Irritable bowel syndrome （IBS） is a functional bowel disorder characterized primarily by abdominal pain and altered defecation habits. In recent years， traditional Chinese medicine （TCM） has made progress in multiple aspects of IBS research and treatment， including syndrome distribution， development of TCM formulas， clinical efficacy evaluation， external therapies， and psychosocial regulation. However， it still faces challenges such as over-reliance on symptomatic manifestations rather than biomarkers for diagnostic criteria， and the lack of high-quality evidence-based data supporting the efficacy of TCM formulas in treating IBS. This paper proposed that TCM diagnosis and treatment of IBS should adhere to the strategy of integrating the holistic concept with syndrome differentiation and treatment， combining TCM external therapies such as acupuncture， moxibustion and acupoint application）， and emphasizing individualized diagnosis and treatment for psychosomatic abnormalities. Future research should integrate multi-omics technologies， artificial intelligence and other methods to deepen the understanding of the pathogenesis of IBS and the mechanisms of TCM formulas， so as to promote the standardization and internationalization of TCM in the diagnosis and treatment of IBS.

Objective The purpose of writing the"expert consensus on humanistic care for patients in hospice care"(hereinafter referred to as the"consensus")aims to standardize the practice of humanistic care in the field of hospice care,ensuring that humanistic care is integrated throughout the entire service process for hospice care patients and their families.Methods A systematic search was conducted in domestic and foreign databases for literature related to hospice care and humanistic care,including guidelines,expert consensuses,systematic reviews or Meta-analyses,and evidence summaries.High-quality evidence was evaluated,extracted,and summarized to form the initial draft of the"consensus".From June to October 2024,20 experts from the fields of hospice care,nursing humanities,and evidence-based nursing were invited to participate in 1 round of expert consultation.Among them,13 experts were selected for 2 rounds of expert demonstration meetings.After collating and analyzing the experts' opinions,the initial draft was revised and refined,ultimately resulting in the final version of the"consensus".Results The effective response rate of the consultation questionnaire was 100％,with expert authority coefficient of 0.880,judgment coefficient of 0.935,and familiarity level of 0.825.The Kendall harmony coefficient of the expert consultation was 0.134(P＜0.05).The"consensus"consisted of 13 aspects,including the targets and objectives,principles,institutional guarantees,environmental requirements,etc.Conclusion This"consensus"possesses strong scientific rigor and practicality,which can provide guidance and references for the practice of humanistic care in the field of hospice care,promoting the standardization and humanization of hospice care services.

Objective The purpose of writing the"expert consensus on humanistic care for patients in hospice care"(hereinafter referred to as the"consensus")aims to standardize the practice of humanistic care in the field of hospice care,ensuring that humanistic care is integrated throughout the entire service process for hospice care patients and their families.Methods A systematic search was conducted in domestic and foreign databases for literature related to hospice care and humanistic care,including guidelines,expert consensuses,systematic reviews or Meta-analyses,and evidence summaries.High-quality evidence was evaluated,extracted,and summarized to form the initial draft of the"consensus".From June to October 2024,20 experts from the fields of hospice care,nursing humanities,and evidence-based nursing were invited to participate in 1 round of expert consultation.Among them,13 experts were selected for 2 rounds of expert demonstration meetings.After collating and analyzing the experts' opinions,the initial draft was revised and refined,ultimately resulting in the final version of the"consensus".Results The effective response rate of the consultation questionnaire was 100％,with expert authority coefficient of 0.880,judgment coefficient of 0.935,and familiarity level of 0.825.The Kendall harmony coefficient of the expert consultation was 0.134(P＜0.05).The"consensus"consisted of 13 aspects,including the targets and objectives,principles,institutional guarantees,environmental requirements,etc.Conclusion This"consensus"possesses strong scientific rigor and practicality,which can provide guidance and references for the practice of humanistic care in the field of hospice care,promoting the standardization and humanization of hospice care services.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

MicroRNAs(miRNAs)comprise a class of endogenous RNA molecules with a typical length of 19～25 nucleotides.They regulate gene expression levels by identifying homologous sequences and intervening in transcription,translation,or epigenetic processes.miRNAs have potential applications in relation to the pathogenesis,progression,and treatment of deep vein thrombosis(DVT).DVT refers to the abnormal coagulation of blood within the lumen of the deep veins,blocking the venous lumen and obstructing the venous return,especially in the lower limbs.Furthermore,detachment of the thrombus and entry into the lungs can lead to death.This article comprehensively reviews recent research findings regarding the diverse mechanisms of action of miRNAs in relation to DVT.Given that the regulation of miRNA expression using targeted therapeutic approaches may promote the recovery of DVT,this article also discusses the potential applications of miRNAs for the clinical diagnosis and treatment of DVT,and aims to provide valuable references and insights for future clinical and basic research in the field of DVT.

