diverse regions and bacterial species. The cgMLST and SNP analyses demonstrated concordant clustering, with all 65 outbreak-related strains forming a single cluster alongside human-derived strains from Guangxi. Conclusion:The ESBL-producing Shigella sonnei responsible for the outbreak shares a homologous relationship with Guangxi human-derived strains, and the detected resistance plasmids and virulence genes underscore the need to strengthen drug resistance surveillance and genome tracing.

Background::The ratio of high-density lipoprotein cholesterol (HDL-C) to low-density lipoprotein cholesterol (LDL-C) predicts cardiovascular disease (CVD) endpoints, yet its prognostic validity in high-risk populations and for type 2 diabetes mellitus (T2DM)-related adverse events remains unestablished.Methods::This study included 32,609 people aged 35-75 years in Fujian Province, China, who were at high risk for CVD. The primary endpoint was all-cause mortality during follow-up. Cox proportional hazard models and restricted cubic spline (RCS) analysis were used to evaluate the correlation between the HDL-C/LDL-C ratio and the endpoints.Result::On the basis of the restricted RCS curve, the participants were classified as having a low (< 0.3), middle (0.3-0.5), or high (> 0.5) HDL-C/LDL-C ratio. Multivariate Cox regression analyses revealed that the risk of all-cause mortality (HR = 1.48, 95% CI 1.14-1.93, p < 0.01 for low; HR = 1.30, 95% CI 1.06-1.58, p < 0.05 for high) was increased in the low and high groups. Participants without T2DM who were at high risk for CVD had similar prognoses (HR = 1.65, 95% CI 1.19-2.28, p < 0.01 for low; HR = 1.35, 95% CI 1.05-1.74, p < 0.01 for high). However, this association was not found in participants with T2DM who were at high risk for CVD. Conclusion::HDL-C/LDL-C can be used to predict the prognosis of individuals at high risk for CVD, and maintaining HDL-C/LDL-C ratios between 0.3 and 0.5 may be the most helpful range for this population. Furthermore, maintaining this ratio range holds clinical significance for cohorts without T2DM, although further exploration is needed in this T2DM cohort.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

This study aimed to investigate the effects of sweet potato leaf extract on production per-formance,systematic and mammary gland oxidative stress status and rumen microbiota of dairy goats fed high concentrate diets.Twenty Guanzhong dairy goats with same parity,similar lactation period(120±15)d and healthy body condition were selected and randomly divided into four groups:low-concentrate(LC),low-concentrate supplemented with 1％sweet potato leaf extract(LCS),high-concentrate(HC)and high-concentrate supplemented with 1％sweet potato leaf ex-tract(HCS).The experimental period was 35 days.The results showed that in the third week,milk yield in the HCS group was significantly higher than that in the LC and LCS groups(P＜0.05).The content of lipopolysaccharide in the rumen fluid of the HC group was significantly higher than that of the other three groups(P＜0.05),the content of malondialdehyde in the serum of the HC group was significantly higher than that of the LCS group(P＜0.05),the content of reactive oxygen species,protein carbonyl,8-hydroxydeoxyguanosine in the milk of the HC group was sig-nificantly higher than that of the LC and LCS groups(P＜0.05),GSH-Px in HCS group was sig-nificantly higher than that in the other three groups(P＜0.05).After the addition of sweet potato leaf extract,there was an increasing trend in the content of Anabaena phylum at the phylum level.In the joint analysis of genera,rumen fluid LPS showed highly significant negative correlation with Succiniclasticum(P＜0.01)and negative correlation with Prevotella(P＜0.05).Valeric acid was negatively correlated with Prevotella(P＜0.05).The pH value was negatively correlated with Treponema(P＜0.05).Butyric acid was positively correlated with Anaeroplasma(P＜0.05).In conclusion,the addition of sweet potato leaf extract to the diet can increase milk production and al-leviate the state of mammary gland oxidative stress,as well as improving rumen microbial diversi-ty of dairy goats.

Background::The ratio of high-density lipoprotein cholesterol (HDL-C) to low-density lipoprotein cholesterol (LDL-C) predicts cardiovascular disease (CVD) endpoints, yet its prognostic validity in high-risk populations and for type 2 diabetes mellitus (T2DM)-related adverse events remains unestablished.Methods::This study included 32,609 people aged 35-75 years in Fujian Province, China, who were at high risk for CVD. The primary endpoint was all-cause mortality during follow-up. Cox proportional hazard models and restricted cubic spline (RCS) analysis were used to evaluate the correlation between the HDL-C/LDL-C ratio and the endpoints.Result::On the basis of the restricted RCS curve, the participants were classified as having a low (< 0.3), middle (0.3-0.5), or high (> 0.5) HDL-C/LDL-C ratio. Multivariate Cox regression analyses revealed that the risk of all-cause mortality (HR = 1.48, 95% CI 1.14-1.93, p < 0.01 for low; HR = 1.30, 95% CI 1.06-1.58, p < 0.05 for high) was increased in the low and high groups. Participants without T2DM who were at high risk for CVD had similar prognoses (HR = 1.65, 95% CI 1.19-2.28, p < 0.01 for low; HR = 1.35, 95% CI 1.05-1.74, p < 0.01 for high). However, this association was not found in participants with T2DM who were at high risk for CVD. Conclusion::HDL-C/LDL-C can be used to predict the prognosis of individuals at high risk for CVD, and maintaining HDL-C/LDL-C ratios between 0.3 and 0.5 may be the most helpful range for this population. Furthermore, maintaining this ratio range holds clinical significance for cohorts without T2DM, although further exploration is needed in this T2DM cohort.

