Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

OBJECTIVE To investigate the mechanism of Yifei xuanfei jiangzhuo formula （YFXF） against vascular dementia （VD）. METHODS The differentially expressed genes of YFXF （YDEGs） were obtained by network pharmacology. High-risk genes were screened from YDEGs by using the nomogram model. The optimal machine learning models in generalized linear， support vector machine， extreme gradient boosting and random forest models were screened based on high-risk genes. VD model rats were established by bilateral common carotid artery occlusion， and were randomly divided into model group and YFXF group （12.18 g/kg， by the total amount of crude drugs）， and sham operation group was established additionally， with 6 rats in each group. The effects of YFXF on behavior （using escape latency and times of crossing platform as indexes）， histopathologic changes of cerebral cortex， and the expression of proteins related to the secreted phosphoprotein 1 （SPP1）/phosphoinositide 3-kinase （PI3K）/protein kinase B （aka Akt） signaling pathway and the mRNA expression of SPP1 in cerebral cortex of VD rats were evaluated. RESULTS A total of 6 YDEGs were obtained， among which SPP1， CCL2， HMOX1 and HSPB1 may be high-risk genes of VD. The generalized linear model based on high-risk genes had the highest prediction accuracy （area under the curve of 0.954）. Compared with the model group， YFXF could significantly shorten the escape latency of VD rats， significantly increase the times of crossing platform （P＜0.05）； improve the pathological damage of cerebral cortex， such as neuronal shrinkage and neuronal necrosis； significantly reduce the expressions of SPP1 protein and mRNA （P＜0.05）， while significantly increase the phosphorylation levels of PI3K and Akt （P＜0.05）. CONCLUSIONS VD high-risk genes SPP1， CCL2， HMOX1 and HSPB1 may be the important targets of YFXF. YFXF may play an anti-VD role by down-regulating the protein and mRNA expressions of SPP1 and activating PI3K/Akt signaling pathway.

Objective:To explore the hematological phenotype and genotypic characteristics of five Chinese individuals with a rare thalassemia mutation HBB: c. 93-21G>A. Methods:A retrospective study was carried out on five individuals identified by the People′s Hospital of Yangjiang and Guangzhou Hybribio Co., Ltd. from May 2018 to September 2022. Routine blood test and hemoglobin electrophoresis were performed, and the genotypes of five subjects were determined by using PCR combined with reverse dot blotting (RDB), nested PCR, Gap-PCR and Sanger sequencing. This study was approved by Medical Ethics Cornmittee of the People′s Hospital of Yangjiang (Ethics No. 20240001).Results:Among the five individuals, hematological data of one was unavailable, and the remaining four had presented with microcytosis and hypochromia. The results of hemoglobin electrophoresis indicated that all of them had a HbA 2 level of ≥4.7%. Genetic analysis showed that one case had harbored compound heterozygous mutations of ααα anti3.7 triplet and HBB: c. 93-21G>A, one had compound heterozygous mutations of -α 3.7 and HBB: c. 93-21G>A, whilst the remaining three were heterozygous for the HBB: c. 93-21G>A mutation. Conclusion:The hematological phenotype of β-thalassemia carriers ( HBB: c. 93-21G>A) is similar to that of other β + thalassemia heterozygotes with mild β-thalassemia characteristics.

