[Objective] To analyze the serological characteristics and molecular biology results for a P^k phenotype. [Methods] One patient with P^k phenotype upon unexpected antibodies at Jining Blood Center in July 2022 was selected as the study subject. The blood groups and unexpected antibodies of the proband and his second son were identified using serological methods. The sequences of 3-β-N-acetylgalactosaminyltransferase gene (B3GALNT1) and the coding region of α-1,4-galactosyltransferase gene (A4GALT) were amplified and analyzed by PCR direct sequencing, and haploid sequence analysis was carried out on the variant sites of the B3GALNT1 gene. PROVEAN, SIFT, PolyPhen2 and Mutation Taster were used to analyze the effect of mutations on the protein. [Results] Serological test results suggested that the proband was a P2^k type, including anti-P antibody in his serum, and his son was the P2 phenotype. Sequencing analysis revealed that the proband had a compound heterozygous variants of the B3GALNT1 in c. 239T>A (p. Leu80Gln) and c. 433C>T (p. Arg145Ter). Further haploid analysis showed that the c. 239T>A had occurred in one haploid while c. 433C>T was present in the other haploid, and his son carried a heterozygous variant of c. 433C>T (p. Arg145Ter); the proband and his son all have homozygous mutation variants of the A4GALT in c. 903C>G. Bioinformatic analysis also predicted that the mutations were harmful to the protein function. [Conclusion] The compound heterozygous variants c. 239T>A (p. Leu80Gln) and c. 433C>T (p. Arg145Ter) in the B3GALNT1 gene may contribute to the P^k phenotype, of which the c. 239T>A variant has not been reported.

OBJECTIVES: To propose a hypothesis that aloe-emodin may inhibit scar tissue fibrosis through thrombospondin-1(THBS1)-PI3K-Akt pathway. METHODS: By cultivating fibroblasts derived from scar tissue after cleft palate surgery in humans, aloe emodin of different concentrations (10, 20, 30, 40 and 50 μmol/L) was added to the cells which activity was detected. At the same time, transcriptome sequencing was performed on scar tissue and cells, and bioinformatics methods were used to explore potential targets and signaling pathways of scar tissue fibrosis. RESULTS: Aloe-emodin had a concentration dependent inhibitory effect on fibroblast proliferation,with the 40 μmol/L concentration group showing the most significant effect. The results of tissue and cell sequencing indicated that differentially expressed genes were significantly enriched in extracellular matrix-receptor interaction pathway, and shared a common differential gene which was THBS1. The ORA analysis results indicated that differentially expressed genes, including THBS1, were significantly enriched in the PI3K-Akt signaling pathway. CONCLUSIONS Aloe emodin may inhibit the PI3K-Akt pathway by downregulating THBS1, thereby reducing the proliferation activity of fibroblasts derived from postoperative palatal scar tissue.
Thrombospondin 1/genetics* ; Humans ; Signal Transduction/drug effects* ; Fibroblasts/cytology* ; Proto-Oncogene Proteins c-akt/metabolism* ; Fibrosis ; Phosphatidylinositol 3-Kinases/metabolism* ; Cicatrix/metabolism* ; Cell Proliferation/drug effects* ; Anthraquinones/pharmacology* ; Cells, Cultured

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.

Objective To explore the doctor's willingness to adopt artificial intelligence aided diagnosis and treatment system(ADTS)and its influencing factors,clarify the key links in the implementation of ADTS,and then propose suggestions for optimizing technology and management.Methods On the basis of UTAUT model,factors such as perceived risk,cognitive trust and legal supervision were added to design a questionnaire.After pre-investigation,226 officially recovered questionnaire data were analyzed for reliability and validity,hypothesis testing and SEM fitting,and the relationship chain model of doctors'ADTS adoption intention was obtained.Results Performance expectation,cognitive trust and social influence have a direct positive impact on doctors'willingness,among which social influence has the greatest effect.Facilitating conditions and legal supervision have indirect influence.It also found that doctors who have used ADTS have higher scores in various aspects such as performance expectations,effort expectations,and learning adaptability.Conclusion To implement the application of ADTS,attention should be paid to training,the software and hardware operation and maintenance system,and risk responsibility regulations should be improved,requiring joint efforts from multiple parties.