MicroRNAs(miRNAs)comprise a class of endogenous RNA molecules with a typical length of 19～25 nucleotides.They regulate gene expression levels by identifying homologous sequences and intervening in transcription,translation,or epigenetic processes.miRNAs have potential applications in relation to the pathogenesis,progression,and treatment of deep vein thrombosis(DVT).DVT refers to the abnormal coagulation of blood within the lumen of the deep veins,blocking the venous lumen and obstructing the venous return,especially in the lower limbs.Furthermore,detachment of the thrombus and entry into the lungs can lead to death.This article comprehensively reviews recent research findings regarding the diverse mechanisms of action of miRNAs in relation to DVT.Given that the regulation of miRNA expression using targeted therapeutic approaches may promote the recovery of DVT,this article also discusses the potential applications of miRNAs for the clinical diagnosis and treatment of DVT,and aims to provide valuable references and insights for future clinical and basic research in the field of DVT.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

OBJECTIVE To systematically evaluate the safety of paroxetine in the treatment of pregnant patients with depression in the second and third trimesters of pregnancy， and provide reference for rational clinical use of it. METHODS Retrieved from Cochrane Library， PubMed， Embase， VIP， CNKI， Wanfang database and SinoMed database， by manual search， randomized controlled studies or observational studies were collected on depression patients who were given paroxetine vs. selective serotonin reuptake inhibitor （SSRI） in the second and third trimesters of pregnancy during the inception to Aug. 2022. Methodological qualities of the included studies were assessed by Cochrane Handbook 5.1.0 or Newcastle-Ottawa Scale （NOS）. Meta-analysis was performed with RevMan 5.4.1 software. RESULTS Finally， 9 observational studies were included， and all included studies were of high quality in NOS scale. Meta-analysis was performed on 8 cohort studies. Meta-analysis showed that the total incidence of adverse pregnancy outcomes of mothers and infants [RR＝0.99， 95%CI（0.89，1.10），P＝0.87]， total incidence of maternal adverse pregnancy outcomes [RR＝0.98， 95%CI （0.87，1.10）， P＝0.69] and premature birth [RR＝0.89， 95%CI （0.43， 1.83）， P＝0.75] in the second and third trimesters of pregnancy were lower than that with other SSRI， without statistical significance. The incidence of neonatal complications with paroxetine in the second and third trimesters of pregnancy was higher than that with other SSRI， but the difference was not statistically significant [RR＝1.02， 95%CI （0.82，1.29）， P＝0.84]. One study reported that the incidence of neonatal pulmonary hypertension in paroxetine group was higher than that in other SSRI group （0.4% vs. 0.3%）. CONCLUSIONS The safety of peroxetine in the second and third trimesters of pregnancy is comparable with that of other SSRI， but it is necessary to be alert to the occurrence of neonatal pulmonary hypertension.

Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.

Myostatin gene (MSTN) encodes a negative regulator for controlling skeletal muscle growth in animals. In this study, MSTN^-/- homozygous mutants with "double muscle" phenotypic traits and stable inheritance were bred on the basis of MSTN gene editing rabbits, with the aim to establish a method for breeding homozygous progeny from primary MSTN biallelic mutant rabbits. MSTN^-/- primary mutant rabbits were generated by CRISPR/Cas9 gene editing technology. The primary mutant rabbits were mated with wild type rabbits to produce F1 rabbits, whereas the F2 generation homozygous rabbits were bred by half-sibling mating or backcrossing with F1 generation rabbits of the same mutant strain. Sequence analysis of PCR products and its T vector cloning were used to screen homozygous rabbits. The MSTN mutant rabbits with 14-19 week-old were weighed and the difference of gluteus maximus tissue sections and muscle fiber cross-sectional area were calculated and analyzed. Five primary rabbits with MSTN gene mutation were obtained, among which three were used for homozygous breeding. A total of 15 homozygous rabbits (5 types of mutants) were obtained (M2-a: 3; M2-b: 2; M3-a: 2; M7-a: 6; M7-b: 2). The body weight of MSTN^-/- homozygous mutant rabbits aged 14-19 weeks were significantly higher than that of MSTN^+/+ wild-type rabbits of the same age ((2 718±120) g vs. (1 969±53) g, P < 0.01, a 38.0% increase). The mean cross sections of gluteus maximus muscle fiber in homozygous mutant rabbits were not only significantly higher than that of wild type rabbits ((3 512.2±439.2) μm² vs. (1 274.8±327.3) μm², P < 0.01), but also significantly higher than that of MSTN^+/- hemizygous rabbits ((3 512.2±439.2) μm² vs. (2 610.4±604.4) μm², P < 0.05). In summary, five homozygous mutants rabbits of MSTN^-/- gene were successfully bred, which showed a clear lean phenotype. The results showed that the primary breeds were non-chimeric mutant rabbits, and the mutant traits could be inherited from the offspring. MSTN^-/- homozygous mutant rabbits of F2 generation could be obtained from F1 hemizygous rabbits by inbreeding or backcrossing. The progenies of the primary biallelic mutant rabbits were separated into two single-allelic mutants, both of which showed a "double-muscle" phenotype. Thus, this study has made progress in breeding high-quality livestock breeds with gene editing technology.
Animals ; CRISPR-Cas Systems/genetics* ; Gene Editing ; Muscle, Skeletal/metabolism* ; Mutation ; Myostatin/metabolism* ; Phenotype ; Rabbits