Objective:A machine learning algorithm was used to develop a predictive model of self-injury among college students and to explore the high-risk factors for self-injury among college students.Methods:From November to December 2022, a convenience sample of 791 college students from a university in Hebei Province was selected.Whether the self-injurious behavior occurred or not was regarded as an outcome variable.The basic demographics data were collected for statistical analysis.The adolescent self-harm questionnaire, the acquired helplessness scale, the Chinese version of the interpersonal needs questionnaire, the adolescent life events scale, and the childhood traumatic experiences questionnaire were used for assessment.The predictor variables were statistically analyzed by SPSS 26.0 software, and the performance of the model was evaluated by random forest, support vector machine and logistic regression so as to predict the self-injury behavior of college students.The model performance was evaluated by the accuracy, F1 score, sensitivity, specificity, and AUC value of the model, and the optimal model was selected.Finally, the optimal model was used to analyze the high-risk factors of college students' self-injury behaviors.Results:(1) The results of one-way ANOVA showed that the detection rate of self-injury behavior among college students was 42.4%(335/791), and the detection rate of male students was significantly higher than that of female students ( χ2=14.139, P<0.05). Individuals with lower-middle monthly household income(RMB 3 000-5 999) had a significantly higher detection rate of self-injury behavior than those with other monthly household income( P<0.05). (2) The accuracy of random forest, support vector machine, and logistic regression models were 85.53%, 85.96%, and 68.86%, F1 scores were 0.853, 0.864, and 0.676, and sensitivities were 83.91%, 89.04%, and 64.91%, respectively.The AUCs of support vector machine, logistic regression models and random forest were 0.89, 0.73 and 0.92.(3) The top ten characteristic variables of high risk factors for college students' self-injury behaviors based on the random forest algorithm with better predictive efficacy were emotional abuse, frustration of belonging, helplessness, interpersonal relationship factor, despair, emotional neglect, academic stress factor, monthly family income, perception of tiredness, and health adaptation factor, in that order. Conclusions:Random forest is optimal for predicting self-injury behavior among college students compared to support vector machine and logistic regression.Factors influencing self-injury behavior among college students originate from environmental factors, individual factors and interpersonal factors.

The vagina contains at least a billion microbial cells,dominated by lactobacilli.Here we perform metagenomic shotgun sequencing on cervical and fecal samples from a cohort of 516 Chinese women of reproductive age,as well as cervical,fecal,and salivary samples from a second cohort of 632 women.Factors such as pregnancy history,delivery history,cesarean section,and breastfeeding were all more important than menstrual cycle in shaping the microbiome,and such information would be necessary before trying to interpret differences between vagino-cervical micro-biome data.Greater proportion of Bifidobacterium breve was seen with older age at sexual debut.The relative abundance of lactobacilli especially Lactobacillus crispatus was negatively associated with pregnancy history.Potential markers for lack of menstrual regularity,heavy flow,dysmenor-rhea,and contraceptives were also identified.Lactobacilli were rare during breastfeeding or post-menopause.Other features such as mood fluctuations and facial speckles could potentially be predicted from the vagino-cervical microbiome.Gut and salivary microbiomes,plasma vitamins,metals,amino acids,and hormones showed associations with the vagino-cervical microbiome.Our results offer an unprecedented glimpse into the microbiota of the female reproductive tract and call for international collaborations to better understand its long-term health impact other than in the settings of infection or pre-term birth.

Hypoxia is beneficial for the differentiation of stem cells transplanted for myocardial injury, but mechanisms underlying this benefit remain unsolved. Here, we report the impact of hypoxia-induced Jagged1 expression in cardiomyocytes (CMs) for driving the differentiation of cardiac stem cells (CSCs). Forced hypoxia-inducible factor 1 (HIF-1) expression and physical hypoxia (5% O) treatment could induce Jagged1 expression in neonatal rat CMs. Pharmacological inhibition of HIF-1 by YC-1 attenuated hypoxia-promoted Jagged1 expression in CMs. An ERK inhibitor (PD98059), but not inhibitors of JNK (SP600125), Notch (DAPT), NF-B (PTDC), JAK (AG490), or STAT3 (Stattic) suppressed hypoxia-induced Jagged1 protein expression in CMs. c-Kit CSCs isolated from neonatal rat hearts using a magnetic-activated cell sorting method expressed GATA4, SM22 or vWF, but not Nkx2.5 and cTnI. Moreover, 87.3% of freshly isolated CSCs displayed Notch1 receptor expression. Direct co-culture of CMs with BrdU-labeled CSCs enhanced CSCs differentiation, as evidenced by an increased number of BrdU/Nkx2.5 cells, while intermittent hypoxia for 21 days promoted co-culture-triggered differentiation of CSCs into CM-like cells. Notably, YC-1 and DAPT attenuated hypoxia-induced differentiation. Our results suggest that hypoxia induces Jagged1 expression in CMs primarily through ERK signaling, and facilitates early cardiac lineage differentiation of CSCs in CM/CSC co-cultures HIF-1/Jagged1/Notch signaling.

