Objective To investigate the infection of 23 kinds of human papillomavirus ( HPV) subtypes in female cervical epithelium samples and to analyze their relationships with age and results of cyto-logic test in Haikou area. Methods A total of 4 037 local healthy women were enrolled in this study from July 2013 to August 2016, 1 967 of whom received cervical cytology test. Cervical cell samples collected from those women were detected for HPV typing by using PCR-reverse dot blot hybridization. Results (1) The total positive rate of HPV in 4 037 samples was 22. 15% (894 cases), and the detection rates of carci-nogenic, possibly carcinogenic and non-carcinogenic HPV were 16. 13%, 3. 99% and 5. 55% (651, 161 and 224 cases), respectively. The positive rates of 6 genotypes were high, which were HPV52, 53, 81, 51, 16 and 58 in turn. (2) The detection rates of carcinogenic, possibly carcinogenic HPV and some of the gen-otypes (HPV18, 52, 53, 66) increased with age (all P<0. 05). (3) Multiple infection in HPV-positive women accounted for 24. 38% (218/894), in which the infection rates of carcinogenic types declined with age and the numbers of HPV genotypes in carcinogenic infections were negatively correlated with age ( both P<0. 05). (4) Only 2. 49% (49/1 967) of the samples were positive for cervical cytologic test, classified into the ≥ASC-US ( atypical squamous cells of undetermined significance ) group. The detection rates of eight kinds of carcinogenic and two kinds of possibly carcinogenic HPV in≥ASC-US group were significantly higher than those in the negative (no intraepithelial lesion or malignant lesion, NILM) group (all P<0. 05). Conclusion This study indicates that Haikou women have higher rates of HPV infection, especially the eld-erly group. HPV subtype infections present some regional characteristics and are mainly single type infec-tion. Cervical cancer screening should be combined with tests for HPV and cytology analysis to improve its effectiveness.

Objective:To investigate the risk factors for death in patients with corona virus disease 2019 (COVID-19).Methods:The clinical data of 141 cases of patients diagnosed with COVID-19 at Renmin Hospital of Wuhan University from February 1 to February 26, 2020 were included in this retrospective analysis. The gender, age, time of hospitalization after the onset, clinical manifestations, underlying diseases, laboratory examination indicators (inculding white blood cell counts, neutrophil counts, lymphocyte counts, lymphocyte subsets, immunoglobulin, complement 3, complement 4, D-dimer, fibrinogen), and short term prognosis were compared between the death group and the survival group. Logistic regression was used to analyze the factors influencing the death of COVID-19 patients. The t test, Mann Whitney U test or chi-square test were used for comparison between groups. Results:Of the 141 COVID-19 patients, 52 died and 89 survived. The age, hypertension, chronic respiratory diseases, cardiovascular and cerebrovascular diseases, fever and wheeze of patients in the death group were all higher than those in the survival group, which were (70.7±13.3) years old vs (50.4±15.3) years old, 51.9%(27/52) vs 14.6%(13/89), 15.4%(8/52) vs 4.5%(4/89), 30.8%(16/52) vs 7.9%(7/89), 80.8%(42/52) vs 61.8%(55/89) and 50.0%(26/52) vs 25.8%(23/89), respectively. The differences were all statistically significant ( t=7.972, χ2=22.104, 3.615, 12.392, 5.503 and 8.447, respectively, all P<0.05). The white blood cell count, neutrophil count, CD4 + /CD8 + T lymphocyte, immunoglobulin E, D-dimer, fibrinogen, CD19 + T lymphocyte proportion and CD19 + T lymphocyte count of patients in the death group were all higher than those in the survival group, which were 8.20(5.26, 13.01)×10 9/L vs 5.29(3.96, 7.04)×10 9/L, 7.40(4.54, 11.46)×10 9/L vs 3.16(2.20, 5.01)×10 9/L, 2.32(1.77, 3.11) vs 1.63(1.25, 2.08), 125.0(42.6, 275.0) IU/mL vs 66.8(38.3, 143.0) IU/mL, 7.27(2.11, 16.21) mg/L vs 0.95(0.38, 2.54) mg/L, 4.37(2.72, 6.78) g/L vs 4.10(2.78, 4.97) g/L, (23.19±13.43)% vs (15.38±6.38)%, and (181.5±115.4)/μL vs (98.89±77.64)/μL, respectively. The differences were all statistically significant ( Z=3.944, 4.210, 2.834, 1.190, 5.497, 1.180, t=3.987, 3.411, respectively, all P<0.05). The lymphocyte count, CD3 + T lymphocyte proportion, CD3 + T lymphocyte count, CD8 + T lymphocyte proportion, CD8 + T lymphocyte count, CD16 + CD56 + T lymphocyte count and CD4 + T lymphocyte count were all lower than those in survival group, which were 0.47(0.37, 0.96)×10 9/L vs 1.33(0.90, 1.55)×10 9/L, 48.72%(42.31%, 76.92%) vs 69.91%(65.05%, 75.36%), 223.0(100.0, 403.0)/μL vs 761.0(499.0, 1 092.0)/μL, 13.82%(10.32%, 19.82%) vs 24.90%(20.87%, 29.57%), 55.5(30.5, 106.0)/μL vs 318.0(162.5, 443.5)/μL, 63.0(29.0, 99.5)/μL vs 140.0(69.5, 195.5)/μL and (209.74±140.13)/μL vs (487.61±232.02)/μL, respectively. The differences were all statistically significant ( Z=6.937, 3.944, 5.883, 3.924, 5.703, 3.517 and t=7.558, respectively, all P<0.01). Age, history of hypertension, white blood cell count, D-dimer, and fibrinogen were the risk factors for death of COVID-19 (odds ratio ( OR)=1.170, 10.405, 3.055, 1.128 and 1.343, respectively, all P<0.05). Conclusion:Age, underlying hypertension, white blood cell count, D-dimer and fibrinogen are independent prognostic factors for COVID-19.