A VLi-net based cell nucleus segmentation method integrating convolutional neural networks(CNN)and Vision Transformer(ViT)is proposed to address the limitation that the U-Net with CNN as its backbone is only proficient in capturing local features and has a restricted receptive field.Firstly,to mitigate challenges such as high cost of data annotation and insufficient annotated data,text annotations are introduced to enhance the network's understanding of image information.Secondly,to improve the segmentation performance of VLi-net,ViT and CNN are combined to fully extract global and local features,with multi-receptive field convolution features incorporating into the ViT structure for effectively mitigating the issues of limited local information interaction and single feature representation in ViT.Finally,an interactive fusion module(ViFusion)is used to efficiently fuse the multi-level features from the CNN and ViT branches.Experimental results show that VLi-net achieves a Dice coefficient of 80.85%and a mean intersection over union(MIoU)of 66.83%on the MoNuSeg dataset,obtains a Dice coefficient of 80.53%and a MIoU of 67.54%on the DSB-2018 dataset,and has a Dice coefficient of 86.87%and a MIoU of 77.44%on the TNBC dataset.These findings confirm that VLi-net outperforms other methods across multiple experimental metrics.

This study aimed to investigate the effects of sweet potato leaf extract on production per-formance,systematic and mammary gland oxidative stress status and rumen microbiota of dairy goats fed high concentrate diets.Twenty Guanzhong dairy goats with same parity,similar lactation period(120±15)d and healthy body condition were selected and randomly divided into four groups:low-concentrate(LC),low-concentrate supplemented with 1％sweet potato leaf extract(LCS),high-concentrate(HC)and high-concentrate supplemented with 1％sweet potato leaf ex-tract(HCS).The experimental period was 35 days.The results showed that in the third week,milk yield in the HCS group was significantly higher than that in the LC and LCS groups(P＜0.05).The content of lipopolysaccharide in the rumen fluid of the HC group was significantly higher than that of the other three groups(P＜0.05),the content of malondialdehyde in the serum of the HC group was significantly higher than that of the LCS group(P＜0.05),the content of reactive oxygen species,protein carbonyl,8-hydroxydeoxyguanosine in the milk of the HC group was sig-nificantly higher than that of the LC and LCS groups(P＜0.05),GSH-Px in HCS group was sig-nificantly higher than that in the other three groups(P＜0.05).After the addition of sweet potato leaf extract,there was an increasing trend in the content of Anabaena phylum at the phylum level.In the joint analysis of genera,rumen fluid LPS showed highly significant negative correlation with Succiniclasticum(P＜0.01)and negative correlation with Prevotella(P＜0.05).Valeric acid was negatively correlated with Prevotella(P＜0.05).The pH value was negatively correlated with Treponema(P＜0.05).Butyric acid was positively correlated with Anaeroplasma(P＜0.05).In conclusion,the addition of sweet potato leaf extract to the diet can increase milk production and al-leviate the state of mammary gland oxidative stress,as well as improving rumen microbial diversi-ty of dairy goats.

A VLi-net based cell nucleus segmentation method integrating convolutional neural networks(CNN)and Vision Transformer(ViT)is proposed to address the limitation that the U-Net with CNN as its backbone is only proficient in capturing local features and has a restricted receptive field.Firstly,to mitigate challenges such as high cost of data annotation and insufficient annotated data,text annotations are introduced to enhance the network's understanding of image information.Secondly,to improve the segmentation performance of VLi-net,ViT and CNN are combined to fully extract global and local features,with multi-receptive field convolution features incorporating into the ViT structure for effectively mitigating the issues of limited local information interaction and single feature representation in ViT.Finally,an interactive fusion module(ViFusion)is used to efficiently fuse the multi-level features from the CNN and ViT branches.Experimental results show that VLi-net achieves a Dice coefficient of 80.85%and a mean intersection over union(MIoU)of 66.83%on the MoNuSeg dataset,obtains a Dice coefficient of 80.53%and a MIoU of 67.54%on the DSB-2018 dataset,and has a Dice coefficient of 86.87%and a MIoU of 77.44%on the TNBC dataset.These findings confirm that VLi-net outperforms other methods across multiple experimental metrics.