Acute myeloid leukemia (AML) is the most common type of leukemia at present. Although clinical treatment has a certain effect on this disease, most patients still die of relapse or its treatment related diseases. Nowadays, chimeric antigen receptor (CAR) T cells therapy technology has developed rapidly, and has become a hot topic in tumor immunotherapy. The high expression of CD123 in AML cells, low expression or non expression in normal hematopoietic stem cells and tissues, make more and more researchers focus on the technology of CD123+cell immunotherapy. Some studies have confirmed that CD123 CAR-T cells have a certain effect on AML, which provides a new way for clinical treatment of relapsed or refractory AML. This review summarizes the structure, production and delivery methods of CD123 CAR-T cells, and the current research status and shortcomings of CD123 CAR-T cells.

AIM: To explore the expression and significance of receptor tyrosine kinase anexelekto (Axl) in nasopharyngeal carcinoma (NPC).METHODS: Immunohistochemistry was used to detect the Axl protein expression of 78 patients with NPC and 32 patients with nasopharyngeal chronic inflammation (NPI).The correlations between the Axl protein levels and the clinical parameters of NPC patients were analyzed.NPC cells were cultured in vitro, and the expression of Axl in well differentiated CNE1 cells, poorly-differentiated CNE2Z cells and undifferentiated C666-1 cells was detected by immunofluorescence staining.After treatment of the CNE1and C666-1 cells with Axl specific inhibitor TP-0903, CCK-8 assay was used to detect cell viability, flow cytometry was adopted to analyze the cell cycle distribution, qPCR was used to examine the mRNA levels of Axl and proliferating cell nuclear antigen (PCNA), and Western blot was used to examine the protein expression of Axl and p-Axl.RESULTS: Axl protein was localized in the cell membrane and cytoplasm.The rate of high expression of Axl in NPC was significantly higher than that in NPI (P<0.01).High Axl expression showed no correlations with NPC patients'' age, gender and M stage, while positively correlated with the clinical stage, T stage and N stage (P<0.05).Axl protein showed a low level in the CNE1 cells, but showed a high level in CNE2Z and C666-1 cells.TP-0903 inhibited cell viability in concentration and time dependent manners.TP-0903 at 2 nmol/L showed significant inhibitory effects, as evidenced by arresting the cell cycle at G0 phase and reducing Axl activity and PCNA expression.CONCLUSION: High expression of Axl promotes the clinical progress of NPC.TP-0903 significantly inhibits the viability of NPC cells, suggesting that Axl may be a valuable target in the NPC treatment.

Objective To study the mechanism of urinary Kallidinogenase combined with aspirin in treat-ment of acute cerebral infarction. Methods Eighty-six patients with acute cerebral infarction were randomly divid-ed into the observation group(n=43)and the control group(n=43).The observation group was treated with uri-nary Kallidinogenase combined with aspirin,while the control group was treated only with aspirin.Two weeks after the treatment,variables of hemorheology,serum Hcy,hs-CRP,VEGF,IL-6,Cys-C,neurological deficit(NI-HSS)and daily living ability(ADL)were compared between the two groups. Results After treatment,the serum Hcy,hs-CRP,VEGF,Cys-C,IL-6 levels,the NIHSS and ADL in the observation group were significantly better improved than those of the control group(P<0.05).The clinical efficacy in the observation group was significantly higher than that of the control group[95.35%(41/43)vs 74.42%(32/43)](P<0.05).Conclusion Urinary Kal-lidinogenase combined with aspirin is more effective in the treatment of acute cerebral infarction. The mechanism may be related to the early improvements of serum Hcy,hs-CRP,VEGF,Cys-C and IL-6 expression.