OBJECTIVE To evaluate the safety and suitability of traditional Chinese medicine prescriptions generated by generative artificial intelligence （AIGC）， and to provide research ideas for empowering the traditional Chinese medicine industry with AIGC. METHODS Using the 2020 edition of Chinese Pharmacopoeia and the 5th edition of Traditional Chinese Medicine as corpus， GPT-4 and the real-time networking model developed based on GPT-4 （referred to as the “networking model”） were used for deep learning. The clinical cases included in the consensus of traditional Chinese medicine experts in recent years were extracted manually to regenerate prescriptions based on diagnosis using the GPT-4 model and networking model； traditional Chinese medicine experts conducted blind evaluation and scoring of GPT-4 generated prescriptions， networking model generated prescriptions， and expert consensus prescriptions. At the same time， Turing testing was used to evaluate whether the GPT-4 model and networking model had the same ability as human intelligence. RESULTS The average score of traditional Chinese medicine prescriptions generated by the GPT-4 model showed no statistically significant difference compared to manual prescriptions （P＞0.05）， while the average score of prescriptions generated by the networking model showed no statistically significant difference compared to traditional Chinese medicine prescriptions generated by the GPT-4 model （P＞0.05）. The proportion of model-generated prescriptions mistakenly judged as manual prescriptions in the Turing test was 51.11%. CONCLUSIONS The traditional Chinese medicine prescriptions generated by the GPT-4 model have reached a certain level of safety and suitability， and the GPT-4 model has passed the Turing test. The introduction of AIGC in the diagnosis and treatment process may provide technical support for the rational use of clinical traditional Chinese medicine.

Although widely applied in treating hematopoietic malignancies, transplantation of hematopoietic stem/progenitor cells (HSPCs) is impeded by HSPC shortage. Whether circulating HSPCs (cHSPCs) in steady-state blood could be used as an alternative source remains largely elusive. Here we develop a three-dimensional culture system (3DCS) including arginine, glycine, aspartate, and a series of factors. Fourteen-day culture of peripheral blood mononuclear cells (PBMNCs) in 3DCS led to 125- and 70-fold increase of the frequency and number of CD34⁺ cells. Further, 3DCS-expanded cHSPCs exhibited the similar reconstitution rate compared to CD34⁺ HSPCs in bone marrow. Mechanistically, 3DCS fabricated an immunomodulatory niche, secreting cytokines as TNF to support cHSPC survival and proliferation. Finally, 3DCS could also promote the expansion of cHSPCs in patients who failed in HSPC mobilization. Our 3DCS successfully expands rare cHSPCs, providing an alternative source for the HSPC therapy, particularly for the patients/donors who have failed in HSPC mobilization.
Antigens, CD34/metabolism* ; Hematopoietic Stem Cell Transplantation ; Hematopoietic Stem Cells ; Humans ; Leukocytes, Mononuclear/metabolism* ; Peptides/metabolism*